DOCTEUR DE L'UNIVERSITÉ DE BORDEAUX

THÈSE PRÉSENTÉE

POUR OBTENIR LE GRADE DE

DOCTEUR DE

L’UNIVERSITÉ DE BORDEAUX

ÉCOLE DOCTORALE

SPÉCIALITÉ PHARMACOÉPIDEMIOLOGIE

Option Pharmaco-épidémiologie, pharmaco-vigilance

Par Nelly Raquel HERRERA COMOGLIO

TITRE :

ÉVÉNEMENTS CARDIOVASCULAIRES MAJEURS ET MORTALITÉ EN PATIENTS TRAITÉS AVEC DES HYPOGLYCÉMIANTS NON

INSULINIQUES Étude de cohortes basée sur une population de Catalogne, Espagne

Sous la direction de : Xavier VIDAL GUITART

Soutenue le 17 Décembre 2019 Membres du jury : Mme. AGUSTI ESCASANY, Antonia Prof. Fundacio Institut Catala de Farmacologia Président, rapporteur Mme. BOSCH Montserrat Prof. Associée Fundacio Institut Catala de Farmacologia Examinateur M. SALVO, Francesco Prof. Université de Bordeaux Examinateur

Titre : ÉVÉNEMENTS CARDIOVASCULAIRES MAJEURS ET MORTALITÉ EN PATIENTS TRAITÉS AVEC DES

HYPOGLYCÉMIANTS NON INSULINIQUES Étude de cohortes basée sur une population de Catalogne, Espagne

Résumé :

Le diabète mellitus Type 2 (DMT2) est une maladie chronique et progressive causée par multiples facteurs. Plus de 422 millions de personnes dans tout le monde ont diabète; la maladie a un profond impact social et économique. La maladie cardiovasculaire est la cause principale de la morbilité et la mortalité chez les patients diabetiques, qui ont des taux de mortalité plues élévées que la population non-diabétique.

La définition de la DMT2 est basée sur ses manifestations métaboliques – surtout celles de glucose sanguin – qui servent comme marqueurs du contrôle et de l’évolution de la maladie. Cependant, tandis qu’on reconnait l’effet du contrôle de la glucose sanguin sur les complications microvasculaires, son impact sur les complications macrovasculaires ne sont pas clairs.

Depuis 2008, les nouveaux agents hypoglycémiants doivent démontrer leur sécurité cardiovasculaire, soit à travers d’une meta-analyse ou d’essais cliniques évaluant les résultat cliniques cardiovasculaires; quelques nouveaux agents ont montré une réduction des effets cliniques (comme infarctus du myocarde et accident cérébro-vasculaire) et de la mortalité. Toutefois, les populations qui faisaient partie de ces essais cliniques a grande échelle ont différences avec la population générale; donc, les résults de ces essais ne sont pas completement généralisables.

Tandis que les essais cliniques randomisés sont toujours considérés le “gold-standard” pour la génération de l’évidence scientifique, les études observationelles qui sont fait à partir de grande bases de données utilisés pour d’autre propos, dites “secondaires”, sont de plus en plus utilisées pour la génération de l’évidence scientifique complémentaire ou confirmatoire de celle provenant des essais cliniques, surtout quand ces essais ne sont pas disponibles ou sont impracticables.

Ce travail montre les résultats d’une étude observationnelle de cohortes, basée sur la population enregistrée en SIDIAP, une large base de données des médecins généraux de Catalogne, qui reccueil les régistres de plus de 5,5 millions de personnes. Les évenements cliniques cardiovasculares et la mortalité ont été évalués dans la population générale, non-sélectionnée, traitée avec des agents hypoglycémiants non-insuliniques. On attend que les résultats de cette investigation soient útiles pour la prise de decisions, tant au niveau des cliniciens comme au niveau de la santé publique.

Mots clés : événements cardiovasculaires majeurs ; mortalité ; diabete mellitus type 2 ; hypoglycémiants non-insuliniques

Title: Assessment of cardiovascular effects of non-insulin blood-glucose-lowering agents

Cardiovascular outcomes and mortality in Type 2 diabetes mellitus patients treated with non-insulin blood glucose-lowering drugs in Catalonia: a six-year retrospective population-based cohort study

Abstract :

Type 2 diabetes mellitus (T2DM) is a multifactorial, chronic, progressive disease,

affecting more than 422 million people over the world, and having a significant

societal and economic impact. Cardiovascular disease is the leading cause of

morbidity and mortality in T2DM patients, who have higher rates of mortality

than the non-diabetic population.

T2DM is defined by its metabolic -mainly glucose-related- manifestations which

serve as markers for controlling the evolution of disease. However, while the

effect of control serum glucose levels on microvascular complications is

acknowledged, its impact on macrovascular complications remains uncertain.

Since 2008, new blood glucose-lowering agents have to demonstrate

cardiovascular safety, and some have shown to reduce cardiovascular outcomes

and mortality. However, the populations included in these large cardiovascular

outcome trials differ from the general population, making results no fully

generalisable.

While randomised controlled trials are the gold standard for generating scientific

evidence, observational studies conducted with secondary data of Electronic

medical records (EMRs) are increasingly used as a source of complementary or

confirmatory evidence, especially when RCTs are not feasible or unavailable.

This work report an observational, population-based cohort study conducted in

SIDIAP, a large Catalan general practitioners database that contains health data

of 5,5 million people. We assessed cardiovascular outcomes and mortality in

general, unselected T2DM population treated with non-insulin blood-glucose-lowering

agents. The results are expected to be useful both for clinical and public

health decision-making.

Keywords : cardiovascular outcomes ; mortality ; Type 2 diabetes mellitus ; non-insulin blood-glucose-lowering agents

Unité de recherche

Institut Catala de Farmacologia

European Programme de Pharmacovigilance and Pharmacoepidemiology

PhD Thesis

Assessment of cardiovascular effects of non-insulin blood glucose-lowering agents

Cardiovascular outcomes and mortality in Type 2 diabetes mellitus patients treated with non-insulin blood glucose-lowering drugs in Catalonia: a six-year

retrospective population-based cohort study.

Year: 2019Bordeaux, France.

PhD candidate

Director

Supervisors

Plenary Doctoral Committee

Nelly Raquel Herrera ComoglioProf. Xavier Vidal GuitartUniversitat Autonoma de Barcelona, Spain Prof. Antonia AgustiProf. Montserrat BoschProf. Antonia AgustiPresident of JuryProf. Montserrat BoschProf. Francesco Salvo

PhD Thesis

Assessment of cardiovascular effects of non-

insulin blood glucose-lowering agents

Cardiovascular outcomes and mortality in Type 2 diabetes mellitus

patients treated with non-insulin blood glucose-lowering drugs in

Catalonia: a six-year retrospective population-based cohort study

PhD candidate Nelly Raquel Herrera Comoglio

Director Prof. Xavier Vidal Guitart

Fundacio lnstitut Catala de Farmacología

Universitat Autonoma de Barcelona,

Spain

Supervisors Prof. Antonia Agusti Fundacio lnstitut Catala de Farmacología

Prof. Montserrat Bosch Fundacio lnstitut Catala de Farmacología

Plenary Doctoral Committee President of Jury: Prof. Antonia

Agusti

Prof. Montserrat Bosch

Prof. Francesco Salvo Université de Bordeaux

Year: 2019

Bordeaux, France

Declaration of good academic conduct

“I Nelly Raquel HERRERA COMOGLIO, hereby certify that this dissertation, Which is

47,571 words in length, has been written by me’that it is a record ofwork carried out by

me, and that it has not been submitted in any previous application for a higher degree.

All sentences or passages quoted in this dissertation from other people’s work (with or

without trivial changes) have been placed within quotation marks, and specifically

acknowledged by reference to the author, WOrk and page. I understand that plagiarism -

the unacknowledged use of such passages - Will be considered grounds for fail皿e in

this dissertation and in the degree prograITme aS a Whole. I also a触m that’Wi血the

exception of the specific acknowledgements, the following dissertation is entirely my

own work.一一

Signature of the

iv

Acknowledgements

To the thesis director, Prof. Xavier Vidal, who was always present and

supported all the instances of this project, since its early beginnings to the final

result.

To the director of Catalan Institut of Pharmacology, Prof. Albert Figueras, to

all the team of professionals and its founder, Prof. Joan-Ramon Laporte, for

the permanent contribution to the education and investigation in

Pharmacoepidemiology in Spain and Latin-American countries.

To the members of Institut Jordi Gol, for the participation in this research

v

Table of contents

Abstract x

Abbreviations xii

Part I Background 1

I Introduction 2

II Cardiovascular Outcomes trials assessing the

effect of non-insulin blood-glucose-lowering

agents on major cardiovascular adverse events

(MACE) and mortality 14

III Generalisability of Cardiovascular Outcomes

Trials to the Real World: Implications for

Clinical Practice 32

Part II Cardiovascular outcomes and mortality

among type 2 diabetes mellitus patients

prescribed first and second-line blood

glucose-lowering drugs: a population-based

cohort study in the Catalan electronic

medical record database, SIDIAP, 2010-

2015

43

IV Protocol Rationale and Design 44

vi

V Cardiovascular outcomes and mortality

in type 2 diabetes mellitus patients prescribed

first-line non-insulin blood-glucose-lowering

agents as monotherapy 78

VI Cardiovascular outcomes and mortality

in type 2 diabetes mellitus patients prescribed

second-line, metformin-based non-insulin

blood-glucose-lowering agents dual therapies 107

VII Discussion and Conclusion 138

References 144

VIII Appendix A 180

IX Annexes 187

IX.1 Cardiovascular outcomes, heart failure and

mortality in type 2 diabetic patients treated with

glucagon-like peptide 1 receptor agonists (GLP-1

RAs): A systematic review and meta-analysis of

observational cohort studies 189

IX.2 Linagliptin and Cardiac Failure 208

IX.3 Glibenclamide/glyburide and palpitations in

Asian population

221

viii

Major cardiovascular outcomes (MACE), mortality and heart failure in Type 2 diabetes mellitus patients treated

with non-insulin blood glucose-lowering drugs in Catalonia: a six-year retrospective population-based

cohort study

Abstract

Diabetes mellitus is a chronic, progressive disease, that affects an increasing

number of people worldwide and present with microvascular and macrovascular

complications. People with Type 2 diabetes mellitus have 2-4 fold of

cardiovascular disease, the leading cause of morbidity and mortality for diabetic

patients. Management of T2DM is based on control of blood- glucose and CV risk

factors. Therapies for Type 2 diabetes mellitus encompass insulins, sulfonylureas,

metformin, meglitinides, thiazolidinediones, dipeptidyl-peptidase inhibitors,

glucagon-like peptide 1 receptor agonists, sodium-glucose 2 cotransporter

inhibitors and other agents. Since 2008, all new blood glucose-lowering agents

have to show CV safety to comply with regulatory recommendations; usually

accomplished through large cardiovascular outcomes randomised trials (CVOTs).

As the clinical outcomes assessed are relatively rare, the populations of these

trials are mostly high CV risk patients. Agents of two classes, GLP-1 RAs and

SGLT-2, have shown 13-14% of MACE risk reduction in T2DM patients, the

results are driven by all-cause mortality for liraglutide and empagliflozin for CV

death. The question that arises is to what extent the results of these CVOTs are

generalisable to unselected populations.

The evidence from pharmacoepidemiologic safety studies conducted in large

electronic healthcare databases has increasing importance as complementary or

confirmatory evidence in regulatory or payers’ decision-making. Observational

research also has a unique significance to assess the effect of drug or drug classes

in a particular setting and real-world conditions. However, observational

research can be flawed by bias in design and analyses and should be rigorously

conducted to provide compelling insights and to minimise the inherent

confounding by indication of non-randomised studies.

ix

The present work hypothesises that, in the study period, the treatment with new

classes of blood glucose-lowering drugs in an adult, T2DM population in

Catalonia, is not associated with a clinically relevant benefit, defined as a 10%

reduction in cardiovascular morbidity and mortality compared with the use of

reference non-insulin glucose-lowering agents, metformin and sulphonylureas

(SU).

This work presents a longitudinal population-based cohort study to assess CV

outcomes and mortality among adults Type 2 diabetes mellitus patients treated

with non-insulin blood-glucose-lowering agents in Catalonia. Patients should

have been registered in the Catalan nationwide healthcare system and their data

recorded in the general practitioners’ Information System for the Development

of Research in Primary Care (SIDIAP) database. We used a new-user design to

avoid prevalent-user bias and assessed exposures through an as-treated

approach, following patients from the first prescription of a given agent to its

discontinuation, switching or the addition of another antidiabetic drug. To

minimise bias, cohorts of patients were compared at the same line of treatment.

Crude incident rates of CV outcomes and mortality were adjusted by

demographic, clinical and socio-economic variables through a Cox multivariate

analyses. Although we minimised selection bias, other biases such as information

bias are likely to be significant in health medical records databases, and residual

confounding can not be ruled out.

x

ABBREVIATIONS

3-p MACE 3-point major adverse cardiovascular event

4-p MACE 4-point major adverse cardiovascular event

AGE Advanced glycation end products

AHT Arterial hypertension

AMI Acute myocardial infarction

BL Baseline

BMI Body mass index

BNP Brain natriuretic peptide

CABG Coronary arterial by-pass graft

CHD Coronary heart disease

CHF Congestive heart failure

CI Confidence interval

CKD Chronic kidney disease

CV Cardiovascular

CVD Cardiovascular disease

DBP Diastolic blood pressure

DCCT Diabetes Control and Complications Trial

DM Diabetes mellitus

DPP-4 Dipeptidyl peptidase – 4

DPP-4i Dipeptidyl peptidase – 4 inhibitor

eGFR Estimated glomerular filtration rate

EMA European Medicines Agency

EU European Union

FDA US Food & Drug Administration

FPG Fasting plasma glucose

GIP Glucose-dependent insulinotropic peptide

GLP-1 Glucagon-like peptide 1

GLP-1 RA Glucagon-like peptide 1 receptor agonist

HbA1c Glycated haemoglobin

HDL-C High-density lipoprotein colesterol

HF Heart failure

HHF Hospitalisation for heart failure

HOPE Heart Outcomes Prevention Evaluation

HR Hazard ratio

xi

HUA Hospitalisation for unstable angina

LDL-C Low-density lipoprotein colesterol

LV Left ventriculum, left ventricular

MACE Major adverse cardiovascular events

MET metformin

MI Myocardial infarction

NIAD Non-insulin blood-glucose-lowering “antidiabetic” drug

N-BNP N-terminal pro-Brain natriuretic peptide

PAD Peripheral arterial disease

PCO Primary composite outcome

PTCA Percutaneous transluminal coronary angioplasty

RCT Randomised controlled trial

RF Renal failure

RR Relative risk

SBP Systolic blood pressure

SCO Secondary composite outcome

SGLT-2 Sodium-glucose cotransporter-2

SGLT-2 i Sodium-glucose cotransporter-2 inhibitors

SIDIAP Information System for the Development of Research in Primary Care

SU Sulfonylurea

T1DM Type 1 Diabetes Mellitus

T2DM Type 2 Diabetes Mellitus

TC Total cholesterol

TG Triglycerides

TIA Transient ischemic attack

TZD Thiazolidinediones

UK United Kingdom

UKPDS United Kingdom Prospective Diabetes Study

UA Unstable angina

US United States

VADT Veterans Affairs Diabetes Trial

VLDL-C Very low-density lipoprotein cholesterol

WHO World Health Organization

Part I: Background

I. Introduction

Introduction 3

Nelly Raquel Herrera Comoglio Eu2P PhD December 2019

I. Introduction

The prevalence and trends of diabetes mellitus

Diabetes mellitus (DM) affects more than 422 million people; by 2035, its prevalence

is foreseen to rise to 592 million. The number of people with diabetes increased almost

4-fold from 1980 to 2014. [1] The global prevalence of diabetes among adults over 18

years of age has risen from 4.7% in 1980 to 8.5% in 2014 (1 every 12 people). [1, 2]

Diabetes mellitus Type 2 (T2DM) accounts or around 90% of all diabetes cases

worldwide.[2]

The substantial increase in diabetes prevalence observed both in developed and

developing countries might be due to either an increased incidence or longer

survival.[3] Diagnosed type 2 diabetes mellitus’ prevalence has been estimated to

increase more than twice between 2000 and 2013 in the UK, up to 5.32%. [4] In

Catalonia diagnosed T2DM prevalence was 7.6% in 2009, being 3-fold higher in

patients aged 75 yr. or older, [5] which is consistent with data reporting a 25% of US

population aged ≥65 years having diabetes. [6] Some more recent studies report a

stabilisation or fall in diabetes incidence in some countries, to which preventive

strategies could have contributed. A recently published review reported an increase of

diagnosed diabetes in most populations from the 1960s to the early 2000s, after which

a pattern emerged of stable trends in 30% and declining trends in 36% of the reported

populations. [3] However, data are limited in low and middle-income countries, where

trends in diabetes incidence could be different.

Introduction 4 Nelly Raquel Herrera Comoglio Eu2P PhD December 2019

Diabetes mellitus vascular complications and mortality

Diabetes is a significant cause of blindness, kidney failure, heart attacks, stroke and

lower limb amputation, [1] WHO projects that diabetes will be the 7th leading cause

of death in 2030, and it has been estimated that diabetes caused 4.9 million deaths in

2014. [1,2] The highest number of people with diabetes is between 40 and 59 years of

age. Patients with Type 2 diabetes mellitus (T2DM) are more likely to die from any

cause and cardiovascular (CV) causes; risks vary and are higher with younger age,

worse glycemic control, and greater severity of renal complications; for younger

people, the risks of dying persists even for those with acceptable glycaemic control.

[7, 8]

Diabetes-related microvascular complications can lead to significant morbidity and

premature mortality; however, the most important cause of death in people with diabetes

is for cardiovascular disease (CVD). [9] It has long been recognised that diabetes is an

independent risk factor for CVD, affecting all components of the cardiovascular system:

microvasculature, larger arteries, the heart, as well as the kidneys; and imparting a 2- to

4-fold risk of CVD. Also, many diabetic patients often have other risk factors for CVD,

such as obesity, hypertension and dyslipidemia. [10] Patients with diabetes have twice

the risk of incident myocardial infarction and stroke as that of the general population,

many do not survive their first event, or their mortality rate is generally higher than that

of the general population. As many as 80% of patients with type 2 diabetes mellitus will

develop and possibly die of macrovascular disease. [11, 12] Older adults with diabetes are

at substantial risk for both acute and chronic microvascular and cardiovascular

complications of the disease. However, cardiovascular disease prevalence is not affected

by older-age onset diabetes. [6]

T2DM people often present with other risk factors for cardiovascular disease (CVD). A

third of people with T2DM have CVD: 29.1% had atherosclerosis, 21.2% had coronary

heart disease (CHD), 14.9% had heart failure (HF), 14.6% had angina, 10.0% had had a

myocardial infarction (MI), and 7.6% had experienced a stroke. CVD causes death in

50% of T2DM patients. [13] In Catalonia, in 2009, the prevalence of CVD prevalence

was 22.0%, being coronary heart disease (18.9%) and peripheral ischemia (4.5%) the

more frequent manifestations. [14]


A study published in 2009 report that adults with diabetes have experienced a 50%

reduction in the rate of incident CVD, although remaining at a consistent, approximate 2-

fold excess for CVD events compared with those without diabetes.[15] Marked

reductions in cardiovascular disease mortality were seen in the last decades as a result of

new therapies and proactive diagnosis. [16, 17] In diabetic patients, CVD mortality rates

have decreased in a greater extent than in non-diabetic, thus reducing the difference.

Regional differences in mortality in T2DM populations have been reported in Spain and

the UK. [17, 18] In US adult diabetic population, 10-year relative changes in mortality

were significant for major CVD (by -33%), ischemic heart disease (by -40 %), and stroke

(by -30%), but not heart failure (by -0.5%, non-significant) or arrhythmia (-12.0%) [16]

The pathogenesis of heart failure includes not only coronary artery disease but also

hypertension and diabetic cardiomyopathy, not fitting clearly into the traditional, binary

classification of diabetes complications as either microvascular or macrovascular.[19] In

the Framingham study, which has found that in non-diabetic patients the incidence rate

of heart failure was higher for men than for women, it has been estimated that in diabetic

patients treated with insulin, diabetes confers more than a two-fold increase in the risk of

heart failure in men and five-fold higher risk in women. [20, 21] As with stroke and

myocardial infarction, in a heart-failure setting in patients with diabetes, mortality rates

are about twice that of the non-diabetic population; individuals with diabetes aged 45–54

years are almost 9-fold more likely to develop heart failure, and the relative risk falls to

1.8 for those aged 75–84 years. [19] Results of 4-yrs follow-up of an international registry

found that diabetes mellitus was associated with a 33% greater risk of hospitalisation for

heart failure. In patients with diabetes mellitus, heart failure at baseline was independently

associated with cardiovascular death, increasing fatal outcome 2.5-fold.[21]

Heart failure is the second most frequent cardiovascular presentation in people with

diabetes, (14,1%), being peripheral artery disease the first one, with 16,2%. A study

conducted in England during 1998-2010 and using data of four linked databases (primary

care, hospital admission, disease registry, and death certificate records) found that 17.9%

people with type 2 diabetes had a first cardiovascular presentation. Patients with Type 2

diabetes were at about three times higher risk of peripheral arterial disease (HR 2.98), and


at increased risk of ischaemic stroke, stable angina, heart failure, and non-fatal myocardial

infarction. [22]

In a decade, in the UK, the proportion of diabetic patients increased from 18% in 2002-

2004 to 26% in 2012-2014.[23] However, the absolute number of newly diagnosed heart

failure individuals increased by 12%, and the estimated absolute number of prevalent

heart failure cases increased even more, by 23%, this mainly due to an increase in

population size and age. Patient age increased 0.79 years and patients had more multi-

morbidity at first presentation of heart failure, from 3.4 to 5.4. In the same period, diabetes

mellitus was the fifth most prevalent comorbidity for incident CVD (11.2%), but the

frequency was higher between 60-69 and 70-79 years (16.3% and17.9% respectively).

[24]

The increased mortality of people with diabetes is due not only to CV death but also to

cancer-related deaths and other causes. [25]

Both the increased prevalence of DM and diabetes-related comorbidities impact on

healthcare costs. Average annual healthcare costs associated with patients with type

2 diabetes are substantially more expensive (72.4%) compared with non-diabetic

subjects. They are higher among diabetic patients with poor glycemic control and

macrovascular complications. [26]

Glycemic markers and DM complications

Despite the extensive clinical research devoted to, diabetes is still defined by its

biochemical manifestations (elevated fasting plasma glucose, glycated haemoglobin,

hyperglycaemia and glucosuria) and complications, the pathogenesis of type 2 diabetes

and its complications remains unknown. [27] Several mechanisms have been proposed to

explain hyperglycemia to increased cardiovascular morbidity and mortality. It has been

suggested that hyperglycemia may produce advanced glycation end products in diabetic

patients and even in those who are prone to developing diabetes before diabetes onset,

contributing to endothelial dysfunction, atherosclerosis and microangiopathy, relevant

factors to CVD and heart failure. [28, 29] Blood glucose binds irreversibly with proteins,

the rate and extent of nonenzymatic glycation of proteins depend mainly on the prevailing


glucose concentration and the protein life span. Covalent, nonreversible glycation of

proteins - the formation of advanced glycation end products (AGE)-, is the final stage of

a sequential process that starts with reversible, non-covalent glycation. The presence of

various AGE is thought to be linked to the normal ageing process and the chronic

complications of diabetes mellitus.[30] Glycated haemoglobin (HbA1c), which indicates

the glycemic level during the previous 3-months – the lifespan time of red blood cells-,

is the surrogate marker that has been the gold standard outcome in diabetic trials for more

than 40 yrs. [31] In healthy subjects, levels of “stable” HbA1C are ∼5–6% of total HbA,

these values can increase up to 15% or more in diabetic individuals. However, there can

also be a “labile” HbA1C formed during the early, reversible stages of the glycation

process and which reflects ambient vs longer-term glucose levels; this reversible HbA1c

may overestimate HbA1C by up 2–3% in healthy subjects and by 10% in subjects with

diabetes. It should be kept in mind that hyperglycemia does not provide the complete

answer to the aetiology of increased early glycated products, given that glycated

haemoglobin is also present in some non-diabetic conditions, including chronic renal

failure. [32]

The beneficial effect of intensive therapy on microvascular outcomes have been

established for insulin-dependent diabetes mellitus in 1993, showing a direct relationship

between increased glycemic levels and microvascular complications. The observational

study UKPD 35 found that in type 2 diabetes mellitus patients, previous hyperglycemia

was strongly associated with microvascular and macrovascular complications, being any

reduction in HbA1c likely to reduce the risk of complications, with the lowest risk being

in those with HbA1c values in the normal range (<6.0%). [33] Each 1% reduction in

updated mean HbA1c was associated with reductions in risk of macrovascular and

microvascular complications: non-significant 14% for myocardial infarction and a

significant 37% for microvascular complications. [34]

A substantial amount of increased cardiovascular risk and all-cause mortality caused by

T2D cannot be explained by traditional vascular risk factors. Only 35% of the excess

cardiovascular risk and 42% of the excess mortality risk caused by T2D have been found

to be mediated by the classical cardiovascular risk factors. For CVD, the most

considerable mediated effects were by insulin resistance, elevated triglycerides and


micro‐albuminuria. For mortality, the largest mediated effects were by micro‐albuminuria

and insulin resistance. [35]

The UKPDS 33, published in 1998, compared the effects of pharmacologic blood-glucose

control (“intensive group”, either sulfonylureas or insulin) with diet in patients with type

2 diabetes. The “intensive treatment” decreased the risk of microvascular complications,

but not the macrovascular disease. In this study, neither sulfonylureas or insulin showed

an adverse effect on cardiovascular outcomes but increased the risk of

hypoglycaemia.[36] Since then, the beneficial effect of blood glucose-lowering agents on

microvascular complications of diabetes mellitus has been almost unanimously

acknowledged by most published statements (77%–100%) and guidelines (95%). [37]

However, their effect on macrovascular complications, such as coronary, cerebral and

peripheral macroangiopathy, remains uncertain.[11, 38, 39] A meta-analysis of 16

guidelines and 328 statements found that this evidence reported no significant impact of

tight glycemic control on the risk of dialysis/transplantation/renal death, blindness, or

neuropathy, and a consistent 15% relative risk reduction of non-fatal myocardial

infarction, with no significant effect on all-cause mortality, cardiovascular mortality, or

stroke. [37] These results are consistent with a previous meta-analysis of more-intensive

vs less intensive glucose control found the same risk reduction of 15% for MI, favouring

the more intensive control. Exploratory analysis in this MA also suggested that

participants with no history of macrovascular disease obtained the benefit, whereas those

with a prior macrovascular disease did not. [40]

Epidemiological studies and meta-analyses of RCTs have clearly shown a direct

relationship between HbA1c and CVD, but the potential of intensive glycemic control to

reduce CVD events has been less clearly defined. [9] A meta-analysis of 102 clinical trials

showed that DM confers about a two-fold excess risk for a wide range of vascular

diseases. Independently from other conventional risk factors, after adjustment for other

risk factors, an increase of 1% in the glycated haemoglobin level is associated with an

increase of 18% in the risk of cardiovascular events.[41] The prospective observational

study UKPDS 35, published in 2000, found that the incidence of clinical complications

was significantly associated with glycaemia reduction, being each 1% mean HbA1c

reduction associated with reductions in risk of 21% for diabetes-related deaths, 14% for


MI, 21% for any endpoint related to diabetes and 37% for microvascular complications,

retinopathy or renal failure. Interestingly, no threshold of risk was observed for these

effects. [34] The association between higher levels of HbA1c and increased CV risks have

been confirmed with more or less consistent results in studies using secondary data from

healthcare databases. [42, 43] It also has been suggested that in no diabetic patients, the

relation between glycated haemoglobin and cardiovascular events would have a linear

association in non-extreme values. [44] The Heart Outcomes Prevention

Evaluation (HOPE) found that in diabetic participants, a 1% absolute rise in the updated

HbA1c predicted future CV events after adjusting for confounders and treatment, and the

analysis of diabetic and non-diabetic patients showed that a 1 mmol/l rise in fasting

plasma glucose was related to an increased risk of CV outcomes, after adjusting for

presence or absence of diabetes, thus indicating an independent progressive relationship

between indices of glycaemia and incident CV events, renal disease and death. [45]

It also has been suggested that the current target of HBA1c level does not predict a

better coronary microcirculatory function in T2DM patients and that there is a possible

link between coronary microvascular disease and LV diastolic function in Type2 diabetic

patients. [46, 47]

Ideally, glycemic control should be attained with no hypoglycaemic events.

Hypoglycaemia produces significant metabolic stress that could trigger major vascular

events such as myocardial infarction and stroke. [48] A decade ago, the potential CV

dangers of intensive treatment regimens and strict glycemic control in T2DM people who

have CV disease (CVD) arose in three trials in which excess mortality was observed. [49

-51]

Intensive blood-glucose control and clinical outcomes

The Diabetes Control and Complications Trial (DCCT, 1993) randomly assigned 1441

patients with insulin-dependent diabetes mellitus to receive intensive therapy or standard

therapy with insulin. In this study, tight glycemic control in type 1 diabetes patients

significantly reduced the development and progression of chronic diabetic complications,


such as retinopathy, nephropathy, and neuropathy. [33] Long-term follow-up of these

patients demonstrated beneficial effects on macrovascular outcomes in the Epidemiology

of Diabetes Interventions and Complications study. The risk of the primary composite

CVD outcome was reduced by 42% in the original and that of fatal or non-fatal MI or

stroke (MACE) by 57% in the intensive vs the control group, but the limited number of

patients with events (only 12) was inadequate to draw conclusions. [52]

The United Kingdom Prospective Diabetes Study (UKPDS 33, 1998) was designed in

order to assess micro and macrovascular complications of diabetes in 3867 newly

diagnosed patients with type 2 diabetes, median age 54 years. After three months of diet,

patients were randomly assigned to standard dietary therapy or pharmacological therapy

based either on sulfonylureas (chlorpropamide, glibenclamide and glipizide) or with

insulin. Patients assigned to diet received pharmacological treatment only if they had

hyperglycemic symptoms or a FPG higher than 15 mm/L. The goal of pharmacological

therapy was to maintain FPG < 6.0 mm/L, with stepwise addition of other hypoglycaemic

agents (metformin or insulin) when the glycaemic goals were not met (i.e., patients

assigned to any of the three sulfonylureas could be given metformin; oral agents could

later be replaced by insulin). Follow-up was up to ten years. HA1c was 7.0% in the

intensive group compared with 7.9% in the conventional group - an 11% reduction, with

no difference in HbA1c among agents in the intensive group. Compared with the

conventional group, the risk in the intensive group was 12% lower for the composite of

any diabetes-related endpoint (sudden death, death from hyperglycaemia or

hypoglycaemia, fatal or non-fatal myocardial infarction, angina, heart failure, stroke,

renal failure, amputation, vitreous haemorrhage, retinopathy requiring photocoagulation,

blindness in one eye, or cataract extraction); 10% lower for any diabetes-related death

(death from myocardial infarction, stroke, peripheral vascular disease, renal disease,

hyperglycaemia or hypoglycaemia, and sudden death); and 6% lower for all-cause

mortality. Most of the risk reduction in any diabetes-related aggregate endpoint was due

to a 25% risk reduction in microvascular endpoints. [36]

In the United Kingdom Prospective Diabetes Study (UKPDS 34, 1998), 753 overweight

patients were included in a randomised controlled trial and were followed for 10.7 years.

Four hundred eleven patients were allocated in standard treatment, primarily with diet


alone, and 342 patients were allocated in pharmacological treatment with metformin,

aiming for FPG < 6 mmol/L. A secondary analysis compared the 342 patients allocated

metformin with 951 overweight patients allocated intensive blood-glucose control with

chlorpropamide (n=265), glibenclamide (n=277), or insulin (n=409). Metformin has

found to have a 34% reduction on cardiovascular outcomes in overweight patients;

sulfonylureas showed a non-significant reduction in risk of myocardial infarction

(MI).[53] It has been noted that these results were obtained in a randomised subgroup of

obese patients (342 patients in the metformin group and 411 in the conventional group)

and have never been reproduced, suggesting design and methodological drawbacks. [54]

In a supplementary trial, patients on maximal doses of sulfonylureas who attained an

HbA1c ≤ 6.1 mmol/L were allocated to be added metformin or to continue on

sulfonylurea alone. Patients who were added metformin had a significant 60% higher all-

cause death compared with those given sulfonylurea alone. [53]

Post-trial monitoring aimed to determine whether this improved glucose control persisted

and whether such therapy had a long-term effect on macrovascular outcomes: 3277

patients were followed through clinical visits or annual questionnaires for five years, with

no intervention to maintain their previously assigned therapies all patients in years 6 to

10 were assessed through questionnaires. Although differences in glycated haemoglobin

levels were lost after the first year, the relative reduction in risk of microvascular

outcomes persisted at ten years and reduction in risk on some CV outcomes emerged. In

the sulfonylurea-insulin group, relative reductions in risk persisted at ten years for

any diabetes-related endpoint (9%, P=0.04) and microvascular disease (24%, P=0.001),

risk reductions for myocardial infarction (15%, P=0.01) and death from any cause (13%,

P=0.007) emerged over time. In the metformin group, significant risk reductions persisted

for any diabetes-related endpoint (21%, P=0.01), myocardial infarction (33%, P=0.005),

and death from any cause (27%, P=0.002). [55]

In the Veterans Affairs Diabetes Trial, (VADT, 2009) no significant effect on the rates of

major cardiovascular events, death, or microvascular complications - except progression

of albuminuria- was obtained through an intensive glucose control in patients with poorly

controlled type 2 diabetes. [56] In this study, 1791 military veterans (mean age, 60.4

years, mean time from diagnosis of diabetes 11.5, yrs., 40% with a history of a previous


cardiovascular event) were randomly assigned to receive intensive vs the standard

pharmacological therapy. Intensive therapy started at maximal doses and standard therapy

at half of the maximal doses. [56] The primary outcome was the time from randomisation

to the first occurrence of a major cardiovascular event, a composite of myocardial

infarction, stroke, death from cardiovascular causes, congestive heart failure, surgery for

vascular disease, inoperable coronary disease, and amputation for ischemic gangrene. The

median follow-up was 5.6 yrs. Patients with a BMI ≥ 27 were given metformin plus

rosiglitazone 27, and those who had a BMI ≤ 27 were started on glimepiride plus

rosiglitazone.[56] In the follow-up extension of VADT trial, after 9.8 years of follow-up,

patients with type 2 diabetes who had been randomly assigned to intensive glucose control

for 5.6 years had fewer major cardiovascular events than those assigned to standard

therapy, but no improvement was seen in the rate of overall survival (VADT follow-up,

2015).[57]

In the ADVANCE trial (2008), with glucose intensive control there were no significant

effects on major macrovascular events ( HR 0.94; 95% CI, 0.84 to 1.06; P=0.32), death

from cardiovascular causes (HR 0.88; 95% CI, 0.74 to 1.04; P=0.12), or death from any

cause (HR 0.93; 95% CI, 0.83 to 1.06; P=0.28). In this study,11,140 patients with type 2

diabetes were allocated to receive either standard glucose control or intensive glucose

control, the latter defined as the use of gliclazide (modified release) plus other drugs as

required to achieve a glycated haemoglobin value of 6.5% or less. [58] After a median of

5 years of follow-up, the haemoglobin target was achieved in the intensive-control group

(6.5%), while in the standard-control group was 7.3%. Intensive control reduced the

incidence of combined major macrovascular and microvascular events, primarily because

of a reduction in the incidence of nephropathy. Severe hypoglycemia was more frequent

common in the intensive-control group (2.7%, vs 1.5% in the standard-control group;

hazard ratio, 1.86; 95% CI, 1.42 to 2.40; [58] However, with intensive control

In the ACCORD trial (2008), the intensive group therapy was discontinued after a follow-

up of 3.5 yrs. because of higher mortality (HR ratio, 1.22; 95% CI, 1.01 to 1.46; P =

0.04).This study assessed the effect of intensive therapy vs glucose-lowering standard on

10,251 patients (mean age, 62.2 years, 38% women, 35% with a history of a

cardiovascular event) with a median glycated haemoglobin level of 8.1%. The target of


the intensive therapy group was an HbA1c level below 6.0%, and the standard therapy

target was from 7.0 to 7.9%. The primary outcome - a composite of non-fatal myocardial

infarction, non-fatal stroke, or death from cardiovascular causes- was no significant

reduced in the intensive therapy group (HR, 0.90; [CI], 0.78 to 1.04; P = 0.16). This result

was due to a lower rate of nonfatal MI in the intensive group than in the standard therapy

group (3.6% vs. 4.6%; HR, 0.76; 95% CI, 0.62 to 0.92; P = 0.004), and a higher rate of

death from cardiovascular causes in the intensive group (2.6% vs. 1.8%; hazard ratio,

1.35; 95% CI, 1.04 to 1.76; P = 0.02); with no significant difference in the rate of nonfatal

stroke (1.3% vs. 1.2%; HR, 1.06; 95% CI, 0.75 to 1.50; P = 0.74). Of note, rates of the

primary outcome began to separate in the two study groups after three years.[59] After

the intensive therapy was discontinued, the target for glycated haemoglobin level was set

from 7 to 7.9% for all participants, and the median HbA1c in this group rose from 6.4%

to 7.2%, and the use of glucose-lowering medications and rates of severe hypoglycemia

were similar in the two groups. The follow-up continued until the planned end of the trial

(5 yrs). The trends in CV mortality and MI persisted during the entire follow-up period

(HR for death, 1.19; 95% CI, 1.03 to 1.38; and HR for non-fatal myocardial infarction,

0.82; 95% CI, 0.70 to 0.96). [60]

Before the ACCORD trial’s results were published, in 2008, a majority of statements

declared valuable to achieve tight glycemic control to prevent macrovascular

complications (47%–59%). In 2009, only 21% of statements favoured strict glycemic

control. [37] The concentrations of glycated haemoglobin (HbA1c), which are used as a

surrogate marker for outcomes that are important to patients, such as blindness or

amputation, do not have a linear relationship with CV outcomes.[61] An intensive glucose

control – aiming to maintain HbA1c levels close to those of healthy patients, has failed

to demonstrate benefits for CV mortality, though showing a trend towards lower MI risks.

Being the control of CV risk factors one of the goals of the diabetes care and CVD, other

surrogates, like blood pressure, lipids, albumin excretion rates, and C reactive protein

have been used to predict CVD outcomes and mortality.

In 2008, as a result of the findings of an increased number of MI in a trial with

rosiglitazone, the CV safety of blood glucose-lowering drugs was required to be assessed

through major adverse cardiovascular events (MACE) endpoints.

II. Cardiovascular Outcomes trials assessing the effect of non-insulin

blood-glucose-lowering agents on major cardiovascular adverse events

(MACE) and mortality

Cardiovascular Outcomes trials 15 Nelly Raquel Herrera Comoglio Eu2P PhD December 2019

II. Cardiovascular Outcomes trials assessing

the effect of non-insulin blood-glucose-lowering

agents on major cardiovascular adverse events

(MACE) and mortality

II.1. Randomised controlled trials assessing cardiovascular

outcomes before the FDA guidance

Sulfonylureas and biguanides

The first RCT for the assessment of cardiovascular effects and mortality in diabetic

patients began in 1961: the University Group Diabetes Program (UGDP) was initiated as

a result of a congressional request about the impact of the treatment with the first-

generation tolbutamide on the cardiovascular complications of diabetes. “The UGDP was

a randomised, controlled, multicenter clinical trial designed to evaluate the effectiveness

of long-term hypoglycaemic drug therapy in preventing or delaying the vascular

complications of diabetes (newly diagnosed, non-insulin dependent, adult-onset

diabetes). The tolbutamide and phenformin treatments were terminated in 1969 and 1971,

respectively, because of lack of efficacy.” [62] It was one of the first large‐scale

cooperative clinical trials designed and implemented in the United States. Patients were

allocated to placebo, tolbutamide, phenformin, or insulin. The study investigators

concluded in 1969 that the combination of diet and tolbutamide therapy was no more

effective than diet alone in prolonging life. [62, 63] Interestingly, the initiative was

impulsed by a congressman who had a daughter in treatment with tolbutamide. The study

was stopped eight years later because of an increase in cardiovascular deaths in those

receiving tolbutamide.


Sulfonylureas, metformin and insulin

In the late 1970s, the UKPDS was set up in Oxford. It included more than 5102 out of

7600 subjects considered for inclusion at 23 centres across the UK. It was the most

extensive study, and the median follow-up was ten years. The primary aim was to

determine the effect of intensive glycaemic control on the incidence of complications; the

secondary objective was to assess whether there were differences between treatments.

Subjects were randomised to “conventional” (diet) or “intensive” treatment; when diet

failed to achieve glycaemic targets, subjects were randomised to sulfonylureas, insulin or

metformin if they were obese. [64, 36, 53] The primary outcome measures were

aggregates of any diabetes-related clinical endpoint, diabetes-related death, and all-cause

mortality. The results of the UKPDS 33 (3867 patients) showed that over ten years,

patients in the intensive group had a reduction of HbA1c of 0.9% compared with

conventional therapy (7.0% vs 7.9%) with no difference among agents in the intensive

group. [36] The UKPDS 34 included 1704 overweight patients who were randomized to

diet alone versus intensive blood-glucose control policy with metformin, or

chlorpropamide, glibenclamide or insulin. The reduction of HbA1c was 0.6% in

metformin-treated patients (7.4% vs 8.0%), and they had risk reductions of 32% for any

diabetes-related endpoint, 42% for diabetes-related death, and 36% for all-cause

mortality. The early addition of metformin in sulfonylurea-treated patients increased the

risk of diabetes-related death compared with a continued sulfonylurea alone. [53] The 10-

years post-trial monitoring showed that the benefit for glycaemic control was evident over

time risk for MI (15%) and death from any cause (13%) in the sulfonylurea insulin group;

in the metformin group, reductions for MI and mortality were 33% and 27%, respectively.

The benefit remained even when between-group differences in glycated haemoglobin

levels were lost after the first year. [55] As mentioned in the “Introduction” section, the

ACCORD, the ADVANCE and the VADT trials, aiming to reach a stricter glycemic

control failed to demonstrate a beneficial effect of intensive glucose lowering on CV risk.

A meta-analysis indicated a modestly reduced risk of non-fatal myocardial infarction

(0.85, 0.74 to 0.96), similar results concerning MI were obtained in the observation

UKPDS 35 [65, 34]


Dual agonists of peroxisome proliferator-activated receptor: muraglitazar

Peroxisome proliferator-activated receptors (PPARs) are nuclear transcription factors that

modulate gene expression, regulating glucose and fatty acid metabolism, apoptosis,

angiogenesis, cell proliferation and differentiation, and immune response. Peroxisome

proliferator-activated receptors gamma agonists increase insulin sensitivity (“glitazones”

rosiglitazone and pioglitazone). The first dual alpha -gamma agonist was muraglitazar. In

2005, a meta-analysis of documents about phase 2 and 3 clinical trials released under

public disclosure laws for the FDA advisory committee meeting evaluated the incidence

of death, myocardial infarction (MI), stroke, congestive heart failure (CHF), and transient

ischemic attack (TIA) in diabetic patients treated with muraglitazar compared with

controls. The primary outcome was a composite of incidence of death, non-fatal MI, or

non-fatal stroke; an extended composite outcome included these events plus the incidence

of CHF and TIA. In the muraglitazar-treated patients, the primary outcome occurred in

1.47% patients compared with 0.67% patients in the combined placebo and pioglitazone

treatment groups (controls) (relative risk 2.23; 95% CI 1.07-4.66). For the expanded

MACE the RR was 2.62; 95% CI, 1.36-5.05. Components of the composite endpoint

exceeded 2.1 but were not statistically significant. [66, 67]

Thiazolidinediones

FDA issued the marketing authorisation for rosiglitazone in late May 1999, and European

authorities did so in July 2000 but required a post-approval clinical outcome trial, known

as the RECORD (Rosiglitazone Evaluated for Cardiovascular Outcomes and Regulation

of glycemia in Diabetes) trial which was published in 2009. Concerns about the safety of

another thiazolidinedione, the pioglitazone, based on preclinical data, prompted that a

cardiovascular safety trial was conducted, the PROActive trial.

The PROActive trial (2005) assessed the effect of pioglitazone on secondary prevention

of macrovascular events in 5238 patients. Patients were followed for a mean of 2.85 years.

The primary endpoint was the composite of all-cause mortality, non-fatal myocardial

infarction (including silent myocardial infarction), stroke, acute coronary syndrome,

endovascular or surgical intervention in the coronary or leg arteries, and amputation


above the ankle; no significant results were achieved for the primary endpoint (HR, 0.90).

The secondary endpoint (composite of all-cause mortality, MI and stroke) was

significantly less frequent in the pioglitazone group (HR, 0.84); meanwhile the incidence

of heart failure hospitalisations was higher in the pioglitazone group. In a subgroup of

2,445 patients with previous MI, pioglitazone achieved a statistically significant

beneficial effect on the prespecified end point of fatal and non-fatal MI (28%) and acute

coronary syndrome (ACS) (37%), but not in the primary endpoint; the incidence of heart

failure and fatal heart failure were higher in the pioglitazone group. [68, 69]

The weaknesses of the design of the RECORD study (the composite of death and

cardiovascular hospitalisations) and conduction (the low rate of events) have been

criticised. [70] The results of an interim analysis were published in 2007 as a response to

the meta-analysis of Nissen. In this meta-analysis had suggested increased CV risk for

patients treated with rosiglitazone, with a significant odds ratio for myocardial infarction

of 1.43 (95% confidence interval: 1.03 to 1.98, p = 0.03) and a border-line significant

increase of the risk of CV mortality. [71, 72] Instead, the interim results from the

RECORD study reported that rosiglitazone was associated with a small, non-significant

increase in the risk of the primary outcome of all hospitalizations and deaths from CV

cause (HR, 1.08; 95% CI 0.89 to 1.31), and for the fatal or non-fatal myocardial infarction

outcome, the HR ratio was 1.16 (95% CI 0.75 to 1.81). [71] The sponsor did a meta-

analysis with data similar to that by Nissen and Wolski had been provided to the FDA

and the European Medicines Agency in August 2006, and prompted the information was

included in product labels in Europe two months later. [70] Observational research using

health care database found that the treatment with TZD monotherapy was associated with

a significantly increased risk of congestive heart failure (adjusted rate ratio [RR], 1.60;

95% CI, 1.21-2.10), acute myocardial infarction (RR, 1.40; 95% CI 1.05-1.86), and death

(RR, 1.29 95% CI 1.02-1.62) compared with other oral hypoglycemic agent combination

therapies. The increased risk of congestive heart failure, acute myocardial infarction, and

mortality associated with TZD use appeared limited to rosiglitazone. [73]


II.2. The “post-rosiglitazone era”: regulatory guidances for new

non-insulin glucose-lowering agents

Three new classes have been introduced since 2005, the glucagon-like peptide-1 (GLP-

1) receptor agonists, the dipeptidyl peptidase-4 (DPP- 4) inhibitors, and the sodium-

glucose cotransporter-2 (SGLT-2) inhibitors. Exenatide b.i.d., the first GLP-1 RA, was

approved in the US in 2005 and sitagliptin, the first DPP-4 i, in 2006, and one year later

in the UE.[74]

In September 2010 US FDA significantly restricted the use of rosiglitazone to patients

who cannot control their Type 2 diabetes on other medications, and required that GSK

develop a restricted access program for Avandia (rosiglitazone) under a risk evaluation

and mitigation strategy - or REMS - available to new patients only if they are unable to

achieve glucose control on other medications and are unable to take pioglitazone, the only

other drug in the class of thiazolidinediones. [75] FDA performed a re-evaluation of the

Rosiglitazone Evaluated for Cardiovascular Outcomes and Regulation of Glycemia in

Diabetes (RECORD) trial and decided to modify the rosiglitazone REMS program

requirements in November 2013. [76] Rosiglitazone was withdrawn from the EU market

in September 2010; the marketing authorisation for Avandia (Rosiglitazone) expired on

11 July 2015 following the decision of the marketing authorisation holder, SmithKline

Beecham Ltd., not to apply for a renewal of the marketing authorisation. [77, 78]

In December 2008, the US Food and Drug Administration (FDA) issued a Guidance for

Industry recommending that “to establish the safety of a new antidiabetic therapy to treat

type 2 diabetes, sponsors should demonstrate that the therapy will not result in an

unacceptable increase in cardiovascular risk”. At the time of NDA submission, all

applicants have to compare the incidence of important CV events occurring with their

investigational agent to the incidence of the same types of events in the control group. At

least three major cardiovascular events (MACE) should be prospectively adjudicated: CV

death, non-fatal myocardial infarction and non-fatal stroke, and can include other

endpoints. This assessment can be accomplished through a meta-analysis of phase 2 and

phase 3 clinical trials and/or throughout a single, large safety trial. [38]


In 2012, the EMA issued guidance stating that a new glucose-lowering agent should

preferably show a neutral or beneficial effect on parameters associated with

cardiovascular risk(e.g. body weight, blood pressure, lipid levels), recommending that

“the emphasis will be on major cardiovascular events (MACE) (CV death, non-fatal

myocardial infarction and stroke) but hospitalization for unstable angina could also be

included in a composite endpoint if the main objective is to exclude a safety signal. Other

events, such as revascularisation and/or worsening of heart failure, will also be evaluated.

[39, 79]

As a result of these regulatory recommendations, an increasing number of large

randomised controlled trials have been designed and conducted to assess the impact of

non-insulin glucose-lowering agents on major cardiovascular outcomes. Due to

randomised allocation and double-blind design, well designed and conducted RCTs are

considered the “gold standard” for scientific evidence: every patient in a study has a

known (usually equal) chance of receiving each of the treatments, the selection bias is

minimised, and both known (and unknown) confounding factors are likely to be

distributed in an unbiased manner between the groups. Random assignment of a large

number of subjects into treatment groups usually leads to a good balance of observed and

unobserved risk factors in all groups. Nevertheless, randomised controlled trials have

major limitations when they are used to assess the role of medications in the aetiology

and management of chronic diseases. The primary limitations arise from selected

populations, the long-time required from trial design to completion, the relatively short

duration of exposure, and under representativeness of frail elderly patients. Results

obtained from trials can be misleading if generalised to the general population because

effect sizes, baseline risks, and comorbidity have been shown to differ between trial

populations and the broader population not represented in trials. [80, 81] Although longer,

with larger sample sizes, and including older patients, CV outcomes large trials for

hypoglycemic agents are not completely free of these limitations. In particular, RCTs

include selected populations (i.e., patients at high cardiovascular risk, exclusion of

patients at the end stage of chronic renal disease, good treatment compliance); and

patients are followed in conditions different from the clinical practice.


FDA’s and EMA guidances recommend that outcomes in RCTs evaluating glucose-

lowering agents for T2DM should include a 3 p. MACE (cardiovascular death, non-fatal

infarction and non-fatal stroke), and possibly another expanded MACE, including

unstable angina, revascularization procedures; EMA included heart failure [79] Being

death the most critical clinical event, it has a very low expected rate in T2DM trials; the

event rate of the rest of CV outcomes are foreseen to be low, even in high CV risk

populations. Then, to reduce the sample size and the length of the study, these RCTs have

a primary composite outcome (PCO) of three or four individual components:

cardiovascular death, and non-fatal events of similar clinical importance. However,

analysis, interpretation and reporting of COs are complex and can be even misleading.

[81]

Up to date, fifteen cardiovascular outcome trials comparing drugs vs placebo have been

published; an additional one, the CAROLINA trial, that assessed the safety of linagliptin

vs placebo. Out of them, all those belonging to the class of dipeptidyl-peptidase -4

inhibitors showed non-inferiority vs placebo but failed to show superiority.

Apart from other studies terminated because of safety concerns (fasiglifam) and some

others finished (ACE [acarbose]) or terminated (omarigliptin, taspoglutide). The trial

assessing omarigliptin in patients with T2DM and CVD, OMNEON (A Study to Assess

Cardiovascular Outcomes Following Treatment With Omarigliptin) was terminated

because of commercial reasons; interim results showed no effect on MACE.

Peroxisome proliferator-activated receptors (PPARs)

AleCardio: Aleglitazar is a dual agonist of PPARs with insulin-sensitising and

glucose-lowering actions. The AleCardio trial enrolled 7226 patients hospitalised

for acute coronary syndrome. The planned follow-up – at least 2.5 years- was

terminated after a median of 104 weeks, upon recommendation of the data and

safety monitoring board due to futility for efficacy and increased rates of safety

endpoints (hospitalisation due to heart failure and changes in renal function). In

July 2013, the sponsor announced that following the results of a regular safety

review, the independent Data and Safety Monitoring Board (DSMB) has


recommended halting the trial due to safety signals and lack of efficacy. The 3-

point MACE was non-significant (HR 0.96). There were increased rates of heart

failure and gastrointestinal bleeding and renal impairment. Heart failure is an

established risk of PPAR-gamma activators and thought to be due to fluid

retention. The increased risk for heart failure associated with aleglitazar in the

AleCardio trial (HR, 1.22) was similar to that attributed to pioglitazone in a meta-

analysis (HR, 1.41) Increased serum creatinine is also a known effect of PPAR-

alpha activators and was associated with aleglitazar in this trial. [82-84]

Dipeptidyl peptidase-4 inhibitors

The incretin-based therapies include the oral dipeptidyl peptidase 4 inhibitors (DPP-4 i)

and glucagon-like peptide-1 receptor (GLP-1R) agonists. While GLP-1RAs exert

glucoregulatory actions by binding to GLP-1 receptors, DPP-4 i prevent inactivation of

GLP-1.

Four CVOTs assessed DPP-4 inhibitors vs placebo: TECOS (sitagliptin)], EXAMINE,

(alogliptin), SAVOR-TIMI 53 (saxagliptin) and CARMELINA (linagliptin). None of

these trials has shown to reduce the risk of MACE in the treatment group. Saxagliptin has

shown a significant increased frequency of heart failure and alogliptin a non-significant

increased risk of HF. Table II.1 shows the characteristics and results of the CVOTs

assessing DPP-4 inhibitors vs placebo.

TECOS (sitagliptin): Sitagliptin was the first marketed dipeptidyl-peptidase

inhibitor, agent approved by the US FDA in October 2006; the European

Commission granted a marketing authorisation valid throughout the European

Union in March 2007. The TECOS evaluated long-term effects on cardiovascular

outcomes of sitagliptin, or placebo added to existing therapy in 14,671 patients

aged ≥50 years with glycated haemoglobin level, 6.5 to 8.0%, established CVD

and no severe renal insufficiency, the median follow-up was 3 yrs. In the TECOS

Study, sitagliptin was non-inferior to placebo for the primary 4-points MACE (+

and hospitalisation for unstable angina), HR, 0.98; 95% CI, 0.88 to 1.09, or

hospitalization for heart failure.[85]


Table II.1: Characteristics of cardiovascular outcomes trials (CVOTs) assessing the effects of dipeptidyl peptidase-4 inhibitors (DPP-4 i) vs placebo

CVOTs: cardiovascular outcomes trials; DPP-4 i: dipeptidyl peptidase-4 inhibitors; MACE: major adverse cardiovascular events (composite outcome); 5-p: 5 points MACE; 4-p: 4 points MACE; 3-p: 3-points MACE. CV: cardiovascular; AMI: acute myocardial infarction; HHF: hospitalisation for heart failure.

All subjets were randomized 1:1 to investigational product and placebo. Significant results are highlighted in bold.

EXAMINE (alogliptin): Alogliptin is a selective DPP-4 i, approved for the

treatment of type 2 diabetes in January 2013 in the US and in September 2013 in

EU. Examination of Cardiovascular Outcomes with Alogliptin versus Standard

of Care (EXAMINE study) assessed the primary 3-point MACE in 5380 T2DM

patients with an acute coronary syndrome (ACS) within the previous 15 to 90

days, and showed no difference between groups, although the glycated

haemoglobin levels were significantly lower with alogliptin than with

placebo.[86]

CVOTs

DPP-4 i

N Follow-up

Median

MACE All-Cause Mortality

CV Mortality

AMI Stroke HHF

TECOS [85] Sitagliptin

14,671 3.0 yrs 0.98 (0.89–1.08) (4 p)

0.99 (0.89–1.10) (3-p)

1.01 (0.90–1.14)

1.03 (0.89–1.19)

0.95 (0.81–1.11)

0.97 (0.79–1.19)

1.00 (0.83–1.20)

EXAMINE [86, 87] Alogliptin

5,380 1.5 yrs 0.96 (≤1.16) 0.88 (0.71–1.09)

0.85 (0.66–1.10)

1.08 (0.88–1.33)

0.91 (0.55–1.50)

(non-significant increase)

SAVOR-TIMI 53 [88] Saxagliptin

16,492 2.1 yrs 1.00 (0.89–1.12) (3-p)

1.02 (0.94–1.11)

1.11 (0.96–1.27)

1.03 (0.87–1.22)

0.95 (0.80–1.12)

1.11 (0.88–1.39)

1.27 (1.07–1.51)

CARMELINA

[93] Linagliptin

6,991 2.2 yrs 1.02 (0.89-1.17)

0.98 (0.84-1.13)

0.96 (0.81-1.14)

1.12 (0.90-1.40)

0.91 (0.67-1.23)

0.90 (0.74-1.08)


The exploratory extended MACE endpoint (plus urgent revascularisation due to

unstable angina, and hospital admission for heart failure) did not show

differences, HR 0.98, 95% CI 0.86–1.12, either the hospital admission for heart

failure HR 1.07, 95% CI 0.79–1.46).65 Alogliptin had no effect on composite

events of cardiovascular death and hospital admission for heart failure in the post

hoc analysis (HR 1.00, 95% CI 0.82–1·21) and results did not differ by baseline

BNP concentration. Patients with a history of heart failure at baseline were older,

more frequently women, and had higher baseline BNP concentrations and lower

eGFR values, than patients with no history of heart failure. [87]

SAVOR-TIMI 53 (saxagliptin): Saxagliptin is DPP-4 inhibitor approved in July

2009 in the US and in October 2009 in EU. The SAVOR-TIMI 53 trial [88]

included 16,492 patients with T2DM, HbA1c 6.5% to 12.0%, and either a history

of established cardiovascular disease (78%) or multiple risk factors for vascular

disease; the follow-up had a median of 2.1 years. Results showed neutral effects

of saxagliptin on primary composite of 3-point MACE, HR, 1.00; 95% CI 0.89 to

1.12, as well as on the major secondary 5-point MACE (plus hospitalization for

unstable angina, coronary revascularization, or heart failure) HR 1.02; 95% CI

0.94 to 1.11; P = 0.66.

However, hospitalization for heart failure was more frequent in the saxagliptin

group than in the placebo group (3.5% vs 2.8%; hazard ratio, 1.27; 95% CI, 1.07

to 1.51). [89] These results were consistent irrespective of the renal function.

Overall, the risk of hospitalisation for heart failure among the three eGFR severity

groups of patients was 2.2% (reference), 7.4% (adjusted HR 2.38), and 13.0%

(adjusted HR 4.59), respectively. The relative risk of hospitalisation for heart

failure with saxagliptin was similar in patients with different levels of eGFR.

Saxagliptin and placebo groups showed similar results in the change in eGFR and

safety renal outcomes, including doubling of serum creatinine, initiation of

chronic dialysis, renal transplantation, or serum creatinine >6.0 mg/dL. However,

patients with renal impairment who were treated with saxagliptin achieved similar

reductions in microalbuminuria than those of the overall trial population. [90] The

change in albumin/creatinine ratio (ACR) did not correlate with that in HbA1c.


[91] In the SAVOR TIMI 53 trial, baseline HbA1c ≥7% was associated with

increased risk of cardiovascular death, myocardial infarction, or ischemic stroke

(adjusted hazard ratio 1.35; 95% CI 1.17-1.58) but not with hospitalisation for

heart failure (adjusted HR 1.09; 95% CI, 0.88-1.36). [92]

CARMELINA (linagliptin): The trial CARMELINA trial included 6979

patients (mean age, 65.9 years; eGFR, 54.6 mL/min/1.73 m; 80.1% with renal

impairment), the median follow-up was 2.2 years. The HR for the 3-points MACE

was 1.02; 95% CI, 0.89-1.17. No differences were observed for the kidney

outcome (time to first occurrence of adjudicated death due to renal failure, end-

stage renal disease, ESRD, or sustained 40% or higher decrease in eGFR from

baseline) HR, 1.04; 95% CI, 0.89-1.22. No difference was found in hypoglycemia,

but there were more cases of confirmed acute pancreatitis in the linagliptin

group.[93]

Glucagon-like peptide 1 receptor agonists (GLP-1 Ras)

Glucagon-like peptide-1 (GLP-1 potentiates the insulin secretion from pancreatic beta

cells and lowers inappropriate high glucagon secretion in a glucose-dependent manner; it

also has effects in extrapancreatic tissues (gastrointestinal tract, heart, vasculature, and

central and peripheral nervous system). Seven studies of the class of the glucagon-like

peptide-1 receptor agonists: LEADER (liraglutide), SUSTAIN-6 (semaglutide),

HARMONY (albiglutide) and the REWIND (dulaglutide) trials showed benefits on

MACE; the PIONEER trial (oral semaglutide), the EXSCEL trial (exenatide) and the

ELIXA (lixisenatide) showed non-inferiority but no beneficial effects on CV outcomes.

Table II.2 shows the characteristic and results of CVOTs assessing GLP-1 RAs vs

placebo.

ELIXA: Lixisenatide is a GLP-1 RA, with a short half-life (i.v. 30 min and 2−3

h after s.c. administration. The ELIXA study included 6068 patients with acute

coronary syndrome within the previous 180 days, mean follow-up was 2.1 years.

The intervention showed neutral results on the 4-points MACE (+ unstable

angina) and in its components or heart failure. [94]


Table II.2: Characteristics of cardiovascular outcomes trials (CVOTs) assessing effects of glucagon-like peptide 1 receptor agonists (GLP-1 RAs) vs placebo

CVOTs

GLP-1 RAs n Follow-

up

Median


CV Mortality

AMI Stroke HHF

ELIXA

[94]

Lixisenatide

6,068 2.1 yr. 1.02 (0.89- 1.17)

0.94 (0.78- 1.13)

0.98 (0.78–1.22)

1.03 (0.87–1.22)

1.12 (0.79–1.58)

0.96 (0.75 -1.23)

LEADER

[95]

Liraglutide

9,340 3.8 yr. 0.87 * (0.78- 0.97)

0.85* (0.74- 0.97)

0.78* (0.66- 0.93)

0.86 (0.73–1.00

0.86 (0.71–1.06)

0.87 (0.73–1.05)

SUSTAIN-6

[96]

Semaglutide

3,297 2.1 yr. 0.74 * (0.58- 0.95)

1.05 (0.74–1.50)

0.98; (0.65 1.48)

0.74 (0.51-1.08)

0.61 (0.38 -0.99)

1.11 (0.77–1.61)

EXSCEL

[97]

Exenatide LAR

14,752 3.2 yr. 0.91 (0.83- 1.00)

0.86* (0.77−0.97)

0.88 (0.76−1.02)

0.97 (0.85−1.10)

0.85 (0.70−1.03)

0.94 (0.78−1.13)

HARMONY

[98]

Albiglutide

9,463

1·6 yr. 0.78 * (0·68–0·90)

0.95 (0.79–1.16)

0.93 (0·73–1·19)

0.75 * (0.61–0.90)

0.86 (0.66–1.14)

prior HF [0.70 (0.54-

0.90)]*

PIONEER-6

[99]

Oral semaglutide

3,183

1.32 0.79; (0.57 to 1.11)

0.51* (0.31- 0.84)

0.49 * (0.27-0.92)

1.18 (0.73 - 1.90)

0.74 (0.35- 1.57)

0.86 (0.48–1.55)

REWIND

[100]

Dulaglutide

9,901

5.4 yr.

[5.1–5.9]

0.88 * (0.79–0.99)

0.90 (0.80–1.01)

0.91 (0.78–1.06)

0.96 (0.79–1.15)

0.76 * (0.62–0.94)

0.93 (0.77–1.12)

CVOTs: cardiovascular outcomes trials; GLP-1 RAs: glucagon-like peptide 1 receptor agonists; MACE: major adverse cardiovascular events (composite outcome4-p: 4 points MACE; 3-p: 3-points MACE. CV: cardiovascular; AMI: acute myocardial infarction; HHF: hospitalisation for heart failure. Significant results are highlighted in bold. Values for follow-up are median. Participants in all trials were randomized 1 :1 to investigational drug or placebo. * no history of HF [0.82 (0.69-0.98)]


The LEADER (liraglutide) trial assessed the effects of liraglutide, a GLP-1

analogue administered once daily. The LEADER assessed the 3-point MACE in

9340 patients with high CV risk T2DM; the median follow-up was 3.8 years. The

primary outcome for the liraglutide group was significant lower (HR, 0.87 95%

CI 0.78-0.97), mainly due to a lower rate in CV death in the liraglutide group

(hazard ratio, 0.78). Non-fatal MI and non-fatal stroke were non-statistically

lower in the liraglutide group: HR, 0.88 95% CI 0.71-1.03 and HR, 0.89 95% CI

0.72-1.11 respectively. The rate of death from any cause was lower in

the liraglutide group (HR, 0.85 95% CI 0.74-0.97). Hospitalisation for heart

failure (HHF) was non-significantly reduced in the liraglutide arm (HR, 0.87 95%

CI 0.73-1.05).The rate of composite outcome of renal or retinal microvascular

events was lower in the liraglutide group than in the placebo group (hazard ratio,

0.84), driven by a lower rate of nephropathy events in the liraglutide group (hazard

ratio, 0.78), retinopathy events were non significantly higher in the liraglutide

group than in the placebo group (0.6 vs 0.5 events per 100 patient-years; HR,

1.15). [95]

SUSTAIN-6: The SUSTAIN study was a non-inferiority large RCT assessing the

effect of semaglutide, a long-acting glucagon-like peptide 1 (GLP-1) analogue

with an extended half-life of approximately 1 week, on cardiovascular outcomes

vs placebo 3297 Type 2 diabetic patients. The primary composite outcome was

the 3 points MACE, HR 0.74; 95% CI 0.58 to 0.95. Nonfatal myocardial infarction

was reduced, HR 0.74; 95% CI, 0.51 to 1.08; as well as nonfatal stroke (HR 0.61;

95% CI, 0.38 to 0.99. Rates of death from cardiovascular causes were similar in

the two groups. [96]

The EXSCEL (exenatide LAR) included 14,752 patients, 73.1% with a previous

CVD; the median follow-up was 3.2 years. Patients treated with exenatide and

placebo group did not show differences in the 3-point MACE, HR 0.91; 95% CI

0.83 to 1.00. The rates of death from cardiovascular causes, fatal or non-fatal

myocardial infarction, fatal or non-fatal stroke, hospitalisation for heart failure,

and hospitalization for acute coronary syndrome, and the incidence of acute


pancreatitis, pancreatic cancer, medullary thyroid carcinoma, and serious adverse

events did not differ significantly between the two groups. [97]

The HARMONY (albiglutide) trial included 9463 participants with CVD; the

median follow-up was at least 1.6 years. Albiglutide was superior to placebo in

reducing the risk of 3 p. MACE (HR 0.78, 95% CI 0·68-0·90).[98]

The PIONEER 6 (oral semaglutide) trial assessed the 3-point composite MACE

in 3183 patients with high cardiovascular risk (mean age 66 years; 84.7% were 50

years of age or older and had cardiovascular or chronic kidney disease; mean

follow-up was 15.9 months) The primary MACE for semaglutide vs placebo was

non-significant, HR 0.79; 95% CI 0.57 to 1.11. For components of the MACE,

death from cardiovascular causes, HR 0.49; 95% CI, 0.27 to 0.92; and death from

any cause (HR 0.51; 95% CI, 0.31 to 0.84) were significantly reduced, while non-

fatal MI and non-fatal stroke showed no differences. [99]

The REWIND (dulaglutide) trial recruited 9901 patients who had either a

previous cardiovascular event or cardiovascular risk factors, 31.5 % of patients

had a history CVD, 22.2% had a baseline renal impairment, and 8.5% had a

history of heart failure; mean HbA1c 7·2%; the mean follow-up was 5.4 years.

Patients treated with dulaglutide had decreased risk of the primary outcome, the

3-point MACE, HR 0.88, 95% CI 0.79-0.99 and stroke HR 0.76 (0.62–0.94. All-

cause and CV mortality, MI and HF did not differ between groups. [100] The

exploratory analysis showed improved renal outcomes in patients treated with

dulaglutide: HR 0.95, 95% CI 0.77-0.93, with a clear effect for microalbuminuria,

HR 0.77, 95% CI 0.68–0.87. More participants assigned to dulaglutide reported a

gastrointestinal adverse event during follow-up compared with participants

assigned to placebo. [101]

Sodium-glucose co-transporter 2 inhibitors (SGLT-2)

The SGLT1 and SGLT2 are primarily responsible for intestinal glucose absorption and

reabsorption of most of the filtered glucose in the kidney. The tree studies belonging to

the class of sodium-glucose cotransporter 2 inhibitors: EMPA-REG (empagliflozin),


CANVAS (canagliflozin) and DECLARE-TIMI 58 (dapagliflozin) showed reduction in

heart failure outcomes. For the group of glucose co-transporter-2 inhibitors (SGLT-2 i),

empagliflozin (EMPA-REG outcome) has shown a substantial relative risk reduction of

death from cardiovascular causes, hospitalisation for heart failure and all-cause mortality.

The hematocrit increases during treatment with SGLT2 inhibitors, which have a diuretic

effect but do not cause sufficient hemoconcentration to increase the risk of cerebral

infarction. Elevation of the hematocrit during SGLT2 inhibitor therapy is presumed to

involve enhancement of erythropoiesis in addition to hemoconcentration.[102] The

increase in the hematocrit and an HR of 1.33 for stroke have been found in a meta-analysis

of RCTs with SGLT2 inhibitors. [103, 104] The characteristics and results of the CVOTs

assessing sodium-glucose cotransporter-2 inhibitors vs placebo are shown in Table III.3.

Table III.3: Characteristics of cardiovascular outcomes trials (CVOTs) assessing effects of sodium-glucose cotransporter-2 inhibitors (SGLT-2 i) vs placebo

CVOTs

SGLT-2 i

N Follow-up

Median


CV Mortality

AMI Stroke HHF

EMPAREG OUTCOMES [105, 106] Empagliflozin

7020 (1:1:1)

3.1 0.86 (0.74–0.99)

0.68 (0.57–0.82)

0.62 (0.49–0.77)

0.87 (0.70–1.09)

1.18 (0.89–1.56)

0.65 (0.50–0.85)

CANVAS [107] Canagliflozin

10,142 (1:1)

2.34 0.86 (0.75–0.97)

0.87 (0.74–1.01)

0.87 (0.72–1.06)

0.89 (0.73–1.09

0.87 (0.69–1.09)

0.67 (0.52–0.87)

DECLARE-TIMI 58 [108, 109] Dapagliflozin

17,160 (1:1)

4.2 0.93 (0.84−1.03)

0.93 (0.82−1.04)

0.98 (0.82−1.17)

0.89 (0.77−1.01)

1.01 (0.84−1.21

0.73 (0.61−0.88)

CVOTs: cardiovascular outcomes trials; SGLT-2 i: sodium-glucose cotransporter-2 inhibitors; MACE: major adverse cardiovascular events (composite outcome4-p: 4 points MACE; 3-p: 3-points MACE. CV: cardiovascular; AMI: acute myocardial infarction; HHF: hospitalisation for heart failure. Significant results are highlighted in bold. Randomisation is shown in n column, as (1 :1), empagliflozin has three arms : placebo, empagliflozin 10 mg and empagliflozin 25 mg.


The EMPA-REG outcomes (empagliflozin) study assessed the effect of

empagliflozin, in 7020 patients with a high risk for cardiovascular events; the

median follow-up time was 3.1 yrs. The primary outcome was the 3-point MACE;

the key secondary composite outcome was the primary outcome plus

hospitalisation for unstable angina. Empagliflozin showed a reduction of CV

death (HR 0.62, 95% CI 0.49-0.77), hospitalisation for HF (HR 0.65, 95% CI

0.50-0.85), and death from any cause (HR 0.68 95% CI 0.57-0.82), but not for the

rates of fatal and non-fatal MI (HR 0.87 95%CI 0.87-1.09) or fatal and non-fatal

stroke (HR 1.18, 95% CI 0.89-1.56). 10.1% had heart failure at baseline. The

composite of heart failure hospitalisation or cardiovascular death (HR: 0.66, 95%

CI 0.55–0.79), hospitalisation for heart failure (HR: 0.65, 95% CI 0.5–0.85), CV

death (HR 0.62 95% CI 0.49–0.77), and all-cause mortality was lower in patients

treated with empagliflozin than with placebo. Still, patients with HF at baseline

had smaller non-significant reductions. [105, 106]

CANVAS (canagliflozin) The CANVAS Program integrated data from two trials

involving a total of 10,142 participants (mean age 63.3 years, 35.8% women,

mean diabetes duration was 13.5 years, 65.6% had a history of cardiovascular

disease. The primary outcome was a composite of 3-point MACE. The rate of the

primary outcome was lower with canagliflozin than with placebo (HR 0.86; 95%

CI 0.75 - 0.97). Canagliflozin showed a possible benefit of canagliflozin

concerning the progression of albuminuria (HR 0.73 [95% CI 0.67-0.79]) and the

renal composite (HR 0.60, 95% CI 0.47-0.77); an increased risk of amputations

has also reported. [107]

The DECLARE-TIMI 58 trial included 17, 160 patients; 60% didn’t have

atherosclerotic CV disease, median follow-up was 4.2 years. Dapagliflozin did

not result in a lower rate of MACE but showed a reduction in CV death, HR 0.83

95% CI 0.73 to 0.95, which reflected a lower rate of hospitalization for heart

failure (HR 0.73; 95% CI, 0.61 to 0.88) By 10% of population included had heart

failure. [108, 109]


Conclusion

This summary shows that most of the CVOTs assessing non-insulin blood glucose-

lowering drugs include high CV risk patients, who, in some trials, are the 100%. The

inclusion of high CV risk patients is aimed to achieve the goal of showing absence of

negative CV outcomes in the shortest possible time. [110, 111] The EMA guidance states

that “the study population included in the studies supporting the marketing authorisation

application should resemble the target population for the medicinal product. However, a

higher representation of subjects with a high baseline risk for cardiovascular diseases and

complications (…) compared to the target population may be acceptable for assessing

cardiovascular safety profile regardless of whether a meta-analytic or a dedicated

cardiovascular outcome trial approach is used.” [79]

These large double-blind, randomised, event-driven RCTs have adopted a non-inferiority

design – in some cases, superiority is tested.

Excepted the CAROLINA trial (that assessed CV outcomes in patients treated with

linagliptin or glimepiride), all the investigational products are compared to placebo in

addition to standard care.

All the compounds of the class of DPP-4 i showed no effects on MACE, but some agents

(saxagliptin, alogliptin) were associated with an increase in the risk of heart failure.

Conversely, because of the mechanism of action of SGLT-2, all these agents show

benefits in terms of heart failure risk; however, the trials had different results. The

differences in results might be caused by characteristics in populations (lower percentage

of high CV risk patients in the dapagliflozin CVOT), but also by drugs’ characteristics.

[112] In the class of GLP-1 RA, three agents have achieved reductions in risk of MACE:

liraglutide, albiglutide and semaglutide; all are long-acting GLP-1 RA.

Liraglutide and empagliflozin received a US FDA approval for the reduction of the risk

of cardiovascular death in adult patients with type 2 diabetes mellitus and cardiovascular

disease prevention. [113, 114] The question arises if the beneficial effect achieved in high

CV risk patients can be obtained when treating unselected populations.

III. Generalizability of Cardiovascular

Outcome Trials to the Real World:

Implications for Clinical Practice

III. Generalizability of Cardiovascular Safety Trials 33


III. Generalizability of Cardiovascular

Outcome Trials to the Real World:

Implications for Clinical Practice

Randomised clinical trials (RCTs) are considered the gold standard for assessing the

efficacy of treatments because randomisation equally distributes known and unknown

factors among control and intervention groups, reducing the potential for confounding:

the different groups are therefore comparable and, if the study is sufficiently powered,

the effect of the intervention can be identified.[80] RCTs generate evidence on the

benefits and harms of therapeutic interventions and are the primary basis for many

regulatory decisions – the first and the more important one is the marketing authorisation

- and clinical guidelines.

Since 2008, large trials evaluating clinically significant CV outcomes (cardiovascular

outcomes trials, CVOTs) have provided insights on the CV safety of new non-insulin

blood glucose-lowering agents (NIADs, non-insulin “antidiabetic” drugs). [38, 115] In

CVOTs, even if they have large sample size and a long follow-up period, mortality and

events such as MI and stroke occur at a relatively low incidence rate. Because of this,

CVOTs need to gather significantly clinical events in one primary composite endpoint;

this allows sufficient power for evaluation. Most CVOTs assessing the effect of NIADs

on Type2 diabetes mellitus population (T2DM) have a primary composite outcome of

three-points (i.e. myocardial infarction, stroke and CV death) or four-points major

adverse cardiovascular events (usually including unstable angina). [116 -118]

III. Generalizability of Cardiovascular Safety Trials 34 Nelly Raquel Herrera Comoglio Eu2P PhD December 2019

As the CV events included in the primary composite outcome are relatively rare, CVOTs

generally include patients with a high risk of CV and with an expected high rate of CV

events (e.g. patients with prior ischemic events occurred in a defined time) to ensure that

the trial will have sufficient statistical power and to achieve the required number of events

in the shortest time – usually more than two years. In CVOTs, many patients had

established CVD, which ranged from 41% in DECLARE-TIMI (dapagliflozin) to 100%

in EXAMINE (alogliptin), ELIXA (lixisenatide), EMPA‐REG OUTCOME

(empagliflozin), and HARMONY (albiglutide). [108, 86, 94, 105, 98] These selected

populations limit the generalizability of the results and prevent the assessment of primary

prevention. [119] For instance, the rate of events in patients with a recent MI or ACS is

much higher than in the general population, [120-122] As this high CV-risk population

represents only a small part of the general T2DM population, the external validity of study

results should be considered restricted to those who could have been enrolled in the study.

Based on the beneficial results of CVOTs, some agents have received the approval to

reduce the risk of MACE, or death in patients with type 2 diabetes who have established

CVD or CV risk factors, consistently to the populations included in the respective

CVOTs: empagliflozin to reduce the risk of CV death (2016), liraglutide to reduce MACE

(2017), canagliflozin to reduce CV events (2018) and dapagliflozin to reduce the risk of

hospitalisation for heart failure (2018) and to reduce the risk of end-stage kidney disease,

worsening of kidney function, heart-related death, and being hospitalised for heart failure

in certain patients with type 2 diabetes and diabetic kidney disease (2019). To December

2019, other applications are still pending (injectable semaglutide to reduce the risk of

major adverse cardiovascular events (MACE) and dulaglutide for the reduction of major

adverse cardiovascular events (MACE) in adults with type 2 diabetes who have

established cardiovascular (CV) disease or multiple cardiovascular risk factors.

The generalisability of RCTs’ results to patients in daily clinical practice is a significant

issue both for clinical decision making and the incorporation of a given agent, or a class,

in updated clinical guidelines. [123] Despite their similar characteristics in study design

and definition of primary endpoints, CVOTs assessing agents of the same class of NIADs

often differ in their inclusion/exclusion criteria. These different criteria impact on

generalizability.


A meta-analysis of CVOTs that analysed results separately in patients with or without

preexisting cardiovascular disease found that, compared with placebo, the use of both

glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors

was associated with a significant 14% lower MACE risk only in patients with preexisting

cardiovascular disease at baseline; instead, the results were neutral for those patients

without cardiovascular disease at baseline. [119]

The comparability of populations included in CVOTs and the “real-world” populations

that could benefit from the good results of these trials is not only a clinical but also an

economic issue. The cost of newer therapies as SGLT-2 i and GLP-1 RA are roughly 5

to 10-fold more expensive than older treatments. [124] For general populations, the

benefit in terms of hospitalisations, adverse events (cardiovascular or not) and mortality

remain to be assessed. When analysing hospital admissions costs, the differences in the

event rates for MI, stroke or death for high CV risk populations and unselected

populations should be taken into account, to not to oversize cost estimations. An

economic-model analysis for “real-world” healthcare costs, but based on RCTs’

populations might be misleading. [125]

Follow-up duration should also be considered in the analysis of benefit because in primary

care, patients usually maintain their treatments for much more extended periods than in

CVOTs, and even observational studies.

III.1. Observational studies assessing the comparability of real-

world populations with CVOTs’ populations

Glucagon-like peptide 1 receptor agonists

Hinton et al. conducted with data of the UK Royal College of General Practitioners

(RCGP) Research and Surveillance Centre (RSC) network database. In this study, only

16.6% out of 84 394 T2DM patients met the inclusion criteria for established or high-risk

CV disease of the LEADER trial (liraglutide). In the general population studied, patients

were older (73.2 vs 64.2 years), had lower mean glycated haemoglobin (67.1 vs 71.6

mmol/mol) and mean body mass index (30.9 vs 32.5). [126]


Boye et al. estimated the proportions of individuals in the reference population (US

IQVIA Real-World Data Adjudicated Claims database) weighted by data from the

National Health and Nutrition Examination Survey (NHANES), represented by subjets in

the CVOTs for age, sex, body mass index (BMI), HbA1c, eGFR category, and prior MI.

The cohort included 113,079 T2DM patients. Based on inclusion/exclusion criteria,

42.6% of the US reference population were eligible for enrolment in REWIND

(dulaglutide), versus 15.9% in EXSCEL (exenatide LAR), 13.0% in SUSTAIN-6

(semaglutide), and 12.9% in LEADER (liraglutide). [127]

Wittbrodt et al. compared data from the US population National Health and Nutrition

Examination Survey (NHANES) with published eligibility of CVOTs evaluating GLP-1

RAs, and estimated the percentage of US patients who would have met eligibility criteria

for enrollment in the GLP-1 RAs CVOTs: 47.2% of US population fulfill criteria for

EXSCEL, 22.4% for REWIND, 15.5% for FREEDOM-CVO, 12.8% for LEADER,

11.8% for SUSTAIN-6, 8% for HARMONY (albiglutide) and 6.4% for the ELIXA trial.

[128]

Sodium-glucose co-transporter inhibitors

In European countries, there were consistent patterns of representativeness of CVOTs

for enrolment criteria. The DECLARE-TMI 58 (dapagliflozin) trial had the highest

representativeness, indicating that it included and examined patients who are most

representative of the general T2D patients in the studied countries. [129]

Wittbrodt et al. conducted a cross-sectional retrospective study to evaluate the

proportions of US adults with T2D meeting the eligibility criteria for each of the 4

sodium-glucose cotransporter-2 (SGLT2) inhibitor CVOTs. Data were extracted from

the National Health and Nutrition Examination Survey, (NHANES) in the periods 2009-

2010 and 2011-2012. Weighted analysis of T2D diagnosis and other relevant clinical and

demographic characteristics was used to estimate the percentage of US adults with T2D

who met the eligibility criteria for the CANVAS program (CANagliflozin cardioVascular

Assessment Study) (canagliflozin); and the DECLARE-TIMI 58 (dapagliflozin), EMPA-

REG OUTCOME (empagliflozin), and VERTIS-CV (ertugliflozin) trials. 23,941,512 US


adults from data on key inclusion criteria and information indicating a diagnosis of T2D.

Of these, 4.1% met the criteria for EMPA-REG OUTCOME, 4.8% for VERTIS-CV,

8.8% for the CANVAS program, and 39.8% for the DECLARE-TIMI 58 trial. [130]

Birkeland et al. conducted a cross-sectional study in four European countries. In 2015, a

total T2D population of 803 836 patients was identified in Germany (29.79%), The

Netherlands (4.51%), Norway (18.63%) and Sweden (47.07%). These cohorts showed

that the general population had 25% to 44% cardiovascular (CV) disease baseline

prevalence and high CV-preventive drug use (>80%). In brief, Type2 DM unselected

population had less prevalent CV disease, and patients were slightly older than those

included in the CVOTs. As in other studies assessing generasibility, the DECLARE-TIMI

58 trial had the highest representativeness, 59% compared to the general T2D population,

2-, 3- and 4-fold higher compared to the CANVAS (34%), EMPA-REG OUTCOME

(21%) and VERTIS-CV (17%) trials, respectively. [123]

Nicolucci et al. conducted a study in 222 Italian clinics, with 455,662 adult patients with

T2DM. Among the population identified, the proportion of patients meeting major

eligibility criteria was 11.7% for EMPA-REG OUTCOME, 29.4% for CANVAS, 55.9%

for DECLARE-TIMI 58, and 12.8% for VERTIS-CV. “Real-world” patients were older,

had longer diabetes duration, lower BMI and HbA1c levels, lower prevalence of the

established cardiovascular and cerebrovascular disease, and higher rates of microvascular

complications and peripheral arterial disease than those participating in the CVOTs,

eligible patients. [131]

III.2. Characteristics of the population registered in the Catalan

SIDIAP database compared with CVOTs’ populations.

The Catalan Institute of Health (CIH) manages the main healthcare system of the

autonomous community of Catalonia, Spain, at all its levels. 76% of the Catalan

population attends at least once the primary care teams. General practitioners

participating in SIDIAP (Information System for the Development of Research in

Primary Care) record patients’ clinical, laboratory, diagnostic and medical procedures,

referrals data and prescriptions, among others. The state CIH funds the prescribed


medicines in part (for workers or employees) or for full (for retired people). The

population included in the SIDIAP database is approximately 74% of the total regional

one; being representative of the Catalan population.

In the second part of this work, the design, methods and results of an observational study

assessing the CV effects of the more used non-insulin blood-glucose-lowering agents

during 2010-2015 in the SIDIAP database are presented. Basal characteristics of the total

were calculated using descriptive statistics. Data for demographic (age and sex) and

clinical variables (HbA1c, diabetes duration, history of CV disease, history of heart and

renal failures) are shown in Table III.1.

This table presents the characteristics of the full cohort (any patient treated with at least

one non-insulin blood-glucose-lowering agent), the cohort of drug-naïve patients treated

with a single NIAD in monotherapy, the cohort of patients treated with two or more

NIADs. To compare these populations’ characteristics with those of the cohorts included

in CVOTs, Table 1 shows the basal characteristics of patients included in CVOTs of

different classes of new NIADs.

When comparing basal characteristics of populations included in non-insulin blood-

glucose-lowering therapies CVOTs and the general population registered in the primary

care SIDIAP database, populations of five CVOTs have a substantially higher proportion

of men (by 70%), in seven CVOTs the proportion of men ranges from 60.7% to 66.6%;

in the PIONEER 6 trial (oral semaglutide) the proportion is slightly higher than the

SIDIAP population prescribed two or more NIADs, and in the REWIND trial

(dulaglutide) this proportion is lower. Except for the EXAMINE trial, the populations

included in all CVOTs had a longer duration of diabetes mellitus. In five CVOTs all

participants had established CVD, in the other studies the proportion of participants with

established CVD were much higher than in SIDIAP populations, ranging from 31.5%

(REWIND trial) to 84.6%. Participants of CVOTs had from 2-fold up to 5-fold- higher

history of HF (from 9.9 to 26.8% vs 5.1%). (Table III.1)


Table III.1. Basal characteristics of cohorts of T2DM patients registered in SIDIAP database and cohorts of CVOTs assessing non-insulin blood-glucose-lowering drugs vs placebo

SIDIAP database 2010-2015 Men % Mean age,

years (SD) HbA1C

% (SD) T2DM duration

Years (SD)

BMI (SD) History CVD %

History HF %

Renal Failure %

SIDIAP Full cohort

55.5 % 66.1 (12.7) 7.9 (1.6) 5.4 (5.7) 30.8 (5.4) 21.2 % 4.8 % 2.6 %

SIDIAP Monotherapy

users

51.1 % 65.7 (13.2) 7.6 (1.6) 4.0 (5.3) 31 (5.4) 20.4 % 5.1 % 2.8 %

SIDIAP ≥ 2 NIADs

users

56.2 % 66.8 (11.7) 8.3 (1.5) 7.8 (5.6) 30.5 (5.3) 22.4 % 4.3 % 2.3%

Cardiovascular Outcomes Trials (CVOTs) Men % Mean age,

years (SD) HbA1C

% (SD) T2DM duration

Years (SD)

BMI (SD) History CVD %

History HF %

Renal Failure %

EMPAREG OUTCOMES [105, 106] Empagliflozin

72 % 63.1 (8.6) ≥ 75 yrs: 9%

8.1 (0.8) ≤5 yrs: 18% >5-10 yrs: 25 % >10 yrs 57 %

30.6±5.3 99% 10.1% 26% eGFR <60:

25.9%

CANVAS [107] Canagliflozin

64.2 % 63.3 ±8.3 8.2±0.9 13.5±7.8 32.0±5.9 65.6% 14.4 % 17.21% eGFR 76.5±

20.5 DECLARE TIMI [108, 109] Dapagliflozin

63.1 % 63.9±6.8 8.3±1.2% 11.0 32.1±6.0 40.6% CVD 59.4% risk

factors

9.9% eGFR 85.4±15.8

ELIXA [94] Lixisenatide

69.7% 59.9 ± 9.7 ≥65 yrs 33%

7.6±1.3 9.4±8.3

30.2±5.8 100% 22.4% eGFR 76.7±21.3

<60: 49.1%


LEADER [95] Liraglutide

64.3% 64.3 ± 7.2 ≥60 yrs 75%

8.7 ± 1.5 12.7 ± 8.0 32.5 ± 6.3 85.1% CVD 14.9 % risk

factors

17.12% 26.7 % eGFR <60:

21.8 % SUSTAIN-6 [96] Semaglutide

60.7% 64.6 ± 7.4 8.2±1.6 14.9 ± 8.5 32.3 ± 6.5 83% 23.6% NA

EXSCEL [97] Exenatide LAR

62% 62.7 (56.4, 68.8 ≥65 yrs: 40.25%

8.0 (7.3, 8.9) 12 (7, 18) 31.8 [28.2- 36.2]

73.1% 16.2% eGFR <60: 18.6 %

HARMONY [98] Albiglutide

70 % 64.1 8.76 (1.5) 14.1 (8.6) 32.3 ±5.9 100% 20% eGFR 79.1 (mean)

PIONEER-6 [99] Oral semaglutide

58.4 % 66.1 (7.1) ≥75 yrs: 12.9%

8.2 (1.6) 14.9 (8.5) 32.3 ± 6.5 84.6% NR eGFR 74.2 ±21. eGFR <60 :

27 % REWIND [100] Dulaglutide

53.7 % 66.2 7.3% (1.1) 10.5 (7.3) 32.3 ±5.7 31.5% CVD 20.8% CV

events

8.5% eGFR 75.3 [61.6–91.8]

TECOS [85] Sitagliptin

70,7% 66.0±8.0 7.3±0.7 9.4 [4.9-15.3] 30.2±5.7 100% 18% Mean eGFR 74.9±21.1

EXAMINE [86-87] Alogliiptin

69.7% 60.9 ± 10.0 8.0±1.1 7.1 median 28.7 median 100% 27.8% Median eGFR 71.1

<60: 28.6 % SAVOR-TIMI [88] Saxagliptin

66.6% 65.1±8.5 8.0±1.4 10.3[5.2–16.7] 31.1±5.5 78.4% 12.8% Mean 72.5±22.6 ≤ 50: 15.7%

CARMELINA [93] Linagliptin

62.9% 65.9 ±9.10 7.95 ±1.01 14.8 ±9.5 31.3 ±5.3 58.5% 26.8% eGFR 54.6 ±25.0 <60: 62.3%

BMI: body mass index; CAD: coronary artery disease; CV: cardiovascular; CVD: cardiovascular disease; eGFR: (mean) estimated glomerular filtration rate ml/min/1.73 m2;

HbA1C: glycated haemoglobin; HF: heart failure; IHD: ischemic heart disease; MA: microalbuminuria; MI: myocardial infarction; SD: standard deviation; SIDIAP: Information

System for the Development of Research in Primary Care; T2DM: type 2 diabetes mellitus.


Conclusion

The results of three CVOTs has led the FDA to approve indications for

antihyperglycaemic medications to reduce the risk of CV death (empagliflozin) and to

reduce the risk of MACE (liraglutide, canagliflozin), both indications specific to patients

with established atherosclerotic cardiovascular disease. [115] The results of a meta-

analysis of CVOTs show that the decreased risk of MACE in both glucagon-like peptide-

1 receptor agonists and sodium-glucose cotransporter-2 inhibitors was confined to

patients with previous CVD (a significant 14% lower MACE) but patients without

preexisting CVD had a nonsignificant 2% higher MACE risk. [119]

The studies mentioned in this section showed that, for SLT-2 i and GLP RAs large

CVOTs, the percentage of unselected populations in European countries and US that

could have met the inclusion criteria varies largely. In the mentioned studies, only 12.8%,

12.9% and 16.6% of the general population met the inclusion criteria for the LEADER

trial (liraglutide) and 4.8% to 21% for the EMPA-REG trial (empagliflozin). Other trials

with more inclusive participation’s criteria, such as EXCEL (exenatide) and DECLARE-

TIMI (dapagliflozin) has obtained less stringent results. The percentage of patients with

established CVD registered in the SIDIAP database were from 21.2 to 22.4%.

The assessment of CV outcomes of non-insulin blood glucose-lowering drugs in “real-

world” populations adds complementary evidence of the effects of these drugs and helps

to update clinical guidelines, in spite of the methodological limitations inherent to

observational research.

Part II

Cardiovascular outcomes and mortality

among type 2 diabetes mellitus patients

prescribed first and second-line blood

glucose-lowering drugs: a population-based

cohort study in the Catalan electronic medical

record database, SIDIAP, 2010-2015

IV. Protocol

Rationale and Design

IV. Protocol Rationale and Design 44 Nelly Raquel Herrera Comoglio Eu2P PhD December 2019

IV

Protocol for the assessment of major

cardiovascular events (MACE) and mortality in new

users of non-insulin glucose-lowering agents:

observational longitudinal study in the Catalan

population-based electronic health record

database, SIDIAP, 2010-2015

IV. 1 Introduction

Randomized clinical trials (RCTs) have revolutionised medical research and improved

the quality of health care by clarifying the benefits and harms of many interventions.

[132] Because of their randomized allocation, well designed and conducted RCTs are

considered as the gold standard for assessing the effects of drugs on populations.

Randomisation distributes equally known and unknown confounding factors in order to

create a control group that is as similar as possible to the treatment group. However, in

some cases clinical trials would be infeasible (e.g., when the safety outcome is very

rare) or when the study of outcomes or exposures in an interventional or prospective

study would be unethical. [133, 134]

It is often unclear to what extent results of clinical trials conducted in selected

populations, as high CV risk, are applicable to unselected ones, composed mainly of

low CV risk patients. Large RCTs select high-risk populations because the low rate of

events would demand otherwise a larger size population - or much longer follow-up - to

reach the intended difference and the statistical power. Conversely, in observational

research, large cohorts and long-time follow-up can be obtained from longitudinal


healthcare databases (LHDs) especially if the number of subjects treated and the time

from marketing authorisation are large enough.

In recent years, Real-World Evidence has an increasingly important role in the

generation of scientific evidence and the US “21 Century Cures Act” established a

program to evaluate the potential regulatory use of real-world evidence. [135]

Healthcare longitudinal databases are increasingly used in evaluating comparative drug

efficacy and harmful effects.

Longitudinal healthcare databases (LHDs) encompass extensive records of medical

practices, diagnostic and laboratory procedures, drug prescribing and/or dispensing, and

diagnosis codes. They include electronic medical records (EMRs), administrative claims

databases and registries. EMR data are generated in the course of routine clinical care

provision and can contain clinical data such body mass index (BMI), smoking or

alcohol consumption, or diabetes duration.

For comparative efficacy research and the study of relatively rare events, LHDs have

many advantages over RCTs: they can provide large size data, include patients often

underrepresented in clinical trials, are representative of routine clinical care and suitable

to study real-world effectiveness and utilization patterns. [136] Retrospective research

avoids long delays for collecting outcomes and are available at relatively low cost. On

the other hand, observational studies almost always have bias because prognostic

factors are unequally distributed between patients exposed or not exposed to an

intervention. The non-randomized design inherent to observational research can

produce misleading results due to a number of flaws arising from design, time-related

bias, matching and analysis. [137] All studies conducted in large longitudinal databases

are subject to information bias. [133] In claims administrative databases, the

discontinuity of coverage can impact on follow-up periods for reasons other than the

outcome of interest or the study period, EMRs can be more stable than administrative

claims data. However, records from one part of the health system, such as primary care,

may not capture health events occurring in other parts of the health system, such as

hospital care. For instance, a study conducted between 2003-2009, found that UK


Clinical Practice Research Datalink (CPRD) captured most cases of MI, but missed

about a 25% of cases, and hospital records alone underestimated the true incidence of

myocardial infarction. [138]

On April 6th 2017, the following protocol for an observational study has been registered

in ENCePP.

This protocol is in accordance with the ENCePP Guide on Methodological Standards in

Pharmacoepidemiology [139] and the guidelines International Society of

Pharmacoepidemiology (ISPE) guidelines, revision 3 dated June 2015. [140]

The protocol has been registered in ENCePP (18513) on April 6th, 2017. [141]

Title: Major cardiovascular events in new users of non-insulin glucose-lowering

agents: observational longitudinal study in the Catalan population-based

electronic health record database, SIDIAP, 2010-2015.

STUDY REFERENCE NUMBER: ENCePP 18510

AUTHORS: Raquel Herrera Comoglio1, Xavier Vidal Guitart2, Luisa Ibáñez Mora2,

Mònica Sabaté Gallego2, Pili Ferrer Argelés2, Maria Elena Ballarin Alins2, Jean-Luc

Faillie3

1 School of Medicine, National University of Cordoba, Argentina

2 Catalan Institut of Pharmacoepidemiology, Autonomous University of Barcelone,

Barcelone, Spain

3 Département de Pharmacologie Médicale et Toxicologie, Centre Régional de

PharmacoVigilance, CHRU Montpellier, 371 avenue du Doyen Gaston Giraud,

34295 Montpellier, France. [email protected]


ABSTRACT:

Background:

Cardiovascular (CV) risk is the leading cause of morbidity and mortality in T2DM

population. The effect of control serum glucose levels on macrovascular

complications remains uncertain. Glucose-lowering agents are currently marketed

based on results of clinical trials with subrogate variables, mainly the percentage of

glycated haemoglobin and other glucose markers. In 2007, concerns about CV safety

of rosiglitazone led to regulatory recommendations regarding CV risk of new

hypoglycemic agents, which are in force since 2008 (FDA, US) and 2012 (EMA,

EU). In order to fulfil these recommendations, since 2008 a number of large

randomized clinical trials have been designed and conducted, with a non-inferiority

design as basis. Among those, three recently published large RCTs showed beneficial

effects on cardiovascular mortality, meanwhile five large RCTs have failed to

demonstrate any beneficial effect on CV outcomes. Other large RCTs, on-going or

recently completed, are currently assessing the CV effect of seven marketed agents–

and are foreseen to be completed up to 2020- are currently unavailable. In spite of

enrolling a large number of diabetic patients with established or at high risk of CVD

and having long follow-up periods, these studies are not free of limitations of RCTs.

In addition, concerns have risen about the effect of some therapeutic groups or agents

on heart failure (HF).

Electronic healthcare data, collected in the course of actual clinical practice by

physicians can provide information of drugs effects in a real-world setting.

Electronic medical records (EMRs) contain demographic and clinical information

tests, and can be linked with other databases (hospitalization or deaths). An

increasing number of population-based observational research focuses on effect of

glucose-lowering agents on CV outcomes in large cohorts of patients. Up to date, no

such studies have been performed in Spain. The Catalan general practitioners (GP)

database SIDIAP contains pharmacoepidemiological data of 80 % of the total

regional population.


Aim: to evaluate the effect of currently marketed non-insulin glucose-lowering

agents on major CV outcomes in cohorts of Spanish population based on records of

population-based EMR SIDIAP.

Design: Longitudinal retrospective observational cohort study, period of observation

of six years (1st January 2010- 31st Dec 2015)

Material and Methods Study population: Cohorts of patients aged 18 yrs. or older

registered in the Catalan general practitioners (GP) database (SIDIAP), with an

active diagnosis of type 2 diabetes mellitus. Exposures: approved blood glucose-

lowering agents since their first prescription. Outcomes: The primary outcome

(PCO) is the composite of all-cause death, non-fatal myocardium infarction (MI) and

non-fatal stroke. Secondary outcomes are individual components of PCO, and

diagnoses of HF and peripheral artery disease. Statistical analysis: For populations’

characteristics, a descriptive statistic report in percentages categorical variables and

in mean (standard deviation) and median [interquartile rank]. Patients will be

stratified by demographic and clinical variables and use of insulins. The crude

incidence rates for each group will reported, and hazard ratios (HRs) for association.

Strengths and limitations: Analysis of these databases could provide an estimation

of the effect of currently marketed glucose-lowering agents on CV outcomes in a

sample of non-selected population. New-users design prevents survivors bias.

Limitations are mainly derived from its observational design (no randomization and

confounding by indication). Concerning exposure data, some more recently marketed

agents will not reach a number of prescriptions or follow-up periods appropriate to

make valid comparisons with older agents. Concerning outcomes data, CV fatal

outcomes not occurring in healthcare setting can be misclassified. Concerning

completeness of data, missing data about a number of patients have been reported in

other studies with SIDIAP database. The study period (6 years, from 2010 to 2015),

which has been chosen according to availability of prescription/dispensing data,

results in a follow-up can be not enough long for new diagnosed T2DM patients or

patients not at high cardiovascular risk.


Expected results: For the drugs and period’s study, adjusted results should be

similar to those obtained in large randomized controlled trials evaluating the effect of

glucose-lowering agents on cardiovascular outcomes. Thus, up to a 10% reduction in

cardiovascular morbidity and mortality compared with the use of reference non-

insulin glucose-lowering agents, metformin and sulfonylureas (SU) can be expected.

IV. 2. Protocol rationale

2.1 Research question:

Background:

Diabetes is an independent risk factor for CV disease. Cardiovascular disease has been

estimated to be 2-4-fold higher in diabetic patients than non-diabetic, affecting 80% of

patients with type 2 diabetes. The rates of CV disease, mortality and HF vary according

T2DM patients’ characteristics.

T2DM is a multifactorial disease that is defined by its glucose-related manifestations,

although its etiology is not fully understood. Glycaemic control has shown to reduce

microvascular complications and to be related in part to macrovascular disease. The

higher prevalence of traditional vascular risk factors in patients with type 2 diabetes

(e.g. hypercholesterolemia, hypertension) can explain only partially the higher CV risk

in patients with type 2 diabetes compared with patients without diabetes. Other

non‐traditional risk factors, such as insulin resistance, micro‐albuminuria or

inflammation, may be important in the pathophysiology of cardiovascular disease and

vascular mortality. [142, 143]

Observational evidence has shown that the risk of CV and CV mortality in higher in

patients with diabetes. An observational study with data of 431 579 patients with T2DM

registered in the Swedish National Diabetes Register found that the excess risk of acute

myocardial infarction (AMI), coronary heart disease (CHD) and death was higher for

patients with type 2 diabetes compared with controls, overall hazard ratio adjusted for

age, sex, level of education, country of birth, diabetes duration and comorbidities was


1.42 (1.41-1.44). However, for T2DM patients with HA1c 6.9% or lower,

normoalbuminuria and eGFR ≥60 mL/min, compared with controls was 0.95 (95% CI

0.92 to 0.98). [144]

Tancredi et al. studied the excess risks of death from any cause and death from

cardiovascular causes among persons with type 2 diabetes and various levels of

glycemic control and renal complications in the Swedish National Diabetes Register

from January 1, 1998 to December 31, 2011. The adjusted HR of CV death was 1.14;

95% CI, 1.13 to 1.15). The excess risks of death from any cause and cardiovascular

death was higher for younger patients, those with worse glycemic control, and greater

severity of renal complications. In patients younger than 55 years who had a glycated

hemoglobin level of 6.9% or less, the hazard ratio for death from any cause was 1.92

(95% CI, 1.75 to 2.11); for patients ≥ 75 years of age or older was 0.95 (95% CI, 0.94 to

0.96). T2DM patients ≤ 55 years of age with a glycated hemoglobin level of 6.9% or

less and normoalbuminuria were at higher risk of death from any cause, HR 1.60 (95%

CI, 1.40 to 1.82); for T2DM patients ≥ 75 years of age the risk of dying was 0.76 (95%

CI, 0.75 to 0.78), and patients 65 to 75 years of age also had a significantly lower risk of

death (hazard ratio, 0.87; 95% CI, 0.84 to 0.91). [145]

An observational study found that, although lower in the contemporary era than

previously, diabetes mellitus remains significantly associated with all-cause and CVD

mortality. A gradient of mortality risk with increasing HbA1c >6% to 6.9% was

observed, suggesting that HbA1c remains useful predictor of outcomes, even if

causality cannot be inferred. These authors retrospectively studied all-cause and CVD

mortality among 963 648 adults in the US Veterans Affairs Healthcare System from

2002 to 2014 during a mean follow-up of 8 years, 34% had diabetes mellitus. Compared

with nondiabetic individuals, patients with diabetes mellitus had higher all-cause and

CVD mortality, respectively. The adjusted HR was 1.29 (95% CI, 1.28-1.31), declined

with adjustment for CVD risk factors (HR 1.18 [95% CI, 1.16-1.19]) and glycemia (HR

1.03 [95% CI, 1.02-1.05]). In T2DM patients, CVD mortality increased as HbA1c

exceeded 7%. For HbA1c 7%-7.9% HR was 1.11 [95% CI, 1.08-1.14]; for 8%-8.9%,


HR was 1.25 [95% CI, 1.22-1.29]; for HA1c ≥9%, HR was 1.52 [95% CI, 1.48-1.56]

relative to HbA1c 6%-6.9%). [146]

While the effect of effect of glucose-lowering agents on microvascular events has been

established in different observational and interventional studies, the effect of glucose

control on cardiovascular outcomes remains unclear.

An overview of the milestones in the diabetes research has been summarized in the

section “Introduction”

Metformin is currently the primary drug of choice for type 2 diabetes patients.

Sulfonylureas (SU) have been associated with weight gain and an increased rate of

myocardial infarction, all-cause mortality, stroke, heart failure and CV death. A number

of new drugs – including insulins analogs- and new drug classes have been developed in

order to achieve the glucose control in type 2 diabetic patients. A meta-analysis showed

an increased risk of MI for rosiglitazone; this led regulatory agencies to require new

glucose-lowering agents to show absence of cardiovascular toxicity. Large RCTs,

designed and conducted in order to comply regulatory requirements, including

populations at high cardiovascular risk, showed results varying from a neutral effect

(DPP-4 i) to an important reduction in the risk of CV mortality, hospitalization for HF

and all-cause mortality with empaglifozin, and some agents among the GLP-1 RAs

agents showed decreased risk of MACE and mortality (liraglutide, semaglutide,

albiglutide) Concerns have arisen because of an increased incidence of heart failure with

the use of some agents, particularly some DPP-4 inhibitors (saxagliptin, alogliptin) and

glitazones. Liraglutide and empagliflozin received a US FDA approval for reducing the

risk of cardiovascular death in adult patients with type 2 diabetes mellitus and

cardiovascular disease prevention as a new indication; [113, 114] in the EMPA-REG

study the decrease on CV mortality is probably related with an increased diuresis, and

the volume depletion and subsequent effect on heart failure occurrence or worsening.

Rationale: Effects of non-insulin glucose-lowering agents on cardiovascular outcomes

in Type 2 diabetic patients are still being assessed.


Experimental evidence: Two new glucose-lowering classes, GLP-1 RAs and SGLT-2 i,

demonstrated a reduction on MACE events and all-cause mortality in large controlled-

placebo trials conducted in high CV-risk T2DM patients. Cardiovascular outcomes

trials (CVOTs) assessing the effects of dipeptidyl-peptidase-4 inhibitors have shown

non-inferiority compared to placebo, but failed to show superiority.

Observational evidence: Previous epidemiological research has obtained variable results

different regarding the association between cardiovascular disease events and exposure

to different blood glucose-lowering agents. Observational research for each class

obtained results of different magnitude in different settings.

The assessment of cardiovascular outcomes in cohorts of patients treated with

hypoglycaemic agents in clinical settings could provide useful information if adequately

collected and analysed, although it can be compromised by uncontrolled biases and

confounding, in particular confounding by indication.

Health care databases’ pharmacoepidemiologic and pharmacoeconomic analysis has a

growing role in supporting health care decision making and efficient management of

health care organizations. [134, 147] While experimental research has well-established

standards for assessing the effects of drug on clinical outcomes, in observational

research results depend on the studies’ design regarding design-related bias or potential

bias, comparator, selection of covariates for adjusting and strategies for minimising

confounding.

Large datasets of healthcare records are increasingly used to study the clinical outcomes

of health care interventions in a general population including older adults who are often

under-represented in clinical trials. [148] Healthcare records often lack of detailed

clinical information—in particular, frailty—that is central to the clinical management of

older adults.

Objective: The aim of this study is to assess the effect in a clinical setting of non-insulin

blood glucose-lowering agents on CV outcomes in cohorts of new users of Catalan

general practitioners’ databases, in the period between Jan 1st 2010 to Dec 31st 2015.


This database is linked with mortality registries through administrative healthcare

database CatSalut, from the Catalan Institut of Health.

3. Research hypothesis: In the study period, non-insulin blood glucose-lowering agents

introduced as add-on therapy (second or third-lines) or as monotherapy for blood-

glucose control and diabetes-related complications’ prevention, do not provide a

clinically relevant benefit defined as a 10% reduction in cardiovascular morbidity and

mortality compared with the use of reference non-insulin glucose-lowering agents,

metformin and sulfonylureas (SUs).

IV.3. Protocol design

Observational, retrospective, longitudinal, population-based cohorts study utilizing

secondary data from electronic healthcare database (de-identified demographic, clinical,

and prescription data of patients registered in general practitioner’s medical health

records linked with databases hospitalizations’ records and death registries).

Setting: Catalonia (Spain, European Union), is a North-Eastern Spanish province of

32,000 km² and 7,5 million inhabitants.

Source population: All residents in Catalonia registered in the public Catalan Institut

of Health, universal health care system, for at least one year, and recorded in the general

practitioners’ “Information System for the Development of Research in Primary Care”

(SIDIAP) healthcare database (https://www.sidiap.org/ )

The Catalan Institute of Health (CIH, http://ics.gencat.cat/es/inici/index.html ) is a state

healthcare system, tax-funded, and mandatory for active workers and retirees. It is the

main provider of health services in Catalonia, has 279 primary care teams (PCT) with

data of 5.564.292 people, approximately 74% of the Catalan population. SIDIAP

(Information System for the Development of Research in Primary Care) is a primary

care population computerized database in Catalonia, Spain, containing anonymised

patient’s records for the 5.8 million people attended by general practitioners in the

Catalan Health Institute. SIDIAP includes data on demographic variables, diagnoses,

clinical variables, prescriptions, specialist referrals, laboratory test results, and

https://www.sidiap.org/

http://ics.gencat.cat/es/inici/index.html


medications claimed from pharmacist offices, obtained from the administrative CatSalut

general database.

SIDIAP has been created in 2006. Pharmacoepidemiologic cross-sectional studies

conducted for T2DM assessed trends in prescription patterns, health care costs and time

to intensification. [149 -1610]

Study population: Catalan population aged ≥18, attending primary care practitioners

registered in the SIDIAP, between January 1st 2010 to December 31st 2015 with an

active diagnosis of Type 2 diabetes mellitus and receiving a first prescription of a non-

insulin glucose-lowering agent.

Study period: from January 1st 2010 until Dec 31 2015

Inclusion criteria (ICD-10 codes):

all patients registered in the SIDIAP database for at least one year previous to

the index date.

aged ≥18,

attending primary care practitioners registered in the SIDIAP, between January

1st 2010 to December 31st 2015

diagnosed of Type 2 diabetes mellitus (Coded E11.0, E11.1, E11.2, E11.3,

E11.4, E11.5, E11.6, E11.7, E11.8, and E11.9)

receiving a first prescription of a non-insulin blood glucose-lowering agent.

(NIAD)

Exclusion criteria (ICD-10 codes):

people < 18 yrs,

diagnosis of Type 1 diabetes mellitus, (Coded E10.0, E10.1, E10.2, E10.3,

E10.4, E10.5, E10.6, E11.8, and E11.9.)


diabetes mellitus due to underlying condition, (E08); drug or chemical induced

diabetes mellitus (E09); gestational diabetes (O24.4); post pancreatectomy

diabetes mellitus (E13); postprocedural diabetes mellitus (E13); secondary

diabetes mellitus NEC (E13)

active prescription of the given non-insulin glucose-lowering agent in the 90

days previous to cohort entry.

SIDIAP provided a coded, de-identified dataset of new-users of NIADs. Patients on

other drug(s) were considered “incident users” for the NIAD newly prescribed. In

accordance, patients with ICD 10 code Z79.84 [long term (current use) of oral

antidiabetic drugs] were included if received a first prescription on another one

non-insulin glucose lowering agent.

Patients registered in the SIDIAP database ≤ 365 days (washout period)

Restriction: In order to have a sample representative of the T2DM population treated

with NIADs, no restrictions were applied other than age (<18) in the inclusion/exclusion

criteria. Patients are not excluded for history of CV events, CVD or other risk factors

for the CV outcomes. Restriction reduces the sample size, compromises the statistical

power, limits the generalizability of the results, and precludes effect modification

analyses by these risk factors. [133] Instead, subgroup analyses were performed in each

of these sub-cohorts of patients (previous CVD yes/no, previous HF diagnosis yes/no,

previous CKD diagnosis yes/no)

Risk of selection bias: The risk of selection bias is low. Patients are not excluded for

future events (such as therapy duration, insulin dispensing during follow-up, number of

visits to GPs or doses of drugs of interest)

Incident-users design: Patients should not have a prescription of the non-insulin blood

glucose-lowering agent in the 90 days before the index date. This period could be too

short of excluding prevalent users.


Patients with use of any NIAD or insulin were excluded for the study on first-line

therapies; however, we cannot rule out that patients who entered in the cohort in early

2010 could have been exposed previously to the agent supposed to be first-prescribed.

For second-line therapies, patients had previous use of a NIAD agent, most patients

were on metformin. The first prescription of the second-line agent added to metformin

was considered the index date.

Risk of misclassification: The 90-days period with no prescriptions of a given NIAD

could result too short if chronic patients have their prescription refilled for more than 3

months. Roumie et al. report that in the US National Veterans Health Administration

databases, seventy percent of our population received 90-day prescriptions, and 93%

and 94% of metformin and sulfonylurea users, respectively, refilled their prescriptions

within 90 days. In our study, the unintended inclusion of prevalent users can be

suspected by the prescription patterns in the monotherapy study, in which 2010 year

were 16% higher than in 2011, and this pattern was not repeated in the following years.

[162]

Washout period:

When assessing effects with a new-user design (i.e., patients newly prescribed a drug),

the washout period warrants that patients have no prior prescription of the drug of

interest in the healthcare system of the study. Washout period minimize the inclusion of

prevalent users, who can be diagnosed of T2DM and treated outside the healthcare

system used for the study. [133]

Primary objective is to compare the time from the first prescription of a given non-

insulin blood glucose-lowering agent to the recorded first occurrence of any component

of a composite of major cardiovascular (CV) outcomes in cohorts of users of non-

insulin glucose-lowering drugs.

Primary outcome is a composite of three-components of mayor cardiovascular events

(MACE): all-cause death, non-fatal myocardial infarction (MI) and non-fatal stroke.


Secondary objectives are to compare, in cohorts of users of non-insulin glucose-

lowering drugs

1. the time from the first prescription of a A10B (ATC code) given agent to:

individual components of PCO:

o all-cause death,

o the first occurrence of non-fatal MI,

o the first occurrence of non-fatal stroke

2. the time from the first prescription of a A10B (ATC code) given agent to:

the record of heart failure diagnosis (HF) (either HF onset or worsening to

another NYHA class)

the first record of incident diagnosis of intermittent claudication, worsening

of intermittent claudication or revascularization procedures for peripheral

vasculopathy.

Secondary outcomes:

All-cause death

Recorded diagnosis of first occurrence of any of the following events:

o non-fatal MI,

o non-fatal stroke

o record of heart failure diagnosis,

o incident diagnosis of intermittent claudication, worsening of intermittent

claudication or hospitalization for peripheral revascularization

procedures.

Rational for the choice of outcomes:


Three points MACE: Major adverse cardiovascular events (MACE) is the composite

endpoint mentioned in the FDA and EMA guidances for the assessment of

cardiovascular safety in new antidiabetic drugs: “These events should include

cardiovascular mortality, myocardial infarction, and stroke, and can include

hospitalization for acute coronary syndrome, urgent revascularization procedures, and

possibly other endpoints.” [163, 79] In RCTs, composite endpoints reduce the sample

size requirements and allow to assess easily the net clinical benefit of an intervention,

avoiding misinterpretations associated with competing risks and the challenge of using a

single outcome to validate the study objectives. [164] All CVOTs assessing the CV

safety of new blood glucose-lowering drugs have a three- or a four-points MACE as

primary outcome. Three-points MACE (MI, stroke and CV death) has long been used

for cardiac drug approvals by the US Food and Drug Administration and since late 2008

has become a primary safety outcome for non-insulin blood glucose-lowering drugs.

Composite end points allow CVOTs to ensure that sample size and duration of follow-

up remain reasonable. [117] The combination of clinical outcomes into a composite end

point increases the numbers of events ascertained and thus statistical power and

precision. [118] Composite CV end points in diabetes trials have included a larger

number of components, while more recent CVOTs almost exclusively use a composite

of CV death, nonfatal myocardial infarction (MI), and nonfatal stroke (three-point major

adverse CV event,3P-MACE) composite-or add hospitalization for unstable angina

(HUA) to these three outcomes (4P-MACE). Marx et al. pointed that “the primary

outcome of 3P-MACE may offer a better balance than 4P-MACE between statistical

efficiency, operational complexity, the likelihood of diagnostic precision (and therefore

clinical relevance) for each of the component outcomes, clinical importance, and the

aim to adequately capture any potential treatment effect of the intervention.”[165]

However, mechanisms of actions of different classes of drugs often differ in their

impact on CV outcomes, but also individual agents within a class – such it is the case

for liraglutide and exenatide. Because of drugs’ differences, no particular individual or

composite end point can be seen as a "gold standard" for CVOTs of all glucose-

lowering drugs. [166]


In the composite MACE, cardiac death is the top marker form benefit, but the rate of

expected events usually makes necessary the addition of less definitive but more

frequently occurring endpoints, such as MI and stroke, to enable outcome trials to be

completed in a reasonable time and with reasonable costs.

In our study, the three-point MACE was chosen as a primary endpoint to emulate

CVOTs and to make comparisons easier. MACE captures the first event of the

composite, and also addresses competing risks. With some exposures, such as

metformin and sulfonylureas, the high number of patients allows the assessment for

each outcome separately. This cannot be the case, however, for exposures with a low

number of patients, such as the ones newly introduced during the study period. The

composite of MACE also allows the identification of the first event that occurred in the

population at risk.

In our study, we included all-cause mortality. Diabetic patients, causes of death are

mainly from CVD and cancer [25] All-cause mortality captures all the events and it

reflect more accurately the benefit or harm of a treatment.

MI: Myocardial infarction is the component of the three-points MACE; and a major

event of CVD, can be fatal or non-fatal; MI can be also silent, with no clinical

manifestations. We included all the codes for MI, with no exclusions.

Stroke: We also included all the stroke events (ischemic, haemorragic and embolic)

Embolic events can be manifestations of other cardiac diseases, namely atrial

fibrillation. The inclusion of embolic stroke may introduce nondifferential outcome

misclassification. [167]

All-cause mortality: All-cause mortality is the most important marker of benefit,

although CV mortality is the end point included in MACE and most of the CVOTs.

Mortality is always pertinent with regard to medications (such as lipid-lowering agents)

used to treat asymptomatic patients, because the only benefit is prevention of future

disease. [168] As SIDIAP has not access to complete registries coding the cause of

death, CV mortality was excluded because of feasibility. SIDIAP database is linked


with administrative database of the CIH and allows a reliable registry of deaths occurred

both in in-hospital settings and outside a hospital.

Heart failure:

As many as 50% of patients with type 2 diabetes may develop heart failure. [169] Heart

failure is a common complication of type 2 diabetes, and its prognostic implications

including high mortality. [165]

Peripheral artery disease: In the Framingham study, the prevalence of PAD at study

entry was 13.6% and the incidence of new PAD was 3.7 per 100 patient-years. Both

prevalent and incident PAD was strongly and independently associated with increasing

age, systolic blood pressure, total serum cholesterol, and especially smoking. [170]

Patients with low ankle-brachial index plus diabetes presented increased mortality,

acute myocardial infarction, and ischemic stroke risk, all at rates similar to those with

previous cardiovascular disease. A retrospective cohort study using the Database of the

Catalan primary care system (SIDIAPQ), for 2006-2015, included 58,118 persons,

mean (SD) age 66.6 (10.7) years, 53.4% were men, the median follow-up was 5.9 years.

Compared to the reference group with no diabetes, no previous cardiovascular disease,

and normal ankle-brachial index (ABI). Participants with low ABI showed increased

mortality, acute myocardial infarction, and ischemic stroke incidence in all the

subgroups. Patients with low ankle-brachial index plus diabetes presented increased

mortality, acute myocardial infarction, and ischemic stroke risk, all at rates similar to

those with previous cardiovascular disease. [171]

Risk of information bias:

SIDIAP contains diagnoses recorded by general practitioners. Although SIDIAP

database can be linked with hospital discharges codes, in this dataset only GPs records

are available. This is a significant source for information bias, that could be in principle

non-differential, and non-fatal events (i.e., non-fatal MI and non-fatal stroke) could

have been under recorded. The risk of information bias is much lower for all-cause

mortality, because SIDIAP is linked with ICH administrative database.


Follow-up period: from January 1st 2010 to December 31st 2015

Data Sources

Clinical data for selecting study population

Selection of study population will be done on records of SIDIAP (Information System

for the Development of Research in Primary Care), a primary care population

computerized database. Data extraction (T2DM population ≥ 18 years, registered in

SIDIAP ≥ 365 days, with a new claimed prescription of a new NIAD agent and no

previous prescribing of this NIAD in the previous 90 days), coding and anonymization

has been performed by SIDIAP.

Demographic and clinical data extraction:

In the coded, de-identified dataset provided by SIDIAP (Information System for the

Development of Research in Primary Care), the following baseline characteristics have

been retrieved for each cohort:

Covariates included in the adjustment were: demographic data, clinical variables,

Demographic data include:

o sex (men, women)

o age (further categorised into age subgroups: ≤44yrs, 45-64yrs; ≥ 65 years

(for sub-group analyses we considered the categories < 75 years and ≥

75 years

o MEDEA deprivation index for socio-economic inequalities in health 94

Rationale for the inclusion of a deprivation index:


The MEDEA score (Mortality in small Spanish areas and economic and environmental

inequalities” summarises the geographic patterns of mortality in Spain cities related to

socioeconomic and environmental characteristics.[172] A study found that in Barcelona,

the mortality rate was 2.33 per 103 in men and 1.15 per 103 in women in Q1 and 3.49

per 103 in men and 1.52 per 103 in women living in Q4. Premature mortality rates

showed higher premature mortality in the most deprived districts. [173]

Clinical variables include:

o Type 2-diabetes related:

standardised glycated haemoglobin (HbA1c) values; (registered up to 3

months before or on the index date [index prescription]

diabetes duration (yrs) at the index date

insulin treatment up to 3 months before or on the index date

insulin treatment during the follow-up

o body mass index (BMI);

o other cardiovascular risk factors

CV risk, according to REGICOR score

smoking status (past, current, never, according to the most recent

information recorded in the medical history)

blood pressure (BP) (systolic [SBP] and diastolic [SBP]); (both SBP and

DBP must be recorded in the same date in order to be included)

lipid levels including total cholesterol (TC), low-density lipoproteins or LDL

cholesterol (LDLc), high-density lipoproteins or HDL cholesterol (HDLc)

and triglycerides (TG) (TG, LDLc and HDLc must be recorded in the same

date in order to be included)


We extracted the value of the local REGICOR index of CV risk during the 15 months

prior the index date. The most recent recorded value of the body mass index (BMI) was

retrieved from records in the last 15 months prior to the index date.

The prevalence of selected cardiovascular risk factors (CVRF) has been assessed in the

North Catalonia. CVRF was: hypertension: 74.5%; dyslipidemia: 77.7%; smoking:

14.9%; obesity 44.9%, and familial CVD: 38.4%. Three or more CVRFs, including

T2DM, were observed in 91.3%. Metabolic syndrome (MS) prevalence was 68.7%.

Framingham score was 10.0%, higher in men than in women. CVD prevalence was

related to: age, number of CVRFs, duration of diabetes, familial history of CVD, waist

circumference, hypertension, lipid profile, kidney disease, and Framingham score, but

not to MS by itself. Normal values of serum lipid and blood pressure were only

observed in 18.9% and 24.0%, respectively. Platelet aggregation inhibitors were only

recorded in 16.1% of the patient cohort. MS presence was not an independent risk factor

of CVD in THIS study.[14]

Hypertension and type 2 diabetes are common comorbidities. Hypertension is twice as

frequent in patients with diabetes compared with those who do not have diabetes.

Moreover, patients with hypertension often exhibit insulin resistance and are at greater

risk of diabetes developing than are normotensive individuals. [174]

The REGICOR index is a local score for cardiovascular risk [175, 176]

Clinical history [ICD-10 codes]

Past morbidities include registered diagnosis previously to the index date of the

following:

Cardiovascular disease

o Acute coronary syndrome (ACS) (I20, I24)

o Myocardial infarction (I21)

o Stroke (hospitalization for ischemic or haemorragic stroke, clinically evident

transient ischemia) (I61, I62.9, I63, I64, I65, I66, I67, I69)


o Diagnosis of heart failure, heart failure (I50, I51.7, I,51.9)

o Peripheric arteriopathy (excluding Buerger and Raynaud), intermittent

claudication

Renal failure (N17-N19)

Co-morbidities are those registered by general practitioners.

Risk of information bias:

For the recorded concomitant medication, there is a risk of information bias: events not

recorded (such as MI or ACS treated in hospitals) or delays in recording.

For Renal failure, there is no data of eGFR creatinine levels or stages of disease. For

heart failure, there is no data about hospitalization or NYHA classification categories.

Concomitant medication [ATC codes]

We extracted data of concomitant medication at baseline (up to 3 months before the

index date)

Rationale for the inclusion of covariates:

Among the universally acknowledged risk factors for coronary artery disease: increased

concentrations of low-density lipoprotein cholesterol, decreased concentrations of high-

density lipoprotein cholesterol, and increased triglyceride concentration, haemoglobin

A1c, systolic blood pressure, fasting plasma glucose concentration, and a history of

smoking. [174, 177]

HbA1c has shown a direct association in the risk of stroke in T2DM patients, with a

stepwise increased risk of death for every 10 mmol/mol categorical increment of HbA1c

for the highest HbA1c category. [178]

Patients with history of myocardial infarction (MI) or stroke are at higher risk of a

recurrent event, but also of other manifestations of cardiovascular (CV) disease such as

stroke. [122] The annual death rate for survivors of MI is 5% six-fold than in people


who do not have coronary heart disease.[179] Diabetes, peripheral artery disease, and

history of stroke are strongly associated with subsequent MI, stroke, or death, additional

risk factors are heart failure, renal disease, and chronic obstructive pulmonary. [180]

Patients older, male, with diabetes, prior stroke, heart failure, unstable angina, and no

revascularization have worse long-term prognosis. [122] After the first year, MI

survivors remain at higher risk than the general population, particularly if there are

additional risk factors such as older age, hypertension, or diabetes, all of which lead to

worse outcomes. [121] Patients with a past stroke are an increased risk of a recurrent

event (approximately 7%/year).

Concomitant medications

Cardiovascular drugs

o Antihypertensives [C02, C03, C07, C08, C09]

o Antithrombotic agents [B01A]

o Lipid-modifying agents [C10]

Drugs that potentially affect cardiovascular risk

o Antidepressants [N06A]

o NSAIDs [M01A]

Insulin and analogues [A10A]

Concomitant medication is defined as every drug with an active prescription at the

index date (the day of prescription of a given glucose-lowering agent)

Rationale for the choice of concomitant medications:

NSAIDs increase CV risk, especially selective cyclo-oxygenase-2 (COX-2) agents, but

also non-selective agents not inhibiting enough the production of thromboxane A2. The

higher cardiovascular risk is for diclofenac


Depression is very prevalent among patients with T2DM and is associated with several

key diabetes-related outcomes. [181]

Exposures

Exposure definition

Subjects are considered exposed if a given non-insulin glucose lowering agent is

prescribed. SIDIAP contains prescription records that are linked with administrative

data of Catalan Institut of Health. However, as prescription is a proxy of dispensing,

which is in turn a proxy of administration drug, treatment compliance could not be

assessed.

SIDIAP also contains information about drug strength, number of units for package and

the number of packages dispensed. This information has not been analysed in the

present protocol.

A patient was considered as exposed to a given agent from the date of the filled

prescription of a first new non-insulin glucose-lowering agent (index date) until the day

of the prescription of a new agent (either addition or switching) or loss of follow-up.

To compare adequately the effect of two or more agents, similar disease severity’s

populations should be selected, comparing similar lines of therapies (i.e., second-line to

second-line) would allow to avoid bias.[182] When a given agent is added for the first

time to a pre-existing glucose-lowering therapy (for instance, metformin or

sulfonylurea), the exposure will be re-assigned to double-therapy (i.e., MET + X or SU

+ X). If a new agent is added to a double therapy, this addition will be compared to the

existing double therapy, as an “add-on” therapy (i.e. “MET+SU+ X” will be compared

to “MET+SU” OR “MET+SU+Y”


For cohorts of new users of metformin and SU, patients will be reassigned to if another

hypoglycemic agent is prescribed.

Initial date of observations: January 1st 2010

Final date of observations: December 31st 2015

Patients will be considered exposed to a non-insulin glucose-lowering agent after the

first prescription of any of the following:

Substances [ATC code] (date of EU approval)

Metformin[A10BA02]

Sulfonylureas [A10BB]:

o First-generation SUs: chlorpropamide [A10BB02], tolbutamide

[A10BB03], acetohexamide [A10BB31], carbutamide [A10BB06],

tolazamide [A10BB05], metahexamide, [A10BB10].

o Second-generation SUs: glibenclamide [A10BB01], glibornuride

[A10BB04], glipizide [A10BB07], gliquidone [A10BB08], gliclazide

[A10BB09], glisoxepide [A10BB11], glimepiride [A10BB12]

Meglitinides

Repaglinide [A10BX02] (authorized in UE on August 17th 1998,

authorized in Spain in 1999); nateglinide [A10BX03] (authorized in UE

on April 3rd 2001)

Alpha glucosidase inhibitors [A10BF]

Acarbose[A10BF01] (authorized in Spain in 1999); Miglitol [A10BF02],

voglibose [A10BF03]

DPP-4 inhibitors:


vildagliptin [A10BH02] (authorized in EU in September 2007),

saxagliptin, [A10BH03] (approved in October 2009 in EU),

linagliptin[A10BH05] (approved in EU in August 2011), alogliptin

[A10BH04] (approved in September 2013 in EU), sitagliptin [A10BH01]

(approved in March 2007 in EU)

GLP-1 Receptor Analogues:

exenatide [A10BJ01] (approved in 2005/2012), liraglutide [A10BJ02]

(approved in 2010), lixisenatide[A10BJ03] (approved in EU in 2013),

albiglutide [A10BJ04] (approved in 2014), dulaglutide [A10BJ05]

(approved in 2014).

Note: Semaglutide was not yet approved in EU or US FDA during the

study period. Aliglutide was withdrawn from the market in July 26,

2017, therefore it was available during the study period.

SGLT-2 inhibitors:

dapagliflozin [A10BK01] (approved in EU in November 2012),

canaglifozin [A10BK02] approved in November 2013 in EU,

empagliflozin [A10BK03] (approved in EU in May 2014).

Thiazolidinediones:

pioglitazone[A10BG03] (authorized in October 2000 in EU),

rosiglitazone [A10BG02] (authorized in EU in and suspended in EU in

2010)

Combined drugs:

When fixed-combinations were prescribed, the exposure was considered as the

addition of the compounds.

Exposure data source and extraction


SIDIAP database contains coded data of individual patients and prescribing health

professional, medicine’s National Code and ATC classification, description of

presentation, number of units per package, daily dose, month of drug dispensing,

monthly number of packages dispensed and dates of start and end of prescription.

We have used only data about the agent. No doses were extracted or analysed.

Exposure validation

The Catalan Health Institute (Servei Català de la Salut [CatSalut]) manages a database

which contains information about the dispensed drug, patient, prescriber and primary

care center, and funds a percentage (for work active population) or the total amount of

dispensed medicines (for retired people). SIDIAP prescription records are linked with

CatSalut dispensing records.

Further validation of exposure has not been performed.

Exposure ascertainment

Exposures have been identified by ATC codes. The exposure is defined though an “as

treated” approach. Patients are considered at risk for a given agent from the date of

prescription (index date) to the date of the prescription of another agent.

Information bias:

There are no data about adherence. Prescription and dispensing are both proxies of

administration.

Low risk of nondifferential information bias. Low risk of nondifferential

misclassification.

Outcomes

Outcomes data source and coding


To obtain the data for the study’s endpoints, outcomes were extracted from SIDIAP

database, with ICD-10 coded diagnoses recorded by general practitioners.

For the present study, incident CV events are identified by ICD-10-CM:

myocardial infarction (I21)

Hospitalization for unstable angina (I20)

stroke (I61. I62, I63, I64),

Code for coronary revascularization procedure

Code for peripheral revascularization procedure or onset or worsening of

intermittent claudication (I73.9)

Code for heart failure (I50.0)

Mortality: SIDIAP database is linked with the Catalan Institute of Health

administrative database, which in turn is linked with mortality registry containing

records of date and mortality causes for all Catalonia-residents’ deaths. Linkage

between databases is based on the CatSalut unique identifier code for each Catalonia

resident.

In our study, we do not have the codes for causes of death, and because of this, the

initial aim to assess specifically CV mortality has given up.

In the initial version of the protocol, cardiovascular (CV) death was defined as:

ICD-10 I46 code (cardiac arrest, I46.0, I46.1, I46.9) and

any death occurred ≤ 30 days after following a recorded event of:

o acute myocardial infarction (I21, I23), stroke (I61. I62, I63), cardiac

arrhythmia (I49.9), hearth failure, cardiovascular procedure,

cardiovascular hemorrhage, non-stroke intracranial hemorrhage, non-

procedural or non-traumatic vascular rupture (e.g., aortic aneurysm), or

pulmonary hemorrhage from a pulmonary embolism, other

cardiovascular causes, such as pulmonary embolism or peripheral arterial

disease.


Outcomes measurement

Incidence rates of primary composite outcome (PCO)and secondary composite outcome

(SCO) and secondary outcomes (SOs) will be retrieved from SIDIAP database.

For each cohort, incidence rate will be calculated from the index prescription (first

prescription of a new non-insulin glucose-lowering agent) until the first event of the

PCO, SCO, or events detailed in SOs, switching to or addition of another agent or end

of the observation period.

Bias

Selection bias: SIDIAP includes data of the 80 % of total Catalan population. Since all

new prescription of a non-insulin glucose-lowering agent defines selection, there is a

low possibility of selection bias.

Differential misclassification of exposure in study and concomitant medications is

unlikely due to the administrative nature of the system.

A possible outcomes’ misclassification can occur if the event occurred out of the

Catalan health system and it hasn’t been recorded in GP records.

In studies comparing drugs that have similar indications, patients will be assigned to

therapies for reasons that are difficult to measure (“channelling”). [168] The

measurement of all factors plausibly related to prognosis is essential.

Analysis plan

Statistical Analyses (as described in the Section V: First-line monotherapies, XVG)

As descriptive statistics of the sample frequencies (percentage) were used for

categorical variables and mean (standard deviation) or median (interquartile range) for

quantitative variables.


Twenty multiple imputations by chained equations were obtained to replace missing

baseline values for HbA1c, body mass index, total cholesterol, high-density lipoprotein

cholesterol, low-density lipoprotein cholesterol, triglycerides, serum creatinine, and

systolic and diastolic blood pressure.

To reduce the selection bias associated with the observational nature of the data, a

propensity score as the conditional probability of being treated with SU drugs was

estimated with the baseline covariates as age, gender, socioeconomic status, BMI, SBP,

DBP, HbA1c, lipid profile, serum creatinine level, use of other medications (ACE

inhibitors, aldosterone antagonists, antiplatelet drugs, beta-blockers, calcium channel

blockers, diuretics, statins, other lipid-lowering drug, NSAIDs, antidepressants), history

of acute coronary syndrome, myocardial infarction, ictus, heart failure, peripheral

arterial disease, renal failure), and the duration of diabetes. Variables were considered

well balanced if the standardised differences between both groups were <0.10 after

adjusting by PS.

Inverse probability of treatment weighting (IPTW), was used to create pseudo-

populations of SUs and metformin initiators with similar covariate distribution. This

approach has been shown to be particularly suitable when the outcome is a time to event

measure.

Weights were stabilised to avoid extreme values and increase precision in the estimates.

Propensity scores and IPTWs were generated for each imputed data set.

Event rates per 10,000 person-years were calculated for events. For each event, time to

follow-up we defined as the time between cohort entry (the first prescription of

metformin or SUs alone) and the event. Patients were followed up until prescription of a

different antidiabetic drug, death, transfer or end of study. Cox proportional hazard

models were used to estimate the hazard ratios with 95% CIs.

All analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC).


Subgroups analysis

For exploring heterogeneity treatment’s effect, descriptive subgroup analysis will be

performed:

Use of insulin during monotherapy (for dual, second-lines only): yes/ no

Age: < 75 years and ≥ 75 years

Sex: male / female

Glycemic control

o HbA1c < 8%)

o HbA1c 8 – 10 %

o HbA1c > 10%

Diabetes duration:

o 0-4 years

o 5-9 years

o 10-14 years

o ≥ 15 years

Body Mass Index:

o < 25 kg/ m2

o 25-29.9 kg/ m2

o 30- 39.9 kg/m2

o ≥ 40 kg/m2

History of cardiovascular disease: yes/no

History of heart failure: yes/ no

History of renal failure: yes/ no


MEDEA index:

o Rural

o U1 (least deprived)

o U5 (most deprived)

Exploratory subgroup analysis will be derived from data.

Quality assurance, feasibility and reporting

Quality assurance:

Data validation for outcomes is not foreseen due to feasibility reasons. For study’s

exposures CatSalut, as an administrative database, performs validations.

Feasibility:

To conduct this study, an agreement has been signed between Institut Jordi Gol and the

Fundacio Catalan Institut of Farmacologia.

Register:

The initial protocol has been registered in ENCePP with the number 18510. Results

have been submitted to publication.

Reporting:

Study results are reported according to STROBE and RECORD-PE guidelines. [183,

184] Recommendations about graphical depiction were followed. [141]

Ethical issues

The protocol has been approved by the Clinical Investigations Ethics Review Board

from the Investigation in Clinical Care Institut Jordi Gol. Local rules of confidentiality

are respected (according to article 5, Ley Organica 15/1999, Regulación del

Tratamiento de Datos de Carácter Personal). The Institut Jordi Gol provided a coded,

de-identified data set of T2D patients ≥ 18 years registered in the SIDIAP database for


at least 365 days. Patient Informed Consent is considered not necessary due to data

anonymisation.

Strength & limitations of the study

Strengths

1. Healthcare data collected in clinical practice by physicians are representative of

routine clinical practice in large populations. All age categories and all socio-

economic areas are represented. Patients have long permanence in the database.

2. Some information about lifestyle is lacking (smoking habit, exercise)

3. Incident users “new-users” design reduces the risk of prevalent users’ bias

4. “As treated” approach reduces the risk of exposure misclassification

5. Adequate comparator: similar disease stage. We compared first-line

monotherapy vs first-line monotherapy and second-line dual therapy vs second-

line dual therapy.

6. Subgroup analysis are performed.

7. The database is linked with administrative database, data about mortality are

reliable.

Limitations

1. Based only in general practitioners’ recorded diagnoses. Hospital data

are not available. Risk of information bias

2. No data about adherence

3. No data about nursing homes residents.

4. The protocol didn’t include cancer or other comorbidities data in order to

assess frailty.

5. Heart failure and renal failure diagnoses are not categorized.

6. The outcomes are comprehensive and encompass different levels of

severity.

7. In the period of the study is a possible relatively reduced number of

patients treated with non-insulin glucose-lowering agents that have been


approved since 2008, especially for the newest agents (alogliptin,

albiglutide, canagliflozin, empagliflozin, dapagliflozin). New users of

rosiglitazone are expected to be recorded until 2010 (date of marketing

suspension). These more recently marketed agents could not reach a

number of prescriptions or follow-up periods appropriate to make valid

comparisons with older agents

8. The study period (6 years, from 2010 to 2015), which has been chosen

according to availability of prescription/dispensing data, could result in a

follow-up not long enough for new diagnosed T2DM patients or patients

not at high cardiovascular risk.

9. Missing data about 25-30% of patients have been reported in other

studies with SIDIAP database.

We haven’t included antipsychotic agents in concomitant medications. Patients with

diabetes are at higher risk of dementia and cognitive decline, antipychotics are used in

dementia. Prospective observational studies in people with diabetes showed 73%

increased risk of all types of dementia, 56% increased risk of Alzheimer dementia, and

127% increased risk of vascular dementia compared with individuals without diabetes.

Conversely, patients with Alzheimer dementia are more likely to develop diabetes than

people without Alzheimer dementia.

V. Cardiovascular outcomes and mortality

in type 2 diabetes mellitus patients prescribed first-line non-insulin blood-glucose-lowering

agents as monotherapy

V. First-line monotherapies 78 Nelly Raquel Herrera Comoglio Eu2P PhD December 2019

V.

Cardiovascular outcomes and mortality in type 2

diabetes mellitus patients prescribed first-line non-

insulin blood-glucose-lowering agents as

monotherapy

A population-based cohort study in the Catalan electronic

medical record database, SIDIAP, 2010-2015

Raquel Herrera Comoglio – Xavier Vidal Guitart

Abstract

Aim: To assess cardiovascular (CV) events and all-cause mortality in type 2 diabetes

mellitus (T2DM) patients treated with first-line monotherapies of non-insulin antidiabetic

drugs.

Methods: Longitudinal retrospective cohort study in the Catalan database SIDIAP

(Information System for the Development of Research in Primary Care). T2DM patients

≥18 years newly prescribed first-line monotherapies during 2010-2015 were followed

since their first prescription until MACE (myocardium infarction MI, stroke and all-cause

death), its components, heart failure (HF) and peripheral artery disease (PAD) or

censoring. Cox proportional hazard models were used to estimate hazard ratios 95%

confidence interval (HR [95%CI]).

Results: Compared with metformin, the use of sulfonylureas, dipeptidyl peptidase-4

inhibitors (DPP-4 i) and meglitinides were significantly associated with higher risk for

MACE (1.55 [1.42 – 1.68]); 1.49 [1.22-1.84] and 2.01 [1.29-3.12]) and all-cause


mortality (1.67 [1.52 – 1.84], 1.65 [1.30 -2.0] and 2.08 [1.26-3.42]. Sulfonylureas users

had increased risk of non-fatal MI (1.38 [1.03 – 1.85]) non-fatal stroke (1.31 [1.11 –

1.54]), HF (1.49 [1.28-1.72]) and PAD (1.24 [1.02-1.51]). Meglitinides users were at

increased risks of MI, HR 2.03 (1.10-3.74).

Conclusion: In first-line monotherapies, compared with metformin, sulfonylureas were

associated with increased risks in all the outcomes; DPP-4 i showed increased risks of

MACE and mortality compared with metformin. Residual confounding cannot be ruled

out, deserving further research

Introduction

The goal of the T2DM treatment is the maintenance of glycaemic control to reduce the

progression to long-term microvascular and macrovascular complications. When

possible, diet and lifestyle modifications are recommended before prescribing drugs.

International guidelines recommend metformin as a first-line treatment of choice in

combination with lifestyle modifications. Sulfonylureas (SUs) are recommended if

contraindications or intolerance exist. [185, 186] The rationale for the use of metformin

as first-line glucose-lowering pharmacological therapy in type 2 diabetes is based on its

glucose-lowering efficacy: as insulin-sensitiser, metformin counters insulin resistance

and lowers basal hyperinsulinaemia, while avoiding significant hypoglycaemia or weight

gain. [187]

Heart failure was a formal contraindication for metformin because of the risk of lactic

acidosis, a very rare but potentially fatal adverse effect related to biguanides; this

contraindication had been removed in 2006 based on observational studies. [188, 189]

Severe renal impairment, heart failure and liver impairment are risk factors for lactic

acidosis. [189, 190] In April 2016, the US Food&Drug Administration modified the

labelling, the use of metformin is contraindicated if eGFR <30 mL/1.73m2 and not

recommended if 30-45 mL/minute/1.73m2.[190]

In spite of the relatively low number of patients randomised to metformin, the United

Kingdom Prospective Diabetes Study (UKPDS34) represents one of the milestones in

T2DM treatment: it found a significant reduction of any diabetes-related end-point,


diabetes-related mortality, and all-cause mortality in 342 obese, newly diagnosed T2DM

people, compared with conventional therapy of insulin or first or second-generation

sulfonylureas. [53] In the UKPDS post-trial monitoring, relative reductions in risk

persisted at 10 years in the SU–insulin group for any diabetes-related endpoint (9%) and

microvascular disease (24%), risk reductions for myocardial infarction (15%) and death

from any cause (13%) emerged over time; in the metformin group, significant risk

reductions were more than two-fold higher than those of the SUs-insulin group: 21%, for

any diabetes-related endpoint, 33% myocardial infarction, and 27% for death from any

cause. [55]

In 2005, the International Diabetes Federation recommended metformin as an initial

glucose-lowering pharmacotherapy for T2DM. In 2011 metformin was included in

WHO’s essential medicines list. [191, 192] Local guidelines in Catalonia, published in

2010, recommended metformin as initial diabetes therapy. [193]

Apart from the UKPDS study, there is scarce experimental evidence regarding

cardiovascular (CV) and mortality outcomes for sulfonylureas (SUs) compared with

metformin: a systematic review found six trials comparing second-generation

sulphonylureas versus metformin. [194] A RCT assessing CV events in 304 high-risk

Chinese T2DM patients obtained favourable results for metformin, compared with

glipizide. [195] Observational studies have almost consistently reported higher risks for

SUs compared with metformin. [196-200, 167, 162]

The CV benefits of metformin have been highlighted in recent publications [192].

Lamanna et al. suggested that the improved CV outcomes shown with metformin

treatment appeared to be more beneficial in longer trials enrolling younger patients and

that it is likely that metformin monotherapy is associated with improved survival; the

concomitant use with sulphonylureas was associated with reduced survival. [201] Bailey

et al. pointed out that metformin has been associated with reduced long-term CV risk in

prospective controlled trials, observational studies and database analyses. [187]

Metformin is the agent of choice for the first-line T2DM treatment, while SUs are

recommended to those patients who are intolerant to metformin. Approximately 5% of


patients do not tolerate metformin, mainly due to diarrhoea and other gastrointestinal

effects. Other agents used in first-line monotherapy are meglitinides, dipeptidyl

peptidase-4 inhibitors (DPP-4 i), thiazolidinediones (TZD) and sodium-glucose co-

transporter 2 inhibitors (SGLT-2 i).

We carried out a longitudinal cohort study to assess the risk of cardiovascular outcomes

and all-cause mortality. The primary objective was to evaluate the risk of the time to the

first event of the composite of major CV events (non-fatal myocardial infarction and non-

fatal stroke) and all-cause mortality (MACE). Secondary objectives are to assess the risk

of the time to the first event of all-cause mortality, myocardial infarction (MI), stroke,

heart failure and peripheral artery disease (PAD) in cohorts of T2DM people registered

in the general practitioner Catalan health care database, SIDIAP.

Cohorts of patients were defined by their exposures to non-insulin blood glucose-

lowering drugs. In this paper, we first focused on monotherapies, metformin,

sulfonylureas, dipeptidyl peptidase-4 inhibitors and meglitinides as first-line therapies in

drug-naïve patients. Other first-line monotherapies were not assessed due to the reduced

number of patients exposed. We also evaluated clinical outcomes in cohorts on the most

used second-line combined treatments, metformin and SUs: we compared patients who

were prescribed SUs added to metformin to patients who initially were treated with SUs

and were prescribed metformin added to SUs. Based on the existing observational

evidence, we hypothesise that patients had a lower risk of mortality and cardiovascular

outcomes in patients newly treated with first-line metformin compared with those newly

prescribed sulfonylureas as monotherapy.

The protocol has been evaluated by the Clinical Investigations Ethics Review Board from

the Investigation in Clinical Care Institut Jordi Gol, and an agreement has been signed

between the Institut Jordi Gol and the Catalan Institute of Pharmacology. According to

local regulation, to maintain data confidentiality and patient anonymity, patient

information was de-identified before data extraction and analysis. The protocol has been

registered in the ENCePP registry (protocol 18510) and reported according to STROBE

and RECORD-PE guidelines. [183, 184]


Design:

Analysis of longitudinal electronic health records data. We did a population-based cohort

study of adult T2DM patients who started a first-line monotherapy. To reduce bias we

used an incident user design. For patients who initiated these treatments in the study

period, we analysed outcomes of patients who were prescribed a second-line therapy with

the combination metformin+ SUs. Figure V.1 depicts the study design.

Study Population

People aged ≥18 years with an active T2DM diagnosis (ICD-10 codes E11) registered in

the SIDIAP database ≥365 days before the cohort entry and without any blood-glucose-

lowering prescription in the 90 days prior the index date (including insulin and non-

insulin). The study period extended from January 1st 2010 to December 31st 2015.

Figure V.2 shows the selection flow.

Exposures:

Metformin and sulfonylureas, as first-line monotherapy or as a second-line dual

combination therapy; and dipeptidyl peptidase-4 inhibitors and meglitinides in

monotherapy. SUs are considered as a class: if a patient on a given SU switched to another

agent, he/she was considered to continue in the same cohort. The index date (ID) is

defined as the day of the first recorded claimed prescription. Patients were considered at

risk from the day of the claimed prescription until the prescription to another non-insulin

blood glucose-lowering agent or censoring.

Follow-up

Patients were followed until the first event of primary and secondary outcomes, the first

prescription of a different NIAD group, or end of study 31st December 2015, whatever

came first.

SIDIAP database

Patients ≥ 18 years registered in the SIDIAP database ≥ 365 days

Diagnosis of T2DM

No records o previfous prescription of any NIAD ≥ 90 days (Whashout window)

Jordi Gol Institut

De-identified dataset

First prescription of a non-insulin blood glucose-lowering as first-line

monotherapy

Catalan Institutof Pharmacology

Data source

Patient selection

Figure V.1: Summary of design characteristics and design-related potential sources of bias

Excluded T1DM

Excludedprior use of insulins orany NIAD

Study design: Observational retrospective cohort studyRisk of bias: confounding by indication

Objective: To compare first-line agents in monotherapy vs first-line agents in monotherapy

Risk of time-lag bias: lowData source: SIDIAP (Information System for the Development of Research in Primary Care)Patient selection:

Risk of selection bias (selection of patient based on future events): No or verylow risk.

Risk of misclassification: Yes. There is a risk of misclassification of patients(prevalent users instead of incident users), mostly in the 2010 cohort entry, because of no access to previous periods’ data.

Exposure: new-users of first-line monotherapies.

Data extraction: ATC codes from recorded prescriptions and linked with Catalan

Health Institute administrative database dispensing.Risk of information bias: low. Prescriptions and dispensing are proxies of

administration. No data on adherenceRisk of survivors bias: No or low risk (incident-users design). Misclassification, as stated above, is possible.

Exposure ascertainment: As-treated approachRisk of exposure misclassification: lower than in the ITT approach

Covariates ascertainment: previous to the index day. Outcomes ascertainment: clinical recorded diagnoses coded by International Classification of Diseases, Revision 10

Risk of information bias for non-fatal outcomes: Yes. GP SIDIAP database isnot linked with hospital databasesMortality data linked with administrative database. Risk of information bias: low

V. First-line monotherapies 83


269,792 T2DM patients

prescribed one or more non-insulin blood glucose -lowering drug (NIAD)

167,176 patients on prevalent and incident monotherapy102,616 ≥2 NIAD agents

123,260 patients newly prescribed monotherapy

110,535 patients onMET monoterapy

(89.68 %)

7, 739 patients on SUsmonoterapy

(6.54 %)

TZDs(0.18 %)

2139DPP-4 i monotherapy

(1.73%)

2233Meglitinides

monotherapy(1.77 %)

GLP-1 RA(0.13 %)S

SGLT-2 i(0.02 %)

614 patients with other NIADs

Others

Figure V.2: Flow chart of cohorts’ selection process from T2DM patients registered in the SIDIAP database from January 1st 2010 to December 31st 2015

SIDIAP (Information System for the Development of Research in Primary Care)NIAD: non-insulin blood glucose-lowering (“antidiabetic”) drug; MET: metformin; SUs: sulfonylureas; DPP-4 i: dipeptidyl-peptidase-4 inhibitors; GLP-1 RA: glucagon-like peptide receptor agonist; SGLT-2 i: sodium-glucose co-transporter 2 inhibitors; TZD: thiazolidinediones




Data extraction:

Selection: Patients with an active diagnosis code of T2DM and a new prescription of any

NIAD in first-line monotherapy (either metformin, sulfonylurea (SU), dipeptidyl

peptidase-4 inhibitors, meglitinides, thiazolidinediones, glucagon-like peptide 1 receptor

agonists and sodium-glucose co-transporter 2 inhibitors). We excluded patients with

T1DM, (Coded E10.0, E10.1, E10.2, E10.3, E10.4, E10.5, E10.6, E11.8, and E11.9.;

patients with gestational diabetes; patients with diabetes mellitus due to underlying

condition, (E08), drug or chemical induced diabetes mellitus (E09), post pancreatectomy

diabetes mellitus (E13), postprocedural diabetes mellitus (E13) and secondary diabetes

mellitus NEC (E13). No further exclusion criteria were applied. This study aimed to be

representative of real-world practice; this is why patients with a history of cardiovascular

disease at cohort entry were not excluded.

Washout period: We extracted data from patients with no prior exposure to any blood-

glucose-lowering agent (including insulins) in the 90 days before the index date.

Covariates ascertainment: Age, sex and time from T2DM diagnosis were retrieved at the

date of the first prescription of the NIAD prescribed in first-line. We extracted the

following values up to 3 months before the ID: standardized glycated haemoglobin

(HbA1c), history of CV disease (including coronary artery disease (ICD-10 codes I20,

I21, I22, I23, or I24), stroke (ICD-10 codes I61. I62, I63, I64), heart failure (ICD-10

I50.0), peripheral artery disease (ICD-10 code I73.9). Values of cholesterol levels (total,

low-density lipoproteins or LDL-cholesterol, and high-density lipoproteins or HDL-

cholesterol), systolic and diastolic blood pressure (BP) were extracted up to 12 months

before the index date. We extracted the value of the local REGICOR index of CV risk

during the 15 months prior to the index date. The most recent recorded value of the body

mass index (BMI) was retrieved from records in the last 15 months before the index date.

We also extracted data of concomitant medication at baseline (any active prescription at

the index date) of the following therapeutic groups: ACE inhibitors/ARBs, (C09)

Aldosterone antagonists (C03), Antiplatelet drugs, (B01A) β-Blockers (C07), Calcium

channel blockers (C08), Diuretics (C03), Statins (C10AA), Other lipid-lowering drugs


(C10A), non-steroidal anti-inflammatory drugs (NSAIDs) (M01A), and Antidepressants

(N06A). Besides the study’s exposures, we extracted the concomitant use of insulin

(A10A) during the study period, to adjust for insulins use.

Outcomes:

The primary outcome is a composite of three-components of major cardiovascular events


Secondary outcomes are components of MACE: myocardial infarction [ICD-10 I21),

stroke (I61. I62, I63, I64), a new diagnosis of heart failure (I50.0) and onset or worsening

of intermittent claudication (I73.9)

Statistical analysis:

As descriptive statistics of the sample frequencies (percentage) were used for categorical

variables and mean (standard deviation) or median (interquartile range) for quantitative

variables.

Twenty multiple imputations by chained equations were obtained to replace missing

baseline values for HbA1c, body mass index, total cholesterol, high-density lipoprotein

cholesterol, low-density lipoprotein cholesterol, triglycerides, serum creatinine, and

systolic and diastolic blood pressure.

To reduce the selection bias associated with the observational nature of the data, a

propensity score as the conditional probability of being treated with each NIAD group

other than metformin (i.e., SUs, DPP-4 i and meglitinides) was estimated with the

baseline covariates as age, gender, socioeconomic status, BMI, SBP, DBP, HbA1c, lipid

profile, serum creatinine level, use of other medications (ACE inhibitors, aldosterone

antagonists, antiplatelet drugs, beta-blockers, calcium channel blockers, diuretics, statins,

other lipid-lowering drug, NSAIDs, antidepressants), history of acute coronary syndrome,

myocardial infarction, ictus, heart failure, peripheral arterial disease, renal failure), and

the duration of diabetes. Variables were considered well balanced if the standardised

differences between both groups were <0.10 after adjusting by PS.


Inverse probability of treatment weighting (IPTW), was used to create pseudo-

populations of each NIAD group initiators with similar covariate distribution. This

approach has been shown to be particularly suitable when the outcome is a time to event

measure.

Weights were stabilized to avoid extreme values and increase precision in the estimates.

Propensity scores and IPTWs were generated for each imputed data set.

Event rates per 1,000 person-years were calculated for events along with their 95%

confidence intervals (CIs). For each event, time to follow-up we defined as the time

between cohort entry (the first prescription of metformin or SUs alone) and the event,

prescription of a different antidiabetic drug, death, or end of study. Cox proportional

hazard models were used to estimate the hazard ratios with 95% CIs.

A sensitivity analysis for the 2011-2015 period was conducted to minimise in a higher

extend the risk of misclassification of prevalent-users as incident-users.

All analyses were performed by Prof. Xavier Vidal using SAS 9.4 (SAS Institute Inc.,

Cary, NC).

Results:

123,260 patients were newly prescribed first-line monotherapy. The most used were

metformin (89.68 %) and SUs (6.54%); 4900 patients (4.04%) received other agents:

DPP-4 i (1.73%), repaglinide (1.77%), thiazolidinediones (0.18%), glucagon-like peptide

1 receptor agonists (0.13%), alpha-glucosidase inhibitors (0.15%), and a negligible

proportion of patients were prescribed dapagliflozin and guar gum.

There were 110,535 patients newly prescribed metformin; 7,739 incident users received

SUs; 2, 139 patients were treated with PPD-4 i and 2,233 patients received meglitinides

as monotherapy.

Since 2011 to 2015, incident prescriptions for first-line monotherapies of metformin,

sulfonylureas and meglitinides decreased gradually by 25%, 75% and 60%, respectively,

while incident prescriptions of DPP-4 i as first-line monotherapy remained stable. The


sharp fall in prescriptions of all therapeutic groups between 2010 and 2011 could be

caused by misclassification of prevalent users, due to the short period (90 days) applied

for the exclusion of prevalent users in the SIDIAP database. Trends in prescriptions are

shown in Figure 3.

Almost all SUs used were second-generation compounds, being the most prescribed

gliclazide (55%), glibenclamide (28%) and glimepiride (15%). For dipeptidyl peptidase-

4 inhibitors the most prescribed agent was sitagliptin (by 51%), followed by vildagliptin

and linagliptin (by 22% and 20% respectively). Repaglinide was the only prescribed agent

in the meglitinides class. Absolute and relative figures of prescriptions for first-line

monotherapies are shown in Table 1.


Table 1. Therapeutic classes and agents prescribed in incident first-line monotherapy for T2DM patients, SIDIAP database, 2010-2015

Therapeutic class n Class % Total %

All classes 123, 260 100.00

Biguanides 110,535 89.68

metformin 110,535 100%

Sulfonylureas 7,739 6.54

chlorpropamide 9 0.12

Glibenclamide/glyburide 2152 27.77

gliclazide 4219 54.45

glimepiride 1132 14.61

glipizide 133 1.72

gliquidone 97 1.25

glisentide 6 0.08

DPP-4 i 2,139 1.73

alogliptin 1 0.05

linagliptin 473 22.11

saxagliptin 142 6.64

sitagliptin 1102 51.52

vildagliptin 421 19.68

Meglitinides 2,233 1.77

repaglinide 2233 100

Thiazolidinediones 229 0.18

pioglitazone 202 90.58

rosiglitazone 21 9.42

Alpha-glucosidase inhibitors 189 0.15

acarbose 160 84.66

miglitol 29 15.34

Glucagon-like peptide 1 RA 164 0.13

exenatide 91 55.49

liraglutide 73 44.51

Guar gum 49 49 0.04

Sodium-glucose co-transporter 2 i 22 0.02

Dapagliflozin 22


There were differences among patients’ basal characteristics in the four cohorts analysed.

At baseline, patients prescribed meglitinides were the oldest cohort, mean age 74.0 (SD

12.4), the subgroup ≥ 65 years had the highest percentage (77.8%); the most prolonged

diabetes duration (mean 5.2 years, SD 6.0); the lowest BMI (mean 29.8, SD 5.1) and the

lowest mean total cholesterol (190, SD 41.0). Patients newly prescribed with repaglinide

(the only meglitinides prescribed) had the high percentage of previous CVD (36.2%), in

all its manifestations; had the highest percentage of renal failure history (14.4%) and heart

failure (10.7%). Patients newly prescribed repaglinide were the group most treated with

antiplatelet drugs (28.0%), diuretics (46.8%) and antidepressants (8.0%). Among the four

cohorts analysed, they were among the less deprived, with 30.7% of patients living in the

least deprived areas and 28% in the most deprived ones.

Patients newly prescribed metformin as first-line monotherapy were more likely to be

men (57.0%), obese (mean BMI 31.4 kg/1.73m2, SD 5.3) and were the youngest cohort

(mean age 63.7 years, SD 12.7). The metformin cohort had the shortest diabetes duration

(2.5 years, SD 3.8), the highest total cholesterol levels (mean 206.2 mg/dL, SD 40.5), the

lowest percentage of history of CVD (15.0%) in all its manifestations, the lowest

percentage of recorded history of heart failure (1.4%) and renal failure (2.6%), had the

lowest percentage of treated patients with aldosterone antagonists (1.2%), antiplatelet

drugs (17.4%), beta-blockers (13..6%), calcium-channel blockers (12.6%) and, diuretics

(31.1%), and the highest percentage of patients treated with NSAIDs (12.6%). Patients

were more likely to live in deprived areas (34.8% in U4 and U5) than in the least deprived

urban areas (26.6% in U1 and U2).

Compared with metformin, patients newly prescribed SUs were older (mean age 70.1, SD

12.8 vs 63.7, SD 12.7), had a longer diabetes duration (mean 5.0 SD 5.3 vs. 2.5 SD 3.8

years), were more likely to have a history of CV events (27.4% vs. 15.0%), chronic kidney

disease (CKD) (4.1% vs. 1.4%) and heart failure (HF) (10.2% vs. 2.6%). Length of follow

up also differed, median follow-up was 2.42 years [0.97-3.97] and 1.54 years [0.53-3.12]

for new users of metformin and SUs, respectively. Concerning the socio-economic status,

31% of patients lived in the most deprived areas and 26.5% in the least deprived.


Table 2: Characteristics of cohorts of new users of NIADs first-line monotherapy at baseline*, SIDIAP database, 2010-2015

n (%) n (%) n (%) n (%)

Metformin monotherapy

Sulfonylureas monotherapy

DPP-4 i monotherapy

Repaglinide monotherapy

Participants (%) 110535 (100.0) 7739 (100.0) 2139 (100.0) 2233 (100.0) Sex Women 47509 (43.0) 3654 (47.2) 1011 (47.3) 1113 (49.8) Men 63026 (57.0) 4085 (52.8) 1128 (52.7) 1120 (50.2) Age, y mean (SD) 63.7 (12.7) 70.1 (12.8) 70.8 (12.4) 74.0 (12.4) Median [IR] 64.0 [55.0 - 73.0] 72.0 [61.0 - 80.0] 72.0 [62.0 - 80.0] 77.0 [66.0 - 83.0] 18-44 7662 (6.9) 243 (3.1) 48 (2.2) 43 (1.9) 45-64 49623 (44.9) 2280 (29.5) 582 (27.2) 453 (20.3) => 65 53250 (48.2) 5216 (67.4) 1509 (70.5) 1737 (77.8) Diabetes duration mean (SD) 2.5 (3.8) 5.0 (5.3) 4.6 (5.1) 5.2 (6.0) Median [IR] 0.5 [0.0 - 4.0] 4.2 [0.1 - 7.7] 3.2 [0.0 - 6.9] 3.8 [0.0 - 8.0] 0-4 y 88276 (79.9) 4251 (54.9) 1285 (60.1) 1246 (55.8) 5-9 y 16657 (15.1) 2350 (30.4) 586 (27.4) 612 (27.4) 10-14 y 4373 (4.0) 799 (10.3) 179 (8.4) 250 (11.2) => 15 y 1229 (1.1) 339 (4.4) 89 (4.2) 125 (5.6) Haemoglobin A1c, % Result not available n (%) 26477 (24.0) 2620 (33.9) 748 (35.0) 887 (39.7) Mean (SD) 7.5 (1.5) 7.8 (1.5) 7.4 (1.3) 7.4 (1.5) median [IQR] 7.1 [6.6 - 8.0] 7.5 [6.7 - 8.4] 7.1 [6.5 - 8.0] 7.2 [6.4 - 8.1] <8 n (%) 62603 (56.6) 3303 (42.7) 1032 (48.2) 976 (43.7) 8 to 10 n (%) 15428 (14.0) 1394 (18.0) 296 (13.8) 291 (13.0) >10 n (%) 6027 (5.5) 422 (5.5) 63 (2.9) 79 (3.5) Body Mass Index (BMI) Result not available n (%) 30891 (27.9) 2652 (34.3) 677 (31.7) 766 (34.3) Mean (SD) 31.4 (5.3) 30.0 (5.2) 30.4 (5.5) 29.8 (5.1) median [IQR] 30.7 [27.7 - 34.3] 29.3 [26.5 - 32.8] 29.7 [26.6 - 33.4] 29.4 [26.4 - 32.7] < 25 6633 (6.0) 743 (9.6) 211 (9.9) 234 (10.5) 25.0 to 29.9 (overweight) 28493 (25.8) 2116 (27.3) 556 (26.0) 566 (25.3) 30.0 to 39.9 (obese I-II) 39112 (35.4) 2019 (26.1) 607 (28.4) 616 (27.6) => 40 (obese III) 5406 (4.9) 209 (2.7) 88 (4.1) 51 (2.3) Cholesterol T-C mean (SD) 206.2 (40.5) 194.0 (42.1) 191.8 (41.6) 190.5 (41.0)

T-C median [IQR] 204.0 [179.0 -

232.0] 191.5 [166.0 -

219.0] 189.0 [164.0 -

218.0] 188.0 [162.0 -

217.0] HDLC mean (SD) 48.6 (12.4) 48.6 (12.9) 48.6 (13.6) 49.2 (14.3) HDLC median [IQR] 47.0 [40.0 - 56.0] 47.0 [40.0 - 56.0] 47.0 [39.0 - 56.0] 47.0 [39.0 - 57.0]


LDL-C mean (SD) 125.5 (35.2) 114.6 (36.1) 112.1 (35.0) 111.6 (34.9)

LDL-C median [IQR] 124.0 [101.0 -

148.0] 112.0 [90.0 -

137.0] 110.0 [88.0 -

134.0 108.0 (87.0 -

133.0) Comorbidities Prior cardiovascular events 16566 (15.0) 2122 (27.4) 642 (30.0) 808 (36.2) Acute coronary syndrome (ACS) 6498 (5.9) 837 (10.8) 242 (11.3) 318 (14.2) Myocardial Infarctiona (MI) 3371 (3.0) 369 (4.8) 120 (5.6) 137 (6.1) Stroke 5287 (4.8) 596 (7.7) 178 (8.3) 253 (11.3) Peripheric artery disease (PAD) 2499 (2.3) 287 (3.7) 108 (5.0) 134 (6.0) Renal Failure (RF) 2898 (2.6) 791 (10.2) 244 (11.4) 321 (14.4) Heart Failure (HF) 1584 (1.4) 320 (4.1) 171 (8.0) 238 (10.7) REGICOR index Result not available n (%) 65371 (59.1) 5644 (72.9) 1576 (73.7) 1755 (78.6) Low CV risk 16791 (15.2) 658 (8.5) 191 (8.9) 180 (8.1) Medium CV risk 19501 (17.6) 977 (12.6) 252 (11.8) 206 (9.2) High CV risk 8872 (8.0) 460 (5.9) 120 (5.6) 92 (4.1) Concomitant medication use Cardiovascular drugs

ACE inhibitors/ARBs 47535 (43.0) 3249 (42.0) 1006 (47.0) 1058 (47.4) Aldosterone antagonists 1359 (1.2) 304 (3.9) 116 (5.4) 115 (5.2) Antiplatelet drugs 19215 (17.4) 1745 (22.5) 503 (23.5) 625 (28.0) β-Blockers 14988 (13.6) 1248 (16.1) 445 (20.8) 455 (20.4) Calcium channel blockers 13926 (12.6) 1196 (15.5) 433 (20.2) 502 (22.5) Diuretics 34401 (31.1) 2636 (34.1) 862 (40.3) 1045 (46.8) Statins 44809 (40.5) 2663 (34.4) 938 (43.9) 855 (38.3) Other lipid-lowering drugs 6738 (6.1) 373 (4.8) 156 (7.3) 104 (4.7)

NSAIDs 13967 (12.6) 862 (11.1) 226 (10.6) 212 (9.5) Antidepressants 6492 (5.9) 439 (5.7) 148 (6.9) 178 (8.0) MEDEA index Rural 19585 (17.7) 1561 (20.2) 464 (21.7) 446 (20.0) Urban 5436 (4.9) 569 (7.4) 156 (7.3) 178 (8.0) U1 – Less deprived 13379 (12.1) 1050 (13.6) 326 (15.2) 351 (15.7) U2 15979 (14.5) 996 (12.9) 311 (14.5) 336 (15.0) U3 17657 (16.0) 1159 (15.0) 331 (15.5) 296 (13.3) U4 18884 (17.1) 1163 (15.0) 304 (14.2) 315 (14.1) U5 – Most deprived 19615 (17.7) 1241 (16.0) 247 (11.5) 311 (13.9) Cohort entry (yr) 2010 23983 (21.7) 3185 (41.2) 583 (27.3) 721 (32.3) 2011 19977 (18.1) 1598 (20.6) 293 (13.7) 451 (20.2) 2012 18756 (17.0) 1090 (14.1) 321 (15.0) 367 (16.4)


2013 17182 (15.5) 885 (11.4) 316 (14.8) 292 (13.1) 2014 15468 (14.0) 595 (7.7) 309 (14.4) 231 (10.3) 2015 15169 (13.7) 386 (5.0) 317 (14.8) 171 (7.7) Exitus at 31Dec2015 n (%) 6226 (5.6) 1351 (17.5) 298 (13.9) 550 (24.6)

SIDIAP: Information System for the Development of Research in Primary Care

MEDEA: Mortality in small Spanish áreas and economic and environmental inequalities

REGICOR index: Framingham-REGICOR (Registre Gironí del Cor) local index of cardiovascular risk

NIAD: non-insulin blood-glucose-lowering “antidiabetic” drugs; DPP-4 i: dipeptidyl-peptidase-4 inhibitors

T-C: Total cholesterol; HDLC: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol * Values for HbA1c, comorbidities, concomitant use of drugs: ≤ 3 months before the index date. Values of cholesterol levels and

blood pressure were extracted up to 12 months before the index date. Values of REGICOR index of CV risk during the 15 months

before the index date. Values of body mass index (BMI) were the most recent ≤15 months before the index date.

Significant results are highlighted in bold.

There were 2139 patients newly prescribed DPP-4 i first-line monotherapy. This cohort

had the highest percentage of patients treated with statins (43.9%) and other lipid-

lowering drugs (7.3%). According to the MEDEA index, the cohort was well-balanced,

28.7 % and 29.7% lived in the most and least deprived areas, respectively.

Baseline REGICOR score is not commented, due to the high percentage of missing data

for all the four cohorts (range 59.1% to 78.6%).

Table 2 shows the baseline characteristics of patients newly prescribed first-line

monotherapies (only metformin, SUs, DPP-4 i and repaglinide, accounting for 99.72% of

the total first-line monotherapy cohort).

In the four assessed cohorts, during the study period, there were 9588 first events of

MACE. There were 1004 myocardial infarctions, 2,952 events of stroke and 6,123

patients died. There were also 2,779 new diagnoses of heart failure and 2,078 of PAD.

After adjusting for all available demographic, clinical and concomitant medications data

at baseline and for the use of insulin after the index date and before or on the date of the

event, compared with MET monotherapy alone, all other classes (SUs, DPP-4 i and

meglitinides) were at higher risk of MACE (for SUs, HR 1.55 [1.42 – 1.68]; for DPP-4 i

HR 1.49 [1.22-1.84] and for meglitinides HR 2.01 [1.29 -3.12]), and all-cause mortality:

for SUs, HR 1.67 (1.52-1.84); for DPP-4 i, HR 1.65 (1.30-2.09) and for meglitinides HR


2.08 (1.26-3.42). Patients on secretagogues monotherapies were significantly at higher

risk of non-fatal MI: for SUs, HR 1.38 (1.03 – 1.85) and for meglitinides, HR 2.03 (1.10-

3.74) while for DPP-4 i there was a trend for higher risk. For non-fatal stroke, SUs

monotherapy showed an excess of risk (HR 1.31 [1.11 – 1.54]). Secretagogues were also

associated with a higher risk of a new record of diagnoses of heart failure (SUs, HR 1.49

[1.28-1.72] and meglitinides HR 2.15 (1.39-3.32). SUs monotherapy was also associated

with a higher risk of PAD (HR 1.24 [1.02-1.51]). Among the analysed cohorts, no class

showed HR favouring the use of other therapeutic groups vs metformin, for any outcome.

Table 3 shows the number of events in each cohort, the person-years period, the crude

incidence rate and the adjusted hazard ratios.

We also stratified the cohorts of metformin and SUs newly prescribed first-line

monotherapy patients by the use of insulin during the follow-up, and by age, sex, HbA1c,

diabetes duration, body mass index (BMI), history of CVD, history of HF, history of renal

failure (RF) and MEDEA deprivation index.

Patients on SUs monotherapy were at higher risk of:

MACE at any age (but at higher risk if they were ≥75years old) if they have not

used insulin during the follow-up period, had no prior history of CVD, HF or RF,

and lived in rural or most deprived areas.

MI, if they had no use of insulin, patients were ≥ 75 years, were women, had

HbA1c < 8%, any time of diabetes duration, but at higher risk if they had 0-4 years

of >15 years, BMI ≥ 30, had no prior CVD, no prior HF and lived in the most

deprived urban areas.

Stroke, at any age, if patients had no use of insulins during the follow-up period,

had HbA1c <8%, shortest diabetes duration, higher BMI, no history of CVD, HF

or CKD, and living in rural areas.


Table 3. Adjusted hazard ratio (95% CI) of MACE, all-cause mortality and CV outcomes in cohorts of T2DM patients treated with NIADs as first-line

monotherapy, SIDIAP healthcare database, 2010-2015

Outcomes N° of events Person-years Crude Incidence Rate per 1000 p-y

Adjusted HR (95% CI)

Composite of MACE (AMI, stroke and all-cause mortality) MET 7764 279677,66 27,76 reference SUs 1073 16369,99 65,55 1.55 (1.42-1.68) DPP-4 i 246 3519,30 69,90 1.49 (1.22-1.84) Repaglinide 505 4710,21 107,21 2.01 (1.29-3.12) All-cause mortality MET 4696 285961,85 16,42 reference SUs 817 16899,92 48,34 1.67 (1.52-1.84) DPP-4 i 185 3616,43 51,16 1.65 (1.30-2.09) Repaglinide 425 4881,87 87,06 2.08 (1.26-3.42) MI MET 865 293664,23 2,95 reference SUs 82 18677,02 4,39 1.38 (1.03-1.85) DPP-4 i 26 3914,00 6,64 1.68 (0.94-2.99) Repaglinide 31 5815,38 5,33 2.03 (1.10-3.74) Stroke MET 2573 290015,76 8,87 reference SUs 251 18354,30 13,68 1.31 (1.11-1.54) DPP-4 i 49 3870,27 12,66 1.08 (0.70-1.65) Repaglinide 79 5712,07 13,83 1.63 (0.93-2.84) Heart failure MET 2478 290560,66 8,53 reference SUs 329 18148,97 18,13 1.49 (1.28-1.72) DPP-4 i 68 3839,36 17,71 1.03 (0.72 – 1.48) Repaglinide 163 5463,92 29,83 2.15 (1.39 – 3.32) PAD MET 1797 291814,75 6,16 reference SUs 172 18522,28 9,29 1.24 (1.02-1.51) DPP-4 i 31 3901,39 7,95 1.12 (0.64 -1.97) Repaglinide 78 5695,23 13,70 1.26 (0.64 – 2.51)

NIADs: non-insulin blood-glucose-lowering (“antidiabetic”) agents MACE: composite of major adverse cardiovascular events (MI, stroke and all-cause mortality); MI: myocardial infarction; PAD: peripheral artery disease; SIDIAP: Information System for the Development of Research in Primary Care; MET: metformin; SUs: sulfonylureas; DPP-4 i: dipeptidyl-peptidase 4 inhibitors.


HF with no insulin use, higher risks when older, male, had lower HbA1c, BMI

from 25 to 39.9, no history of HF and rural or living in most deprived urban areas.

The risks for HF were high irrespective of previous events of CV or CKD

diagnosis, but the magnitude was greater for those with no prior events of CV or

CKD diagnosis.

PAD with no use of insulin, elderly, HbA1c <8%, diabetes duration 5-9 years,

BMI 30-39.9, and no history of HF or CKD. The risk of PAD for SUs users was

higher than for metformin users irrespective of they had past diagnoses of CVD

or not, or area of residence.

In general, SUs new users were at higher risk of for all the outcomes if they had no

insulin therapy during the follow-up, HbA1c < 8%, shorter diabetes duration, older age,

no prior history of CVD, HF or CKD and living in most deprived areas. Women using

SUs were at higher risk of MI and men had a higher risk of HF. Diabetes duration > 15

years was positively associated with the occurrence of MI and PAD.

Living in deprived areas was positively associated with MACE, all-cause mortality, MI

and HF, but not with stroke or PAD.

Results of subgroup analyses are shown in Table V.4.

Among patients on metformin monotherapy who received a second-line agent, 17,541

patients received SUs (52.68%); while among those who had been newly prescribed

SUs, 3,217 patients (41.57%) were added MET as second-line combined treatment.

For second-line dual therapies, compared with the addition of SUs to metformin, the

cohort of patients receiving metformin added to SUs did not present a statistically

higher risk in any of the endpoints analyzed. Results are shown in Table V.5.

A sensitivity analysis was performed with drug-naïve patients who initiated their first-

line monotherapy from January 1st 2011 to December 31st 2015. Results are shown in

Table V.6.


Table V.4. Adjusted HR (95% CI) of subgroup analyses for MACE, mortality, MI, stroke events and HF and PAD diagnosed in T2DM patients treated with first-line sulfonylureas (SUs) compared with metformin (MET) in the SIDIAP database, 2010-2015

MACE All-cause mortality MI Stroke HF PAD MET (total) reference reference reference reference reference Reference SUs (total) 1.55 (1.42-1.68) 1.67 (1.52-1.84) 1.38 (1.03-1.85) 1.31 (1.11-1.54) 1.49 (1.28-1.72) 1.24 (1.02-1.51) Use of insulins after the index date Ins Yes 1.23 (0.97 – 1.55) 1.35 (1.07 – 1.71) 0.70 (0.22- 2.20) 0.78 (0.35-1.72) 1.10 (0.67-1.82) 1.18 (0.67-2.08) Ins No 1.72 (1.57- 1.87) 1.90 (1.71 – 2.10) 1.63 (1.22-2.18) 1.45 (1.23-1.71) 1.69 (1.45-1.96) 1.40 (1.15-1.72) Age < 75 1.45 (1.32-1.59) 1.46 (1.32 – 1.62) 1.46 (0.99 - 2.14) 1.30 (1.07-1.58) 1.46 (1.24-1.71) 1.12 (0.84-1.51) ≥ 75 1.65 (1.43-1.91) 2.00 (1.66 – 2.41) 1.72 (1.16-2.54) 1.36 (1.05- 1.77) 1.55 (1.17- 2.06) 1.50 (1.17- 1.92) Sex Male 1.70 (1.52 – 1.89) 1.82 (1.61 – 2.07) 1.38 (0.98-1.94) 1.42 (1.13-1.77) 1.78 (1.45-2.20) 1.34 (1.07- 1.67) Female 1.74 (1.53 – 1.97) 1.74 (1.51 – 2.00) 2.13 (1.29- 3.51) 1.40 (1.11- 1.77) 1.48 (1.22-1.80) 1.53 (1.03- 2.26) HbA1c HbA1c < 8 1.73 (1.56-1.93) 1.91 (1.70-2.15) 1.65 (1.13-2.40) 1.42 (1.13-1.79) 1.79 (1.50-2.14) 1.37 (1.06-1.79) HbA1c 8-10 1.37 (1.12-1.69) 1.38 (1.10-1.75) 1.34 (0.74 -2.44) 1.31 (0.86-1.99) 1.14 (0.76 -1.70) 1.37 (0.95- 1.99) HbA1c > 10 1.71 (1.12-2.62) 2.04 (1.25- 3.32) 1.36 (0.40-4.66) 1.54 (0.70 - 3.38) 1.66 (0.74- 3.76) 1.36 (0.62 -3.01) Diabetes duration 0-4 y 1.92 (1.73 – 2.13) 2.03 (1.80- 2.30) 1.67 (1.18- 2.37) 1.62 (1.32-1.97) 1.83 (1.52-2.20) 1.27 (0.99-1.64) 5-9 y 1.32 (1.14 – 1.53) 1.46 (1.24-1.73) 1.22 (0.71-2.11) 0.97 (0.71-1.31) 1.21 (0.94 -1.56) 1.67 (1.17- 2.39) 10-14 y 1.30 (1.05 – 1.61) 1.21 (0.94 - 1.57) 1.37 (0.63- 2.95) 1.09 (0.70-1.70) 1.33 (0.89-1.99) 1.19 (0.71 -1.98) ≥ 15 y 0.92 (0.64 – 1.33) 0.95 (0.62- 1.43) 1.84 (0.51-6.64) 0.66 (0.29-1.51) 1.00 (0.52 -1.90) 2.07 (0.88- 4.87) BMI < 25 1.35 (1.08-1.68) 1.34 (1.04 -1.72) 1.51 (0.72-3.17) 1.26 (0.81 -1.94) 0.97 (0.56 -1.67) 1.20 (0.67- 2.15) 25 - 29.9 1.41 (1.21-1.64) 1.57 (1.32 -1.87) 1.15 (0.66 -2.00) 1.17 (0.88-1.56) 1.87 (1.44- 2.43) 1.22 (0.85- 1.75) 30 - 39.9 1.90 (1.63- 2.21) 2.13 (1.79- 2.53) 1.77 (1.07-2.95) 1.59 (1.21-2.09) 1.75 (1.39-2.19) 1.55 (1.15- 2.10) ≥ 40 1.94 (1.13-3.35) 1.74 (0.89- 3.42) 2.47 (0.48-12.69) 1.87 (0.73-4.76) 1.03 (0.42-2.56) 1.46 (0.38- 5.59) CVD history Prior CVD no 1.83 (1.65-2.03) 2.06 (1.82-2.32) 1.58 (1.10 - 2.27) 1.56 (1.29-1.89) 1.68 (1.37- 2.05) 1.37 (1.08 -1.75) Prior CVD yes 1.20 (1.05 -1.37) 1.25 (1.08 - 1.44) 1.39 (0.88 -2.18) 0.96 (0.72-1.29) 1.41 (1.15-1.72) 1.30 (0.95- 1.79) HF history Prior HF no 1.71 (1.57 – 1.87) 1.90 (1.72-2.10) 1.61 (1.20- 2.15) 1.42 (1.20- 1.69) 1.63 (1.40- 1.91) 1.41 (1.15- 1.73) Prior HF yes 0.92 (0.76- 1.12) 0.95 (0.77 - 1.18) 0.57 (0.22 - 1.52) 1.04 (0.63- 1.69) 1.37(0.95- 1.97) 0.92 (0.54- 1.58) CKD CKD no 1.66 (1.52-1.81) 1.81 (1.64-2.00) 1.54 (1.15 -2.07) 1.41 (1.20 - 1.67) 1.61 (1.38-1.88) 1.37 (1.12- 1.67) CKD yes 1.13 (0.83 -1.54) 1.02 (0.73 -1.42) 1.74 (0.62 -4.91) 1.10 (0.59-2.03 1.54 (1.01-2.33) 1.40 (0.73 - 2.68) MEDEA index Rural 1.68 (1.42-2.00) 1.89 (1.56-2.30) 1.23 (0.67- 2.25) 1.38 (0.98-1.95) 1.48(1.09-2.02) 0.98 (0.58-1.66) U1 (least deprived) 1.30 (1.02-1.66) 1.41 (1.04-1.91) 0.83 (0.36 -1.90) 1.41 (0.95 -2.09) 1.59 (1.00 -2.52) 1.31 (0.75-2.27) U5 (most deprived) 1.68 (1.3- 2.15) 2.09 (1.55- 2.80) 2.58 (1.25- 5.34) 0.89 (0.53 -1.50) 1.59 (1.08- 2.32) 0.94 (0.52-1.71)

SIDIAP: Information System for the Development of Research in Primary Care; MEDEA: Mortality in small Spanish areas and economic and environmental inequalities; MACE: composite of major adverse cardiovascular events; MI: myocardial infarction; PAD: peripheral artery disease MET: metformin; SUs: sulfonylureas



Table V.5. Adjusted HRs for MACE, all-cause mortality, MI, stroke, HF and PAD in cohorts of T2DM patients treated with metformin and sulfonylureas, as

monotherapy or as second-line dual therapy, SIDIAP healthcare database, 2010-2015


Adjusted HR

Composite of MACE (AMI, stroke and all-cause mortality) MET 7764 279677,66 27,76 reference SUs 1073 16369,99 65,55 1.49 (1.37-1.62) MET + SUs 1053 32190,60 32,71 reference SU + MET 361 6340,48 56,94 1.13 (0.97-1.31)

All-cause mortalityMET 4696 285961,85 16,42 reference SUs 817 16899,92 48,34 1.56 (1.42-1.72) MET + SUs 751 32771,49 22,92 Reference SU + MET 277 6536,35 42,38 1.13 (0.95-1.35)

MIMET 865 293664,23 2,95 Reference SUs 82 18677,02 4,39 1.46 (1.11-1.93) MET + SUs 91 32614,02 2,79 Reference SU + MET 28 6496,67 4,31 1.19 (0.78-1.83)

StrokeMET 2573 290015,76 8,87 Reference SUs 251 18354,30 13,68 1.31 (1.11-1.54) MET + SUs 269 32341,79 8,32 Reference SU + MET 87 6373,20 13,65 1.13 (0.87-1.46)

Heart failureMET 2478 290560,66 8,53 Reference SUs 329 18148,97 18,13 1.46 (1.26-1.69) MET + SUs 294 32337,17 9,09 Reference SU + MET 88 6417,06 13,71 1.01 (0.79-1.30)

PADMET 1797 291814,75 6,16 Reference SUs 172 18522,28 9,29 1.29 (1.07-1.55) MET + SUs 245 32381,58 7,57 Reference SU + MET 56 6450,40 8,68 0.98 (0.72-1.31)

MACE: composite of major adverse cardiovascular events (MI, stroke and all-cause mortality); MI: myocardial infarction; PAD: peripheral artery disease; SIDIAP: Information System for the Development of Research in Primary Care; MET: metformin; SUs: sulfonylureas; MET + SU: a sulfonylurea was added to previous metformin monotherapy; SU + MET: metformin was added to previous sulfonylurea monotherapy


Table V.6. Adjusted hazard ratio (95% CI) of MACE, all-cause mortality and CV outcomes in cohorts of T2DM patients treated with NIADs as first-line

monotherapy, SIDIAP healthcare database, 2011-2015.


Adjusted HR (95% CI) 2011-2015 period

Composite of MACE (AMI, stroke and all-cause mortality) MET 5436 212161.30 25.62 reference SUs 702 11506.55 61.01 1.49 (1.35 – 1.66) DPP-4 i 198 2786.45 71.06 1.58 (1.24 – 2.01) Repaglinide 356 3415.41 104.23 2.06 (1.32 – 3.23) All-cause mortality MET 3123 216308.05 14.44 reference SUs 506 11846.04 42.71 1.59 (1.41 – 1.80) DPP-4 i 150 2862.54 52.40 1.76 (1.30 – 2.38) Repaglinide 288 3528.49 81.62 2.20 (1.28 – 3.79) MI MET 674 220345.80 3.06 reference SUs 57 12750.93 4.47 1.36 (0.97 – 1.91) DPP-4 i 20 3073.88 6.51 1.82 (0.96 – 3.46) Repaglinide 25 4038.38 6.19 1.65 (0.89 – 3.05) Stroke MET 1929 218084.82 8.85 reference SUs 184 12547.63 14.66 1.33 (1.10 – 1.61) DPP-4 i 36 3041.05 11.84 1.07 (0.67 – 1.71) Repaglinide 62 3982.37 15.57 1.74 (0.94 – 3.19) Heart failure MET 1842 218454.12 8.43 reference SUs 234 12445.29 18.80 1.47 (1.24 – 1.76) DPP-4 i 53 3030.45 17.49 1.00 (0.63 – 1.58) Repaglinide 107 3859.18 27.73 1.94 (1.26 – 2.99) PAD MET 1396 219132.97 6.37 reference SUs 117 12648.87 9.25 1.22 (0.97 – 1.53) DPP-4 i 23 3074.43 7.48 1.07 (0.54 – 2.13) Repaglinide 57 3981.46 14.32 1.38 (0.72 – 2.66)

NIADs: non-insulin blood-glucose-lowering (“antidiabetic”) agents MACE: composite of major adverse cardiovascular events (MI, stroke and all-cause mortality); MI: myocardial infarction; PAD: peripheral artery disease; SIDIAP: Information System for the Development of Research in Primary Care; MET: metformin; SUs: sulfonylureas; DPP-4 i: dipeptidyl-peptidase 4 inhibitors. Covariates included estimating propensity-score are the same than the full-study period set.


Discussion

In our study, in first-line monotherapy, second-generation sulfonylureas were associated

with a significant excess risk of MACE, all-cause mortality, MI and stroke events and

risk of HF and PAD diagnoses, compared with metformin. Interestingly, SUs users with

no history of CVD, HF or CKD had increased risk for all outcomes. These results are

consistent in direction with those obtained in observational studies comparing SUs and

MET in monotherapy, although the magnitude of estimates varied.

The meta-analysis of Lamanna et al. found that metformin was not associated with

significant harm or benefit on cardiovascular events (MH-OR 0.94[0.82-1.07], p = 0.34);

although a significant benefit was observed in trials versus placebo/no therapy (MH-OR

0.79[0.64-0.98], p = 0.031), but not in active-comparator trials (MH-OR 1.03[0.72-1.77],

p = 0.89). There was a significant correlation of the effect of metformin on cardiovascular

events with trial duration and with minimum and maximum age for inclusion.[201]

Except for a few prospective studies [202, 203.] and a meta-analysis [204], most of the

observational evidence regarding the safety of newly treated T2DM patients, either with

SUs or metformin, reported a higher risk of all-cause and CV mortality for SUs users.

[196-200, 167, 162]

A meta-analysis of 82 RCTs and 26 observational studies found that SUs increase risks

of all-cause mortality and cardiovascular-related mortality compared with all other

treatments combined; the risk of myocardial infarction was significantly higher for SUs

compared with DPP-4 inhibitors and sodium-glucose co-transporter-2 inhibitors, and the

risk of stroke was significantly higher for SUs than for DPP-4 inhibitors, GLP-1 RAs,

thiazolidinediones (TZD) and insulin. [205]

Schramm et al. found that monotherapy with the most used insulin secretagogues,

including glimepiride, glibenclamide, glipizide, and tolbutamide was associated with

increased mortality and cardiovascular risk compared with metformin; while gliclazide

and repaglinide appear to be associated with a lower risk than other insulin

secretagogues.[200]


In second-line therapies combining metformin and sulfonylureas, there were no

differences in any outcome between cohorts of patients previously treated with SUs

compared with patients who were first treated with metformin and were added SUs as

second-line treatment. These results suggest that patients on combined therapies

including metformin can have lesser risks than those on SUs alone, irrespective of the

initial monotherapy. To avoid immortal-time bias and time-lag bias, we didn’t do

comparisons between patients on first-line and second-line therapies. Instead, Gulliford

et al. reported [206] reported no differences in mortality between the second-lines of

combined therapies metformin and SUs and their respective first-lines monotherapies; in

this study, only patients who have survived to the first-line therapy could be included in

the combination one, thus presenting with immortal-time bias. Fisman et al. in their

prospective observational study, compare mortality in patients with metformin

monotherapy and combination therapy, thus comparing different severity stages and

presenting with lag-time bias, reported an increase of mortality with the combination

therapy. [203]

Other monotherapies assessed in our study didn’t show any benefit compared with

metformin: both meglitinides (repaglinide) and DPP-4 i increased risk of mortality and

MACE. Evidence from CVOTs with DPP-4 inhibitors (TECOS [sitagliptin], SAVOR-

TIMI [saxagliptin], and CARMELINA [linagliptin]) obtained neutral results on

cardiovascular outcomes and mortality vs placebo.[85, 89, 93-] On the other hand, most

of the observational evidence obtained favourable results for the use of DPP-4 i.

(Appendix A ) However, the CAROLINA trial assessed a head-to-head comparison of

linagliptin vs glimepiride and didn’t show differences between agents; this result should

raise scepticism about the “no effect” of this DPP-4 i, given that glimepiride belongs to

the SU class. [206] Of note, in a recently published observational study linagliptin was

associated with a non-significant 9% decreased risk in the composite cardiovascular

outcome (HR 0.91 [0.79- 1.05]) [207] In our study patients on DPP-4 i monotherapy were

at a significantly increased risk of MACE and all-cause mortality; and an excess of risk

of MI cannot ruled out, this finding deserves further research.

Scheller et al. didn’t find differences between sitagliptin monotherapy compared with

metformin monotherapy in the risk of all-cause mortality or the composite endpoint


MACE but was associated with an increased likelihood of changing glucose-lowering

treatment. [209] Sitagliptin was the monotherapy used by almost 51% of patients treated

with DPP-4 i in our study.

Ou et al. found that metformin users had significantly lower risks for composite CVD

risk (aHR 0.87, 95 % CI 0.79-0.94), as well as those for MI, HF, and hypoglycemia, as

compared to those of DPP4i users. [210]

Results of 19 observational studies assessing the use of DPP-4 i vs no use, metformin,

SUs, insulin and other antidiabetic drugs (OADs) are summarised in the Appendix A.

Only one cohort study [211] found an excess of risk of MI and percutaneous

revascularisation with sitagliptin vs non-use in patients with chronic kidney disease and

myocardial infarction. Another cohort study in Korean patients [212] found that,

compared with metformin, sitagliptin was not associated with an elevated risk of CV

complications including myocardial infarction, ischemic stroke, heart failure, and

coronary revascularisation, compared to metformin. In patients with high CV risk, results

were similar.

Repaglinide was the only meglitinide prescribed in our study. Repaglinide users were

significantly at higher risk of MACE, all-cause mortality and MI. There are a few

observational studies assessing meglitinides in first-line treatments. Huang et al. found an

increased risk of MACE, HR 1.69 (1.25–2.59) and all-cause mortality, HR 1.88 (1.45–

2.43) in repaglinide users, compared with glimepiride users. [213] In the study conducted

by Ou et al., compared to DPP4i users, meglitinides users were at higher risk of the

composite of CV events (HR 1.3 [1.20-1.43]), as well as those for stroke, MI, HF, and

hypoglycemia. [210] In our study, patients treated with meglitinides had a higher risk

than those on SUs for MACE, all-cause mortality and MI. Basal characteristics of patients

prescribed meglitinides show more basal comorbidities than all other groups. Although

these unbalanced characteristics are controlled for confounding, other unassessed

covariates could have contributed to residual confounding.

Our study has several strengths: it presents observational data from large cohorts of

unselected patients with and without a history of past cardiovascular disease, heart and


renal failure who attend routine clinical care, fully representative of the “real-world” use.

The SIDIAP database of the nationwide healthcare system has extensively used for

diabetes research. DM has been associated with increased mortality: all-cause mortality,

involves cardiovascular, cancer and other morbidities’ death, being a proper measure to

assess therapies’ benefits or harms. [25] Mortality records are linked with an

administrative database and reflect all deaths, either within and outside hospitals. Another

strength is that we assessed the first recorded diagnosis of PAD; a few observational

studies included PAD in their outcomes. [214] PAD has been found to be the first

manifestation of CVD in T2DM patients. [215]

As significant limitations, we have no data on the population’s smoking status, alcohol

consumption or physical activity. The dataset is not linked with hospital registries,

which is a source of non-differential information bias regarding events not recorded by

GPs. In people with a history of HF or PAD at baseline, we can’t know if the new

diagnosis refers to a worsening or hospitalisation. We have not retrieved data on

hypoglycaemic events, that can be related to falls, hip fractures and CV and overall

mortality. The number of patients in nursing homes was not available; elder people

living in nursing homes are at higher risk of mortality.[216]. Data about the

administered medication during hospitalisation, if any, were not available, and this can

introduce risk of non-differential misclassification bias. Another limitation of our study

is the lack of data on adherence, though prescriptions and dispensing are both proxies of

drug administration. Additionally, we have not analysed different doses of the same

agent, or stratified by agents in the cases of SUs and DPP-4 i. Another important remark

is that prescriptions of first-line monotherapy agents fell sharply between 2010 and

2011, while steadily and slightly decrease since 2011, with differences among groups.

As the coded, de-identified dataset which serves as a basis for our study excluded

patients with a previous prescription of the given agent within the 90 days before the

index day, it is plausible that this period was not long enough, and prevalent-users were

included instead of incident-users only. (Fig. V.4) The exclusion of prescriptions

initiated in 2009 could not have been enough to remove prevalent users entirely. A

sensibility analysis was performed with data of 2011-2015 monotherapy cohorts to

exclude more extensively possible prevalent patients. In the sensibility analysis, the


results for the primary and secondary outcomes of the 2011-2015 period are similar in

magnitude and direction to those of 2010-2015 period. (Table V.6)

High-quality observational studies have suggested an increased CV risk for SUs. One of

the shortcomings highlighted in these studies is the use of a composite endpoint, [167]

introducing possible masking of the associations with the individual components. We

assessed both the composite – as primary - and individual components – as secondary

outcomes, and results were consistent for an increased risk of SUs. However, our results

showed higher estimates than those reported in other studies, but are close to the one

reported by Filion et al. for all-causes mortality.[197, 167] Residual confounding and

bias might explain these differences, but also they can be due to the choice of metformin

as a comparator, [197] and the outcome all-cause mortality instead of CV mortality both

in primary composite and in the individual secondary outcome. In all-cause mortality,

hypoglycaemia events and possibly cancer or other causes could play a major role. [197,

25]

Excluding the data of 2010, trends in incident first-line monotherapies prescriptions

could reflect a decrease in new diagnoses of T2DM, as highlighted by other authors. [3]

Prescriptions of meglitinides and sulfonylureas decreased more than twice those of

metformin (60% and 75% vs 25%), thus showing adherence to the local guidelines.

[193]

A population-based study recently published compared major adverse cardiovascular

events (MACE) among patients with diabetes who reached a reduced kidney function

threshold and continued treatment with metformin or a sulfonylurea.[217] The study

found better cardiovascular and mortality outcomes for patients treated with metformin

monotherapy compared with SUs monotherapy. Patients who continued metformin

monotherapy had reduced risk for the primary composite outcome of hospitalization for

acute myocardial infarction (AMI), ischemic or hemorrhagic stroke, transient ischemic

attack (TIA), or date of cardiovascular death, HR 0.80 (0.75 to 0.86) and cardiovascular

death (HR 0.70 [0.63 - 0.78]).


In our study, in patients with first-line treatment, 89,68% of patients were treated with

metformin, according to local and international recommendations. A low percentage of

patients who were newly prescribed metformin had a history of HF and or renal failure

(4% adding the two proportions), suggesting that the contraindications amended in 2006

(for HF) and 2016 (for RF) could still be taken into account by prescribers. Among all

the first-line newly prescribed patient, 7,739 patients (6.54%) were treated with SUs. In

the SUs cohort, 10.2 % of patients had a history of RF, and 4.1% of HF, accounting for a

total of 13.6% of patients with both conditions.

Conclusion

In this study, compared with metformin, the use of sulfonylureas in first-line

monotherapy is associated with higher risks of all-cause mortality, MI, stroke, HF, PAD

and MACE. This observational evidence strengths the concept of first-line use of

metformin and adds confirmatory concerns about mortality and CV effects of

sulfonylureas monotherapy. Additionally, in our study, the use of other agents

(secretagogues such as repaglinide and DPP-4 i) didn’t show beneficial effects over

metformin. The results of second-line comparison suggest that metformin has beneficial

effects when added to SUs monotherapy.

In this study, roughly 86.4% of patients on SUs were free both of HF and RF at baseline

could have benefited from metformin-based regimes.

If no sound contraindications exist, the use of metformin-based treatments should

always be considered. Recommendations for metformin use should be strengthened in

clinical practice.

VI. Cardiovascular outcomes and mortality

in type 2 diabetes mellitus patients prescribed second-line, metformin-based non-insulin blood-

glucose-lowering agents dual therapies

VI. Second-line, metformin-based, dual therapies 107 Nelly Raquel Herrera Comoglio Eu2P PhD December 2019

VI. Second-line, metformin-based, dual therapies

Cardiovascular outcomes and mortality in type 2 diabetes

mellitus patients prescribed second-line, metformin-based

non-insulin blood-glucose-lowering agents dual therapies

Raquel Herrera Comoglio, Xavier Vidal Guitart

Background: Type 2 diabetes mellitus is a progressive disease and patients need

intensification therapies as the condition worsens. Cardiovascular disease is the leading

cause of morbidity and mortality among T2DM patients, the choice of non-insulin

blood-glucose-lowering (“antidiabetic”) agents (NIAD) should consider potential

cardiovascular (CV) effects.

Aim: To evaluate the risk of major adverse cardiovascular events (MACE), myocardial

infarction (MI), stroke, all-cause mortality, heart failure (HF) and peripheral artery

disease (PAD) in cohorts of type 2 diabetes patients newly prescribed a NIAD as a

second-line agent added to metformin.

Methods: Longitudinal, observational study of cohorts of T2DM adult patients who

received a second-line NIAD added to previous metformin monotherapy. Patients were

followed since the date of the first prescription of the second-line agent (ID, index date)

to the first event of the primary outcome (MACE) or secondary outcomes (all-cause

mortality, MI, stroke, HF and PAD) or censoring. Patients’ basal characteristics were


ascertained before the ID. Cox proportional hazard models were used to estimate hazard

ratios with 95% confidence interval (HR [95%CI]).

Results: Among 110,535 patients first-prescribed metformin monotherapy during the

study period, 28,539 patients (25.81%) received a second-line NIAD as intensification

therapy: SUs, Compared to MET+SUs, no difference was found in the risk of the

primary and secondary outcomes in patients treated with DPP-4 i. Patients on MET +

meglitinides were at an increased risk of MACE and all-cause mortality (HR 1.19 [1.01-

1.42] and all-cause mortality (HR1.38 [1.14-1.67])

Conclusion : With metformin plus sulfonylureas as reference, no significant differences

were found between in the cohort treated with DPP-4 i. Patients with meglitinides were

at higher risk of the composite MACE and all-cause mortality. Residual confounding

cannot be ruled out, deserving further research.

Keywords : metformin, sulfonylureas, meglitinides dipeptidyl peptidase-4 inhibitors

(DPP-4 i), glucagon-like peptide-1 receptor agonists (GLP-1 RA), sodium-glucose

transporter 2 inhibitors (SGLT-2 i), thiazolidinediones.

Introduction

Type 2 Diabetes Mellitus is a chronic progressive condition disease characterised by

elevated blood glucose levels. Diabetes can lead to cardiovascular disease, and

microvascular and nerves damage, and premature death. [218] As the glycaemic control

fails as the condition worsens, most patients need multiple therapies to attain these

glycemic target levels in the longer term.[219]

Glycemic control of diabetes mellitus did not improve substantially in the past ten

years; the proportion of diabetic patients achieving a haemoglobin A1c (A1C) target

<7% is still around 50%.[220] The Catalan Health Institute has issued clinical


guidelines, current objectives of diabetes clinical care include an A1C value <7%, blood

pressure (BP) < 140/90, and when possible, 130/80 mmHg24, LDL cholesterol (LDL-

C) value ≤ 130 mg/dL (3,35 mmol/L), in primary prevention and ≤ 100 mg/dL (2,6

mmol/L) in secondary prevention.[193]

Metformin is currently the first drug of choice for T2DM. The Catalan guideline states

that metformin should be used in all patients, unless contraindicated or not tolerated.

Other blood-glucose-lowering drugs are recommended to be used in second-line, in

combination with metformin. Other agents should be added in a third-line treatment if

glycaemic targets are not achieved. In all stages, the control of cardiovascular risk

factors includes lifestyle interventions and pharmacological treatment.

Sulfonylureas stimulate the insulin release by binding to the sulfonylurea receptor 1

(SUR1), which is part ATP-sensitive potassium channel. Agents within the class vary

considerably in their pharmacokinetic properties, have high bioavailability and reach

peak plasma concentrations within 1.5–4.0 h, have long half-lives, being their effect

more extended. SUs bind to plasma proteins, which can lead to interactions with other

drugs such as salicylates, sulfonamides and warfarin. SUs are metabolized in the liver

and eliminated via the bile and urine, which restrains their use patients with hepatic

and/or renal impairment.[221] Hypoglycaemia and weight gain are known adverse

effects of this class of drugs. The cardiovascular safety of SUs has raised concerns since

the early 1970s, though in the ten years follow-up analysis mortality and MI were

reduced in the cohort initially treated with intensive therapy. [55, 187] (see also Section

I, Introduction) Meta-analysis of RCTs have found SUs to increase mortality and CV

mortality. [198, 205, 222,] Observational studies have also found more harm with the

use of SUs than with other therapies. [205] [Appendix B]

As sulfonylureas (SUs), meglitinide analogues are insulin secretagogues that stimulate

insulin release by inhibiting ATP-sensitive potassium channels of the beta-cell

membrane, though binding to a receptor distinct from that of SUs. Both repaglinide and

nateglinide are absorbed rapidly, stimulate insulin release within a few minutes, are

rapidly metabolised in the liver and are mainly excreted in the bile. Therefore, following


the preprandial administration of these drugs, insulin is more readily available during

and just after the meal. Pharmacokinetic characteristics allow a significant reduction in

postprandial hyperglycaemia without the danger of hypoglycaemia between meals. The

short action and biliary elimination make repaglinide and nateglinide especially suitable

for geriatric patients or in whom one of the other first-line antidiabetic drugs, i.e.

metformin, is strictly contraindicated (e.g. nephropathy with creatinine clearance < or =

50 ml/min). [223] A study found that glibenclamide and repaglinide have similar

efficacy in reducing blood-glucose markers over an eight week period when these

agents are used to treat patients with inadequate glycaemic control on metformin

monotherapy; Repaglinide improved postprandial glycaemic control by stimulating

early-phase insulin release. [224] Meglitinides have found to have similar blood-

glucose-lowering potency than metformin. [225] Repaglinide, in addition to metformin,

seems to be effective in reducing glucose-related markers. In a meta-analysis of 11

RCTs, repaglinide plus metformin was significantly more effective in decreasing two

hours postprandial glucose-levels than glimepiride plus metformin; there were fewer

events of hypoglycemia in the repaglinide plus metformin group, and no differences in

HbA1c and fasting blood glucose.[226] However, there is scarce evidence concerning

cardiovascular risk.

Dipeptidyl peptidase-4 inhibitors (DPP-4i) inactivates the proteolysis of the incretin

hormones glucagon-like peptide 1 (GLP-1) and glucose dependent insulinotropic peptide

(GIP), resulting in an increased glucose dependent insulin secretion. Native glucagon-

like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) account

for up to 60% of postprandial insulin release, termed “incretin effect”. GLP-1 receptor’s

activation potentiates insulin secretion from pancreatic beta cells and lower inappropriate

high glucagon secretion in a glucose-dependent manner. GLP-1 receptor is also expressed

in extrapancreatic tissues (gastrointestinal tract, heart, vasculature, and central and

peripheral nervous system), what is thought to have pleiotropic effects. DPP-4 i stimulate

both post-meal insulin secretion and inhibit glucagon secretion in a glucose-dependent

manner, with low risk of hypoglycaemia. Their effect on weight is neutral. [227, 228].


Four large CVOTs has shown no harmful or beneficial effects of DPP-4 i in CV outcomes

[85, 87, 89, 93]

We conducted an observational, population-based cohort study to assess major

cardiovascular outcomes and all-cause mortality in patients who were prescribed a

second-line non-insulin blood glucose-lowering agent (“antidiabetic” agent, NIAD)

added to metformin.

In chronic and evolving diseases, to differentiate clinical events as manifestations of the

natural progression of the disease or as effects of treatments is challenging; moreover

when treatment vary over time, and patients characteristics also change over the time.

[182] We performed comparison in second-line treatment added to metformin, to avoid

comparisons between different severity of the disease. hen assessing the effects of

treatments. As diabetes is an established risk factor for cardiovascular disease, longer

diabetes duration or more severe disease are both associated with higher cardiovascular

risk, as well as history of cardiovascular disease. Patients with poor glycaemic control

requiring drug intensification in second or third-line therapies are likely to experience

more CV events. To reduce confounding, comparisons between cohorts of patients

should be made in the same lines of treatment.

The general characteristics of the study design have been described in other sections,

only specifically those related to this study are reported.

Design

Analysis of longitudinal electronic health records data. We did a population-based cohort

study of adult T2DM patients who had started a first-line monotherapy with metformin

during the study period (1st January 2010 to 31st December 2015) and were first

prescribed a second-line non-insulin blood-glucose-lowering drug. Thus, we used a new-

user design to reduce bias. Fig V.1 represents graphically the study design and risks of

bias.

Figure VI.1: Summary of design characteristics and design-related potential sources of bias

Study design: Observational retrospective cohort study*: confounding by indication. Residual confoundingRisk of bias

Objective: To compare metformin-based second-line combined dual therapies. *Risk of time-lag bias: low. Intensification with a second NIAD was considered a

proxy of disease severityData source: SIDIAP (Information System for the Development of Research in Primary Care)Selection process:

*Risk of selection bias: very low risk. Patients initiating metformin monotherapy during the study period were selected only for new prescription of NIAD.

Risk of misclassification: Low. Data extraction:Exposure: new-users of any NIAD prescribed as second-line therapy added to metformin monotherapy.Data extraction: ATC codes from recorded prescriptions and linked to the Catalan Institut of Health (CIH) administrative database dispensing

*Risk of information bias: Low concerning exposures. Prescriptions and dispensing are proxies of administration. No data on adherence

* Risk of survivors bias: Low risk (incident-users design). Misclassification, as stated above, is possible.Exposure ascertainment: As-treated approach

*Risk of exposure misclassification: lower than in the ITT approachCovariates ascertainment: previous to the index dayOutcomes ascertainment: clinical recorded diagnoses coded by International Classification of Diseases, Revision 10 (ICD-10)

*Risk of information bias: Yes. GP SIDIAP database is not linked with Hospital databases

Mortality through administrative database linkage: low risk of information bias for mortality-

SIDIAP database

Jord

i Gol

Inst

itut

Patients ≥ 18 years registered in the CIH database≥ 365 days

Patients Diagnosed of T2DM

No records o previfous prescription of any NIAD ≥ 90 days (Whashout window)

ExcludedT1DM

Data source

Patient selection

Cata

lan

Inst

ituto

f Ph

arm

acol

ogy

De-identified dataset

Cohort of patients first prescription metformin as first-line monotherapy during the study period

Patients on metformin monotherapy first added a second NIAD as second-line dual therapy

Excludedpatients withprior use of

insulin

ATC: Anatomical-Therapeutic-Chemical code; AT: As-treated; CIH: Catalan Institut of Health; ITT: Intention-to-treat; NIAD: non-insulin blood-glucose lowering “antidiabetic” drug. SIDIAP: Information System for the Development of Research in Primay Care; T1DM: Type 1 diabetes mellitus; T2DM: Type 2 diabetes mellitus.

Study population

VI. Second-line, metformin-based, dual therapies 112Nelly Raquel Herrera Comoglio Eu2P PhD December 2019

269,792 T2DM patients prescribed one or more non-insulin blood glucose -lowering drug (NIAD)

123,260 patients newly prescribed monotherapy

110,535 drug-naïve patients started MET monoterapy during the study period

28,539 patients were added a second NIAD to metformin monotherapy

SUsn=17,541(61.46%)

DPP-4 in= 8,035(28.15%),

Meglitinidesn= 2,261(7.92%),

OthersGLP-1 RAs:1.35%; SGLT-2 i 0.48%; TZD:0.62%

Figure VI.2 : Flow chart of cohorts’ selection process from T2DM patients registered in the SIDIAP database from January 1st 2010 to December 31st 2015

SIDIAP (Information System for the Development of Research in Primary Care); NIAD: non-insulin blood glucose-lowering (“antidiabetic”) drug MET: metformin; SUs: suphfonylureas; DPP-4 i: dipeptidyl-peptidase-4 inhibitors; GLP-1 RA: glucagon-like peptide receptor agonist, SGLT-2 i: sodium-glucose co-transporter 2 inhibitors; TZD: thiazolidinediones



Study Population

People aged ≥18 years with an active T2DM diagnosis (ICD-10 codes E11) who had been

first-prescribed metformin during the study period and who were included in the cohort

study assessing first-line monotherapies. The study period extended from January 1st

2010 to December 31st 2015. Patients on metformin monotherapy entered in the second-

line cohorts as their condition worsened and needed treatment intensification. The flow

of patients into the cohort increased as time passed, and the percentage in the 2010

second-line cohort is much lower than in the following years. Figure VI.2 shows the

selection flow.

Exposures:

Any non-insulin blood-glucose-lowering drug added to metformin as a second-line dual

combination therapy: sulfonylureas (A10BB), meglitinides (repaglinide [A10BX02],

nateglinide [A10BX03]), DPP-4 inhibitors (vildagliptin [A10BH02], saxagliptin

[A10BH03],linagliptin [A10BH05], alogliptin [A10BH04], and sitagliptin [A10BH01];

glucagon-like peptide 1 receptor agonists (GLP-1 RA) (exenatide [A10BJ01], liraglutide

[A10BJ02], lixisenatide[A10BJ03], albiglutide [A10BJ04], dulaglutide [A10BJ05]);

SGLT-2 inhibitors: dapagliflozin [A10BK01], canagliflozin [A10BK02] , empagliflozin

[A10BK03]; thiazolidinediones (pioglitazone[A10BG03] and rosiglitazone [A10BG02])

and other agents. Switching between drugs of the same class doesn’t change the patient’s

cohort. The index date (ID) is defined as the day of the first recorded claimed prescription

of the NIADs first added to metformin. Patients were considered at risk from the day of

the claimed prescription until the prescription to another non-insulin blood glucose-

lowering class agent or censoring.

Follow-up

Patients were followed until the first event of primary and secondary outcomes, the first

prescription of a different NIAD group, loss of follow-up (defined as no prescription of

the drug during 12 months), or the end of study 31st December 2015.


Data extraction:

Selection: Patients with an active diagnosis code of T2DM, who had been first prescribed

metformin during the study period and who received a new prescription of any NIAD in

second-line monotherapy. The previous use of insulin (after the first prescription of

metformin monotherapy) was no exclusion criterium. No further exclusion criteria were

applied.

We extracted data from patients with no prior exposure to the given second-line blood-

glucose-lowering agent before the index date. We extracted data of the use of insulin

(A10A) during the study period, and after the first prescription of insulin, to adjust for

insulins use.

Covariates ascertainment: Age, sex and time from T2DM diagnosis were retrieved at the

date of the first prescription of the second-line agent added to metformin, with the same

criteria used in the first-line cohorts.

Outcomes:

The primary outcome is a composite of three-components of major cardiovascular events


Secondary outcomes are components of MACE: myocardial infarction [ICD-10 I21),

stroke (I61. I62, I63, I64), a new diagnosis of heart failure (I50.0) and onset or worsening

of intermittent claudication (I73.9)

Statistical analysis:

Statistical analyses have been described in the section “Firs-line therapies” All analyses

were performed by Prof. Xavier Vidal using SAS 9.4 (SAS Institute Inc., Cary, NC).

Results

Among 110,535 patients initiating treatment with metformin monotherapy 28,539

patients received intensification with other therapies: 17,541 were on MET + SUs

(61.46%), 8,035 were on MET+ DPP-4 i (28.15%), 2,261 were on meglitinides


(7.92%), 386 on MET+GLP-1 RA (1.35%), 138 on MET+SGLT-2 i (0.48%) and 178

on thiazolidinediones (0.62%). Table VI.1 shows the frequency of the different agents

within a class.

Table VI.1. Therapeutic classes and agents prescribed in incident second-line

therapies added to basal metformin, for T2DM patients, SIDIAP database, 2010-

2015

Therapeutic class n Class % Total %

All classes 28,539 100.00

Sulfonylureas 17,541 61.46

Glibenclamide/glyburide 3313 18.87

Glipizide 196 1.12

Gliquidone 34 0.19

Gliclazide 11803 67.24

Glimepiride 2207 12.57

Glisentide 1 0.01

DPP-4 i 8,035 28.15

sitagliptin 4541 56.42

vildagliptin 2237 27.79

saxagliptin 235 2.92

alogliptin 3 0.04

linagliptin 1033 12.83

Meglitinides 2,261 7.81

Guar gum 33 1.46

Repaglinide 2233 98.50

Nateglinide 1 0.04

Thiazolidinediones 178 0.62

rosiglitazone 9 5.06

pioglitazone 169 94.94

Glucagon-like peptide 1 RA 164 1.35

exenatide 106 27.25

liraglutide 283 72.75

Sodium-glucose co-transporter 2 i 138 0.48

Dapagliflozin 138 100.00


As patients are selected from the cohort of first-line monotherapy new-users of

metformin, the graphic shows that there is low risk of misclassification in prevalent and

incident users. All curves increased over time, reflecting the incorporation of patients

who needed intensification therapies. Fig. VI. 3 shows the patients’ entry to each cohort.

Figure VI.3. Yearly percent of second-line, metfomin-based dual therapies for each

NIAD class, 2010-2015, SIDIAP database

Patients’ basal characteristics (i.e., at the time of the prescription of a second-line agent,

in the temporal window already mentioned in the section of first-line therapies) are

shown in Table VI.2

The MET + SUs and the MET+DPP-4 i cohorts were similar in mean age (61.0,

SD 12.4 and 61.4, SD 12.4 years). The MET + SUs cohort had the highest mean

level of HbA1c (8.4, SD 1.6)

The MET+ DPP-4 i cohort had the highest percentage of men and the highest

use of statins.


Table VI. 2. Basal characteristics of patients newly treated with NIADs added to previous metformin as dual, second-line therapies SIDIAP database, 2010-2015

MET + SUs MET+DPP-4 i MET + meglitinides MET + GLP-1 RA MET + SGLT-2 i MET + TZD

n (%) n (%) n (%) n (%) n (%) n (%)

Total 17541 (100.0) 8035 (100.0) 2261 (100.0) 386 (100.0) 138 (100.0) 178 (100.0)

Sex Women 7613 (43.4) 3376 (42.0) 1068 (47.2) 211 (54.7) 70 (50.7) 80 (44.9)

Men 9928 (56.6) 4659 (58.0) 1193 (52.8) 175 (45.3) 68 (49.3) 98 (55.1)

Age

Mean (SD) 61.0 (12.4) 61.4 (12.6) 66.9 (13.3) 51.4 (10.8) 56.6 (11.2) 62.6 (12.4)

Median [IQR] 61 [52 – 70] 61 [53 – 70] 68 [57 – 78] 51 [45 – 59] 57 [49 – 65] 64 [53 – 72]

18-44 1654 (9.4) 731 (9.1) 128 (5.7) 96 (24.9) 22 (15.9) 12 (6.7)

45-64 9079 (51.8) 4089 (50.9) 807 (35.7) 246 (63.7) 79 (57.2) 84 (47.2)

=> 65 6808 (38.8) 3215 (40.0) 1326 (58.6) 44 (11.4) 37 (26.8) 82 (46.1)

T2DM duration

Mean (SD) 2.6 (3.8) 2.5 (3.9) 3.2 (4.3) 1.8 (3.4) 1.7 (2.8) 3.4 (4.6)

Median [IQR] 0.8 [0.0 - 4.4] 0.4 [0.0 - 4.0] 1.1 [0.0 - 5.3] 0.0 [0.0 - 2.3] 0.1 ([0.0 - 2.3] 1.4 [0.0 - 5.6]

0-4 y 13706 (78.1) 6436 (80.1) 1658 (73.3) 334 (86.5) 119 (86.2) 126 (70.8)

5-9 y 2935 (16.7) 1230 (15.3) 431 (19.1) 41 (10.6) 18 (13.0) 37 (20.8)

10-14 y 697 (4.0) 283 (3.5) 128 (5.7) 7 (1.8) . (.) 9 (5.1)

=> 15 y 203 (1.2) 86 (1.1) 44 (1.9) 4 (1.0) 1 (0.7) 6 (3.4)

HBA1c

Missing 1626 (9.3) 1131 (14.1) 340 (15.0) 77 (19.9) 21 (15.2) 31 (17.4)

Mean (SD) 8.4 (1.6) 8.2 (1.6) 7.8 (1.6) 8.1 (1.7) 8.1 (1.7) 8.0 (1.8)

Median [IQR] 8.0 [7.2 - 9.1] 7.8 [7.1 - 8.9] 7.4 [6.7 - 8.5} 7.8 [6.9 - 9.0] 7.7 [6.8 - 8.8] 7.6 [6.8 - 9.0]

< 8 7620 (43.4) 3692 (45.9) 1220 (54.0) 168 (43.5) 71 (51.4) 86 (48.3)

8 to 10 5986 (34.1) 2344 (29.2) 514 (22.7) 100 (25.9) 32 (23.2) 40 (22.5)


> 10 2309 (13.2) 868 (10.8) 187 (8.3) 41 (10.6) 14 (10.1) 21 (11.8)

BMI

Missing 1831 (10.4) 891 (11.1) 297 (13.1) 30 (7.8) 7 (5.1) 25 (14.0)

Mean (SD) 30.9 (5.4) 31.1 (5.4) 30.4 (5.4) 40.5 (6.3) 35.5 (6.3) 31.2 (6.1)

Median [IQR] 30.3 [27.2 - 34.0] 30.5 [27.4 - 34.2] 29.6 [26.7 - 33.3] 39.9 [35.8 - 44.5] 34.7 [31.4 - 38.9] 29.8 [27.2 - 34.9]

< 25 1747 (10.0) 755 (9.4) 275 (12.2) 1 (0.3) 4 (2.9) 19 (10.7)

25.0 to 29.9 (overweight) 5767 (32.9) 2506 (31.2) 776 (34.3) 7 (1.8) 19 (13.8) 59 (33.1)

30.0 to 39.9 (obese I-II) 7224 (41.2) 3400 (42.3) 810 (35.8) 171 (44.3) 82 (59.4) 64 (36.0)

=> 40 (obese III) 972 (5.5) 483 (6.0) 103 (4.6) 177 (45.9) 26 (18.8) 11 (6.2)

Comorbidities Prior cardiovascular events 2215 (12.6) 1196 (14.9) 473 (20.9) 36 (9.3) 12 (8.7) 23 (12.9)

ACS 1029 (5.9) 585 (7.3) 222 (9.8) 15 (3.9) 11 (8.0) 10 (5.6)

MI 531 (3.0) 345 (4.3) 116 (5.1) 14 (3.6) 4 (2.9) 4 (2.2)

Stroke 809 (4.6) 444 (5.5) 178 (7.9) 7 (1.8) 3 (2.2) 10 (5.6)

PAD 494 (2.8) 316 (3.9) 120 (5.3) 7 (1.8) 2 (1.4) 7 (3.9)

Heart failure 593 (3.4) 366 (4.6) 200 (8.8) 11 (2.8) 4 (2.9) 3 (1.7)

Renal failure 324 (1.8) 345 (4.3) 229 (10.1) 5 (1.3) 3 (2.2) 8 (4.5)

REGICOR CV index

Missing 8125 (46.3) 4083 (50.8) 1378 (60.9) 181 (46.9) 60 (43.5) 93 (52.2)

Low CV risk 3232 (18.4) 1364 (17.0) 286 (12.6) 89 (23.1) 25 (18.1) 30 (16.9)

Medium CV risk 4185 (23.9) 1731 (21.5) 415 (18.4) 98 (25.4) 41 (29.7) 40 (22.5)

High CV risk 1999 (11.4) 857 (10.7) 182 (8.0) 18 (4.7) 12 (8.7) 15 (8.4)

Concomitant medications

Insulin use 155 (0.9) 261 (3.2) 127 (5.6) 23 (6.0) 5 (3.6) 11 (6.2)

ACE inhibitors 8383 (47.8) 4107 (51.1) 1252 (55.4) 232 (60.1) 78 (56.5) 83 (46.6)

Aldosterone antagonists 225 (1.3) 174 (2.2) 84 (3.7) 11 (2.8) 4 (2.9) 2 (1.1)

Antiplatelet drugs 3362 (19.2) 1831 (22.8) 613 (27.1) 64 (16.6) 27 (19.6) 41 (23.0)

Beta-Blockers 2628 (15.0) 1446 (18.0) 492 (21.8) 70 (18.1) 29 (21.0) 27 (15.2)


Calcium channel blockers 2356 (13.4) 1419 (17.7) 501 (22.2) 65 (16.8) 23 (16.7) 24 (13.5)

Diuretics 5870 (33.5) 2832 (35.2) 1018 (45.0) 155 (40.2) 52 (37.7) 45 (25.3)

Statins 8030 (45.8) 4089 (50.9) 1095 (48.4) 169 (43.8) 78 (56.5) 76 (42.7) Other lipid-lowering drug 1431 (8.2) 908 (11.3) 137 (6.1) 45 (11.7) 13 (9.4) 15 (8.4)

NSAIDs 1982 (11.3) 902 (11.2) 198 (8.8) 59 (15.3) 23 (16.7) 24 (13.5)

Antidepressants 1091 (6.2) 541 (6.7) 202 (8.9) 43 (11.1) 16 (11.6) 17 (9.6)

MEDEA deprivation index

Rural 2999 (17.1) 1510 (18.8) 466 (20.6) 54 (14.0) 23 (16.7) 29 (16.3)

Urban 820 (4.7) 362 (4.5) 142 (6.3) 20 (5.2) 6 (4.3) 8 (4.5)

U1 (least deprived) 1931 (11.0) 1071 (13.3) 261 (11.5) 50 (13.0) 13 (9.4) 24 (13.5)

U2 2232 (12.7) 1172 (14.6) 290 (12.8) 55 (14.2) 15 (10.9) 23 (12.9)

U3 2807 (16.0) 1335 (16.6) 365 (16.1) 58 (15.0) 19 (13.8) 22 (12.4)

U4 3179 (18.1) 1323 (16.5) 344 (15.2) 67 (17.4) 29 (21.0) 30 (16.9)

U5 (most deprived) 3573 (20.4) 1262 (15.7) 393 (17.4) 82 (21.2) 33 (23.9) 42 (23.6)

Year Cohort entry 2010 1630 (9.3) 419 (5.2) 138 (6.1) 16 (4.1) 0 (0.) 43 (24.2)

2011 2644 (15.1) 793 (9.9) 359 (15.9) 30 (7.8) 0 (0) 38 (21.3)

2012 3127 (17.8) 1169 (14.5) 331 (14.6) 52 (13.5) 0 (0) 19 (10.7)

2013 3166 (18.0) 1408 (17.5) 451 (19.9) 77 (19.9) 2 (1.4) 21 (11.8)

2014 3389 (19.3) 1808 (22.5) 493 (21.8) 98 (25.4) 56 (40.6) 22 (12.4)

2015 3585 (20.4) 2438 (30.3) 489 (21.6) 113 (29.3) 80 (58.0) 35 (19.7) Vital Status at 31Dec2015 n (%) 883 (5.0) 358 (4.5) 250 (11.1) 3 (0.8) 3 (2.2) 9 (5.1)

NIAD: non-insulin blood glucose-lowering (“antidiabetic”) drugs; SIDIAP: Information System for the Development of Research in Primary Care. MET: metformin; SUs: sulfonylureas; DPP-4 i: dipeptidyl-peptidase 4 inhibitors; GLP-1 RAs: glucagon-like peptide 1 receptor agonists; SGLT-2 i: sodium-glucose co-transporters 2 inhibitors; TZD: thiazolidinediones

MEDEA: Mortality in small Spanish áreas and economic and environmental inequalities Values for HbA1c and comorbidities: ≤ 3 months before the index date. Values of REGICOR index of CV risk during the 15 months prior the index date. Values of body mass index (BMI) were the most recent ≤15 months before the index date. Concomitant use of drugs refers to any active prescription at the index date.


Mean age of patients of the MET+ meglitinides cohort was 66.9 years (SD

13.3). Patients of this cohort had the highest percentage of comorbidities, and

also the highest percentage of aldosterone antagonists, antiplatelet drugs, beta-

blockers, calcium-channel blockers and diuretics.

54.7 % of patients who were prescribed GLP-1 RA were women, mean age was

51.4 years, had the shortest mean diabetes duration, the highest BMI, and the

highest percentage of use of lipid-lowering drugs other than statins.

The cohort of patients on MET + TZD had the highest previous use of insulin.

The shortest mean follow-up was 0.75 years (SD 0.56) for the cohort of MET+SGLT-2

and the longer one of 2,2 years (SD 1.54) for MET + SUs. Mean follow-was 1.62 years

(SD 1.38) for MET + DPP-4 i; 1.91 years (SD 1.44) for MET + meglitinides; 1.86 years

(SD 1.66) for MET + TZD and 1.73 years (SD 1.31) for MET + GLP-1 RA.

In the six cohorts, during the study period, there were 1,834 first events of MACE. There

were 174 myocardial infarctions, 484 events of stroke and 1,261 patients died. There were

also 578 new diagnoses of heart failure and 465 of PAD. Cohorts of dual second-line

metformin-based regimes were compared with MET + SUs as reference, because this

combination is the recommended in local guidelines and the most widely used dual

combined therapy in the study population.

After adjusting for all available demographic, clinical and concomitant medications data

at baseline and for the use of insulin after the index date and before or on the date of the

event, compared with MET + SUs dual therapy, only patients on MET + SUs dual

therapy were at significant higher risk of MACE, HR 1.19 (1.01 – 1.42) and all-cause

mortality, HR 1.38 (1.14 – 1.67). In all the other outcomes, meglitinides showed no

difference compared with MET + SUs. The use of MET + DPP-4 i showed no statistical

differences with the use of MET + SUs.Other combined therapies (MET + GLP-1 RA,

MET + SGLT-2 i and MET + TZD) do not reach statistical significance or had 0 events.


Table VI.3. Adjusted HRs of first event of MACE, all-cause mortality, CV events and HF and PAD diagnoses and all-cause mortality in cohorts of T2DM patients treated with NIADs added to metformin as a dual second-line therapy, SIDIAP

healthcare database, 2010-2015

Outcomes N° of events Person-years Crude Incidence Rate per 1000 p/y

Adjusted HR

Composite of MACE (AMI, stroke and all-cause mortality) MET + SU 1120 55269.56 20.26 reference MET + DPP-4 i 432 12754.07 33.87 1.10 (0.98-1.23) MET + meglitinides 267 4240.40 62.97 1.19 (1.01-1.42) MET + GLP-1 RA 6 662.51 9.06 MET + SGLT-2 i 2 104.13 19.21 MET + TZD 7 327.12 21.40 All-cause mortality MET + SU 751 56470.74 13.30 reference MET + DPP-4 i 283 12984.49 21.80 1.08 (0.94-1.25) MET + meglitinides 218 4313.03 50.54 1.38 (1.14-1.67) MET + GLP-1 RA 3 666.72 4.50 MET + SGLT-2 i 2 104.13 19.21 MET + TZD 4 331.33 12.07 MI MET + SU 113 56168.71 2.01 reference MET + DPP-4 i 49 12906.75 3.80 1.23 (0.87-1.75) MET + meglitinides 9 4304.33 2.09 0.70 (0.30-1.61) MET + GLP-1 RA 0 666.72 0.00 MET + SGLT-2 i 0 104.13 0.00 MET + TZD 3 327.12 9.17 Stroke MET + SU 315 55544.50 5.67 reference MET + DPP-4 i 117 12829.00 9.12 1.03 (0.83-1.29) MET + meglitinides 49 4249.10 11.53 0.86 (0.60-1.24) MET + GLP-1 RA 3 662.51 4.53 MET + SGLT-2 i 0 104.13 0.00 MET + TZD 0 331.33 0.00 Heart failure MET + SU 347 55565.41 6.24 reference MET + DPP-4 i 122 12856.76 9.49 0.91 (0.74-1.13) MET + meglitinides 100 4182.76 23.91 1.25 (0.96-1.64) MET + GLP-1 RA 5 660.51 7.57 MET + SGLT-2 i 1 103.25 9.69 MET + TZD 3 326.20 9.20


PAD MET + SU 307 55763.33 5.51 reference MET + DPP-4 i 104 12855.39 8.09 0.95 (0.76-1.20) MET + meglitinides 51 4256.88 11.98 1.35 (0.94-1.94) MET + GLP-1 RA 1 666.48 1.50 MET + SGLT-2 i 0 104.13 0.00 MET + TZD 2 330.79 6.05

HR: hazard ratio; (95% CI): 95% confidence interval MACE: composite of major adverse cardiovascular events (MI, stroke and all-cause mortality); MI: myocardial infarction; PAD: peripheral artery disease; SIDIAP: Information System for the Development of Research in Primary Care; MET: metformin; SUs: sulfonylureas; DPP-4 i: dipeptidyl-peptidase 4 inhibitors; GLP-1 RA: glucagon-like peptide 1 receptor agonists; SGLT-2 i: sodium glucose co-transport 2 inhibitors; TZD: thiazolidinediones

Table VI.3 shows the number of events in each cohort, the person-years period, the crude

incidence rate/1000 p/y and the adjusted hazard ratios (95% CI)

We also stratified the cohorts of MET+SUs, MET+DPP-4 i; MET + meglitinides newly

prescribed second-line dual patients by the use of insulin during the follow-up, and by

age, sex, HbA1c, diabetes duration, body mass index (BMI), history of CVD, history of

HF, history of renal failure (RF) and MEDEA deprivation index

Among patients on MET+DPP-4 i, compared with MET + SU dual therapy:

For MACE, patients were at higher risk if they had previous history of H, were

overweight and female. There was a trend toward higher risk for patients with

previous history of CVD and for those with no previous use of insulin.

For all-cause mortality, patients were at higher risk if they had history of HF,

there was a trends toward increased risk for women.

For MI, patients were at higher risk if they had history of CVD, an increased risk

for patients with history of HF cannot ruled out.

For stroke, there was no differences between subgroups

For heart failure (HF) and peripheral artery disease (PAD), patients were at

higher risk if they had history of HF.

Results of the sub-group analysis for patients on MET+DPP-4 i are shown in Table

VI.4. Results of subgroup analysis for MACE are shown in the Figure VI.4 a.


Table VI.4. Adjusted HRs Subgroup analyses for MACE, mortality, CV events and HF and PAD diagnoses in T2DM patients treated with dual second-line therapy of MET+ DPPP-4 i vs. MET+SUs in the SIDIAP database, 2010-2015

MACE All-cause mortality MI Stroke HF PAD MET+SU (total) reference reference reference reference reference reference MET+DPP-4I(total) 1.10 (0.98 – 1.23) 1.08 (0.94 – 1.25) 1.23 (0.87 – 1.75) 1.03 (0.83 – 1.29) 0.91 (0.74 – 1.13) 0.95 (0.76 – 1.20)

Use of insulins after the index date Ins Yes 0.96 (0.69 – 1.34) 0.94 (0.65 – 1.36) 2.40 (0.55 – 10.35) 1.00 (0.45 – 2.21) 1.47 (0.78 – 2.79) 1.02 (0.45 – 2.30) Ins No 1.12 (0.99 – 1.26) 1.11 (0.96 – 1.30) 1.19 (0.83 – 1.70) 1.04 (0.83 – 1.30) 0.87 (0.78 – 2.79) 0.95 (0.75 – 1.20) Age < 75 1.09 (0.93 – 1.28) 1.05 (0.85 – 1.10) 1.31 (0.89 – 1.93) 1.03 (0.78 – 1.35) 0.95 (0.70 – 1.29) 0.89 (0.68 – 1.17) ≥ 75 1.17 (0.98 – 1.38) 1.18 (0.97 – 1.43) 1.00 (0.46 – 2.17) 1.10 (0.76 – 1.57) 0.92 (0.69 – 1.25) 1.19 (0.76 – 1.86) Sex Male 0.94 (0.78 – 1.14) 0.94 (0.75 – 1.18) 1.53 (0.77 – 3.04) 0.80 (0.57 – 1.13) 0.80 (0.58 – 1.11) 0.79 (0.47 – 1.33) Female 1.21 (1.04 – 1.39) 1.19 (1.00 – 1.43) 1.15 (0.77 – 1.72) 1.24 (0.94 – 1.65) 1.02 (0.77 – 1.35) 1.00 (0.77 – 1.33) HbA1c HbA1c < 8 1.02 (0.87 – 1.20) 1.01 (0.83 – 1.23) 1.19 (0.72 – 1.96) 0.96 (0.70 – 1.31) 0.89 (0.67 – 1.19) 0.96 (0.68 – 1.36) HbA1c 8-10 1.23 (0.99 – 1.53) 1.18 (0.91 – 1.55) 1.40 (0.76 – 2.58) 1.24 (0.83 – 1.83) 1.04 (0.71 – 1.54) 1.01 (0.67 – 1.51) HbA1c > 10 1.07 (0.75 – 1.54) 1.16 (0.73 – 1.83) 1.00 (0.37 – 2.72) 0.84 (0.38 – 1.83) 0.62 (0.28 – 1.39) 0.84 (0.47 – 1.51) Diabetes duration 0-4 y 1.09 (0.95 – 1.25) 1.02 (0.86 – 1.22) 1.39 (0.94 – 2.05) 1.08 (0.84 – 1.39) 0.91 (0.70 – 1.17) 0.92 (0.70 – 1.21) 5-9 y 1.12 (0.88 – 1.43) 1.23 (0.93 – 1.65) 1.02 (0.46 – 2.26) 0.99 (0.61 – 1.61) 1.02 (0.66 – 1.58) 0.85 (0.52 – 1.36) 10-14 y 1.35 (0.85 – 2.13) 1.47 (0.87 – 2.48) - 1.09 (0.46 – 2.57) 0.92 (0.35 – 2.42) 1.67 (0.58 – 4.80) ≥ 15 y 0.58 (0.27 – 1.21) 0.62 (0.27 – 1.46) 0.55 (0.05 – 5.66) 0.25 (0.03 – 1.99) 0.32 (0.07 – 1.50) 4.96 (0.88 – 28.06) BMI < 25 0.94 (0.70 – 1.26) 0.86 (0.61 – 1.22) 0.61 (0.15 – 2.53) 1.05 (0.58 – 1.89) 0.73 (0.34 – 1.60) 1.27 (0.75 – 2.13) 25 - 29.9 1.24 (1.03 – 1.49) 1.23 (0.98 – 1.55) 1.56 (0.93 – 2.61) 1.14 (0.80 – 1.64) 0.92 (0.63 – 1.35) 1.29 (0.91 – 1.82) 30 - 39.9 1.07 (0.88 – 1.29) 1.13 (0.88 – 1.44) 1.18 (0.67 – 2.08) 0.90 (0.64 – 1.28) 1.02 (0.75 – 1.39) 0.58 (0.37 – 0.92) ≥ 40 0.98 (0.54 – 1.76) 0.90 (0.44 – 1.87) 0.62 (0.06 – 6.21) 1.53 (0.52 – 4.50) 0.49 (0.19 – 1.23) 0.59 (0.12 – 2.86) CVD history Prior CVD no 1.07 (0.93 – 1.23) 1.05 (0.88 – 1.26) 1.00 (0.66 – 1.52) 1.09 (0.85 – 1.40) 0.84 (0.65 – 1.09) 0.88 (0.67 – 1.15) Prior CVD yes 1.22 (0.99 – 1.49) 1.20 (0.95 – 1.52) 2.38 (1.24 – 4.57) 0.91 (0.59 – 1.42) 1.15 (0.80 – 1.65) 1.26 (0.82 – 1.94) HF history Prior HF no 1.05 (0.92 – 1.19) 1.03 (0.88 – 1.21) 1.13 (0.78 – 1.65) 1.00 (0.79 – 1.25) 0.82 (0.65 – 1.04) 0.90 (0.71 – 1.14) Prior HF yes 1.59 (1.18 – 2.14) 1.51 (1.08 – 2.12) 2.66 (0.96 – 7.35) 1.60 (0.79 – 3.21) 1.82 (1.07 – 3.10) 2.69 (1.06 – 6.81)


Table VI.4. Adjusted HRs Subgroup analyses for MACE, mortality, CV events and HF and PAD diagnoses in T2DM patients

treated with dual second-line therapy of MET+ DPPP-4 i vs. MET+SUs in the SIDIAP database, 2010-2015 (cont.)

MACE All-cause mortality MI Stroke HF PAD Renal failure history Prior RF no 1.11 (0.98 – 1.25) 1.07 (0.92 – 1.24) 1.25 (0.88 – 1.77) 1.09 (0.88 – 1.36) 0.91 (0.73 – 1.13) 0.97 (0.76 – 1.22) Prior RF yes 1.01 (0.63 – 1.61) 1.66 (0.98 – 2.80) 0.60 (0.05 – 6.60) 0.24 (0.07 – 0.85) 1.18 (0.59 – 2.36) 0.79 (0.31 – 2.04) MEDEA index Rural 0.89 (0.69 – 1.16) 0.78 (0.56 – 1.07) 0.95 (0.45 – 2.01) 1.21 (0.75 – 1.97) 0.95 (0.61 – 1.49) 0.88 (0.49 – 1.58) U1 (least deprived) 1.18 (0.85 – 1.65) 2.01 (1.35 – 3.01) 0.14 (0.02 – 1.08) 0.64 (0.33 – 1.24) 0.66 (0.35 – 1.26) 0.63 (0.31 – 1.26) U5 (most deprived) 1.10 (0.79 – 1.53) 0.99 (0.64 – 1.54) 1.02 (0.38 – 2.72) 1.18 (0.69 – 2.04) 1.68 (0.98 – 2.87) 1.15 (0.67 – 1.97)

Table VI.5. Adjusted HR of subgroup analyses for MACE, mortality, MI, stroke events and HF and PAD diagnosed in T2DM patients treated with dual second-line therapy of meglitinides added to metformin, SIDIAP database, 2010-2015

MACE All-cause mortality MI Stroke HF PAD MET+SU (total) reference reference reference reference reference reference MET+meglitinides (total)

1.19 (1.01 – 1.42) 1.38 (1.14 – 1.67) 0.70 (0.30 – 1.61) 0.86 (0.60 – 1.24) 1.25 (0.96 – 1.64) 1.35 (0.94 – 1.94)

Use of insulins after the index date Ins Yes 1.74 (1.16 – 2.62) 1.80 (1.16 – 2.77) - 1.54 (0.55 – 4.31) 1.63 (0.80 – 3.35) 1.46 (0.49 – 4.38) Ins No 1.10 (0.91 – 1.33) 1.28 (1.04 – 1.58) 0.73 (0.31 – 1.68) 0.80 (0.54 – 1.18) 1.22 (0.91 – 1.62) 1.34 (0.91 – 1.96) Age < 75 1.24 (0.96 – 1.62) 1.46 (1.06 – 2.02) 0.88 (0.36 – 2.17) 0.96 (0.59 – 1.57) 1.11 (0.71 – 1.74) 1.36 (0.87 – 2.12) ≥ 75 1.07 (0.87 – 1.32) 1.24 (0.99 – 1.55) 0.19 (0.04 – 0.91) 0.68 (0.40 – 1.15) 1.28 (0.92 – 1.79) 1.29 (0.74 – 2.26) Sex Male 0.95 (0.73 – 1.24) 1.17 (0.87 – 1.58) – 0.45 (0.25 – 0.81) 1.37 (0.94 – 1.99) 1.20 (0.59 – 2.46) Female 1.38 (1.11 – 1.71) 1.55 (1.21 – 1.98) 0.89 (0.38 – 2.07) 1.24 (0.79 – 1.97) 1.14 (0.78 – 1.65) 1.39 (0.91 – 2.11) HbA1c HbA1c < 8 1.21 (0.98 – 1.50) 1.39 (1.10 – 1.75) 0.91 (0.34 – 2.44) 0.78 (0.50 – 1.24) 1.55 (1.12 – 2.16) 1.32 (0.81 – 2.15) HbA1c 8– 10 1.16 (0.79 – 1.70) 1.27 (0.84 – 1.93) 0.40 (0.03 – 5.55) 1.13 (0.57 – 2.24) 0.98 (0.54 – 1.77) 1.70 (0.93 – 3.11) HbA1c > 10 1.18 (0.59 – 2.34) 1.57 (0.76 – 3.23) – 0.57 (0.12 – 2.77) 0.53 (0.12 – 2.44) 0.83 (0.26 – 2.62)


Table VI.5. Adjusted HR of subgroup analyses for MACE, mortality, MI, stroke events and HF and PAD diagnosed in T2DM

patients treated with dual second-line therapy of meglitinides added to metformin, SIDIAP database, 2010-2015 (cont.)

MACE All-cause mortality MI Stroke HF PAD Diabetes duration 0– 4 y 1.20 (0.97 – 1.48) 1.44 (1.13 – 1.83) 0.63 (0.23 – 1.71) 0.73 (0.44 – 1.18) 1.09 (0.77 – 1.54) 1.56 (1.03 – 2.37) 5– 9 y 1.05 (0.73 – 1.51) 1.15 (0.78 – 1.70) 1.01 (0.25 – 4.06) 0.93 (0.45 – 1.89) 1.58 (0.94 – 2.66) 0.55 (0.21 – 1.44) 10– 14 y 1.80 (1.05 – 3.08) 1.55 (0.83 – 2.90) – 2.20 (0.84 – 5.72) 2.05 (0.89 – 4.70) 2.28 (0.76 – 6.85) ≥ 15 y 0.86 (0.42 – 1.76) 1.14 (0.54 – 2.39) – 0.13 (0.02 – 1.08) 0.41 (0.07 – 2.49) 8.86 (0.95 – 82.44) BMI < 25 1.04 (0.69 – 1.55) 1.17 (0.77 – 1.77) – 0.89 (0.36 – 2.17) 1.36 (0.60 – 3.07) 1.38 (0.55 – 3.43) 25 – 29.9 1.39 (1.06 – 1.82) 1.60 (1.18 – 2.17) 1.14 (0.37 – 3.46) 0.84 (0.46 – 1.56) 1.37 (0.87 – 2.15) 1.13 (0.60 – 2.14) 30 – 39.9 1.08 (0.80 – 1.45) 1.29 (0.92 – 1.79) 0.36 (0.08 – 1.63) 0.89 (0.49 – 1.63) 1.11 (0.72 – 1.71) 1.42 (0.79 – 2.56) ≥ 40 1.06 (0.39 – 2.88) 1.19 (0.40 – 3.53) 2.27 (0.23 – 22.24) – 1.42 (0.52 – 3.89) 2.69 (0.37 – 19.53) CVD history Prior CVD no 1.18 (0.95 – 1.46) 1.36 (1.06 – 1.74) 0.80 (0.32 – 1.97) 0.87 (0.56 – 1.34) 1.35 (0.98 – 1.86) 1.37 (0.90 – 2.09) Prior CVD yes 1.32 (1.01 – 1.73) 1.53 (1.15 – 2.06) 0.28 (0.06 – 1.42) 0.91 (0.47 – 1.77) 1.12 (0.70 – 1.79) 1.38 (0.70 – 2.73) HF history Prior HF no 1.19 (0.99 – 1.44) 1.37 (1.11 – 1.69) 0.73 (0.30 – 1.75) 0.94 (0.65 – 1.35) 1.20 (0.90 – 1.61) 1.26 (0.86 – 1.86) Prior HF yes 1.35 (0.92 – 1.97) 1.69 (1.13 – 2.52) 0.43 (0.05 – 4.10) 0.07 (0.01 – 0.54) 1.94 (1.03 – 3.63) 4.32 (1.57 – 11.86) Renal failure history Prior RF no 1.21 (1.01 – 1.44) 1.39 (1.14 – 1.69) 0.7 (0.3 – 1.64) 0.88 (0.6 – 1.29) 1.24 (0.94 – 1.66) 1.39 (0.96 – 2.02) Prior RF yes 0.94 (0.56 – 1.58) 1.19 (0.64 – 2.22) 0.74 (0.07 – 8.21) 0.64 (0.23 – 1.78) 1.33 (0.68 – 2.63) 0.61 (0.21 – 1.8) MEDEA index Rural 0.94 (0.65 – 1.36) 1.03 (0.68 – 1.55) 0.11 (0.01 – 0.82) 1.17 (0.56 – 2.42) 0.89 (0.50 – 1.60) 2.00 (0.91 – 4.42) U1 (least deprived) 1.63 (0.98 – 2.70) 2.09 (1.15 – 3.79) 1.66 (0.40 – 6.82) 1.08 (0.42 – 2.79) 2.05 (1.02 – 4.11) 0.70 (0.23 – 2.10) U5 (most deprived) 0.94 (0.56 – 1.60) 0.95 (0.50 – 1.80) 1.76 (0.50 – 6.21) 0.43 (0.13 – 1.46) 1.53 (0.74 – 3.17) 1.58 (0.67 – 3.72)

MET+SUs is reference. HR: hazard ratio; (95% CI): 95% confidence interval; Significant results in bold.

MACE: composite of major adverse cardiovascular events (MI, stroke and all-cause mortality); MI: myocardial infarction; PAD: peripheral artery disease; HF: heart failure; RF: renal failure; MET: metformin; SUs: sulfonylureas; DPP-4 i: dipeptidyl-peptidase 4 inhibitors; GLP-1 RA: glucagon-like peptide 1 receptor agonists; SGLT-2 i: sodium-glucose co-transporter 2 inhibitors; TZD: thiazolidinediones. SIDIAP: Information System for the Development of Research in Primary Care; MEDEA: Mortality in small Spanish áreas and economic and environmental inequalities

Fig VI.4: Subgroup analyses of risk of MACE in cohorts of metformin patients who were added DPP-4 i (A) or meglitinides (B) compared to the addition of sulfonylureas (SUs).

All estimators: adjusted HR: hazard ratio; (95% CI): 95% confidence interval. MET+SUs is reference.MACE: composite of major adverse cardiovascular events (MI, stroke and all-cause mortality); MI: myocardial infarction; HF: heart failure; RF: renal failure; MET: metformin; SUs: sulfonylureas; DPP-4 i: dipeptidyl-peptidase 4 inhibitors.SUs: sulphonylureas. SUs: Gliclazide 67.24%; Glibenclamide/glyburide 18.87%; Glimepiride 12.57%.DPP-4 i: dipeptidyl-peptidase 4 inhibitors. DPP-4 i: Sitagliptin 56.42%; Vildagliptin: 27.49%; Linagliptin 12.83%.Meglitinides: Repaglinide 98.50%

A: MET + DPP-4 i vs MET + SUs B: MET + meglitinides vs MET + SUs



Among patients on MET + meglitinides, compared with MET+ SUs dual therapy:

For MACE, patients were at increased risk if they were women, overweighted,

diabetes duration 10-14 years long, had previous history of CVD and no history

of renal failure.

For all-cause mortality, patients were at higher risk irrespective of their previous

use of insulin, CVD and HF history. Patients < 75 years, women, overweighted

(BMI between 25 and 29.9 kg/1.73 m2), with glycated haemoglobin < 8%,

shorter diabetes duration (0-4 years) and no history of renal failure were at

increased risk of dying. Higher risk for people > 75 years cannot ruled out.

For MI and stroke, there was no differences between subgroups.

For heart failure (HF) and peripheral artery disease (PAD), patients were at

increased risk if they had history of HF at baseline. Patients with HbA1c < 8%

also showed higher risk of HF.

In brief, in patients who were prescribed MET+ DPP-4 i and compared with the

reference, MET+SUs, there was no difference in overall estimators for all the outcomes

assessed. In subgroup analysis, history of HF was associated with increased risk of

MACE, all-cause mortality, new onset or worsening of HF and PAD; female patients

were also at higher risk of MACE.

For patients who were prescribed MET+meglitinides, compared with MET + SUs as a

reference, overall estimators reflect a higher risk of MACE and all-cause mortality. In

subgroup analysis, female patients were at increased risks both for MACE and all-cause

mortality. For HF and PAD, a history of HF was associated with increased risks.

Patients were at increased risk of all-cause mortality irrespective of their CVD or HF

history, or the past use or not of insulins, but people with no history of renal failure also

had increased risks.

Results of the subgroup analysis for patients on MET+meglitinides compared with

MET+SUs are shown in Table VI.5. Results of subgroup analysis for MACE are shown

in Figure VI.4 b.


Interestingly, both for MET+SUs and MET+meglitines, patients living in the least

deprived areas were at higher risks of all-cause mortality. Patients on MET + DPP-4 i

who lived in the least deprived areas had a non-significant lower risk for MI (HR 0.14

[0.02-1.08]) All the patients included in these cohorts are within the primary care public

health and medicines are funded partially or in full by the administration; this allows

patients’ economic situation to have little impact on the choice of the agent. In this

study, there were no differences in prescription on more expensive agents, such as GLP-

1 RA and, to a lesser extent, SGLT-2. 38% and 44% of patients prescribed GLP-1 RA

and SGLT-2 lived in the most deprived urban areas.

Summary of the currently available evidence

RCTs and MA of RCTs

There is scarce RCTs evidence for comparisons between two metformin-based

combination plus SUs or other NIADs assessing major clinical outcomes.

1. A systematic review of RCTs found inconclusive evidence whether metformin and

SUs combination therapy compared with metformin plus another glucose-lowering

intervention results in benefit or harm for most patient-important outcomes (mortality,

SAEs, macrovascular and microvascular complications) except for hypoglycaemia.

However, more adverse effects were found with the combination of metformin and SUs.

[229]

Five trials compared (n = 1194) with MET+GLP-1 RA, (n = 1675): all-cause

mortality was 11/1057 (1%) versus 11/1537 (0.7%), risk ratio (RR) 1.15 (95%

confidence interval (CI) 0.49 to 2.67).

Nine trials compared MET + SUs ( n= 5414) with MET + DPP-4 i (n = 6346):

all-cause mortality was 33/5387 (0.6%) versus 26/6307 (0.4%), RR 1.32 (95%

CI 0.76 to 2.28). For CV mortality 11/2989 (0.4%) versus 9/3885 (0.2%), RR

1.54 (95% CI 0.63 to 3.79); 9 trials; 11,694 participants; non-fatal stroke

14/2098 (0.7%) versus 8/2995 (0.3%), RR 2.21 (95% CI 0.74 to 6.58); ); 4 trials;

5093 participants;; non-fatal MI 15/2989 (0.5%) versus 13/3885 (0.3%), RR

1.45 (95% CI 0.69 to 3.07); 6 trials; 6 trials; 6874 participants; low-certainty


evidence; SAE 735/5387 (13.6%) versus 779/6307 (12.4%), RR 1.07 (95% CI

0.97 to 1.186874; one trial in 64 participants reported no microvascular

complications were observed .

Eleven trials compared MET + SUs (n = 3626) with MET + TZD (N = 3685):

all-cause mortality was 123/3300 (3.7%) versus 114/3354 (3.4%), RR 1.09

(95% CI 0.85 to 1.40); 6 trials; 6654 participants; low-certainty evidence;

cardiovascular mortality 37/2946 (1.3%) versus 41/2994 (1.4%), RR 0.78 (95%

CI 0.36 to 1.67); 4 trials; 5940 participants; low-certainty evidence; SAE

666/3300 (20.2%) versus 671/3354 (20%), RR 1.01 (95% CI 0.93 to 1.11); 6

trials; 6654 participants; very low-certainty evidence; non-fatal stroke 20/1540

(1.3%) versus 16/1583 (1%), RR 1.29 (95% CI 0.67 to 2.47); P = 0.45; 2 trials;

3123 participants; very low-certainty evidence; non-fatal MI 25/1841 (1.4%)

versus 21/1877 (1.1%), RR 1.21 (95% CI 0.68 to 2.14); P = 0.51; 3 trials; 3718

participants; very low-certainty evidence; three trials (3123 participants)

reported no microvascular complications (very low-certainty evidence)

Three trials compared MET + SUs (n = 462) with MET + meglitinides (n =

476): one person died in each intervention group (3 trials; 874 participants; low-

certainty evidence); no cardiovascular mortality (2 trials; 446 participants; low-

certainty evidence); SAE 34/424 (8%) versus 27/450 (6%), RR 1.68 (95% CI

0.54 to 5.21); P = 0.37; 3 trials; 874 participants; low-certainty evidence; no

fatal stroke (1 trial; 233 participants; very low-certainty evidence); non-fatal

MI 2/215 (0.9%) participants in the M+S group; 2 trials; 446 participants; low-

certainty evidence; no microvascular complications (1 trial; 233 participants;

low-certainty evidence)

Four trials compared MET + SUs (n = 2109) with metformin plus a sodium-

glucose co-transporter 2 inhibitor (SGLT-2 i) (n = 3032): all-cause mortality

was 13/2107 (0.6%) versus 19/3027 (0.6%), RR 0.96 (95% CI 0.44 to 2.09); CV

mortality 4/1327 (0.3%) versus 6/2262 (0.3%), RR 1.22 (95% CI 0.33 to 4.41);

SAE 315/2107 (15.5%) versus 375/3027 (12.4%), RR 1.02 (95% CI 0.76 to

1.37); non-fatal stroke 3/919 (0.3%) versus 7/1856 (0.4%), RR 0.87 (95% CI


0.22 to 3.34); non-fatal MI 7/890 (0.8%) versus 8/1374 (0.6%), RR 1.43 (95%

CI 0.49 to 4.18; amputation of lower extremity 1/437 (0.2%) versus 1/888

(0.1%)

SUs and DPP-i in combined metformin-based regimes:

1. RCTs

The CAROLINA trial compared linagliptin vs glimepiride in standard care.

Of 6042 participants randomized, 6033 (mean age, 64.0 years; 2414 [39.9%]

women; mean glycated haemoglobin, 7.2%; median duration of diabetes, 6.3

years; 42% with macrovascular disease; 59% had undergone metformin

monotherapy) did not find differences in the 3 points MACE (HR, 0.98 [95%

CI, 0.84-1.14]). In the subgroup analyses, all patients with metformin

5005/6033, 83%) showed no differences either (HR,1.01 [95% CI 0.86,

1.19); there were not a separate subgroup analysis for patients with prior

metformin monotherapy (Table S2. Hazard ratios for the primary outcome

(3-point MACE) in subgroups) [207]

2. Observational evidence:

Using the UK Clinical Practice Research Datalink, a cohort of patients newly

treated with metformin or sulfonylurea monotherapy between January 1,

1988, and December 31, 2011, was identified and was followed until

December 31, 2012. The cohort consisted of 11,807 patients that included

2286 on a DPP-4 inhibitor-metformin combination and 9521 on a

sulfonylurea-metformin combination. The crude incidence rates (95% CIs)

of the composite endpoint (myocardial infarction, stroke and all-cause

mortality) were 1.2% (0.8% to 1.7%) and 2.2% (1.9% to 2.5%) per year for

the DPP-4 inhibitor-metformin and sulfonylurea-metformin combinations,

respectively. In the high-dimensional propensity score-adjusted model, the

use of the DPP-4 inhibitor-metformin combination was associated with a

38% decreased risk for the composite endpoint (adjusted HR 0.62; 95% CI

0.40 to 0.98), compared with the sulfonylurea-metformin combination.[230]


In a large Danish population-based observational cohort study, Mogensen et

al. compared MET + DPP-4 i (n= 11,138) with MET+SUs (25,092) as a

reference. Results showed a decreased risk for DPP-4 i users: RR of 0.65

(0.54–0.80) for mortality, RR of 0.57 (0.40–0.80) for CV mortality and RR

of 0.70 (0.57–0.85) for the 3 point MACE of MI, stroke and CV

mortality.[231]

Seong et al. found decreased risks for MET+ DPP-4 i users. Compared with

MET + DPP-4 i as a reference (n= 74,720), MET+SUs users (n=253,563)

were at higher risks of CVD, MI and ischemic stroke. For total CVD, the

result was HR 1.20 (1.09-1.32) ; for MI, HR 1.14 (1.04-1.91); for HF, HR

1.07 (0.71-1.62); and for ischemic stroke 1.51 (1.28-1.79). [232]

Compared with MET + SUs (n=9,419) as a reference, Kannan et al. didn’t

find differences in MET + DPP-4 i users (n=1,487) for all-cause mortality

(HR 1.029 [0.81–1.31]) or risk of coronary artery disease, (HR1.056 [0.99–

1.13]), but found an increased risk of HF (HR 1.104 [1.04–1.17]), stroke

(HR 1.28 [1.08-1.52]), ischemic heart disease (HR 1.35 [1.16-1.57]) and

peripheral artery disease (HR 1.65 [1.16-2.36]) [233]

Ekström et al. compared MET+ DPP-4 i with MET + SUs as a reference.

The study didn’t find statistical differences in mortality (HR 0.79 [0.60,

1.04]) or stroke (HR 0.82 [0.53, 1.25]). Results for heart failure (HR 0.54

[0.38, 0.76]), fatal CVD (HR 0.34 [0.17, 0.68] , CVD (HR 0.70 [0.58,

0.84]), fatal coronary heart disease (HR 0.40 [0.22, 0.73]) or CHD (HR 0.66

[0.54, 0.80]) favoured the use of MET+ DPP-4 i. [234]

O’Brien et al. compared MET + SUs vs. MET + DPP-4 i. Patients treated

with MET + SUs were at higher risk of MACE (HR1.36 [1.23-1.49]) and

congestive HF (HR 1.47 [1.23-1.75]) [235]

In a Taiwan nationwide study using National Health Insurance Research

Database, DPP-4 inhibitors added to metformin were associated with lower

risks for all-cause death (hazard ratio [HR], 0.63 [95% CI, 0.55 to 0.72]),

VI. Second-line, metformin-based, dual therapies 133


MACEs (HR, 0.68 [CI, 0.55 to 0.83]), ischemic stroke (HR, 0.64 [CI, 0.51 to

0.81]), and hypoglycemia (HR, 0.43 [CI, 0.33 to 0.56]) compared with

sulfonylureas as add-on therapy to metformin. There were no differences on

risks for myocardial infarction and hospitalization for heart failure.[236]

Table VI.6 shows a summary of the current observational evidence concerning the

effect of DPP-4 i added to metformin compared with metformin plus SUs.

Table VI.6 Observational evidence of cardiovascular outcomes with second-line

DDP-4 i vs sulfonylureas added to metformin

Metformin + DPP-4 i vs metformin + SUs as reference

MACE Death CV death

CVD MI Stroke HF IHD PAD

Yu [230]

0.62; (0.40 - 0.98)

0.53 (0.29 - 0.97)

0.74 (0.38 - 1.43)

Mogensen [231]

0.70 (0.57 -0.85)

0.65 (0.54 – 0.80)

0.57 (0.40-0.80)

Seong [232]

0.83 (0.76-0.92)

0.88 (0.52-0.96)

0.66 (0.56-0.78)

0.93 (0.62-1.41)

Kannan [233]

1.03 (0.81-1.31)

1.06 (0.99-1.13)

1.28 (1.08-1.52)

1.104 (1.04-1.17)

1.35 (1.16-1.57)

1.65 (1.16-2.36)

Ekström [234]

0.79 (0.60-1.04)

0.70 (0.34 – 0.68) 0.40 (0.22-0.73)

0.70 (0.58-0.84)

0.82 (0.53-1.25)

0.54 (0.38-0.76)

0.66 (0.54-0.80)

O’Brien [235]

0.74 (0.57- 0.81)

0.68 (0.57- 0.81)

Ou

[236]

0.68 (0.55 - 0.83)

0.63 (0.55 - 0.72)

0.64 (0.51 - 0.81)


MET + meglitinides compared with MET + SUs

Using nationwide administrative Danish registries, Mogensen et al. found

that users of MET+ repaglinide (n= 2,118) showed no differences in

mortality compared with the use of MET + glimepiride as reference

(n=40,026). For mortality were 0.81(0.62-1.05), CV mortality Hr 0.81 (0.56-

1.19) and for 3-points MACE 0.87 (0.68-1.10) [237]

Ekström et al. compared MET+ meglitinides (n= 2,254) with MET + SUs

(n= 8,801) as reference. The study didn’t find statistical differences for any

outcome: mortality (HR 0.90 [0.75, 1.08]), stroke (HR 0.96 [0.70, 1.33]),

heart failure (HR 1.00 [0.81, 1.24)]), fatal CVD (0.81 [0.56- 1.15]), CVD

(1.08 [0.95- 1.23]), fatal coronary heart disease (0.76 [0.54 -1.06]) or CHD

(1.08 [0.93-1.24]). [234]

Table VI.7 shows a summary of the current observational evidence concerning the

effect of meglitinides added to metformin compared with metformin plus SUs.

Table VI.7 Observational evidence of cardiovascular outcomes with second-line

meglitinides vs sulfonylureas added to metformin

Metformin + meglitinides vs Metformin + SUs as reference Mogensen 0.87

(0.68-1.10)

0.81 (0.62-1.05)

0.81 (0.56-1.19)

Ekström 0.90 (0.70-1.33)

0.81 (0.56 - 1.15)

1.08 (0.95- 1.23)

0.96 (0.70-1.33)

1.00 (0.81-1.24)

1.08 (0.93 - 1.24)


Discussion

In our study, 28,539 patients who had received the first-line treatment with monotherapy

were added other NIADs as intensification second-line dual therapies: most on MET +

SUs (61.46%), 28.15% on MET+DPP-4 i and by 8 % with MET + meglitinides; other

treatments were used in a much lower proportion, and because of this they have not been

included in our analysis. The cohorts of second-line therapy were not well balanced in

basal characteristics. Patients who were prescribed meglitinides were the oldest ones and

had more comorbidities. (Table VI.2) Compared with MET + SUs as a reference, the

cohort of MET + DPP-4 i users did not show statistically significant differences in the

overall HRs for any outcomes. For MET + meglitinides compared with MET + SUs, we

found an increased risk of MACE and all-cause mortality; the higher risk of dying was

consistently increased across many subgroups (Table VI.5).

We have not found statistical differences in the risks of MACE comparing the MET +

DPP-4 i users vs the MET+SUs users. Our results are in line with the CAROLINA trial,

though in this trial the only studied SUs was glimepiride. [207] in mortality and other

outcomes, with observational research, [233, 234] although other studies observed

improved outcomes for MET + DPP-4 i users [230, 231, 232, 235, 236] or in CV events

[233, 234].

In our study, in the subgroup analyses of the combined second-line MET + DPP-4 i

compared with MET + SUs, patients with a history of heart failure were at increased risk

for MACE, all-cause mortality, HF and PAD, and there was also at a trend for increased

risk of MI. In the CARMELINA trial, linagliptin did not affect the risk of hospitalisation

for HF or other selected HF-related outcomes, including participants with and without a

history of HF. [238] It has been hypothesised that other circulating peptides that are

substrates for DPP4 that could have independent cardiovascular effects. A consensus is

emerging that DPP4is might not be a preferred agent in patients with T2DM with

increased risk for CVD with concomitant HF. [239] A history of HF is a risk factor for

mortality; in the subgroup analyses of patients treated with meglitinides, patients were at

increased risk of dying irrespective than they had or no previous history of HF (although

the risk was higher in patients with previous HF). In patients treated with SUs


monotherapy, the risk of all-cause mortality, MACE and all the outcomes was higher in

patients with no history of HF (Section V, Table V.4). The analyses of subgroups of other

monotherapies and dual combined treatments could help to clarify whether there is or not

an association with the use of DPP-4 i therapies. This is an area for further research.

Concerning the increased risk in MACE and all-cause mortality we have observed in

MET + meglitinides users compared with MET + SUs users, the results are not consistent

with those obtained in Swedish and Danish databases. Ekström et al. included cancer and

smoking among basal covariates and Mogensen et al. the Charlson score, which is more

comprehensive and includes cancer, AIDS and other comorbidities. In our study, patients

on MET+ meglitinides were significantly older and sicker than those from different

cohorts. Due to better pharmacodynamic and pharmacokinetic characteristics of

meglitinides compared with SUs, general practitioners could have preferred meglitinides

to treat frail patients. Thus, a channelling bias cannot be ruled out, and results deserve

further reappraisal. If an unmeasured confounder is correlated with observed covariates,

the potential for confounding can be mitigated through direct covariate adjustment and

create a cohort balanced on observed covariates. [240] The propensity score matching

(PS matching) produces well-balanced cohorts, at expenses of reducing the sample size;

the characteristics of the PS matched cohorts could tend to be closer to one of the groups,

in this case, it would be the MET+ meglitinides group, and results would be restricted to

a population of this characteristics.

Other limitations and strengths have been already mentioned in the Section V, that report

the results of a companion study about monotherapies.

Conclusion

In this study, no differences were found in mortality and CV outcomes between the dual

second-line treatments for metformin-based regimes either with the addition of SUs or

DPP-4 i. We also observed an increased risk of MACE and all-cause mortality in the

MET+meglitinides cohort, channelling bias, and residual confounding cannot be ruled

out and deserve further research.

VII. Discussion and Conclusion

VII. Discussion and Conclusion 138 Nelly Raquel Herrera Comoglio Eu2P PhD December 2019

VII. Discussion and Conclusion

The regulatory framework for non-insulin blood-glucose-lowering agents (“NIADs”) is

based on surrogate blood glucose-related markers, mainly glycated haemoglobin. This

surrogate marker has shown to be associated with microvascular complications of T2DM,

but its relationship with the cardiovascular disease remains uncertain. In 2008 the FDA

issued guidelines recommending the sponsors to demonstrate that the therapy will not

result in an unacceptable increase in cardiovascular risk - as a result of the meta-analysis

finding an increased risk of MI with rosiglitazone; the European Medicines Agency made

a similar decision in 2012.[38, 39] The sponsor can get a marketing authorisation before

cardiovascular safety is demonstrated; therefore, some new non-insulin blood glucose-

lowering drugs are used in clinical practice before the results of cardiovascular safety

become available. [163]

One decade later, fifteen large randomised controlled trials (RCTs) assessing CV

outcomes have been published, as it has been summarised in Section II. In spite of some

criticism raised by their non-inferiority design basis of these trials, the contribution of

cardiovascular outcomes trials to the scientific knowledge and decision-making, both in

clinical practice and public health, is outstanding. Instead, for older – and widely used -

therapeutic groups, there is scarce RCTs evidence.


Along with the conducting of these trials, the development of some agents have been

terminated: this is the case of fasiglifam, in which events of hepatotoxicity led to an

unfavourable balance risk/benefit; the hepatotoxicity has been later explained by the

effects of the drug, TKD-875, on bile acid and bilirubin homeostasis. Other agents failed

to show any beneficial effects, such as the aleglitazar, a dual peroxisome proliferator-

activated receptors (PPARs). [241-243] The AleCardio (aleglitazar) trial was stopped due

to futility for efficacy at an unplanned interim analysis and increased rates of safety

endpoints, which included heart failure, gastrointestinal and renal dysfunction. [82]

In 2014, the first sentence of the paper reporting the AleCardio study results was: “No

therapy directed against diabetes has been shown to unequivocally reduce the excess risk

of cardiovascular complications”. At that time, this was true. Hereafter, some

cardiovascular outcome trials (CVOTs) assessing some therapeutic groups of non-insulin

blood glucose-lowering drugs have shown a reduction in mortality and cardiovascular

outcomes. In terms of CV benefit of the absence of harm, the group of dipeptidyl

peptidase-4 inhibitors showed no harmful or beneficial effects on CV outcomes, although

in April 2016 added “Warnings and Precautions” to the labels of medicines that contain

saxagliptin or alogliptin to inform of the potential increased risk of heart failure. [244]

In the group of glucagon-like peptide 1 receptor agonists, liraglutide (LEADER trial),

semaglutide (SUSTAIN-6) and albiglutide (HARMONY) had shown a reduction of

MACE, for liraglutide was driven by a decrease in all-cause and CV mortality, for

semaglutide by a decreased risk of stroke and albiglutide by a reduction of the risk of MI.

In the group of sodium-glucose co-transporter-2 inhibitors, CVOTs with empagliflozin

(EMPA-REG trial), canagliflozin (CANVAS trial) and dapagliflozin (DECLARE-TIMI

trial) achieved all significant reductions in hospitalization for heart failure, but only

empagliflozin achieved a significant reduction in CV mortality and all-cause mortality.

Large RCTs also detected other safety concerns. The aggregated data of DPP-4 i trials

have highlighted a safety signal regarding the risk of pancreatitis. [112] The example of

fasiglifam highlights the importance of safety monitoring processes within the


randomised design of adequately sized clinical trials (7,600 patients in eight Phase III

clinical trials)

However, the high CV risk populations included in CVOTs trials are substantially

different from the “real-world” unselected ones - who are the people receiving

medications, and, ultimately, pay for them- as summarized in the Section III

“Generalizability of RCTs”. The observational research, especially the one conducted

with data of thousands, hundred thousand or even million patients of routine clinical care

has become increasingly important in terms of decision-making and public health policy.

Observational research is included in the US 21st Century Cures Act as a source of

complementary or confirmatory evidence. [247]

In spite of the obvious methodological limitations of the observational research due to

non-randomised design and of restrictions in the availability of some clinical data – while

they are retrieved in clinical trials, namely adherence and lifestyle habits – observational

research plays now a more relevant role in the assessment of the clinical impact of

therapies - either old and new agents used - in a given population. Individuals belonging

to these “real-world” populations fund these therapies, by their own or through either

private or public healthcare systems.

We conducted the present study with the data of a large, comprehensive, healthcare

database that gather clinical, diagnoses and prescriptions and dispensing data of the 80%

of the Catalan population. As this database serves to administrative purposes, it is linked

with mortality registries; this allows a very reliable available information on deaths in

individuals.

Once data have been analysed, our study’s results revealed some shortcomings in design

and conducting; but also made evident some unique strengths. Respect to the all-cause

mortality outcome, one of the more important limitations in the design is that we haven’t

included data of cancer or other relevant morbidities diagnoses among covariates.

Besides, data about people living in nursing homes were not available. The original

protocol registered in ENCePP included CV mortality in the MACE (the primary


outcome) and all-cause mortality as one of the secondary outcomes; however, full data of

mortality causes were unavailable, and only all-cause mortality has been assessed.

Frailty may be independently associated with antidiabetic medication exposure and risk

of mortality, thus could introduce confounding.[240] In our study, unlikely to subgroups

analyzed for DPP-4 i a and SUs, patients of many MET+ meglitinides cohort’s subgroups

were at higher risk of dying; this could reveal a residual confounding by indication.

Meglitinides are secretagogues that produce a rapid and short-lived insulin output;

therefore, the potential for hypoglycaemia and other adverse effects are supposed to be

reduced. There are no RCTs that report the effect of meglitinides on mortality or diabetes-

related complications. [225] Frail patients might have been prescribed meglitinides

because of their safer profile compared with SUs. The lack of cancer diagnosis or other

covariates indicating frailty prevents us drawing any sound conclusion based on the

results of all-cause mortality in the MET+ meglitinides group compared with the MET +

SUs group (HR 1.38 [95% CI 1.14-1.67]) or the results of the monotherapy meglitinides

vs metformin (HR 2.08 [1.26-3.42]). The measurement of all factors plausibly related to

prognosis is essential. [168] Specific covariates allowing to control appropriately for

confounding for indication were not available in our study. Even in studies that control

for relevant measured confounders but have unmeasured or poorly measured frailty, the

possibility of residual confounding by frailty exists. [240] Another strategy as the

propensity score matching would have reduced the sample size and also produced a cohort

representative of all MET + meglitinide users, but not fully representative of MET + SUs

users, thus limiting its generalizability.

Another limitation of the design is that the washout time of 90 days before the first

prescription is not enough long to avoid the inclusion of prevalent users: in the selection

process performed by the Institut Jordi Gol (IJG), the subset of patients entering in the

2010 cohort is much larger than in the following years. For the first-line monotherapy

analysis, patients having previous exposures to a blood glucose-lowering agent were not

included, but it is likely that the process was not effective in the 2010 cohort, because

data of the year 2009 were not available in the coded de-identified data set provided by


the IJG. A sensitivity analysis excluding the 2010 cohort was performed and showed

results consistent with the ones of the primary analysis.

Other limitations have been already mentioned in the Section IV “Protocol design and

rationale”, such as the inclusion of all events of stroke -which can retrieve embolic events

not necessarily related with macrovascular atherosclerosis instead of only ischemic

events. As data of hospitalisation are not available, the outcome referred to heart failure

is based on recorded diagnoses (as well as those of peripheral arteriopathy) and

encompasses both ambulatory diagnoses and hospitalisations. However, this outcome

might be more sensitive in detecting the incidence of heart failure.

As it had been foreseen in the protocol submitted to ENCePP, we haven’t enough sample

size to obtain significant results in patients treated with SGLT-2 i, GLP-1 RAs and TZD.

An extension of this study, including 2016-2019 years, could overcome this limitation.

This study also has unique strengths. It is the first longitudinal study conducted with data

of SIDIAP assessing cardiovascular outcomes and mortality in patients with T2DM

treated with NIADs, in first and second-lines. The results of the first-line treatments are

consistent with those obtained by observational research.

We also compared two cohorts, both treated with the combination metformin and SUs

but with different previous monotherapies -either metformin or SUs, the analysis resulted

in no differences. We found that combined metformin and sulfonylurea therapy had

similar outcomes in patients previously treated with metformin o with SUs, suggesting a

beneficial effect of metformin. Douros et al. found that, in patients on metformin who

added or switching SUs, patients who switched to SUs were at increased risks of MI and

all-cause mortality than those who added SUs (myocardial infarction, HR 1.5 95% CI

1.03 to 2.24; and all-cause mortality, HR 1.23, 95% CI 1.00 to 1.50). [245] No differences

were observed for ischaemic stroke, cardiovascular death, or severe hypoglycaemia.

These findings support the beneficial effect of metformin, [245] also showed in our study.

The results of some agents in the GLP-1 RAs and SGLT-2 i have risen enthusiasm

regarding the potential of these new therapies. It should be kept in mind that SGLT-2 i,


while achieving a significant reduction in mortality, mainly due to their mechanism of

action -volume depletion because of osmotic diuresis - have been associated to adverse

effects such as amputations, especially canagliflozin, and ketoacidosis. Concerning GLP-

1 RA, oral formulations (semaglutide) can achieve better adherence, but the percentage

of patients who are intolerant to their gastrointestinal adverse effects is much higher than

the one of the patients intolerant to metformin. Besides, GLP-1 RAs are the more

expensive agents among the NIADs, and CVOTs included mainly people with high CV

risk, making it the results challenging to be extrapolated to the “real-world” populations.

As shown in the Section II, populations’characteristics of published CVOTs and the one

registered in SIDIAP differ, therefore, the results of CVTs should not be fully applicable

to the latter.

In the meta-analysis of observational studies assessing CV outcomes and mortality in

GLP-1 RAs users (see Annex 1) we found that the reduction in the risk of death had a

larger size than those obtained in CVOTs. Crude incidence rate ratio observed in our

study (second-line therapies) also suggest a reduced risk of mortality for GLP-1 users.

This deserves further study, with a larger sample size and, probably, a longer follow-up.

Observational research is produced more and more and has obtained substantive evidence

about the most used drugs; in some cases, prompted regulatory decision or modifications

of clinical guidelines. The study design and analysis of observational data have evolved

– as well as the RCTs- and are continuously improving. Standardisation of design and

methods would allow the reproducibility and comparability among studies. Well-

conducted observational research assessing the comparative effectiveness and safety of

drugs used in clinical practice is now essential for an appropriate evaluation of the

medicines, especially when RCTs are not available or their population do not represent

the one that use the drug.

References

Bibliography 145 Nelly Raquel Herrera Comoglio Eu2P PhD December 2019

References:

[1] http://www.who.int/mediacentre/factsheets/fs312/en/ Reviewed Nov 2016.

Accessed on January 15th 2017

[2] International Diabetes Federation, http://www.idf.org/diabetesatlas/update-2014,

accessed on January 17th 2017.

[3] Magliano DJ, Islam RM, Barr ELM, Gregg EW, Pavkov ME, Harding JL, Tabesh

M Koye DN, Shaw JE. Trends in incidence of total or type 2 diabetes: systematic

review. BMJ. 2019 Sep 11;366:l5003.

[4] Sharma M, Nazareth I, Petersen I. Trends in incidence, prevalence and prescribing

in type 2 diabetes mellitus between 2000 and 2013 in primary care: a retrospective

cohort study. BMJ Open. 2016 Jan 13;6(1):e010210.

[5] Vinagre I, Mata-Cases M, Hermosilla E, Morros R, Fina F, Rosell M, Castell C,

Franch-Nadal J, Bolíbar B, Mauricio D. Control of Glycemia and Cardiovascular

Risk Factors in Patients With Type 2 Diabetes in Primary Care in Catalonia (Spain).

Diabetes Care 35:774–779, 2012

[6] Kirkman MS, Briscoe VJ, Clark N, Florez H, Haas LB, Halter JB, Huang ES,

Korytkowski MT, Munshi MN, Odegard PS, Pratley RE, Swift CS. Diabetes in

older adults. Diabetes Care. 2012 Dec;35(12):2650-64.

[7] Shahnam Sharif, Rolf H. H. Groenwold, Yolanda van der Graaf, Gijs F. N.

Berkelmans, Maarten J. Cramer, Frank L. J. Visseren, Jan Westerink, On behalf of

the SMART study group Mediation analysis of the relationship between type 2

diabetes and cardiovascular events and all‐cause mortality: Findings from the

SMART cohort Diabetes Obes Metab. 2019 Aug; 21(8): 1935–1943.

[8] Tancredi M, Rosengren A, Svensson AM, Kosiborod M, Pivodic A, Gudbjörnsdottir

S, Wedel H, Clements M, Dahlqvist S, Lind M. Excess Mortality among Persons

with Type 2 Diabetes N Engl J Med. 2015 Oct 29;373(18):1720-32

[9] Skyler JS, Bergenstal R, Bonow RO, Buse J, Deedwania P, Gale EA, Howard BV,

Kirkman MS, Kosiborod M, Reaven P, Sherwin RS; American DiabetesAssociation;


American College of Cardiology Foundation; American Heart Association.

Intensive Glycemic Control and the Prevention of Cardiovascular Events:

Implications of the ACCORD, ADVANCE, and VA Diabetes Trials. A Position

Statement of the American Diabetes Association and a Scientific Statement of the

American College of Cardiology Foundation and the American Heart Association

2009

[10] Leon BM, Maddox TM. Diabetes and cardiovascular disease: Epidemiology,

biological mechanisms, treatment recommendations and future research. World J

Diabetes. 2015 Oct 10;6(13):1246-58.

[11] Diabetes mellitus: a major risk factor for cardiovascular disease. A joint editorial

statement by the American Diabetes Association; The National Heart, Lung, and

Blood Institute; The Juvenile Diabetes Foundation International; The National

Institute of Diabetes and Digestive and Kidney Diseases; and The American Heart

Association. Circulation. 1999 Sep 7;100(10):1132-3.

[12] Buse JB, Ginsberg HN, Bakris GL, Clark NG, Costa F, Eckel R, Fonseca V,

Gerstein HC, Grundy S, Nesto RW, Pignone MP, Plutzky J, Porte D, Redberg R,

Stitzel KF, Stone NJ; American Heart Association; American Diabetes Association

Primary Prevention of Cardiovascular Diseases in People With Diabetes Mellitus

Circulation. 2007;115:114-126

[13] Einarson TR, Acs A, Ludwig C, Panton UH. Prevalence of cardiovascular

disease in type 2 diabetes: a systematic literature review of scientific evidence from

across the world in 2007-2017. Cardiovasc Diabetol. 2018 Jun 8; 17(1):83.

[14] Jurado J, Ybarra J, Solanas P, Caula J, Gich I, Pou JM, Romeo JH Prevalence of

cardiovascular disease and risk factors in a type 2 diabetic population of the North

Catalonia diabetes study. J Am Acad Nurse Pract. 2009 Mar;21(3):140-8. doi:

10.1111/j.1745-7599.2008.00377.x.

[15] Fox CS, Coady S, Sorlie PD, Levy D, Meigs JB, D'Agostino RB Sr, Wilson PW,

Savage PJ. Trends in cardiovascular complications of diabetes. JAMA. 2004 Nov

24;292(20):2495-9.


[16] Cheng YJ, Imperatore G, Geiss LS, Saydah SH, Albright AL, Ali MK, Gregg

EW. Trends and Disparities in Cardiovascular Mortality Among U.S. Adults With

and Without Self-Reported Diabetes, 1988-2015. Diabetes Care. 2018

Nov;41(11):2306-2315. doi: 10.2337/dc18-0831.

[17] Orozco-Beltrán D, Sánchez E, Garrido A, Quesada JA, Carratalá-Munuera MC,

Gil-Guillén VF. Trends in Mortality From Diabetes Mellitus in Spain: 1998-2013.

Rev Esp Cardiol (Engl Ed). 2017 Jun;70(6):433-443. doi:

10.1016/j.rec.2016.09.022. Epub 2016 Nov 5. [Article in English, Spanish]

[18] Bhatnagar P, Wickramasinghe K, Wilkins E, Townsend N. Trends in the

epidemiology of cardiovascular disease in the UK. Heart. 2016 Dec

15;102(24):1945-1952. doi: 10.1136/heartjnl-2016-309573. Epub 2016 Aug 22.

[19] Gilbert RE, Krum H. Heart failure in diabetes: effects of anti-hyperglycaemic

drug therapy. Lancet. 2015 May 23;385(9982):2107-17.

[20] McKee PA, Castelli WP, McNamara PM, Kannel WB. The natural history of

congestive heart failure: the Framingham study. N Engl J Med. 1971 Dec

23;285(26):1441-6.

[21] Kannel WB, Hjortland M, Castelli WP. Role of diabetes in congestive heart

failure: the Framingham study. Am J Cardiol. 1974 Jul;34(1):29-34

[22] Shah AD, Langenberg C, Rapsomaniki E, Denaxas S, Pujades-Rodriguez M,

Gale CP, Deanfield J, Smeeth L, Timmis A, Hemingway H. Type 2 diabetes and

incidence of cardiovascular diseases: a cohort study in 1·9 million people. Lancet

Diabetes Endocrinol. 2015 Feb;3(2):105-13.

[23] Conrad N, Judge A, Tran J, Mohseni H, Hedgecott D, Crespillo AP, Allison M,

Hemingway H, Cleland JG, McMurray JJV, Rahimi K. Temporal trends and

patterns in heart failure incidence: a population-based study of 4 million individuals.

Lancet. 2018 Feb 10;391(10120):572-580.

[24] Tran J, Norton R, Conrad N, Rahimian F, Canoy D, Nazarzadeh M, Rahimi K.

Patterns and temporal trends of comorbidity among adult patients with incident


cardiovascular disease in the UK between 2000 and 2014: A population-based

cohort study. PLoS Med. 2018 Mar 6;15(3):e1002513.

[25] Baena-Díez JM, Peñafiel J, Subirana I, Ramos R, Elosua R, Marín-Ibañez A,

Guembe MJ, Rigo F, Tormo-Díaz MJ, Moreno-Iribas C, Cabré JJ, Segura A,

García-Lareo M, Gómez de la Cámara A, Lapetra J, Quesada M, Marrugat J,

Medrano MJ, Berjón J, Frontera G, Gavrila D, Barricarte A, Basora J, García JM,

Pavone NC, Lora-Pablos D, Mayoral E, Franch J, Mata M, Castell C, Frances A,

Grau M; FRESCO Investigators. Risk of Cause-Specific Death in Individuals With

Diabetes: A Competing Risks Analysis. Diabetes Care. 2016 Nov;39(11):1987-

1995.

[26] Mata-Cases M, Casajuana M, Franch-Nadal J, Casellas A, Castell C, Vinagre I,

Mauricio D, Bolíbar B. Direct medical costs attributable to type 2 diabetes mellitus:

a population-based study in Catalonia, Spain. Eur J Health Econ. 2016

Nov;17(8):1001-1010.

[27] Gale EA. Is type 2 diabetes a category error? Lancet. 2013 Jun

1;381(9881):1956-7.

[28] Piarulli F, Sartore G, Lapolla A. Glyco-oxidation and cardiovascular

complications in type 2 diabetes: a clinical update. Acta Diabetol. 2013

Apr;50(2):101-10.

[29] Goode KM, John J, Rigby AS, Kilpatrick ES, Atkin SL, Bragadeesh T, Clark

AL, Cleland JG. Elevated glycated haemoglobin is a strong predictor of mortality in

patients with left ventricular systolic dysfunction who are not receiving treatment

for diabetes mellitus. Heart. 2009 Jun;95(11):917-23. doi: 10.1136/hrt.2008.156646.

Epub 2009 Feb 19.

[30] Clark SL, Santin AE, Bryant PA, Holman R, Rodnick KJ. The initial

noncovalent binding of glucose to human hemoglobin in nonenzymatic glycation.

Glycobiology. 2013 Nov;23(11):1250-9.

[31] Kingwell A FDA eyes new diabetic endpoints. Nat Rev Drug Discov. 2016 Sep

29;15(10):666-7.


[32] Selvaraj N, Bobby Z, Sridhar MG. Increased glycation of hemoglobin in chronic

renal failure: [corrected] potential role of oxidative stress. Arch Med Res. 2008

Apr;39(3):277-84. doi: 10.1016/j.arcmed.2008.01.001.

[33] Diabetes Control and Complications Trial Research Group, Nathan DM, Genuth

S, et al. The effect of intensive treatment of diabetes on the development and

progression of long-term complications in insulin-dependent diabetes mellitus. N

Engl J Med. 1993;329(14):977–986. doi:10.1056/NEJM199309303291401

[34] Stratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, Cull CA, Hadden D,

Turner RC, Holman RR. Association of glycaemia with macrovascular and

microvascular complications of type 2 diabetes (UKPDS 35): prospective

observational study. BMJ. 2000 Aug 12;321(7258):405-12.

[35] Sharif S, Groenwold RHH, van der Graaf Y, Berkelmans GFN, Cramer MJ,

Visseren FLJ, Westerink J; SMART study group. Mediation analysis of the

relationship between type 2 diabetes and cardiovascular events and all-cause

mortality: Findings from the SMART cohort. Diabetes Obes Metab. 2019

Aug;21(8):1935-1943

[36] UK Prospective Diabetes Study Group (UKPDS) Intensive blood-glucose

control with sulphonylureas or insulin compared with conventional treatment and

risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective

Diabetes Study (UKPDS) Group. Lancet. 1998 Sep 12;352(9131):837-53.

[37] Rodríguez- Gutiérrez R, Montori VM. Glycemic Control for Patients With Type

2 Diabetes Mellitus: Our Evolving Faith in the Face of Evidence. Circ Cardiovasc

Qual Outcomes. 2016 Sep;9(5):504-12.

[38] U.S. Department of Health and Human Services Food and Drug Administration

Center for Drug Evaluation and Research (CDER). Guidance for Industry.

“Diabetes Mellitus – Evaluating CV Risk in New Anti-diabetic Therapies to Treat

Type 2 Diabetes”. Food &Drug Administration, Dec. 2008. http://www.fda.gov/

downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/

ucm071627.pdf.


[39] http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2

012/06/WC500129256.pdf

[40] Control Group, Turnbull FM, Abraira C, Anderson RJ, Byington RP, Chalmers

JP, Duckworth WC, Evans GW, Gerstein HC, Holman RR, Moritz TE, Neal BC,

Ninomiya T, Patel AA, Paul SK, Travert F, Woodward M. Intensive glucose control

and macrovascular outcomes in type 2 diabetes. Diabetologia. 2009

Nov;52(11):2288-98

[41] Emerging Risk Factors Collaboration, Sarwar N, Gao P, Seshasai SR, Gobin R,

Kaptoge S, Di Angelantonio E, Ingelsson E, Lawlor DA, Selvin E, Stampfer M,

Stehouwer CD, Lewington S, Pennells L, Thompson A, Sattar N, White IR, Ray

KK, Danesh J. Diabetes mellitus, fasting blood glucose concentration, and risk of

vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet.

2010 Jun 26;375(9733):2215-22.

[42] Chen YY, Lin YJ, Chong E, Chen PC, Chao TF, Chen SA, Chien KL. The

impact of diabetes mellitus and corresponding HbA1c levels on the future risks of

cardiovascular disease and mortality: a representative cohort study in Taiwan. PLoS

One. 2015 Apr 13;10(4):e0123116.

[43] Monesi L, Tettamanti M, Cortesi L, Baviera M, Marzona I, Avanzini F, Monesi

G, Nobili A, Riva E, Fortino I, Bortolotti A, Fontana G, Merlino L, Trevisan R,

Roncaglioni MC. Elevated risk of death and major cardiovascular events in subjects

with newly diagnosed diabetes: findings from an administrative database. Nutr

Metab Cardiovasc Dis. 2014 Mar;24(3):263-70

[44] Schöttker B, Rathmann W, Herder C, Thorand B, Wilsgaard T, Njølstad I,

Siganos G, Mathiesen EB, Saum KU, Peasey A, Feskens E, Boffetta P,

Trichopoulou A, Kuulasmaa K, Kee F Brenner H; CHANCES group. HbA1c levels

in non-diabetic older adults -No J-shaped associations with primary vascular events,

cardiovascular and all-causes mortality after adjustment for confounders in a meta-

analysis of individual participant data from six cohort studies. BMC Med. 2016 Feb

11;14:26.

http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2012/06/WC500129256.pdf



[45] Gerstein HC, Pogue J, Mann JF, Lonn E, Dagenais GR, McQueen M, Yusuf S;

HOPE investigators. The relationship between dysglycemia and cardiovascular and

renal risk in diabetic and non-diabetic participants in the HOPE study: a prospective

epidemiological analysis. Diabetologia. 2005 Sep;48(9):1749-55.

[46] Valenzuela-Garcia LF, Matsuzawa Y, Sara JD, Kwon TG, Lennon RJ, Lerman

LO, Ruiz-Salmeron RJ, Lerman A. Lack of correlation between the optimal

glycaemic control and coronary micro vascular dysfunction in patients with diabetes

mellitus: a cross sectional study. Cardiovasc Diabetol. 2015 Aug 14;14:106.

[47] Kawata T, Daimon M, Miyazaki S, Ichikawa R, Maruyama M, Chiang SJ, Ito C,

Sato F, Watada H, Daida H. Coronary microvascular function is independently

associated with left ventricular filling pressure in patients with type 2 diabetes

mellitus. Cardiovasc Diabetol. 2015 Aug 5;14:98.

[48] Frier BM, Schernthaner G, Heller SR. Hypoglycemia and cardiovascular risks.

Diabetes Care. 2011 May;34 Suppl 2:S132-7

[49] Gerstein HC, Miller ME, Byington RP, et al. Effects of intensive glucose

lowering in type 2 diabetes. N Engl J Med 2008; 358:2545–2559

[50] Patel A, MacMahon S, Chalmers J, et al. Intensive blood glucose control and

vascular outcomes in patients with type 2 diabetes. N Engl J Med 2008;358:2560–

2572

[51] Duckworth W, Abraira C, Moritz T, et al. Glucose control and vascular

complications in veterans with type 2 diabetes. N Engl J Med 2009;360:129–139

[52] Lachin JM, Orchard TJ, Nathan DM; DCCT/EDIC Research Group. Update on

cardiovascular outcomes at 30 years of the diabetes control and complications trial/

epidemiology of diabetes interventions and complications study. Diabetes Care.

2014;37(1):39-43.

[53] UK Prospective Diabetes Study (UKPDS) Group. Effect of intensive blood-

glucose control with metformin on complications in overweight patients with type 2

diabetes (UKPDS 34). Lancet. 1998 Sep 12;352(9131):854-65.


[54] Boussageon R, Gueyffier F, Cornu C. Metformin as firstline treatment for type 2

diabetes: are we sure? BMJ. 2016 Jan 8;352:h6748.

[55] Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of

intensive glucose control in type 2 diabetes. N Engl J Med. 2008 Oct

9;359(15):1577-89.

[56] Duckworth W, Abraira C, Moritz T, Reda D, Emanuele N, Reaven PD, Zieve

FJ, Marks J, Davis SN, Hayward R, Warren SR, Goldman S, McCarren M, Vitek

ME, Henderson WG, Huang GD; VADT Investigators Glucose control and vascular

complications in veterans with type 2 diabetes. N Engl J Med. 2009 Jan

8;360(2):129-39. doi: 10.1056/NEJMoa0808431.

[57] Hayward RA, Reaven PD, Wiitala WL, Bahn GD, Reda DJ, Ge L, McCarren M,

Duckworth WC, Emanuele NV; VADT Investigators. Follow-up of glycemic

control and cardiovascular outcomes in type 2 diabetes.N Engl J Med. 2015 Jun

4;372(23):2197-206.

[58] ADVANCE Collaborative Group, Patel A, MacMahon S, Chalmers J, Neal B,

Billot L, Woodward M, Marre M, Cooper M, Glasziou P, Grobbee D, Hamet P,

Harrap S, Heller S, Liu L, Mancia G, Mogensen CE, Pan C, Poulter N, Rodgers A,

Williams B, Bompoint S, de Galan BE, Joshi R, Travert F. Intensive blood glucose

control and vascular outcomes in patients with type 2 diabetes. N Engl J Med. 2008

Jun 12;358(24):2560-72.

[59] Action to Control Cardiovascular Risk in Diabetes Study Group., Gerstein HC,

Miller ME, Byington RP, Goff DC Jr, Bigger JT, Buse JB, Cushman WC, Genuth S,

Ismail-Beigi F, Grimm RH Jr, Probstfield JL, Simons-Morton DG, Friedewald WT.

Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med. 2008 Jun

12;358(24):2545-59

[60] ACCORD Study Group, Gerstein HC, Miller ME, Genuth S, Ismail-Beigi F,

Buse JB, Goff DC Jr, Probstfield JL, Cushman WC, Ginsberg HN, Bigger JT,

Grimm RH Jr, Byington RP, Rosenberg YD, Friedewald WT. Long-term effects of


intensive glucose lowering on cardiovascular outcomes. N Engl J Med. 2011 Mar

3;364(9):818-28. doi: 10.1056/NEJMoa1006524.

[61] Yudkin JS, Lipska KJ, Montori VM. The idolatry of the surrogate.BMJ. 2011

Dec 28;343:d7995. doi: 10.1136/bmj.d7995.

[62] Blackburn H, Jacobs DR Jr. The University Group Diabetes Program 1961-1978:

pioneering randomized controlled trial. Int J Epidemiol. 2017 Oct 1;46(5):1354-1364.

doi: 10.1093/ije/dyw168.

[63] .Meinert CL, Knatterud GL, Prout TE, Klimt CR. A study of the effects of

hypoglycemic agents on vascular complications in patients with adult-onset diabetes.

II. Mortality results. Diabetes. 1970;19:Suppl:789-830.

[64] King P, Peacock I, Donnelly R. The UK prospective diabetes study (UKPDS):

clinical and therapeutic implications for type 2 diabetes.Br J Clin Pharmacol. 1999

Nov;48(5):643-8.

[65] Boussageon Control Group, Turnbull FM, Abraira C, Anderson RJ, Byington RP,

Chalmers JP, Duckworth WC, Evans GW, Gerstein HC, Holman RR, Moritz TE, Neal

BC, Ninomiya T, Patel AA, Paul SK, Travert F, Woodward M. Intensive glucose

control and macrovascular outcomes in type 2 diabetes. Diabetologia. 2009

Nov;52(11):2288-98. doi: 10.1007/s00125-009-1470-0.

[66] Cefalu WT, Kaul S, Gerstein HC, Holman RR, Zinman B, Skyler JS, Green JB,

Buse JB, Inzucchi SE, Leiter LA, Raz I, Rosenstock J, Riddle MC. Cardiovascular

Outcomes Trials in Type 2 Diabetes: Where Do We Go From Here? Reflections From

a Diabetes Care Editors' Expert Forum. Diabetes Care. 2018 Jan;41(1):14-31. doi:

10.2337/dci17-0057.

[67] Nissen SE, Wolski K, Topol EJ. Effect of muraglitazar on death and major adverse

cardiovascular events in patients with type 2 diabetes mellitus. JAMA. 2005 Nov

23;294(20):2581-6.

[68] Dormandy JA, Charbonnel B, Eckland DJ, Erdmann E, Massi-Benedetti M et al,

PROactive investigators. Secondary prevention of macrovascular events in patients

with type 2 diabetes in the PROactive Study (PROspective pioglitAzone Clinical Trial


In macroVascular Events): a randomised controlled trial. Lancet. 2005 Oct

8;366(9493):1279-89.

[69] Erdmann E, Dormandy JA, Charbonnel B, Massi-Benedetti M, Moules IK, Skene

AM; PROactive Investigators The effect of pioglitazone on recurrent myocardial

infarction in 2,445 patients with type 2 diabetes and previous myocardial infarction:

results from the PROactive (PROactive 05) Study. J Am Coll Cardiol. 2007 May

1;49(17):1772-80.

[70] Psaty BM, Furberg CD. The record on rosiglitazone and the risk of myocardial

infarction. N Engl J Med. 2007 Jul 5;357(1):67-9.

[71] Nissen SE, Wolski K. Effect of rosiglitazone on the risk of myocardial infarction

and death from cardiovascular causes. N Engl J Med 2007;356:2457-71.

[72] Home PD, Pocock SJ, Beck-Nielsen H, et al. Rosiglitazone evaluated for cardiac

outcomes an interim analysis. N Engl J Med 2007;357:28-38.

[73] Lipscombe LL, Gomes T, Lévesque LE, Hux JE, Juurlink DN, Alter DA.

Thiazolidinediones and cardiovascular outcomes in older patients with diabetes.

JAMA. 2007 Dec 12;298(22):2634-43.

[74] Blind E, Janssen H, Dunder K2, de Graeff . he European Medicines Agency's

approval of new medicines for type 2 diabetes.Diabetes Obes Metab. 2018

Sep;20(9):2059-2063. doi: 10.1111/dom.13349.

[75] PA3.https://www.fda.gov/Drugs/DrugSafety/PostmarketDrugSafetyInformationf

orPatientsandProviders/ucm226956.htm. Accessed on March 03 2017

[76] https://www.fda.gov/Safety/MedWatch/SafetyInformation/SafetyAlertsforHuma

nMedicalProducts/ucm376683.htm Accessed on March 03 2017

[77] http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm531517.h

tm, accessed on Dec. 2nd 2016.

[78] http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2

016/03/WC500203804.pdf


[79] EMA. Reflection paper on assessment of cardiovascular safety profile of

medicinal products; 2016. https://www.ema.europa.eu/en/documents/scientific-

guideline/reflection-paper-assessment-cardiovascular-safety-profile-medicinal-

products_en.pdf Accessed 31 March 2018

[80] Rawlins M. De Testimonio: on the evidence for decisions about the use of

therapeutic interventions. Clin Med (Lond). 2008 Dec;8(6):579-88..

[81] Cordoba G, Schwartz L, Woloshin S, Bae H, Gøtzsche PC. Definition, reporting,

and interpretation of composite outcomes in clinical trials: systematic review. BMJ.

2010 Aug 18;341:c3920. Toxicol. 2016 Sep 19:1-5.

[82] Lincoff AM, Tardif JC, Neal B, Nicholls SJ, Rydén L, Schwartz GG, Malmberg

K, Buse JB, Henry RR, Wedel H, Weichert A, Cannata R, Grobbee DE. Evaluation

of the dual peroxisome proliferator-activated receptor α/γ agonist aleglitazar to reduce

cardiovascular events in patients with acute coronary syndrome and type 2 diabetes

mellitus: rationale and design of the AleCardio trial. Am Heart J. 2013

Sep;166(3):429-34. doi: 10.1016/j.ahj.2013.05.013.

[83] Lincoff AM, Tardif JC, Schwartz GG, Nicholls SJ, Rydén L, Neal B, Malmberg

K, Wedel H, Buse JB, Henry RR, Weichert A, Cannata R, Svensson A, Volz D,

Grobbee DE; AleCardio Investigators. Effect of aleglitazar on cardiovascular

outcomes after acute coronary syndrome in patients with type 2 diabetes mellitus: the

AleCardio randomized clinical trial. JAMA. 2014 Apr 16;311(15):1515-25. doi:

10.1001/jama.2014.3321

[84] http://www.worldpharmanews.com/roche/2488-roche-halts-investigation-of-

aleglitazar

[85] Green JB, Bethel MA, Armstrong PW, Buse JB, Engel SS, Garg J, Josse R,

Kaufman KD, Koglin J, Korn S, Lachin JM, McGuire DK, Pencina MJ, Standl E,

Stein PP, Suryawanshi S, Van de Werf F, Peterson ED, Holman RR; TECOS Study

Group. Effect of Sitagliptin on Cardiovascular Outcomes in Type 2 Diabetes. N Engl

J Med. 2015 Jul 16;373(3):232-42.

https://www.ema.europa.eu/en/documents/scientific-guideline/reflection-paper-assessment-cardiovascular-safety-profile-medicinal-products_en.pdf



http://www.worldpharmanews.com/roche/2488-roche-halts-investigation-of-aleglitazar

http://www.worldpharmanews.com/roche/2488-roche-halts-investigation-of-aleglitazar


[86] Zannad F, Cannon CP, Cushman WC, Bakris GL, Menon V, Perez AT, Fleck PR,

Mehta CR, Kupfer S, Wilson C, Lam H, White WB; EXAMINE Investigators. Heart

failure and mortality outcomes in patients with type 2 diabetes taking alogliptin versus

placebo in EXAMINE: a multicentre, randomized, double-blind trial. Lancet. 2015

May 23;385(9982):2067-76.

[87] White WB, Cannon CP, Heller SR, Nissen SE, Bergenstal RM, Bakris GL, Perez

AT, Fleck PR, Mehta CR, Kupfer S, Wilson C, Cushman WC, Zannad F; EXAMINE

Investigators. Alogliptin after acute coronary syndrome in patients with type 2

diabetes. N Engl J Med. 2013 Oct 3;369(14):1327-35.

[88] Scirica BM, Bhatt DL, Braunwald E, Steg PG, Davidson J, Hirshberg B, Ohman

P, Price DL, Chen R, Udell J, Raz I. The design and rationale of the saxagliptin

assessment of vascular outcomes recorded in patients with diabetes mellitus-

thrombolysis in myocardial infarction (SAVOR-TIMI) 53 study. Am Heart J. 2011

Nov;162(5):818-825.e6

[89] Scirica BM, Bhatt DL, Braunwald E, Steg PG, Davidson J, Hirshberg B, Ohman

P, Frederich R, Wiviott SD, Hoffman EB, Cavender MA, Udell JA, Desai NR,

Mosenzon O, McGuire DK, Ray KK, Leiter LA, Raz I; SAVOR-TIMI 53 Steering

Committee and Investigators. Saxagliptin and cardiovascular outcomes in patients

with type 2 diabetes mellitus. N Engl J Med. 2013 Oct 3;369(14):1317-26.

[90] Udell JA, Bhatt DL, Braunwald E, Cavender MA, Mosenzon O, Steg PG,

Davidson JA, Nicolau JC, Corbalan R, Hirshberg B, Frederich R, Im K, Umez-

Eronini AA, He P, McGuire DK, Leiter LA, Raz I, Scirica BM; SAVOR-TIMI 53

Steering Committee and Investigators. Saxagliptin and cardiovascular outcomes in

patients with type 2 diabetes and moderate or severe renal impairment: observations

from the SAVOR-TIMI 53 Trial. Diabetes Care. 2015 Apr;38(4):696-705.

[91] Mosenzon O, Leibowitz G, Bhatt DL, Cahn A, Hirshberg B, Wei C, Im K,

Rozenberg A, Yanuv I, Stahre C, Ray KK, Iqbal N, Braunwald E, Scirica BM, Raz I.

Effect of Saxagliptin on Renal Outcomes in the SAVOR-TIMI 53 Trial. Diabetes

Care. 2017 Jan;40(1):69-76.


[92] Cavender MA, Scirica BM, Raz I, Steg PG, McGuire DK, Leiter LA, Hirshberg

B, Davidson J, Cahn A, Mosenzon O, Im K, Braunwald E, Bhatt DL. Cardiovascular

Outcomes of Patients in SAVOR-TIMI 53 by Baseline Hemoglobin A1c. Am J Med.

2016 Mar;129(3):340.e1-8.

[93] Rosenstock J, Perkovic V, Johansen OE, Cooper ME, Kahn SE, Marx N,

Alexander JH, Pencina M, Toto RD, Wanner C, Zinman B, Woerle HJ, Baanstra D,

Pfarr E, Schnaidt S, Meinicke T, George JT, von Eynatten M, McGuire DK;

CARMELINA Investigators. Effect of Linagliptin vs Placebo on Major

Cardiovascular Events in Adults With Type 2 Diabetes and High Cardiovascular and

Renal Risk: The CARMELINA Randomized Clinical Trial. JAMA. 2019 Jan

1;321(1):69-79. doi: 10.1001/jama.2018.18269.

[94] Pfeffer MA, Claggett B, Diaz R, Dickstein K, Gerstein HC, Køber LV, Lawson

FC, Ping L, Wei X, Lewis EF, Maggioni AP, McMurray JJ, Probstfield JL, Riddle

MC, Solomon SD, Tardif JC; ELIXA Investigators. Lixisenatide in Patients with

Type 2 Diabetes and Acute Coronary Syndrome. N Engl J Med. 2015 Dec

3;373(23):2247-57

[95] Marso SP, Daniels GH, Brown-Frandsen K, Kristensen P, Mann JF, Nauck MA,

Nissen SE, Pocock S, Poulter NR, Ravn LS, Steinberg WM, Stockner M, Zinman B,

Bergenstal RM, Buse JB; LEADER Steering Committee; LEADER Trial

Investigators. Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes. N Engl

J Med. 2016 Jul 28;375(4):311-22.

[96] Marso SP, Bain SC, Consoli A, Eliaschewitz FG, Jódar E, Leiter LA, Lingvay I,

Rosenstock J, Seufert J, Warren ML, Woo V, Hansen O, Holst AG, Pettersson J,

Vilsbøll T; SUSTAIN-6 Investigators. Semaglutide and Cardiovascular Outcomes in

Patients with Type 2 Diabetes. N Engl J Med. 2016 Nov 10;375(19):1834-184

[97] Holman RR, Bethel MA, Mentz RJ, Thompson VP, Lokhnygina Y, Buse JB, Chan

JC, Choi J, Gustavson SM, Iqbal N, Maggioni AP, Marso SP, Öhman P, Pagidipati

NJ, Poulter N, Ramachandran A, Zinman B, Hernandez AF; EXSCEL Study Group.

Effects of Once-Weekly Exenatide on Cardiovascular Outcomes in Type 2 Diabetes.

N Engl J Med. 2017 Sep 28;377(13):1228-1239.


[98] Hernandez AF, Green JB, Janmohamed S, D'Agostino RB Sr, Granger CB, Jones

NP, Leiter LA, Rosenberg AE, Sigmon KN, Somerville MC, Thorpe KM, McMurray

JJV, Del Prato S; Harmony Outcomes committees and investigators. Albiglutide and

cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease

(Harmony Outcomes): a double-blind, randomised placebo-controlled trial. Lancet.

2018 Oct 1. pii: S0140-6736(18)32261-X

[99] Husain M, Birkenfeld AL, Donsmark M, Dungan K, Eliaschewitz FG, Franco DR,

Jeppesen OK, Lingvay I, Mosenzon O, Pedersen SD, Tack CJ, Thomsen M, Vilsbøll

T1, Warren ML1, Bain SC1; PIONEER 6 Investigators. Oral Semaglutide and

Cardiovascular Outcomes in Patients with Type 2 Diabetes. N Engl J Med. 2019 Aug

29;381(9):841-851. doi: 10.1056/NEJMoa1901118.

[100] Gerstein HC, Colhoun HM, Dagenais GR, Diaz R, Lakshmanan M, Pais P,

Probstfield J, Riddle MC,; REWIND Trial Investigators. Design and baseline

characteristics of participants in the Researching cardiovascular Events with a Weekly

INcretin in Diabetes (REWIND) trial on the cardiovascular effects of dulaglutide.

Diabetes Obes Metab. 2018 Jan;20(1):42-49.

[101] Gerstein HC, Colhoun HM, Dagenais GR, Diaz R, Lakshmanan M, Pais P,

Probstfield J, Botros FT, REWIND Investigators. Dulaglutide and renal outcomes in

type 2 diabetes: an exploratory analysis of the REWIND randomised, placebo-

controlled trial. Lancet. 2019 Jul 13;394(10193):131-138. doi: 10.1016/S0140-

6736(19)31150-X.

[102] Wu JH, Foote C, Blomster J, Toyama T, Perkovic V Sundström J, Neal B. Effects

of sodium-glucose cotransporter-2 on cardiovascular events, death, and major safety

outcomes in adults with type 2 diabetes: a systematic review and meta-analysis.

Lancet Diabetes Endocrinol. 2016 May;4(5):411-9.

[103] Sano M, Takei M, Shiraishi Y, Suzuki Y. Increased Hematocrit During Sodium-

Glucose Cotransporter 2 Inhibitor Therapy Indicates Recovery of Tubulointerstitial

Function in Diabetic Kidneys. J Clin Med Res. 2016 Dec;8(12):844-847.


[104] Imprialos KP, Boutari C, Stavropoulos K, Doumas M, Karagiannis AI. Stroke

paradox with SGLT-2 inhibitors: a play of chance or a viscosity-mediated reality? J

Neurol Neurosurg Psychiatry. 2017 Mar;88(3):249-253.

[105] Zinman B, Wanner C, Lachin JM, Fitchett D, Bluhmki E, Hantel S, Mattheus M,

Devins T, Johansen OE, Woerle HJ, Broedl UC, Inzucchi SE; EMPA-REG

OUTCOME Investigators. Empagliflozin, Cardiovascular Outcomes, and Mortality

in Type 2 Diabetes. N Engl J Med 2015; 373:2117-2128November 26, 2015

[106] Fitchett D, Zinman B, Wanner C, Lachin JM, Hantel S, Salsali A, Johansen OE,

Woerle HJ, Broedl UC, Inzucchi SE; EMPA-REG OUTCOME® trial investigators

Heart failure outcomes with empagliflozin in patients with type 2 diabetes at

highcardiovascular risk: results of the EMPA-REG OUTCOME® trial. Eur Heart J.

2016 May 14;37(19):1526-34

[107] Neal B, Perkovic V, Mahaffey KW, de Zeeuw D, Fulcher G, Erondu N, Shaw W,

Law G, Desai M, Matthews DR; CANVAS Program Collaborative Group.

Canagliflozin and Cardiovascular and Renal Events in Type 2 Diabetes. N Engl J

Med. 2017 Aug 17;377(7):644-657. doi: 10.1056/NEJMoa1611925

[108] Wiviott SD, Raz I, Bonaca MP, Mosenzon O et al.; DECLARE–TIMI 58

Investigators. Dapagliflozin and Cardiovascular Outcomes in Type 2 Diabetes. N

Engl J Med. 2019 Jan 24;380(4):347-357. doi: 10.1056/NEJMoa1812389. Epub 2018

Nov 10.

[109] McMurray JJV, Solomon SD, Inzucchi SEet al.; DAPA-HF Trial Committees and

Investigators. Dapagliflozin in Patients with Heart Failure and Reduced Ejection

Fraction. N Engl J Med. 2019 Sep 19. doi: 10.1056/NEJMoa1911303

[110] Tomlinson B, Hu M, Zhang Y, Chan P, Liu ZM. Effects of glucose-lowering drugs

on cardiovascular outcomes in patients with type 2 diabetes. Expert Opin Drug Metab

Toxicol. 2016 Sep 19:1-5.


[111] Thompson PL, Davis TM. Cardiovascular Effects of Glucose-Lowering Therapies

for Type 2 Diabetes: New Drugs in Perspective. Clin Ther. 2016 Nov 15. pii: S0149-

2918(16)30793-7.

[112] Home P. Cardiovascular outcome trials of glucose-lowering medications: an

update. Diabetologia. 2019 Mar;62(3):357-369. doi: 10.1007/s00125-018-4801-1

[113] Press release Victoza® (liraglutide) is approved in the US as the only type 2

diabetes treatment indicated to reduce the risk of three major adverse cardiovascular

events http://press.novonordisk-us.com/2017-08-25-Victoza-R-liraglutide-is-

approved-in-the-US-as-the-only-type-2-diabetes-treatment-indicated-to-reduce-the-

risk-of-three-major-adverse-cardiovascular-events accessed on 06 June 2018.

[114] https://www.fda.gov/news-events/press-announcements/fda-approves-jardiance-

reduce-cardiovascular-death-adults-type-2-diabetes 02 December 2016

[115] McGuire DK, Marx N, Johansen OE, Inzucchi SE, Rosenstock J, George JT. FDA

guidance on antihyperglyacemic therapies for type 2 diabetes: One decade later.

Diabetes Obes Metab. 2019 May;21(5):1073-1078. doi: 10.1111/dom.13645.

[116] Marx N, McGuire DK, Perkovic V, Woerle HJ, Broedl UC, von Eynatten M,

George JT, Rosenstock J. Composite Primary End Points in Cardiovascular Outcomes

Trials Involving Type 2 Diabetes Patients: Should Unstable Angina Be Included in

the Primary End Point? Diabetes Care. 2017 Sep;40(9):1144-1151. doi:

10.2337/dc17-0068.

[117] Freemantle N, Calvert M, Wood J, Eastaugh J, Griffin C. Composite outcomes in

randomized trials: greater precision but with greater uncertainty? JAMA. 2003 May

21; 289(19):2554-9

[118] Montori VM, Permanyer-Miralda G, Ferreira-González I, Busse JW, Pacheco-

Huergo V, Bryant D, Alonso J, Akl EA, Domingo-Salvany A, Mills E, Wu P,

Schünemann HJ, Jaeschke R, Guyatt GH Validity of composite end points in clinical

trials. BMJ. 2005 Mar 12; 330(7491):594-6.

https://www.fda.gov/news-events/press-announcements/fda-approves-jardiance-reduce-cardiovascular-death-adults-type-2-diabetes%2002%20December%202016

https://www.fda.gov/news-events/press-announcements/fda-approves-jardiance-reduce-cardiovascular-death-adults-type-2-diabetes%2002%20December%202016


[119] Giugliano D, Maiorino MI, Bellastella G, Chiodini P, Esposito K. Glycemic

Control, Preexisting Cardiovascular Disease, and Risk of Major Cardiovascular

Events in Patients with Type 2 Diabetes Mellitus: Systematic Review With Meta-

Analysis of Cardiovascular Outcome Trials and Intensive Glucose Control Trials. J

Am Heart Assoc. 2019 Jun 18;8(12):e012356. doi: 10.1161/JAHA.119.012356.

[120] Jokhadar M, Jacobsen SJ, Reeder GS, Weston SA, Roger VL. Sudden death and

recurrent ischemic events after myocardial infarction in the community. Am J

Epidemiol. 2004 Jun 1;159(11):1040-6.

[121] Johansson S, Rosengren A, Young K, Jennings E. Mortality and morbidity trends

after the first year in survivors of acute myocardial infarction: a systematic review.

BMC Cardiovasc Disord. 2017 Feb 7;17(1):53. doi: 10.1186/s12872-017-0482-9.

[122] Tangri N, Ferguson TW, Whitlock RH, Rigatto C, Jassal DS, Kass M, Toleva O,

Komenda P. Long term health outcomes in patients with a history of myocardial

infarction: A population based cohort study. PLoS One. 2017 Jul 12;12(7):e0180010.

doi: 10.1371/journal.pone.0180010.

[123] Birkeland KI, Bodegard J, Norhammar A, Kuiper JG, Georgiado E, Beekman-

Hendriks WL, Thuresson M, Pignot M, Herings RMC, Kooy A. How representative

of a general type 2 diabetes population are patients included in cardiovascular

outcome trials with SGLT2 inhibitors? A large European observational study.

Diabetes Obes Metab. 2018 Dec 9. doi: 10.1111/dom.13612

[124] .American Diabetes Association. 9. Pharmacologic Approaches to Glycemic

Treatment: Standards of Medical Care in Diabetes-2019. Diabetes Care. 2019

Jan;42(Suppl 1):S90-S102. doi: 10.2337/dc19-S009.

[125] Barrull C, Massafrets M, Planellas L, Perulero N. Análisis de coste-efectividad

de empagliflozina en pacientes con diabetes mellitus tipo 2 y enfermedad

cardiovascular establecida en España

http://www.aes.es/Jornadas2017/pdfs/posters/Aes_2017_23_1.pdf accessed on 22

October 2019.


[126] Hinton W, Feher M, Munro N, Walker M, de Lusignan S . Real-world prevalence

of the inclusion criteria for the LEADER trial: Data from a national general practice

network. Diabetes Obes Metab. 2019 Jul;21(7):1661-1667

[127] Boye KS, Riddle MC, Gerstein HC, Mody R, Garcia-Perez LE, Karanikas CA,

Lage MJ, Riesmeyer JS, Lakshmanan MC. Generalizability of glucagon-like peptide-

1 receptor agonist cardiovascular outcome trials to the overall type 2 diabetes

population in the United States. Diabetes Obes Metab. 2019 Jun;21(6):1299-1304.

[128] Wittbrodt ET, Eudicone JM, Bell KF, Enhoffer DM, Latham K, Green JB.

Generalizability of glucagon-like peptide-1 receptor agonist cardiovascular outcome

trials enrollment criteria to the US type 2 diabetes population. Am J Manag Care.

2018 Apr;24(8 Suppl):S146-S155.

[129] Wittbrodt E, Chamberlain D, Arnold SV, Tang F, Kosiborod M. Eligibility of

patients with type 2 diabetes for sodium-glucose co-transporter-2 inhibitor

cardiovascular outcomes trials: An assessment using the Diabetes Collaborative

Registry. Diabetes Obes Metab. 2019 Aug;21(8):1985-1989. doi:

10.1111/dom.13738. Epub 2019 Apr 24.

[130] Wittbrodt ET, Eudicone JM, Bell KF, Enhoffer DM, Latham K, Green JB.

Eligibility varies among the 4 sodium-glucose cotransporter-2 inhibitor

cardiovascular outcomes trials: implications for the general type 2 diabetes US

population. Am J Manag Care. 2018 Apr;24(8 Suppl):S138-S145.

[131] .Nicolucci A, Candido R, Cucinotta D, Graziano G, Rocca A, Rossi MC,

Tuccinardi F, Manicardi V. Generalizability of Cardiovascular Safety Trials on

SGLT2 Inhibitors to the Real World: Implications for Clinical Practice. Adv Ther.

2019 Aug 13.

[132] Bothwell LE, Greene JA, Podolsky SH, Jones DS. Food and Drug Assessing the

Gold Standard--Lessons from the History of RCTs. N Engl J Med. 2016 Jun

2;374(22):2175-81. doi: 10.1056/NEJMms1604593.

[133] Food and Drug Administration Best Practices for Conducting and Reporting

Pharmacoepidemiologic Safety Studies Using Electronic Healthcare Data. May


2013 https://www.fda.gov/regulatory-information/search-fda-guidance-

documents/best-practices-conducting-and-reporting-pharmacoepidemiologic-safety-

studies-using-electronic Accessed on 25 May 2016

[134] Franklin JM, Schneeweiss S. When and How Can Real-World Data Analyses

Substitute for Randomized Controlled Trials? Clin Pharmacol Ther. 2017

Dec;102(6):924-933. doi: 10.1002/cpt.857.

[135] https://www.congress.gov/bill/114th-congress/house-bill/34/text accessed on

May 21st 2018

[136] Schneeweiss S, Avorn J. A review of uses of health care utilization databases for

epidemiologic research on therapeutics. J Clin Epidemiol. 2005 Apr;58(4):323-37

[137] Najafzadeh M, Schneeweiss S. From Trial to Target Populations - Calibrating

Real-World Data. N Engl J Med. 2017 Mar 30;376(13):1203-1205.

[138] Herrett E, Shah AD, Boggon R, Denaxas S, Smeeth L, van Staa T, Timmis A,

Hemingway H. Completeness and diagnostic validity of recording acute myocardial

infarction events in primary care, hospital care, disease registry, and national

mortality records: cohort study. BMJ. 2013 May 20;346:f2350.

[139] http://www.encepp.eu/standards_and_guidances/documents/ENCePPGuideonM

ethStandardsinPE_Rev7.pdf

[140] https://www.pharmacoepi.org/resources/policies/guidelines-08027/

[141] http://www.encepp.eu/encepp/openAttachment/fullProtocol/18513;jsessionid=m

ClXTymXcRntbO4vtPMPVS82xGlCU52KWPDKwDWmkbizKC2P8AmR!119696

1961

[142] Fonseca V, Desouza C, Asnani S, Jialal I. Nontraditional risk factors for

cardiovascular disease in diabetes. Endocr Rev. 2004;25(1):153–175.

doi:10.1210/er.2002-0034

[143] Martín-Timón I, Sevillano-Collantes C, Segura-Galindo A, Del Cañizo-Gómez

FJ. Type 2 diabetes and cardiovascular disease: Have all risk factors the same

strength? World J Diabetes. 2014 Aug 15;5(4):444-70. doi: 10.4239/wjd.v5.i4.444.

http://www.encepp.eu/standards_and_guidances/documents/ENCePPGuideonMethStandardsinPE_Rev7.pdf

http://www.encepp.eu/standards_and_guidances/documents/ENCePPGuideonMethStandardsinPE_Rev7.pdf

https://www.pharmacoepi.org/resources/policies/guidelines-08027/

http://www.encepp.eu/encepp/openAttachment/fullProtocol/18513;jsessionid=mClXTymXcRntbO4vtPMPVS82xGlCU52KWPDKwDWmkbizKC2P8AmR!1196961961




[144] Tancredi M, Rosengren A, Svensson AM, Pivodic A,5, Gudbjörnsdottir S,

Wedel H, Lind M. Glycaemic control and excess risk of major coronary events in

patients with type 2 diabetes: a population-based study. Open Heart. 2019 Sep

9;6(2):e000967.

[145] Tancredi M, Rosengren A, Svensson AM, Kosiborod M, Pivodic A,

Gudbjörnsdottir S, Wedel H, Clements M, Dahlqvist S, Lind M. Excess Mortality

among Persons with Type 2 Diabetes. N Engl J Med. 2015 Oct 29;373(18):1720-32.

[146] Raghavan S, Vassy JL, Ho Y, Song R, Gagnon DR, Cho K, Wilson PWF,

Phillips LS. Diabetes Mellitus-Related All-Cause and Cardiovascular Mortality in a

National Cohort of Adults. J Am Heart Assoc. 2019 Feb 19;8(4):e011295.

[147] Schneeweiss S, Rassen JA, Brown JS, Rothman KJ, Happe L, Arlett P, Dal Pan

G, Goettsch W, Murk W, Wang SV Graphical Depiction of Longitudinal Study

Designs in Health Care Databases. Ann Intern Med. 2019 Mar 19;170(6):398-406.

[148] Schneeweiss S, Patrick AR, Stürmer T, Brookhart MA, Avorn J, Maclure M,

Rothman KJ, Glynn RJ. Increasing levels of restriction in pharmacoepidemiologic

database studies of elderly and comparison with randomized trial results. Med Care.

2007 Oct;45(10 Supl 2):S131-42.

[149] Barrot-de la Puente J, Mata-Cases M, Franch-Nadal J, Mundet-Tudurí X,

Casellas A, Fernandez-Real JM, Mauricio D. Older type 2 diabetic patients are more

likely to achieve glycaemic and cardiovascular risk factors targets than younger

patients: analysis of a primary care database. Int J Clin Pract. 2015

Dec;69(12):1486-95.

[150] Ramón Gomisa, Sara Artolab, Pedro Conthec, Josep Vidala, Ricard Casamord,

Beatriu Fontd. Prevalence of type 2 diabetes mellitus in overweight or obese

patients outpatients in Spain. Grupo de Estudio OBEDIA 14 May 2013.

[151] Bolíbar B, Fina Avilés F, Morros R, Garcia-Gil M del M, Hermosilla E, Ramos

R, Rosell M, Rodríguez J, Medina M, Calero S, Prieto-Alhambra D; Grupo SIDIAP.

[SIDIAP database: electronic clinical records in primary care as a source of


information for epidemiologic research]. [Article in Spanish] Med Clin (Barc). 2012

May 19;138(14):617-21.

[152] Canivell S, Mata-Cases M, Real J, Franch-Nadal J, Vlacho B, Khunti K,

Gratacòs M, Mauricio D. Glycaemic control after treatment intensification in

patients with type 2 diabetes uncontrolled on two or more non-insulin antidiabetic

drugs in a real-world setting. Diabetes Obes Metab. 2019 Jun;21(6):1373-1380

[153] Losada E, Soldevila B, Ali MS, Martínez-Laguna D, Nogués X, Puig-Domingo

M, Díez-Pérez A, Mauricio D, Prieto-Alhambra D. Real-world antidiabetic drug use

and fracture risk in 12,277 patients with type 2 diabetes mellitus: a nested case-

control study. Osteoporos Int. 2018 Sep;29(9):2079-2086.

[154] Suárez-Ortegón MF, McLachlan S, Price AH, Fernández-Balsells M, Franch-

Nadal J, Mata-Cases M, Barrot-de la Puente J, Mundet-Tudurí X, Mauricio D,

Ricart W, Wild SH, Strachan MWJ, Price JF, Fernández-Real JM. Decreased iron

stores are associated with cardiovascular disease in patients with type 2 diabetes

both cross-sectionally and longitudinally. Atherosclerosis. 2018 May;272:193-199.

[155] Tebé C, Martinez-Laguna D, Moreno V, Cooper C, Diez-Perez A, Collins GS,

Prieto-Alhambra D. Differential Mortality and the Excess Rates of Hip Fracture

Associated With Type 2 Diabetes: Accounting for Competing Risks in Fracture

Prediction Matters. J Bone Miner Res. 2018 Aug;33(8):1417-1421. doi:

10.1002/jbmr.3435. Epub 2018 May 3.

[156] Pontes C, Marsal JR, Elorza JM, Aragón M, Prieto-Alhambra D, Morros R.

Analgesic Use and Risk for Acute Coronary Events in Patients With Osteoarthritis:

A Population-based, Nested Case-control Study. Clin Ther. 2018 Feb;40(2):270-

283.

[157] Ramos R, Comas-Cufí M, Martí-Lluch R, Balló E, Ponjoan A, Alves-Cabratosa

L, Blanch J, Marrugat J, Elosua R, Grau M, Elosua-Bayes M, García-Ortiz L,

Garcia-Gil M. Statins for primary prevention of cardiovascular events and mortality

in old and very old adults with and without type 2 diabetes: retrospective cohort

study. BMJ. 2018 Sep 5;362:k3359. doi: 10.1136/bmj.k3359.


[158] Mata-Cases M, Franch-Nadal J, Real J, Mauricio D. Glycaemic control and

antidiabetic treatment trends in primary care centres in patients with type 2 diabetes

mellitus during 2007-2013 in Catalonia: a population-based study. BMJ Open. 2016

Oct 5;6(10):e012463.

[159] Bolíbar B, Pareja C, Astier-Peña MP, Morán J., Rodríguez-Blanco T, Rosell-

Murphy M et al. Variability in the performance of preventive services and in the

degree of control of identified health problems: A primary care study protocol..

BMC Public Health. 2008; 8: 281.

[160] http://www.sidiap.org/images/stories/docs/SIDIAP_angles_v2.pdf

[161] Mata-Cases M, Mauricio D, Franch-Nadal J. Clinical characteristics of type 2

diabetic patients on basal insulin therapy with adequate fasting glucose control who

do not achieve HbA1c targets. J Diabetes. 2017 Jan;9(1):34-44

[162] Roumie CL, Min JY, D'Agostino McGowan L, Presley C, Grijalva CG,

Hackstadt AJ, Hung AM, Greevy RA, Elasy T, Griffin MR. Comparative Safety of

Sulfonylurea and Metformin Monotherapy on the Risk of Heart Failure: A Cohort

Study. J Am Heart Assoc. 2017 Apr 19;6(4). pii: e005379. doi:

10.1161/JAHA.116.005379.

[163] U.S. Department of Health and Human Services Food and Drug Administration

Center for Drug Evaluation and Research. Guidance for Industry. Diabetes Mellitus:

Developing Drugs and Therapeutic Biologics for Treatment and Prevention

[Internet]. Center for Drug Evaluation and Research. February 2008.

http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/

Guidances/UCM071624.pdf. Accessed on June 22 2018.

[164] Chi GY . Some issues with composite endpoints in clinical trials. Fundam Clin

Pharmacol. 2005 Dec;19 (6):609-19

[165] Marx N, McGuire DK, Perkovic V, Woerle HJ, Broedl UC, von Eynatten M,

George JT, Rosenstock J.Composite Primary End Points in Cardiovascular

Outcomes Trials Involving Type 2 Diabetes Patients: Should Unstable Angina Be


Included in the Primary End Point? Diabetes Care. 2017 Sep;40(9):1144-1151. doi:

10.2337/dc17-0068.

[166] Ferreira-González I, Busse JW, Heels-Ansdell D, Montori VM, Akl EA, Bryant

DM, Alonso-Coello P, Alonso J, Worster A, Upadhye S, Jaeschke R, Schünemann

HJ, Permanyer-Miralda G, Pacheco-Huergo V, Domingo-Salvany A, Wu P, Mills

EJ, Guyatt GH. Problems with use of composite end points in cardiovascular trials:

systematic review of randomised controlled trials. BMJ. 2007 Apr

14;334(7597):786. Epub 2007 Apr 2.

[167] Filion KB, Douros A, Azoulay L, Yin H, Yu OH, Suissa S.

Sulfonylureas as initial treatment for type 2 diabetes and the risk of adverse

cardiovascular events: A population-based cohort study. Br J Clin Pharmacol. 2019

Oct;85(10):2378-2389. D

[168] Ray WA. Observational studies of drugs and mortality. N Engl J Med. 2005 Dec

1;353(22):2319-21.

[169] Rosano GM, Vitale C, Seferovic P. Heart Failure in Patients with Diabetes

Mellitus. Card Fail Rev. 2017 Apr;3(1):52-55. doi: 10.15420/cfr.2016:20:2.

[170] Norman PE, Davis WA, Bruce DG, Davis TM. Peripheral arterial disease and

risk of cardiac death in type 2 diabetes: the Fremantle Diabetes Study. Diabetes

Care. 2006 Mar;29(3):575-80.

[171] Alves-Cabratosa L, Garcia-Gil M, Comas-Cufí M, et al. Role of Low Ankle-

Brachial Index in Cardiovascular and Mortality Risk Compared with Major Risk

Conditions. J Clin Med. 2019;8(6):870. Published 2019 Jun 18.

doi:10.3390/jcm8060870

[172] Domínguez-Berjóna MF, Borrell C, Cano-Serral G, Esnaola S, Nolasco A,

Pasarín MI, Ramis R, Saurina C, Escolar-Pujolar A (Constructing a deprivation

index based on census data in large Spanish cities [the MEDEA project])

Construcción de un índice de privación a partir de datos censales en grandes

ciudades españolas (Proyecto MEDEA) Gac Sanit. 2008;22(3):179-87

https://www-ncbi-nlm-nih-gov.proxy.insermbiblio.inist.fr/pubmed/31276600

https://www-ncbi-nlm-nih-gov.proxy.insermbiblio.inist.fr/pubmed/31276600


[173] Álvarez A, Vicente Sánchez M, Alejos B , Pascual C, Regidor E. Process and

Results of Constructing a Deprivation Index for the Districts of Madrid and

Barcelona, Spain Rev Esp Salud Pública 2013; 87:317-329.

[174] Petrie JR, Guzik TJ, Touyz RM. Diabetes, Hypertension, and Cardiovascular

Disease: Clinical Insights and Vascular Mechanisms Can J Cardiol. 2018 May;

34(5): 575–584. doi: 10.1016/j.cjca.2017.12.005

[175] Baena Díez JM, del Val Garcia JL, Héctor Salas Gaetgens L, Sánchez Pérez R,

Altes Vaques E, Deixens Martínez B, Amatller Corominas M, Katia Núñez Casillas

D. [Comparison of the SCORE and REGICOR models for calculating

cardiovascular risk in cardiovascular disease-free individuals at a healthcare center

in Barcelona, Spain]. Rev Esp Salud Publica. 2005 Jul-Aug;79(4):453-64.

[176] Buitrago F, Cañón-Barroso L, Díaz-Herrera N, Cruces-Muro E, Escobar-

Fernández M, Serrano-Arias JM. [Comparison of the REGICOR and SCORE

function charts for classifying cardiovascular risk and for selecting patients for

hypolipidemic or antihypertensive treatment]. Rev Esp Cardiol. 2007

Feb;60(2):139-47. [Article in Spanish

[177] Turner RC, Millns H, Neil HA, Stratton IM, Manley SE, Matthews DR, Holman

RR. Risk factors for coronary artery disease in non-insulin dependent diabetes

mellitus: United Kingdom Prospective Diabetes Study (UKPDS: 23) BMJ. 1998

Mar 14;316(7134):823-8.

[178] Zabala A, Darsalia V, Holzmann MJ, Franzén S, Svensson AM, Eliasson B,

Patrone C, Nyström T, Jonsson M. Risk of first stroke in patients with type 2

diabetes and the relation to glycaemic control: a nationwide observational study.

Diabetes Obes Metab. 2019 Oct 1

[179] https://www.who.int/cardiovascular_diseases/priorities/secondary_prevention/co

untry/en/index1.html Accessed on July 23rd 2019

[180] Rapsomaniki E, Thuresson M, Yang E, Blin P, Hunt P, Chung SC, Stogiannis D,

Pujades-Rodriguez M, Timmis A, Denaxas SC, Danchin N, Stokes M, Thomas-

Delecourt F, Emmas C, Hasvold P, Jennings E, Johansson S, Cohen DJ, Jernberg T,


Moore N, Janzon M, Hemingway H. Using big data from health records from four

countries to evaluate chronic disease outcomes: a study in 114 364 survivors of

myocardial infarction. Eur Heart J Qual Care Clin Outcomes. 2016 Jul 1; 2(3):172-

183.

[181] Salinero-Fort MA, Gómez-Campelo P, San Andrés-Rebollo FJ, Cárdenas-

Valladolid J, Abánades-Herranz JC, Carrillo de Santa Pau E, Chico-Moraleja RM,

Beamud-Victoria D, de Miguel-Yanes JM, Jimenez-Garcia R, López-de-Andres A,

Ramallo-Fariña Y, De Burgos-Lunar C; MADIABETES Research Group.

Prevalence of depression in patients with type 2 diabetes mellitus in Spain (the

DIADEMA Study): results from the MADIABETES cohort. BMJ Open. 2018 Sep

24;8(9):e020768. doi: 10.1136/bmjopen-2017-020768.

[182] Patorno E, Patrick AR, Garry EM, Schneeweiss S, Gillet VG, Bartels DB,

Masso-Gonzalez E, Seeger JD. Observational studies of the association between

glucose-lowering medications and cardiovascular outcomes: addressing

methodological limitations. Diabetologia. 2014 Nov;57(11):2237-50. doi:

10.1007/s00125-014-3364-z. Epub 2014 Sep 12.

[183] von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of

Observational Studies in Epidemiology (STROBE) statement: guidelines for

reporting observational studies. Lancet. 2007;370(9596):1453–1457.

doi:10.1016/S0140-6736(07)61602-X

[184] Langan SM, Schmidt SA, Wing K, Ehrenstein V, Nicholls SG, Filion KB,

Klungel O, Petersen I, Sorensen HT, Dixon WG1, Guttmann A, Harron K, Hemkens

LG, Moher D, Schneeweiss S, Smeeth L, Sturkenboom M, von Elm E, Wang SV,

Benchimol EI. The reporting of studies conducted using observational routinely

collected health data statement for pharmacoepidemiology (RECORD-PE). BMJ.

2018 Nov 14;363:k3532

[185] International Diabetes Federation. Clinical Practice Recommendations for

managing Type 2 Diabetes in Primary Care. 2017


[186] Inzucchi SE, Bergenstal RM, Buse JB, Diamant M, Ferrannini E, Nauck M,

Peters AL, Tsapas A, Wender R, Matthews DR; American Diabetes Association

(ADA); European Association for the Study of Diabetes (EASD). Management of

hyperglycemia in type 2 diabetes: a patient-centered approach: position statement of

the American Diabetes Association (ADA) and the European Association for the

Study of Diabetes (EASD).Diabetes Care. 2012 Jun;35(6):1364-79. doi:

10.2337/dc12-0413.

[187] Bailey CJ, Marx N. Cardiovascular protection in type 2 diabetes: Insights from

recent outcome trials. Diabetes Obes Metab. 2019 Jan;21(1):3-14. doi:

10.1111/dom.13492.

[188] Inzucchi SE, Masoudi FA, McGuire DK. Metformin in heart failure. Diabetes

Care. 2007 Dec;30(12):e129.

[189] Lipska KJ. Metformin Use in Patients With Historical Contraindications. Ann

Intern Med. 2017 Feb 7;166(3):225-226. doi: 10.7326/M16-2712.

[190] https://www.fda.gov/drugs/drug-safety-and-availability/fda-drug-safety-

communication-fda-revises-warnings-regarding-use-diabetes-medicine-metformin-

certain and https://www.fda.gov/media/96771/download. Accessed on July 17th

2017.

[191] Bailey CJ. Metformin: historical overview. Diabetologia. 2017 Sep;60(9):1566-

1576. doi: 10.1007/s00125-017-4318-z.

[192] Scheen AJ, Paquot N. Metformin revisited: a critical review of the benefit-risk

balance in at-risk patients with type 2 diabetes. Diabetes Metab. 2013

May;39(3):179-90. doi: 10.1016/j.diabet.2013.02.006.

[193] Manel Mata, Francesc Xavier Cos, Rosa Morros, Laura Diego, Joan Barrot,

Maria Berengué, Montserrat Brugada, Teresa Carrera, Juan Francisco Cano,

Montserrat Estruch, Josep Maria Garrido, Genoveva Mendoza, Jordi Mesa, Marifé

Muñoz, Assumpció Recasens, Joan Antoni Vallés.

http://ics.gencat.cat/web/.content/documents/assistencia/gpc/GuiaDiabetis2015.pdf


[194] Hemmingsen B, Schroll JB, Lund SS, Wetterslev J, Gluud C, Vaag A, Sonne

DP, Lundstrøm LH, Almdal T. Sulphonylurea monotherapy for patients with type 2

diabetes mellitus. Cochrane Database Syst Rev. 2013 Apr 30;(4):CD009008. doi:

10.1002/14651858.CD009008.pub2

[195] Han Y, Xie H, Liu Y, Gao P, Yang X, Shen Z. Effect of metformin on all-cause

and cardiovascular mortality in patients with coronary artery diseases: a systematic

review and an updated meta-analysis. Cardiovasc Diabetol. 2019 Jul 30;18(1):96.

doi: 10.1186/s12933-019-0900-7.

[196] Powell WR, Christiansen CL, Miller DR. Meta-Analysis of Sulfonylurea

Therapy on Long-Term Risk of Mortality and Cardiovascular Events Compared to

Other Oral Glucose-Lowering Treatments. Diabetes Ther. 2018 Aug;9(4):1431-

1440. doi: 10.1007/s13300-018-0443-z.

[197] Azoulay L, Suissa S. Sulfonylureas and the Risks of Cardiovascular Events and

Death: A Methodological Meta-Regression Analysis of the Observational Studies.

Diabetes Care. 2017 May;40(5):706-714. doi: 10.2337/dc16-1943.

[198] Phung OJ, Schwartzman E, Allen RW, Engel SS, Rajpathak SN. Sulphonylureas

and risk of cardiovascular disease: systematic review and meta-analysis. Diabet

Med. 2013 Oct;30(10):1160-71. doi: 10.1111/dme.12232.

[199] Roumie CL, Hung AM, Greevy RA, et al. Comparative effectiveness of

sulfonylurea and metformin monotherapy on cardiovascular events in type 2

diabetes mellitus: a cohort study. Ann Intern Med 2012;157:601–610

[200] Schramm TK, Gislason GH, Vaag A, Rasmussen JN, Folke F, Hansen ML,

Fosbøl EL, Køber L, Norgaard ML, Madsen M, Hansen PR, Torp-Pedersen C.

Mortality and cardiovascular risk associated with different insulin secretagogues

compared with metformin in type 2 diabetes, with or without a previous myocardial

infarction: a nationwide study. Eur Heart J. 2011 Aug;32(15):1900-8. doi:

10.1093/eurheartj/ehr077.

[201] Lamanna C, Monami M, Marchionni N, Mannucci E. Effect of metformin on

cardiovascular events and mortality: a meta-analysis of randomized clinical trials.


Diabetes Obes Metab. 2011 Mar;13(3):221-8. doi: 10.1111/j.1463-

1326.2010.01349.x

[202] Fisman EZ, Tenenbaum A, Benderly M, Goldbourt U, Behar S, Motro M.

Antihyperglycemic treatment in diabetics with coronary disease: increased

metformin-associated mortality over a 5-year follow-up. Cardiology.

1999;91(3):195-202.

[203] Fisman EZ, Tenenbaum A, Boyko V, Benderly M, Adler Y, Friedensohn A,

Kohanovski M, Rotzak R, Schneider H, Behar S, Motro M. Oral antidiabetic

treatment in patients with coronary disease: time-related increased mortality on

combined glyburide/metformin therapy over a 7.7-year follow-up.Clin Cardiol.

2001 Feb;24(2):151-8.

[204] Varvaki Rados D, Catani Pinto L Reck Remonti L, Bauermann Leitão C, Gross

JL. The Association between Sulfonylurea Use and All-Cause and Cardiovascular

Mortality: A Meta-Analysis with Trial Sequential Analysis of Randomized Clinical

Trials. PLoS Med. 2016 Apr 12;13(4):e1001992

[205] Bain S, Druyts E, Balijepalli C, Baxter CA, Currie CJ, Das R, Donnelly R,

Khunti K, Langerman H, Leigh P, Siliman G, Thorlund K, Toor K, Vora J, Mills EJ.

Cardiovascular events and all-cause mortality associated with sulphonylureas

compared with other antihyperglycaemic drugs: A Bayesian meta-analysis of

survival data. Diabetes Obes Metab. 2017 Mar;19(3):329-335. doi:

10.1111/dom.12821.

[206] Gulliford M, Latinovic R. Mortality in type 2 diabetic subjects prescribed

metformin and sulphonylurea drugs in combination: cohort study. Diabetes Metab

Res Rev. 2004 May-Jun;20(3):239-45.

[207] Rosenstock J, Kahn SE, Johansen OE, Zinman B, Espeland MA, Woerle HJ,

Pfarr E, Keller A, Mattheus M, Baanstra D, Meinicke T, George JT, von Eynatten

M, McGuire DK, Marx N; CAROLINA Investigators. Effect of Linagliptin vs

Glimepiride on Major Adverse Cardiovascular Outcomes in Patients With Type 2


Diabetes: The CAROLINA Randomized Clinical Trial. JAMA. 2019 Sep 19. doi:

10.1001/jama.2019.13772

[208] Patorno E, Schneeweiss S, Gopalakrishnan C, Martin D, Franklin JM. Using

Real-World Data to Predict Findings of an Ongoing Phase IV Cardiovascular

Outcome Trial: Cardiovascular Safety of Linagliptin Versus Glimepiride. Diabetes

Care. 2019 Jun 25. pii: dc190069. doi: 10.2337/dc19-0069.

[209] Scheller NM, Mogensen UM, Andersson C, Vaag A, Torp-Pedersen C. All-

cause mortality and cardiovascular effects associated with the DPP-IV inhibitor

sitagliptin compared with metformin, a retrospective cohort study on the Danish

population. Diabetes Obes Metab. 2014 Mar;16(3):231-6. doi: 10.1111/dom.12197.

[210] Ou HT1, Chang KC2, Li CY3,4, Wu JS5,6. Risks of cardiovascular diseases

associated with dipeptidyl peptidase-4 inhibitors and other antidiabetic drugs in

patients with type 2 diabetes: a nation-wide longitudinal study. Cardiovasc Diabetol.

2016 Mar 1;15:41. doi: 10.1186/s12933-016-0350-4.

[211] Chen DY, Wang SH, Mao CT, Tsai ML, Lin YS, Chou CC, Wen MS, Wang

CC, Hsieh IC, Hung KC, Chen TH. Sitagliptin and cardiovascular outcomes in

diabetic patients with chronic kidney disease and acute myocardial infarction: A

nationwide cohort study. Int J Cardiol. 2015 Feb 15;181:200-6. doi:

10.1016/j.ijcard.2014.12.029.

[212] Shin S, Kim H. The effect of sitagliptin on cardiovascular risk profile in Korean

patients with type 2 diabetes mellitus: a retrospective cohort study. Ther Clin Risk

Manag. 2016 Mar 15;12:435-44. doi: 10.2147/TCRM.S105285.

[213] Huang HK, Yeh JI. Comparison of mortality and cardiovascular event risk

associated with various insulin secretagogues: A nationwide real-world analysis.

Diabetes Res Clin Pract. 2019 Jun;152:103-110. doi: 10.1016/j.diabres.2019.04.032.

[214] Yang TY, Liaw YP, Huang JY, Chang HR, Chang KW, Ueng KC. Association

of Sitagliptin with cardiovascular outcome in diabetic patients: a nationwide cohort

study. Acta Diabetol. 2016 Jun;53(3):461-8. doi: 10.1007/s00592-015-0817-x.


[215] Shah AD, Langenberg C, Rapsomaniki E, Denaxas S, Pujades-Rodriguez M,

Gale CP, Deanfield J, Smeeth L, Timmis A, Hemingway H. Type 2 diabetes and

incidence of cardiovascular diseases: a cohort study in 1.9 million people. Lancet

Diabetes Endocrinol. 2015 Feb;3(2):105-13.

[216] Szczerbińska K, Topinková E, Brzyski P, van der Roest HG, Richter T, Finne-

Soveri H, Denkinger MD, Gindin J, Onder G, Bernabei R. The characteristics of

diabetic residents in European nursing homes: results from the SHELTER study. J

Am Med Dir Assoc. 2015 Apr;16(4):334-40. doi: 10.1016/j.jamda.2014.11.009.

Epub 2014 Dec 19.

[217] Roumie CL, Chipman J, Min JY, Hackstadt AJ, Hung AM, Greevy RA Jr,

Grijalva CG, Elasy T, Griffin MR. Association of Treatment With Metformin vs

Sulfonylurea With Major Adverse Cardiovascular Events Among Patients With

Diabetes and Reduced Kidney Function. JAMA. 2019 Sep 19:1-11. doi:

10.1001/jama.2019.13206.

[218] Guidelines on second- and third-line medicines and type of insulin for the

control of blood glucose levels in non-pregnant adults with diabetes mellitus.

Geneva: World Health Organization; 2018. Licence: CC BY-NCSA 3.0 IGO.

https://apps.who.int/iris/bitstream/handle/10665/272433/9789241550284-eng.pdf

[219] Turner RC, Cull CA, Frighi V, Holman RR. Glycemic control with diet,

sulfonylurea, metformin, or insulin in patients with type 2 diabetes mellitus:

progressive requirement for multiple therapies (UKPDS 49). UK Prospective

Diabetes Study (UKPDS) Group. JAMA. 1999 Jun 2;281(21):2005-12.

[220] Lipska KJ, Yao X, Herrin J, McCoy RG, Ross JS, Steinman MA, Inzucchi SE,

Gill TM, Krumholz HM, Shah ND. Trends in drug utilization, glycemic control, and

rates of severe hypoglycemia, 2006–2013. Diabetes Care. 2017;40:468–475.

[221] Tahrani AA, Barnett AH, Bailey CJ. Pharmacology and therapeutic implications

of current drugs for type 2 diabetes mellitus. Nat Rev Endocrinol. 2016

Oct;12(10):566-92. doi: 10.1038/nrendo.2016.86.


[222] Monami M, Genovese S, Mannucci E. Cardiovascular safety of sulfonylureas: a

meta-analysis of randomized clinical trials. Diabetes Obes Metab. 2013;15:938-953.

[223] Landgraf R. Meglitinide analogues in the treatment of type 2 diabetes mellitus.

Drugs Aging. 2000 Nov;17(5):411-25.

[224] Stephens JW, Bodvarsdottir TB, Wareham K, Prior SL, Bracken RM, Lowe GD,

Rumley A, Dunseath G, Luzio S, Deacon CF, Holst JJ, Bain SC. Effects of short-

term therapy with glibenclamide and repaglinide on incretin hormones and oxidative

damage associated with postprandial hyperglycaemia in people with type 2 diabetes

mellitus. Diabetes Res Clin Pract. 2011 Nov;94(2):199-206. doi:

10.1016/j.diabres.2011.07.014.

[225] Black C, Donnelly P, McIntyre L, Royle PL, Shepherd JP, Thomas S.

Meglitinide analogues for type 2 diabetes mellitus Cochrane Database Syst Rev.

2007 Apr 18;(2):CD004654

[226] Xie J, Li N, Jiang X, Chai L, Chen JJ, Deng W. Short-term efficacy and safety

of repaglinide versus glimepiride as augmentation of metformin in treating patients

with type 2 diabetes mellitus. Diabetes Metab Syndr Obes. 2019 Apr 17;12:519-

526. doi: 10.2147/DMSO.S198154.

[227] Scheen AJ. Cardiovascular Effects of New Oral Glucose-Lowering Agents:

DPP-4 and SGLT-2 Inhibitors.Circ Res. 2018 May 11;122(10):1439-1459. doi:

10.1161/CIRCRESAHA.117.311588

[228] Scheen AJ. Cardiovascular safety of DPP-4 inhibitors compared with

sulphonylureas: Results of randomized controlled trials and observational studies.

Diabetes Metab. 2018 Nov;44(5):386-392. doi: 10.1016/j.diabet.2018.05.007.

[229] Madsen KS, Kähler P, Kähler LKA, Madsbad S, Gnesin F, Metzendorf MI,

Richter B, Hemmingsen B. Metformin and second- or third-generation

sulphonylurea combination therapy for adults with type 2 diabetes mellitus.

Cochrane Database Syst Rev. 2019 Apr 18;4:CD012368. doi:

10.1002/14651858.CD012368.pub2.


[230] Yu OH, Yin H, Azoulay L. The combination of DPP-4 inhibitors versus

sulfonylureas with metformin after failure of first-line treatment in the risk for major

cardiovascular events and death. Can J Diabetes. 2015;39(5):383-389.

doi:10.1016/j.jcjd.2015.02.002

[231] Mogensen UM, Andersson C, Fosbøl EL, Schramm TK, Vaag A, Scheller NM,

Torp-Pedersen C, Gislason G, Køber L. Cardiovascular safety of combination

therapies with incretin-based drugs and metformin compared with a combination of

metformin and sulphonylurea in type 2 diabetes mellitus-a retrospective nationwide

study. Diabetes Obes Metab. 2014 Oct;16(10):1001-8. doi: 10.1111/dom.12314..

[232] Seong JM, Choi NK, Shin JY, Chang Y, Kim YJ, Lee J, Kim JY, Park BJ.

Differential cardiovascular outcomes after dipeptidyl peptidase-4 inhibitor,

sulfonylurea, and pioglitazone therapy, all in combination with metformin, for type

2 diabetes: a population-based cohort study. PLoS One. 2015 May

20;10(5):e0124287. doi: 10.1371/journal.pone.0124287.

[233] Kannan S, Pantalone KM, Matsuda S, Wells BJ, Karafa M, Zimmerman RS.

Risk of overall mortality and cardiovascular events in patients with type 2 diabetes

on dual drug therapy including metformin: A large database study from the

Cleveland Clinic. J Diabetes. 2016 Mar;8(2):279-85. doi: 10.1111/1753-

0407.12301.

[234] Ekström N, Svensson AM, Miftaraj M, Franzén S, Zethelius B, Eliasson B,

Gudbjörnsdottir S. Cardiovascular safety of glucose-lowering agents as add-on

medication to metformin treatment in type 2 diabetes: report from the Swedish

National Diabetes Register. Diabetes Obes Metab. 2016 Oct;18(10):990-8. doi:

10.1111/dom.12704.

[235] O'Brien MJ, Karam SL, Wallia A, Kang RH, Cooper AJ, Lancki N, Moran MR,

Liss DT, Prospect TA, Ackermann T. Association of Second-line Antidiabetic

Medications With Cardiovascular Events Among Insured Adults With Type 2

Diabetes. JAMA Netw Open. 2018 Dec 7;1(8):e186125. doi:

10.1001/jamanetworkopen.2018.6125.


[236] Ou SM, Shih CJ, Chao PW, et al. Effects on Clinical Outcomes of Adding

Dipeptidyl Peptidase-4 Inhibitors Versus Sulfonylureas to Metformin Therapy in

Patients With Type 2 Diabetes Mellitus. Ann Intern Med. 2015;163(9):663-672.

doi:10.7326/M15-0308 [Abstract in PubMed]

[237] Mogensen UM, Andersson C, Fosbøl EL, Schramm TK, Vaag A, Scheller NM,

Torp-Pedersen C, Gislason G, Køber L. Metformin in combination with various

insulin secretagogues in type 2 diabetes and associated risk of cardiovascular

morbidity and mortality--a retrospective nationwide study. Diabetes Res Clin Pract.

2015 Jan;107(1):104-12. doi: 10.1016/j.diabres.2014.09.047.

[238] McGuire DK, Alexander JH, Johansen OE, Perkovic V, Rosenstock J, Cooper

ME, Wanner C, Kahn SE, Toto RD, Zinman B, Baanstra D, Pfarr E, Schnaidt S,

Meinicke T, George JT, von Eynatten M, Marx N; CARMELINA Investigators.

Linagliptin Effects on Heart Failure and Related Outcomes in Individuals With

Type 2 Diabetes Mellitus at High Cardiovascular and Renal Risk in CARMELINA

Circulation. 2019 Jan 15;139(3):351-361. doi:

10.1161/CIRCULATIONAHA.118.038352.

[239] Kenny HC, Abel ED. Heart Failure in Type 2 Diabetes Mellitus. Circ Res. 2019

Jan 4;124(1):121-141. doi: 10.1161/CIRCRESAHA.118.311371

[240] Presley CA, Chipman J, Min JY, Grijalva CG, Greevy RA, Griffin MR, Roumie

CL. Evaluation of frailty as an unmeasured confounder in observational studies of

antidiabetic medications. J Gerontol A Biol Sci Med Sci. 2018 Sep 26. doi:

10.1093/gerona/gly224.

[241] Menon V, Lincoff AM, Nicholls SJ, Jasper S, Wolski K, McGuire DK, Mehta

CR, Rosenstock J, Lopez C, Marcinak J, Cao C, Nissen SE; GRAND 306

Investigators. Fasiglifam-Induced Liver Injury in Patients With Type 2 Diabetes:

Results of a Randomized Controlled Cardiovascular Outcomes Safety Trial.

Diabetes Care. 2018 Dec;41(12):2603-2609. doi: 10.2337/dc18-0755.


[242] Shavadia JS, Sharma A, Gu X, Neaton J, DeLeve L, Holmes D, Home P, Eckel

RH, Watkins PB, Granger CB. Determination of fasiglifam-induced liver toxicity:

Insights from the data monitoring committee of the fasiglifam clinical trials

program. Clin Trials. 2019 Jun;16(3):253-262. doi: 10.1177/1740774519836766.

[243] Diabetes Care. 2018 Dec;41(12):2603-2609. doi: 10.2337/dc18-0755.Li X,

Zhong K, Guo Z, Zhong D, Chen X. Fasiglifam (TAK-875) Inhibits Hepatobiliary

Transporters: A Possible Factor Contributing to Fasiglifam-Induced Liver Injury.

Drug Metab Dispos. 2015 Nov;43(11):1751-9. doi: 10.1124/dmd.115.064121.

[244] FDA Drug Safety Communication: FDA adds warnings about heart failure risk

to labels of type 2 diabetes medicines containing saxagliptin and alogliptin

https://www.fda.gov/drugs/drug-safety-and-availability/fda-drug-safety-

communication-fda-adds-warnings-about-heart-failure-risk-labels-type-2-diabetes

Accessed on May 13 2018

[245] Douros A, Dell’Aniello S, Yu OHY, Filion KB, Azoulay L, Suissa S.

Sulfonylureas as second-line drugs in type 2 diabetes and the risk of cardiovascular

and hypoglycaemic events: population-based cohort study. BMJ 2018;362:k2693.

[246] McGowan LD, Roumie CL. Sulfonylureas as second-line treatment for type 2

diabetes. BMJ. 2018 Jul 18;362:k3041. doi: 10.1136/bmj.k3041

[247] H.R.34 - 21st Century Cures Act https://www.congress.gov/114/plaws/publ255/PLAW-114publ255.pdf Accessed on May 21st 2018

https://www.fda.gov/drugs/drug-safety-and-availability/fda-drug-safety-communication-fda-adds-warnings-about-heart-failure-risk-labels-type-2-diabetes

https://www.fda.gov/drugs/drug-safety-and-availability/fda-drug-safety-communication-fda-adds-warnings-about-heart-failure-risk-labels-type-2-diabetes

VIII. Appendix A

Appendix A 181 Nelly Raquel Herrera Comoglio Eu2P PhD December 2019

Appendix A: Results of retrospective observational studies conducted in LHDs assessing cardiovascular outcomes and mortality in DPP-4 i users

Study drugs MACE All-cause mortality MI Stroke HF PAD

[1] Scheller Sitagliptin vs metformin

1.22 (0.92-1.61) 1.25 (0.92-1.71)

[2] Wang KL Sitagliptin vs no use

0.87 (0.74–1.03) 1.21 (1.03–1.42)

[3] Chen DY Sitagliptin vs no use

1.32 (0.97–1.79) 0.90 (0.67–1.23) 1.62 (1.05–2.52) 1.73 (1.15–2.58)

[4] Wang SH Post-MI

Sitagliptin vs no use

0.97 (0.73–1.29) 0.65 (0.39–1.10)a 1.07 (0.72–1.59) 1.30 (0.75–2.26)

[5] Chen DY Post-stroke

Sitagliptin vs no use

1.00 (0.82–1.22) 0.95 (0.78–1.16) 1.07 (0.55– 2.11) b

[6] Shih CJ DPP-4 i vs no users

0.79 (0.75-0,83) 0.54 (0.52-0.56) 0.79 (0.72-0.87) 0.79 (0.75-0.84)

[7] Ou HT DPP-4 i vs no users

0.83 (0.76-0.91) c

[8] Shin S Sitagliptin vs metformin

0.831 (0.536–1.289)

1.863 (0.376–9.230) a

0.657 (0.237–1.826) HUA 1.165 (0.791–1.715)

0.783 (0.470–1.306)

0.762 (0.389–1.495)

[9] Fu AZ DPP-4 i vs SUs Prior CVD: 0.95 (0.78–1.15) No prior CVD: 0.59 (0.38–0.89)


[10] Toh S Saxagliptin vs SUs

0.69 (0.54- 0.87) DRS stratified: 0.86 (0.77-0.95)

[11] Yang TY Sitagliptin vs no use

0.56 (0.41-0.74) CVD: 0.59 (0.48-0.72) d

0.75 (0.59-0.96) 0.86 (0.45-1.65)

[12] Chan SY DPP-4 i vs non-users

0.76 (0.65–0.90) 0.43 (0.39–0.47)

No difference 0.77 (0.61–0.97) No difference

[13] Nyström T MET + insulin vs MET + DPP-4 i

0.59 (0.51-0.69) CVD (fatal and non-fatal): 0.71 (0.62-0.82)

[14] Ou SM Patients prior HF

DPP-4 i vs DPP-4 i non-users.

0.67 (0.64- 0.70) 0.81 (0.76 - 0.87) 0.96 (0.88-1.04)

0.80 (0.71 - 0.89) 0.83 (0.76- 0.89)

[15] Chin HJ DPP-4 i vs glimepiride

0.87 (0.75-1.01)

[16] Lo Re V Saxagliptin vs OADs

0.92 (0.86 - 0.98)

[17] Kim YG DPP-4 i vs SUs 0.76 (0.67–0.87) 0.63 (0.60–0.67) Full cohort: 0.78 (0.67–0.86) Baseline CVD: 0.77 (0.68–0.79) No baseline CVD: 0.71 (0.56–0.90)

[18] Williams R Vildagliptin vs NIADs

IRR: 0.22 to 1.02 IRR: 0.61 to 0.97 ACS: 0.55 to 1.60

IRR: 0.02 to 0.77 CHF: 0.49 to 1.03

[19] Yu OH Case-control

DPP-4 i alone or in combination

0.88 (0.63–1.22)


with OADs vs SUs

[20] Filion K Nested case-control

DPP-4 i vs OADs

0.87 (0.63-1.21)

a Cardiovascular mortality; b Haemorragic stroke; c Cardiovascular disease

LHDs: longitudinal healthcare databases; IRR: incident rate ratio DPP-4 i: dipeptidyl-peptidase inhibitors MET: metformin; SUs: sulfonylureas; ACS: acute coronary syndrome CHD: coronary heart disease; CHF: congestive heart failure; CVD: cardiovascular disease; IHD: ischemic heart disease; IS: ischemic stroke; MI: myocardial infarction

References

[1] Scheller NM, Mogensen UM, Andersson C, Vaag A, Torp-Pedersen C. All-cause mortality and cardiovascular effects associated with the

DPP-IV inhibitor sitagliptin compared with metformin, a retrospective cohort study on the Danish population. Diabetes Obes Metab. 2014

Mar;16(3):231-6. doi: 10.1111/dom.12197. Epub 2013 Sep 10.

[2] Wang KL, Liu CJ, Chao TF, Huang CM, Wu CH, Chen SJ, Yeh CM, Chen TJ, Lin SJ, Chiang CE.Sitagliptin and the risk of hospitalization

for heart failure: a population-based study. Int J Cardiol. 2014 Nov 15;177(1):86-90. doi: 10.1016/j.ijcard.2014.09.038.

[3] Chen DY, Wang SH, Mao CT, Tsai ML, Lin YS, Chou CC, Wen MS, Wang CC, Hsieh IC, Hung KC, Chen TH. Sitagliptin and

cardiovascular outcomes in diabetic patients with chronic kidney disease and acute myocardial infarction: A nationwide cohort study. Int J

Cardiol. 2015 Feb 15;181:200-6. doi: 10.1016/j.ijcard.2014.12.029.


[4] Wang SH, Chen DY, Lin YS, Mao CT, Tsai ML, Hsieh MJ, Chou CC, Wen MS, Wang CC, Hsieh IC, Hung KC, Chen TH.Cardiovascular

Outcomes of Sitagliptin in Type 2 Diabetic Patients with Acute Myocardial Infarction, a Population-Based Cohort Study in Taiwan. PLoS

One. 2015 Jun 26;10(6):e0131122. doi: 10.1371/journal.pone.0131122.

[5] Chen DY, Wang SH, Mao CT, Tsai ML, Lin YS, Chou CC, Wen MS, Wang CC, Hsieh IC, Hung KC, Chen TH. Cardiovascular Outcomes of

Sitagliptin in Type 2 Diabetic Patients with Acute Myocardial Infarction, a Population-Based Cohort Study in Taiwan. Int J Cardiol. 2015

Feb 15;181:200-6. doi: 10.1016/j.ijcard.2014.12.029.

[6] Shih CJ, Chen HT, Kuo SC, Ou SM, Chen YT. Cardiovascular Outcomes of Dipeptidyl Peptidase-4 Inhibitors in Elderly Patients With Type

2 Diabetes: A Nationwide Study. J Am Med Dir Assoc. 2016 Jan;17(1):59-64. doi: 10.1016/j.jamda.2015.10.009.

[7] Ou HT, Chang KC, Li CY, Wu JS. Risks of cardiovascular diseases associated with dipeptidyl peptidase-4 inhibitors and other antidiabetic

drugs in patients with type 2 diabetes: a nation-wide longitudinal study. Cardiovasc Diabetol. 2016 Mar 1;15:41. doi: 10.1186/s12933-016-

0350-4.

[8] Shin S, Kim H. The effect of sitagliptin on cardiovascular risk profile in Korean patients with type 2 diabetes mellitus: a retrospective cohort

study. Ther Clin Risk Manag. 2016 Mar 15;12:435-44. doi: 10.2147/TCRM.S105285.

[9] Fu AZ, Johnston SS, Ghannam A, Tsai K, Cappell K, Fowler R, Riehle E, Cole AL, Kalsekar I, Sheehan J. Association Between

Hospitalization for Heart Failure and Dipeptidyl Peptidase 4 Inhibitors in Patients With Type 2 Diabetes: An Observational Study. Diabetes

Care. 2016 May;39(5):726-34. doi: 10.2337/dc15-0764


[10] Toh S, Hampp C, Reichman ME, Graham DJ, Balakrishnan S, Pucino F, Hamilton J, Lendle S, Iyer A, Rucker M, Pimentel M, Nathwani

N, Griffin MR, Brown NJ, Fireman BH. Risk for Hospitalized Heart Failure Among New Users of Saxagliptin, Sitagliptin, and Other

Antihyperglycemic Drugs: A Retrospective Cohort Study. Ann Intern Med. 2016 Jun 7;164(11):705-14. doi: 10.7326/M15-2568

[11] Yang TY, Liaw YP, Huang JY, Chang HR, Chang KW, Ueng KC. Association of Sitagliptin with cardiovascular outcome in diabetic

patients: a nationwide cohort study. Acta Diabetol. 2016 Jun;53(3):461-8. doi: 10.1007/s00592-015-0817-x.

[12] Chan SY, Ou SM, Chen YT, Shih CJ. Effects of DPP-4 inhibitors on cardiovascular outcomes in patients with type 2 diabetes and end-

stage renal disease. Int J Cardiol. 2016 Sep 1;218:170-175. doi: 10.1016/j.ijcard.2016.05.062.

[13] Nyström T, Bodegard J, Nathanson D, Thuresson M, Norhammar A, Eriksson JW. Second line initiation of insulin compared with DPP-4

inhibitors after metformin monotherapy is associated with increased risk of all-cause mortality, cardiovascular events, and severe

hypoglycemia. Diabetes Res Clin Pract. 2017 Jan;123:199-208. doi: 10.1016/j.diabres.2016.12.004.

[14] Ou SM, Chen HT, Kuo SC, Chen TJ, Shih CJ, Chen YT. Dipeptidyl peptidase-4 inhibitors and cardiovascular risks in patients with pre-

existing heart failure. Heart. 2017 Mar;103(6):414-420. doi: 10.1136/heartjnl-2016-309687.

[15] Chin HJ, Nam JH, Lee EK, Shin JY. Comparative safety for cardiovascular outcomes of DPP-4 inhibitors versus glimepiride in patients

with type 2 diabetes: A retrospective cohort study. Medicine (Baltimore). 2017 Jun;96(25):e7213. doi: 10.1097/MD.0000000000007213.

[16] Lo Re V, Carbonari DM, Saine ME, Newcomb CW, Roy JA, Liu , Wu Q, Cardillo S, Haynes K, Kimmel SE, Reese PP, Margolis DJ,

Apter AJ, Reddy KR, Hennessy S, Bhullar H, Gallagher AM, Esposito DB, Strom BL. Postauthorization safety study of the DPP-4 inhibitor

saxagliptin: a large-scale multinational family of cohort studies of five outcomes. BMJ Open Diabetes Res Care. 2017 Jul 31;5(1):e000400.

doi: 10.1136/bmjdrc-2017-000400.


[17] Kim YG, Yoon D, Park S, Han SJ, Kim DJ, Lee KW, Park RW, Kim HJ. Dipeptidyl Peptidase-4 Inhibitors and Risk of Heart Failure in

Patients With Type 2 Diabetes Mellitus: A Population-Based Cohort Study. Circ Heart Fail. 2017 Sep;10(9). pii: e003957. doi:

10.1161/CIRCHEARTFAILURE.117.003957.

[18] Williams R, de Vries F, Kothny W, Serban C, Lopez-Leon S, Chu C. Schlienger R4. Cardiovascular safety of vildagliptin in patients with

type 2 diabetes: A European multi-database, non-interventional post-authorization safety study. Diabetes Obes Metab. 2017 Oct;19(10):1473-

1478. doi: 10.1111/dom.12951

[19] Yu OH, Filion KB, Azoulay L, Patenaude V, Majdan A, Suissa S. Incretin-based drugs and the risk of congestive heart failure. Diabetes

Care. 2015 Feb;38(2):277-84. doi: 10.2337/dc14-1459.

[20] Filion KB, Azoulay L, Platt RW, Dahl M, Dormuth CR, Clemens KK, Hu N, Paterson JM, Targownik L, Turin TC, Udell JA, Ernst P;

CNODES Investigators. A Multicenter Observational Study of Incretin-based Drugs and Heart Failure. N Engl J Med. 2016 Mar

24;374(12):1145-54. doi: 10.1056/NEJMoa1506115.

IX. Annexes

XI.1. Cardiovascular outcomes, heart failure and mortality in

type 2 diabetic patients treated with glucagon-like peptide 1

receptor agonists (GLP-1 RAs): A systematic review and meta-

analysis of observational cohort studies

Herrera Comoglio R, Vidal Guitart X.. Int J Clin Pract. 2020;74(9):e13553. doi:10.1111/ijcp.13553

XI.2. Linagliptin and Cardiac failure

WHO Pharmaceuticals Newsletter, N°1, 2015, World Health Organization, p. 20-26

XI.3. Glibenclamide/glyburide and palpitations in Asian

population

WHO Pharmaceutical Newsletter, N° 2, 2019, World Health Organization, p. 17-22

IX. 1

Cardiovascular outcomes, heart failure and mortality

in type 2 diabetic patients treated with glucagon-like peptide 1 receptor

agonists (GLP-1 RAs): A systematic review and meta-analysis of

observational cohort studies

Int J Clin Pract. 2020;74:e13553. wileyonlinelibrary.com/journal/ijcp | 1 of 18https://doi.org/10.1111/ijcp.13553

© 2020 John Wiley & Sons Ltd

Received:19September2019 | Revised:6May2020 | Accepted:18May2020DOI: 10.1111/ijcp.13553

M E T A - A N A LY S I S

METABOLISM & ENDOCRINOLOGY

Cardiovascular outcomes, heart failure and mortality in type 2 diabetic patients treated with glucagon-like peptide 1 receptor agonists (GLP-1 RAs): A systematic review and meta-analysis of observational cohort studies

Raquel Herrera Comoglio1,2 | Xavier Vidal Guitart2,3

1SchoolofMedicine,UniversidadNacionaldeCórdoba,Córdoba,Argentina2Eu2P European Programme in Pharmacovigilance and Pharmacoepidemiology,UniversityofBordeauxSegalen,Bordeaux,France3FundacioInstitutCataladeFarmacologia,UniversitatAutonomadeBarcelona,Barcelona,Spain

CorrespondenceRaquelHerreraComoglio,HospitalNacionaldeClínicas,FacultaddeCienciasMédicas,UniversidadNacionaldeCórdoba,UNC–SantaRosa1564,5000Córdoba,Argentina.Email: [email protected]; [email protected]

AbstractBackground: Cardiovascular outcomes trials (CVOTs) have assessed the effects of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) on major adverse cardio-vascularevents (MACE)andmortality inhighcardiovascular (CV)riskpopulations.Observational research can provide complementary evidence about these effects in unselected populations.Aim: To systematically review retrospective observational cohort studies conducted inelectronichealthcaredatabases (EHDs)assessingGLP-1RAs´effectsonMACEand/orhospitalisation forheart failure (HHF) and/or all-causemortality inType2diabetesmellitus(T2DM)patients.Methods: Wesystematicallysearchedstudiesmeetinginclusioncriteria,compareddesign,methodsandpopulationcharacteristics,assessedriskforbiasanddidameta-analysis(MA)usingarandom-effectsmodeltocalculateoverallhazardratios(HRs)and 95% CI (confidence intervals).Results: Sixteen studies included 285,436 T2DMpatients exposed toGLP-1 RAs(exenatidebid,liraglutide,lixisenatide,long-actingexenatide),n ranged from 219 to 160,803 patients. Comparators included: no exposure, other antidiabeticmedica-tions (OADs), combinedOADs, canagliflozin ormultiple comparators. Ten studiesestimated all-causemortality, hazard ratios (HRs) ranged from0.17 (95%CI 0.02-1.22) to 1.29 (95% CI 0.54-3.13). Thirteen studies assessed cardiovascular events and/orMACE;HRsrangedfrom0.27(95%CI0.14-0.53)to1.11(95%CI0.99-1.24).EightstudiesassessedHHF,HRsrangedfrom0.12(95%CI0.02-0.66)to1.64(95%CI1.28-2.13).Excludingtwostudiesbecauseoftemporalbias,weobtainedpooledestimatesforall-causemortality:HR0.63(0.44-0.89),CVoutcomesHR0.84(0.75-0.94) andHHF;HR0.94 (0.78-1.14), (high between-study variability: I2 = 83.35%;I2 = 70.3%; and I2=90.1%,respectively).Conclusion: PooledresultsofEHDs’studiesassessingGLP-1RAseffectsfavouredGLP-1RAs forall-causemortalityandMACEwhilewereneutral forHHF.Resultsshould be interpreted cautiously because of studies’ substantial heterogeneity and limitations of observational research.

www.wileyonlinelibrary.com/journal/ijcp

mailto:

mailto:

https://orcid.org/0000-0002-2810-1749

https://orcid.org/0000-0001-6705-4298

mailto:[email protected]

mailto:[email protected]

http://crossmark.crossref.org/dialog/?doi=10.1111%2Fijcp.13553&domain=pdf&date_stamp=2020-06-17

2 of 18 | HERRERA COMOGLIO And VIdAL GUITART

1 | INTRODUC TION

Inpatientswithtype2diabetesmellitus(T2DM),morbidityandmor-talityratefrommacrovasculardiseaseisupto80%1,2; patients are also at increased risk of heart failure and have a worse prognosis.3,4 Blood-glucose levels control has shown to be effective to reduce microvascularcomplicationsofdiabetes,mainlydiabeticnephropa-thy,buttheeffectoncardiovasculardisease(CVD)isunclear.5 Since 2005,15newnon-insulinblood-glucose-loweringdrugs(non-insulin“antidiabeticdrugs,”NIADs)havebeenmarketed,addingthreenewclasses of non-insulin products: glucagon-like peptide-1 receptor agonists (GLP-1 RAs), sodium-glucose cotransporter-2 inhibitors(SGLT2 i) and inhibitors of dipeptidyl-peptidase 4 (DPP-4 i).6

NativeGLP-1andglucose-dependentinsulinotropicpolypeptide(GIP)accountforupto60%ofpost-prandialinsulinrelease.Theacti-vationofGLP-1receptorincreasesinsulinsecretion,lowersinappro-priate high glucagon secretion in a glucose-dependent manner and haseffectsinextrapancreatictissues,deceleratinggastricemptying,reducing food intake—and body weight—and lowering circulating li-poproteins,inflammationandsystolicbloodpressure.7,8GLP-1RAshave longer T1/2 than native GLP-1 and are the only injectable class among the marketed non-insulin blood-glucose-lowering agents. The route of administration and gastrointestinal adverse effects arethemainreasonsforthenon-adherencetotreatment,relativelyfrequent in patients initiatingGLP—1RAs.9 The first agent of the GLP-1RAclass,exenatideb.i.d.,isavailableinclinicalpractisesince2005. Semaglutide has been approved in late 2017-2018 and theonlyGLP-1RAwithanoralformulation,oralsemaglutide,in2019.10

Non-insulinglucose-loweringagentsaremarketedbasedonre-sultsofclinicaltrialswithsurrogatevariables,mainlythepercentageof glycated haemoglobin and other glucose markers.11However,inspiteoftheclearcorrelationbetweendiabetesandCVD,theeffectof glucose-lowering therapies and glycemic control on CVD is un-clear.5Since2008,glucose-loweringagentsthatreceivedmarketingauthorisation based on results of clinical trials with surrogate vari-ables have to comply with regulatory recommendations regarding CV safety.12,13BecauseofthelowrateofCVevents,thesecardio-vascular outcomes trials (CVOTs) enrol a large number of diabetic pa-tientsusuallywithestablishedorathighriskofCVD,andhavelongfollow-up (FU)periods.Up todate, sevenCVOTsassessingGLP-1RAs on cardiovascular outcomes in high CV risk populations arecompleted and published (Table 1).14–20 Liraglutide and semaglutide showed beneficial effects on major adverse cardiovascular events (MACE); for liraglutide,theseresultsweredrivenbythereductioninCVmortalityandforsemaglutide,bythereductionofnon-fatalstrokes.InAugust2017,theUSFDAincludedasanewindicationforliraglutide the reduction of the risk of CV events in adults with type 2 diabetes who have established CVD.

However, it is often unclear to what extent results of clini-cal trials conducted in selected high-risk CV populations apply to unselected ones, composedmainly of lowCV risk patients. If ad-equately conducted, studies using data of electronic healthcaredatabases containing secondary information (EHDs) can be useful

to provide complementary or confirmatory evidence of the effects ofdrugsinreal-worldpopulations,especiallyforthoseunderrepre-sented or excluded in RCTs. EHDs containing secondary informa-tion—(EHDs)—are increasingly used as source data in observational research evaluating comparative drug effectiveness and safety and have an increasingly important role in the generation of scientific evidence.21 For comparativeeffectiveness researchand the studyof relatively rare events, EHDshavemany advantagesoverRCTs:have large size data, include patients often underrepresented inclinicaltrials,arerepresentativeofroutineclinicalcareandsuitableto study real-world effectiveness and utilisation patterns.22 On the contrary,observational studiesalmostalwayshaveabiasbecauseof the unequal distribution of prognostic factors between patients exposed or not exposed to an intervention. The non-randomised design inherent to observational research can produce misleading results because of several flaws arising from design, time-relatedbias,matchingandanalysis.23Moreover,whenassessingtreatmenteffectsinchronicandprogressiveconditions,asT2DMis,research-ershavetodealwithadditionalchallenges,suchastheadditionorswitching to other ant-diabetic therapies to control blood-glucose biomarkers.24

The objective of this systematic review is to describe and compare characteristics of designs, baseline study populationsand results of observational cohort studies conducted in EHDs assessing MACE, hospitalisation for heart failure (HHF) and all-cause mortality in Type 2 diabetes patients treated with GLP-1

How did you gather the information you considered in your review?

Medline,ScopusandWebofSciencewereusedtoidentifyabstractsandfulltextspublishedasofJanuary31,2019.Searches included individual and class terms for GLP-1 RAs, studydesignandsourcesofdata. Inclusionandex-clusion criteria were clearly defined. The process is sum-marisedinFigure1;andthelistofretrievedandexcludedpapersareshowninTablesS1andS2,respectively.

What is the “take-home” message for the clinician?

• It is often unclear to what extent results of clinical tri-als conducted in selected high-risk cardiovascular (CV) populationscanapplyto“realworld”ones.

• The effect of glucagon-like peptide 1 receptor agonists (GLP-1RAs)onType2diabetesmellitus(T2DM)unse-lectedpopulations,composedmainlyoflowCVriskpa-tients,hasbeenassessedthroughobservationalcohortstudies in Western healthcare databases.

• Pooled results of these observational studies suggest abeneficialeffectofGLP-1RAsonall-causemortality,andtoalesserextent,onCVevents;resultsfortheHHFwere neutral.

| 3 of 18HERRERA COMOGLIO And VIdAL GUITART

TAB

LE 1

PublishedRCTsassessingCVoutcomesinType2diabetesmellituspatientstreatedwithGLP-1RAs:characteristics,populationandresults

GLP

-1 R

A C

VOT

n= (r

ando

mi-

satio

n)Fo

llow

-up

Popu

latio

n ch

arac

teris

tics

Resu

lts

Men

%; e

lder

lyH

isto

ry

CV

DH

isto

ry

HF

MA

CEA

-C M

CV

MA

MI

Stro

keH

HF

ELIXALixisenatide15

6,068(1:1)

2.1

y69.7%;≥65y33%

100%

22.4

%1.02(0.89-1.17)

0.94(0.78to1.13)

0.98(0.78-1.22)

1.03(0.87-1.22)

1.12(0.79-1.58)

0.96(0.75−1.23)

LEADERLiraglutide16

9,340(1:1)

3.8y

64.3%;≥60y75%

100%

17.1

2%0.

87* (0.78-0.97)

0.85

* (0.7

4 to

0.9

7)0.

78* (0.66-0.93)

0.86(0.73-1.00

0.86(0.71-1.06)

0.87(0.73-1.05)

SUSTAIN-6Semaglutide

173,297(1:1)

2.1

y60.7%;meanage

64.6±7.4

83%IHD

23.6%

0.74

* (0.58-0.95)

1.05

(0.7

4-1.

50)

0.98;(0.651.48)

0.74(0.51-1.08)

0.61(0.38−0.99)

1.11(0.77-1.61)

EXSCELexenatideLAR1814,752(1:1)

3.2

y62%;≥65y

40.2

5%73

.1%

16.2%

0.91(0.83-1.00)

0.86

* (0.7

7-0.

97)

0.88(0.76-1.02)

0.97(0.85-1.10)

0.85(0.70-1.03)

0.94(0.78-1.13)

HARM

ONYAlbiglutide19

9,463

(1:1

)median1.6y

70%

;Meanage64.1y.

70%CAD;

47%IM;

17%

Str

oke;

20%HF

20%

0.78

* (0.68-0.90)

0.95(0.79-1.16)

0.93

(0.7

3-1.

19)

0.75

* (0.61-0.90)

0.86(0.66-1.14)

priorHF[0.70

(0.5

4-0.

90)]

no

his

tory

of

HF[0.82

(0.69-0.98)]

REWINDDulaglutide20

9,901

(1:1

)m

edia

n 5.

4 y

(IQR

5.1-

5.9)

53.7

%Mean66.2years

31.5

%C

VD

20.8%

CV

E

8.5%

0.88

* (0.7

9-0.

99)

0.90(0.80-1.01)

0.91(0.78-1.06)

0.96(0.79-1.15)

0.76

* (0.62-0.94)

0.93

(0.7

7-1.

12)

*PIONEER-6Oral

sem

aglu

tide21

3,183

(1:1

)58.4%

Median66y.

0.79

; (0.

57 to

1.1

1)0.

51* (0.31-0.84)

0.49

* (0.2

7 to

0.9

2)1.18(0.73to1.90)

0.74

(0.3

5 to

1.5

7)0.86(0.48-1.55)

Abbreviations:A-CM,all-causemortality;AMI,acutemyocardialinfarction;CVM,cardiovascularmortality;CVOT,cardiovascularoutcometrial;HHF,hospitalisationforheartfailure;MACE,major

adversecardiovascularevents;RCTs,randomisedcontrolledtrials.

AllresultsHazardratio(95%CI).

HARMONYtrialreportsthecompositeofdeathfromcardiovascularcausesorhospitaladmissionforheartfailure.ThetableshowsresultsofthesubgroupanalysisforHHF.

*Sig

nific

ant r

esul

ts a

re h

ighl

ight

ed in

bol

d.


RAs, and to descriptively and quantitatively summarise their re-sults. This systematic review has been registered in PROSPERO (CRD42019122102).Tothebesttoourknowledge,nosystematicreview (SR) has been performed exclusively for observational co-hortstudiesassessingtheeffectofGLP-1RAsonCVoutcomes,all-causemortalityandHF.ThisarticlehasbeenreportedaccordingtotheMOOSEGuidelinesforMeta-AnalysesandSystematicReviewsof Observational Studies.25

2 | METHODS

2.1 | Data sources and searches

WeusedMedline,ScopusandWebofScience to identifyabstractsandfulltextspublishedasofJanuary31,2019.Searchesincludedspe-cificandclasstermsforGLP-1RAs(exenatideORliraglutideORlixi-senatideORalbiglutideORdulaglutideORsemaglutideORGLP-1RA),studydesign (observational,observationalcohort,population-based)and sources of data (healthcare databases). We performed additional manualsearchesinreferencesor“relatedarticles”upto31Jan2019.Nofiltersforlanguageordatewereapplied.Weexcludednestedcase-control studies because of their different design and analysis.

2.2 | Study selection and quality assessment

We selected abstracts, read in-depth papers matching the in-clusion criteria and analysed full text and additional files if any. Inclusion criteria: 1. Retrospective observational cohort studies 2. Conducted using data of EHDs. 3. Including T2DM patientstreatedwithGLP-1RAs.4.Assessingmajorcardiovascularevents(ie,cardiovascularmortality,acutemyocardialinfarction(MI)andstroke—eitherincompositeorindividualoutcomes),HHFandall-cause mortality. Exclusion criteria:1.StudiesnotincludingGLP-RAs2.Assessingexclusivelyotherclinicaloutcomes,suchasglycemiccontrol,bodyweightand/orhypoglycemicevents,adherence,eco-nomicoutcomes,differentclinicaloutcomes(cancer,pancreatitis).3.Nested case-control studies. The flowdiagram (Figure1) andthe list of retrieved and excluded papers show the results of the selectionprocess.(TablesS1andS2,respectively)Methodologicalquality/riskofbiaswasassessedusingtheROBINS-Iassessmenttool.26Funnelplotgraphicsforeachoutcomewereusedtoevalu-atepublicationbiasvisually(Figure2).

2.3 | Data extraction and analysis

We descriptively synthesised data about data sources (country and type),studypopulations’baselinecharacteristics,numberofsub-jectsincludedinfullandanalysedcohorts,studyperiod,patientse-lection,start,endandlengthofFU,comparatordrug(s),outcomes,covariates and the period in which covariates were assessed. We

searched for some various aspects in study design and analysis that could impacton study results: studypopulations, study andcomparatordrugs, lengthofFU,exposureascertainment,andthenumber and nature of covariates included in matching and/or ad-justment. We searched to identify major bias that can explain even-tual differences in the results of individual studies. We summarised analysis’ results (primary, secondary, subgroup and sensitivityanalysis), and strategies to control for confounding (restriction,matching and analysis adjustment). Three separate meta-analyses wereperformed(all-causemortality,CVeventsandHHF)usingarandom-effectsmodeltocalculateoverallhazardratios(HRs)andtwo-sided95%CI(confidenceintervals).AnalyseswereperformedusingStata,version13.1(StataCorp.).Between-studyheterogene-ity was assessed by I2statistic,athresholdofI2 greater than 50% indicateshighheterogeneity.IntheprimaryMA,weexcludedtheresults of two studies with a temporal bias that could have affected estimates.AfurthersensitivityMAincludedthesetwostudies.TheMAwasreportedfollowingtheMOOSEchecklist.

3 | RESULTS

We identified sixteen observational cohort studies assessing CV outcomes,HHFandorall-causemortalityinT2MDpatientstreatedwithGLP-1RAs:Bestetal,27Mogensenetal,28Pauletal,29Vélezetal,30Patornoetal,31Kannanetal,32Ekströmetal,33Anyanwaguetal,34Suissaetal,35Zimmermanetal,36Toulisetal,37Anyanwaguet al,38 Patorno et al,39 Dawwas et al,40 Svanström et al 41 and O'Brien et al.42ThetotalnumberofpatientsexposedtoGLP-1RAswas285,436;rangingfrom21933to160,803.40 (Table 2) Study peri-ods span from 200527 to 2015 37(FigureS1).

3.1 | Data sources

AllstudieswereconductedinWesterndatabasesvalidatedforinves-tigational purposes for the condition and outcomes of interest: most inUS27,29–32,36,39,40,42,43andUK33,35,37,38;Denmark,28Sweden,34 and Sweden and Denmark.41 European databases are electronic medi-calrecords(EMRs),withpartial(intheUK,theHealthImprovementNetwork[THIN]andtheClinicalPracticeResearchDatalink[GPRD])or universal general population coverage (Denmark and Sweden); in Danish databases28,41 information about clinical variables is lack-ing.USstudieswereconductedinclaimsEHDs27,29–32,37,39,40,42 and EMRs.32,36

3.2 | Study populations

AllstudiesincludedT2DMpatientsaged≥18yearsandexcludepa-tients with diagnosis codes related to Type 1 diabetes; two stud-iesalsoexcludedpatientswithahistoryofmalignancies,end-stagerenaldisease,humanimmunodeficiencyvirus,organtransplant31,39


and nursing home admission at baseline.31Allstudypopulationshadlow CV risk; six studies excluded participants with a history of as-sessed outcomes27–29,33,38,40 Table 3 shows the populations’ baseline characteristics.

3.3 | Study drug(s)

Six studies did not report the individual GLP-1 RAs included intheir analysis.30,32–35,42 One study focused on incretin thera-pies.30 Four studies studiedGLP-1RAs exclusively as second-linetherapies,28,31–33 and one study assessed only liraglutide added to metformin(MET).41 Three studies evaluated a single agent: exena-tideb.i.d. (theonlyGLP-1RA licencedat studies’periods27,29) and

liraglutide.41 In two studies assessing the effect of canagliflozin39 and DPP4 i40 onHF,GLP-1RAswere the reference drugs. Threestudies reported the percentage of use,36–38 being exenatide and liraglutide the most used GLP-1 RAs; two studies37,38 made sub-group analysis for each agent. Two studies reported the use of dula-glutide—approved in September 2014—and albiglutide39,40 no study included semaglutide (marketed in December 2017). Table S3 shows thereportingifany,andthenumberofindividualGLP-1RAused.

3.4 | Comparator drug(s)

In five studies, comparators were other non-insulin antidia-betic agents.27,29,30,36–38 Eight studies compared GLP-1 RAs vs

F I G U R E 1 Flowchartoftheselectionprocess(PRISMAstatement)


awell-defined class: vs SUs,35 vs DPP-4 i40,41 (two with prior use ofMET at baseline) or evaluatedGLP-1RAs vs other second-linetherapies, all added tometformin:metforminwithSUsas a refer-ence28,31–33 or DPP-4 i,31,42 thiazolidinediones or insulin.31 In one study, the studied drug was canagliflozin and GLP-1 RAs one of

the comparator groups.39Intwostudies,thecomparatorwasinsu-lin29,34:oneassessedthird-linetherapies,GLP-1RAsvsinsulin(bothaddedtometformin+SUs)(TableS4).34

If prescribed according to existing guidelines, drugs and linesof treatment are proxies of disease severity. Studies comparing

F I G U R E 2 Funnelplotsof16observationallongitudinalcohortstudiesassessingeffectsofGLP-1RAsinType2diabetesmellituspatients.A,All-causemortality.B,Cardiovascularevents.C,Hospitalizationforheartfailure


TAB

LE 2

Observationalcohortstudiesassessingtheeffectofglucagon-likepeptidereceptoragonists(GLP-1RAs)oncardiovascularoutcomes,all-causemortalityand/orheartfailure

Aut

hor,

yrSt

udy

and

com

para

tor d

rugs

Des

ign

Expo

sure

an

alys

isFo

llow

-up

(yea

rs)

Resu

lts A

ll-ca

use

mor

talit

yRe

sults

MAC

ERe

sults

CV

eve

nts

Resu

lts H

HF

Best

et a

l28Ex

enat

ide

b.i.d

. (n

= 2

1 75

4)no

n-ex

enat

ide

(n=361771)

Inc

ATNR

Non-fatalMI,ischemicstrokeor

coro

nary

reva

scul

aris

atio

n H

R 0.80(0.74-0.86)

CV

rela

ted

hosp

italis

atio

nHR0.88(0.79-0.98);

All-causehospitalisation

HR

0.94

(0.9

1- 0

.97)

ITT

CVDevent:HR0.86;0.81-0.92

CV-

rela

ted

hosp

italis

atio

n:HR0.88;(0.79−0.98)

All-causehospitalisation:

Mogensen

et a

l29MET+GLP-

1(n

= 43

45)

vsMET+SU(n=25

092),

Inc

AT2.

1 y.

RR 0

.77

(0.5

1-1.

17)

RR0.82(0.55-1.21)

CVmortality0.89

(0.47-1.68);

Mat

ched

coh

ort:

MET+GLP-1

RAs(n=1901)

vsMET+SUs

(n =

190

1)

ITT

RR 0

.70

(0.4

0-1.

23)

RR0.64(0.34-1.20)

Paul

et a

l30ex

enat

ide

BID+OADs

(n=2,804);vs

exen

atid

e +

insu

lin

(n=7,870);

insulin+OADs

(n=28,551)

Inc

ITT

3.5

y.MI+stroke:

Fullcohort:0.50(0.32,0.79)

With

out p

revi

ous

CV

D:

0.33(0.18-0.63)

With

out p

revi

ous

CV&

rena

l D:

0.33(0.18,0.63)

Fullcohort:

Non-fatalMI:0.52(0.23,

1.19);Non-fatalStroke;

0.50(0.28-0.84)

NopreviousCVD:

Non-fatalAMI0.24(0.06-

0.99

); no

n-fa

tal s

trok

e 0.

37

(0.180.75)

Fullcohort:0.34

(0.2

2- 0

.52)

NopreviousCVD:

0.34

(0.2

2- 0

.52

NopreviousCV

& R

enal

D: 0

.32

(0.2

1- 0

.50)

exen

atid

e BI

D +

insu

lin

(n=7,870);vs

insulin+OADs

(n=28,551)

Fullcohort:0.44(0.34,0.57)

With

out p

revi

ous

CV

D:

0.33(0.23,0.47)

With

out p

revi

ous

CV&

rena

l D:

0.32(0.22,0.46)

Fullcohortnon-fatalMI:

0.65(0.44,0.98)

NopreviousCVD:Non-fatal

AMI0.52(0.31,0.91)

NopreviousCVandrenalD

0.45(0.28,0.85)

Fullcohort:0.40

(0.32,0.50)

With

out p

revi

ous

CV

D:

0.40(0.3-,0.50)

With

out p

revi

ous

CV&

rena

l D: 0

.35

(0.28-0.45)

Velezetal31

Incr

etin

age

nts

(GLP-1RAs

n =

205)

vs n

ot d

efin

ed

cont

rol g

roup

(n=2,798)

Inci

dent

G

LP-1

ag

ents

ITT

2.03

y.

(742

day

s)H

R 0.

17

(0.0

2-1.

22)

1event,no

estim

ator (C

ontin

ues)


Aut

hor,

yrSt

udy

and

com

para

tor d

rugs

Des

ign

Expo

sure

an

alys

isFo

llow

-up

(yea

rs)

Resu

lts A

ll-ca

use

mor

talit

yRe

sults

MAC

ERe

sults

CV

eve

nts

Resu

lts H

HF

Pato

rno

et a

l 32MET+GLP1RAs

(n=14466)

vsMET+SU

(n=114.480);

vsMET+DPP-4

i(n=18658vs

n=69807);

vsMET+insulin

(29343vs42982)

inci

dent

ITT

0.8y

vsDPP-4i1.02(0.84-1.24)

vsSUs0.86(0.69-1.09)

vsinsulin0.72(0.62-0.84)

AftermatchingforPSthatincludedHbA1:

vsDPP-4iHR1:20(0.76-1.89)

vsSUs:HR1.05(0.63-1.74)

vs in

sulin

: HR

1.01

(0.7

3-1.

74)

AT8.7y

vsDPP-4iHR0.94(0.75-1.18)

vsSUsHR0.78(0.59-1.04)

vsinsulinHR0.88(0.62-1.26)

Kan

nan

et a

l 33MET+GLP-1RAs

(n =

433

)vsMET+SUs

(n =

941

9)

inci

dent

ITT

4 y

HR

0.57

(0

.30-

1.07

)C

oron

ary

arte

ry d

isea

se:

HR

1.11

(0.9

9-1.

24)

Con

gest

ive

Hea

rt

failu

re: H

R 1.

11

(0.9

9-1.

22)

Ekst

röm

et

al34

MET+GLP-1ra

agen

t(s) (

n =

219

GLP-1RA)

MET+SU(n=4293)

inci

dent

ITT

3.6y

HR0.88

(0.3

5- 2

.25)

CVD:HR0.26(0.10-0.67)

CVD0.26(0.10-0.67);

fata

l CV

D 0

.20

(0.0

3- 1

.45)

:CHD0.31(0.12-0.81);fatalCHD0.17(0.02-1.22);

AT

Anyanwagu

etal201635

MET-SU+GLP-1

RAs(n=419)

MET+SU+insulin

(n=1584)

Matchedcohorts:

n =

419

each

inci

dent

ATFullcohort

3.74

y†

FUupto

5 ye

ars

Full

coho

rt H

R 0.21(0.08−0.51)

BMI ≥

30

kg/

m2 : H

R 0.

24

(0.1

0- 0

.59)

BMI ≥

40

kg/

m2 : H

R 0.

33

(0.1

2-0.

92)

MACEPSmatchedcohort

(n =

419

)HR0.27(95%CI0.28,0.97)

BMI≥30kg/m

2 : HR

0.31

0.16-0.61

BMI≥40kg/m

2 : HR

0.31

(95%

CI

0.13,0.75)

MI

Fullcohort:HR0.45(0.12-1.69)

BMI≥30kg/m2:HR0.57(0.15-2.14)

BMI≥40kg/m2:HR0.33(0.03-3.29)

Stro

keFullcohort:HR0.39(0.10-1.44)

BMI≥30kg/m

2 : HR

0.45

(0.1

2- 1

.72)

BMI≥40kg/m

2 :HR0.48(0.8-2.8)

Suis

sa e

t al 36

GLP

-1 ra

(firs

t use

2007)(n=6196)

SUs(firstuse2000)

(n=79682)

Prev

alen

t ne

w u

sers

ITT

> 2

yHHF0.73(95%CI:

0.57

-0.9

3

Zim

mer

man

et

al37

GLP-1RAs

(n=8362)

Non-GLP-1

(n =

97

500)

Prev

alen

t +

inci

dent

AT1.2(0.4,2.8)y

Full

coho

rt:All-

caus

e m

orta

lity

HR0.48(0.41-

0.57

) Prio

r C

VD

:All-cause

mor

talit

y H

R 0.

70 (0

.50-

0.99

)

Full

coho

rt:

HR0.82(0.74-0.91)

Prio

r CV

D:HR0.76(0.63-0.92;

Full

coho

rt:

Non-fatalAMI0.80

(0.65-0.99);

non-fatalstroke0.82

(0.7

4- 0

.91)

;Pr

ior C

VD

:Non-fatalAMI

0.78(0.54-1.12;

non-fatalstroke0.76,95%

CI0.63-0.93;

TAB

LE 2

(C

ontin

ued)

(Con

tinue

s)


Aut

hor,

yrSt

udy

and

com

para

tor d

rugs

Des

ign

Expo

sure

an

alys

isFo

llow

-up

(yea

rs)

Resu

lts A

ll-ca

use

mor

talit

yRe

sults

MAC

ERe

sults

CV

eve

nts

Resu

lts H

HF

inci

dent

HR0.80(0.47,

1.34

)HR0.49(0.34,0.69)

AMIHR0.53(0.21,1.30)

StrokeHR0.54(0.38,0.77)

prev

alen

tHR0.46(0.38,

0.55

)HR0.86(0.78,0.96)

AMIHR0.82(0.66,1.03)

StrokeHR0.20(0.18,0.23)

Toul

is e

t al38

GLP-1RAs

(exenatide,

liraglutide,

lixis

enat

ide)

(n=8345)vs

unex

pose

d toGLP-1RAs

(n=16541).

inci

dent

ITT

2.72

y(3

2.7

mo.

)Fu

ll co

hort

:aIRR0.64

(0.56-0.74),

low

-ris

k pa

tient

s (aIRR:0.64

(0.53-0.76)

in lo

w-r

isk

patie

nts

(aIR

R:

0.93(0.83-1.12).

Lira

glut

ide

aIRR

0.56(46-0.67

Exen

atid

e 0.

72

(0.6-0.85)

Anyanwagu

et a

l39in

sulin

+ G

LP-1

ra(exenatide,

liraglutide,

lixis

enat

ide)

179

3 vsinsulin+OADs

(PS

mat

ched

coh

ort)

N=1793vsnon-

expo

sed

to G

LP-1

ra

inci

dent

AT3.

2 y

(SD

: 3.

07)

TotalFUup

to 5

y

Coho

rt w

ith n

o pr

ior C

V e

vent

s:

0.35

(0.1

7-0.

73)

Full

coho

rt:MACE:0.64

(0.42-0.98

prio

r CV

eve

nts:

Com

posi

te

of n

onfa

tal C

V e

vent

s:

0.76(0.41-1.42)

AMI:0.62(0.06-6.53)

Non-fatalstroke:0.93

(0.54-1.61)

Full

coho

rt: 1

.22

(0.76-1.94)

Lira

glut

ide:

0.1

7 (0.06-0.0.47)

Exen

atid

e: 0

.49

(0.2

4—0.

99)

Lixi

sena

tide:

-

Liraglutide:0.53(0.32-0.89)

Exenatide:0.81(0.50-1.31)

Lixi

sena

tide:

-

Liraglutide:0.48(0.22-1.04)

Exenatide:0.75(0.36-1.56)

Lixi

sena

tide:

-

Lira

glut

ide:

1.2

0 (0.69-2.08)

Exen

atid

e: 1

.22

(0.66-2.24)

Lixisenatide:1.66

(0.2

3-12

.13)

Pato

rno

et a

l4020

539

pai

rs in

itiat

or

ofcanagliflozinora

com

para

tor

inci

dent

AT0.6y(0.5)

HR

0.1.

29

(0.5

4-3.

13)

(Acutemyocardialinfarction,

ischemicstroke,or

haem

orrh

agic

str

oke)

HR

0.97

(0.7

4-1.

27)

MI:HR0.97(0.69-1.37)

Stroke:HR0.93(0.61-1.45)

UA:HR1.37(0.85-2.17)

CR:HR0.99(0.76-1.29)

HR:(1.28-2.13)

ITT

1 y

HR1.03(0.81-1.33)

HR:1.47(1.16-1.85)

TAB

LE 2

(C

ontin

ued)

(Con

tinue

s)


Aut

hor,

yrSt

udy

and

com

para

tor d

rugs

Des

ign

Expo

sure

an

alys

isFo

llow

-up

(yea

rs)

Resu

lts A

ll-ca

use

mor

talit

yRe

sults

MAC

ERe

sults

CV

eve

nts

Resu

lts H

HF

Daw

was

et

al41

DPP

-4 i

vs G

LP-1

RAs

(321,606patients,

each

mat

ched

cohort160803)

inci

dent

ATFU170(±290

d.) i

n D

PP4

i and

159

(±285d.)in

GLP-1RAs

grou

p

HR:1.16

(1.1

1- 1

.20)

NoHFhistory:1.18

(1.1

2-1.

22)

PriorHFhistory:

1.11

(0.9

3-1.

35)

Svan

strö

m42

Lira

glut

ide

vs D

PP-4

i w

ith p

revi

ous/

basalMET

inci

dent

ITT

3.5

y (S

D

2.1)

for

lirag

lutid

e an

d 3.

2 y

(2.0

) for

DPP

-4

inhi

bito

rs.

HR0.83,

(0.7

7-0.

90)

MACE:HR0.90(95%CI

0.83-0.98)

historyofmajorCVD:0.81

(0.7

1-0.

92)

withoutCVD:0.96(0.86-1.06)

ExpandedMACE(+other

ischaemicheartdisease,

coronaryrevascularisation,and

perip

hera

l art

eria

l dis

ease

.) (H

R 0.95,(0.89-1.01).

CVdeathHR0.78

(0.68-0.91),

MI0.94,(0.84-1.06)

stroke0.88(0.77-1.01)

HR0.90(0.80-1.03)

AT

(sen

sitiv

ity

anal

ysis)

2.1

y(SD1.8)for

lirag

lutid

e an

d 1.

7 y

(1.6)

ForDPP-4i

HR0.84(95%CI0.75-0.94)

Fullcohortwithmultivariate

anal

ysis

inst

ead

of P

S m

atch

ing:

HR0.88,95%CI0.82-0.94)

O’B

rien

et a

l43

n =

11 3

51

GLP-1RAassecond-

line

afte

r tak

ing

METaloneOR

noADMvsDPP-4

as re

fere

nce

inci

dent

ITT

Upto2y

Mean1.3y.

HR:0.78(0.63-0.96)

PriorCVevents:0.78(0.43-1.42)

NohistoryCVevents0.81

(0.65-1.01)

PriorMET0.92(0.90-1.22)

NopriorMET0.66(0.48-0.90)

Adherentpatients:0.74

(0.5

0-1.

10)

Stroke0.65(0.44-0.97)

IHD0.91(0.67-1.24)

PAD0.90(0.42-1.95)

0.65(0.43-1.02)

ATTime-varyingmodel:0.81

(0.61-1.08)

Not

e: Allestimates(95%CI).

Abbreviations:AMI,Acutemyocardialinfarction;AT,As-treatedapproach;BMI,Bodymassindex;CR,Coronaryrevascularisation;CV,Cardiovascular;CVD,Cardiovasculardisease;DPP-4i,Dipeptidyl

peptidase4inhibitors;FU,Follow-up;GLP-1RA,Glucagon-likepeptide1receptoragonists;HF,Heartfailure;HHF,Hospitalisationsforheartfailure;HR,HazardRatio;INS,Insulins;ITT,Intention-to-

treat;MACE,Majoradversecardiovascularevents;MET,Metformin;MI,Myocardialinfarction;PS,Propensityscore;SU,Sulphonylureas;UA,Unstableangina.

† 5.19y.forINS;1.42forGLP-1RAs.

TAB

LE 2

(C

ontin

ued)


TAB

LE 3

CharacteristicsofpopulationsexposedtoGLP-1RAsinobservationalcohortstudies

Stud

y id

entif

icat

ion:

A

utho

rm

ean

age

% m

enT2

DM

dur

atio

nm

edia

n H

bA1c

% h

isto

ry o

f C

VD

% H

yper

tens

ion

% h

isto

ry H

F

%

hist

ory

stro

ke%

his

tory

MI

curr

ent o

r pas

t sm

oker

sBM

I kg/

m2

Best

et a

l2852.7±8.7

(exe

natid

e)43.8%

NA

NA

12.7

% IH

D65.4%

3.5%

0% †

0% †

NA

Obese16.2%

exen

atid

e an

d 9.

1%

non-

exen

atid

e

Mogensen

et a

l29total60.6±13

y; G

LP-1

54.3±11.7;

53.5

(11.

9)

in m

atch

ed

coho

rt

Tota

l 59%

GLP-156.1%

(initi

atio

n m

onot

hera

py

(IQR)

2.0

y (0

.4-4

.5 y

); G

LP-1

3.0

(1.4

-5.1

)

NA

IHD

: tot

al

6,1%;GLP-1

5.6%

NA(useofARA

Tota

l: 3.

2;

GLP-12.6%

0% †

0% †

NA

NA

Paul

et a

l3058y.GLP-1

tota

l 59%

1.0(0.2,1.8)y

7.6%

IHD6.53%

83%(useof

anti-

hype

rte

nsiv

es)

1.71

%0.

50%

0.78%

51%

≥3089%intheEBID

and

EBID

+ in

sulin

groupsand65%in

insu

lin g

roup

Velezetal31

(re

fers

to

incr

etin

th

erap

ies)

60.0±11.4

45.4

%2.

47 y

7.98±1.77

1.6%-

45%

0% †

0%0.

2%NA

NA

Pato

rno

et a

l3250

.7 to

53.

247

.2 to

60.0.6%

NA(proxiesas

indi

cato

rs o

f dia

bete

s se

verit

y)

7.8to9.9%

7.6to13.1%

44.4

%1.

2%0.

4%NR

5.6to7.7%

11.2

to 2

1.5%

Ekst

röm

et a

l33GLP--1:56.0y;

SU:62.9y

55.7

%GLP-1RA5.0(2.0,8.0)

y;SUs5.0(3.0,8.0)y

GLP-1ra7.8%

(3.4);SUs7.7%

(3.2

)

GLP

-1 ra

12.8%;SUs

15.2

%

75.3

% (u

se o

f ant

i-hy

pert

ensi

ves)

GLP

-1 ra

4.1

%;

SUs3.8%

NR

NR

current18,1%

Mean34.9(5.1):

SUs30.7(4.8)

Kan

nan

et a

l3460.6±12.6y;

MET+GLP-1

RAs55y,

MET+SUs

62y

42%

: for

MET+GLP-1

and

54.9

% fo

r MET+SU

NR(proxies:

neuropathy,

retinopathy,CKD)

NR

0% †

42.5

%0%

(exc

lusi

on

crite

rium

)2.8%

0% †

MET+GLP-1

RAs:Current

9.5%andNon-

smok

ers

52.3

%

ForMET+SU

15.8%and

46.7%

MET+GLP--1RAs

median36.98

(32.2,43.0):

MET+SUs32.28

(28.2,37.5)

Anyanwagu

et a

l3549.60±

10

.5 y

;54

.2%

mea

n 2.

7 y

(3.0

)9.

4(2.

0) %

1.6%for

othe

r C

oron

ary

Hea

rt

Disease,

50.6%(useofanti-

hipe

rten

sive

s)0.

2%NR

NR

Cur

rent

22.

4%Pa

st s

mok

er

38.4%

meanBM

I39.6(7.1)

kg/m

2

Suis

sa e

t al36

56.0±10.5

54.9

%5.2±3.0y

≤7%:

7.1%

-8.0%:

>8%:

6.7%

15.6%

77%

23.2

%63.0.6%

4.6%

3.1%

NR

57.5

%O

bese

93.

2%

Zim

mer

man

et

al37

Median(IQR)

61(51,70)

51.8%

NA

NA

13.3

%65%

NA

5%1.

7%current12%,

form

er 2

7%medianBM

I(IQR)

32.1(27.9,37.6)

Toul

is38

57.5

(10.

4)54.6%

9.8(5.7)

8.4(3.9)%

16.5%

63.8%

3.8%

5.3%

NR

Cur

rent

14.

1%37

.4 (7

.1) (C

ontin

ues)


the same treatment lines (ie, first, secondor third-lines therapies)comparetreatmentsatthesamestage,whileinstudiescomparingthe use of a drug vs non-use there is a source for time-lag bias. In oursearch,sixstudiescomparedsecond-linetherapies: theuseofMET+GLP-1RAsvsMET+SUs,28,31–33vsotheragents,31 liraglu-tide vs DPP-4 i41 and vs DPP4 i.42 One study compared third-line therapies,34 and other the intensification either with insulin or GLP-1 RA.38

3.5 | Outcomes

3.5.1 | All-cause mortality

Ten studies estimated all-cause mortality as primary or second-ary endpoint,28,30,32–34,36–39,41: hazard ratios (HRs) ranged from0.17 (95% CI 0.02-1.22)30 to 1.29 (95% CI 0.54-3.13).39 Significant HRsspannedfrom0.21(95%CI0.08-0.51)(third-lineGLP-1RAsvs insulin therapy33) to 0.83 (95%CI 0.77-0.90),/ liraglutide vsDPP-4 i41). In four studies comparingGLP-1RAs vs non-GLP-1RAs30,36–38 significant estimators ranged from HR 0.35 (0.17-0.73)to0.64(0.56-0.74):liraglutidesubgroups37,38 and prevalent users36 showed more beneficial results than exenatide and in-cidentusers, respectively.Threestudiescomparingsecond-linetherapiesMET+GLP-1RAsvsMET+SU28,32,33 obtained non-significant results. The study comparing canagliflozin to GLP-1RAs39(meanfollow-up(FU)0.5year),obtainedthehighestnon-significant estimator benefiting canagliflozin. (Table 2) The pri-maryMAexcludedtwostudieswithlackofalignmentofthestartofexposurewithstartofFU.34,37Whenpoolingresults,irrespec-tiveofdifferentstudydrugsandcomparators,all-causemortalityfavouredGLP-1 RAs, HR 0.63 (0.44-0.89) (with high between-study variability I2 =83.3%). (Figure3) In a sensitivity analysis,theadditionofthetwopreviouslyexcludedstudies,witha6-mo.and 9-mo. delays, respectively, between the start of exposureandthestartoftreatmentdidnotalterresults (HR0.61, [0.44-0.77])(Figure4).

3.5.2 | Cardiovascular events

Eleven studies estimated non-fatal cardiovascular events and/or MACEnot includingdeaths; for individualCVevents,HRs rangedfrom0.24 (95%CI 0.06-0.99),with insulin as reference,29 to 0.82(95% CI 0.74-0.91)37 (stroke in prevalent users vs non-GLP-1 use). MACE includingnon-fatal events ranged from0.27 (0.28-0.97) vsinsulin34to0.80(95%CI0.74-0.86)vsnon-GLP-1use.27 Six stud-iesassessedMACEincludingmortality,significantHRrangedfrom0.26 (95%CI0.1-0.67)32 to0.90 (95%CI0.83-0.98) for liraglutidevs DPP-4 i.41 (Table 2) Pooled results of nine studies suggest a ben-efitofGLP-1RAsoncardiovascularoutcomes(HR0.82,0.73-0.92)(high between-study variability: I2=75.5%)(Figure3).Thesensibilityanalysisdidnotvaryresults(Figure4).St

udy

iden

tific

atio

n:

Aut

hor

mea

n ag

e%

men

T2D

M d

urat

ion

med

ian

HbA

1c%

his

tory

of

CV

D%

Hyp

er te

nsio

n%

his

tory

HF

%

hist

ory

stro

ke%

his

tory

MI

curr

ent o

r pas

t sm

oker

sBM

I kg/

m2

Anyanwagu

et a

l3962.7(13.8)y

53.2

%4.

9 (4

.5)

8.6%

(1.8%).

20%

90%

use

of a

nti-

hype

rten

sive

s6%

0% †

0% †

Current13%,

former38%

32.4(6.9)

Pato

rno

et a

l40

n =

20.5

3956.7(10.8)

52.8%

NA

8.8(1.8)%

IHD11.8%

53.5

%3.

4%1.

2%C

oron

ary

Reva

scul

-arization

2.3%

7.4%

22.3

%

Daw

as e

t al41

n=160.803

53 (1

0.2)

44.1

%NR

NR

12.5

%33.%ACEi

14.9

% C

CB

0% (in th

e pr

evio

us

60d.)

NR

NR

NR

NR

Abbreviations:AMI,Acutemyocardialinfarction;BMI,Bodymassindex;CV,Cardiovascular;CVD,Cardiovasculardisease;DPP-4i,Dipeptidylpeptidase4inhibitors;EBID,Exenatideb.i.d.;GLP-1

RA,Glucagon-likepeptide1receptoragonists;HbA1c,Glycatedhaemoglobin;HF,Heartfailure;IHD,Ischemicheartdisease;INS,Insulins;IQR,Interquartilerange;MET,Metformin;MI,Myocardial

infarction;NA,Notavailable;NR,Notreported;SU,Sulphonylureas;T2DM,Type2diabetesmellitus.

†It i

s an

exc

lusi

on c

riter

ium

.

TAB

LE 3

(C

ontin

ued)


3.5.3 | Hospitalisation for heart failure

Eight studies assessedHHF, being the only outcome evaluated intwo studies (vs sulphonylureas35 and vs DPP-4 I40). HRs ranged from 0.12(95%CI0.02-0.66)forMET+GLP-1RAsvsMET+SUs32 to 1.64 (95%CI1.28-2.13) forHFwhencomparedwithcanagliflozin(GLP-1RAsweretakenasreference,canagliflozinshowedariskre-duction of 39%)39SignificantHRsrangedfrom0.12(0.02-0.66)32 to 1.16(1.11-1.20)vsDPP-4i.40Whenpoolingresults,weobtainedaHR0.94(0.78-1.14)(between-studyvariabilityI2=90.1%)(Figure5).

3.6 | User design

The incident user design identifies patients starting a new treat-ment,FUbeginsaftertreatmentinitiation.Theincidentuser'sdesignreduces thestudysize,andthus,compromise theprecisionof thecomparative estimates.43–45

Patients were incident new users in all studies but two.35,36 Zimmerman et al36 selected both current and incident users of GLP-1 RAs(vsnon-GLP-1RAsuse).Suissaetal35 proposed the prevalent new-usercohortdesigntocompareGLP-1RAs—irrespectivetothe

F I G U R E 3 Meta-analysisofobservationalcohortstudiesperformedinLHDsassessingall-causemortality(A)andCVevents(B)inType2DMpatientstreatedwithGLP-1RAs.A,all-causemortality.B,CVevents

(A)

(B)

F I G U R E 4 Meta-analysisofobservationalcohortstudiesperformedinLHDsassessingall-causemortality(A)andCVevents(B)inType2DMpatientstreatedwithGLP-1Ras.Sensitivityanalysisincluding16studies.A,Sensitivityanalysisofall-causemortality.B,Sensitivityanalysis of cardiovascular (CV) events

(A) (B)


previoususeofthecomparatordrug—toSUsincidentorprevalentusers: a purely incident new-user design would have restricted the 6196GLP-1RAsuserstoonly1633newusersofGLP-1RAs.TheGLP-1RAsuserswerematchedtoSUsusersemployingatime-con-ditional propensity score matching based both on time and number ofprescriptions,tocomparepatientswiththesameseverityofthedisease.35

3.7 | Exposure ascertainment

Intheintention-to-treatapproach(ITT),theinitialdrugexposureisas-sumedtobeunchangeduntiltheendofFU,irrespectiveoftreatment'schanges.46Theas-treatedapproach(AT) isconsideredmoresuitablefortheexposureascertainmentinobservationalcohortstudies;theATapproach computes the specific drug exposure's time from exposure's startuntilthedrug'sdiscontinuation.Forthemainanalysis,eightstud-ies used the ITT approach29–33,35,37,42andeightstudiesusedtheATapproach.27,28,33,36,38–40 Seven studies used the opposite approach as asensitivityanalysis,andresultswereconsistent.27,28,31,32,39,41,42

3.8 | Covariates

In most studies, covariates for matching and/or adjustment in-cluded demographics, clinical and laboratory data, concomitantmedications (antidiabetic, anti-hypertensives, lipid-loweringtherapies, antiplatelet therapy andother cardiovascularmedica-tions) co-morbidities. Some included socio-economic factors and durationofdiabetes,aninteractiontermtoaccounttheeffectofstatin use and LDL cholesterol.36 Best et al selected more than 300covariates,amongthem,thetop50ofeachpre-exposurepa-tient characteristics. In Patorno et al,31 three propensity score-matched cohorts included matching of more than 200 patients’ characteristics.

Covariate information should be assessed in the longitu-dinal health care data in the time preceding the cohort entry. “Intermediates” covariates should be assessed a defined time be-fore the inclusion date because they can represent an effect of treatment or a reason for the prescription.47 In different studies,covariates were derived from the pharmacy and medical claims ren-dered during the 9 months preceding cohort entry27 or during the 6monthsprecedingdruginitiation31,39; Charlson co-morbidity score 10yearspriorthebaseline,andconcomitantCVtherapieswithinthelast year.28PauletalincludedmeasuresofHbA1cwithina3-monthwindow before the index date.29Inothers,baselinevariableswereretrievedclosesttothedateofdualtherapy'sstart;comorbidities,including CAD and CHF,were screened back up to 10 years ear-lier.32,34 In Anyanwagu 201634 study covariates were collected at least180daysbeforeintensificationofMET+SU.TableS4showscovariates used in the different studies.

3.8.1 | Study duration

study periods ranged from 2.539 to 14 years.35(Figure5)Twostud-ies30,35 started in2000,while the firstGLP-1RA,exenatideb.i.d.,was marketed in 2005. The chart of studies’ periods and the most relevantdrugapprovalsareshowninFigureS1.

3.9 | Follow-up

For the primary analysis, the shortest mean FU periods were0.44 years40(studydurationof7.5yrs.)and0.6years.39 (study dura-tion2.5years,with55%-68%ofpatientscensoredbecauseoftheendofstudy),toupto4years32ThemostprolongedFUperiodwas8.7 years31 (AT approach, secondary analysis), and in two studieswas up to 5 years.34,38

3.10 | Diabetes duration

Diabetesusuallyworsenwith longerduration,and the risk forCVdisease increases. Diabetes duration is reported in eight stud-ies28–30,32,34,35,37,38 and not-reported in the remaining ones. Proxies ofdiseaseseverity,suchasdiabetescomplications,arereportedintwo studies (Table 3).31,33

3.11 | Bias

WeusedtheROBINS-Iassessmenttooltoassesstheriskofbias.26 Allstudiesconductedinlargelongitudinaldatabasesaresubjecttoinformation bias and have a risk of non-differential exposure mis-classification and outcome misclassifications, that could be morelikelyinclaimsdatabasesthaninEMRs.22 Studies with a specific risk of bias are mentioned below.

F I G U R E 5 Meta-analysisofobservationalcohortstudiesperformedinLHDsassessinghospitalizationforheartfailure(HHF)inType2DMpatientstreatedwithGLP-1Ras


3.11.1 | Time-related bias

Threestudieswiththenew-userdesigndidnotstarttheFUatthesametimewiththestartoftreatment.EkströmstartedtheFUatthesecondfilledprescriptionoftheagentaddedtoMET;adelaynottemporallyde-fined,andconsiderednottobeimportant.Toulisetalsettheindexdatefor each exposed patient as the date of the fourth quarter consecutive GLP-1RAsprescriptions(atleast9monthsafterthestartofGLP-1RAstherapy): this would imply a selection bias for tolerant/responsiveness or adherent patients and could have affected outcomes’ ascertainment. Besides,patientswithlessthanfourquarterconsecutiveprescriptionscould be part of the non-exposed group (risk of misclassification).37 In bothstudies,“immortal-timebias”(ie,person-timethatisevent-freebydefinition) was avoided by matching the unexposed cohort at the index dateoftheirrespectiveexposedpatients,andassigningthesameindexas their respective exposed patients.

In Anyanwagu 2016,34 outcomes must have occurred at least 180daysafter the intensificationofMET+SUwitheither INSorGLP-1RA; although therewerenodeaths in this period, theout-come's ascertainment could have been affected.

In the study comparing canagliflozin to non-gliflozin, amongthemGLP-1RAs,thereisariskofattritionbiasbecause55%-68%ofpatients were censored because of the end of the study.39

3.11.2 | Selection bias

If patients are excludedbasedon future events, there is a risk ofselectionbias.Astudyrequiredthecompletenessofinformationoneventdates forHF,MIandstroke following the indexdate,29 and inclusion criteria required the availability of two prescriptions within 60daysofindexdate,thispotentialtemporalbiaswascontrolledbyadjusting the time to event for the survival analyses to avoid possible immortal-timebias.Inotherstudies,patientshadtorefillaprescrip-tionofthesecondglucose-loweringagentwithin180daysandmain-taincontinuoustreatmentwithMETthroughoutthe180days34; or had to have at least two encounters after initiating the combination therapy to be included.32 InZimmermanet al,36 patients were in-cluded if they received T2 diabetes medications during the study period and if they had to have at least two medical visits. In a study thatdefinedthecohortentrybyinsulinintensification,(ie,eithertheadditionofGLP-1RAsorOADstobaselineinsulin),theanalysisoftwopointsMACE(MIandstroke)wasrestrictedtomatchedcohortsexcludingallpatientswithaCVeventbeforeor180daysafterin-sulin initiation.

3.12 | Methods to control for confounding

Inobservational studies, careful controlof confounding isneededbecause physicians chose treatments based on patients’ character-istics,andtherefore,cohorts’characteristicsdiffer.Propensityscorematchingwasthemostfrequentlyusedmethod,butnotexclusively,

since some studies used multivariable analyses or combinations of both. The differences that can probably be more relevant in terms of impact on results refer to the use of time-dependent variables in the analyses and the management of missing values. Table S5 shows covariates,methodsandmissingdataapproachusedineachstudyreporting.

4 | DISCUSSION

In our systematic search of retrospective cohort studies performed inEHDs,therewasalowriskofpublicationbias.Otherrisksofbiashave been mentioned previously. All studies were performed inWesternEHDs(US,UK,SwedenandDenmark)andincludedlowCV-riskpatients,beingwidelyrepresentativeofthe“real-world”T2DMpopulation.Sixstudiesexcludedpatientswithpreviousevents,ap-proximately67%.TheresultsofourMAssuggestabeneficialeffectofGLP-1RAsonall-causemortalityand,toalesserextent,onCVeventsandneutralresultsontheHHF.

InCVOTsassessingtheeffectsofGLP-1RAsonMACEinhighCV-risk populations, liraglutide (LEADER) has shown significantresults in reducingMACE, all-causemortality andCVmortality;semaglutide (SUSTAIN-6) and dulaglutide (REWIND) reducedMACE and stroke; albiglutide (HARMONY) significantly reducedMACE(includedHHF);exenatideQW(EXSCEL)reducedall-causemortality,andoralsemaglutidereducedCVmortalityandall-causemortality.14–20NoCVOThasbeenperformedforexenatideb.i.d.Resultsofmeta-analysesofRCTs—includingresultsofSUSTAIN-6suggested12% lowerriskofall-causemortality,12%-16%ofCVmortalitywiththeuseofGLP-1RAsinT2DMpatients,anda10%riskreductionforMACE.48–52(TableS6)AstudypointedoutthatonlyasmallproportionofUSpopulationcouldbeincludedinmostoftheCVOTsassessingGLP-1RAs,andbecauseofthis,theresultsofCVOTscouldnotapplytoUST2DMgeneralpopulations.53 We found that pooled results of the observational studies included in thisreviewaretrendedinthesamedirectionthanthoseofMAsofRCTs,althoughtheyhadamuchlargersizeforall-causemortalityandCVevents.However,individualresultsvaryinmagnitudeand,insomecases,alsoindirection.

DifferencesofdifferentGLP-1RAsonCVoutcomeshavebeenexplained not only because of the respective T1/2 that could im-pactonglycemiccontrol,butalsobecauseoftheactiononotherorgans.7,54,55 In our search, study drug(s) varied over time, fromexenatide b.i.d. in the oldest studies27,28 to agents with more pro-longedaction,mainlyliraglutideandexenatidelong-actingrelease(LAR)inthestudiespublishedmorerecently.Twostudies—assess-ing only exenatide b.i.d.27,29—obtainedHR0.81andHR0.44 forMACEnot includingdeaths.Thestudyevaluatingliraglutidevsaneutralantidiabeticdrugclass,DPP-4i,41 got HRs similar to those ofLEADERtrial,althoughcharacteristicsoftheirpopulationswerenot (100%highCV-riskpatients−81%withestablishedCVD—inthe LEADER and 16% of patients with ischemic heart diseasein the observational study). In two studies reporting subgroup


analysis37,38 liraglutide has obtained better estimates than exen-atideLAR(Table3).Noneofthestudiesincludedinthissystematicreviewassessessemaglutide,whichhasbeenmarketedaftertheend of studies periods.

Thechoiceofcomparatordrugcanalsoimpactonresults,eitherbecauseoftheireffectsonCVoutcomesandHFortheconfoundingforindication,notenoughcontrolled.

It has been suggested that the decrease in major CV events ob-servedinRCTswithGLP-1RAscouldbeasaresultofanantiathero-genic effect, and thus, relatedwith durationof exposure.55 In the sameway,anestedcase-control study found theeffectof liraglu-tide was dose and duration dependent.56 Zimmerman et al included incidentandprevalentusersofGLP-1RAs; forall-causemortality,the subgroup of prevalent-users obtained more benefit from the use ofGLP-1RAs thanoneof the incident users; theoverallHR0.48(0.41-0.57)wasdrivenbyprevalentusers,whatcouldberelatedtomore prolonged exposure. Kannan et al, with the most extendedmeanFUandanITTapproach,obtainedanon-significantestimateforall-causemortality (HR0.57 [0.30-1.07]). InonestudywithATapproach results for CVOs and all-cause mortality were similar at a year,butbecamesignificantlydifferentafter5yearsofFU.38 This delayedbenefitwouldbeconsistentwiththefindingoftheLEADERstudy,15withamedianFUof3.8years,inwhichthebeneficialeffectof liraglutide on CV mortality and all-cause mortality became evident about12-18monthsafterstartedthetreatment54 (a similar—although smaller—difference has become apparent in the EXSCEL trial—me-dianFU—12monthsafterexenatideLARtreatmentstarted).17

Six studies analysed subgroups of prior o no history of CV events. Three studies estimated benefits for subgroups with no previous CV events,and three found thatpatientswithahistoryofCVeventshad better outcomes than the no-history of CV events.

In observational cohort studies assessing chronic and progres-sive conditions, such diabetes, the ascertainment of exposure(s)period (s) during FU can be blurred by drug discontinuation, in-tensificationwith other drugs, and/or switching to other antidi-abetic agents. The “as-treated” approach for the ascertainmentofexposureismoresuitablebecausethe“intention-to-treat”ap-proach can introduce exposuremisclassification. For exposures’ascertainment, in the studies included in this review, the choiceof either the ITT orAT strategies seems not to have influencedthe results. Patorno et al31 used an ITT analysis of exposure withashortperiodofFU(upto1year,meanFU0.8years)inPSmatchedcohorts;anATapproachandaFUof8.7years,obtainedasimilarnon-significantestimate.AnotherstudywiththeITTap-proach28 that reappraiseddata inanATanalysis, themedianFUwas2.1years,resultswereconsistent.

This systematic review has several limitations. Studies have substantialdifferencesinsizeandnumberofcovariatesincludedin the matching and/or analysis. Study drugs and comparators also differ, some studies consider individual GLP-1 RAs agents, andothers take the whole class; agents vary according to the study period,anystudyincludedpatientstreatedwithsemaglutidebe-cause of the date of its marketing authorisation. The small sample

size andnumberof events in some studiesdonot contribute torobusteffectestimates,resultinginwideCIs.Wehavejustsum-marised the differences in methods to control for confounding,with no further analysis; 10 large cohorts analysed propensity score-matched populations. The 37% risk difference in mortality obtainedinourMAisthreefoldtheoneobtainedinMAsofRCTs.Study populations are only from three European countries and the US, thuspooled resultswouldbenotgeneralisableacrossotherhealth care systems.

Onthecontrary, thissetofstudies represent theretrospectiveobservational research conducted in Western EHDs available at pres-ent.Thepooledresults,evenifoversised,areclinicallyrelevantandtrendinthesamedirectionfoundintheMAsofRCTs.Wedescribedandanalysedsimilaritiesanddifferencesinmanyaspects.Finally,thisisthefirstSRandMAofobservationalcohortstudiesconductedinEHDs,whereasanotheroneconsideredbothRCTsandobservationalresearch forHHF—57 and because of this add the “real-world evi-dence”ontheeffectsofGLP-1RAsonlowCV-riskpopulations.

5 | CONCLUSION

Inthisreviewofobservationalcohortstudiesinnon-selectedT2DMpopulations, pooled results suggest a beneficial effect of GLP-1RAs on all-causemortality and, to a lesser extent, onCV events,whiletheuseofGLP-1RAwouldhavenoimpactontheHHF.Theseresults are in line with those obtained in some CVOTs and meta-analysesofRCTs.However, themagnitudeofcombinedestimatesshouldbeconsideredwithcaution,becauseofinherentlimitationsof the non-randomised design, inter-study variability and residualconfounding. Further research rigorously conducted is needed toaddcomplementaryevidenceof theeffectsofGLP-1RAson lowCV-risk populations.

CONFLIC T OF INTERE S TThe authors have no conflicts of interest relevant to this article to disclose.

AUTHORS' CONTRIBUTIONSBothauthorscontributedequallyintheconceptionofwork,searchandselection,analysesandinterpretationofresults.XVGmadesta-tistical analysis. The manuscript was drafted by RHC and reviewed by XVG.

ORCIDRaquel Herrera Comoglio https://orcid.org/0000-0002-2810-1749 Xavier Vidal Guitart https://orcid.org/0000-0001-6705-4298

R E FE R E N C E S 1. Diabetesmellitus:amajorriskfactorforcardiovasculardisease.A

jointeditorialstatementbytheAmericanDiabetesAssociation;TheNational Heart, Lung and Blood Institute; The JuvenileDiabetes

https://orcid.org/0000-0002-2810-1749

https://orcid.org/0000-0002-2810-1749

https://orcid.org/0000-0002-2810-1749

https://orcid.org/0000-0001-6705-4298

https://orcid.org/0000-0001-6705-4298


Foundation International; The National Institute of Diabetesand Digestive and Kidney Diseases; and The American HeartAssociation.Circulation. 1999;100(10):1132-1133.

2. BuseJB,GinsbergHN,BakrisGL,etal.;AmericanHeartAssociation,American Diabetes Association. Primary prevention of cardio-vascular diseases in people with diabetes mellitus. Circulation. 2007;115:114-126.

3. LehrkeM,MarxN.Diabetesmellitusandheartfailure.Am J Cardiol. 2017;120(1S):S37-S47.

4. GilbertRE,KrumH.Heartfailureindiabetes:effectsofanti-hyper-glycaemic drug therapy. Lancet.2015;385(9982):2107-2117.

5. Bejan-AngoulvantT,CornuC,ArchambaultP,etal.IsHbA1cavalidsurrogate for macrovascular and microvascular complications in type 2 diabetes? Diabetes Metab. 2015;41(3):195-201.

6. Blind E, Janssen H, Dunder K, de Graeff PA. The EuropeanMedicinesAgency'sapprovalofnewmedicinesfortype2diabetes.Diabetes Obes Metab.2018;20(9):2059-2063.

7. NauckMA,Meier JJ, CavenderMA,Abd El AzizM,DruckerDJ.Cardiovascular actions and clinical outcomes with glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors. Circulation.2017;136(9):849-870.

8. BonnetF.GLP-1receptoragonistconfertargetorganprotectionintype 2 diabetes. Diabetes Metab. 2017;1:2S1-2S2.

9. SikiricaMV,MartinAA,WoodR,LeithA,PiercyJ,HigginsV.Reasonsfor discontinuation of GLP1 receptor agonists: data from a re-al-world cross-sectional survey of physicians and their patients with type 2 diabetes. Diabetes Metab Syndr Obes. 2017;29(10):403-412.

10. DaviesM,PieberTR,Hartoft-NielsenML,HansenOKH, JabbourS, Rosenstock J. Effect of oral semaglutide compared with pla-cebo and subcutaneous semaglutide on glycemic control in pa-tients with type 2 diabetes: a randomized clinical trial. JAMA. 2017;318(15):1460-1470.

11. U.S. Department of Health andHuman Services Food andDrugAdministrationCenterforDrugEvaluationandResearch.Guidance for Industry. Diabetes Mellitus: Developing Drugs and Therapeutic Biologics for Treatment and Prevention [Internet]. Center for Drug Evaluation and Research. 2008. http://www.fda.gov/downloads/Drugs /Guida nceCo mplia nceRe gulat oryIn forma tion/Guida nces/UCM071624.pdf.AccessedJune22,2018

12. http://www.ema.europa.eu/docs/en_GB/docum ent_libra ry/Scien tific_guideline/2012/06/WC500129256.pdf. Accessed June 22,2018

13. U.S.FoodandDrugAdministration.GuidanceforIndustry.DiabetesMellitus Evaluating Cardiovascular Risk in New AntidiabeticTherapies to Treat Type 2 Diabetes, 2008. www.fda.gov/downloads/Drugs /Guida nceCo mplia nceRe gulat oryIn forma tion/Guida nces/ucm071627.pdf.AccessedJune22,2018

14. Pfeffer MA, Claggett B, Diaz R, et al. Lixisenatide in patientswith type 2 diabetes and acute coronary syndrome. N Engl J Med. 2015;373(23):2247-2257.

15. Marso SP, Daniels GH, Brown-Frandsen K, et al. Liraglutideand cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2016;375(4):311-322.

16. Marso SP, Bain SC, Consoli A, et al. Semaglutide and cardiovas-cular outcomes in patients with type 2 diabetes. N Engl J Med. 2016;375(19):1834-1844.

17. HolmanRR, BethelMA,Mentz RJ, et al. Effects of once-weeklyexenatide on cardiovascular outcomes in type 2 diabetes. N Engl J Med.2017;377(13):1228-1239.

18. HernandezAF,GreenJB,JanmohamedS,etal.;HarmonyOutcomescommitteesand investigators.Albiglutideandcardiovascularout-comes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-con-trolled trial. Lancet.2018;.pii:S0140–6736(18)32261-X.

19. Gerstein HC, Colhoun HM, Dagenais GR, et al.; REWIND TrialInvestigators. Design and baseline characteristics of participants in theResearching cardiovascular Eventswith aWeekly INcretin inDiabetes(REWIND)trialonthecardiovasculareffectsofdulaglu-tide. Diabetes Obes Metab.2018;20(1):42-49.

20. Husain M, Birkenfeld AL, Donsmark M, et al.; PIONEER 6Investigators. Oral semaglutide and cardiovascular outcomes in pa-tients with type 2 diabetes. N Engl J Med.2019;381(9):841-851.

21. Schneeweiss S, Avorn J. A review of uses of health care utiliza-tion databases for epidemiologic research on therapeutics. J Clin Epidemiol.2005;58(4):323-337.

22. Food and Drug Administration Best Practices for Conducting and Reporting Pharmacoepidemiologic Safety Studies Using Electronic Healthcare Data. 2013. https://www.fda.gov/regul atory -infor matio n/searc h-fda-guida nce-docum ents/best-pract ices-condu cting and-repor ting-pharm acoep idemi ologi c-safet y-studi es-using -elect ronic.AccessedJune15,2015.

23. NajafzadehM,SchneeweissS.Fromtrialtotargetpopulations-cal-ibrating real-world data. N Engl J Med.2017;376(13):1203-1205.

24. Patorno E, PatrickAR,Garry EM, et al.Observational studies ofthe association between glucose-lowering medications and car-diovascular outcomes: addressing methodological limitations. Diabetologia. 2014;57(11):2237-2250.

25. Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of obser-vational studies in epidemiology: a proposal for reporting.Meta-analysisOfObservationalStudiesinEpidemiology(MOOSE)group.JAMA.2000;283(15):2008-2012.

26. SterneJA,HernánMA,ReevesBC,etal.ROBINS-I:atoolforas-sessing risk of bias in non-randomised studies of interventions. BMJ.2016;12(355):i4919.

27. Best JH,HoogwerfBJ,HermanWH,etal.Riskof cardiovasculardisease events in patients with type 2 diabetes prescribed the glu-cagon-like peptide 1 (GLP-1) receptor agonist exenatide twice daily or other glucose-lowering therapies: a retrospective analysis of the LifeLink database. Diabetes Care. 2011;34(1):90-95.

28. MogensenUM,AnderssonC,FosbølEL,etal.Cardiovascularsafetyof combination therapies with incretin-based drugs and metformin compared with a combination of metformin and sulphonylurea in type2diabetesmellitus–aretrospectivenationwidestudy.Diabetes Obes Metab.2014;16(10):1001-1008.

29. PaulSK,KleinK,MaggsD,BestJH.Theassociationofthetreat-ment with glucagon-like peptide-1 receptor agonist exenatide or insulin with cardiovascular outcomes in patients with type 2 dia-betes: a retrospective observational study. Cardiovasc Diabetol. 2015;24(14):10.

30. VelezM,PetersonEL,WellsK,etal.Associationofantidiabeticmed-ications targeting the glucagon-like peptide 1 pathway and heart failure events in patients with diabetes. J Card Fail.2015;21(1):2-8.

31. PatornoE,EverettBM,GoldfineAB,etal.Comparativecardiovas-cular safety of glucagon-like peptide-1 receptor agonists versus other antidiabetic drugs in routine care: a cohort study. Diabetes Obes Metab.2016;18(8):755-765.

32. Kannan S, Pantalone KM, Matsuda S, Wells BJ, Karafa M,Zimmerman RS. Risk of overall mortality and cardiovascular events in patients with type 2 diabetes on dual drug therapy including met-formin:AlargedatabasestudyfromtheClevelandClinic.J Diabetes. 2016;8(2):279-285.

33. EkströmN,SvenssonAM,MiftarajM,etal.Cardiovascularsafetyofglucose-lowering agents as add-on medication to metformin treat-mentintype2diabetes:reportfromtheSwedishNationalDiabetesRegister. Diabetes Obes Metab.2016;18(10):990-998.

34. AnyanwaguU,MamzaJ,MehtaR,DonnellyR,IdrisI.Cardiovascularevents and all-cause mortality with insulin versus glucagon-like pep-tide-1 analogue in type 2 diabetes. Heart.2016;102(19):1581-1587.

http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM071624.pdf





http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm071627.pdf



https://www.fda.gov/regulatory-information/search-fda-guidance-documents/best-practices-conductingand-reporting-pharmacoepidemiologic-safety-studies-using-electronic





35. SuissaS,MoodieEE,Dell'AnielloS.Prevalentnew-usercohortde-signs for comparative drug effect studies by time-conditional pro-pensity scores. Pharmacoepidemiol Drug Saf.2017;26(4):459-468.

36. ZimmermanRS,HobbsTM,WellsBJ, et al.Associationof gluca-gon-like peptide-1 receptor agonist use and rates of acute myocar-dialinfarction,strokeandoverallmortalityinpatientswithtype2diabetes mellitus in a large integrated health system. Diabetes Obes Metab.2017;19(11):1555-1561.

37. ToulisKA,HanifW,SaravananP,etal.All-causemortalityinpatientswith diabetes under glucagon-like peptide-1 agonists: a popula-tion-based,opencohortstudy.Diabetes Metab.2017;43(3):211-216.

38. AnyanwaguU,MamzaJ,DonnellyR,IdrisI.EffectofaddingGLP-1RAonmortality,cardiovascularevents,andmetabolicoutcomesamong insulin-treated patients with type 2 diabetes: a large retro-spectiveUKcohortstudy.Am Heart J.2018;196:18-27.

39. Patorno E, GoldfineAB, Schneeweiss S, et al. Cardiovascular out-comesassociatedwithcanagliflozinversusothernon-gliflozinantidi-abetic drugs: population based cohort study. BMJ.2018;6(360):k119.

40. DawwasGK, SmithSM,ParkH.Riskof heart failurehospitaliza-tion among users of dipeptidyl peptidase-4 inhibitors compared to glucagon-like peptide-1 receptor agonists. Cardiovasc Diabetol. 2018;17(1):102.

41. SvanströmH,UedaP,MelbyeM,etal.Useofliraglutideandriskofmajor cardiovascular events: a register-based cohort study in Denmark and Sweden. Lancet Diabetes Endocrinol.2018;7(2):106-114.

42. O’BrienMJ,KaramSL,WalliaA,etal.Associationof second-lineantidiabetic medications with cardiovascular events among insured adults with type 2 diabetes. JAMA Netw Open.2018;1(8):e186125.

43. Schneeweiss S. A basic study design for expedited safety signalevaluation based on electronic healthcare data. Pharmacoepidemiol Drug Saf.2010;19(8):858-868.

44. JohnsonES,BartmanBA,BriesacherBA,etal.The incidentuserdesign in comparative effectiveness research. Pharmacoepidemiol Drug Saf.2013;22(1):1-6.

45. RayWA. Evaluating medication effects outside of clinical trials:new-user designs. Am J Epidemiol.2003;158(9):915-920.

46. KiriVA,MacKenzieG.Howrealisintention-to-treat(ITT)analysisinnon-interventionalpostauthorizationsafetystudies?Wecandobetter. Curr Drug Saf. 2009;4(2):137-142.

47. Patorno E,Garry EM, PatrickAR, et al. Addressing limitations inobservational studies of the association between glucose-low-ering medications and all-cause mortality: a review. Drug Saf. 2015;38(3):295-310.

48. KristensenSL,RørthR,JhundPS,etal.Cardiovascular,mortality,andkidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: a systematic review and meta-analysis of cardiovascular outcome trials. Lancet Diabetes Endocrinol.2019;7(10):776-785.

49. ZhengSL,RoddickAJ,Aghar-JaffarR, et al. associationbetweenuse of sodium-glucose cotransporter 2 inhibitors, glucagon-like

peptide1agonists,anddipeptidylpeptidase4 inhibitorswithall-cause mortality in patients with type 2 diabetes: a systematic re-view and meta-analysis. JAMA.2018;319(15):1580-1591.

50. Bethel MA, Patel RA, Merrill P, et al.; EXSCEL Study Group.Cardiovascular outcomes with glucagon-like peptide-1 receptor agonists in patients with type 2 diabetes: a meta-analysis. Lancet Diabetes Endocrinol.2018;6(2):105-113.

51. ZhangZ,ChenX,LuP,etal.Incretin-basedagentsintype2diabeticpatients at cardiovascular risk: compare the effect of GLP-1 ago-nists and DPP-4 inhibitors on cardiovascular and pancreatic out-comes. Cardiovasc Diabetol.2017;16(1):31.

52. MonamiM,ZannoniS,PalaL,etal.Effectsofglucagon-likepep-tide-1 receptor agonists on mortality and cardiovascular events: a comprehensivemeta-analysisofrandomizedcontrolledtrials.Int J Cardiol. 2017;1(240):414-421.

53. BoyeKS,RiddleMC,GersteinHC,etal.Generalizabilityofgluca-gon-like peptide-1 receptor agonist cardiovascular outcome trials to theoveralltype2diabetespopulationintheUnitedStates.Diabetes Obes Metab.2019;21(6):1299-1304.

54. Bailey CJ, Marx N. Cardiovascular protection in type 2 diabe-tes: insights from recent outcome trials. Diabetes Obes Metab. 2019;21(1):3-14.

55. VergèsB,CharbonnelB.After theLEADER trial andSUSTAIN-6,how do we explain the cardiovascular benefits of some GLP-1 re-ceptor agonists? Diabetes Metab. 2017;1:2S3-2S12

56. GejlM,Starup-LindeJ,Scheel-ThomsenJ,GregersenS,VestergaardP. Risk of cardiovascular disease: the effects of diabetes and an-ti-diabetic drugs - a nested case-control study. Int J Cardiol. 2015;15(178):292-296.

57. LiL,LiS,LiuJ,etal.Glucagon-likepeptide-1receptoragonistsandheart failure in type 2 diabetes: systematic review and meta-anal-ysis of randomized and observational studies. BMC Cardiovasc Disord.2016;11(16):91.

SUPPORTING INFORMATIONAdditional supporting information may be found online in theSupporting Information section.

How to cite this article:HerreraComoglioR,VidalGuitartX.Cardiovascularoutcomes,heartfailureandmortalityintype2 diabetic patients treated with glucagon-like peptide 1 receptoragonists(GLP-1RAs):Asystematicreviewandmeta-analysis of observational cohort studies. Int J Clin Pract. 2020;74:e13553. https://doi.org/10.1111/ijcp.13553

https://doi.org/10.1111/ijcp.13553

IX. 2

Linagliptin and Cardiac Failure

The aim of the Newsletter is to disseminate information on the

safety and efficacy of pharmaceutical products, based on communications received from our

network of "drug information officers" and other sources such as

specialized bulletins and journals, as well as partners in WHO.

The information is produced in the form of résumés in English, full

texts of which may be obtained on request from:

Safety and Vigilance,

EMP-HIS, World Health Organization,

1211 Geneva 27, Switzerland, E-mail address: [email protected]

This Newsletter is also available on our Internet website:

http://www.who.int/medicines

Further information on adverse reactions may be obtained from the

WHO Collaborating Centre for International Drug Monitoring

Box 1051 751 40 Uppsala

Tel: +46-18-65.60.60 Fax: +46-18-65.60.80

E-mail: [email protected]: http://www.who-umc.org

The WHO Pharmaceuticals Newsletter provides you

with the latest information on the safety of medicines

and legal actions taken by regulatory authorities across

the world. It also provides signals based on information

derived from Individual Case Safety Reports (ICSRs)

available in the WHO Global ICSR database,

VigiBase®.

Contents

Regulatory matters

Safety of medicines

Signal

WHO Pharmaceuticals

NEWSLETTER

2015

No.1Prepared in collaboration with the WHO Collaborating Centre for International Drug Monitoring, Uppsala Sweden

© World Health Organization 2015

All rights reserved. Publications of the World Health Organization can be obtained from WHO Press, World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland (tel.: +41 22 791 3264; fax: +41 22 791 4857; e-mail: [email protected]). Requests for permission to reproduce or translate WHO publications – whether for sale or for non-commercial distribution – should be addressed to WHO Press, at the above address (fax: +41 22 791 4806; e-mail: [email protected]).

The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted lines on maps represent approximate border lines for which there may not yet be full agreement.

The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters.

All reasonable precautions have been taken by the World Health Organization to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the World Health Organization be liable for damages arising from its use.

Printed by the WHO Document Production Services, Geneva, Switzerland

WHO Pharmaceuticals Newsletter No. 1, 2015 • 3

Table of Contents

Regulatory Matters

Amoxicillin containing products ...................................................................... 4

Cabazitaxel acetonate ................................................................................... 4

Combined hormonal contraceptives ................................................................ 4

Dimethyl fumarate ....................................................................................... 5

Epoetin alfa ................................................................................................. 5

Freeze-dried live attenuated mumps virus vaccine ............................................ 6

Galantamine hydrobromide ............................................................................ 6

Hydroxychloroquine or chloroquine ................................................................. 6

Hydroxyethyl starch intravenous infusions ....................................................... 7

Interferon beta products ............................................................................... 8

Ivabradine .................................................................................................. 9

Levetiracetam .............................................................................................. 9

Metoclopramide ........................................................................................... 9

Simeprevir sodium ...................................................................................... 10

Sodium-glucose co-transporter 2 inhibitors (ipragliflozin, dapagliflozin, tofogliflozin, luseogliflozin, canagliflozin and empagliflozin) ............................... 11

Tofogliflozin hydrate .................................................................................... 11

Topiramate ................................................................................................ 11

Ustekinumab .............................................................................................. 12

Ziprasidone ................................................................................................ 12

Safety of medicines

Agomelatine ............................................................................................... 13

Bromocriptine ............................................................................................. 13

Hydrogen peroxide ...................................................................................... 14

Ipilimumab ................................................................................................. 14

Isotretinoin ................................................................................................ 15

Nonsteroidal anti-inflammatory drugs (NSAIDs), opioids, and acetaminophen ...... 16

Valproate-containing medicines ..................................................................... 16

Vascular endothelial growth factor receptor inhibitors ....................................... 16

Signal

Hexetidine and Severe hypersensitivity reactions ............................................. 18

Linagliptin and Cardiac failure ....................................................................... 20

Temsirolimus and Myocardial infarction .......................................................... 29


SIGNAL

Discussion and Conclusion

The available information on hexetidine and E122, the significant number of reports listing hexetidine as only or only suspected drug, the range, severity and consistent pattern of adverse reactions reported, the plausible time to onset and positive dechallenge (and rechallenge in one case) support the causal association between hexetidine preparations and severe hypersensitivity reactions. Three reports list co-administered medications also known to elicit allergic reactions; these might also have contributed to the reactions reported.

Given the available information from VigiBase®, it is not possible to determine if the reactions were elicited by hexetidine itself or E122. Individuals with a history of salicylate intolerance seem to be at a higher risk of developing an allergic reaction to products containing azorubin. The information currently available to prescribers and patients does not list severe, potentially life-threatening hypersensitivity reactions adequately.

References

1. MHRA, SPC for hexetidine (Oraldene). URL: http://www.mhra.gov.uk/home/groups/spcpil/ documents/spcpil/con1384326269327.pdf Accessed: 17 July 2014.

2. Gell PGH, Coombs RRA, eds. Clinical aspects of immunology. 1st ed. Oxford, England: Blackwell; 1963.

3. Luskin AT, Luskin SS, Anaphylaxis and anaphylactoid reactions: Diagnosis and management. Am J Ther. 1996 Jul;3(7):515-20.

4. Merk H, Ebert L, Goerz G, Allergic contact dermatitis due to the fungicide hexetidine. Contact Dermatitis. 1982 May;8(3):216.

5. Irish Medicines Board, SPC for hexetidine (Oraldene). URL: https://www.hpra.ie/img/ uploaded/swedocuments/LicenseSPC_PA0823-026-001_18062013164055. pdf Accessed: 17 July 2014.

6. Swissmedic, SPC for hexetidine (Hextril). URL: http://www.swissmedicinfo.ch/default.aspx Accessed 17 July 2014.

Linagliptin and Cardiac failure

Dr Raquel Herrera Comoglio, Argentina

Summary

Linagliptin is a reversible, selective inhibitor of the enzyme dipeptidyl peptidase-4 (DPP4), which is responsible for the metabolic inactivation of the incretin glucagon-like peptide 1 (GLP-1), thus extending the GLP-1 half-life. GLP-1 acts on glucose control by stimulating glucose-dependent insulin secretion and suppressing glucagon release. Linagliptin was approved in 2011 for patients with type 2 diabetes mellitus as monotherapy or in combination with other antidiabetic agents in the United States of America (US), the European Union, in Australia and in other countries.

Heart failure can be caused by structural or functional abnormalities of the heart. Up to 6 May 2014, 15 ISCRs associating cardiac failure with linagliptin had been received in the WHO Global ICSR Database, VigiBase®. All but two of these ISCRs were reported as serious, and there was one death reported. Linagliptin was reported as the only suspected drug in 13 cases. Age, reported in nine cases, ranged from 60 to 88 years. Five patients were 83 years and older.

Dipeptidyl peptidase-4 inhibitors (DPP4i) are expected to have beneficial effects on cardiac outcomes, mainly through the prolonged effect of GLP-1. However, two large trials assessing the impact of saxagliptin and alogliptin on cardiovascular death, non-fatal myocardial infarction and non-fatal stroke failed to show any beneficial effect of these drugs on the composite of major cardiovascular outcomes. In addition, concerns arose about the effect of saxagliptin and alogliptin on cardiac failure. A meta-analysis suggested that cardiac failure could be a DPP4i class effect; if it is a class effect, its mechanisms are unknown.

In spite of their inherent limitations, spontaneous reports from VigiBase® add observational data in support of the association cardiac failure - linagliptin/ (DPP4i) as a drug-related effect in some patients with risk factors (e.g. old age), underlying concomitant conditions or pre-existing cardiac failure and/or other concomitant medication. The pair linagliptin-cardiac failure should be considered as a signal, and deserves further investigation.


SIGNAL

Introduction

Linagliptin is a synthetic, reversible inhibitor of dipeptidyl peptidase-4 (DPP4). Acting through inhibition of DPP4, linagliptin inhibits the proteolytic degradation of the incretin hormones glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP), resulting in increased glucose dependent insulin secretion. Linagliptin is indicated in the treatment of type 2 diabetes mellitus (T2DM) to improve glycaemic control in adults, as monotherapy or as combination therapy with metformin, or sulphonylurea and metformin, or in combination with insulin with or without metformin.1-3

Cardiac failure, or heart failure (HF), is a pathophysiologically complex clinical syndrome, not fully understood, which results from an impaired function of the heart as a pump supporting physiological circulation. Symptoms are dyspnoea, exercise intolerance, and sodium and water retention, often manifested as oedema. Cardiac failure can be caused by any abnormality of the structure, mechanical function or electrical activity of the heart, or as a secondary dysfunction of other organs and tissues, e.g. kidneys, liver or muscles; other systemic processes, as neurohumoral activation, are also involved.4,5

Receptors of GLP-1 (GLP-1R) are expressed in pancreas and extrapancreatic tissues (lung, kidney, central, enteric and peripheral nervous systems, lymphocytes, blood vessels, and heart). GLP-1 exerts direct actions on the cardiovascular system, the heart, vessels and kidney, mainly via GLP-1R. In preclinical studies, incretin-based agents control body weight, improve glycaemic control with a low risk of hypoglycaemia, decrease blood pressure, inhibit the secretion of intestinal chylomicrons, and reduce inflammation.6

GIP and GLP-1 are rapidly inactivated by DPP4, 1-3, 6-8 a transmembrane protein that removes N-terminal dipeptides from various substrate hormones, chemokines, neuropeptides, growth factors and incretins. Other cardioactive peptides cleaved by DPP4, are brain natriuretic peptide (BNP) and neuropeptide Y. BNP is a cardiac neurohormone with natriuretic and vasodilatory actions, secreted into the plasma from the ventricles in response to ventricular volume expansion and pressure overload.6-8 BNP has been established as a diagnostic and prognostic marker of left ventricular (LV) systolic and diastolic dysfunction.9 BNP plasma levels have been shown to be significantly higher in patients with decompensated chronic HF.10

Reports in VigiBase®

Fifteen ICSRs were retrieved from the WHO Global ICSR Database, VigiBase®, up to 6 May 2014

(Table 1). All but one (a case from literature) were spontaneous reports. All but two cases were serious (one case doesn’t state seriousness, another one was reported as not serious), and there was one death reported. The patients were 11 women (73%) and three men (20%), while the literature case doesn’t report the patient’s sex. Age was reported in nine cases (60%), being all patients 60 years or older; age ranged from 83 to 88 years in four out of nine patients (44%). Another patient’s age (case 11) was estimated to 85-90 years.

Relevant medical history was reported in six cases (cases 8, 9, 10, 11, 12 and 13). In case 8, concomitant diseases included renal artery stenosis, renal insufficiency, renal disease (shrinked kidneys), renal anaemia, renal osteodystrophy, hyperuricaemia, dyslipidaemia, hypertension, and chronic obstructive pulmonary diseases (COPD). In case 9, relevant medical history mentions cardiovascular disease (CVD) (percutaneous coronary intervention, angina pectoris and myocardial ischaemia), hypertension, hyperlipidaemia, chronic renal failure and diabetes mellitus. Of note, both patients had low weight (43 kg and 40 kg). Case 10 reports hypertension pulmonale, cor pulmonale and lung fibrosis. Case 11 reports unspecified heart disease, case 12 mentions hypertension and atrial fibrillation and case 13 renal insufficiency and hypertension.

Reports come from Europe (six cases), the US (three), Canada (two), Australia (two) and Japan (one). ICSRs were sent by physicians (ten cases), manufacturers (two), pharmacist (one) consumers/non health professionals (one case) and other health professionals (one case). The completeness score of the ICSRs was low (0.17-0.27) in 31%, medium (0.33-0.53) in 44% and high (0.75-0.95) in 25% of the reports. One report mentions a recently published article about the effect of saxagliptin on cardiac outcomes.

Seven reports provide time to onset, which varied from 6 days (two reports) to 295 days.

In all cases but two, linagliptin is the only suspected drug. In the case from literature, metformin 2 g/day (part of the investigational product) was also suspected. In case 9, cilostazol was started nine days before the adverse event and was also mentioned as a suspected drug.

Co-administered medication was reported in nine cases, although it was not specified in one case. Three cases included calcium-channel blockers (amlodipine in two cases and nifedipine in one). T2DM medication was reported in six cases, metformin was the only antidiabetic drug concomitantly administrated in four (in the case from a clinical trial it was reported as a suspected drug), there was one case with metformin and pioglitazone, and another with insulin.


SIGNAL

Dechallenge action was reported in nine cases (60%): linagliptin was reported as withdrawn in eight of these, and in one linagliptin dose was reported as not changed, the patient recovered with sequelae.

The fatal case (case 8) refers to a 66 year-old woman with renal insufficiency (renal artery stenosis, renal anaemia, renal dystrophia), hyperuricemia, dyslipemia, hypertension and COPD, who presented with acute dyspnoea and cardiac decompensation 16 days after having started therapy with linagliptin. The patient died 15 days later, and linagliptin was considered “implied”.

The first spontaneous reporting (case 1) referred to a woman presenting with cardiac failure 44 days after having started her treatment with linagliptin 5 mg. All the other adverse events (urinary infection, pulmonary infection) occurred at least 11 days after the HF onset, and were probably related to complications derived from hospitalization. Linagliptin was withdrawn after the patient had recovered from her cardiac failure, and hypothetically, after hospitalization.

In the case reported by a manufacturer (case 2), a female patient was also treated with pioglitazone, amlodipine, metformin and irbesartan. Case 9 refers to a Japanese 87 year-old woman, weight 40 kg, treated with several drugs including nifedipine for hypertension, hyperlipidaemia, chronic renal failure and myocardial ischaemia. The patient presented with congestive HF and aggravated renal failure six days after having started linagliptin 5 mg/day and nine days after start of cilostazol treatment with 200 mg/day. In case 10, a 67 year-old woman with a history of scleroderma, lung fibrosis and pulmonary hypertension presented with atrial fibrillation and cardiac failure after approximately six months in treatment with linagliptin. The reporter, a physician, mentions an article of Scirica et al., published the previous month.11

In case 12, an 88 year-old woman who had started linagliptin 45 days earlier, went to a hospital for routine pacemaker battery replacement, and cardiac insufficiency was detected; the patient lost 13 kg with appropriate therapy (this indicates the amount of fluid retention), and her ejection fraction was 45%.

Case 13 reports that an 83 year-old man with renal insufficiency and hypertension presented with cardiac failure. Linagliptin was withdrawn, but there is no information about the outcome. Concomitant poly-medication is mentioned but details are not provided.

The case from literature (case 14) was extracted from a 52 weeks multifactorial design study, and reports two drug related serious adverse events

(SAEs) in the same patient on day 295 after starting the study treatment (2.5 mg linagliptin + 1000 mg metformin). The patient experienced cardiogenic shock and supraventricular tachycardia within one hour of administration of medication. The patient required hospitalisation and study medication was discontinued; the patient recovered.12

Literature and Labelling

The EMA Summary of Product Characteristics for linagliptin, the Australian Public Assessment report and the product label for linagliptin (US FDA) do not mention heart/cardiac failure as an event associated with or described for linagliptin therapy.1-3

In a published clinical trial report, cardiac failure is mentioned as having occurred in a patient after 295 days of linagliptin 5 mg/metformin 2 g treatment.12 This case has been retrieved in VigiBase® and already mentioned in the “Reports in VigiBase®” section.

A meta-analysis, published in February 2013, found that treatment with DPP4i reduces the risk of cardiovascular events (particularly myocardial infarction) and all-cause mortality in patients with type 2 diabetes. Although HF was a pre-specified component of major cardiovascular events (MACE), meta-analysis’s results don’t mention HF.13 A trial in older patients does not mention cardiac failure among adverse events.14 Results posted on Clinicaltrials.gov mention only rhythm abnormalities and coronary artery diseases as serious cardiac adverse events.15

A meta-analysis of 50 DPP4-inhibitors trials, enrolling 55,141 participants, found a statistically significant trend towards increased risk of HF outcomes with no increase in risk with regards to all-cause mortality, cardiovascular mortality, acute coronary syndrome (ACS) or stroke. Most of the HF cases were retrieved from results of SAVOR-TIMI-53 (saxagliptin, 66.2% of the data of HF), EXAMINE (alogliptin, 21.3%) and the VIVIDD trial (vildagliptin, 6.9%), the latter enrolled only patients with left ventricular fraction <40%. Seven clinical trials for linagliptin with 5,260 participants were included in the analysis.16

A meta-analysis of clinical trials with vildagliptin, sitagliptin, saxagliptin, alogliptin, linagliptin, and dutogliptin found that the overall risk of acute HF was higher in patients treated with DPP4i in comparison with those treated with placebo/active comparators, and suggested that DPP4i could be associated with an increased risk of HF.17

A recently published analysis of pooled data of 22 placebo-controlled trials found a negative relationship, with an incidence of HF adverse


SIGNAL

events (AEs) for linagliptin- and placebo-treated patients of 0.2% (n = 11) for linagliptin and 0.3% (n = 7) for placebo.18

Discussion and Conclusion

Ageing, dyslipidaemia, hypertension, renal insufficiency and diabetes are risk factors for developing cardiac failure. As stated, all patients with a reported age were 60 years or older: four ISCRs refer to patients 83 year-old and older, and for the other five the patient’s age was between 60 to 71 years. Another patient’s age was estimated to 85-90 years.

Co-morbidities predisposing to HF (dyslipidaemia, hypertension, prior cardiac disease, renal insufficiency) are mentioned in six cases.

Concomitant medication was reported in nine cases (although not specified in one case). Linagliptin was the only suspected drug in thirteen ISCRs, in two cases another drug was reported as suspected (cilostazol and metformin, respectively). In three cases (cases 7, 8, and 9), cholesterol lowering agents, i.e. statins or ezetimibe or both, are reported among concomitant medication. Three patients (3, 8 and 9) were under diuretic treatment (furosemide/amiloride, hydrochlorotiazide and furosemide respectively). Two patients (7 and 9) were treated with angiotensin converting enzyme inhibitors (quinapril and enalapril), two other patients with angiotensin receptors antagonists (2 and 9, irbesartan and losartan respectively). Three patients were treated with calcium channel blockers and two patients with doxazocin (α-blocker).

Calcium channel blockers can lead to worsening HF and have been associated with an increased risk of cardiovascular events, especially the non vasoselective ones.5 In three cases, patients were also treated with calcium channel blockers, two with amlodipine and one patient with nifedipine. Time to onset was reported in two out of these three cases (6 days and 16 days).

In case 8 (fatal outcome), a 66 year-old woman presented with HF 16 days after having started linagliptin. She was also treated with amlodipine 10 mg, quinapril 5 mg, bisoprolol 5 mg, gliquidone 30 mg, and simvastatin 20 mg. Recent studies have found that amlodipine does not exert favourable effects on the clinical course of patients with HF;19,20 other not significant interactions for HF seem unlikely, such as simvastatin with amlodipine (amlodipine increases the systemic exposure of simvastatin, this patient being on amlodipine low-dose).

In case 9 (an 87 year-old woman treated with nifedipine and other cardiovascular therapies), cilostazol and linagliptin 5 mg had been added

respectively nine days and five days before the HF onset. Cilostazol is a reversible phosphodiesterase III inhibitor with anti-platelet, vasodilatory and antithrombotic properties, metabolized by CYP3A4 and CYP2C19. Cilostazol is formally contraindicated in patients with pre-existing HF. Nifedipine is metabolized by CYP2C19 (interaction described in product information).

For the patient treated with pioglitazone/ metformin (case 2), no time to onset was reported. Pioglitazone is a thiazolidinedione, which selectively ligands the nuclear transcription factor peroxisome proliferator-activated receptor-γ (PPAR-γ). Thiazolidinediones improve glycaemic control by increasing insulin sensitivity. Fluid retention, that can cause or exacerbate HF in some patients, is a known effect of PPAR-γ, and pioglitazone can cause or exacerbate congestive HF in some patients.21

In eight cases, linagliptin was withdrawn. Except for the fatal case and one with unknown outcome, dechallenge was positive. No cases with rechallenge were reported. In case 3, the dose was not changed and the patient recovered with hospitalization.

Time to onset (reported in seven cases) was <6 months in six patients (44 days, 6 days, 147 days, 16 days, 6 days and 164 days respectively). In a large study with saxagliptin, the risk for HF hospitalization associated with the use of saxagliptin was highest in the first six months and declined thereafter.

DDP4 inhibitors have been expected to have beneficial effects on cardiac outcomes, both due to GLP-1 actions and to other peptide hormones with direct cardiorenal effects. Preclinical data and mechanistic studies suggested a possible additional non-glycaemic beneficial action on blood vessels and the heart, via both GLP-1 dependent and GLP-1-independent effects.22 It has been suggested that DPP4 inhibitors reduce the risk for the multiple co-morbidities associated with obesity/T2DM including hypertension, cardiovascular and kidney disease.23


SIGNAL

Table 1. Characteristics of reports in VigiBase® for linagliptin and cardiac failure

Case Age/

Sex

Time to

onset

Duration of

treatment

Other suspected (S) or

concomitant (C) drugs

Reported adverse reactions/

adverse events

Dechallenge

action

Outcome

1 -/F 44 days 68 days - Urinary tract infection,

pulmonary congestion,

hypotension, pain, nausea,

pleural fibrosis, hypoxia,

cardiac congestive failure

Drug withdrawn Recovered

(before drug

withdrawal)

2* -/F - - Pioglitazone, metformin,

amlodipine, irbesartan (all C)

Cardiac failure - Unknown

3 84/M 6 days - Metformin, clarithromycin,

doxazocin, fluticasone,

lactulose, senna, latanoprost,

furosemide/amiloride,

clopidogrel (all C)

Oedema, respiratory rate

increased, wheezes,

orthopnoea, condition

aggravated, congestive heart

failure

Dose not

changed

Recovered

with sequelae

4 71/F - - - Heart failure - -

5 -/F 147 days 147 days - Anaemia, congestive heart

failure, hypertension

pulmonary


with sequelae

6 60/F - - - Congestive heart failure - -

7* 69/F - - - Dyspnoea, swelling, cardiac

failure

- -

8** 66/F 16 days 16 days Amlodipine, quinapril ,

colecalciferol, sodium

ascorbate/ferrous sulfate,

calcium acetate, calcium

carbonate, sodium bicarbonate,

cloxazolam, acetylsalicylic acid,

bisoprolol, gliquidone ,

simvastatin (all C)

Vomiting, renal failure acute,

acute dyspnoea, myocardial

decompensation, general

physical health deterioration

Drug withdrawn Death

9 87/F 6 days 6 days Cilostazol (S)

Zopiclone doxazocin,

haloperidol, ezetimibe,

famotidine, nifedipine,

hydrochlorotiazide/losartan

potassium, acetylsalicylic acid

(all C)

Renal failure aggravated,

cardiac failure congestive


10 67/F 164 days 176 days Estradiol, terbutaline, formoterol

fumarate/ budesonide,

prednisolone, insulin aspart,

insulin glargine, acetylsalicylic

acid, magnesium oxide,

simvastatin, ezetimibe, enalapril,

potassium, furosemide, sildenafil,

cyclophosmamide (all C)

Disease progression, oedema,

cardiac arrest, atrial

fibrillation, myocardial

decompensation


11 ***/F - - Metformin (C) Myocardial decompensation Drug withdrawn Recovered

12 88/F - 45 days Metformin (C) Oedema, cardiac failure - Recovered

13 83/M - - Polymedication (not further

specified)

Cardiac failure Drug withdrawn Unknown

14 -/- 295 days 295 days Metformin (S) Supraventricular tachycardia,

cardiogenic shock


15 -/M - - - Asthenia, hyperglycaemia,

tremor, nocturnal dyspnoea,

abdominal discomfort

Unknown Unknown

*causality reported as possible, ** causality reported as ‘implied’, *** estimated age: 85-90 years


SIGNAL

However, a recently published meta-analysis suggested that HF is a class effect of DPP4i.17 Two large trials specifically designed to assess composite cardiovascular outcomes contributed largely to this conclusion. A large trial found a higher statistically significant risk of hospitalization due to HF in the saxagliptin group than in the placebo group. Patients had T2DM and established cardiovascular disease or multiple cardiovascular risks factors, and were followed for a median of 2.1 years; HF was included in a composite secondary endpoint. 12.8% of participants had prior HF.11 The events were more frequent in patients with diabetes and HF.24 In a large trial assessing the effect of alogliptin on cardiovascular outcomes in 5,380 patients followed for a median of 18 months, 28% of the participants had HF at baseline; HF was not part of the primary composite outcome or secondary outcomes. Although the heterogeneity of sub-groups is mentioned, no specific details are provided.25

Prior HF was the strongest predictor of hospitalization during the study, followed by impaired kidney function. In the EXAMINE study, the risk increase for hospitalization due to HF associated with alogliptin was apparently less clear in spite of the percentage of patients with prior HF, the higher use of ß-blocking agents, and the more frequent medical controls with treatment adaptations in EXAMINE might be one of the potential explanations.26

Mechanisms of the hypothesized effect of DDP4 inhibitors on HF are unknown. As previously mentioned, DPP4 cleaves not only GLP-1 and GIP, but also other cardioactive peptides, such as substance P, brain natriuretic peptide (BNP), neuropeptide Y, CXCL12, bradykinin, and related peptides.6-7 BNP is increased in HF, being both a diagnostic and prognostic marker.9,10 It has also been suggested that DPP4 is abnormally increased in patients with T2DM and these increased DPP4 levels are independently associated with asymptomatic left ventricular both diastolic and systolic dysfunction in T2DM patients which have a higher risk of presenting left ventricular dysfunction.27 Neuropeptide Y (NPY1-36) is released from sympathetic neurons; DPP4 removes the N-terminal from NPY1-36 to generate NPY3-36, which binds to Y2 receptors that have relative antagonist properties to Y1 receptor activation. Any decrease in the DPP4 mediated generation of NPY3-36 would decrease the activity of Y2 inhibitory autoreceptors; and so augment sympathetic and parasympathetic neurotransmitter release.28 A clinical study in 53 patients found that peptide Y is augmented in diabetic patients.29

Linagliptin also has significant inhibitory activity on the human M1, M2 and M3 muscarinic receptors, with half minimal inhibitory concentration (IC50) values of 295 to 1000 nM (more than 22 times the clinical peak concentration (Cmax), which has not been considered clinically relevant.1,2 In animal models a relationship between B-adrenergic and M2 muscarinic receptors and diminished ventricular contractility has been suggested.30

In spite of their inherent limitations, spontaneous reports from VigiBase® add observational data in support of the association cardiac failure-linagliptin/ (DPP4i) as a drug-related effect in some patients with risk factors (e.g. old age), underlying concomitant conditions or pre-existing cardiac failure and/or other concomitant medication. The pair linagliptin-cardiac failure should be considered as a signal, and deserves further clinical and pharmacoepidemiological investigation.

References

1. Australian Public Assessment Report for Linagliptin. Department of Health and Ageing, Therapeutic Goods Administration (TGA). December 2011.

2. Summary of Product Characteristics for Linagliptin. European Medicines Agency. URL: http://www.ema. europa.eu/ema/… Accessed: June 2014

3. Product Label for linagliptin (Tradjenta®). US Food & Drug Administration. URL: http://www. accessdata.fda.gov/scripts/cder/drugsatfda/index.cfm?fuseaction=Search.Search_Drug_Name. Accessed: June 2014.

4. Braunwald E. Heart failure. JACC Heart Fail. 2013 Feb;1(1):1-20. doi: 10.1016/j.jchf.2012.10.002. Epub 2013 Feb 4.

5. Jessup M, Abraham WT, Casey DE, Feldman AM, Francis GS, Ganiats TG et al. 2009 focused update: ACCF/AHA Guidelines for the diagnosis and Management of Heart Failure in Adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines: developed in collaboration with the International Society for Heart and Lung Transplantation. Circulation. 2009 Apr 14;119(14):1977-2016.

6. Ussher JR, Drucker DJ. Cardiovascular actions of incretin-based therapies. Circulation research. 2014; 114:1788-1803.


SIGNAL

7. Ussher JR, Drucker DJ. Cardiovascular biology of the incretin system. Endocr Rev. 2012 Apr;33(2):187-215.

8. Drucker DJ. Dipeptidyl peptidase-4 inhibition and the treatment of type 2 diabetes: preclinical biology and mechanism of action. Diabetes care. 2007 Jun;30(6):1335-43.

9. Braunwald E. Biomarkers in heart failure. N Engl J Med. 2008 May 15;358(20):2148-59.

10. Gong H, Wang X, Ling Y, Shi Y, Shi H. Prognostic value of brain natriuretic peptide in patients with heart failure and reserved left ventricular systolic function. Exp Ther Med. 2014 Jun;7(6):1506-1512.

11. Scirica BM, Bhatt DL, Braunwald E, Steg PG, Davidson J, Hirshberg B et al. Cardiovascular outcomes in patients with type 2 diabetes mellitus. N Engl J Med. 2013 Oct 3;369(14):1317-26.

12. Haak T, Meinicke T, Jones R, Weber S, von Eynatten M, Woerle HJ. Initial combination of linagliptin and metformin in patients with type 2 diabetes: efficacy and safety in a randomised, double-blind 1-year extension study. Int J Clin Pract. 2013 Dec;67(12):1283-93. 9.

13. Monami M, Ahrén B, Dicembrini I, Mannucci E. Dipeptidyl peptidase-4 inhibitors and cardiovascular risk: a meta-analysis of randomized clinical trials. Diabetes Obes Metab. 2013 Feb;15(2):112-20.

14. Barnett AH, Huisman H, Jones R, von Eynatten M, Patel S, Woerle HJ. Linagliptin for patients aged 70 years or older with type 2 diabetes inadequately controlled with common antidiabetes treatments: a randomised, double-blind, placebo-controlled trial. Lancet. 2013 Oct 26;382(9902):1413-23.

15. Clinicaltrials.gov (identifier NCT01084005). URL: http://www.clinicaltrials.gov. Accessed: June 2014.

16. Wu S, Hopper I, Skiba M, Krum H. Dipeptidyl peptidase-4 inhibitors and cardiovascular outcomes: Meta-analysis of randomized clinical trials with 55,141 participants. Cardiovasc Ther. 2014 Aug;32(4):147-58.

17. Monami M, Dicembrini I, Mannucci E. Dipeptidyl peptidase-4 inhibitors and heart failure: A meta-analysis of randomized clinical trials. Nutr Metab Cardiovasc Dis. 2014 Jul;24(7):689-97.

18. Lehrke M, Marx N, Patel S, Seck T, Crowe S, Cheng K et al. Safety and Tolerability of Linagliptin in Patients With Type 2 Diabetes: A

Comprehensive Pooled Analysis of 22 Placebo-Controlled Studies. Clin Ther. 2014 Jul 8. pii: S0149-2918(14)00371-3.

19. Lee SA, Choi HM, Park HJ, Ko SK, Lee HY. Amlodipine and cardiovascular outcomes in hypertensive patients: meta-analysis comparing amlodipine-based versus other antihypertensive therapy. Korean J Intern Med. 2014 May;29(3):315-24.

20. Packer M, Carson P, Elkayam U, Konstam MA, Moe G, O’Connor C et al. Effect of amlodipine on the survival of patients with severe chronic heart failure due to a nonischemic cardiomyopathy: results of the PRAISE-2 study (prospective randomized amlodipine survival evaluation 2). JACC Heart Fail. 2013 Aug;1(4):308-14.

21. Information for Healthcare Professionals: Pioglitazone HCl. URL: http://www.fda.gov/Drugs/DrugSafety/ost- marketDrugSafetyInformationforPatientsandProviders/ ucm124178.htm. Accessed: 2014.

22. Scheen AJ. Cardiovascular effects of dipeptidyl peptidase-4 inhibitors: from risk factors to clinical outcomes. Postgrad Med. 2013 May;125(3):7-20.

23. Aroor AR, Sowers JR, Jia G, DeMarco VG. Pleiotropic Effects of the Dipeptidylpeptidase-4 Inhibitors on the Cardiovascular System. Am J Physiol Heart Circ Physiol. 2014 Jun 13

24. Scirica BM, Raz I, Cavender MA, Steg PG, Hirshberg B, Davidson J et al. Abstract 17503: Outcomes of Patients With Type 2 Diabetes and Known Congestive Heart Failure Treated With Saxagliptin: Analyses of the SAVORTIMI 53 Study. Cardiovasc Ther. 2014 Apr 21.

25. White WB, Cannon CP, Heller SR, Nissen SE, Bergenstal RM, Bakris GL, et al. Alogliptin after acute coronary syndrome in patients with type 2 diabetes. N Engl J Med. 2013 Oct 3;369(14):1327-35.

26. Schernthaner G, Sattar N. Lessons from SAVOR and EXAMINE: Some important answers, but many open questions. J Diabetes Complications. 2014 Jul-Aug;28(4):430-3. doi: 10.1016/j. jdiacomp.2014.02.011.

27. Ravassa S, Barba J, Coma-Canella I, Huerta A, López B, González A, et al. The activity of circulating dipeptidyl peptidase-4 is associated with subclinical left ventricular dysfunction in patients with type 2 diabetes mellitus. Cardiovasc Diabetol. 2013 Oct 7;12:143.


SIGNAL

28. Baraniuk JN, Jamieson MJ. Rhinorrhea, cough and fatigue in patients taking sitagliptin. Allergy, Asthma & Clinical Immunology. 2010;6(1):8-

29. Matyal R, Mahmood F, Robich M, Glazer H, Khabbaz K, Hess P, et al. Chronic type II diabetes mellitus leads to changes in neuropeptide Y receptor expression and distribution in human myocardial tissue. Eur J Pharmacol. 2011 Aug 31;665(1-3):19-28.

30. Kashihara T, Hirose M, Shimojo H, Nakada T, Gomi S, Hongo M, et al. β(2)-Adrenergic and M(2)-muscarinic receptors decrease basal t-tubular L-type Ca2+ channel activity and suppress ventricular contractility in heart failure. Eur J Pharmacol. 2014 Feb 5 ;724:122-31.

Response from Boehringer Ingelheim

Thank you for the opportunity to provide our comments. Boehringer Ingelheim completed a cumulative assessment of HF on 14 February 2014 following an internally detected signal after publication of the results for the saxagliptin and alogliptin cardiovascular outcome trials (CVOT) in September 2013.

A cumulative review of linagliptin and linagliptin+metformin fixed dose combination (FDC) reports entered in the Boehringer Ingelheim global drug safety database until 27AUG2014 was undertaken using the narrow MedDRA v17.0 SMQ cardiac failure. Data from clinical trials, epidemiologic studies and the published literature were also reviewed.

92 events (87 serious) in 83 reports were identified in association with linagliptin; no reports were identified in association with the linagliptin+metformin FDC. Forty-six events in 42 reports were from clinical trials, 32 events in 28 reports were spontaneous, 3 events in 3 reports were from Health Authorities and 11 events in 10 reports were from observational studies. The main reason for seriousness was hospitalization alone or associated with life-threatening or fatal reports. Forty six patients were female (55%), 35 were male (42%) and 2 did not report gender.

The 78 reports which provide age range between 39 and 92 years with most (75%) over 60. Twenty one patients (25%) were older than 80.

All of the reports were individually assessed and had a plausible alternative explanation, factors (e.g. temporality) that make a causal relationship to linagliptin unlikely or insufficient information for a more full causal assessment.

A dechallenge is described in 28 reports: 12 positive, 7 negative and 2 unknown outcomes. Four rechallenges are described: 2 negative, one unknown outcome. Only one positive rechallenge was described, in a patient with end stage renal and chronic cardiac failure with critical coronary occlusion. In 4 reports the patient recovered while linagliptin continued unchanged. Six reports were not temporally related to linagliptin; one patient had viral cardiomyopathy 5 months after discontinuation, another with pre-existing chronic renal failure experienced HF 3 months after discontinuation, 3 had evidence of worsening of pre-existing HF prior to starting linagliptin and one event occurred on the day that linagliptin was started, when hyperthyroidism was also diagnosed.

Time to onset was reported in 71 of the 92 events. Thirty eight events (54%) were <6 months, and 11 (15%) events occurred more than 1 year after starting linagliptin.

Of the 15 ICSRs identified on the Vigibase database, 14 are included in the search results. Case 15 is not because the Preferred Term Nocturnal dyspnoea is not in the narrow cardiac failure SMQ definition. In addition to the information presented, case 1 reported a patient in a Boehringer Ingelheim sponsored trial in patients with renal impairment. The patient was taking furosemide for congestive heart failure (CHF) and spironolactone and metolazone was added 3 months before linagliptin was started indicating worsening or instability of the disease prior to linagliptin.

The evaluation of HF is complex in the type 2 Diabetes Mellitus (T2DM) population. As stated in the article above, the risk factors for HF would be expected to be higher in the T2DM population compared with non- diabetics. An assessment was conducted of past or concomitant conditions that are known risk factors for HF. Seventy seven patients (93%) had at least one risk factor and 55 patients (66%) had 3 or more risk factors. Six patients did not have at least one risk factor for HF and all 6 reports had limited or no medical history provided.

Hypertension may be the single most important modifiable risk factor for HF.1 Fifty eight patients (70%) reported hypertension.

Obesity and insulin resistance are also important risk factors for cardiac failure.1 Using the international classification of body mass index (BMI)2, of 54 patients reporting a BMI, 12 (22%) were pre-obese and 27 (50%) were obese. Only 10 reports described patients with normal BMI and the 5 underweight patients had renal failure.

Linagliptin does not require dose alteration in patients with renal insufficiency and may be more


SIGNAL

likely to be used in this patient population than other DPP-4 inhibitors. Thirty six patients (43%) reported renal insufficiency, acute or chronic renal failure or diabetic nephropathy which are recognised risk factors for HF.1

The reported incidence rate of HF in patients with T2DM varies across studies largely reflecting differences in ascertainment and adjustment approaches.3 A study conducted in multiple countries across Europe and North Africa estimated an annual incidence rate of CHF requiring hospitalisation of 10 per 1000 persons.4 In the US, the CDC reported the annual age-adjusted hospital discharge rate with HF as first-listed diagnosis in diabetes patients to be 13.4 per 1000 in 2006.5 Another US study estimated a crude incidence rate of about 11.8 per 1000 PY.6

The incidence rate of patients with unadjudicated narrow MedDRA SMQ cardiac failure events for linagliptin is 5.8 per 1000PY (N=9060).7 Pooled analysis of safety data from 23 randomized clinical trials (N=5488 linagliptin, 3290 placebo) showed same overall incidence of cardiac disorders (3.3% and 3.3%, respectively).7 Using the narrow MedDRA SMQ cardiac failure, the frequency was 0.5% (linagliptin) and 0.2% (placebo).8 This frequency in patients with a history of cardiac failure was 5.1% (linagliptin) and 5.5% (placebo).7 External adjudication of events of hospitalization for HF in 8 randomized, double-blind studies, (N=2039 linagliptin, 1275 placebo) showed 9 (0.4%) and 5 (0.4%) patients respectively were adjudicated to have HF. Further adjudicated analyses will be available when the large ongoing CVOTs CAROLINA and CARMELINA complete.

It is not possible to demonstrate a direct causal effect of linagliptin with HF due to the confounding of the reports with risk factors for the condition and the known relatively high background incidence in the T2DM population. In addition, the observed incidence rate in the clinical trials appears to be within the published range. Data does not demonstrate an increased frequency in patients with previous HF. Boehringer Ingelheim concluded that linagliptin is not casually associated with HF however this topic will continue to be closely monitored and the linagliptin CVOTs CAROLINA and CARMELINA will provide important further information.

References

1. Yancy CW, Jessup M, Bozkurt B, Butler J, Casey Jr DE, Drazner MH, et al. 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation. 2013;128:e240-e327.

2. http://apps.who.int/bmi/index.jsp?introPage=intro_3. html Accessed 8 September 2014.

3. Roger VL. Epidemiology of heart failure. Circ Res 2013. 113(6):646-659.

4. Vaur L, Gueret P, Lievre M, Chabaud S, Passa P, DIABHYCAR Study Group. Development of congestive heart failure in type 2 diabetic patients with microalbuminuria or proteinuria: observations from the DIABHYCAR (type 2 DIABetes, Hypertension, CArdiovascular Events and Ramipril) study. Diabetes Care 2003. 26(3):855-860.

5. Centres for Disease Control and Prevention Diabetes Public Health Resource. http://www.cdc.gov/diabetes/ statistics/ cvdhosp/hf/fig3.htm Accessed 10 September 2014.

6. Kanaya AM, Adler N, Moffet HH, Liu J, Schillinger D, Adams A, Ahmed AT, Karter AJ. Heterogeneity of diabetes outcomes among Asians and Pacific Islanders in the US: the Diabetes Study of Northern California (DISTANCE). Diabetes Care 2011. 34(4):930-937.

7. Internal Boehringer Ingelheim data.

8. Schernthaner G, Khunti K, Patel S, Cheng K, Mattheus M, Woerle HJ. Safety of linagliptin in 8778 patients with type 2 diabetes mellitus: pooled analysis of 23 placebo-controlled randomized clinical trials. 74th Sci Sess of the American Diabetes Association (ADA), San Francisco, 13 - 17 Jun 2014 (Poster) 2014.

IX. 3

Glibenclamide/glyburide and palpitations in

Asian population

WHO Vision for Medicines Safety No country left behind:

worldwide pharmacovigilance for safer medicines, safer patients

The aim of the Newsletter is to disseminate regulatory

information on the safety of pharmaceutical products,

based on communications received from our network of

national pharmacovigilance centres and other sources such as

specialized bulletins and journals, as well as partners in WHO.

The information is produced in the form of résumés in English,

full texts of which may be obtained on request from:

Safety and Vigilance: Medicines,

EMP-HIS, World Health Organization,

1211 Geneva 27, Switzerland, E-mail address: [email protected]

This Newsletter is also available at: http://www.who.int/medicines

The WHO Pharmaceuticals Newsletter provides you with the latest information on the safety of medicines and legal actions taken by regulatory authorities around the world. It also provides signals based on information derived from the WHO global database of individual case safety reports, VigiBase.

This newsletter also includes a brief report on WHO missions to Lebanon and Ethiopia, for strengthening the national pharmacovigilance systems.

Contents

Regulatory matters

Safety of medicines

Signal

Feature

WHO Pharmaceuticals

NEWSLETTER 2019

No.2

© World Health Organization 2019

Some rights reserved. This work is available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo).

Under the terms of this licence, you may copy, redistribute and adapt the work for non-commercial purposes, provided the work is appropriately cited, as indicated below. In any use of this work, there should be no suggestion that WHO endorses any specific organization, products or services. The use of the WHO logo is not permitted. If you adapt the work, then you must license your work under the same or equivalent Creative Commons licence. If you create a translation of this work, you should add the following disclaimer along with the suggested citation: “This translation was not created by the World Health Organization (WHO). WHO is not responsible for the content or accuracy of this translation. The original English edition shall be the binding and authentic edition”.

Any mediation relating to disputes arising under the licence shall be conducted in accordance with the mediation rules of the World Intellectual Property Organization.

Suggested citation. WHO Pharmaceuticals Newsletter No.2, 2019: World Health Organization; 2019. Licence: CC BY-NC-SA 3.0 IGO.

Cataloguing-in-Publication (CIP) data. CIP data are available at http://apps.who.int/iris.

Sales, rights and licensing. To purchase WHO publications, see http://apps.who.int/bookorders. To submit requests for commercial use and queries on rights and licensing, see http://www.who.int/about/licensing.

Third-party materials. If you wish to reuse material from this work that is attributed to a third party, such as tables, figures or images, it is your responsibility to determine whether permission is needed for that reuse and to obtain permission from the copyright holder. The risk of claims resulting from infringement of any third-party-owned component in the work rests solely with the user.

General disclaimers. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of WHO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement.

The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by WHO in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters.

All reasonable precautions have been taken by WHO to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall WHO be liable for damages arising from its use.

Printed in Switzerland

WHO Pharmaceuticals Newsletter No. 2, 2019 3

Table of Contents

Regulatory Matters

Baloxavir marboxil ......................................................................... 5

Carbimazole .................................................................................. 5

Deferiprone .................................................................................. 5

Eliglustat ...................................................................................... 5

Febuxostat ................................................................................... 6

Fenspiride .................................................................................... 6

Finasteride ................................................................................... 6

Fingolimod .................................................................................... 6

Glecaprevir hydrate/pibrentasvir combination .................................... 7

Hydrochlorothiazide ....................................................................... 7

Lithium ........................................................................................ 7

Macitentan ................................................................................... 8

Nivolumab (genetical recombination) ................................................ 8

Opioids ........................................................................................ 8

Oseltamivir ................................................................................... 9

Palbociclib .................................................................................... 9

Pembrolizumab ............................................................................. 9

Sodium-glucose co-transporter 2 (SGLT2) inhibitors ........................... 9

Trastuzumab (genetical recombination) .......................................... 10

Safety of medicines

Carbimazole ................................................................................ 11

Chlorhexidine digluconate ............................................................. 11

Dipeptidyl peptidase-4 (DPP-4) inhibitors ........................................ 12

Fluoroquinolones ......................................................................... 12

Paracetamol (modified-release) ..................................................... 12

Rivaroxaban ............................................................................... 12

Tofacitinib .................................................................................. 13

Signal

Esomeprazole and gynaecomastia in obese adults ............................ 14

Glibenclamide/Glyburide and palpitations in the Asian population ....... 17

Phenprocoumon – Accidental overdose ........................................... 23


Table of Contents

Selegiline and hypoglycaemia in underweight adults ......................... 23

Feature

Strengthening Pharmacovigilance in Lebanon and Ethiopia ................ 27


Signal

References

1. Meyboom, RH et al. Proton-pump inhibitors and gynecomastia. Nederlands Bijwerkingen Centrum Lareb, August 2005.

2. electronic Medicines Compendium: Summary of Product Characteristics for esomeprazole (Emozul®). Available from: https://www.medicines.org.uk/emc/product/4727. Accessed: 2018-03-29.

3. US Food and Drug Administration: Product label for esomeprazole (Nexium®). Available from: https://dailymed.nlm.nih.gov/dailymed/drugInfo. cfm?setid=f4853677-1622-4037-688b-fdf533a11d96. Accessed: 2018-03-29.

4. Johnson RE, Murad MH. Gynecomastia: Pathology, Evaluation and Management. Mayo Clin Proc. 2009;84(11):1010-15.

5. Roberto G, Melis M, Biagi C. Drug-induced gynecomastia. Focus Farmacovigilanza. 2013;77(7):2.

6. Satoh T, Munakata H, Fujita K, Itoh S, Kamataki T, Yoshizawa I. Studies on the interactions between drug and estrogen: II. On the inhibitory effect of 29 drugs reported to

induced gynaecomastia on the oxidation of estradiol at C-2 or C-17. Biol Pharm Bull. 2003;26(5):695-700.

7. Carvajal A, Macias D, Gutierrez A, Ortega S, Sáinz M, Martín Arias LH, et al. Gynaecomastia associated with proton pump inhibitors: a case series from the Spanish Pharmacovigilance System. Drug Saf. 2007;30(6):527-31.

8. Goldstein JA, Ishizaki T, Chiba K, de Morais SM, Bell D, Krahn PM, et al. Frequencies of the defective CYP2C19 alleles responsible for the mephenytoin poor metabolizer phenotype in various Oriental, Caucasian, Saudi Arabian and American black populations. Pharmacogenetics. 1997;7:59-64.

9. Desta Z, Zhao X, Shin JG, Flockhart DA. Clinical significance of the cytochrome P4502C19 genetic polymorphism. Clin Pharmacokinet. 2002;41(12):913-58.

10. Lardinois CK, Mazzaferri EL. Cimetidine blocks testosterone synthesis. Arch Intern Med. 1985;145(5):920-2.

11. Nexium and Gynaecomastia - from FDA reports [histogram]. 2018. Available from: https://www.ehealthme.com/ds/nexium/gynaecomastia/. Accessed: 2018-01-30.

Glibenclamide/glyburide and palpitations in the Asian population Raquel Herrera Comoglio, Argentina

Summary

Glibenclamide (glyburide in the US) is an oral blood glucose lowering drug (BGLD), a second-generation, long-acting sulfonylurea (SU) approved for use in the US in 1984, following its introduction in Europe by several years; in other countries it was approved later (in Singapore in 1990). SUs are currently first-line agents or an add-on therapy to other oral hypoglycaemic agents (OHAs), usually metformin. About a quarter of newly-diagnosed patients initiate therapy with SUs.

Sulfonylureas stimulate insulin release by binding to specific sites on the beta cells (insulin secretagogue agent). As other SUs, glibenclamide is metabolized in the liver and excreted by the kidney. The genetically polymorphic cytochrome P450 (CYP), enzyme CYP2C9 is mainly responsible for the

hepatic metabolism of sulfonylureas (SUs). It has been found that pharmacokinetics of glibenclamide depends significantly on CYP2C9 genotypes. CYP2C9 pharmacogenetic variants are more frequent in South-Asian populations (10-25%) than in Caucasian ones (2%–6%).

Hypoglycaemia, a very well-known type A adverse effect of glibenclamide and other BGLDs, can manifest through a variety of symptoms. Palpitations are an unspecific symptom that can be the expression of the adrenergic counter-regulation to hypoglycaemia.

One hundred individual case safety reports (ICSRs) with the combination glibenclamide and palpitations were retrieved from VigiBase, the WHO global database of ICSRs on 15 January 2018. Many also mention other related terms, including


Signal

hypoglycaemia, sweating and blurred vision. Half of the set came from South-Asian countries. In the 59 reports in which time to onset (TTO) could be calculated, it ranged from 0 days to 20 years, with the majority (30 reports) in the 0 to 10 days group. Of these 30 reports, 21 originated from South-Asian countries.

To the best of our knowledge, for glibenclamide there is no regulatory labelling or warning that refers to an increased risk of hypoglycaemia in patients with defective isoenzyme genetics.

ICSRs contain limited information, and a patient’s pharmacogenetic status is usually never stated. In spite of these known limitations, we have identified characteristics of ICSRs mentioning glibenclamide as a suspected drug, and palpitations as an early hypoglycaemic symptom that could be suggestive of a defective metabolism (short time-to-onset, geographic region). We hypothesize that patients presenting with hypoglycaemia and with a short TTO could have defective genetic variants, and therefore this is considered a signal.

Introduction

Glibenclamide is an oral blood glucose lowering drug (BGLD), a second-generation sulfonylurea (SU). SUs induce glucose-independent insulin release from the pancreatic β-cells by binding to the ATP-sensitive potassium (KATP) channel. The polymorphic enzyme cytochrome P450 (CYP) 2C9 is the main enzyme catalysing the biotransformation of SUs.

Hypoglycaemia is a pharmacological, dose-dependent (type A) adverse effect of blood glucose lowering agents, especially insulin and insulin secretagogues. Mild hypoglycaemia is associated with adrenergic and neurogenic symptoms, such as tremor, palpitations and perspiration. Severe hypoglycaemia is characterized by symptoms related to reduced glucose to the brain, such as weakness, poor concentration, slurred speech, confusion or even seizure or coma.

In normal individuals, hypoglycaemic counter regulation is a multifactorial process that involves reduction of insulin secretion, increasing glucagon secretion, adrenergic activation, increased growth hormone and cortisol secretion. Hypoglycaemia increases plasma levels of both epinephrine and norepinephrine, released primarily from the adrenal medulla. Recovery from hypoglycaemia is dependent on the adrenergic response. Individuals with preserved autonomic neurological response manifest these higher levels of catecholamines through palpitations, increased heart rate, piloerection, etc.1

A number of factors can increase the possibility of hypoglycaemic events: over-prescribing, inappropriate dosing, changes in exercise or diet, pharmacodynamic (PD) interactions (other blood glucose lowering agents),2 pharmacokinetic (PK)

interactions (agents with high protein-binding), antibiotics, substandard or fake medicines, comorbidities (infection, pancreatic cancer, cancer, renal, hepatic and cardiovascular disease) and patient characteristics (elderly, BMI, poor nutrition).2,3 In addition, published case reports and case series have highlighted the association between CYP2C9 genetic polymorphisms and hypoglycaemic events.

Glibenclamide’s daily dosage is between 1.25 and 20 mg. The usual starting dose of glibenclamide tablets is 2.5 to 5 mg daily.4 The defined daily dose (DDD) is 7 mg or 10 mg (micronized and non-micronized, respectively).5 Glibenclamide is not recommended in the elderly or in individuals with a glomerular filtration rate (GFR)<50 mL/min. The combination of glibenclamide and metformin may have a synergistic effect, since both agents act to improve glucose tolerance by different, but complementary mechanisms.4

Several studies which evaluated the safety of SUs consistently showed that glibenclamide is associated with a higher risk of hypoglycaemia when compared to other SUs, including glipizide, gliclazide and glimepiride.6-9 Glibenclamide was associated with a 52% greater risk of experiencing at least one episode of hypoglycaemia compared with other secretagogues (relative risk 1.52 [95% CI 1.21-1.92]) and with an 83% greater risk compared with other sulfonylureas (1.83 [1.35-2.49]).10

According to the National List of Essential Medicines (NLEMs), glibenclamide (2.5 and 5.0 mg) is an essential SU in five countries, India, Pakistan, Nepal, Sri Lanka and Bangladesh. In Sri Lanka, glibenclamide is the most commonly prescribed SU by GPs.11

The polymorphic CYP2C9 isoenzyme catalyses the biotransformation of SUs in the liver. The mutant alleles CYP2C9*2 and CYP2C9*3 are known to have a reduced drug-metabolizing activity than the wildtype CYP2C9*1, the decrease in catalysing activity of the *3 allele being more pronounced. The *3 variant is most common in Asians with a frequency of 10%–25% compared to that of 2%–6% in Caucasians. Involvement of CYP2C19 in the metabolism of SUs is also reported. CYP2C19*2 and CYP2C19*3 are variants that encode a non-functional CYP2C19 enzyme. Individuals with either of the variants are labelled as poor metabolizers.12

In Asian populations, genetic variability of CYP2C9 is dominated by the less functioning allele *3 (3.4% in East Asians and 11.3% in South Asians) while in Europeans, genetic variability expresses mainly the *2 variant (Europeans 11.7% and admixed Americans 6.6%), Other allelic variants are also present in South Asian and African populations. 13


Signal

Reports in VigiBase

A total of 100 Individual Case Safety Reports (ICSRs), with the combination glibenclamide and palpitations were retrieved from VigiBase, the WHO global database of ICSRs, on 15 January 2018, and were reviewed case by case. Four repeated reports were identified, and there was a likely duplicate from the US, therefore only 95 cases were considered. Of these, 47 reports were from Asian countries and 48 reports from non-Asian countries. Most reports included hypoglycaemic symptoms (dizziness, sweating, vision blurred, etc.), and 18 explicitly reported hypoglycaemia. Most of the Asian ICSRs (34/47, 72%) had a completeness score (level of documentation) >0.50. Case series distribution of gender, region and completeness score is set out in Table 1.

Table 1. Case series characteristics: gender, region and completeness score

Gender

Total n (%)

Female 60 (63.16)

Male 30 (31.6)

Unknown 5 (5.3)

Number of reports (%)

Asian countries n 47 (49.5%)

Other countries n 48 (50.5%)

Thailand 26 (27.3%) US 29 (30.5%)

India 8 (8.4%) Germany 4 (4.2%)

Singapore 6 (6.3%) Peru 3 (3.2%)

China, Malaysia

6 (6.3%) 3 each

Canada, Sweden

4 (4.2%) 2 each

Japan 1 (1.1%) Australia, Italy, Denmark, Eritrea, Namibia, Oman, Spain and United Kingdom

8 (8.4%) 1 each

Completeness Score

Total n (%)

≥ 0.70 23 (24.2)

0.31 - 0.69 43 (45.3)

0.1 - 0.28 29 (30.5)

In Asian ICSRs, the reporter is given in 35 reports (74.5%), and all but one were physicians, the remaining one, a pharmacist. In 32 ICSRs, the sender’s comment highlighted the Asian origin.

Patients were relatively younger in Asia than in the other countries: 36/47 (76.6%) were ≤ 64 years old. The age distribution is described in Table 2.

Table 2. Age distribution

Age Asian countries n/n total (%)

Other countries n/n total (%)

N Total (%)

Reported 47/47 (100%) 39/48 (81%)

6 years 0 1/48 (2%) 1 (1.1%)

24-44 years 11/47 (23%) 0 11 (11.6%)

45-64 years 25/47 (53%) 22/48 (46%) 47 (49.5%)

65-74 years 6/47 (13%) 7/48 (15%) 13 (13.7%)

above 75 years 5/47 (11%) 9/48 (19%) 14 (14.7%)

Unknown 0 9 (19%) 9 (9.5%)

Total ≥65 years

11/47 (23%) 16/48 (31%) 26/95 (27.4%)

Total ≤ 64 years

36/47 (77%) 23/48 (52%) 61/95 (64.2%)

Onset date and at least start date of glibenclamide therapy were stated in 59 out of 95 ICSRs (57.9%); so it was possible to calculate time-to-onset (TTO) in this subset of reports. In three ICSRs, the onset date was stated as previous to the reported start of drug administration.

In 28 Asian reports, TTO ranged from 0 to 60 days, and in 21 reports, TTO was up to 10 days. In six ICSRs symptoms manifested the same day, and in 11 ICSRs TTO was 1 day. Time to onset is set out in Table 3.

Table 3. Time to onset

Time to onset (TTO) n (total) n (Asia)

0-10 days 30 21

12-28 days 4 4

Within a month* 4 2

33-36 days 2 1

43-61 days 3 2

90-270 days 9 4

1-20 years 7 2

Total 59 36

* Reports with the same year and month reported for drug start and reaction onset The dose for glibenclamide was reported in 65 cases (68.4%) and ranged from 1.8 mg to 20 mg daily. Fifty ICSRs reported low doses: one 1.8 mg, 13 cases between 2-2.5 mg, 33 a dose of 5 mg, two with 3.5 mg and one with 7.5 mg. Nine patients were treated with 10 mg, three with 15 mg and another three patients with 20 mg. The percentage of patients with doses ≤ 5 mg was similar in Asian countries and in non-Asian countries (77% and 76% respectively).

Thirteen reports (14%) were classed as serious, two of them were fatal, and 18 (19%) were classed as non-serious. In the two fatal cases, other BGLDs


Signal

were suspected (ipragliflozin and sitagliptin); glibenclamide is reported as not withdrawn. Two ICSRs reported syncope as an adverse reaction, which indicates a more serious state.

Alternative causes of hypoglycaemia

Possible interactions: An ICSR from a non-Asian country reports hypoglycaemia in a 78-year-old woman treated with hydroxychloroquine because of a rheumatoid arthritis. Three Asian country reports mention drugs possibly interacting: one of a 75-year-old woman, who started a triple therapy with metformin 1 g, pioglitazone 30 mg and glibenclamide 5 mg, and presented with palpitations and dizziness on the seventh day; another one mentions phenytoin administration with confusing TTO dates; another report mentions hypoglycaemic symptoms the same day that amoxicillin was administered, and 10 days after the therapy with glibenclamide was started.

Changes in physical activity: An ICSR from a non-Asian country reports the case of a 62-year-old woman under therapy with glibenclamide and metformin who developed bouts of palpitations, confusion, and chest discomfort, particularly at times of unpredicted physical activity.

Incident comorbidities and incident concomitant therapies: An ICSR from an Asian country reports hypoglycaemia in a 52-year-old woman who was started on dual therapy with metformin 1 g daily and glibenclamide 5 mg; the TTO was 10 days. On day 8, a prescription of 2 g of amoxicillin is reported (only one-day of treatment), and doxycycline 200 mg daily, given for three days, because of an acute upper respiratory infection. Some anti-infective agents may enhance the hypoglycaemic effect of glibenclamide and infection itself can trigger hypoglycaemia.

An ICSR from a non-Asian country reports hypoglycaemia and palpitations in a 59-year-old woman treated with glibenclamide 5 mg over nine months. Two days before the hypoglycaemic event occurred, the patient had received medication for a cardiac event (morphine, nifedipine, isosorbide dinitrate, furosemide, enalapril).

Other alternative causes: In two reports from a non-Asian country (TTO 170 days and 215 days), palpitations are more likely related with other morbidities (atrial fibrillation, reduced left ventricular ejection fraction). In another ICSR from an Asian country (TTO 270 days), other medications suggesting an acute coronary syndrome are reported.

Literature and labelling

The US FDA label only mentions CYP2C9 as induced by rifampicin, and potentially reducing glibenclamide plasma levels as a consequence. There is no mention of poor metabolizers.4 The

Health Sciences Authority of Singapore issued a warning about glibenclamide in older patients and renal impairment. However, there is no mention of pharmacogenetic variability.14

A statement from the Royal Dutch Pharmacists Association Working Group concluded that there are no dose recommendations based on patients’ pharmacogenetic status to give at this time (2011).15

“Place of sulfonylureas in the management of type 2 diabetes mellitus in South Asia: A consensus statement”, an initiative of the South Asian Federation of Endocrine Societies (SAFES), developed in accordance with the American Association of Clinical Endocrinologists/ American College of Endocrinology (AACE/ACE) doesn’t mention pharmacogenetic variability as a cause of sulfonylurea intolerance.11

According to a paper published in 2014, gefitinib product information is the only EMA label containing a warning about CYP2C9 metabolization.16

SUs Metabolism and isoenzymes genetic polymorphisms

A PK study performed in healthy male volunteers showed that in homozygous carriers of the genotype *3/*3, total oral clearance was less than half of that of the wildtype genotype *1/*1 (P <.001). Correspondingly, insulin secretion measured within 12 hours after glyburide ingestion was higher in carriers of the genotype *3/*3 compared with the other genotypes (P =.028), with no clinical effects.17

In a case-control study of 20 diabetic patients admitted to the emergency department with severe hypoglycaemia during SU drug treatment, it was found that the CYP2C9 genotypes *3/*3 and *2/*3 that are predictive of low enzyme activity were more common in the hypoglycaemic group than in the comparison groups (10% vs < 2%, respectively). Other factors in the group with severe hypoglycaemia were lower body mass indexes, higher rates of renal failure, older age, and higher doses of glibenclamide.18

In a study assessing the frequency of CYP2C9 genetic variants in Type 2 diabetes mellitus (T2DM) patients receiving sulfonylureas (92 reporting drug-associated hypoglycaemia, and 84 having never experienced hypoglycaemia), it was found that the presence of the allele CYP2C9*3 increased the risk of hypoglycaemia (OR: 1.687, adjusted for age, BMI, mean daily dose of SU, duration of T2DM and renal function; p = 0.011).19

A study performed in Chinese healthy male volunteers found that CYP2C9 polymorphism appears to exert a dominant influence on glibenclamide pharmacokinetics and pharmacodynamics in vivo; hypoglycaemia developed in 3 of 6 CYP2C9*1/*3 carriers and 2 of 12 CYP2C9*1/*1 carriers.20


Signal

A prospective population-based study did not observe over-representation of the CYP2C9 slow metabolizer genotypes in the hypoglycaemic patients group. However, in the control group, patients with CYP2C9 genotypes predicting slower metabolism of SU drugs were treated with significantly lower doses than were extensive metabolizers.21

A study performed in Turkey with 108 diabetic patients treated for ≥ 3 months with SUs (glimepiride, gliclazide, glipizide) found that heterozygosity and homozygosity for CYP2C9 variant alleles (*2 or *3) tended to be more frequent among patients who reported hypoglycaemic attacks.22

Interactions: Several published cases report hypoglycaemia induced by hydroxychloroquine, and observational studies suggest a dose-dependent protective effect of hydroxychloroquine on drug-induced diabetes in rheumatic patients treated with corticosteroids.23-26 Co-administration of anti-infective agents that are CYP2C9 inhibitors can increase the risk of hypoglycaemia in glipizide and glyburide users.26 Therefore, co-administration of other CYP2C9 inhibitors might also increase the risk of hypoglycaemia, although an increased risk of hypoglycaemia might not be present with less strong (non-clinically-relevant) CYP2C9 inhibitors. P-Glycoprotein inhibitors might also increase the risk of hypoglycaemia.26 A few ICSRs of this set mentioned suspected or concomitant drugs that could interact with glibenclamide. It is worth noting that an interacting agent can theoretically exert additive effects on an isoenzyme with genetically reduced functionality.

Discussion and conclusion

Hypoglycaemia is a sulfonylureas dose-related type A adverse effect. In subjects with preserved autonomic function, the fall in blood glucose levels triggers the adrenergic counter regulation, which manifests through palpitations. Glibenclamide is metabolized by CYP2C9 isoenzyme, which is highly polymorphic. The frequency of polymorphism of defective CYP2C9 alleles (CYP2C9*2 and CYP2C9*3 variants) in South-Asian populations is reported to be 10-25% of a total population; defective isoforms are also present – but much less frequently - in Caucasian populations. Poor metabolizers (CYP2C9*2 and CYP2C9*3 alleles carriers) can show higher plasmatic levels of glibenclamide, leading to low blood glucose levels and triggering counter regulation mechanisms, such as adrenergic response.

Even though observational studies have not shown conclusive results, a literature review supports a relationship between clinical effects and CYP2C9 polymorphic variants. A PK study conducted in Caucasian volunteers saw no clinical effects of the difference in metabolization, but hypoglycaemia was more frequent in one conducted with Chinese volunteers.

To date, no regulatory labelling or warning has highlighted the contribution of poor metabolizer status on higher frequency or severity of hypoglycaemia in patients treated with glibenclamide.

Even though ICSRs provide no information on the pharmacogenetic status of patients, short TTO and, to a lesser extent source countries may suggest a pharmacogenetic cause. This can also be a class characteristic.

In this set of VigiBase reports, the proportion of cases with short TTO, and the number of good quality reports coming from Asian countries in relatively young patients might be considered as a signal of the potential association of an increased frequency of palpitations as symptoms of hypoglycaemia in patients treated with glibenclamide who are poor metabolisers. Also, in 32 ICSRs, the sender’s comments highlighted the Asian origin. This hypothesis would need specific pharmacogenetic studies in patients treated with glibenclamide and experiencing hypoglycaemia with short TTO or interacting medications.

References

1. Hoffman RP. Sympathetic mechanisms of hypoglycemic counterregulation. Curr Diabetes Rev. 2007 Aug;3(3):185-93.

2. Scheen AJ. Drug Interactions of Clinical Importance with Antihyperglycaemic Agents: An Update. Drug Saf. 2005 Jul;28(7):601-631.

3. Thulé PM, Umpierrez G. Sulfonylureas: a new look at old therapy. Curr Diab Rep. 2014 Apr;14(4):473.

4. Glyburide FDA label: https://www.accessdata.fda. gov/drugsatfda_docs/label/2009/017532s030lbl.pdf

5. ATC/DDD Index 2018 2018 https://www.whocc.no/ atc_ddd_index/?code=A10BB01

6. Leonard CE, Han X, Brensinger CM, Bilker WB, Cardillo S, Flory JH, Hennessy S. Comparative risk of serious hypoglycemia with oral antidiabetic monotherapy: A retrospective cohort study. Pharmacoepidemiol Drug Saf. 2018 Jan;27(1):9-18.

7. Douros A, Yin H, Yu OHY, Filion KB, Azoulay L, Suissa S. Pharmacologic Differences of Sulfonylureas and the Risk of Adverse Cardiovascular and Hypoglycemic Events. Diabetes Care. 2017 Nov;40(11):1506-1513.

8. Zhou M, Wang SV, Leonard CE, Gagne JJ, Fuller C, Hampp C, Archdeacon P, Toh S, Iyer A, Woodworth TS, Cavagnaro E, Panozzo CA, Axtman S, Carnahan RM, Chrischilles EA, Hennessy S. Sentinel Modular Program for Propensity Score-Matched Cohort Analyses:


Signal

Application to Glyburide, Glipizide, and Serious Hypoglycemia. Epidemiology. 2017 Nov;28(6):838-846.

9. Shorr RI, Ray WA, Daugherty JR, Griffin MR. Individual sulfonylureas and serious hypoglycemia in older people. J Am Geriatr Soc. 1996 Jul;44(7):751-5.

10. Gangji AS, Cukierman T, Gerstein HC, Goldsmith CH, Clase CM. A systematic review and meta-analysis of hypoglycemia and cardiovascular events: a comparison of glyburide with other secretagogues and with insulin. Diabetes Care. 2007 Feb;30(2):389-94.

11. Kalra S, Aamir AH, Raza A, Das AK, Azad Khan AK, Shrestha D, Qureshi MF, Md Fariduddin, Pathan MF, Jawad F, Bhattarai J, Tandon N, Somasundaram N, Katulanda P, Sahay R, Dhungel S, Bajaj S, Chowdhury S, Ghosh S, Madhu SV, Ahmed T, Bulughapitiya U2.Place of sulfonylureas in the management of type 2 diabetes mellitus in South Asia: A consensus statement. Indian J Endocrinol Metab. 2015 Sep-Oct;19(5):577-96.

12. Dawed AY, Zhou K, Pearson ER. Pharmacogenetics in type 2 diabetes: influence on response to oral hypoglycemic agents. Pharmgenomics Pers Med. 2016 Apr 6;9:17-29.

13. Zhou Y, Ingelman-Sundberg M, Lauschke VM. Worldwide Distribution of Cytochrome P450 Alleles: A Meta-analysis of Population-scale Sequencing Projects. Clin Pharmacol Ther. 2017 Oct;102(4):688-700.

14. Singapore government, Health Science Authority HSA Recommendations to avoid use of glibenclamide in the elderly and renal-impaired. 31 Dec 2013: http://www.hsa.gov.sg/content/hsa/en/ Health_Products_Regulation/Safety_Information_ and_Product_Recalls/Product_Safety_Alerts/2013/ recommendations_to.html

15. Annotation of DPWG Guideline for glibenclamide and CYP2C9. https://www.pharmgkb.org/guideline/ PA166104953 Accessed 18 February 2018.

16. Ehmann F, Caneva L, Papaluca M. European Medicines Agency initiatives and perspectives on pharmacogenomics. Br J Clin Pharmacol. 2014 Apr;77(4):612-7.

17. Kirchheiner J, Brockmöller J, Meineke I, Bauer S, Rohde W, Meisel C, Roots I. Impact of

CYP2C9 amino acid polymorphisms on glyburide kinetics and on the insulin and glucose response in healthy volunteers. Clin Pharmacol Ther 2002; 71: 286–96.

18. Holstein A, Plaschke A, Ptak M, Egberts EH, El-Din J, Brockmöller J, Kirchheiner J Association between CYP2C9 slow metabolizer genotypes and severe hypoglycaemia on medication with sulphonylurea hypoglycaemic agents. Br J Clin Pharmacol. 2005 Jul;60(1):103-6.

19. Ragia G, Petridis I, Tavridou A, Christakidis D, Manolopoulos VG.Presence of CYP2C9*3 allele increases risk for hypoglycemia in Type 2 diabetic patients treated with sulfonylureas. Pharmacogenomics. 2009 Nov;10(11):1781-7.

20. Yin OQ, Tomlinson B, Chow MS. CYP2C9, but not CYP2C19, polymorphisms affect the pharmacokinetics and pharmacodynamics of glyburide in Chinese subjects. Clin Pharmacol Ther. 2005 Oct;78(4):370-7.

21. Holstein A, Hahn M, Patzer O, Seeringer A, Kovacs P, Stingl J. Impact of clinical factors and CYP2C9 variants for the risk of severe sulfonylurea-induced hypoglycemia Eur J Clin Pharmacol. 2011 May;67(5):471-6.

22. Gökalp O, Gunes A, Cam H, Cure E, Aydın O, Tamer MN, Scordo MG, Dahl ML. Mild hypoglycaemic attacks induced by sulphonylureas related to CYP2C9, CYP2C19 and CYP2C8 polymorphisms in routine clinical setting. Eur J Clin Pharmacol. 2011 Dec;67(12):1223-9.

23. Winter EM, Schrander-van der Meer A, Eustatia-Rutten C, Janssen M. Hydroxychloroquine as a glucose lowering drug. BMJ Case Rep. 2011 Oct 28;2011.

24. Shojania K, Koehler BE, Elliott T. Hypoglycemia induced by hydroxychloroquine in a type II diabetic treated for polyarthritis. J Rheumatol. 1999 Jan;26(1):195-6.

25. Chen YM, Lin CH, Lan TH, Chen HH, Chang SN, Chen YH, Wang JS, Hung WT, Lan JL, Chen DY. Hydroxychloroquine reduces risk of incident diabetes mellitus in lupus patients in a dose-dependent manner: a population-based cohort study. Rheumatology (Oxford). 2015 Jul;54(7):1244-9

26. Schelleman H, Bilker WB, Brensinger CM, Wan F, Hennessy S. Anti-infectives and the risk of severe hypoglycemia in users of glipizide or glyburide. Clin Pharmacol Ther. 2010 Aug;88(2):214-22.

Type 2 diabetes mellitus (T2DM) is a multifactorial, chronic, progressive disease, affecting more than 422 million people over the world, and having a significant societal and economic impact. Cardiovascular disease is the leading cause of morbidity and mortality in T2DM patients, who have higher rates of mortality than the non-diabetic population.

T2DM is defined by its metabolic -mainly glucose-related- manifestations which serve as markers for controlling the evolution of disease. However, while the effect of control serum glucose levels on microvascular complications is acknowledged, its impact on macrovascular complications remains uncertain.

Since 2008, new blood glucose-lowering agents have to demonstrate cardiovascular safety, and some have shown to reduce cardiovascular outcomes and mortality. However, the populations included in these large cardiovascular outcome trials differ from the general population, making results no fully generalizable.

While randomised controlled trials are the gold standard for generating scientific evidence, observational studies conducted with secondary data of Electronic medical records (EMRs) are increasingly used as a source of complementary or confirmatory evidence, especially when RCTs are not feasible or unavailable.

This work report an observational, population-based cohort study conducted in SIDIAP, a large Catalan general practitioners database that contains health data of 5,5 million people. We assessed cardiovascular outcomes and mortality in general, unselected T2DM population treated with non-insulin blood-glucose-lowering agents. The results are expected to be useful both for clinical and public health decision-making.

DOCTEUR DE L'UNIVERSITÉ DE BORDEAUX

Documents

Transcript of DOCTEUR DE L'UNIVERSITÉ DE BORDEAUX