Pitfalls of using microalbuminuria as a screening tool to ...

Pitfalls of using microalbuminuria as a screening tool to

identify subjects with increased risk for kidney and

cardiovascular disease

Results of the Unreferred Renal Insufficiency (URI) study and the Early Renal

Impairment and Cardiovascular Assessment in Belgium study (ERICABEL).

Arjan van der Tol

Promotor: Prof. dr. Wim Van Biesen

Co-promotor: Prof. dr. Raymond Vanholder

Thesis submitted in fulfillment of the requirements for the degree of ‘Doctor in Health Sciences’ 2014

Contents

TABLE OF CONTENTS

LIST OF ABBREVIATIONS ....................................................................................................... 5

CHAPTER 1: INTRODUCTION ................................................................................................ 7

1.1 Preface ................................................................................................................................... 9

1.2. Normal renal handling of albuminuria ................................................................................. 11

1.3. Mechanisms of tubular and glomerular albuminuria ........................................................... 13

1.4. Albuminuria as a manifestation of chronic kidney disease ................................................. 18

1.5. Albuminuria as a manifestation of cardiovascular disease .................................................. 21

1.6. The prevalence of albuminuria ............................................................................................ 25

1.7. How to measure albuminuria ............................................................................................... 27

1.8. Outline and aims of the thesis .............................................................................................. 30

1.9. Participants and methods ..................................................................................................... 31

1.10. References ............................................................................................................................ 33

CHAPTER 2: RESULTS ............................................................................................................ 43

2.1. Screening for cardiovascular and renal markers in unselected subjects: results

from URI trial ...................................................................................................................... 43

2.1.1. Towards a rational screening strategy for albuminuria ............................................. 45

2.1.2. Microalbuminuria is more consistent in presence of risk factors ............................... 65

2.1.3. Should screening of renal markers be recommended in a working population ......... 78

2.2. Screening for kidney disease in selected hypertensive subjects: results from the

ERICABEL study ................................................................................................................ 96

2.2.1 Statin use and the presence of microalbuminuria ....................................................... 97

CHAPTER 3: GENERAL DISCUSSION AND CONCLUSIONS ....................................... 115

3.1. The prevalence of microalbuminuria and macroalbuminuria, and the associations

with traditional cardiovascular risk factors in a general population .................................. 118

3.2. Should we use microalbuminuria as a screening tool to identify subjects with unrecognized

cardiovascular risk factors in a presumably healthy population ........................................ 120

Contents

3.3. Conditions which could lead to false positive test results of microalbuminuria ............... 121

3.4. Can we prevent ESRD by screening for microalbuminuria in a general population ......... 123

3.4.1. The disease should be an obvious burden for the individual and the community

in terms of death, poor quality of life and socio-economic factors including

health care costs ........................................................................................................ 124

3.4.2. The natural course of disease should be well-known and the disease should go

through an initial latent stage or be determined by risk factors, which can be

detected by appropriate tests .................................................................................... 125

3.4.3. A suitable test is highly sensitive and specific for the disease as well as being

acceptable to the person screened ............................................................................ 126

3.4.4. Screening followed by diagnosis and interventions in an early stage of the disease

should provide a better prognosis than intervention after spontaneously sought

treatment ................................................................................................................... 127

3.4.5. Adequate treatment or other intervention possibilities are indispensable.

Adequacy is determined both by proven medical effect and ethical and legal

acceptability .............................................................................................................. 128

3.4.6. The cost of case-finding should be economically balanced in relation to possible

expenditure on medical care as a whole ................................................................... 129

3.4.7. Case-finding should be a continuing process and the interval of testing should be

known ....................................................................................................................... 130

3.5 Conclusions ........................................................................................................................ 132

3.6 References .......................................................................................................................... 135

3.7 Conclusies .......................................................................................................................... 143

3.8 References .......................................................................................................................... 146

Dankwoord .................................................................................................................................. 149

Curriculum Vitae ......................................................................................................................... 151

List of abbreviations

LIST OF ABBREVIATIONS

ACR urinary albumin creatinine ratio (mg/g)

ACE-I angiotensin concerting enzyme inhibitor

ADA American Diabetes Association

ADVANCE Preterax and Diamicron Modified Released Control Education study

AER albumin excretion rate (mg/24 h)

AKI acute kidney injury

ARB angiotensin receptor blocker

Aus Diab Kidney study Australian Diabetes, Obesity and Lifestyle Study

BMI body mass index

BP blood pressure

CGA CKD classification: Cause, Glomerular filtration rate, Albuminuria

CVD cardiovascular disease

CKD chronic kidney disease

CKD-EPI Chronic Kidney Disease Epidemiology Collaboration

DALY disability adjusted life year

DM diabetes mellitus

EPIC-Norfolk European Prospective Investigation into Cancer in Norfolk

ERA-EDTA European Renal Association-European Dialysis Transplantation Association

ERICABEL Early Renal Impairment and Cardiovascular Assessment in Belgium study

ESRD end stage renal disease

GDP gross domestic product

GFR glomerular filtration rate

HMG-CoA reductase 3-hydroxy-3-methylglutaryl-CoA reductase

HOPE Heart Outcomes and Prevention Evaluation Study

HPLC high performance liquid chromatography

HUNT Nord-Trøndelag Health Study

HT hypertension

IDMS isotope dilution mass spectrometry

IFCC International Federation of Clinical Chemistry

IGT impaired glucose tolerance

IMAU intermittent microalbuminuria

List of abbreviations

K/DOQI Kidney Disease Outcomes Quality Initiative

KDIGO Kidney Disease Improving Global Outcomes

KEAPS Kidney Evaluation and Awareness Program in Sheffield

NCEP National Cholesterol Education Program

NHANES National Health and Nutrition Examination Survey

NKF National Kidney Foundation

NPHS1/2 congenital nephrotic syndrome of Finnish type

MAU microalbuminuria

MDRD Modification of Diet in Renal Disease

OCRL-1 oculocerebrorenal locus 1

ONTARGET Ongoing Telmisartan Alone and in combination with Ramipril Global

Endpoint Trial

PCER physicochemical exposure risk

PCR protein creatinine ratio

PMAU persistent microalbuminuria

PolNef Early detection of chronic kidney disease in Poland

PREVEND Prevention of Renal and Vascular End Stage Disease Study Group

QALY Quality Adjusted Life Year

RAAS renin angiotensin aldosterone system

RCT randomized control trial

RENAAL Reduction of Endpoints in NIDDM with Angiotensin II Antagonist Losartan

ROADMAP Randomized Olmesartan and Diabetes Microalbuminuria Prevention

RRT renal replacement therapy

SGLT-2 sodium glucose co-transporter 2

SNP single nucleotide polymorphism

TRPC transient receptor potential canonical

UAC urinary albumin concentration (mg/l)

USRDS United States Renal Data System

URIS Unreferred Renal Insufficiency Study

WHO World Health Organization

Zo-1 Zona Occludens 1

CHAPTER 1: INTRODUCTION

CHAPTER 1: Introduction

9

1.1 Preface

Around 20-25% of the Belgian population has hypertension and 5% has diabetes mellitus (1, 2).

Patients with diabetes mellitus and/or hypertension have an increased risk for chronic kidney

disease (CKD), yet only a small proportion of these patients progresses to end stage renal disease

(ESRD) to a degree that renal replacement therapy (RRT) in the form of dialysis or kidney

transplantation is required (3, 4). The prevalence of RRT was 1200 per million persons in

Belgium at 31 December 2012 (5). Asymptomatic microalbuminuria might precede the

development of ESRD by years (6, 7). Timely detection of microalbuminuria in patients with

diabetes and hypertension, followed by administration of renal protective agents, might reduce

the number of patients with ESRD (8). Consequently, the American Diabetes Association (ADA)

recommends annual screening for microalbuminuria in diabetes patients (1). Yet, in only one fifth

of the Belgian RRT patients, renal failure was caused by diabetes (5). Additionally,

microalbuminuria is common in the general population and an independent predictor of

cardiovascular and renal outcome (7, 9). It has been suggested to organize mass screening using

microalbuminuria to identify subjects with increased risk for cardiovascular and renal events (3,

10, 11). Testing for microalbuminuria is easy to perform and does not cause inconvenience for

the tested person. In the present thesis, we measured cardiovascular risk factors and albuminuria

in a Belgian working cohort (URI: unreferred renal insufficiency study) and in a Belgian

hypertensive cohort (ERICABEL: Early Renal Impairment and Cardiovascular Assessment in

Belgium study). We investigated which parameters are associated with microalbuminuria in the

URI and ERICABEL study and whether screening for microalbuminuria is useful to identify

persons with cardiovascular and renal disease.


10

Figure 1: normal renal handling of albumin

Figure 1a. Normal glomerulus and proximal tubule. Figure 1b. The glomerular filtration barrier

comprises the innermost fenestrated glomerular endothelial cells, glomerular basement membrane and the

outermost podocytes. The endothelial cells synthesize a negatively charged glycocalyx that forms a plug

in the fenestrae. Podocytes attach to the outermost aspect of the glomerular filtration barrier by foot

processes, between which there is a protein layer comprising the size barrier slit diaphragm. Figure 1c.

Proximal tubular cell. Filtered albumin is taken up by the megalin/cubilin mediated endocytosis of

proximal tubular cells, is internalized by endosomes, and degradated by the lysozymes. Adapted from (12).

Cubilin Albumin

Proximal tubular cell

Parietal

epithelial cell

Glomerular basement membrane

Podocyte

Glomerular endothelial cell

Fenestrae

Parietal epithelial cell

me

galin


11

1.2 Normal renal handling of albuminuria

Under normal conditions, the glomerular filtration barrier is nearly impermeable for albumin. The

histologic components of the ’glomerular filtration barrier’ include the endothelial surface layer

of glomerular fenestrated capillaries; the glomerular basement membrane and the slit diaphragm

(fig. 1b). Albumin filtration is restricted by the negatively charged endothelial glycocalyx and the

glomerular basement membrane, and by the fine pores of the slit diaphragm (13-15). The slit

diaphragm is a dynamic junction between podocyte foot processes (16). These foot processes

form a scaffolding around the capillary loops that are anchored to the glomerular basement

membrane via α3β1 integrin and α-/β-dystroglycans (17). Although the diameter of the

glomerular slit pores is slightly smaller than the diameter of albumin (3.5 versus 3.6 nm), some

albumin molecules might pass through the slit diaphragm by the variation in diameter of the slit-

pores and the shape of albumin (14, 18). The flux of albumin across the glomerular filter is driven

by different forces: convection, diffusion and electrokinetic effects (19). Experimental research in

normal rats shows that the glomerular-sieving coefficient of albumin is 0.00062 (14). As the

filtration of albumin in humans is considered to be small too, the amount of albumin filtered per

day is significant because the glomerular filtration rate is so high (filtered albumin = glomerular

filtration rate x plasma albumin x glomerular sieving coefficient of albumin: thus daily filtered

albumin = 144 L/day (100 mL/min x 60 min. x 24h) x 37 g/l x 0.00062 = 3.3 g/day) (14). Most

part of the filtered albumin (fig. 1a) is reabsorbed by the proximal tubular cells (fig. 1c). The

tubular uptake of albumin is mediated by megalin and cubilin. Cubilin was originally identified

as the intestinal intrinsic factor-B12 receptor and megalin is a member of the LDL receptor

family (20).


12

Figure 2a shows a normally functioning proximal tubular cell with uptake of albumin, including

carrier albumin for hormones, calcium, folate and vitamin A, D, B12 (20). Following uptake and

degradation of albumin into albumin fragments, the compound is metabolized and/or returned to

the circulation (20).

Figure 2: Endocytic receptors in the proximal tubular renal cells

Figure 2a: Megalin and/or cubilin normally mediate the uptake of filtered proteins, including carrier

protein for vitamins and hormones. Following uptake and degradation of protein, the compound is

metabolized and/or returned to the circulation. 2b: Loss of functional receptor expression, as observed in

rare inherited disorders, leads to defective uptake and tubular proteinuria with the possible loss of

important nutrients and associated deficiencies. 2c: Tubular protein overload, leads to increased tubular

uptake of proteins causing tubular cell apoptosis and change of function with activation of

proinflammatory and profibrotic mediators. 2d: Shedding of receptors, as may be observed in diabetes,

leads to increased urinary excretion of receptors and possible tubular cell dysfunction. Adapted from (20).

a: Normal receptor

mediated uptake of

filtered proteins

b: Loss of receptor

function leading to

tubular proteinuria

c: Tubular protein

overload

d: Shedding of

receptors

Interstitial

inflammation and

fibrosis, nephron

loss

megalin cubilin albumin

vitamin binding protein

lumen

blood


13

1.3 Mechanisms of tubular and glomerular albuminuria

Albuminuria occurs by dysfunction of the glomerular filtration barrier and/or of the receptor

mediated endocytosis process of the proximal tubular cells. Figure 2b shows that dysfunction of

proximal tubular receptors results in albuminuria and loss of nutrients (20). Whether this

mechanism could lead to progressive CKD in humans has not been investigated yet. Mutations of

the megalin encoding gene occur in rare genetic disorders, such as Donnai-Barrow and Facio-

Oculo-Acustico-Renal Syndrome, characterized by multiple developmental anomalies and

asymptomatic tubular albuminuria (20). Mutations of the cubulin encoding gene occur in

Imerslund-Gräsebeck disease, characterized by asymptomatic tubular albuminuria and

megaloblastic anemia due to intestinal and tubular vitamin B12 malabsorption (20-22). A

missense variant (SNP) in the cubulin gene is associated with tubular albuminuria in the general

and diabetic populations (23). The presence of tubular albuminuria in Dent’s disease is related to

genetic mutations (CLCN5 or OCRL1) causing abnormal endosomal and lysosomal trafficking in

the proximal tubular cells (24, 25). The tubular dysfunction in Dent’s disease is associated with

Fanconi syndrome, albuminuria, hypercalciuria, nephrocalcinosis, nephrolithiasis and renal

failure (26).

Increased protein synthesis or glomerular albumin leakage might result in albuminuria as tubular

albumin receptor mediated endocytosis is oversaturated (figure 2c). Tubular overflow proteinuria

might be an expression of a hematological disorder, yet it might also occur by exposure of toxic

agents, such as Chinese herbs, mercury or cadmium (27, 28). Both cadmium and mercury

increase the liver synthesis of metallothionein. This heavy metal binding low molecular weight

protein is freely filtered and is excreted by the kidney as the tubular reabsorption is saturated (29).

Conceivably, albuminuria protects the body, including the kidneys against the accumulation of

toxic heavy metals. However, in case of glomerular induced nephrotic range albuminuria,

receptor mediated endocytosis acts as a biological Trojan horse, by reabsorption of toxic fatty

acids and lipoproteins bound to albumin, leading to activation of pro-inflammatory and pro-

fibrotic mediators, tubular cell apoptosis, interstitial inflammation, fibrosis and nephron loss (20,

30-32). These receptors could be a potential target for therapy. Adenoviral delivery of antisense

RNA leading to partial knockdown of cubilin was shown to protect against adriamycine-induced

glomerulosclerosis and tubulointerstitial renal damage in rats despite resulting in higher levels of


14

albuminuria (33). In patients with nephrotic range proteinuria, statins might abrogate the toxic

effects of modified albumin by inhibiting protein uptake via inhibition of HMG-CoA reductase

and reduced prenylation of proteins involved in the endocytosis of the proximal tubular cell (34-

37), while in subjects with no albuminuria, statins induce tubular albuminuria by the same

mechanism (38-40).

Figure 2d shows that a reduction of megalin expression on the proximal tubular cells and the

coinciding increase of urinary megalin excretion, results in albuminuria. Shedding of the

receptors has been observed in diabetic patients, yet the underlying mechanism remains to be

elucidated (41). Additionally, diabetic patients with poor glycemic control have a higher

proportion of glycated albumin in urine than in plasma, which has been attributed to lower

efficiency of tubular uptake of modified glycated albumin (42). Reduction of albuminuria is

observed after correcting hyperglycemia (43). In vitro, megalin expression is reduced by

activation of tubular angiotensin II type 1A receptors (44). In diabetic rats, angiotensin II

blockade restores megalin expression associated with a decrease in albuminuria (45).

Angiotensin II plays a key role in renal hemodynamic and molecular mechanisms which lead to

albuminuria. Angiotensin II decreases renal blood flow and increases glomerular filtration for

albumin by inducing vasoconstriction of the efferent glomerular arterioles (46). Besides,

angiotensin II upregulates the expression of sodium-glucose cotransporter-2 (SGLT-2) in the

proximal tubules of patients with diabetes (47). As sodium reabsorption increases, the

concentration of sodium in the distal tubules decreases (47). This results in an inhibition of the

tubuloglomerular feedback that causes hyperfiltration by vasodilatation of afferent arterioles and

rising intraglomerular pressure (47). Interestingly, in adults with obesity and essential

hypertension glomerular hyperfiltration is also associated with increased sodium reabsorption in

the proximal tubules of the kidney (48). Additionally, direct release of angiotensin II, insulin,

prostaglandins, glucagon and growth hormones increases the intraglomerular pressure (46).

These hemodynamic disturbances lead to shedding of the endothelial surface layer of the

glomerular fenestrated capillaries and subsequent leakage of albumin into the sub-podocyte space

(15, 49). This consequently exposes the glomerular basal membrane and the podocytes to the

deleterious effects of modified albumin, resulting in thickening of basal membrane and retraction

of podocyte foot processes that lead to increased glomerular permeability for albumin (50).


15

The accompanying structural abnormalities, such as glomerular endothelial injury, foot process

detachment and podocyte loss are more marked in diabetic patients with macroalbuminuria than

in those with microalbuminuria or normoalbuminuria (51, 52). Traditionally, the presence of

microalbuminuria in diabetic patients is considered as a sign of early nephropathy. In the eighties,

Mogensen et.al. published retrospective data (figure 3) of 20 type 1 diabetic males who had poor

metabolic control and hyperfiltration, and who subsequently developed microalbuminuria, overt

nephropathy and decline of renal function over years (53-56).

Figure 3

Figure 3 Landmark publication of the natural course of diabetic nephropathy in 20 male patients with

insulin dependent diabetes mellitus (GFR measured with iothalamate). Poor metabolic control is

associated with GFR > 150 mL/min. After 7-14 years of diabetes, microalbuminuria develops, followed

by clinically overt nephropathy. After 18 years of diabetes blood pressure rises, GFR starts to decline and

ESRD is reached after 25 years. Adapted from (56).


16

This hyperfiltration theory has been universally accepted as the natural cause of diabetic

nephropathy, and extended to individuals with impaired glucose tolerance, metabolic syndrome

and hypertension since glomerular hyperfiltration in these conditions also predicts de novo

microalbuminuria (56-60). For this reason, it is discussed whether a screening of

microalbuminuria in the general population to detect subjects with early nephropathy should be

recommended (11). Microalbuminuria is not only an early predictor for overt nephropathy, it

predicts independently also cardiovascular disease (CVD) and mortality (61, 62). Moreover,

whether the presence of microalbuminuria inevitably results in overt nephropathy is uncertain,

especially nowadays more and more patients are treated for their underlying risk factors (4, 63).

In primary kidney disease the pathophysiologic course of proteinuria might be different.

Mutations in molecular signaling pathways of the slit diaphragm or in podocyte genes have been

implicated in the susceptibility for idiopathic focal and segmental glomerulosclerosis, minimal

change nephropathy and membranous glomerulopathy (16, 64, 65). The sudden onset of overt

nephropathy in non-diabetic glomerular diseases, is conceivably not preceded by the presence of

longterm microalbuminuria in most cases. A mass screening for microalbuminuria will

consequently not be effective to detect these relatively rare disorders. Studies of podocyte

proteins, including nephrin (NPHS1), podocin (NPHS2), zona occludens-1 (ZO-1), α-actinin-4,

CD2 associated protein and transient receptor potential canonical (TRPC) channels, and their

mutation analysis have shed light on the pathogenesis of glomerular kidney disease and

manifestation of albuminuria and emphasized the critical role of the podocyte and the slit

diaphragm in maintaining the function of the glomerular barrier (17, 50). Beside genetic factors,

also drugs, immunologic mechanisms, and bacterial toxins might induce the expression of

podocyte co-stimulatory molecule B7-1 (also termed CD 80), leading to foot process effacement

and albuminuria (17, 66). It is unclear why molecule B7-1 is expressed on the podocyte, as it is

normally expressed on antigen presenting cells and B-cells (67). It is suggested that the disruption

of the glomerular filter by the expression of molecule B7-1 may help the innate immune system

in clearing the circulation from harmful agents through transient urinary albumin loss (68).

Noteworthy, the onset of nephrotic syndrome in minimal change nephropathy is often preceded

by an infection or allergic reaction (69). However, it is unknown whether screening for

albuminuria in presence of infectious or allergic symptoms would be effective, since the patho-

physiologic mechanisms in these diseases are entirely different from those leading to albuminuria


17

in metabolic disorders, including diabetes, hypertension and obesity. The main focus in this thesis

will be on cardiovascular risk factors, as the prevalence of these risk factors was high in the

populations we investigated.


18

1.4 Albuminuria as a manifestation of chronic kidney disease

To identify patients at risk for progressive kidney disease, the National Kidney Foundation’s -

Kidney Disease Outcomes Quality Initiative (NKF-K/DOQI) developed a definition and

classification for CKD that is updated by the Kidney Disease Improving Global Outcomes

(KDIGO) CKD recommendations in 2013 (70, 71). CKD is defined as abnormalities of kidney

structure or function, present for more than 3 months, with implications for health (71). The CGA

staging system classifies subjects with CKD based on the cause of CKD (C), renal dysfunction,

manifested by reduced glomerular filtration rate (G) and kidney damage, manifested by presence

of persistent albuminuria (A). Based on this subdivision, table 1 shows the risk for progressive

CKD (71).

GFR: glomerular filtration rate, adapted from (71).

Notice that the respective misnomers “normoalbuminuria”, “microalbuminuria” and

“macroalbuminuria” have been replaced by “normal to mildly increased albuminuria”,

“moderately increased albuminuria” (CKD stage A2) and “severely increased albuminuria”

(CKD stage A3). Throughout this manuscript, we will continue to use the terminology

“normoalbuminuria”, “microalbuminuria” and macroalbuminuria” as several of the publications

Table 1. Classification of chronic kidney disease (71)

ACR: urinary albumin creatinine ratio

A1 A2 A3

< 30 mg/g 30-300 mg/g >300mg/g

G

FR

cate

go

ries

(

ml/

min

/1.7

3m

²)

G1 ≥ 90 Normal Low High

G2 60-89 Normal Low High

G3a 45-59 Low High Very high

G3b 30-44 High Very high Very high

G4 15-29 Very high Very high Very high

G5 <15 or RRT Very high Very high Very high


19

included in the present thesis were submitted before the publication of the CGA staging system at

January 2013 (71).

The prevalence of CKD in the general population is estimated around 10-15% (72, 73). The

ageing of the population along with the growing global prevalence of diabetes, obesity and other

chronic non communicable diseases has led to a corresponding worldwide increase in prevalence

of CKD (74). The prevalence of all stages CKD is 35% in elderly (age > 60 years), 40% in

diabetes, 23% in hypertension, 17% in obesity and 41% in CVD, as estimated by the USRDS in

2013 (73). Yet, the majority of CKD patients is identified by ACR of 30-300 mg/g and/or

reduced estimated GFR of 30-60 ml/min/1.73m² while a minority (10%) has developed an ACR

higher than 300mg/g and/or an estimated GFR lower than 30 ml/min/1.73m² (73). Only 10% of

the latter subgroup progresses to a degree that RTT is required (75). Consequently, the incidence

rate for RRT is around 10-30 per 100,000 person-years and the prevalence of RRT in the general

population is around 0.1% (3, 72).

It is debatable whether an uniform CKD definition is valid and appropriate for implementing

patients into renal care (76, 77). One point of discussion is the physiological reduction of kidney

function with increasing age and consequently, whether a reduction of GFR of a certain degree

has the same pathophysiological meaning throughout different age strata (72, 73, 77). A

substantial part of patients with an eGFR < 60 ml/min/1.73m² did not have preceding albuminuria

and do not develop progressive CKD (78, 79).

Another concern is whether it is appropriate to include subjects with microalbuminuria in the

definition of CKD, as microalbuminuria is frequently associated with cardiovascular risk factors

(80). In addition, fluctuations in microalbuminuria occur commonly by temporary physiological

responses after strenuous exercise, meal, fever, extreme cold or standing (43, 81-84). For this

reason, repeated measurements of albuminuria should be obtained, and consequently persistent

microalbuminuria is required for diagnosing CKD stage 1 and 2. As most trials do not include

follow-up examinations, CKD stage 1 and 2 could not be identified directly. Therefore, the

prevalence rates of microalbuminuria were assumed to be 51% and 75% for persons with

eGFRs > 90 and, 60-90 ml/min/1.73m², respectively (85). This persistence ratio to define

microalbuminuria acts as self-fulfilling prophecy to establish microalbuminuria as marker for

progressive CKD, whereas macroalbuminuria has a higher specificity to detect renal damage and


20

decline of renal function (52, 86). Of note, diabetic patients with ACR > 3000 mg/g had an 8-fold

higher risk for ESRD than those with ACR < 1500 mg/g (figure 4).

Figure 4

Figure 4. Effect of baseline albuminuria on ESRD in the RENAAL study. Adjusted for baseline serum

creatinine, blood pressure, HbA1c, age and ethnicity. Adapted from (87).


21

1.5 Albuminuria as a manifestation of cardiovascular disease

Figure 5 shows the relation between CKD, cardiovascular risk factors, CVD and mortality.

Patients with primary kidney lesions develop cardiovascular lesions, and inversely patients with

cardiovascular diseases are more prone to develop kidney dysfunction (88-91). This reciprocal

relationship between kidney disease and cardiovascular disease accelerates both general and renal

vascular damage and progression of kidney disease and mortality.

Figure 5

Figure 5. Ageing, hypertension and diabetes are the main causes of cardiovascular disease (CVD) and

chronic kidney disease (CKD). CVD can be both a cause or a consequence of CKD both leading to

increased risk for mortality.


22

In the 19th

century, James Goodhart observed albuminuria in sedentary and unhealthy subjects,

“bulky men or women complain of a certain amount of ill health, and it is found that they eat and

drink to much, they take no exercise, very possibly have gouty antecedents, they have a congested

state of the capillaries of the face, short breath, and are fat and unhealthy looking. The urine of

such as these is often of high specific gravity, and contains little albumen. Give these people

periodical purges, diet them, and make them live altogether more according to natural law, and

albumen disappears” (92). Nowadays, one third of the US population are identified with

“metabolic syndrome” clustering high blood pressure, abnormal lipid status, high glycaemia

and/or large waist circumference (93-95). It is suggested that the Western sedentary lifestyle is an

important cause of the metabolic syndrome (96). The metabolic syndrome is considered to be a

pre-diabetes condition with increased cardiovascular risk (96). Cardiovascular risk factors trigger

oxidative stress that upregulates the expression of inflammatory cytokines and induces

endothelial dysfunction, leading to concomitant kidney damage that might manifest as

microalbuminuria (50, 97-102). In a cross-sectional study of hypertensive patients, those with

microalbuminuria combined to inflammation had more obesity, metabolic syndrome, cardiac

hypertrophy and smokers, while patients with microalbuminuria in absence of inflammation had

no increased cardiovascular risk factors (102). However, prospective studies show that higher

levels of albuminuria are an independent prognostic predictor for cardiovascular morbidity and

mortality in patients with diabetes, metabolic syndrome, vascular disease, hypertension and even

in the general population (62, 103-108). The increased relative risk for CVD according to

microalbuminuria is approximately the same in different cohorts (106, 108-111), but the absolute

risk for CVD is much higher in diabetic versus non-diabetic cohorts (table 2). These findings are

important, as they indicate that a screening program will be more successful targeting specific

groups (such as diabetics and hypertensives). The panels of figure 6 shows Kaplan-Meier curves

for free survival of CVD according to microalbuminuria in different populations. Figure 6a

shows that slightly elevated levels of urinary albumin are already associated with CVD. Figure 6c

show that diabetic populations have a higher absolute risk for CVD than general cohorts (figure

6a and 6b).


23

Table 2: Relative risk and absolute risk for adverse outcomes according to microalbuminuria

(MAU) vs. no MAU in different studies.

Framingham(106): General population, no diabetes, no hypertension, no previous CVD at baseline, high

ACR ≥ 3.9 mg/g in men and ≥ 7.5 mg/g in women.

EPIC(108): General population, no previous CVD, 2% diabetes, 11% hypertension, high ACR > 20 mg/g.

CCHS(109): Copenhagen City Heart Study III: 100% hypertensive patients without previous CVD, 4%

diabetes, high UAE > 6.9 mg/day.

ADVANCE(110): Diabetic patients, history of previous CVD 32%, high ACR ≥ 30mg/g.

Diabetes(111): Diabetic patients without macroalbuminuria, high UAE; 30-300 mg/d.

Study: Relative risk according to MAU

vs. no MAU

Absolute risk (per 1000

person years)

RR 95% CI p No MAU MAU

Framingham (n= 1,568)

-Cardiovascular disease 2.9 1.6-5.4 <0.001 3 9

-All-cause mortality 1.8 1.0-3.2 0.08 3 7

EPIC (n=22,368)


-All-cause mortality 1.5 1.2-1.8 <0.001 5 9

-Cardiovascular mortality 2.0 1.6-2.7 <0.001 2 4

CCHS (n=1,734)


-All-cause mortality 1.5 1.2-1.8 <0.001 13 29

ADVANCE (n= 10,640)


-Cardiovascular mortality 1.7 1.2-2.6 <0.001 5 9

Diabetes (n=199)

-Cardiovascular disease 2.2 2.1-2.5 0.03 26 70

-All-cause mortality 2.5 0.7-8.4 0.15 NA NA


24

Figure 6. Kaplan-Meier survival curves for CVD in population-based cohorts (Framingham: fig.

6a. and EPIC: fig. 6b) and in type 2 diabetic cohort (fig. 6c) according to baseline ACR.

Median ACR = 3.9 mg/g in men

and = 7.5 mg/g in women

Figure 6a

Figure 6b Figure 6c

Years


25

1.6 The prevalence of albuminuria

The prevalence of microalbuminuria and macroalbuminuria ranged from 6 to 12% and 0.6 to

1.3% respectively, according to population cohorts (9, 72, 85, 112-115).

Table 3. Clinical studies reporting the prevalence of albuminuria in the general population

Study HUNT II EPIC KEAPS PolNef PREVEND NHANES AusDiab

Country Norway UK UK Poland Netherlands US Australia

Invited 92,703 77,630 2,199 9,700 85,421 All ?

Responded 65,181 25,112 1,208 2,501 40,856 13,233 11,247

Year 1995-97 93-97 04-05 04-05 1997 99-04 99-20

Age > 20 40-79 > 18 > 18 28-75 > 20 ?

THT 11 14.4 16.7 13.1 11.2 14 12

Diabetes 3.4 2.3 4.4 6.6 2.6 6.8 7

CVD 8 9.8 11.6 ? 3.6 15 0.8

Micro 7.1 11.8 7.1 11.9 7.2 8.2 6.0

Macro 0.8 0.9 0.7 1.3 0.6

Threshold 17-250/ 25-

355**

20-200* > 21/ > 30** > 20° 20-200° 30-300* 30-300*

Collection morning°° random morning random morning random morning

Table 3 shows the numbers of subjects who were invited and responded, year of enrollment, age of

inclusion, THT: prevalence of treated hypertension (%), prevalence diabetes (%), CVD: prevalence of

cardiovascular disease, prevalence of micro= microalbuminuria and macro= macroalbuminuria (%),

Threshold to define microalbuminuira: by (*): ACR = urinary albumin creatinine ratio (mg/g), by (°):

UAE = urinary albumin excretion (mg/l) or by (**): ACR gender specific thresholds: male/female.

Collection: how urine specimens were collected: first morning or random. (°°) in 9,598 subjects 3 first

morning urine specimens were collected.


26

The prevalence of micro- or macro-albuminuria varied in these cohorts as a result of the different

proportions of subjects with CVD, definitions of microalbuminuria and macroalbuminuria, age

range and timing of urinary sample collection (table 3). According to the NHANES cohort, the

prevalence of albuminuria was 34.2% in diabetes (microalbuminuria 28.1%, macroalbuminuria

6.1%), 14.5% in non-diabetic hypertension (microalbuminuria 12.8%, macroalbuminuria 1.7%),

and 5.1% in non-diabetes non-hypertension (microalbuminuria 4.8%, macroalbuminuria

0.3%)(116). Yet, the prevalence of albuminuria (micro- and macro-albuminuria) varied also

markedly between cohorts in the presence of diabetes (6-39%), hypertension (4-47%) and CVD

(15-59%) (6, 117).


27

1.7 How to measure albuminuria

Clinical guidelines on how to measure albuminuria are not uniform with regard to patient

preparation, timing of urine samples, units of reporting, thresholds to define micro- or macro-

albuminuria and the methods which are used to measure urinary albumin and creatinine (42).

Overnight urinary albumin excretion is considered as the golden standard, but is impractical in

clinical settings and in epidemiologic studies. A practical alternative is measurement of urinary

albumin creatinine ratio (ACR) in milligrams albumin per gram creatinine as the units of measure,

in early or first morning urine samples (42, 118). Yet, albuminuria is commonly measured in

random urine samples due to practical reasons. However, a higher within-subjects biological

variability for ACR has been reported with random urine samples compared with first morning

urine samples (119).

Preparation of the patient. Measuring albuminuria during circumstances of pyrexia, exposure to

cold, gout attacks, glycosuria, anemia, hematuria, urinary tract infections, alcohol excess, heart

failure, medication, after exercise and mental anxiety should be interpreted with caution as all

these conditions might affect the level of albuminuria (92, 120). To exclude postural albuminuria,

measuring albuminuria should be performed in a first morning sample, particularly in relatively

young subjects (42).

Molecular forms of albumin. Albumin is the most abundant plasma protein, serving multiple

functions as a carrier of metabolites (long-chain fatty acids, bilirubin…), ions (calcium..),

hormones (thyroxin), organic molecules, vitamins and drugs, as an acid/base buffer, as

antioxidant and by supporting the oncotic pressure and volume of the blood (121). Albumin can

be degraded into albumin fragments of 0.5-5kD by time, proximal tubular cells (cubilin-megalin),

proteolysis during passage through the urinary tract, oxidation in the urine and during specimen

storage (42). Mutations of the gene encoding albumin (<1/1000) might affect renal albumin

clearance, producing different proportions of modified albumin in urine compared to serum (122).

Methods for measuring urine albumin. Urinary albumin can be quantified with immunochemical

methods, such as radioimmunoassay (RIA), immunoturbidimetry and immunonephelometry (42,

123). These routine methods use either polyclonal or monoclonal antibodies. Immunoassays with

polyclonal antisera react with many modified albumins whereas immunoassays with monoclonal


28

antisera can only react with immunoreactively intact albumin (42). However, current

immunoassays have a considerable inter-assay coefficient of variation. Additionally, the

variability between-laboratory appears to be tremendous (124). Size exclusion high performance

liquid chromatography (HPLC) measures both immunoreactive and immunounreacive intact

albumin (125). Albuminuria can also be measured semiquantitatively with point of care methods

by colorimetric test strips (“dipsticks”), such as HemoCue Albumin 201 system (HemoCue®),

Clinitek Microalbumin Reagent strips (Bayer Health Care®) and the Chemstrip Micral Test strips

(Roche Diagnostic®) with a good sensitivity and specificity for urinary albumin excretion (24

hours)(126). Interestingly, a quantitative dye-binding based test strip analysis seems to be reliable

to detect microalbuminuria (127). Additionally, this test might be suitable to perform in a mass

screening as it is very cheap (2 Eurocent per test)(128). The National Kidney Disease Education

Program-International Federation of Clinical Chemistry (IFCC) Working Group is establishing

standardization of albumin measurements (42, 129).

Which threshold for microalbuminuria should be used. Microalbuminuria has been introduced as

a surrogate marker for early diabetic nephropathy based on a small retrospective study of 63

diabetic patients; seven of 8 patients with overnight albuminuria between 30-140 µg/min

developed overt nephropathy after 14 years (130). Another small study, defined early diabetic

nephropathy as an overnight urine albumin excretion between 30-140 µg/min, which was

predictive for the development of clinical proteinuria (131). These values are considered to be

approximately equivalent to an ACR of 20-100 mg/g in a random untimed urine sample. KDIGO

recommends to use a threshold for AER of 30-300 mg/24 hours (ACR of 30-300 mg/g) to

indicate microalbuminuria (71). This threshold is based on the “normal” values of the distribution

of ACR in apparently healthy young subjects and on the increased risk of all cause mortality,

cardiovascular mortality, acute kidney injury (AKI) and CKD progression once ACR exceeds 30

mg/g (6, 117). Also, the Scientific Advisory Board on the prevention of diabetic nephropathy of

the US National Kidney Foundation recommends to use an ACR risk-based cut off above 30

mg/g in random urine samples (132). This threshold can however be debated, as albuminuria is a

continuous parameter that over its entire range is related to cardiovascular events, even with

values below the threshold (133). In addition, such a cut-off of 30 mg/g does not give weight to

the prognostic values of substantially higher degrees of albuminuria (macroalbuminuria), which

carry a much higher risk to be associated with rapid decline of renal function than these relatively


29

modest values of 30 mg/g (86). Beside the amount of urinary albumin concentration, ACR

depends also on muscle mass as it is measured in 24 hour creatinine collection. Consequently,

ACR in females, elderly and in patients with muscle wasting overestimates true AER. Warram

et. al. found a lower threshold in men (ACR ≥ 17 mg/g) than in women (ACR ≥ 25 mg/g) with

serial specimens in 1,613 patients with diabetes mellitus type 1. These gender specific thresholds

corresponded to the 95th

percentiles of distribution of the ACR in 218 healthy control subjects

(134). Higher values of ACR in women than in men are caused by the lower muscle mass and

thus the lower urinary creatinine excretion. For that reason, we decided to use gender specific

thresholds as proposed by Warram et. al., as these values are equivalent to an albumin excretion

rate of 30 µg/min in men and 31 µg/min in women. As a matter of fact, thresholds of

approximately 30 µg/min were also the ones used in the first studies (130, 131).


30

1.8 Outline and aims of the thesis

The presence of microalbuminuria is considered to be an independent indicator for cardiovascular

and/or kidney disease in general populations (7, 88, 135). Microalbuminuria seems to be a

suitable screening tool in persons with diabetes, as treatment of microalbuminuria delays the

development of overt nephropathy (136). Although, screening for microalbuminuria in a general

population, followed by treatment did not improve renal and cardiovascular outcome (137, 138).

One reason for this disappointed result might be that microalbuminuria is an inappropriate test to

select persons with cardiovascular and renal risk. This thesis discusses the pitfalls of using

microalbuminuria to identify subjects with cardiovascular and renal events.

The aims of this thesis are:

1) To evaluate the prevalence of microalbuminuria and the associations with traditional

cardiovascular risk factors in a general population.

2) To evaluate whether we can use microalbuminuria as a screening tool to identify subjects

with unrecognized cardiovascular risk factors in a presumably healthy population.

3) To evaluate which conditions could lead to false positive test results of microalbuminuria.

4) Whether the risk of ESRD could be reduced by screening for microalbuminuria according

to the literature.


31

1.9 Participants and methods

The Unreferred Renal Insufficiency (URI) cohort and Early Renal Impairment and

Cardiovascular Assessment in Belgium (ERICABEL) cohort were applied for this thesis.

The URI study is a non-interventional prospective study that included 1,486 Causcasian workers

who presented at a routine yearly occupational check-up between January 2007 and December

2009. The first study; “towards a rational screening strategy for albuminuria”, excluded subjects

with hematuria, leucocyturia, known comorbidities as diabetes, cardiovascular disease, renal

disease, and on antihypertensive drugs and lipid lowering drugs, leaving a cross-sectional cohort

of 1,191 apparently healthy workers with a mean age of 38 ± 10 years. The second study,

“microalbuminuria is more consistent in presence of risk factors” included 341 workers of the

URI cohort who underwent at least two consecutive occupational heath care examinations.

Subjects with macroalbuminuria and those with incomplete data were excluded, leaving a

prospective cohort of 239 subjects with known and unknown cardiovascular risk factors. The

third study “screening for kidney disease in a presumed healthy working population” included all

screened workers with known and unknown cardiovascular risk factors. The presence of

hematuria and subjects with incomplete data were exclusion criteria for further analysis, leaving a

cross-sectional cohort of 1,398 workers.

Description of procedures: All subjects were investigated during their yearly check up by their

occupational physician. Body weight was recorded to the nearest 0.1 kg and height was measured

to the nearest centimeter. Waist circumference was measured by trained nurses following

recommendations by WHO(139). Body mass index (BMI) was calculated as body weight in kg

divided by height² (kg/m²). Blood pressure and resting heart rate were measured in sitting

position by a calibrated electronic device (OMRON®). A questionnaire about current cigarette

smoking, physical activity and prescribed medication was taken by an occupational physician in

each participant. A random blood and urine spot specimen was collected and analyzed on the

same day in one central laboratory (no frozen samples). Urinary albumin was measured by an

immune turbidimetric method with an inter-assay coefficient of variation of 11.2% at a mean

level of 82 mg/L and an inter-assay coefficient of variation of 4.9% at a mean level of 580 mg/l.

Serum creatinine was analyzed by a colorimetric assay (compensated Jaffe reaction), calibrated

by Isotope Dilution Mass Spectrometry (IDMS), with an inter-assay coefficient of variation of


32

1.75% at a mean level of 104 µmol/l (Roche). Serum cystatin C was determined by nephelometry

with an inter-assay coefficient of variation of 5.1% at a mean level of 0.95 mg/l (Siemens DII).

Serum CRP was measured with an immune turbidimetric method with an inter-assay coefficient

of variation of 4.6% at a mean level of 3.2 mg/l and inter-assay coefficient of variation of 2.5%

at a mean level of 5.5 mg/l.

The ERICABEL study included patients with hypertension, aged 40-70 years. We used the

baseline data of the ERICABEL cohort, a non-interventional epidemiological study with a follow

up of 5 years that included 1,076 Caucasian patients with hypertension, defined as systolic blood

pressure ≥140 mmHg and/or intake of at least one antihypertensive drug, recruited by 96 general

practitioners, between 2006 and 2007, in Belgium. Of the 1076 patients included in this cross-

sectional study, 420 patients had a missing value for at least one of the variables under

investigation. Multiple imputation techniques were used to account for the missing data, using

20 imputations (140). All characteristics and outcome (MAU) were simultaneously used in the

imputation model. The imputation was done using the R function aregImpute from the Hmisc

package (141).

Description of procedures: Each participating primary care physician was asked to include 10

consecutive hypertensive patients aged between 40-70 years, in a 1:1 sex ratio. The eligible

persons were evaluated at baseline and if eligible, sociodemographic information (age, sex, race,

and education level), personal and family medical history, smoking status and medication use

were collected prospectively in an online database. Body weight was recorded to the nearest 0.1

Kg and height was measured to the nearest centimeter. Body mass index (BMI) was calculated as

body weight in Kg divided by height² (kg/m²). Blood pressure was measured according to the

WHO criteria with a calibrated Omron HEM-907 device (average of 2 measurements, sitting,

with 5 minutes in between). All these measurements were done by the primary care physician.

After exclusion of a urinary infection or hematuria (negative Combur® test), MAU was screened

by a Micral® dipstick test. MAU was considered present if measured albuminuria was ≥ 20mg/l

on a morning midstream urine sample. Blood sampling was performed by the general practitioner

in fasting patients.


33

1.10 References

1. International Diabetes Federation. Available at http://www.idf.org/atlasmap/atlasmap.

2. Wolf M, Heuten HG, De Swaef A, de Falleur M, Verpooten GA. The evolution of hypertension

treatment in Belgium, a pharmacoepidemiological study. Acta Cardiol 2012; 67: 147-152. Available at

<Go to ISI>://WOS:000303851000001.

3. van der Velde M, Halbesma N, de Charro FT, Bakker SJL, de Zeeuw D, de Jong PE, Gansevoort RT.

Screening for Albuminuria Identifies Individuals at Increased Renal Risk. Journal of the American

Society of Nephrology 2009; 20: 852-862. Available at <Go to ISI>://000264831300023.

4. de Boer IH, Rue TC, Cleary PA, Lachin JM, Molitch ME, Steffes MW, Sun W, Zinman B, Brunzell

JD, Diabetes C, Complications Trial/Epidemiology of Diabetes I, Complications Study Research G,

White NH, Danis RP, Davis MD, Hainsworth D, Hubbard LD, Nathan DM. Long-term renal outcomes

of patients with type 1 diabetes mellitus and microalbuminuria: an analysis of the Diabetes Control and

Complications Trial/Epidemiology of Diabetes Interventions and Complications cohort. Arch Intern Med 2011; 171: 412-420. Available at http://www.ncbi.nlm.nih.gov/pubmed/21403038.

5. ERA-EDTA Registry: ERA-EDTA Registry 2011 Annual report. Academic Medical Center,

Amsterdam, the Netherlands, june 2011. Available at http://www.era-edta-

reg.org/files/annualreports/pdf/AnnRep2011.pdf.

6. Mahmoodi BK, Matsushita K, Woodward M, Blankestijn PJ, Cirillo M, Ohkubo T, Rossing P, Sarnak

MJ, Stengel B, Yamagishi K, Yamashita K, Zhang L, Coresh J, de Jong PE, Astor BC, Chronic Kidney

Disease Prognosis C. Associations of kidney disease measures with mortality and end-stage renal

disease in individuals with and without hypertension: a meta-analysis. Lancet 2012; 380: 1649-1661.

Available at http://www.ncbi.nlm.nih.gov/pubmed/23013600.

7. Gansevoort RT, Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE,

Coresh J, Chronic Kidney Disease Prognosis C. Lower estimated GFR and higher albuminuria are

associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk

population cohorts. Kidney Int 2011; 80: 93-104. Available at

http://www.ncbi.nlm.nih.gov/pubmed/21289597.

8. Palmer AJ, Valentine WJ, Chen R, Mehin N, Gabriel S, Bregman B, Rodby RA. A health economic

analysis of screening and optimal treatment of nephropathy in patients with type 2 diabetes and

hypertension in the USA. Nephrology Dialysis Transplantation 2008; 23: 1216-1223. Available at <Go

to ISI>://WOS:000254472400024.

9. Hillege HL, Janssen WMT, Bak AAA, Diercks GFH, Grobbee DE, Crijns H, van Gilst WH, de Zeeuw

D, de Jong PE, Prevend Study G. Microalbuminuria is common, also in a nondiabetic, nonhypertensive

population, and an independent indicator of cardiovascular risk factors and cardiovascular morbidity.

Journal of Internal Medicine 2001; 249: 519-526. Available at <Go to ISI>://000169568600005.

10. Ozyilmaz A, Bakker SJ, de Zeeuw D, de Jong PE, Gansevoort RT. Selection on albuminuria enhances

the efficacy of screening for cardiovascular risk factors. Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association 2010;

25: 3560-3568. Available at http://www.ncbi.nlm.nih.gov/pubmed/20702530.

11. de Jong PE, Hillege HL, Pinto-Sietsma SJ, de Zeeuw D. Screening for microalbuminuria in the general

population: a tool to detect subjects at risk for progressive renal failure in an early phase? Nephrology

Dialysis Transplantation 2003; 18: 10-13. Available at <Go to ISI>://000180293800002.

12. Jefferson JA, Shankland SJ, Pichler RH. Proteinuria in diabetic kidney disease: a mechanistic

viewpoint. Kidney International 2008; 74: 22-36. Available at


13. Haraldsson B, Nystrom J, Deen WM. Properties of the glomerular barrier and mechanisms of

proteinuria. Physiol Rev 2008; 88: 451-487. Available at <Go to ISI>://000257595300005.

14. Tojo A, Kinugasa S. Mechanisms of glomerular albumin filtration and tubular reabsorption.

International journal of nephrology 2012; 2012: 481520. Available at


http://www.idf.org/atlasmap/atlasmap

http://www.ncbi.nlm.nih.gov/pubmed/21403038

http://www.era-edta-reg.org/files/annualreports/pdf/AnnRep2011.pdf








34

15. Haraldsson B, Nystrom J. The glomerular endothelium: new insights on function and structure. Curr

Opin Nephrol Hypertens 2012; 21: 258-263. Available at


16. Mundel P, Reiser J. Proteinuria: an enzymatic disease of the podocyte? Kidney Int 2010; 77: 571-580.


17. Reiser J, von Gersdorff G, Loos M, Oh J, Asanuma K, Giardino L, Rastaldi MP, Calvaresi N,

Watanabe H, Schwarz K, Faul C, Kretzler M, Davidson A, Sugimoto H, Kalluri R, Sharpe AH,

Kreidberg JA, Mundel P. Induction of B7-1 in podocytes is associated with nephrotic syndrome. J Clin Invest 2004; 113: 1390-1397. Available at http://www.ncbi.nlm.nih.gov/pubmed/15146236.

18. Solling K, Mogensen CE. Studies on the mechanism of renal tubular protein reabsorption. Proceedings

of the European Dialysis and Transplant Association European Dialysis and Transplant Association

1977; 14: 543-549. Available at http://www.ncbi.nlm.nih.gov/pubmed/341145.

19. Hausmann R, Grepl M, Knecht V, Moeller MJ. The glomerular filtration barrier function: new

concepts. Curr Opin Nephrol Hypertens 2012; 21: 441-449. Available at


20. Christensen EI, Birn H, Storm T, Weyer K, Nielsen R. Endocytic receptors in the renal proximal tubule.

Physiology 2012; 27: 223-236. Available at http://www.ncbi.nlm.nih.gov/pubmed/22875453.

21. Imerslund O. Idiopathic chronic megaloblastic anemia in children. Acta paediatrica Supplementum

1960; 49(Suppl 119): 1-115. Available at http://www.ncbi.nlm.nih.gov/pubmed/13852753.

22. Broch H, Imerslund O, Monn E, Hovig T, Seip M. Imerslund-Grasbeck anemia. A long-term follow-up

study. Acta paediatrica Scandinavica 1984; 73: 248-253. Available at


23. Boger CA, Chen MH, Tin A, Olden M, Kottgen A, de Boer IH, Fuchsberger C, O'Seaghdha CM,

Pattaro C, Teumer A, Liu CT, Glazer NL, Li M, O'Connell JR, Tanaka T, Peralta CA, Kutalik Z, Luan

J, Zhao JH, Hwang SJ, Akylbekova E, Kramer H, van der Harst P, Smith AV, Lohman K, de Andrade

M, Hayward C, Kollerits B, Tonjes A, Aspelund T, Ingelsson E, Eiriksdottir G, Launer LJ, Harris TB,

Shuldiner AR, Mitchell BD, Arking DE, Franceschini N, Boerwinkle E, Egan J, Hernandez D, Reilly

M, Townsend RR, Lumley T, Siscovick DS, Psaty BM, Kestenbaum B, Haritunians T, Bergmann S,

Vollenweider P, Waeber G, Mooser V, Waterworth D, Johnson AD, Florez JC, Meigs JB, Lu X,

Turner ST, Atkinson EJ, Leak TS, Aasarod K, Skorpen F, Syvanen AC, Illig T, Baumert J, Koenig W,

Kramer BK, Devuyst O, Mychaleckyj JC, Minelli C, Bakker SJ, Kedenko L, Paulweber B, Coassin S,

Endlich K, Kroemer HK, Biffar R, Stracke S, Volzke H, Stumvoll M, Magi R, Campbell H, Vitart V,

Hastie ND, Gudnason V, Kardia SL, Liu Y, Polasek O, Curhan G, Kronenberg F, Prokopenko I, Rudan

I, Arnlov J, Hallan S, Navis G, Consortium CK, Parsa A, Ferrucci L, Coresh J, Shlipak MG, Bull SB,

Paterson NJ, Wichmann HE, Wareham NJ, Loos RJ, Rotter JI, Pramstaller PP, Cupples LA, Beckmann

JS, Yang Q, Heid IM, Rettig R, Dreisbach AW, Bochud M, Fox CS, Kao WH. CUBN is a gene locus

for albuminuria. J Am Soc Nephrol 2011; 22: 555-570. Available at


24. Devuyst O, Jouret F, Auzanneau C, Courtoy PJ. Chloride channels and endocytosis: new insights from

Dent's disease and ClC-5 knockout mice. Nephron Physiology 2005; 99: p69-73. Available at


25. Hryciw DH, Ekberg J, Pollock CA, Poronnik P. ClC-5: a chloride channel with multiple roles in renal

tubular albumin uptake. The international journal of biochemistry & cell biology 2006; 38: 1036-1042.


26. Devuyst O, Thakker RV. Dent's disease. Orphanet journal of rare diseases 2010; 5: 28. Available at


27. Kabanda A, Jadoul M, Lauwerys R, Bernard A, van Ypersele de Strihou C. Low molecular weight

proteinuria in Chinese herbs nephropathy. Kidney Int 1995; 48: 1571-1576. Available at


28. Roels HA, Hoet P, Lison D. Usefulness of biomarkers of exposure to inorganic mercury, lead, or

cadmium in controlling occupational and environmental risks of nephrotoxicity. Ren Fail 1999; 21:

251-262. Available at http://www.ncbi.nlm.nih.gov/pubmed/10416202.
















35

29. Vacca CV, Hines JD, Hall PW, 3rd. The proteinuria of industrial lead intoxication. Environ Res 1986;


30. Abbate M, Zoja C, Remuzzi G. How does proteinuria cause progressive renal damage? J Am Soc Nephrol 2006; 17: 2974-2984. Available at http://www.ncbi.nlm.nih.gov/pubmed/17035611.

31. Caruso-Neves C, Pinheiro AAS, Cai H, Souza-Menezes J, Guggino WB. PKB and megalin determine

the survival or death of renal proximal tubule cells. P Natl Acad Sci USA 2006; 103: 18810-18815.

Available at <Go to ISI>://000242689800076.

32. Arici M, Chana R, Lewington A, Brown J, Brunskill NJ. Stimulation of proximal tubular cell apoptosis

by albumin-bound fatty acids mediated by peroxisome proliferator activated receptor-gamma. J Am

Soc Nephrol 2003; 14: 17-27. Available at http://www.ncbi.nlm.nih.gov/pubmed/12506134.

33. Liu J, Li K, He Y, Zhang J, Wang H, Yang J, Zhan J, Liang H. Anticubilin antisense RNA ameliorates

adriamycin-induced tubulointerstitial injury in experimental rats. Am J Med Sci 2011; 342: 494-502.


34. Verhulst A, D'Haese PC, De Broe ME. Inhibitors of HMG-CoA reductase reduce receptor-mediated

endocytosis in human kidney proximal tubular cells. Journal of the American Society of Nephrology :

JASN 2004; 15: 2249-2257. Available at http://www.ncbi.nlm.nih.gov/pubmed/15339974.

35. Sidaway JE, Davidson RG, McTaggart F, Orton TC, Scott RC, Smith GJ, Brunskill NJ. Inhibitors of 3-

hydroxy-3-methylglutaryl-CoA reductase reduce receptor-mediated endocytosis in opossum kidney

cells. Journal of the American Society of Nephrology : JASN 2004; 15: 2258-2265. Available at


36. Douglas K, O'Malley PG, Jackson JL. Meta-analysis: the effect of statins on albuminuria. Annals of internal medicine 2006; 145: 117-124. Available at http://www.ncbi.nlm.nih.gov/pubmed/16847294.

37. Sandhu S, Wiebe N, Fried LF, Tonelli M. Statins for improving renal outcomes: a meta-analysis.

Journal of the American Society of Nephrology : JASN 2006; 17: 2006-2016. Available at


38. Deslypere JP, Delanghe J, Vermeulen A. Proteinuria as Complication of Simvastatin Treatment.

Lancet 1990; 336: 1453-1453. Available at <Go to ISI>://A1990EL85100061.

39. Kostapanos MS, Milionis HJ, Saougos VG, Lagos KG, Christine K, Bairaktari ET, Elisaf MS. Dose-

dependent effect of rosuvastatin treatment on urinary protein excretion. J Cardiovasc Pharmacol Ther

2007; 12: 292-297. Available at <Go to ISI>://000251110600003.

40. Wehlou CMJ, Speeckaert MM, Fiers T, De Buyzere ML, Delanghe JR. alpha(1)-

Microglobulin/albumin ratio may improve interpretation of albuminuria in statin-treated patients.

Clinical Chemistry and Laboratory Medicine 2013; 51: 1529-1534. Available at <Go to

ISI>://WOS:000321104900031.

41. Thrailkill KM, Nimmo T, Bunn RC, Cockrell GE, Moreau CS, Mackintosh S, Edmondson RD,

Fowlkes JL. Microalbuminuria in Type 1 Diabetes Is Associated With Enhanced Excretion of the

Endocytic Multiligand Receptors Megalin and Cubilin. Diabetes Care 2009; 32: 1266-1268. Available

at <Go to ISI>://000267878300027.

42. Miller WG, Bruns DE, Hortin GL, Sandberg S, Aakre KM, McQueen MJ, Itoh Y, Lieske JC,

Seccombe DW, Jones G, Bunk DM, Curhan GC, Narva AS, Urine NKDEP-IWGSA. Current Issues in

Measurement and Reporting of Urinary Albumin Excretion. Clinical Chemistry 2009; 55: 24-38.

Available at <Go to ISI>://WOS:000262303900008.

43. Mogensen CE, Vestbo E, Poulsen PL, Christiansen C, Damsgaard EM, Eiskjaer H, Froland A, Hansen

KW, Nielsen S, Pedersen MM. Microalbuminuria and Potential Confounders - a Review and Some

Observations on Variability of Urinary Albumin Excretion. Diabetes Care 1995; 18: 572-581.

Available at <Go to ISI>://A1995QQ21600025.

44. Hosojima M, Sato H, Yamamoto K, Kaseda R, Soma T, Kobayashi A, Suzuki A, Kabasawa H,

Takeyama A, Ikuyama K, Iino N, Nishiyama A, Thekkumkara TJ, Takeda T, Suzuki Y, Gejyo F, Saito

A. Regulation of Megalin Expression in Cultured Proximal Tubule Cells by Angiotensin II Type 1A

Receptor- and Insulin-Mediated Signaling Cross Talk. Endocrinology 2009; 150: 871-878. Available at

<Go to ISI>://WOS:000262851200035.










36

45. Tojo A, Onozato ML, Kurihara H, Sakai T, Goto A, Fujita T. Angiotensin II blockade restores albumin

reabsorption in the proximal tubules of diabetic rats. Hypertens Res 2003; 26: 413-419. Available at

<Go to ISI>://WOS:000184027600009.

46. Wolf G. New insights into the pathophysiology of diabetic nephropathy: from haemodynamics to

molecular pathology. Eur J Clin Invest 2004; 34: 785-796. Available at


47. Vallon V. The proximal tubule in the pathophysiology of the diabetic kidney. Am J Physiol-Reg I 2011;

300: R1009-R1022. Available at <Go to ISI>://000290149800001.

48. Semplicini A, Ceolotto G, Sartori M, Maresca A, Baritono E, De Toni R, Paparella I, Calo L.

Regulation of glomerular filtration in essential hypertension: Role of abnormal Na+ transport and atrial

natriuretic peptide. Journal of Nephrology 2002; 15: 489-496. Available at <Go to

ISI>://WOS:000179183400004.

49. Deckert T, Feldt-Rasmussen B, Borch-Johnsen K, Jensen T, Kofoed-Enevoldsen A. Albuminuria

reflects widespread vascular damage. The Steno hypothesis. Diabetologia 1989; 32: 219-226.


50. Garg P, Rabelink T. Glomerular proteinuria: a complex interplay between unique players. Advances in chronic kidney disease 2011; 18: 233-242. Available at


51. Toyoda M, Najafian B, Kim Y, Caramori ML, Mauer M. Podocyte detachment and reduced glomerular

capillary endothelial fenestration in human type 1 diabetic nephropathy. Diabetes 2007; 56: 2155-2160.


52. Nakamura T, Ushiyama C, Suzuki S, Hara M, Shimada N, Ebihara I, Koide H. Urinary excretion of

podocytes in patients with diabetic nephropathy. Nephrol Dial Transplant 2000; 15: 1379-1383.


53. Mogensen CE, Christensen CK. Predicting diabetic nephropathy in insulin-dependent patients. N Engl

J Med 1984; 311: 89-93. Available at http://www.ncbi.nlm.nih.gov/pubmed/6738599.

54. Mogensen CE. Microalbuminuria, blood pressure and diabetic renal disease: origin and development

of ideas. Diabetologia 1999; 42: 263-285. Available at

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids

=10096778.

55. Mogensen CE, Schmitz A, Christensen CK. Comparative renal pathophysiology relevant to IDDM and

NIDDM patients. Diabetes/metabolism reviews 1988; 4: 453-483. Available at


56. Mogensen CE. Prediction of clinical diabetic nephropathy in IDDM patients. Alternatives to

microalbuminuria? Diabetes 1990; 39: 761-767. Available at


57. Jerums G, Premaratne E, Panagiotopoulos S, MacIsaac RJ. The clinical significance of hyperfiltration

in diabetes. Diabetologia 2010; 53: 2093-2104. Available at


58. Melsom T, Mathisen UD, Ingebretsen OC, Jenssen TG, Njolstad I, Solbu MD, Toft I, Eriksen BO.

Impaired Fasting Glucose Is Associated With Renal Hyperfiltration in the General Population.

Diabetes Care 2011; 34: 1546-1551. Available at <Go to ISI>://WOS:000293261200020.

59. Premaratne E, Macisaac RJ, Tsalamandris C, Panagiotopoulos S, Smith T, Jerums G. Renal

hyperfiltration in type 2 diabetes: effect of age-related decline in glomerular filtration rate.

Diabetologia 2005; 48: 2486-2493. Available at http://www.ncbi.nlm.nih.gov/pubmed/16261309.

60. Palatini P, Mormino P, Dorigatti F, Santonastaso M, Mos L, De Toni R, Winnicki M, Dal Follo M,

Biasion T, Garavelli G, Pessina AC, Group HS. Glomerular hyperfiltration predicts the development of

microalbuminuria in stage 1 hypertension: the HARVEST. Kidney Int 2006; 70: 578-584. Available at


61. Hallan SI. Chronic kidney disease: a new opportunity for better cardiovascular risk stratification.


62. Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, Coresh J,

Gansevoort RT. Association of estimated glomerular filtration rate and albuminuria with all-cause and







http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10096778








37

cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet 2010; 375:


63. Perkins BA, Ficociello LH, Silva KH, Finkelstein DM, Warram JH, Krolewski AS. Regression of

microalbuminuria in type 1 diabetes. The New England Journal of Medicine 2003; 348: 2285-2293.


64. Williams ME. Diabetic nephropathy: the proteinuria hypothesis. Am J Nephrol 2005; 25: 77-94.


65. Henique C, Tharaux PL. Targeting signaling pathways in glomerular diseases. Curr Opin Nephrol Hypertens 2012; 21: 417-427. Available at http://www.ncbi.nlm.nih.gov/pubmed/22660552.

66. Wharram BL, Goyal M, Wiggins JE, Sanden SK, Hussain S, Filipiak WE, Saunders TL, Dysko RC,

Kohno K, Holzman LB, Wiggins RC. Podocyte depletion causes glomerulosclerosis: diphtheria toxin-

induced podocyte depletion in rats expressing human diphtheria toxin receptor transgene. J Am Soc

Nephrol 2005; 16: 2941-2952. Available at http://www.ncbi.nlm.nih.gov/pubmed/16107576.

67. Abbas AK, Sharpe AH. T-cell stimulation: an abundance of B7s. Nature medicine 1999; 5: 1345-1346.


68. Reiser J, Mundel P. Danger signaling by glomerular podocytes defines a novel function of inducible

B7-1 in the pathogenesis of nephrotic syndrome. J Am Soc Nephrol 2004; 15: 2246-2248. Available at


69. Eddy AA, Schnaper HW. The nephrotic syndrome: from the simple to the complex. Semin Nephrol 1998; 18: 304-316. Available at http://www.ncbi.nlm.nih.gov/pubmed/9613871.

70. National Kidney F. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation,

classification, and stratification. Am J Kidney Dis 2002; 39: S1-266. Available at


71. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 Clinical

Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. . Kidney Int 2013:

1-150. Available at http://www.nature.com/kisup/journal/v3/n1/index.html.

72. Hallan SI, Coresh J, Astor BC, Asberg A, Powe NR, Romundstad S, Hallan HA, Lydersen S, Holmen J.

International comparison of the relationship of chronic kidney disease prevalence and ESRD risk.

Journal of the American Society of Nephrology 2006; 17: 2275-2284. Available at <Go to

ISI>://000239449000029.

73. United States Renal Data System. USRDS Annual Data Report: Atlas of Chronic Kidney Disease and

End-Stage Renal Disease in US. . National Institute of Health, National Institute of Diabetes and Digestive and Kidney Disease. Available at www.usrds.org/adr.htm.

74. James MT, Hemmelgarn BR, Tonelli M. Early recognition and prevention of chronic kidney disease.

Lancet 2010; 375: 1296-1309. Available at http://www.ncbi.nlm.nih.gov/pubmed/20382326.

75. de Jong PE, van der Velde M, Gansevoort RT, Zoccali C. Screening for chronic kidney disease: Where

does Europe go? Clinical Journal of the American Society of Nephrology 2008; 3: 616-623. Available

at <Go to ISI>://000253709800041.

76. Glassock RJ, Winearls C. CKD--fiction not fact. Nephrol Dial Transplant 2008; 23: 2695-2696; author

reply 2696-2699. Available at http://www.ncbi.nlm.nih.gov/pubmed/18567889.

77. Hallan SI, Orth SR. The conundrum of chronic kidney disease classification and end-stage renal risk

prediction in the elderly--what is the right approach? Nephron Clin Pract 2010; 116: c307-316.


78. Hoerger TJ, Wittenborn JS, Segel JE, Burrows NR, Imai K, Eggers P, Pavkov ME, Jordan R, Hailpern

SM, Schoolwerth AC, Williams DE, Centers for Disease C, Prevention CKDI. A health policy model

of CKD: 1. Model construction, assumptions, and validation of health consequences. Am J Kidney Dis


79. Retnakaran R, Cull CA, Thorne KI, Adler AI, Holman RR, Group US. Risk factors for renal

dysfunction in type 2 diabetes: U.K. Prospective Diabetes Study 74. Diabetes 2006; 55: 1832-1839.


80. Hillege HL, Janssen WM, Diercks GF, Pinto-Sietsma SJ, Bak AA, Van Gilst WH, De Zeeuw D, De

Jong PE. Microalbuminuria, a better predictor for clinical outcome than hypertension and/or

hypercholesterolemia? Circulation 2000; 102: 4113. Available at <Go to ISI>://000090072304104.










http://www.nature.com/kisup/journal/v3/n1/index.html

http://www.usrds.org/adr.htm







38

81. Rytand DA, Spreiter S. Prognosis in postural (orthostatic) proteinuria: forty to fifty-year follow-up of

six patients after diagnosis by Thomas Addis. N Engl J Med 1981; 305: 618-621. Available at


82. Naderi ASA, Reilly RF. Primary Care Approach to Proteinuria. J Am Board Fam Med 2008; 21: 569-

574. Available at <Go to ISI>://000260721500014.

83. Poortmans JR, Vanderstraeten J. Kidney function during exercise in healthy and diseased humans. An

update. Sports medicine 1994; 18: 419-437. Available at


84. Fink HA, Ishani A, Taylor BC, Greer NL, Macdonald R, Rossini D, Sadiq S, Lankireddy S, Kane RL,

Wilt TJ. Screening for, monitoring, and treatment of chronic kidney disease stages 1 to 3: a systematic

review for the u.s. Preventive services task force and for an american college of physicians clinical

practice guideline. Annals of internal medicine 2012; 156: 570-581. Available at


85. Coresh J, Selvin E, Stevens LA, Manzi J, Kusek JW, Eggers P, Van Lente F, Levey AS. Prevalence of

chronic kidney disease in the United States. JAMA 2007; 298: 2038-2047. Available at


=17986697.

86. Bakris GL, Williams M, Dworkin L, Elliott WJ, Epstein M, Toto R, Tuttle K, Douglas J, Hsueh W,

Sowers J, Diab NKFH. Preserving renal function in adults with hypertension and diabetes: A

consensus approach. American Journal of Kidney Diseases 2000; 36: 646-661. Available at <Go to

ISI>://000089227300027.

87. de Zeeuw D, Remuzzi G, Parving HH, Keane WF, Zhang Z, Shahinfar S, Snapinn S, Cooper ME,

Mitch WE, Brenner BM. Proteinuria, a target for renoprotection in patients with type 2 diabetic

nephropathy: lessons from RENAAL. Kidney Int 2004; 65: 2309-2320. Available at


88. Astor BC, Matsushita K, Gansevoort RT, van der Velde M, Woodward M, Levey AS, Jong PE, Coresh

J, Chronic Kidney Disease Prognosis C, Astor BC, Matsushita K, Gansevoort RT, van der Velde M,

Woodward M, Levey AS, de Jong PE, Coresh J, El-Nahas M, Eckardt KU, Kasiske BL, Wright J,

Appel L, Greene T, Levin A, Djurdjev O, Wheeler DC, Landray MJ, Townend JN, Emberson J, Clark

LE, Macleod A, Marks A, Ali T, Fluck N, Prescott G, Smith DH, Weinstein JR, Johnson ES, Thorp

ML, Wetzels JF, Blankestijn PJ, van Zuilen AD, Menon V, Sarnak M, Beck G, Kronenberg F,

Kollerits B, Froissart M, Stengel B, Metzger M, Remuzzi G, Ruggenenti P, Perna A, Heerspink HJ,

Brenner B, de Zeeuw D, Rossing P, Parving HH, Auguste P, Veldhuis K, Wang Y, Camarata L,

Thomas B, Manley T. Lower estimated glomerular filtration rate and higher albuminuria are associated

with mortality and end-stage renal disease. A collaborative meta-analysis of kidney disease population

cohorts. Kidney Int 2011; 79: 1331-1340. Available at http://www.ncbi.nlm.nih.gov/pubmed/21289598.

89. Dickinson WH. The Croonian Lectures on the Pathology and Relations of Albuminuria. Br Med J 1876;


90. Vanholder R, Massy Z, Argiles A, Spasovski G, Verbeke F, Lameire N, European Uremic Toxin Work

G. Chronic kidney disease as cause of cardiovascular morbidity and mortality. Nephrol Dial

Transplant 2005; 20: 1048-1056. Available at http://www.ncbi.nlm.nih.gov/pubmed/15814534.

91. Van Biesen W, De Bacquer D, Verbeke F, Delanghe J, Lameire N, Vanholder R. The glomerular

filtration rate in an apparently healthy population and its relation with cardiovascular mortality during

10 years. Eur Heart J 2007; 28: 478-483. Available at http://www.ncbi.nlm.nih.gov/pubmed/17223665.

92. Goodhart JF. Two Clinical Lectures on Albuminuria. Br Med J 1890; 1: 1121-1123. Available at


93. Lorenzo C, Williams K, Hunt KJ, Haffner SM. The National Cholesterol Education Program - Adult

Treatment Panel III, International Diabetes Federation, and World Health Organization definitions of

the metabolic syndrome as predictors of incident cardiovascular disease and diabetes. Diabetes Care


94. Expert Panel on Detection E, Treatment of High Blood Cholesterol in A. Executive Summary of The

Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection,














39

Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA


95. Ervin RB. Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race

and ethnicity, and body mass index: United States, 2003-2006. National health statistics reports 2009:


96. Thomas G, Sehgal AR, Kashyap SR, Srinivas TR, Kirwan JP, Navaneethan SD. Metabolic Syndrome

and Kidney Disease: A Systematic Review and Meta-analysis. Clinical Journal of the American

Society of Nephrology 2011; 6: 2364-2373. Available at <Go to ISI>://WOS:000295657000008.

97. Karalliedde J, Viberti G. Proteinuria in diabetes: bystander or pathway to cardiorenal disease? J Am

Soc Nephrol 2010; 21: 2020-2027. Available at http://www.ncbi.nlm.nih.gov/pubmed/21051738.

98. Mimran A, Ribstein J, Du Cailar G. Microalbuminuria in essential hypertension. Curr Opin Nephrol Hypertens 1999; 8: 359-363. Available at http://www.ncbi.nlm.nih.gov/pubmed/10456269.

99. Bianchi S, Bigazzi R, Campese VM. Microalbuminuria in essential hypertension: Significance,

pathophysiology, and therapeutic implications. American Journal of Kidney Diseases 1999; 34: 973-


100. Klausen KP, Parving HH, Scharling H, Jensen JS. The association between metabolic syndrome,

microalbuminuria and impaired renal function in the general population: impact on cardiovascular

disease and mortality. Journal of Internal Medicine 2007; 262: 470-478. Available at <Go to

ISI>://000249947500010.

101. Bohm M, Reil JC, Danchin N, Thoenes M, Bramlage P, Volpe M. Association of heart rate with

microalbuminuria in cardiovascular risk patients: data from I-SEARCH. Journal of Hypertension 2008;

26: 18-25. Available at <Go to ISI>://000252095900007.

102. Pedrinelli R, Dell'Omo G, Di Bello V, Pellegrini G, Pucci L, Del Prato S, Penno G. Low-grade

inflammation and microalbuminuria in hypertension. Arteriosclerosis, thrombosis, and vascular biology 2004; 24: 2414-2419. Available at http://www.ncbi.nlm.nih.gov/pubmed/15486313.

103. Hillege HL, Fidler V, Diercks GFH, van Gilst WH, de Zeeuw D, van Veldhuisen DJ, Gans ROB,

Janssen WMT, Grobbee DE, de Jong PE, Grp PS. Urinary albumin excretion predicts cardiovascular

and noncardiovascular mortality in general population. Circulation 2002; 106: 1777-1782. Available at

<Go to ISI>://000178385700010.

104. Hallan SI, Matsushita K, Sang Y, Mahmoodi BK, Black C, Ishani A, Kleefstra N, Naimark D,

Roderick P, Tonelli M, Wetzels JF, Astor BC, Gansevoort RT, Levin A, Wen CP, Coresh J, Chronic

Kidney Disease Prognosis C. Age and association of kidney measures with mortality and end-stage

renal disease. JAMA 2012; 308: 2349-2360. Available at


105. Romundstad S, Holmen J, Kvenild K, Hallan H, Ellekjaer H. Microalbuminuria and all-cause mortality

in 2,089 apparently healthy individuals: A 4.4-year follow-up study. The Nord-Trondelag Health Study

(HUNT), Norway. American Journal of Kidney Diseases 2003; 42: 466-473. Available at <Go to

ISI>://000185518500004.

106. Arnlov J, Evans JC, Meigs JB, Wang TJ, Fox CS, Levy D, Benjamin EJ, D'Agostino RB, Vasan RS.

Low-grade albuminuria and incidence of cardiovascular disease events in nonhypertensive and

nondiabetic individuals: the Framingham Heart Study. Circulation 2005; 112: 969-975. Available at


=16087792.

107. Yuyun MF, Khaw KT, Luben R, Welch A, Bingham S, Day NE, Wareham NJ. Microalbuminuria

independently predicts all-cause and cardiovascular mortality in a British population: The European

Prospective Investigation into Cancer in Norfolk (EPIC-Norfolk) population study. Int J Epidemiol


108. Yuyun MF, Khaw KT, Luben R, Welch A, Bingham S, Day NE, Wareham NJ. A prospective study of

microalbuminuria and incident coronary heart disease and its prognostic significance in a British

population: the EPIC-Norfolk study. Am J Epidemiol 2004; 159: 284-293. Available at












40

109. Klausen KP, Scharling H, Jensen G, Jensen JS. New definition of microalbuminuria in hypertensive

subjects - Association with incident coronary heart disease and death. Hypertension 2005; 46: 33-37.


110. Ninomiya T, Perkovic V, de Galan BE, Zoungas S, Pillai A, Jardine M, Patel A, Cass A, Neal B,

Poulter N, Mogensen CE, Cooper M, Marre M, Williams B, Hamet P, Mancia G, Woodward M,

Macmahon S, Chalmers J, Group AC. Albuminuria and kidney function independently predict

cardiovascular and renal outcomes in diabetes. J Am Soc Nephrol 2009; 20: 1813-1821. Available at


111. Viana LV, Gross JL, Camargo JL, Zelmanovitz T, da Costa Rocha EP, Azevedo MJ. Prediction of

cardiovascular events, diabetic nephropathy, and mortality by albumin concentration in a spot urine

sample in patients with type 2 diabetes. J Diabetes Complications 2012; 26: 407-412. Available at


112. Atkins RC, Polkinghorne KR, Briganti EM, Shaw JE, Zimmet PZ, Chadban SJ. Prevalence of

albuminuria in Australia: the AusDiab Kidney Study. Kidney Int Suppl 2004: S22-24. Available at


113. Yuyun MF, Khaw KT, Luben R, Welch A, Bingham S, Day NE, Wareham NJ. Microalbuminuria,

cardiovascular risk factors and cardiovascular morbidity in a British population: The EPIC-Norfolk

population-based study. European Journal of Cardiovascular Prevention & Rehabilitation 2004; 11:

207-213. Available at <Go to ISI>://000222392100005.

114. Bello AK, Peters J, Wight J, El Nahas M. The Kidney Evaluation and Awareness Program in Sheffield

(KEAPS): a community-based screening for microalbuminuria in a British population. Nephron Clin Pract 2010; 116: c95-103. Available at http://www.ncbi.nlm.nih.gov/pubmed/20502045.

115. Krol E, Rutkowski B, Czarniak P, Kraszewska E, Lizakowski S, Szubert R, Czekalski S, Sulowicz W,

Wiecek A. Early Detection of Chronic Kidney Disease: Results of the PolNef Study. American Journal of Nephrology 2009; 29: 264-273. Available at <Go to ISI>://000261132000015.

116. Garg AX, Kiberd BA, Clark WF, Haynes RB, Clase CM. Albuminuria and renal insufficiency

prevalence guides population screening: results from the NHANES III. Kidney Int 2002; 61: 2165-

2175. Available at http://www.ncbi.nlm.nih.gov/pubmed/12028457.

117. Fox CS, Matsushita K, Woodward M, Bilo HJ, Chalmers J, Heerspink HJ, Lee BJ, Perkins RM,

Rossing P, Sairenchi T, Tonelli M, Vassalotti JA, Yamagishi K, Coresh J, de Jong PE, Wen CP,

Nelson RG, Chronic Kidney Disease Prognosis C. Associations of kidney disease measures with

mortality and end-stage renal disease in individuals with and without diabetes: a meta-analysis. Lancet 2012; 380: 1662-1673. Available at http://www.ncbi.nlm.nih.gov/pubmed/23013602.

118. Levin A, Rocco M. KDOQI clinical practice guidelines and clinical practice recommendations for

diabetes and chronic kidney disease. American Journal of Kidney Diseases 2007; 49: S10-S179.


119. Witte EC, Lambers Heerspink HJ, de Zeeuw D, Bakker SJ, de Jong PE, Gansevoort R. First morning

voids are more reliable than spot urine samples to assess microalbuminuria. J Am Soc Nephrol 2009;

20: 436-443. Available at


=19092125.

120. Johnson G. On the Etiology of Albuminuria. Br Med J 1873; 2: 112-113. Available at


121. Birn H, Christensen EI. Renal albumin absorption in physiology and pathology. Kidney International


122. Iwao Y, Hiraike M, Kragh-Hansen U, Mera K, Noguchi T, Anraku M, Kawai K, Maruyama T, Otagiri

M. Changes of net charge and alpha-helical content affect the pharmacokinetic properties of human

serum albumin. Biochimica et biophysica acta 2007; 1774: 1582-1590. Available at


123. Viberti GC, Vergani D. Detection of Potentially Reversible Diabetic Albuminuria - a 3-Drop

Agglutination-Test for Urinary Albumin at Low Concentration. Diabetes 1982; 31: 973-975. Available

at <Go to ISI>://WOS:A1982PN97900007.













41

124. Jones GR. Laboratory reporting of urine protein and albumin. The Clinical biochemist Reviews /

Australian Association of Clinical Biochemists 2011; 32: 103-107. Available at


125. Brinkman JW, Bakker SJL, Gansevoort RT, Hillege HL, Kema IP, Gans ROB, De Jong PE, De Zeeuw

D. Which method for quantifying urinary albumin excretion gives what outcome? A comparison of

immunonephelometry with HPLC. Kidney International 2004; 66: S69-S75. Available at <Go to

ISI>://WOS:000224751000020.

126. Sarafidis PA, Riehle J, Bogojevic Z, Basta E, Chugh A, Bakris GL. A comparative evaluation of

various methods for microalbuminuria screening. American Journal of Nephrology 2008; 28: 324-329.


127. Decavele AS, Fiers T, Penders J, Delanghe JR. A sensitive quantitative test strip based point-of-care

albuminuria screening assay. Clinical chemistry and laboratory medicine : CCLM / FESCC 2012; 50:


128. Tugirimana PL, Delanghe JR. Development of an affordable dye-stained microalbuminuria screening

test. Nephrol Dial Transplant 2009; 24: 1485-1490. Available at


129. Speeckaert MM, Speeckaert R, Van De Voorde L, Delanghe JR. Immunochemically unreactive

albumin in urine: fiction or reality? Critical reviews in clinical laboratory sciences 2011; 48: 87-96.


130. Viberti GC, Hill RD, Jarrett RJ, Argyropoulos A, Mahmud U, Keen H. Microalbuminuria as a

predictor of clinical nephropathy in insulin-dependent diabetes mellitus. Lancet 1982; 1: 1430-1432.


131. Mogensen CE. Microalbuminuria predicts clinical proteinuria and early mortality in maturity-onset

diabetes. N Engl J Med 1984; 310: 356-360. Available at


132. Bennett PH, Haffner S, Kasiske BL, Keane WF, Mogensen CE, Parving HH, Steffes MW, Striker GE.

Screening and management of microalbuminuria in patients with diabetes mellitus: recommendations

to the Scientific Advisory Board of the National Kidney Foundation from an ad hoc committee of the

Council on Diabetes Mellitus of the National Kidney Foundation. American journal of kidney diseases : the official journal of the National Kidney Foundation 1995; 25: 107-112. Available at


133. Chase HP, Marshall G, Garg SK, Harris S, Osberg I. Borderline increases in albumin excretion rate

and the relation to glycemic control in subjects with type I diabetes. Clin Chem 1991; 37: 2048-2052.


134. Warram JH, Gearin G, Laffel L, Krolewski AS. Effect of duration of type I diabetes on the prevalence

of stages of diabetic nephropathy defined by urinary albumin/creatinine ratio. Journal of the American

Society of Nephrology 1996; 7: 930-937. Available at <Go to ISI>://A1996UT21600017.

135. van der Velde M, Matsushita K, Coresh J, Astor BC, Woodward M, Levey A, de Jong P, Gansevoort

RT, Chronic Kidney Disease Prognosis C, van der Velde M, Matsushita K, Coresh J, Astor BC,

Woodward M, Levey AS, de Jong PE, Gansevoort RT, Levey A, El-Nahas M, Eckardt KU, Kasiske

BL, Ninomiya T, Chalmers J, Macmahon S, Tonelli M, Hemmelgarn B, Sacks F, Curhan G, Collins AJ,

Li S, Chen SC, Hawaii Cohort KP, Lee BJ, Ishani A, Neaton J, Svendsen K, Mann JF, Yusuf S, Teo

KK, Gao P, Nelson RG, Knowler WC, Bilo HJ, Joosten H, Kleefstra N, Groenier KH, Auguste P,

Veldhuis K, Wang Y, Camarata L, Thomas B, Manley T. Lower estimated glomerular filtration rate

and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative

meta-analysis of high-risk population cohorts. Kidney Int 2011; 79: 1341-1352. Available at


136. Patel A, MacMahon S, Chalmers J, Neal B, Woodward M, Billot L, Harrap S, Poulter N, Marre M,

Cooper M, Glasziou P, Grobbee DE, Hamet P, Heller S, Liu LS, Mancia G, Mogensen CE, Pan CY,

Rodgers A, Williams B. Effects of a fixed combination of perindopril and indapamide on

macrovascular and microvascular outcomes in patients with type 2 diabetes mellitus (the ADVANCE

trial): a randomised controlled trial. Lancet 2007; 370: 829-840. Available at <Go to

ISI>://000249733300028.












42

137. Romundstad S, Holmen J, Kvenild K, Aakervik O, Hallan H. Clinical relevance of microalbuminuria

screening in self-reported non-diabetic/non-hypertensive persons identified in a large health screening-

-the Nord-Trondelag Health Study (HUNT), Norway. Clin Nephrol 2003; 59: 241-251. Available at


138. Asselbergs FW, Diercks GF, Hillege HL, van Boven AJ, Janssen WM, Voors AA, de Zeeuw D, de

Jong PE, van Veldhuisen DJ, van Gilst WH. Effects of fosinopril and pravastatin on cardiovascular

events in subjects with microalbuminuria. Circulation 2004; 110: 2809-2816. Available at


=15492322.

139. WHO STEPS Surveillance, part 3. Training and Practical Guides, section 3: guide to physical

Measurements. Available at http://www.who.int/chp/steps/Part3_Section3.pdf.

140. Rubin DB. multiple imputation for nonrespons in surveys. New York: John Wiley & Sons 1987.

141. Frank EH. Hmisc: Harrell Miscerllaneous. R package version 3.7-0. 2009. Available at CRAN.R-

project.org/package=Hmisc.




http://www.who.int/chp/steps/Part3_Section3.pdf

CHAPTER 2: RESULTS

2.1. SCREENING FOR CARDIOVASCULAR AND RENAL MARKERS IN UNSELECTED

SUBJECTS: RESULTS FROM URI TRIAL.

CHAPTER 2: Results

45

2.1.1 Towards a rational screening strategy for albuminuria: Results from the

Unreferred Renal Insufficiency Trial

Arjan van der Tol1, Wim Van Biesen

1, Francis Verbeke

1, Guy De Groote

2, Frans Vermeiren

3,

Kathleen Eeckhaut3, Raymond Vanholder

1

1 Renal Division, Department of Internal Medicine, University Hospital Gent

2 CRI laboratory, Gent

3 Occupational Health Care Adhesia, Belgium.

PLoS ONE October 2010; 5(10): e13328

Available at: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0013328

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0013328

CHAPTER 2: Results

46

Abstract.

Background. There remains debate about the screening strategies for albuminuria. This study

evaluated whether a screening strategy in an apparently healthy population based on basic clinical

and biochemical parameters could be more effective than a strategy where screening for

albuminuria is performed unselectively.

Methodology/Principal Findings. The Unreferred Renal Insufficiency (URI) Study is a cross-

sectional study on the prevalence of metabolic risk factors in Belgian workers, volunteering to be

screened during a routine yearly occupational check-up. Subjects (n=295) with treated

hypertension, known diabetes, treated dyslipidaemia, cardiovascular and renal disease were

excluded. Among 1,191 apparently healthy subjects, 23% had unknown hypertension, 13% had

impaired glucose tolerance, 15.4% had normoalbuminuria, 4.2% had microalbuminuria and 0.4%

had macroalbuminuria. Subjects with resting heart rate ≥85 bpm, plasma glucose ≥5.6 mmol/L

and blood pressure ≥140/90mmHg were associated with albuminuria of any degree. A strategy

where only subjects with at least one of these risk factors (n=431) were screened for albuminuria,

would identify all subjects with macroalbuminuria (5/5), 64% of those with microalbuminuria

(32/50), and less than half of those with normoalbuminuria (81/183). An alternative strategy

whereby subjects were first screened for presence of albuminuria, and additional cardiovascular

risk factors were only measured in subjects positive for albuminuria (n=238), would identify only

27% (118/431) of the subjects with additional and potentially modifiable cardiovascular risk

factors. On the other hand, half of the subjects in this study with albuminuria (120/238, of which

102 had normoalbuminuria), had no additional cardiovascular risk factor at all.

Conclusions. Screening an apparently healthy population directly for albuminuria will result in a

high percentage of false positives, mostly measured in the normal range. Screening for

microalbuminuria and macroalbuminuria based on presence of additional, potentially modifiable

risk factors appears to be more beneficial.

CHAPTER 2: Results

47

Introduction

The number of patients with end stage renal disease (ESRD) in need of renal replacement therapy

has dramatically increased over the last decades (1). A substantial part of this increase is

attributable to the rising prevalence of diabetes and/or hypertension (2-4). Microalbuminuria

seems to be an important predictor of progressive renal disease and end stage renal disease in

diabetic and hypertensive subjects (5, 6). It has been demonstrated that in these populations,

preventive measures can delay the evolution to macroalbuminuria and potentially also the

progression to renal failure (7, 8). Consequently, it is advised to screen these high risk subjects

with diabetes and hypertension, in order to identify and eventually treat those at risk for

progressive renal disease. Extending this line of reasoning, some authors advocate to screen also

low risk groups, or even the general population, arguing that most persons with albuminuria

and/or reduced eGFR (<60 ml/min/1.73m²) are asymptomatic (9). Van der Velde et al e.g. found

that 45% of subjects with microalbuminuria were younger than 55 years and had no hypertension

or diabetes (9). It is debated whether such a large scale screening project should be advocated

(10, 11). Concerns are not only the cost of screening itself, but more importantly, the risk and the

cost of treating false positive subjects (12). In the study by van der Velde et al, out of 40,854

subjects screened, 7.8% (n=3,200) had at least microalbuminuria, but only 45 of those developed

ESRD over a 9 year follow up period (9). In addition, effective screening presumes that an

intervention to alter the course of the disease is available (13), which is not the case if the subject

only has albuminuria and no other modifiable risk factor. It would therefore be very useful to

develop a screening strategy based on additional, modifiable, risk factors to enhance the yield of

screening without having too much false positives, and to make interventions possible.

In this study, we used data collected in an apparently healthy working population, aged 17 to 65

years, to evaluate which easily obtainable parameters or combinations of them are associated

with albuminuria, and whether using these risk factors can be of help to increase the effectiveness

of a screening program.

CHAPTER 2: Results

48

Methods

Objectives: The primary aim was to determine the prevalence of different levels of albuminuria,

some (cardiovascular) risk factors and their associations, to develop a rational screening strategy

for albuminuria in the healthy population.

Participants: The Unreferred Renal Insufficiency (URI) is a cross sectional study that included

only Caucasian workers (n=1,486) who presented at a routine yearly occupational check-up

between January 2007 and December 2009, in Belgium. The presence of more than 100

leukocytes/μl (2.2%), and/or more than 50 erythrocytes/μl (2.4%) in the urinary sediment were

considered as confounders for reliable measurement of urinary albumin; these subjects and

subjects with incomplete data (1.8%) were consequently excluded from further analysis. Subjects

with known comorbid conditions or risk factors of which it is well established that they could

affect albuminuria, such as diabetes (1.2%), cardiovascular disease (0.3%), renal disease (0.4%),

subjects on antihypertensive drugs (8.3%) and on lipid lowering drugs (3.3%) were also

excluded, leaving a cohort of 1,191 apparently healthy subjects for analysis.

Description of procedures: All subjects were investigated during their yearly check up by their

occupational physician. Body weight was recorded to the nearest 0.1 kg and height was measured

to the nearest centimeter. Waist circumference was measured by trained nurses following

recommendations by WHO (14). Body mass index (BMI) was calculated as body weight in kg


position by a calibrated electronic device (OMRON®). A questionnaire about current cigarette

smoking, physical activity and prescribed medication was taken by an occupational physician in

each participant. A random blood and urine spot specimen was collected and analyzed on the

same day in one central laboratory (no frozen samples). Urinary albumin was measured by an

immune turbidimetric method with an inter-assay coefficient of variation of 11.2% at a mean

level of 82 mg/L and an inter-assay coefficient of variation of 4.9% at a mean level of 580 mg/l.

Serum creatinine was analyzed by a colorimetric assay (compensated Jaffe reaction), calibrated

by Isotope Dilution Mass Spectrometry (IDMS), with an inter-assay coefficient of variation of

1.75% at a mean level of 104 µmol/l (Roche). Serum CRP was measured with an immune

turbidimetric method with an inter-assay coefficient of variation of 4.6% at a mean level of 3.2

mg/l and inter-assay coefficient of variation of 2.5% at a mean level of 5.5 mg/l.

CHAPTER 2: Results

49

Definitions: Gender specific urinary albumin creatinine ratio (uACR) cutoff values as proposed

by Warram et al. were used because men have a higher urinary excretion of creatinine than

women due to higher muscle mass. Normoalbuminuria was defined as uACR: 0.6-1.8 mg/mmol

(5-16 mg/g) in men and uACR 0.8-2.7 mg/mmol (7-24 mg/g) in women, microalbuminuria was

defined as uACR 1.9-27 mg/mmol (17-249 mg/g) in men and uACR 2.8-39 mg/mmol (25-354

mg/g) in women, macroalbuminuria was defined as uACR ≥ 28 mg/mmol (250 mg/g) in men and

uACR ≥ 40 mg/mmol (355mg/g) in women (15). The Modification of Diet in Renal disease

(MDRD) equation was used to assess the estimated GFR: eGFR= 175× standardized Scr −1.154

×

age−0.203

× 1.212 [if black] × 0.742 [if female](16). Impaired glucose tolerance (IGT) was defined

as a plasma glucose level ≥ 5.6 mmol/L (17). Hypertension was defined as diastolic blood

pressure ≥ 90 mmHg and/or systolic blood pressure ≥140 mmHg. Obesity was defined as BMI >

30 kg/m². Abdominal adiposity was defined according the National Cholesterol Education

Program (NCEP) criteria: waist circumference >102 cm in men and >88 cm in women.

Hypercholesterolemia was defined as serum total cholesterol >6.5 mmol/L.

Ethics: A written informed consent was obtained from all participants. The Ethics Committee of

University Hospital Ghent approved the study (2006-038).

Statistical methods: SPSS 15.0 was used for all calculations. Results are presented as percentages

or as mean ± standard deviation. The baseline characteristics of groups were compared by use of

ANOVA: post-hoc Scheffé test (continuous variables), Kruskal-Wallis (continuous variable with

a skewed distribution) and a Chi-square test (categorical variables). Ordinal regression analysis

was performed to select the associated risk factors with different categories of albuminuria, in a

random selection of 50% of the subjects. The significant continuous variables were

dichotomized. These risk factors were validated in the other 50% of the sample. The prevalence

and test characteristics, of normo-, micro- and macroalbuminuria were calculated, if at least one

of these risk factors was present. The sensitivity was defined as the number of subjects with true-

positive test results divided by the total number of subjects with albuminuria. The specificity was

defined as the number of true-negative test results divided by the total of number of subjects

without albuminuria. The positive predictive value was defined as the number of true-positive

test results divided by the total number of positive test results. The negative predictive value was

defined as the number of true negative test results divided by the total number of negative test

CHAPTER 2: Results

50

results. The positive likelihood ratio was defined as sensitivity divided by 1-specificity; the

negative likelihood ratio was defined as 1-sensitivity divided by specificity.

CHAPTER 2: Results

51

Results

Our cohort (n=1,191) of apparently healthy subjects, after excluding those with treated

hypertension, treated dyslipidaemia, known diabetes, cardiovascular or renal disease, still had a

high prevalence of unknown hypertension, dyslipidaemia and impaired glucose metabolism.

Table 1. Basic characteristics and metabolic risk factors of 1,191 apparently healthy subjects.

Unknown impaired glucose tolerance (IGT)/diabetes: plasma glucose ≥ 5.6 mmol/L.

Hypercholesterolemia: serum cholesterol >6.5 mmol/L. Hypertension: RR ≥140 and/or 90 mmHg.

Table 1 shows the basic characteristics and cardiovascular risk factors of the cohort.

Almost all subjects (98.9%) had an estimated GFR higher than 60 ml/min/1.73m² and none had

an estimated GFR lower than 50 ml/min/1.73m². As expected, the cohort was rather young (age

38.3 ± 9.7 years, range 17-64). Albumin was detected in the urine of one fifth. The large majority

of albuminuric subjects had normoalbuminuria, fewer subjects had microalbuminuria and

macroalbuminuria was only rarely observed (table 1).

Parameters N %

Male gender 998 83.8

Unknown hypertension 279 23.4

Unknown hypercholesterolemia 86 7.2

Unknown IGT/diabetes 150 13.3

Obesity (BMI>30kg/m²) 160 13.5

Abdominal adiposity (%) 168 16.0

No physical activity 491 49.2

Current smoking 370 32.4

Normoalbuminuria 183 15.4

Microalbuminuria 50 4.2

Macroalbuminuria 5 0.4

CHAPTER 2: Results

52

Table 2 shows the distribution of some measured clinical and biochemical parameters in

subgroups according to different levels of albuminuria.

Table 2. Metabolic risk factors in subjects with different level of albuminuria.

Unknown impaired glucose tolerance (IGT)/diabetes: plasma glucose ≥ 5.6 mmol/L.

Hypertension: RR ≥140 and/or 90 mmHg. *p<0.05, **p<0.01 versus no albuminuria.

In a randomly selected cohort (n=599), a multivariate ordinal regression model selected resting

heart rate, plasma glucose and hypertension as significant independent predictors for presence of

albuminuria at any degree (table 3).

Albuminuria no Normo- Micro- Macro-

n=1,191 (%) 953 183 (15.4) 50 (4.2) 5 (0.4)

Age (years) 38.2±9.6 38.7± 10.1 38.8± 10.7 39.0± 9.1

Men (%) 799 (83.8) 147 (80.3) 47 (94) 5 (100)

Systolic blood pressure (mmHg) 126.9± 13.8 129.8± 14.1 132.6± 18.3* 142.6± 11.2

Diastolic blood pressure (mmHg) 77.4± 9.9 79.9± 10.6* 80.8± 13.2 90± 8.9*

Unknown hypertension 199 (20.9) 55(30.1)* 20 (40)** 5(100)**

Resting heart rate 69.5± 10.3 72.6± 11.3** 75.9± 16.1** 78.8± 3.7

Total cholesterol (mmol/L) 5.0± 1.0 5.1± 1.0 4.9± 1.1 7.0± 1.0**

Plasma glucose (mmol/L) 4.8± 0.7 5.0± 1.2* 5.0± 0.9 5.2± 1.1

Unknown IGT/diabetes (%) 103 (11.5) 34(19.3)* 12(24)* 1(20)

Body mass index (kg/m²) 25.9± 3.9 25.5± 4.3 25.3± 5.3 27.5± 2.7

Abdominal adiposity (%) 135 (16.1) 21 (12.7) 11 (25.6) 1 (25)

Serum uric acid (µmol/L) 315± 71 315± 77 339± 77 482± 119**

White blood cell count (109/L) 7.0± 1.9 7.2± 1.9 7.9± 2.3** 8.0± 1.8

C-reactive protein (mg/L) 2.2± 4.8 2.5± 4.2 4.1± 1.0 3.4± 3.6

Current smoking (%) 283 (31) 64 (35.6) 20 (40) 3 (60)

CHAPTER 2: Results

53

Table 3. Multivariate regression analyses to predict different categories of albuminuria

Random sample n=599, R²=0.07 E St error P

Resting heart rate (beats per min) 0.026 0.009 0.006

Plasma glucose (mmol/L) 0.341 0.141 0.015

Unknown Hypertension 0.487 0.237 0.040

White blood cell count 0.079 0.053 0.132

Serum uric acid (µmol/L) 0.001 0.001 0.464

Total serum cholesterol (mmol/L) -0.106 0.104 0.310

The continuous variables were dichotomized (0 or 1) as follows: resting heart rate ≥85 bpm

(cutoff level according to the 90th

percentile); plasma glucose: ≥5.6 mmol/L (impaired glucose

tolerance). Consequently, the prevalence of normoalbuminuria, microalbuminuria and

macroalbuminuria in this randomly selected cohort, was higher in subjects with at least one risk

factor (n=206) than in subjects with no risk factors (n=393), respectively: 21.4 vs 14.1% (n=44 vs

58); 8.7 vs 3.1% (n=18 vs 12) and 1.5 vs 0% (n=3 vs 0), p<0.001. Our risk assessment was

validated in the other randomly selected cohort (n=592). The prevalence of normoalbuminuria,

microalbuminuria and macroalbuminuria in this validation cohort, was also higher in subjects

with at least one risk factor (n=225) than in subjects with no risk factors (n=367), respectively:

16.4 vs 12% (n=37 vs 44); 6.2 vs 1.6% (n=14 vs 6) and 0.9 vs 0% (n=2 vs 0), p=0.001. Table 4

shows the test characteristics for subjects with at least one risk factor, to identify

normoalbuminuria, microalbuminuria and macroalbuminuria in both randomly selected cohorts.

CHAPTER 2: Results

54

Table 4. Test characteristics to identify normo-, micro- and macroalbuminuria with at least one

risk factor in randomly selected populations and in the complete “healthy” population.

Population albuminuria sensitivity specificity PPV NPV LR+ LR-

random cohort

n=599

Normo- 43 70 24 85 1.4 0.8

Micro- 60 70 11 96 2.0 0.6

Macro- 100 70 2 100 3.3 ≈0

validation cohort

n=592

Normo- 46 65 18 88 1.3 0.8

Micro- 70 65 8 98 2.0 0.5

Macro- 100 65 1 100 2.9 ≈0

complete population

n=1191

Normo- 44 67 21 86 1.3 0.8

Micro- 64 67 9 97 1.9 0.6

Macro- 100 67 1 100 3.0 ≈0

PPV: positive predictive value; NPV: negative predictive value; LR+: positive likelihood and LR-:

negative likelihood ratio

Because our risk assessment fitted quite well in both cohorts, we applied it in the complete

population. The prevalence of normoalbuminuria, microalbuminuria and macroalbuminuria in the

complete population, was higher in subjects with at least one risk factor (n=431) than in subjects

with no risk factors (n=760), respectively; 18.8 vs 13.4% (n=81 vs 102); 7.4 vs 2.4% (n=32 vs

18) and 1.2 vs 0% (n=5 vs 0), p<0.001 (figure 1).

CHAPTER 2: Results

55

Figure 1. The prevalence of albuminuria in subjects with at least one vs. none risk factors

We evaluated two strategies for screening albuminuria in our population, one strategy where a set

of additional risk factors were screened as first line, with later screening for albuminuria only in

subjects with at least one of those additional risk factors and an alternative strategy where

albuminuria was screened as first line, and additional risk factors were measured only in those

with albuminuria.

A strategy where only subjects with at least one modifiable risk factor (n=431) were screened for

albuminuria, would identify all subjects with macroalbuminuria (5/5), 64% of subjects with

microalbuminuria (32/50), but less than half of those with normoalbuminuria (81/183), table 4

shows the corresponding likelihoods and predictive values.

An alternative strategy whereby subjects were first screened for presence of albuminuria, and

additional cardiovascular risk factors were only measured in subjects positive for albuminuria

(n=238), would identify only 27% (118/431) of the subjects with additional and potentially

modifiable cardiovascular risk factors. On the other hand, half of the subjects in this study with

albuminuria (120/238, of which 102 had normal range albuminuria), had no additional

cardiovascular risk factor at all.

CHAPTER 2: Results

56

Table 5 shows that subjects with no modifiable risk factor had also lower levels of risk factors

which were not included in our screening model.

Table 5. Cardiometabolic profile in subjects with none versus one or more risk factors.

Risk factors None ≥ 1 p

N 760 431

Age 37.0 ± 9.4 40.6 ± 9.8 <0.001

Male gender (%) 604 (79.5) 394 (91.48) <0.001

Resting heart rate (bpm) 67.7 ± 8.3 74.9 ± 13.1 <0.001

Systolic blood pressure (mmHg) 121.9± 10.4 137.9 ± 14.1 <0.001

Diastolic blood pressure (mmHg) 74.4 ± 8.3 84.3 ± 10.2 <0.001

Plasma glucose (mmol/L) 4.6 ±0.5 5.3 ±1.0 <0.001

White blood cell count (10*109/L) 6.9 ± 1.9 7.3 ± 1.9 <0.001

Body mass index (kg²/m) 25.2 ± 3.6 26.9 ± 4.4 <0.001

Abdominal obesity (%) 72 (10.8) 96 (24.8) <0.001

Obesity >30 kg/m² (%) 71 (9.4) 89 (20.6) <0.001

Total cholesterol (mmol/L) 4.9 ±0.9 5.3 ±1.0 <0.001

Serum uric acid (µmol/L) 305 ± 70 336 ± 77 <0.001

C-reactive protein (mg/L) 2.0 ± 3.4 3.0 ±7.0 <0.001

Current smoking 225 (30.7) 145 (34.9) 0.130

No physical activity 282 (44.3) 209 (57.9) <0.001

CHAPTER 2: Results

57

Discussion

Our data indicate that albuminuria, unknown hypertension and impaired glucose metabolism are

quite prevalent findings in an apparently healthy population. When present, albuminuria was

mostly measured in the normal range, and was frequently found in subjects without other

modifiable risk factors, making its relevance as a predictor of outcome questionable. A screening

strategy for albuminuria starting from assessment of simple and easy to obtain risk factors, such

as resting heart rate, blood pressure and plasma glucose level identified subjects at risk for micro-

and macroalbuminuria in a more effective way than a strategy of screening a healthy population

for albuminuria alone.

There is little debate that screening for albuminuria should be performed in patients with diabetes

and/or hypertension, where early intervention can slow down deterioration of renal function.

Whether it should also be performed in the general population remains ambigiuous (12, 18, 19).

There is heated debate whether screening the healthy population for presence of albuminuria is

fulfilling all conditions requested to define a successful screening program (13).

A first request is that the screened factor either relates to an important health risk, or is prevalent

in the population. In our healthy population, the prevalence of albuminuria was 20%. However,

many cases had normoalbuminuria without additional risk factors. Those in favor of screening

the general population argue that an urinary albumin excretion > 5µg/min is related with

increased risk of cardiovascular morbidity and mortality (20-22). In some of these studies, an

adjustment for presence of hypertension and diabetes was performed, indicating that albuminuria

is an independent risk factor on top of diabetes and hypertension, but it is not clear whether the

increased risk was also present in those with normal blood pressure and glucose levels (21). In

the PREVEND cohort, hypertension and diabetes were not actually assessed objectively, but

were based on “self declared” status (23). In our study, the prevalence of unknown hypertension

and impaired glucose levels was high, even after excluding subjects with known risk factors, so

most likely also in the PREVEND cohort a substantial part of “false negatives” for hypertension

and impaired glucose tolerance are present. Accordingly, the definition of “healthy population” in

the PREVEND cohort is probably not correct, making also the recommendation to screen the

healthy population incorrect, as most likely a substantial number of subjects in this “healthy

cohort” would have hypertension or elevated glucose levels if these would have been measured.

CHAPTER 2: Results

58

In other studies restricted to non-diabetic and non-hypertensive subjects, although the increased

risk for renal disease in those with microalbuminuria seems dramatic, the absolute risk remains

low, with less than 0.1% of persons with microalbuminuria ending up on renal replacement

therapy, or 0.6% developing cardiovascular disease over an 8 year period (9, 24). In the

PREVEND study, the relation between albuminuria and decline of renal function in subjects

without known risk factors, was only observed in those individuals with macroalbuminuria (9).

As mentioned, in our study, all subjects with macroalbuminuria would be detected if only

subjects with more than one risk factor were screened, as none of the subjects without risk factors

(the truly healthy population) had macroalbuminuria. In our cohort, the prevalence of

microalbuminuria, in subjects without risk factors, was 2.4%. A similar figure was found in a

New Zealand study (2.0%) in subjects without diabetes, impaired glucose tolerance, hypertension

or dyslipidaemia (25). In the Copenhagen City Heart Study, the prevalence of microalbuminuria

was also 2.0% in subjects without any feature of the metabolic syndrome, and these subjects had

no increased risk for cardiovascular disease and death, suggesting that microalbuminuria by itself

might not be an independent determinant of outcome without presence of associated risk factors

(26). It can be that this microalbuminuria is the equivalent of “exertional”(27) or “orthostatic”

albuminuria.

Another request for a screening program to be meaningful and effective, is that risk factors

should be modifiable. As a prospective trial to test the hypothesis that medical management of

microalbuminuria affects patient-centered events independent of blood pressure reduction is still

lacking (28), screening for microalbuminuria in subjects without measured additional risk factors,

appears not to be justified from a general health care perspective.

In our cohort of apparently healthy subjects, the likelihood of having albuminuria was related to

the well established and potentially modifiable risk factors blood pressure and plasma glucose

level, but also to resting heart rate. Two thirds of our cohort had none of these risk factors. Table

5 shows that those subjects had also much lower levels of other cardiovascular risk factors not

included in our risk score. Nevertheless, 13.4% of these subjects had normoalbuminuria, but none

had macroalbuminuria. Most likely, these subjects have thus a very low absolute risk for

cardiovascular or renal disease, and the potential benefit of a treatment should be considered very

low. Consequently, a strategy of screening only in those with at least one risk factor would miss

only few potentially relevant cases, at the same time avoiding many “false positives”, who

CHAPTER 2: Results

59

compose nearly 50% of albuminuric subjects when unrestricted screening for albuminuria is

performed first. However, testing for albuminuria in subjects with additional cardiovascular risk

factors is warranted, as a more aggressive treatment can be defended in subjects with additional

risk factors in presence of albuminuria compared to those with risk factors but without

albuminuria.

There is substantial evidence that reduction of blood pressure, decreases the progression of renal

disease and reduces cardiovascular events (29-31). Disturbed glucose metabolism, as indicated by

increased plasma glucose levels, is a condition with increased risk for the development of overt

diabetes. Life style modification, and the use of drugs such as metformin and acarbose can slow

down progression to overt diabetes and/or cardiovascular disease in these subjects (32). The

association of resting heart rate with albuminuria was previously mentioned (33, 34). An

explanation could be that a high resting heart rate may cause mechanical stress that might

contribute to renal endothelial dysfunction leading to albuminuria (35). Previous reports

mentioned that tachycardia as a sign of sympathetic overactivity, is an independent risk factor for

chronic kidney disease, cardiovascular and noncardiovascular mortality, even in an apparently

healthy population (34, 36-38). Reduction of sympathetic overactivity by regular physical activity

and smoking cessation appear to be beneficial in this patient group (39). Carvedilol appears to

reduce proteinuria to a higher degree than expected by the blood pressure lowering effect alone in

patients with hypertension, but it is unclear whether this effect is due to the reduction in heart rate

modification or to genuine metabolic effects (40).

A strength of this study is the nearly 100% participation rate of a relatively young and apparently

healthy occupational population. A limitation is that we only measured urinary albumin to

creatinine ratio at one occasion, guidelines recommend to have at least two positive albumin to

creatinine ratio’s in three consecutive first morning urine samples before labeling a person with

microalbuminuria(41). Furthermore, the prevalence of microalbuminuria was underestimated if

albumin to creatinine ratio was measured in morning urine samples (42), while an overestimation

was observed if random samples were obtained (43).Thus, the association between subjects with

albuminuria and additional risk factors could be confounded by measuring the urinary albumin to

creatinine ratio at only one random occasion.

CHAPTER 2: Results

60

In conclusion, our data provide evidence to support the concept that screening for albuminuria

should only be performed in subjects with additional and potentially modifiable risk factors, and

that this strategy is more beneficial than screening the general population. We identified 3

parameters that are easy and cheap to obtain: blood pressure, plasma glucose and resting heart

rate to identify subjects in whom further assessment of presence of albuminuria might be

relevant.

Acknowledgment We thank all concerned employees of Adhesia (Occupational Heath Care,

Ghent, Belgium) and the recruited participants who made this study possible.

CHAPTER 2: Results

61

References

1. Kramer A, Stel V, Zoccali C, Heaf J, Ansell D, Gronhagen-Riska C, Leivestad T, Simpson K, Palsson

R, Postorino M, Jager K. An update on renal replacement therapy in Europe: ERA-EDTA Registry

data from 1997 to 2006. Nephrol Dial Transplant 2009; 24: 3557-3566. Available at


=19820003.



reg.org/files/annualreports/pdf/AnnRep2009_new.pdf.

3. ERA-EDTA Registry: ERA-EDTA Registry 1998 Annual Report. Academic Medical Center,

Amsterdam, The Netherlands, May 2003. Available at http://www.era-edta-


4. The concise 2009 Annual Data Report Atlas of Chronic Kidney Disease & End-Stage Renal Disease in

the United States Available at www.usrds.org.

5. Bigazzi R, Bianchi S, Baldari D, Campese VM. Microalbuminuria predicts cardiovascular events and

renal insufficiency in patients with essential hypertension. Journal of Hypertension 1998; 16: 1325-


6. Mogensen CE. Microalbuminuria, blood pressure and diabetic renal disease: origin and development

of ideas. Diabetologia 1999; 42: 263-285. Available at


=10096778.

7. Effects of ramipril on cardiovascular and microvascular outcomes in people with diabetes mellitus:

results of the HOPE study and MICRO-HOPE substudy. Heart Outcomes Prevention Evaluation Study

Investigators. Lancet 2000; 355: 253-259. Available at


=10675071.

8. Keane WF, Zhang Z, Lyle PA, Cooper ME, de Zeeuw D, Grunfeld JP, Lash JP, McGill JB, Mitch WE,

Remuzzi G, Shahinfar S, Snapinn SM, Toto R, Brenner BM. Risk scores for predicting outcomes in

patients with type 2 diabetes and nephropathy: the RENAAL study. Clin J Am Soc Nephrol 2006; 1:

761-767. Available at


=17699284.




10. Hallan SI, Dahl K, Oien CM, Grootendorst DC, Aasberg A, Holmen J, Dekker FW. Screening

strategies for chronic kidney disease in the general population: follow-up of cross sectional health

survey. Brit Med J 2006; 333: 1047-1050. Available at <Go to ISI>://000242229800016

http://www.bmj.com/cgi/reprint/333/7577/1047.pdf.

11. Fried L. Are we ready to screen the general population for microalbuminuria? J Am Soc Nephrol 2009;



=19279125.


screening in self-reported non-diabetic/non-hypertensive persons identified in a large health screening -



http://www.era-edta-reg.org/files/annualreports/pdf/AnnRep2009_new.pdf

http://www.era-edta-reg.org/files/annualreports/pdf/AnnRep2009_new.pdf



http://www.usrds.org/







http://www.bmj.com/cgi/reprint/333/7577/1047.pdf



CHAPTER 2: Results

62

The Nord-Trondelag Health Study (HUNT), Norway. Clin Nephrol 2003; 59: 241-251. Available at

<Go to ISI>://000181878200001.

13. Wilson JMG, Jungner G. principles and practice of screening for disease. public health papers, Geneva,

World Health Organization 1968; 34: . Available at http://whqlibdoc.who.int/php/WHO_PHP_34.pdf.

14. WHO STEPS Surveillance, part 3. Training and Practical Guides, section 3: guide to physical

Measurements. Available at http://www.who.int/chp/steps/Part3_Section3.pdf.




16. Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, Kusek JW, Van Lente F. Using

standardized serum creatinine values in the modification of diet in renal disease study equation for

estimating glomerular filtration rate. Ann Intern Med 2006; 145: 247-254. Available at


=16908915.

17. Genuth S, Alberti KGMM, Bennett P, Buse J, DeFronzo R, Kahn R, Kitzmiller J, Knowler WC,

Lebovitz H, Lernmark A, Nathan D, Palmer J, Rizza R, Saudek C, Shaw J, Steffes M, Stern M,

Tuomilehto J, Zimmet P, Classificat ECD. Follow-up report on the diagnosis of diabetes mellitus.

Diabetes Care 2003; 26: 3160-3167. Available at <Go to ISI>://000186269100030.

18. Hallan SI, Stevens P. Screening for chronic kidney disease: which strategy? J Nephrol 2010; 23: 147-

155. Available at


=20155721.

19. Nielen MM, Schellevis FG, Verheij RA. The usefulness of a free self-test for screening albuminuria in

the general population: a cross-sectional survey. BMC Public Health 2009; 9: 381. Available at


=19818129.

20. Klausen KP, Scharling H, Jensen JS. Very low level of microalbuminuria is associated with increased

risk of death in subjects with cardiovascular or cerebrovascular diseases. Journal of Internal Medicine


21. Klausen K, Borch-Johnsen K, Feldt-Rasmussen B, Jensen G, Clausen P, Scharling H, Appleyard M,

Jensen JS. Very low levels of microalbuminuria are associated with increased risk of coronary heart

disease and death independently of renal function, hypertension, and diabetes. Circulation 2004; 110:





<Go to ISI>://000178385700010.





24. Arnlov J, Evans JC, Meigs JB, Wang TJ, Fox CS, Levy D, Benjamin EJ, D'Agostino RB, Vasan RS.

Low-grade albuminuria and incidence of cardiovascular disease events in nonhypertensive and

nondiabetic individuals: the Framingham Heart Study. Circulation 2005; 112: 969-975. Available at


=16087792.

http://whqlibdoc.who.int/php/WHO_PHP_34.pdf

http://www.who.int/chp/steps/Part3_Section3.pdf









CHAPTER 2: Results

63

25. Metcalf PA, Scragg RK, Dryson E. Associations between body morphology and microalbuminuria in

healthy middle-aged European, Maori and Pacific Island New Zealanders. Int J Obes Relat Metab

Disord 1997; 21: 203-210. Available at


=9080259.




ISI>://000249947500010.

27. Heathcote KL, Wilson MP, Quest DW, Wilson TW. Prevalence and duration of exercise induced

albuminuria in healthy people. Clin Invest Med 2009; 32: E261-265. Available at


=19640328.

28. Weir MR, Bakris GL. Should Microalbuminuria Ever Be Considered as a Renal Endpoint in Any

Clinical Trial? American Journal of Nephrology 2010; 31: 469-470. Available at <Go to

ISI>://000277757300014.

29. Turnbull F. Effects of different blood-pressure-lowering regimens on major cardiovascular events:

results of prospectively-designed overviews of randomised trials. Lancet 2003; 362: 1527-1535.


30. Ruilope LM, Salvetti A, Jamerson K, Hansson L, Warnold I, Wedel H, Zanchetti A. Renal function

and intensive lowering of blood pressure in hypertensive participants of the hypertension optimal

treatment (HOT) study. Journal of the American Society of Nephrology : JASN 2001; 12: 218-225.


31. Walker WG, Neaton JD, Cutler JA, Neuwirth R, Cohen JD. Renal function change in hypertensive

members of the Multiple Risk Factor Intervention Trial. Racial and treatment effects. The MRFIT

Research Group. JAMA : the journal of the American Medical Association 1992; 268: 3085-3091.


32. 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; 359: 1577-1589. Available at


=18784090.

33. Bohm M, Reil JC, Danchin N, Thoenes M, Bramlage P, Volpe M. Association of heart rate with

microalbuminuria in cardiovascular risk patients: data from I-SEARCH. Journal of Hypertension 2008;

26: 18-25. Available at <Go to ISI>://000252095900007.

34. Inoue T, Iseki K, Iseki C, Ohya Y, Kinjo K, Takishita S. Heart rate as a risk factor for developing

chronic kidney disease: longitudinal analysis of a screened cohort. Clin Exp Nephrol 2009; 13: 487-

493. Available at


=19444548.

35. Facchini FS, Stoohs RA, Reaven GM. Enhanced sympathetic nervous system activity. The linchpin

between insulin resistance, hyperinsulinemia, and heart rate. Am J Hypertens 1996; 9: 1013-1017.

Available at


=8896654.

36. Gillman MW, Kannel WB, Belanger A, D'Agostino RB. Influence of heart rate on mortality among

persons with hypertension: the Framingham Study. Am Heart J 1993; 125: 1148-1154. Available at














CHAPTER 2: Results

64


=8465742.

37. Palatini P. Elevated heart rate: a "new" cardiovascular risk factor? Prog Cardiovasc Dis 2009; 52: 1-5.


38. Jouven X, Empana JP, Escolano S, Buyck JF, Tafflet M, Desnos M, Ducimetiere P. Relation of heart

rate at rest and long-term (>20 years) death rate in initially healthy middle-aged men. The American

journal of cardiology 2009; 103: 279-283. Available at


39. Ritz E, Ogata H, Orth SR. Smoking: a factor promoting onset and progression of diabetic nephropathy.

Diabetes Metab 2000; 26 Suppl 4: 54-63. Available at


=10922974.

40. Bakris GL, Fonseca V, Katholi RE, McGill JB, Messerli FH, Phillips RA, Raskin P, Wright JT, Jr.,

Oakes R, Lukas MA, Anderson KM, Bell DS. Metabolic effects of carvedilol vs metoprolol in patients

with type 2 diabetes mellitus and hypertension: a randomized controlled trial. JAMA 2004; 292: 2227-

2236. Available at


=15536109.

41. Clinical Practice Guidelines for Chronic Kidney Disease: Evaluation C, and Stratification, part 5.

Evaluation of laboratory measurements for clinical assessment of kidney disease, guidelines 5.

Assesment of proteinuria

Accessed March 10, 2012; . Available at

http://www.kidney.org/professionals/kdoqi/pdf/ckd_evaluation_classification_stratification.pdf.

42. Stehouwer CD, Fischer HR, Hackeng WH, den Ottolander GJ. Identifying patients with incipient

diabetic nephropathy. Should 24-hour urine collections be used? Arch Intern Med 1990; 150: 373-375.

Available at


=2302012.





=19092125.









http://www.kidney.org/professionals/kdoqi/pdf/ckd_evaluation_classification_stratification.pdf





CHAPTER 2: Results

65

2.1.2. Microalbuminuria is more consistent in presence of cardiovascular risk

factors: Results from the Unreferred Renal Insufficiency Trial


1, Guy De Groote

2, Paul Verbeke

3, Frans Vermeiren

3,


1

1 Renal Division, Department of Internal Medicine, University Hospital Ghent

2 CRI laboratory, Ghent

3 Occupational Health Care Adhesia, Belgium

J Nephrol, May-Jun 2013, 26(3): 580-5.

Available at http://www.ncbi.nlm.nih.gov/pubmed/22865595


CHAPTER 2: Results

66

Abstract

Background. The use of microalbuminuria (MAU) to screen for cardiovascular and renal risk

might be hampered by its intermittent character. This prospective observational study assessed

traditional risk factors in presumed healthy workers with intermittent MAU (IMAU) compared to

persistent MAU (PMAU).

Methods. A cohort of 239 Belgian workers underwent at least two consecutive occupational

check-ups with a median time of 12 months. Hypertension (HT) was defined as blood pressure ≥

140/90 mmHg. Impaired glucose tolerance (IGT) was defined as plasma glucose ≥ 5.6 mmol/L.

MAU was defined as urinary albuminin to creatinine ratio of 17-249 mg/g in men and 25-354

mg/g in women. Workers with IMAU had one positive MAU and workers with PMAU had two

positive MAU during follow up.

Results. The mean age of this mainly male (95%) cohort was 41± 9 years. The prevalence of

persistent risk factors (HT and/or IGT) was higher in workers with PMAU than without MAU

(8/12[67%] vs. 55/210[26%], p= 0.005) while workers with IMAU had no increased risk

(5/17[29%]). The prevalence of PMAU was higher in workers with vs. without persistent risk

factors (8/68= 12% vs.4/171= 2%, p= 0.005) while the prevalence of IMAU was the same (5/68=

7% vs. 12/171= 7%, p= 0.93). The reproducibility of initial MAU at consecutive visits was

higher in workers with vs. without persistent risk factors (8/9[89%] vs.4/11[36%], p= 0.03).

Conclusions. The use of MAU as a first step screening strategy in an occupational health care

setting is hampered by false positives and low sensitivity to identify subjects with cardiovascular

and renal risk. Trial registration 2006 NCT00365911

CHAPTER 2: Results

67

Introduction

There is still debate whether a large scale screening starting from MAU (microalbuminuria) may

identify more accurately subjects at risk for cardiovascular and renal events than measuring

simple clinical index factors (1-4). Supporters of direct screening for MAU substantiate that the

presence of asymptomatic MAU can be measured easily in a simple urine spot whereas

opponents substantiate that the use of MAU might be hampered by its intermittent character,

leading to false positives (5-7). False positive measurements of MAU are provoked by temporary

inflammation, exercise, stature, diet, urinary tract infection, hematuria and medication (8, 9). The

HUNT study data revealed that subjects with persistent MAU (PMAU) had an increased

mortality whereas subjects with intermittent MAU (IMAU) had no increased risk (10).

Nevertheless, many outcome studies including MAU as a risk marker include only single

measurements, most likely for practical reasons (11-13). The primary aim of this study is to

assess the prevalence of traditional cardiovascular risk factors in workers with IMAU as

compared to PMAU to asses whether IMAU or PMAU are related to enhanced cardiovascular

risk.

CHAPTER 2: Results

68

Subjects and methods

The Unreferred Renal Insufficiency study is a non-interventional observational study that

included 1486 voluntary Caucasian workers in Belgium (14). Some of these subjects (n= 341)

underwent consecutive occupational check-ups between January 2007 and December 2009.

Subjects with urinary albumin creatinine ratio (ACR) > 300 mg/g (n=1) and subjects with

missing values of interest were excluded, leaving a cohort of 239 “presumed healthy” workers

with at least two measurements of ACR, blood pressure and plasma glucose. This small

subcohort (n=239) had approximately the same prevalence of hypertension and/or impaired

glucose tolerance as the original population (38 vs.39%).

All participants were investigated annually by their occupational physician. At every

investigation, body weight, height and waist circumference was measured by trained nurses

following WHO recommendations. Body mass index (BMI) was calculated as body weight in kg


position by a calibrated electronic device (OMRON®) and a questionnaire about current cigarette

smoking, physical activity and prescribed medication was performed by a physician in each

participant. In addition, a random blood and urine spot specimen was collected and analyzed on

the same day in one laboratory, at every examination, to mimic a routine screening procedure.

Urinary albumin was measured by an immune turbid metric method with an inter-assay

coefficient of variation of 11.2% at a mean level of 82 mg/L and an inter-assay coefficient of

variation of 4.9% at a mean level of 580 mg/L. Serum creatinine was analyzed by a colorimetric

assay (compensated Jaffe reaction), calibrated by Isotope Dilution Mass Spectrometry, with an

inter-assay coefficient of variation of 1.75% at a mean level of 104 µmol/L. Serum CRP was

measured with an immune turbid metric method with an inter-assay coefficient of variation of

4.6% at a mean level of 3.2 mg/L and inter-assay coefficient of variation of 2.5% at a mean level

of 5.5 mg/L.

Definitions: MAU was defined as ACR between 17-249 mg/g in men and 25-354 mg/g in women

as proposed by Warram et al. (15). Subjects with only one positive MAU were determined as

IMAU and those with at least two positive measurements were labeled as PMAU. Hypertension

(HT) was defined as BP ≥ 140/90 mmHg. Persistent hypertension (PHT) was defined as at least

two measurements were positive. Less than half of the subjects (n=10) treated with

CHAPTER 2: Results

69

antihypertensive agents (n=26) had PHT. Impaired glucose tolerance (IGT) was defined as

plasma glucose ≥ 5.6 mmol/L. Persistent IGT (PIGT) was defined as at least two positive

measurements. All subject who were treated for diabetes (n=6) had PIGT. The Modification of

Diet in Renal disease (MDRD) equation was used to assess the estimated GFR: eGFR= 175 × Scr

−1.154 × age

−0.203 × 1.212 [if black] × 0.742 [if female] (16). Abdominal adiposity was defined

according the National Cholesterol Education Program criteria: waist circumference >102cm in

men and >88 cm in women (17). The Framingham risk score was calculated in every subject (18).

Institutional Review Board (IRB)/Ethics Committee approval was obtained at University

Hospital Ghent (2006-038). This study was in adherence with the Declaration of Helsinki A

written informed consent was obtained from all participants.

Statistical methods: SPSS Statistics 19 was used for all calculations. Results are presented as

percentages or as mean ± standard deviation. The baseline characteristics of groups were

compared by use of ANOVA: post-hoc Scheffé test (continuous variables), Kruskal-Wallis

(continuous variable with a skewed distribution) or a Chi-square test (categorical variables).

CHAPTER 2: Results

70

Results

We followed 239 workers during annual consecutive clinical occupational check-ups with a

median follow up of 12 months (range 8-35 months). Three examinations were performed on 38

subjects. In 220 of the 239 workers (92%) the time between the initial examination and the

confirmation test was within 3-13 months and in 20 subjects this time interval was within 14-26

months. The mean age of this mainly male cohort (95%) was 41.1 ± 9.3 years, range 21-61 years.

All workers had an estimated GFR higher than 60 ml/min/1.73m². At the first visit, 72 of 239

workers had hypertension (30%), 33 had impaired glucose tolerance (14%), 90 had hypertension

and/or impaired glucose tolerance (38%) and 20 had MAU (8%) which was reproducible in 50

(70%), 23 (70%), 68 (76%) and 12 (60%), respectively, at subsequent visits (table 1).

Table 1. Reproducibility of microalbuminuria (MAU), hypertension (HT), impaired glucose

tolerance (IGT) and HT and/or IGT at initial testing and during follow up.

Initial test (n) Follow-up (n)

MAU No MAU

MAU 20 12 8

No MAU 219 9 210

HT No HT

HT 72 50 22

No HT 167 27 140

IGT No IGT

IGT 33 23 10

No IGT 206 35 171

HT and/or IGT No risk

HT and/or IGT 90 68 22

No risk 149 47 102

HT = hypertension; IGT = impaired glucose tolerance; MAU is microalbuminuria. MAU is defined as

ACR 17-249 mg/g in men and 25-354 mg/g in women. HT was defined as BP ≥140/90 mmHg. IGT was

defined as plasma glucose ≥5.6 mmol/L.

CHAPTER 2: Results

71

Table 2. Cardiovascular risk factors in subjects with no microalbuminuria (MAU), intermittent

MAU (IMAU) and persistent MAU (PMAU).

No MAU IMAU PMAU p

N (%) 210 17 (7) 12 (5)

Male gender (%) 200 (95) 15 (88) 12 (100) 0.22

Age(yr) 41± 9 40± 12 45± 11 0.32

Heart rate (bpm) 70± 10 74± 16 72± 12 0.24

SBP (mmHg) 131± 14 129± 17 147± 30**°° 0.002

DBP (mmHg) 80± 10 76± 13 91± 16**°° <0.001

BMI (kg/m²) 26.4± 3.6 25.9± 5.5 29.1± 7.6 0.09

Cholesterol (mmol/l) 5.2± 0.9 5.1± 1.2 5.5± 1.0 0.58

C-Reactive protein (mg/L) 1.9± 2.4 3.0± 2.8 3.9± 5.8 0.03

WBC count (109/L) 7.0± 2.0 7.9± 1.5 7.4± 1.9 0.18

Glucose (mmol/l) 5.0± 1.4 5.1± 1.2 6.7± 4.4* 0.05

PHT and/or PIGT (%) 55 (26) 5 (29) 8 (67)*° 0.01

Adiposity (%) 37 (18) 3 (18) 4 (36) 0.32

Framingham score (%) 7± 5 7± 5 13± 11**°° <0.001

Current smoking (%) 68 (32) 6 (35) 4 (33) 0.97

No physical acitivity (%) 81 (39) 6 (37) 9 (75)* 0.05

MAU is defined as ACR 17-249 mg/g in men and 25-354 mg/g in women, SBP: systolic blood pressure,

DBP: diastolic blood pressure, BMI: body mass index, WBC: white blood cell count, PHT: persistent

hypertension was defined as BP ≥140/90 mmHg in at least two occasions, PIGT was defined as plasma

glucose ≥5.6 mmol/L in at least two occasions, adiposity defined by waist circumference >88 cm in

women and >102 cm in men.

*p < 0.05, **p<0.001 vs no albuminuria, °p<0.05, °°p< 0.005 vs IMAU

Approximately one quarter (n=68) of the 239 workers had persistent hypertension and/or

impaired glucose tolerance and only 5% of the workers (n=12) had MAU confirmed during

follow up.

Workers with PMAU had higher mean blood pressure, mean plasma glucose, Framingham risk

score and higher frequency of inactivity than workers without MAU, while workers with IMAU

had no increased cardiovascular risk (table 2).

The prevalence of persistent HT and/or IGT was higher in workers with PMAU (8/12 [67%])

than in those with IMAU (5/17 [29%], p= 0.05) or without MAU (55/210 [26%], p= 0.005, table

CHAPTER 2: Results

72

2). The prevalence of PMAU was higher in workers with risk factors (HT and/or IGT) than in

workers without persistent risk factors (8/68= 12% vs. 4/171= 2%, p= 0.005) while the

prevalence of IMAU was the same in workers with risk factors (HT and/or IGT) vs. those without

persistent risk factors (5/68= 7% vs. 12/171= 7%, p= 0.93). Workers with risk factors (HT and/or

IGT) had a higher reproducibility of initial MAU than workers without persistent risk factors

(8/9 [89%] vs. 4/11 [36%] p= 0.03, table 3).

Table 3. Reproducibility of microalbuminuria (MAU) at initial testing and during follow-up in

workers with and without persistent hypertension (PHT) and/or persistent impaired glucose

tolerance (PIGT).

Initial test (n) Follow-up (n)

MAU No MAU

With PHT and/or PIGT (n=68) MAU 9 8 1

No Mau 59 4 55

Without PHT and/or PIGT (n=171) MAU 11 4 7

No MAU 160 5 155

MAU is defined as ACR 17-249 mg/g in men and 25-354 mg/g in women, PHT: persistent hypertension

was defined as BP ≥140/90 mmHg in at least two occasions; PIGT was defined as plasma glucose ≥5.6

mmol/L in at least two occasions

CHAPTER 2: Results

73

Discussion

Our data indicate that in subjects with initial MAU, the presence of MAU should be confirmed,

to establish it as a renal and cardiovascular risk marker in an occupational screening programme.

Screening for modifiable risk factors, such as blood pressure and plasma glucose, will identify

proportionally more subjects who could benefit from available therapies than screening for MAU

(14). We now add that individuals with intermittent MAU have approximately the same

percentage of cardiovascular risk factors than subjects without MAU, and that subjects with

persistent MAU have higher cardiovascular risk (19). As a consequence, screening for

cardiovascular and/or renal risk in an occupational population should commence from traditional

risk factors rather than from MAU as a first step. In Addition, this study suggests that MAU is

more consistent in presence of cardiovascular risk factors; thus, screening for MAU in subjects

with persistent risk factors might be more effective than screening a presumed healthy

population for MAU. Of note, subjects with cardiovascular risk factors are more likely to be

screened for MAU as a surrogate marker for cardiovascular and renal outcome (20, 21).

According to our data, subjects with intermittent MAU were not associated with increased

established cardiovascular risk factors and thus should be considered as “false positives” in a

screening programme that uses MAU as first step to identify subjects with cardiovascular and

renal risk. Besides the low reproducibility of MAU in our population, the use of MAU was

further hampered by a low sensitivity to detect traditional risk factors. Only 13% (9/68) of the

cases with persistent HT and/or IGT were identified when MAU was used as a first step to screen

for cardiovascular and renal risk (table 3). The PREVEND trial also observed a low sensitivity of

12% to detect at least one traditional risk factor but the authors argued that the 10 years

cardiovascular disease incidence in hypertensive subjects in absence of MAU is sufficiently low

so as not to require treatment (22). However this claim is inconsistent with PREVEND data that

revealed that half of the subjects on renal replacement therapy and 77% of cardiovascular deaths,

had no MAU when they were screened (23, 24). Moreover, medical management in diabetes

and/or hypertension even without influencing MAU can slow down the clinical course of

progressive CKD, end stage renal disease, cardiovascular morbidity and mortality, whereas it is

unproven whether making MAU as such disappear without influencing other risk factors would

have a benefit on outcomes (25-27). When a screening program to detect cardiovascular and renal

risk starts from MAU another question also arises: how to handle subjects with MAU in the

CHAPTER 2: Results

74

absence of cardiovascular risk factors. According to our data, more than half of the subjects (n=

11) with initial MAU had no persistent cardiovascular risk factors. These subjects could be

considered as having masked or premature cardiovascular risk; however, MAU was only

confirmed in four subjects (36%). Some prospective trials showed that such subjects have a

higher risk of developing de novo hypertension and diabetes (11, 12, 28). However, it is unclear

whether preventive steps focusing on MAU would be effective to postpone this risk. More likely,

there is a high probability that such subjects would pay a price, that is, the side effects of life-long

medication and medical intimidation (1, 7).

Unfortunately, this small sample of young, white and mainly male subjects who were screened in

a working environment, is not entirely representative of the global population. However, as the

association between cardiovascular risk factors and MAU is weaker in women, the

reproducibility of MAU would have been even lower if a similar percentage of women and men

had been recruited (29, 30). Another limitation is that we could not exclude exercise-induced

MAU in this cohort as MAU was measured during working time. MAU might fluctuate more in

random urine samples than in early morning urine samples. Participants with only one positive

MAU in at least two examinations, were considered as false positive. Indeed this study confirmed

that at least two consecutive MAU measurements are necessary to identify subjects with an

increased risk for cardiovascular and renal disease .

In conclusion, a first line screening for MAU in an occupational health care setting as a whole as

an index for renal and cardiovascular risk appears inefficient because of low reproducibility of

MAU and low sensitivity to detect traditional cardiovascular risk factors. Screening for MAU in

subjects with modifiable risk factors might be more effective, because MAU seems to be more

consistent in these subjects than in those without risk factors.

Acknowledgments

We thank all concerned employees of Adhesia (Occupational Health Care, Ghent, Belgium) and

the recruited participants who made this study possible.

CHAPTER 2: Results

75

References


screening in self-reported non-diabetic/non-hypertensive persons identified in a large health screening -

The Nord-Trondelag Health Study (HUNT), Norway. Clin Nephrol 2003; 59: 241-251. Available at

<Go to ISI>://000181878200001.

2. Hallan SI, Stevens P. Screening for chronic kidney disease: which strategy? J Nephrol 2010; 23: 147-

155. Available at


=20155721.

3. Nielen MM, Schellevis FG, Verheij RA. The usefulness of a free self-test for screening albuminuria in

the general population: a cross-sectional survey. BMC Public Health 2009; 9: 381. Available at


=19818129.

4. Haynes R, Winearls C. Screening for risk with albuminuria: should we start from here? Nephrology,

dialysis, transplantation : official publication of the European Dialysis and Transplant Association -

European Renal Association 2010; 25: 3463-3465. Available at


5. Bottomley MJ, Kalachik A, Mevada C, Brook MO, James T, Harden PN. Single Estimated Glomerular

Filtration Rate and Albuminuria Measurement Substantially Overestimates Prevalence of Chronic

Kidney Disease. Nephron Clin Pract 2010; 117: c348-c352. Available at




ISI>://000277757300014.










9. van der Tol A, Van Biesen W, Van Laecke S, Bogaerts K, De Lombaert K, Warrinnier H, Vanholder R.

Statin Use and the Presence of Microalbuminuria. Results from the ERICABEL Trial: A Non-

Interventional Epidemiological Cohort Study. PloS one 2012; 7: e31639. Available at





ISI>://000185518500004.

11. Wang TJ, Evans JC, Meigs JB, Rifai N, Fox CS, D'Agostino RB, Levy D, Vasan RS. Low-grade

albuminuria and the risks of hypertension and blood pressure progression. Circulation 2005; 111:










CHAPTER 2: Results

76


=15738353.

12. Halimi JM, Bonnet F, Lange C, Balkau B, Tichet J, Marre M. Urinary albumin excretion is a risk

factor for diabetes mellitus in men, independently of initial metabolic profile and development of

insulin resistance. The DESIR Study. J Hypertens 2008; 26: 2198-2206. Available at


=18854761.

13. Yuyun MF, Khaw KT, Luben R, Welch A, Bingham S, Day NE, Wareham NJ. Microalbuminuria,

cardiovascular risk factors and cardiovascular morbidity in a British population: The EPIC-Norfolk

population-based study. European Journal of Cardiovascular Prevention & Rehabilitation 2004; 11:


14. van der Tol A, Van Biesen W, Verbeke F, De Groote G, Vermeiren F, Eeckhaut K, Vanholder R.

Towards a rational screening strategy for albuminuria: results from the unreferred renal insufficiency

trial. PloS one 2010; 5: e13328. Available at http://www.ncbi.nlm.nih.gov/pubmed/20967254.




16. Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, Kusek JW, Van Lente F. Using

standardized serum creatinine values in the modification of diet in renal disease study equation for

estimating glomerular filtration rate. Annals of internal medicine 2006; 145: 247-254. Available at


17. Cleeman JI, Grundy SM, Becker D, Clark LT, Cooper RS, Denke MA, Howard WJ, Hunninghake DB,

Illingworth DR, Luepker RV, McBride P, McKenney JM, Pasternak RC, Stone NJ, Van Horn L,

Brewer HB, Ernst ND, Gordon D, Levy D, Rifkind B, Rossouw JE, Savage P, Haffner SM, Orloff DG,

Proschan MA, Schwartz JS, Sempos CT, Shero ST, Murray EZ, Expe NCEP. Executive summary of

the Third Report of the National Cholesterol Education Program (NCEP) expert panel on detection,

evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). Jama-Journal

of the American Medical Association 2001; 285: 2486-2497. Available at <Go to

ISI>://000168547600031.

18. Wilson PWF, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of

coronary heart disease using risk factor categories. Circulation 1998; 97: 1837-1847. Available at <Go

to ISI>://000073519200014.

19. Van der Tol A, Van Biesen W, De Groote G, Verbeke P, Vermeiren F, Eeckhaut K, Vanholder R.

Mislabeling Cardiovascular Risk by One Single Measurement of Microalbuminuria. Acta Clin Belg


20. Degli Esposti L, Saragoni S, Buda S, Sturani A, Quintaliani G, Bartolucci S, Di Turi R, Lilli P, Didoni

G, Degli Esposti E. Awareness of albuminuria in an Italian population-based cohort of patients treated

with hypoglycemic drugs. J Nephrol 2011: 0. Available at


21. Uchida J, Machida Y, Iwai T, Iguchi T, Kamada Y, Naganuma T, Kumada N, Kim T, Kawashima H,

Nakatani T. Low-grade albuminuria reduction with angiotensin II type 1 receptor blocker in renal

transplant recipients. J Nephrol 2011; 24: 515-521. Available at



the efficacy of screening for cardiovascular risk factors. Nephrology, dialysis, transplantation : official









CHAPTER 2: Results

77

publication of the European Dialysis and Transplant Association - European Renal Association 2010;








<Go to ISI>://000178385700010.





=10675071.

26. Keane WF, Zhang Z, Lyle PA, Cooper ME, de Zeeuw D, Grunfeld JP, Lash JP, McGill JB, Mitch WE,

Remuzzi G, Shahinfar S, Snapinn SM, Toto R, Brenner BM. Risk scores for predicting outcomes in

patients with type 2 diabetes and nephropathy: the RENAAL study. Clin J Am Soc Nephrol 2006; 1:



=17699284.





=15492322.

28. Brantsma AH, Bakker SJ, Hillege HL, de Zeeuw D, de Jong PE, Gansevoort RT. Urinary albumin

excretion and its relation with C-reactive protein and the metabolic syndrome in the prediction of type

2 diabetes. Diabetes Care 2005; 28: 2525-2530. Available at


=16186291.

29. Halimi JM, Forhan A, Balkau B, Novak M, Wilpart E, Tichet J, Marre M, Grp DS. Is

microalbuminuria an integrated risk marker for cardiovascular disease and insulin resistance in both

men and women? Journal of Cardiovascular Risk 2001; 8: 139-146. Available at <Go to

ISI>://000169768400004.

30. Verhave JC, Hillege HL, Burgerhof JGM, Navis G, De Zeeuw D, De Jong PE, Grp PS. Cardiovascular

risk factors are differently associated with urinary albumin excretion in men and women. Journal of the

American Society of Nephrology 2003; 14: 1330-1335. Available at <Go to ISI>://000182492900025.










CHAPTER 2: Results

78

2.1.3. Should screening of renal markers be recommended in a working

population?


1, Guy De Groote

2, Paul Verbeke

3, Frans Vermeiren

3,


1

1 Renal Division, Department of Internal Medicine, University Hospital Ghent

2 CRI laboratory, Ghent

3 Occupational Health Care Adhesia, Belgium.

Int Urology Nephrology 2014 Aug 46(8): 1601-8

Available at: http://link.springer.com/article/10.1007%2Fs11255-014-0718-x

http://link.springer.com/article/10.1007%2Fs11255-014-0718-x

CHAPTER 2: Results

79

Abstract

Introduction. It is debated whether the general population should be screened for kidney disease.

This study evaluated whether screening of albuminuria and estimated glomerular filtration rate

(eGFR) in a working population should be recommended to detect subjects with CKD.

Methods. The Unreferred Renal Insufficiency (URI) study is a cross-sectional study in 1,398

workers aged 17-65. Markers of cardiovascular and renal disease were measured. Cardiovascular

risk (CVR) was defined by hypertension (n= 416), diabetes (n= 45), dyslipidemia (n= 159) and/or

history of a cardiovascular event (n= 10).

Results. In our population, 5% of the workers had microalbuminuria, 0.5% had

macroalbuminuria and < 0.1% had eGFR < 60 ml/min/1.73m². All workers with an eGFR < 60

ml/min/1.73m² and/or macroalbuminuria (8/8) had at least one cardiovascular risk factor whereas

this was the case in only half of workers with microalbuminuria (36/73, p= 0.007). In workers

without cardiovascular risk factors, the presence of microalbuminuria was associated with low

body mass index (BMI, p < 0.001) or physiochemical exposure risk (p < 0.001).

Conclusions. Screening of renal markers in a working population, identified only a few subjects

with an eGFR < 60 ml/min/1.73m² or macroalbuminuria. Although microalbuminuria was more

prevalent, it might not necessarily indicate kidney disease, as it may have a completely different

meanings depending of the phenotype of the screened subjects. Besides underlying

cardiovascular risk factors, microalbuminuria was also associated with low BMI in absence of

any risk factor, suggesting presence of benign postural proteinuria. In addition, microalbuminuria

also seemed to be related to physicochemical exposure. In view of the impossibility to further

analyse this finding in the present study, the meaning of this observation needs to be further

investigated.

CHAPTER 2: Results

80

Introduction

Chronic kidney disease (CKD) is increasingly considered as a prevalent and important condition

(1). Subjects with CKD are identified by presence urinary albumin creatinine ratio (ACR) > 30

mg/g and/or an estimated glomerular filtration rate (GFR) < 60 ml per minute per 1.73m² of body

surface area (2). Several epidemiologic studies suggested that CKD is an independent risk factor

for acute kidney injury, need for renal replacement therapy, cardiovascular events, all cause

mortality and increased health care costs (3-7). However, the thesis that screening for CKD

would improve clinical outcome is not established by interventional studies, and is thus still a

matter of debate (8-10). A systematic review on the effect of screening programs in the general

population, demonstrated that such programs detect large numbers of subjects with an eGFR < 60

ml/min/1.73m² and ACR > 30 mg/g, but that these subjects have a lower relative risk for

progressive kidney disease than patients in a clinical setting (11). Hence, there is lot of debate

whether the general population should be screened for CKD, even if it is considered as an

important prevalent health problem (8, 9, 11). Misclassification of CKD frequently occurs in

subjects with microalbuminuria, as a consequence of the high intra-individual variability of

albuminuria (8). Moreover, in many subjects microalbuminuria tends to regress to normal levels,

especially nowadays more and more patients are treated for their underlying risk factors (12). The

current study investigated whether screening of renal markers should be recommended in a

working population to detect CKD.

CHAPTER 2: Results

81

Methods

Aims: This study was performed to investigate whether screening for renal markers, such as

microalbuminuria or macroalbuminuria and eGFR < 60 ml/min/1.73m², is useful to detect CKD

in an unselected working population.

Participants: The Unreferred Renal Insufficiency (URI) study is a cross sectional evaluation

including only Caucasian workers (n=1486) who presented at a routine annual occupational

check-up between January 2007 and December 2009, in Belgium. The presence of more than

100 erythrocytes/µl in the urinary sediment was considered as a confounder for reliable

measurements of urinary albumin; subjects showing this degree of hematuria (n=17) and

subjects with incomplete data (n=71) were excluded from further analysis, leaving a cohort of

1,398 subjects. In our study, 351 (25%) workers were exposed to physicochemical hazard, such

as silica, heavy metals, solvents.

Ethics: A written informed consent was obtained from all participants. The Ethics Committee of

University Hospital Ghent approved the study (2006-038) (13).

Description of procedures

All subjects were investigated during their yearly check up by their occupational physician. Body

weight was recorded to the nearest 0.1 kg and height was measured to the nearest centimeter.

Waist circumference was measured by trained nurses following recommendations by WHO.

Body mass index (BMI) was calculated as body weight in kg divided by height² (kg/m²). Blood

pressure and resting heart rate were measured in sitting position by a calibrated electronic device

(OMRON®). A questionnaire about current cigarette smoking, physical activity and prescribed

medication was taken by an occupational physician in each participant. A random blood and

urine spot specimen was collected and analyzed on the same day in one central laboratory (no

frozen samples). Urinary albumin was measured by an immune turbidimetric method with an

inter-assay coefficient of variation of 11.2% at a mean level of 82 mg/L and an inter-assay

coefficient of variation of 4.9% at a mean level of 580 mg/l. Serum creatinine was analyzed by a

colorimetric assay (compensated Jaffe reaction), calibrated by Isotope Dilution Mass

Spectrometry (IDMS), with an inter-assay coefficient of variation of 1.75% at a mean level of

104 µmol/l (Roche). Serum CRP was measured with an immune turbidimetric method with an

CHAPTER 2: Results

82

inter-assay coefficient of variation of 4.6% at a mean level of 3.2 mg/l and inter-assay coefficient

of variation of 2.5% at a mean level of 5.5 mg/l (13).

Definitions: Gender specific urinary albumin creatinine ratio (ACR) cut-off values as proposed

by Warram et al. were used. Microalbuminuria was defined as ACR 17-250 in men and ACR

25-355 mg/g in women, macroalbuminuria as ACR > 250 in men and ACR > 355 mg/g in

women (14). GFR was estimated by the combined creatinine-cystatin C based CKD-EPI equation

(15). Abdominal adiposity was defined according to the National Cholesterol Education Program

(NCEP) criteria as a waist circumference > 102 cm in men and > 88 cm in women (16).

Hypertension was defined as blood pressure ≥140/90 or anti-hypertensive treatment. Diabetes

was defined as plasma glucose ≥ 126 mg/dl or glucose lowering treatment (17). Dyslipidemia

was defined as serum cholesterol ≥ 250 mg/dl or lipid lowering treatment.

Statistical methods: SPSS statistics 22 was used for all calculations. Results are presented as

percentages or as mean ± standard deviation. The baseline characteristics of more than 2 groups

were compared by use of ANOVA: post-hoc Scheffé test (continuous variables), Kruskal-Wallis

(continuous variable with a skewed distribution). The baseline characteristics of two groups were

compared by the use of t-test for continuous variables, Mann-Whitney for continuous variables

with a skewed distribution and Chi-square test for categorical variables. Multivariate logistic

regression analysis was performed for the association of microalbuminuria with other parameters.

Subjects were classified by < 10th

, 10-50th

, 50th

-90th

and >90th

percentile of BMI in presence of

physicochemical exposure risk (PCER), CVR or no risk factor (table 3, figure 1). Subjects were

classified according to median of age (17-39, 40-65 years) (table 4).

CHAPTER 2: Results

83

Results

In this population of 1,398 workers we found 416 subjects (30%) with hypertension (119 treated),

45 (3%) with diabetes (15 treated), 159 (11%) with dyslipidemia (44 treated) and 10 (0.7%) with

a history of cardiovascular event. Since sometimes some of these risk factors coincided in the

same patient, the total number of subjects with one or more cardiovascular risk factors (CVR)

was 512 (37%), of whom only 151 were treated for at least one risk factor, resulting in 361 (26%)

of this population having at least one untreated risk factor. In this cohort, 5% (n= 73) had

microalbuminuria, 0.5% (n= 7) had macroalbuminuria and 0.1% (n= 1) had eGFR< 60 ml per

minute per 1.73m² of body surface area in presence of microalbuminuria. Workers with vs.

without cardiovascular risk factors had higher prevalence of microalbuminuria (7.0 vs. 4.2%, p =

0.001) and macroalbuminuria (1.4 vs. 0, p = 0.001). The worker with an eGFR < 60

ml/min/1.73m² and all workers with macroalbuminuria (8/8) had at least one cardiovascular risk

factor whereas this was the case in only half of workers with microalbuminuria (36/73, p= 0.007).

Only, 15 of these 36 workers with microalbuminuria (42%) and one of 7 (14%) workers with

macroalbuminuria were treated for diabetes, hypertension and/or dyslipidemia.

In the overall cohort, workers with versus without physicochemical exposure risk (PCER) had a

higher prevalence of microalbuminuria (7.4 vs. 4.6%, p = 0.04), but not a higher percentage of

CVR (36 vs. 37%, p = 0.61). As cardiovascular risk factors confound the association between

microalbuminuria and PCER, we split our cohort in workers with and without CVR. As expected,

the workers with CVR had more additional risk factors than those without CVR (table 1). In the

population with CVR, workers with vs. without PCER were only more likely to be male. In the

population without CVR, workers with PCER (n= 226) had a higher prevalence of

microalbuminuria (p <0.001), were younger (p < 0.001) and were more likely to be male (p<

0.001) than those without any risk (n = 660) (table1).

CHAPTER 2: Results

84

Table 1. Characteristics according to without vs. with physicochemical exposure risk (PCER) in

workers without vs. with cardiovascular risk (CVR).

No CVR (n=886) CVR (n=512) P

No PCER PCER No PCER PCER

N 660 226 387 125

Age (years) 38 ± 10 36 ± 9° 44 ± 10 42 ± 9 <0.001

Female (%) 183 (28) 12 (5)° 57 (15) 5 (4)° <0.001

ACR (mg/g) 4 (3-5) 5 (3-6) 13 (4-22) 14 (0-27) 0.02

Microalbuminuria 18 (2.7) 19 (8.4)° 29 (7.5) 7 (5.6) 0.001

Macroalbuminuria 0 0 5 (1.3) 2 (1.6) 0.002

Smoking (%) 199 (31) 73 (36) 118 (31) 35 (30) 0.57

Adiposity (%) 69 (13) 18 (8) 115 (33) 39 (33) <0.001

Inactive (%) 245 (44) 98 (51) 192 (59) 60 (55) <0.001

BMI (kg/m²) 25.1 ± 3.7 25.2 ± 3.2 27.9 ± 4.72 27.8 ± 4.4 <0.001

SBP(mmHg) 122 ± 11 123 ± 10 139 ± 16 142 ± 14 <0.001

DBP (mmHg) 74 ± 9 75 ± 8 86 ± 11 88 ± 11 <0.001

Heart rate (bpm) 69 ± 9 68 ± 10 73 ± 12 72 ± 13 <0.001

Glucose (mg/dl) 85 ± 12 85 ± 12 94 ± 27 91 ± 22 <0.001

Cholesterol (mg/dl) 186 ± 31 186 ± 32 216 ± 45 206 ± 40 <0.001

CRP (mg/l) 2.2 ± 4.0 2.2 ± 4.2 2.9 ± 7.0 2.6 ± 3.7 <0.001

CVR: cardiovascular risk defined by hypertension, diabetes, dyslipidemia and/or cardiovascular event.

ACR (mg/g): urinary albumin creatinine ratio (95% CI of the mean) Microalbuminuria (MAU) is defined

as UACR 17-250 in men and 25-355 mg/g in women. Adiposity: waist circumference > 102 in men and >

88 cm in women. BMI: body mass index. SBP: systolic blood pressure. DBP: diastolic blood pressure,

CRP: C-reactive protein.

°p < 0.01 PCER vs. no PCER.

CHAPTER 2: Results

85

Table 2. Characteristics according to absence or presence of microalbuminuria (MAU) in

workers without vs. with physicochemical exposure risk (PCER) in absence vs. presence of

cardiovascular risk (CVR)

No CVR (n=886) No PCER (n=660) PCER (n=226)

No MAU MAU P No MAU MAU P

N 642 18 (3) 207 19 (8)

Age (years) 38 ± 9 37 ± 11 0.59 36 ± 9 35 ± 11 0.90

Female (%) 178 (28) 5 (28) 1 9 (4) 3 (16) 0.11

Smoking (%) 193 (31) 6 (33) 0.80 66 (36) 7 (37) 0.90

Adiposity (%) 68 (13) 1 (8) 0.60 17 (9) 1 (6) 0.70

Inactive (%) 236 (44) 9 (53) 0.47 87 (50) 11 (58) 0.53

Low BMI <10th

per. 62 (10) 10 (56) <0.001 18 (9) 4 (21) 0.18

High BMI >90th

per. 67 (11) 0 0.29 13(6) 1(5) 1

BMI (kg/m²) 25.2 ± 3.7 21.8 ± 3.2 <0.001 25.3 ± 3.2 23.7± 3.5 0.04

SBP(mmHg) 122 ± 11 120 ± 12 0.53 123 ± 10 121 ± 11 0.33

DBP (mmHg) 74 ± 9 70 ± 11 0.09 75 ± 8 75 ± 9 0.98

Heart rate (bpm) 69 ± 9 71 ± 15 0.57 68± 10 73 ± 12 0.04

Glucose (mg/dl) 85 ± 12 87 ± 11 0.40 85 ± 12 87 ± 16 0.46

Cholesterol (mg/dl) 186 ± 31 177 ± 31 0.25 187 ± 31 174 ± 39 0.46

CRP (mg/l) 2.2 ± 3.4 4.4 ± 1.4 0.48 2.1± 3.7 3.1± 8.0 0.32

CVR (n =505) No PCER (n=382) PCER (n=123)

No MAU MAU P No MAU MAU P

N 353 29 (7) 116 7 (6)

Age (years) 44 ± 10 44 ± 11 0.88 41 ± 9 50 ± 5 0.01

Female (%) 52 (15) 4 (14) 0.58 5 (4) 0 1

Smoking (%) 102 (30) 12(43) 0.15 32 (30) 2(29) 0.92

Adiposity (%) 97 (31) 15 (60) 0.005 35 (32) 3 (60) 0.19

Inactive (%) 170 (59) 15(60) 0.926 55 (54) 4(57) 0.89

Low BMI <10th

per. 30 (9) 1 (4) 0.34 7 (6) 1 (4) 0.39

High BMI >90th

per. 30 (9) 6 (21) 0.03 12 (10) 1 (14) 0.74

BMI (kg/m²) 27.7 ± 4.6 30.0 ± 5.3 0.01 27.6 ± 4.2 30.0 ± 5.8 0.16

SBP(mmHg) 138 ± 16 146 ± 19 0.02 142 ± 13 154 ± 27 0.29

DBP (mmHg) 85 ± 11 90 ± 11 0.03 88 ± 10 97 ± 14 0.02

Heart rate (bpm) 72 ± 11 81 ± 14 0.02 71 ± 12 85 ± 17 0.004

Glucose (mg/dl) 93 ± 21 110 ± 64 0.19 90 ± 21 108 ± 37 0.26

Cholesterol (mg/dl) 215 ± 45 216 ± 40 0.89 204 ± 40 224 ± 37 0.19

CRP (mg/l) 2.8 ± 6.6 6.2 ± 11.7 0.02 2.6 ± 3.8 2.9 ± 0.7 0.002

CVR: cardiovascular risk defined by hypertension, diabetes, dyslipidemia and/or cardiovascular events.

Microalbuminuria (MAU) is defined as UACR 17-250 in men and 25-355 mg/g in women. Adiposity:

waist circumference >102 in men and >88 cm in women. BMI: body mass index. SBP: systolic blood

pressure. DBP: diastolic blood pressure, CRP: C-reactive protein.

CHAPTER 2: Results

86

In the population without CVR, workers with microalbuminuria had a higher percentage of low

BMI (< 10th

percentile of BMI; 38 vs. 12%, p < 0.001) and PCER (51 vs. 25%, p < 0.001) as

compared to those without microalbuminuria. In the population without CVR in presence of

PCER (table 2), workers with microalbuminuria had a higher heart rate (p= 0.04), lower BMI

(p=0.04) but no higher percentage of low BMI (21 vs. 9%, p = 0.18) as compared to those

without microalbuminuria. In the population without CVR and low BMI (table 3), workers with

microalbuminuria had no higher percentage of PCER than those without microalbuminuria (29

vs. 23%, p = 0.62). In the population without any risk (table 2), workers with microalbuminuria

had a higher percentage of low BMI (56 vs. 10%, p< 0.001) as compared to those without

microalbuminuria. After a multivariate adjustment, high heart rate (p = 0.02) was retained as the

only significant predictor for microalbuminuria in workers with PCER, whereas low BMI (p <

0.001) was retained as the only significant predictor for microalbuminuria in workers without any

risk.

In the population with CVR (table 2), workers with microalbuminuria had a higher blood

pressure, heart rate and CRP as compared to those without microalbuminuria, independent of

PCER. After multivariate adjustment, high heart rate (p = 0.02) and CRP (p = 0.02) were retained

as significant predictors for microalbuminuria in workers without PCER, whereas older age was

retained as a significant predictor for microalbuminuria in workers with PCER.

Figure 1 shows the prevalence of microalbuminuria in lean (< 10th

percentile of BMI), normal

(between 10-50th

percentile of BMI), overweight (between 50-90th

percentile of BMI) and obese

workers (> 90th

percentile of BMI) according to no risk, PCER and CVR. In presence of CVR,

overweight or obese workers had a higher prevalence of microalbuminuria than normal or lean

workers (p < 0.05), whereas in absence of risk factors lean workers had a higher prevalence of

microalbuminuria than workers with BMI > 10th

percentile (p < 0.001, table 3). In the population

without CVR, microalbuminuria was not affected by different BMI groups in workers with PCER

(table 3).

CHAPTER 2: Results

87

Table 3. Prevalence of microalbuminuria (MAU) according to percentile of BMI in workers with

no risk, with physicochemical exposure risk (PCER) and cardiovascular risk (CVR).

BMI < 10th

percentile

BMI 10-50th

percentile

BMI 50-90th

percentile

BMI > 90th

percentile

P

No risk

(n=658)

BMI < 21 21-25 25-30 > 30

n 72 284 235 67

Age 34 ± 9 37 ± 10* 40 ± 9**° 38 ± 10* <0.001

MAU 10 (13.9) 5 (1.8)** 3 (1.3)** 0** <0.001

PCER

(n= 226)

n 22 86 104 14

Age 30 ± 7 35 ± 9 38 ± 9* 33 ± 9 0.002

MAU 4 (18.2) 9 (10.5) 5 (4.8) 1 (7.1) 0.17

CVR

(n=503)

BMI < 22 22-28 28-34 > 34

n 40 234 179 50

Age 38 ± 12 43 ± 10 45 ± 9* 46 ± 9* <0.001

MAU 2 (5) 10 (4.3) 16 (9)° 7 (14)° 0.01

CVR is defined by hypertension, diabetes, dyslipidemia and/or cardiovascular event. Microalbuminuria is

defined as UACR 17-250 in men and 25-355 mg/g in women. BMI = body mass index (kg/m²). *p< 0.05,

**p< 0.001 vs. BMI ≤ 10th percentile, °p = 0.01-0.05 vs. BMI 10-50

th percentile.

CHAPTER 2: Results

88

Figure 1. Prevalence of albuminuria in lean (BMI <10th

percentile), normal (BMI between 10-

50th

percentile, overweight (BMI between 50-90th

percentile) and obese workers (BMI > 90th

percentile) in absence of risk factors, in presence of physicochemical exposure risk (PCER) and

presence of cardiovascular risk factors (CVR).

0

5

10

15

20

BMI <10thpercentile BMI 10-50th

percentile BMI 50-90thpercentile BMI > 90th

percentile

pre

vale

nce

of

alb

um

inu

ria

no risk PCER CVR

Microallbuminuria is defined as UACR17-250 in men and 25-355 mg/g in women. CVR risk is defined by

hypertension, diabetes, dyslipidemia and/or cardiovascular event. BMI (body mass index).

CHAPTER 2: Results

89

The prevalence of CVR increased above the age of 40, from 24 to 49% (p < 0.001). We showed

that age is an effect modifier; the presence of microalbuminuria was associated with CVR and

PCER in workers above the age of 40 while it was associated with low BMI and PCER in

workers under the age of 40 years (table 4).

Table 4. Mean BMI (body mass index), the number of workers with cardiovascular risk factors

(CVR) and of physicochemical exposure risk (PCER) according to median of age in absence or

presence of microalbuminuria (MAU).

No MAU MAU

Age n n BMI n BMI P

17-39 682 649 25.3 ± 3.9 33 23.3 ± 4.4 0.004

40-65 705 666 26.9 ± 4.2 39 28.9 ± 5.6 0.04

CVR CVR

17-39 682 649 152 (23) 33 9 (27) 0.61

40-65 709 669 317 (47) 40 27 (68) 0.01

PCER PCER

17-39 511 487 134 (28) 24 12 (50) 0.02

40-65 358 345 73 (21) 13 7 (54) 0.01

CVR: cardiovascular risk defined by hypertension, diabetes, dyslipidemia and/or cardiovascular events.

Microalbuminuria (MAU) is defined as UACR 17-250 in men and 25-355 mg/g in women. BMI: body

mass index in kg/m².

CHAPTER 2: Results

90

Discussion

Screening for renal markers in a working population containing 1,398 workers, identified only

one subject with an eGFR < 60 ml/min/1.73m² (n= 1) and a few subjects with macroalbuminuria

(n= 7), all of whom had at least one traditional cardiovascular risk factor. More workers with

microalbuminuria were detected, but half of these had no modifiable traditional cardiovascular

risk factor (37/73). Microalbuminuria in these subjects was associated with low BMI or PCER.

Young workers had a lower BMI in presence vs. absence of microalbuminuria. Conceivably, a

substantial number of young workers had postural proteinuria and might be mislabeled as having

a risk for cardiovascular and/or renal disease. Whether the presence of microalbuminuria in

workers with PCER is related to early reversible kidney disease might be considered as

potentially possible but remains to be proven. On the other hand, only a limited number of

patients with traditional cardiac risk factors did already receive treatment for that risk factor.

Screening for renal markers in a healthy population might thus identify patients without

modifiable risk factors, whereas screening for and treating traditional cardiac risk factors in this

group might be more appropriate (13).

The presence of microalbuminuria in the general population is independently considered as a

renal and cardiovascular risk marker (18, 19). However, whether this simple marker can be easily

used to start uniform treatment to improve renal and cardiovascular outcome remains uncertain

(10, 20). Our data showed that the presence of microalbuminuria has completely different

meanings according to the population concerned. Microalbuminuria is associated with CVR or

PCER in workers above the age of 40, while it associated with low BMI or PCER in workers

under the age of 40. Consequently, a substantial number of young, healthy and lean workers with

microalbuminuria might have postural proteinuria. It has been suggested that postural

albuminuria commonly occurs in healthy lean adolescents (21, 22). Microalbuminuria might

develop in normal subjects by a physiological response after strenuous exercise or upon standing

(23, 24). Postural albuminuria might be a sign of renal congestion (25), due to the compression

of the left renal vein between the superior mesenteric artery and the abdominal aorta (nutcracker

syndrome) (26). Lean subjects are more likely to develop postural proteinuria as the left renal

vein is at higher risk to be compressed in the space between the aorta and the superior mesenterial

artery which is smaller than on the right side (27). Adolescents with postural proteinuria have an

CHAPTER 2: Results

91

excellent outcome (28). Mostly the problem disappears beyond the age of 30, pointing to an

irrelevant condition, rather than a risk factor (29).

Although it might be useful to know the potentially toxic conditions in a working population, in

our database we had not registered the type of work nor measured toxic agents. However, we

classified our patients into a subgroup with and without PCER. According to the literature,

subjects who are exposed to heavy metals, such as cadmium, mercury might develop tubular

overflow microalbuminuria (30-32). But, whether this microalbuminuria is an indicator for

kidney disease remains unknown. In our study, no worker with PCER had macroalbuminuria.

Thus, it seems unlikely that the presence of microalbuminuria in workers with PCER might

contribute to the development of progressive CKD. However, microalbuminuria was associated

with high heart rate in workers with PCER. Whether this might be related to CKD or CVD is

unknown. This finding underlines the need of prospective studies of the long-term effects of toxic

agents on markers of outcome in CKD and CVD.

One third of the workers had one or more traditional cardiovascular risk factor. Most of these

were however untreated. In terms of prevention and cost-effectiveness, it would thus be more

effective to screen for and treat traditional cardiovascular risk factors, rather than screening of

renal markers. However, as the prevalence of renal markers was much higher in those with

traditional cardiovascular risk factors, this patient group should be explored for presence of

chronic kidney disease, of course one of the problems is that most of these risk factors are

undetected or at least untreated. In this case the screening physician may remain unaware of the

patient’s condition, and not recognize the direct reason to do the appropriate screening test for

CKD. Therefore, traditional risk factors as fasting hyperglycemia and hypertension may be seen

as important part of basic screening that is often neglected.

A strength of this study is that we not only used a “self-declared” status of diabetes, hypertension

and dyslipidemia, but that we also measured plasma glucose, blood pressure and cholesterol, to

identify subjects with unknown traditional risk factors. A limitation is that all parameters in this

cohort were measured at one random occasion. Microalbuminuria might fluctuate more in

random urine samples than in early morning urine samples (33, 34).

We conclude that direct screening of renal markers is not useful in a working population, as half

of the subjects with microalbuminuria had no modifiable cardiovascular risk factors. The

CHAPTER 2: Results

92

presence of microalbuminuria in these workers might not be indicative for progressive CKD as

none of them had macroalbuminuria. The presence of microalbuminuria in these mainly young

workers was associated with low BMI or PCER. Consequently, young, healthy and lean workers

with microalbuminuria might have postural proteinuria and thus an irrelevant condition, rather

than that it would display a risk factor. The occurrence of microalbuminuria in workers with

PCER was unrelated to CVR and should be further investigated. Most likely intervention in this

population may engender medical frustration, unnecessary anxiety, harm, and worries in those

affected and excess of costs to society, employers and individuals. To reduce these false positive

results, a mass screening strategy to identify cardiovascular disease by using microalbuminuria

seems to make sense only when limiting inclusion to subjects above the age of 40. However,

screening for traditional cardiovascular risk factors identifies more subjects at risk for

cardiovascular and renal disease than screening for microalbuminuria for potential preventive

intervention (13).

Acknowledgment We thank all concerned employees of Adhesia (Occupational Heath Care,

Ghent, Belgium) and the recruited participants who made this study possible.

CHAPTER 2: Results

93

References

1. Couser WG, Remuzzi G, Mendis S, Tonelli M. The contribution of chronic kidney disease to the

global burden of major noncommunicable diseases. Kidney Int 2011; 80: 1258-1270. Available at


2. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 Clinical

Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. . Kidney Int 2013:

1-150. Available at http://www.nature.com/kisup/journal/v3/n1/index.html.






4. Van Biesen W, De Bacquer D, Verbeke F, Delanghe J, Lameire N, Vanholder R. The glomerular

filtration rate in an apparently healthy population and its relation with cardiovascular mortality during

10 years. Eur Heart J 2007; 28: 478-483. Available at http://www.ncbi.nlm.nih.gov/pubmed/17223665.

5. Vanholder R, Massy Z, Argiles A, Spasovski G, Verbeke F, Lameire N, European Uremic Toxin Work

G. Chronic kidney disease as cause of cardiovascular morbidity and mortality. Nephrol Dial Transplant






7. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death,

cardiovascular events, and hospitalization. N Engl J Med 2004; 351: 1296-1305. Available at











screening in self-reported non-diabetic/non-hypertensive persons identified in a large health screening-

-the Nord-Trondelag Health Study (HUNT), Norway. Clin Nephrol 2003; 59: 241-251. Available at


11. Black C, Sharma P, Scotland G, McCullough K, McGurn D, Robertson L, Fluck N, MacLeod A,

McNamee P, Prescott G, Smith C. Early referral strategies for management of people with markers of

renal disease: a systematic review of the evidence of clinical effectiveness, cost-effectiveness and

economic analysis. Health Technol Assess 2010; 14: 1-184. Available at






http://www.nature.com/kisup/journal/v3/n1/index.html










CHAPTER 2: Results

94

13. van der Tol A, Van Biesen W, Verbeke F, De Groote G, Vermeiren F, Eeckhaut K, Vanholder R.

Towards a rational screening strategy for albuminuria: results from the unreferred renal insufficiency

trial. PloS one 2010; 5: e13328. Available at http://www.ncbi.nlm.nih.gov/pubmed/20967254.




15. Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, Kusek JW, Manzi J, Van

Lente F, Zhang YL, Coresh J, Levey AS, Investigators C-E. Estimating Glomerular Filtration Rate

from Serum Creatinine and Cystatin C. New Engl J Med 2012; 367: 20-29. Available at <Go to

ISI>://000305979400002.

16. Cleeman JI, Grundy SM, Becker D, Clark LT, Cooper RS, Denke MA, Howard WJ, Hunninghake DB,

Illingworth DR, Luepker RV, McBride P, McKenney JM, Pasternak RC, Stone NJ, Van Horn L,

Brewer HB, Ernst ND, Gordon D, Levy D, Rifkind B, Rossouw JE, Savage P, Haffner SM, Orloff DG,

Proschan MA, Schwartz JS, Sempos CT, Shero ST, Murray EZ, Expe NCEP. Executive summary of

the Third Report of the National Cholesterol Education Program (NCEP) expert panel on detection,


of the American Medical Association 2001; 285: 2486-2497. Available at <Go to

ISI>://000168547600031.

17. Genuth S, Alberti KGMM, Bennett P, Buse J, DeFronzo R, Kahn R, Kitzmiller J, Knowler WC,

Lebovitz H, Lernmark A, Nathan D, Palmer J, Rizza R, Saudek C, Shaw J, Steffes M, Stern M,

Tuomilehto J, Zimmet P, Classificat ECD. Follow-up report on the diagnosis of diabetes mellitus.

Diabetes Care 2003; 26: 3160-3167. Available at <Go to ISI>://000186269100030.





19. Klausen K, Borch-Johnsen K, Feldt-Rasmussen B, Jensen G, Clausen P, Scharling H, Appleyard M,

Jensen JS. Very low levels of microalbuminuria are associated with increased risk of coronary heart

disease and death independently of renal function, hypertension, and diabetes. Circulation 2004; 110:






=15492322.

21. Hirschler V, Molinari C, Maccallini G, Aranda C. Is albuminuria associated with obesity in school

children? Pediatr Diabetes 2010; 11: 322-330. Available at


22. Nguyen S, McCulloch C, Brakeman P, Portale A, Hsu CY. Being overweight modifies the association

between cardiovascular risk factors and microalbuminuria in adolescents. Pediatrics 2008; 121: 37-45.


23. Robinson RR, Coggins CH, Shapiro K, Cohen JJ, Harrington JT, Kassirer JP. Isolated Proteinuria in

Asymptomatic Patients. Kidney International 1980; 18: 395-406. Available at <Go to

ISI>://WOS:A1980KK18700014.





CHAPTER 2: Results

95

24. Smithies O. Why the kidney glomerulus does not clog: a gel permeation/diffusion hypothesis of renal

function. Proc Natl Acad Sci U S A 2003; 100: 4108-4113. Available at


25. Cho LS, Choi YM, Kang HH, Park SJ, Lim JW, Yoon TY. Diagnosis of nut-cracker phenomenon

using renal Doppler ultrasound in orthostatic proteinuria. Nephrology Dialysis Transplantation 2001;

16: 1620-1625. Available at <Go to ISI>://WOS:000170410000019.

26. Mazzoni MB, Kottanatu L, Simonetti GD, Ragazzi M, Bianchetti MG, Fossali EF, Milani GP. Renal

vein obstruction and orthostatic proteinuria: a review. Nephrol Dial Transplant 2011; 26: 562-565.


27. Milani G, Bianchetti MG, Bozzani S, Bettinelli A, Fossali EF. Body mass index modulates postural

proteinuria. International urology and nephrology 2010; 42: 513-515. Available at





29. Naderi ASA, Reilly RF. Primary Care Approach to Proteinuria. J Am Board Fam Med 2008; 21: 569-





31. Bernard A, Lauwerys RR. Proteinuria: changes and mechanisms in toxic nephropathies. Critical

reviews in toxicology 1991; 21: 373-405. Available at http://www.ncbi.nlm.nih.gov/pubmed/1741950.



33. Clinical Practice Guidelines for Chronic Kidney Disease: Evaluation C, and Stratification, part 5.

Evaluation of laboratory measurements for clinical assessment of kidney disease, guidelines 5.

Assesment of proteinuria Accessed March 10, 2012; . Available at

http://www.kidney.org/professionals/kdoqi/pdf/ckd_evaluation_classification_stratification.pdf.





=19092125.








http://www.kidney.org/professionals/kdoqi/pdf/ckd_evaluation_classification_stratification.pdf



CHAPTER 2: Results

96

2.2. Screening for kidney disease in selected hypertensive subjects: results

from the ERICABEL trial: a non interventional epidemiological cohort study.

CHAPTER 2: Results

97

2.2.1. Statin use and the presence of microalbuminuria.


1, Steven Van Laecke

1, Kris Bogaerts

2, Koen De Lombaert

3,

Hans Warrinnier3 and Raymond Vanholder

1.

1 Renal Divison, Department of Internal Medicine, University Hospital Ghent, Belgium.

2 Interuniversity centre for biostatistics and statistical bioinformatics, Leuven, Belgium.

3 F. Hoffman-La Roche LTD, Brussels, Belgium.

PLoS ONE February 2012; 7 (2): e31639



CHAPTER 2: Results

98

Abstract

Background. Microalbuminuria (MAU) is considered as a predictor or marker of cardiovascular

and renal events. Statins are widely prescribed to reduce cardiovascular risk and to slow down

progression of kidney disease. But statins may also generate tubular MAU. The current

observational study evaluated the impact of statin use on the interpretation of MAU as a predictor

or marker of cardiovascular or renal disease.

Methodology/Principal Findings. We used cross-sectional data of ERICABEL, a cohort with

1,076 hypertensive patients. MAU was defined as albuminuria ≥ 20mg/l. A propensity score was

created to correct for “bias by indication” to receive a statin. As expected, subjects using statins

vs. no statins had more cardiovascular risk factors, pointing to bias by indication. Statin users

were more likely to have MAU (OR: 2.01, 95% CI: 1.34-3.01). The association between statin

use and MAU remained significant after adjusting for the propensity to receive a statin based on

cardiovascular risk factors (OR: 1.82, 95% CI: 1.14-2.91). Next to statin use, only diabetes (OR:

1.92, 95% CI: 1.00-3.66) and smoking (OR: 1.49, 95% CI: 0.99-2.26) were associated with MAU.

Conclusions. Use of statins is independently associated with MAU, even after adjusting for bias

by indication to receive a statin. In the hypothesis that this MAU is of tubular origin, statin use

can result in incorrect labeling of subjects as having a predictor or marker of cardiovascular or

renal risk. In addition, statin use affected the association of established cardiovascular risk factors

with MAU, blurring the interpretation of multivariable analyses.

CHAPTER 2: Results

99

Introduction

Microalbuminuria (MAU) is considered as a predictor or marker of cardiovascular morbidity and

mortality, particularly in patients with other risk factors [1-4], and as a surrogate for early kidney

damage especially in subjects with diabetes and hypertension [5,6]. Statins are frequently

prescribed in patients with hypertension, diabetes and metabolic syndrome, to reduce

cardiovascular morbidity and mortality [7]. Statins can reduce existent proteinuria [8-10] through

a positive impact on endothelial dysfunction. In contrast, there is in vitro [11,12] and in vivo [13-

16] evidence that statins are associated with de novo albuminuria and proteinuria. It is of

importance to establish the association between statin use and MAU to correctly interpret

presence of MAU as a predictor or marker of cardiovascular or renal disease in observational

trials with a mixed population of subjects taking and not taking a statin. If statin use is associated

with MAU, there is a risk of incorrect labeling of subjects as having a predictor or marker of

cardiovascular or renal risk. The current study evaluated the association between statin use and

MAU, and used a propensity score analysis to adjust for bias by indication. For this goal, we used

the baseline data of the early renal impairment and cardiovascular assessment in Belgium

(ERICABEL) trial, a prospective cohort of hypertensive patients followed by primary care

physicians created to evaluate the impact of metabolic syndrome on cardiovascular and renal

endpoints.

CHAPTER 2: Results

100

Methods

Objectives. The primary aim of the ERICABEL study was to determine the effect of metabolic

risk factors on the evolution of renal function and cardiovascular outcome over 5 years, in

patients aged between 40 and 70 years with diagnosed hypertension, and followed by their

primary care physician. The current analysis was designed to evaluate 1° the association between

statin treatment and MAU and 2° the impact of statin treatment on the interpretation of the

association between individual cardiovascular risk factors and MAU in epidemiological studies.

Particpants. We used the baseline data of the ERICABEL cohort, a non-interventional

epidemiological study with a follow up of 5 years that included 1,076 Caucasian patients with

hypertension, defined as systolic blood pressure ≥140mmHg and/or intake of at least one

antihypertensive drug, recruited by 96 general practitioners, between 2006 and 2007, in Belgium.

Of the 1076 patients included in this cross-sectional study, 420 patients had a missing value for at

least one of the variables under investigation (see appendix table S1 for detailed list). Multiple

imputation techniques were used to account for the missing data, using 20 imputations [17]. All

characteristics and outcome (MAU) were simultaneously used in the imputation model. The

imputation was done using the R function aregImpute from the Hmisc package [18].

Description of procedures. Each participating primary care physician was asked to include 10

consecutive hypertensive patients aged between 40-70 years, in a 1:1 sex ratio. The eligible

persons were evaluated at baseline and if eligible, sociodemographic information (age, sex, race,

and education level), personal and family medical history, smoking status and medication use

were collected prospectively in an online database. Body weight was recorded to the nearest 0.1

Kg and height was measured to the nearest centimeter. Body mass index (BMI) was calculated as

body weight in Kg divided by height² (kg/m²). Blood pressure was measured according to the

WHO criteria with a calibrated Omron HEM-907 device (average of 2 measurements, sitting,

with 5 minutes in between). All these measurements were done by the primary care physician.

After exclusion of a urinary infection or hematuria (negative Combur® test), MAU was screened

by a Micral® dipstick test. MAU was considered present if measured albuminuria was ≥ 20mg/l

on a morning midstream urine sample. Blood sampling was performed by the general practitioner

in fasting patients.

CHAPTER 2: Results

101

Definitions. The metabolic syndrome was defined as three or more of the following criteria,

according to the National Cholesterol Education Program Third Adult Treatment Panel guidelines

ATP III criteria [19]: elevated blood pressure ≥ 130/85 mmHg and/or antihypertensive

medication (by definition 100% in this cohort), (2) high plasma triglycerides (> 1.7 mmol/l), low

HDL cholesterol (< 1.0 mmol/l in men and < 1.3 mmol/l in women), (4) abdominal adiposity

(waist circumference > 102/88 cm men/women) and/or impaired glucose tolerance (IGT)

(fasting plasma glucose ≥ 6.1 mmol/l and/or known diabetes).

Ethics. The study was approved by an independent ethics committee review board, protocol

number: ML 19208. A written informed consent was obtained from all participants.

Statistical methods. All analyses have been performed using SAS software (SAS software,

version 9.2 of the SAS System for Windows. Copyright © 2002 SAS Institute Inc.) or R Version

2.12.0 (R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, 2009)

[20]. MAU was considered as a dichotomic variable. Continuous variables were described by

their mean, standard deviation, median and interquartile range. Categorical variables were

summarised by frequencies and percentages.

A propensity score for statin use was created to correct for “bias by indication”. Propensity score

analysis is a well established method to adjust for confounding by indication in observational

trials [21,22]. Primarily, for the statistical analyses, 20 imputed samples were created. In a second

step, within each of the 20 samples separately, a propensity model was constructed and the

resulting propensity score was calculated for each of these patients. The propensity model

included the following variables that were deemed to be possibly related to statin use: age, gender,

BMI, waist circumference, SBP, previous CV event, CRP, fasting glucose, diabetes, serum uric

acid, HDL and LDL cholesterol, triglycerides, use of angiotensin converting enzyme

inhibitor/angiotensin receptor blocker (ACE-I/ARB) and smoking. Continuous variables in the

model were included using restricted cubic splines: each continuous variable was included in the

model using 3 dummy variables, called var1, var2 and var3. For the first 5 imputed samples, a

histogram of the propensity scores was presented by statin use (see appendix, table S2). In

addition, in order to check the ability of the propensity scores to balance the two statin groups for

baseline characteristics, tables were presented for the first 5 imputed samples, comparing the

baseline characteristics between the groups (see appendix, table S3). In this way, patients with the

CHAPTER 2: Results

102

same “likelihood” or “propensity” to receive a statin (i.e. in this setting mainly with comparable

cardiovascular risk factors), but in one case taking and in the other case not taking a statin, were

compared for presence of MAU.

For comparison of continuous variables, ANOVA was used, adjusted for propensity scores,

whereas for binary variables, logistic regression analyses, also adjusted for propensity scores,

were employed. Logistic regression analyses were used to assess the association between statin

use and MAU using the “GENMOD” procedure in SAS. The associations were assessed in each

of the 20 imputation samples separately and the results were combined using the SAS procedure

“MIANALYZE”. The following logistic regression models were used: 1°: Univariable model

only including statin use; 2° a model including statin use and propensity scores (using a restricted

cubic spline); 3° a model including statin use, propensity scores and all relevant variables

mentioned above. Since the linearity assumption was deemed appropriate for all continuous

variables in the model (p> 0.1 for the assessment of linearity in the full model), the final model

only included linear terms for all variables.

CHAPTER 2: Results

103

Results

The baseline characteristics of the population are provided in tables 1 and 2.

Table 1. Baseline characteristics (categorical variables)

MS: metabolic syndrome, MAU: microalbuminuria, ACE-I/ARB: angiotensin converting enzyme

inhibitor /angiotensin receptor blocker, CVevent: cardiovascular event

p*:p values between all three groups; p§: p value between users vs. non statin users

There was an equal distribution in gender (51.3% males) in the overall cohort. There was a high

prevalence of metabolic syndrome (44.5%), diabetes (19.8%), current smokers (36.5%) and

MAU (16.4%) in the overall cohort. ACE-I and/or ARB were the most commonly prescribed

antihypertensive agents (55.9%). History of a cardiovascular event was recorded in 11.3% of the

patients. Mean age of the cohort was 57.5 ± 7.5 years. One third (30.8%) of the patients used a

statin.

parameter No statins

(N=724)

Statins

(N=332)

Statin use

unknown (N=20)

Total

(N=1076)

P* P§

Female 384/724

(53%)

132/332

(39.8%)

7/19

(36.8%)

523/1075

(48.7%)

<.001 <.001

MS 239/624

(38.3%)

172/299

(57.5%)

2/4

(50.0%)

413/927

(44.6%)

<.001 <.001

Diabetes 98/700

(14.0%)

104/327

(31.8%)

2/5

(40.0%)

204/828

(19.8%)

<.001 <.001

Smoker 249/698

(35.7%)

123/325

(37.9%)

3/5

(60.0%)

375/653

(36.5%)

0.438 0.501

MAU 68/529

(12.9%)

60/248

(24.2%)

0/4

(0.0%)

128/653

(16.4%)

<.001 <.001

ACE-

ARB

358/724

(49.5%)

232/332

(69.9%)

590/1056

(55.9%)

<.001 <.001

CV event 38/693

(5.5%)

77/325

(23.7%)

1/5

(20.0%)

116/1023

(11.3%)

<.001 <.001

CHAPTER 2: Results

104

Table 2. Baseline characteristics (continuous variables)

Patient

Characteristic

Statistic No statin Statin Statin use

unknown

Total P* P§

Age (years) Mean±SD 56.8± 7.6 59.1± 7.1 58.2± 6.0 57.5± 7.5 < .001 < .001

Median 57.2 60.0 58.6 58.5

N 724 332 19 1075

BMI (kg/m²) Mean±SD 29.3± 5.4 30.6± 5.5 30.5± 4.4 29.7± 5.4 0.001 < .001

Median 28.5 29.7 31.7 28.8

N 689 326 5 1020

SBP (mmHg) Mean±SD 144± 15 143± 16 149± 11 143± 16 0.527 0.394

Median 142.5 140.0 152.0 142.0

N 704 331 5 1040

DBP (mmHg) Mean±SD 84± 10 83± 10 87± 6 84± 10 0.054 0.020

Median 84.5 82.0 86.5 83.5

N 704 331 5 1040

Glucose (mmol/l) Mean±SD 5.6± 2.9 6.3± 2.1 6.8± 1.4 5.9± 2.7 0.001 < .001

Median 5.2 5.7 6.9 5.3

N 644 311 4 959

Uric acid (µmol/l) Mean±SD 339± 89 357± 83 291± 83 345± 89 0.008 0.004

Median 333 357 286 339

N 629 306 4 939

Triglycerides

(mmol/l)

Mean±SD 1.6± 1.1 1.9± 1.1 2.0±1.7 1.7± 1.1 0.011 0.003

Median 1.3 1.6 1.4 1.4

N 638 316 4 958

LDL-Cholesterol

(mmol/l)

Mean±SD 3.2± 0.8 2.7± 1.0 2.9± 0.8 3.0± 0.9 < .001 < .001

Median 3.2 2.6 3.2 3.0

N 628 312 4 944

HDL-Cholesterol

(mmol/l)

Mean±SD 1.5± 0.5 1.4± 0.4 1.3± 0.4 1.5± 0.5 0.004 0.001

Median 1.4 1.3 1.4 1.4

N 637 315 4 956

CRP (mg/dl) Mean±SD 0.5± 0.8 0.4± 0.5 0.4± 0.4 0.5± 0.7 0.148 0.055

Median 0.3 0.2 0.4 0.3

N 608 291 4 903

BMI: body mass index, SBP: systolic blood pressure, DBP diastolic blood pressure, CRP: C-reactive

protein. p*:p values between all three groups; p§: p value between users vs. non statin users

CHAPTER 2: Results

105

Table 3. Association between statin use and microalbuminuria (MAU)

$ Pscore= propensity score; The propensity score was fit using a restricted cubic spline.

ACE-I/ARB: angiotensin converting enzyme inhibitor/angiotensin receptor blocker, CV event:

cardiovascular event, BMI: body mass index, BP: Blood pressure, CRP: C-reactive protein.

Odds Ratio

Logistic regression

model for presence of

MAU

Parameter Estimate 95% Confidence

Interval

P-value

A. Univariable

association

Statin use 2.01 (1.34;3.01) < 0.001

B. Association

adjusted for

propensity score

Statin use 1.82 (1.14; 2.91) 0.01

Pscore$ . 0.6

C. Association fully

adjusted

Statin use 1.90 (1.15; 3.11) 0.01

Pscore 7.92 (0.18; 357.3) 0.28

Age 1.02 (0.91; 1.14) 0.74

Diabetes 1.92 (1.004; 3.66) 0.05

Smoker 1.49 (0.99; 2.26) 0.06

ACE-I/ARB 1.47 (0.92; 2.34) 0.11

CV event 1.32 (0.59; 2.95) 0.50

BMI 1.03 (0.86; 1.24) 0.74

Mean BP 0.96 (0.89; 1.04) 0.35

Fasting glucose 0.97 (0.91; 1.04) 0.44

Uric acid 0.89 (0.50; 1.50) 0.61

Triglycerides 0.99 (0.98; 1.01) 0.44

Cholesterol 1.00 (0.98; 1.02) 0.77

CRP 1.49 (0.01; 359.73) 0.89

CHAPTER 2: Results

106

In univariable analysis, statin users were more likely to be male (p< 0.001), had a higher

frequency of metabolic syndrome (p< 0.001), diabetes type 2 (p< 0.001), MAU (p< 0.001), ACE-

i/ARB treatment (p< 0.001), and previous cardiovascular events (p< 0.001), were older (p<

0.001), had larger BMI (p< 0.001), lower diastolic blood pressure (p= 0.02), higher fasting

glucose (p< 0.001), serum uric acid (p= 0.004), and triglycerides (p= 0.003), had lower levels of

LDL (p< 0.001) and HDL cholesterol (p= 0.001) compared to patients not taking statins (tables 1

and 2). The univariable odds ratio of MAU in patients using vs. not using a statin was 2.01 (95%

CI: 1.34-3.01, p= 0.0009, table 3A).

After multivariable analysis including the propensity score for statin use, the odds of MAU was

still significantly higher in patients taking a statin (OR: 1.82, 95% CI: 1.14-2.91, p= 0.01, table

3B). When all other variables were forced into the model, use of statin still was independently

associated with a higher odds of MAU (OR: 1.90, 95% CI: 1.15-3.11, p= 0.01, table 3C). Next to

statin use, only diabetes (OR: 1.92, 95% CI: 1.00-3.66, p= 0.05) and smoking (OR: 1.49, 95% CI:

0.99-2.26, p= 0.06) were independently associated with MAU after adjusting for the likelihood

of receiving a statin, suggesting that prescription of a statin overrides the association between

cardiovascular risk factors, MAU and creates collinearity by acting as a surrogate.

CHAPTER 2: Results

107

Figure S1. Flow chart of hypotheses to explain the observation of higher prevalence of MAU in

statin users

CHAPTER 2: Results

108

Discussion

Our data create concern on the use of MAU as a predictor or marker of cardiovascular or renal

disease in cohorts with patients using statins. Statin use apparently could blur the interpretation of

MAU by two potential mechanisms: 1° higher prevalence of MAU in patients using a statin, even

after correction for bias by indication and 2° masking of cardiovascular risk factors in

multivariable analyses, as statin use behaves as a surrogate for these markers. In epidemiological

studies evaluating the association between cardiovascular risk factors and MAU, statin use can

induce incorrect labeling of patients as having a cardiovascular or renal risk factor, and

interpretation of other risk factors for cardiovascular or renal disease can be confounded by the

way the use of statins is handled in the analysis. In this cross-sectional analysis, we observed a

two-fold higher prevalence of MAU in subjects who use vs. those who do not use a statin.

However, part of this association (figure S1) can be attributed to bias by indication, as patients

are often prescribed statins because they have cardiovascular risk factors which are by themselves

associated with enhanced risk for MAU. Indeed, we observed a higher prevalence of

cardiovascular risk factors in patients taking vs. not taking a statin in our study. We tried to

exclude this bias by indication by the use of a propensity score analysis. Adjusting for the

propensity score allows to analyze the difference in occurrence of MAU between patients with a

comparable propensity to receive a statin, while one group does whereas the other does not

receive the drug. The technique of propensity score is well established to address confounding

and bias by indication in observational studies [23,24]. However, this increased odds ratio

remained present even after correcting for the fact that statins are usually prescribed in patients

with cardiovascular risk factors which by themselves are associated with MAU, using the robust

technique of propensity score. This observation can either be due to residual or unmeasured

confounding or there can really be an induction of MAU by statin use (figure S1). Our data stress

that statin use confounds the impact of the individual risk factors on MAU, as statin use behaves

as a surrogate for presence of cardiovascular risk factors. As a consequence, in studies where

MAU is either used as a marker or as a surrogate endpoint, the association between outcomes and

certain cardiovascular risk factors can be blurred, and this in an unpredictable and variable

fashion, depending upon the prevalence of statin use in the cohort. On the other hand, if statins

really induce MAU, theoretically it can be both of glomerular or of tubular origin (figure S1). We

did not find any publication, either human, animal or in vitro, indicating that statin associated

CHAPTER 2: Results

109

proteinuria is of glomerular origin, but at least three in vitro or animal studies demonstrated that

statins do inhibit tubular reabsorption of filtered albumin and in this way could generate MAU in

a dose-dependent manner and in absence of cytotoxicity [11,12,25]. There is also growing

evidence that also in other conditions MAU can be the consequence of tubular dysfunction, even

in presence of an entirely intact glomerulus [26,27]. One epidemiological study in humans also

coined statin induced proteinuria as being tubular in origin, and even demonstrated a dose-effect

relation with rosuvastatin [28]. This would explain why statins fail to consistently result in

reduction of MAU in subjects with low grade MAU, or why higher vs. lower doses of statin fail

to further reduce MAU [29,30], as the beneficial effect of statins on the glomerular MAU is

counterbalanced by the induction of tubular MAU. It is very unlikely that statin induced MAU is

associated with an increased cardiovascular risk, but its impact on the functional capacity and the

morphological integrity of the kidneys is unknown. Even when the tubular albuminuria induced

by statins is harmless [31], it interferes with the implication of MAU as predictor or marker of

cardiovascular and renal risk by incorrectly labeling a subject as having a risk factor. Of note, this

would imply that the prognostic impact of glomerular MAU (so not induced by statin use) in

populations with a high prevalence of statin use, would be underestimated. The hypothesis that

statin induced MAU is tubular in origin would also fit with the favorable effect of statins on

cardiovascular disease and on progression of renal disease, as these effects are related to the

reduction of glomerular MAU associated with the improvement of endothelial dysfunction

[32,33].

Limitations. This cross-sectional study could not prove a causal connection between statin use

and de novo MAU. Using the technique of propensity score we achieved “pseudo-

randomization”, which in fact obviates the drawbacks imposed by the method of the study, but

which is not free from unmeasured biases [24]. Unfortunately, although dose dependency and a

higher frequency of more potent inhibitors of HMG coA reductase could add further strength to

the association with MAU, the prescribed daily dose and type of statin were as per protocol not

registered in our study. Another limitation of our study is that MAU was measured in a single

morning urine, whereas guidelines recommend to have at least two positive MAU in three

consecutive first morning urine samples before labeling a person as having MAU [34]. However,

in view of the mechanism of inhibition of tubular endocytosis, it is unlikely that statin associated

MAU would disappear by repeated testing, unless the statin would be stopped temporarily to

CHAPTER 2: Results

110

confirm the diagnosis. Second, intermittent MAU should not be considered as a predictor or

marker of cardiovascular or renal risk, as it is not linked to endothelial dysfunction [35]. In line

with this, we demonstrated in another cohort, that patients with intermittent MAU have far less

cardiovascular risk factors as compared to patients with persisting MAU [36]. Our study shows

that, in addition to the problems caused by single vs. multiple sampling, the use of a statin can

also lead to an in incorrect labeling of subject as having MAU.

The strength of this study is that it reflects routine clinical practice in hypertensive patients. To

the best of our knowledge, this is the first study pointing to an independent association between

statin use and MAU, even after correction for bias by indication by the use of a propensity score,

underlining the potential consequences of confounding induced by statin use on the interpretation

of MAU as a predictor or marker of cardiovascular or renal disease in epidemiological trials.

According to our data, statins are independently associated with an increased prevalence of

MAU, even after correction for bias by indication. As this MAU is most likely of tubular origin,

it is uncertain and rather unlikely whether it has the same prognostic impact for renal and

cardiovascular disease as endothelial dysfunction induced glomerular MAU. As such, it can lead

to incorrect labeling of subjects as having a cardiovascular risk factor.

CHAPTER 2: Results

111

Acknowledgments

We thank the steering committee members: JM Billiouw, M Couttenye, J Donck, M Dratwa, A

Dufour, B Georges, M Henckes, M Jadoul, E. Lessafre, JM Krzesinski, N Lameire, P Leenaerts,

R Lins, Y Pirson, JC Stolear, C Tielemans, W Van Biesen, R Vanholder, Y Vanrenterghem, D

Verbeelen, G Verpooten and the investigators: F Bare, A Bernaers, I Bijnens, P Blondeel, L

Bonami, L Bostoen, N Boussemaere, I Buntinx, D Chevalier, A Cleys, H Cuypers, G D'Hont, E

De Ganck, J De Jaeger, A De Leeuw, K De Praeter, K De Schutter, JL Delattre, P Delhaye, A

Delvosalle, A Demeester, B Dewaele, M Dooms, B Englebienne, L Erpicum, P Feys, H Flamee,

R Follet, J Galot, K Geens, W Geerts, C Gilio, P Govaert, J Helincks, K Hendrickx, Y

Henoumont, G Hoogmartens, S Hoppenbrouwers, J Ingelaere, S Konings, G Lannoo, R

Leuridan, M Lootens, J Luyts, D Mebis, J Mestdagh, L Meyvis, B Morelle, D Mulkers, M

Nevelsteen, F Nollomont, L Phang, A Pollet, E Pycke, V Rémy, M Rombouts, P Schleich, E

Schoofs, H Symons, C Tack, L Tanghe, J Van Assche, J Van Boxem, O Van Damme, E Van der

Putte, H Van der Stockt, J Van Massenhove, N Van Mulders, M Vande Voorde, D Vanmansart, J

Vermaete, J Vernijns, L Vernijns, B Verstraete, A Villé, E Willekens, S Delville, M Deroubaix,

M Ferro, M Ghesquiere, L Rouvroy, P Soetaert, N Van Nieuwenhuyse, E Vanden Eynde

Funding: The funder of this study was F. Hoffmann-La Roche Ltd. The funder had a role in data

collection because the ERICABEL study was initiated by F. Hoffmann-La Roche Ltd. The funder

had no other role in the study design, analysis, decision to publish, or preparation of the

manuscript.

CHAPTER 2: Results

112

References

1. Gerstein HC, Mann JF, Yi Q, Zinman B, Dinneen SF, et al. (2001) Albuminuria and risk of

cardiovascular events, death, and heart failure in diabetic and nondiabetic individuals. JAMA 286:

421-426.

2. Hallan H, Romundstad S, Kvenild K, Holmen J (2003) Microalbuminuria in diabetic and hypertensive

patients and the general population - Consequences of various diagnostic criteria - The Nord-Trondelag

Health Study (HUNT). Scandinavian Journal of Urology and Nephrology 37: 151-158.

3. Klausen KP, Scharling H, Jensen G, Jensen JS (2005) New definition of microalbuminuria in

hypertensive subjects - Association with incident coronary heart disease and death. Hypertension 46:

33-37.

4. van der Tol A, Van Biesen W, Verbeke F, De Groote G, Vermeiren F, et al. (2010) Towards a rational

screening strategy for albuminuria: results from the unreferred renal insufficiency trial. PloS one 5.

5. Bigazzi R, Bianchi S, Baldari D, Campese VM (1998) Microalbuminuria predicts cardiovascular

events and renal insufficiency in patients with essential hypertension. Journal of Hypertension 16:

1325-1333.

6. Mogensen CE (1999) Microalbuminuria, blood pressure and diabetic renal disease: origin and

development of ideas. Diabetologia 42: 263-285.

7. Baigent C, Keech A, Kearney PM, Blackwell L, Buck G, et al. (2005) Efficacy and safety of

cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14

randomised trials of statins. Lancet 366: 1267-1278.

8. Douglas K, O'Malley PG, Jackson JL (2006) Meta-analysis: the effect of statins on albuminuria.

Annals of internal medicine 145: 117-124.

9. Sandhu S, Wiebe N, Fried LF, Tonelli M (2006) Statins for improving renal outcomes: a meta-analysis.

Journal of the American Society of Nephrology : JASN 17: 2006-2016.

10. Fried LF, Orchard TJ, Kasiske BL (2001) Effect of lipid reduction on the progression of renal disease:

a meta-analysis. Kidney International 59: 260-269.

11. Verhulst A, D'Haese PC, De Broe ME (2004) Inhibitors of HMG-CoA reductase reduce receptor-

mediated endocytosis in human kidney proximal tubular cells. Journal of the American Society of

Nephrology : JASN 15: 2249-2257.

12. Sidaway JE, Davidson RG, McTaggart F, Orton TC, Scott RC, et al. (2004) Inhibitors of 3-hydroxy-3-

methylglutaryl-CoA reductase reduce receptor-mediated endocytosis in opossum kidney cells. Journal

of the American Society of Nephrology : JASN 15: 2258-2265.

13. van Zyl-Smit R, Firth JC, Duffield M, Marais AD (2004) Renal tubular toxicity of HMG-CoA

reductase inhibitors. Nephrology, dialysis, transplantation : official publication of the European

Dialysis and Transplant Association - European Renal Association 19: 3176-3179.

14. Wolfe SM (2004) Dangers of rosuvastatin identified before and after FDA approval. Lancet 363: 2189-

2190.

15. Deslypere JP, Delanghe J, Vermeulen A (1990) Proteinuria as Complication of Simvastatin Treatment.

Lancet 336: 1453-1453.

16. Atthobari J, Brantsma AH, Gansevoort RT, Visser ST, Asselbergs FW, et al. (2006) The effect of

statins on urinary albumin excretion and glomerular filtration rate: results from both a randomized

clinical trial and an observational cohort study. Nephrology Dialysis Transplantation 21: 3106-3114.

17. Rubin DB (1987) multiple imputation for nonrespons in surveys. New York: John Wiley & Sons.

18. Frank EH (2009) Hmisc: Harrell Miscerllaneous. R package version 3.7-0.

CHAPTER 2: Results

113

19. Cleeman JI, Grundy SM, Becker D, Clark LT, Cooper RS, et al. (2001) Executive summary of the

Third Report of the National Cholesterol Education Program (NCEP) expert panel on detection,


of the American Medical Association 285: 2486-2497.

20. http://www.R-project.org (Accessed 2012 jan 18) The R project for statistical computing. .

21. Heinze G, Juni P (2011) An overview of the objectives of and the approaches to propensity score

analyses. European heart journal.

22. Zhehui L, Gardiner JC, Bradley CJ (2010) Applying propensity score methods in medical research:

pitfalls and prospects. Medical care research and review : MCRR 67: 528-554.

23. Barnieh L, James MT, Zhang J, Hemmelgarn BR (2011) Propensity score methods and their

application in nephrology research. Journal of nephrology.

24. Rosenbaum PR, Rubin DB (1983) The Central Role of the Propensity Score in Observational Studies

for Causal Effects. Biometrika 70: 41-55.

25. Corna D, Sangalli F, Cattaneo D, Carrara F, Gaspari F, et al. (2007) Effects of rosuvastatin on

glomerular capillary size-selectivity function in rats with renal mass ablation. American Journal of

Nephrology 27: 630-638.

26. Pollock CA, Poronnik P (2007) Albumin transport and processing by the proximal tubule: physiology

and pathophysiology. Current opinion in nephrology and hypertension 16: 359-364.

27. Jefferson JA, Shankland SJ, Pichler RH (2008) Proteinuria in diabetic kidney disease: a mechanistic

viewpoint. Kidney International 74: 22-36.

28. Kostapanos MS, Milionis HJ, Saougos VG, Lagos KG, Christine K, et al. (2007) Dose-dependent

effect of rosuvastatin treatment on urinary protein excretion. Journal of cardiovascular pharmacology

and therapeutics 12: 292-297.

29. Sorof J, Berne C, Siewert-Delle A, Jorgensen L, Sager P, et al. (2006) Effect of rosuvastatin or

atorvastatin on urinary albumin excretion and renal function in type 2 diabetic patients. Diabetes

Research and Clinical Practice 72: 81-87.

30. Rutter MK, Prais HR, Charlton-Menys V, Gittins M, Roberts C, et al. (2011) Protection Against

Nephropathy in Diabetes with Atorvastatin (PANDA): a randomized double-blind placebo-controlled

trial of high- vs. low-dose atorvastatin. Diabetic Medicine 28: 100-108.

31. http://www.fda.gov/Drugs/DrugSafety/PostmarketDrugSafetyInformationforPatientsandProviders/Drug

SafetyInformationforHeathcareProfessionals/PublicHealthAdvisories/UCM051756 (accessed 2012 jan

18) FDA Public Health Advisory for Crestor (Rosuvastatin).

32. Knight SF, Yuan J, Roy S, Imig JD (2010) Simvastatin and tempol protect against endothelial

dysfunction and renal injury in a model of obesity and hypertension. American journal of physiology

Renal physiology 298: F86-94.

33. Park JK, Mervaala EM, Muller DN, Menne J, Fiebeler A, et al. (2009) Rosuvastatin protects against

angiotensin II-induced renal injury in a dose-dependent fashion. Journal of Hypertension 27: 599-605.

34. http://www.kidney.org/professionals/kdoqi/guidelines_ckd/p5_lab_g5.htm (accessed 2012 jan 18)

KDOQI Clinical Practice Guidelines for Chronic Kidney Disease: Evaluation, Classification, and

Stratification, part 5. Evaluation of laboratory measurements for clinical assessment of kidney disease,

guidelines 5. Assesment of proteinuria

35. Romundstad S, Holmen J, Kvenild K, Hallan H, Ellekjaer H (2003) Microalbuminuria and all-cause

mortality in 2,089 apparently healthy individuals: A 4.4-year follow-up study. The Nord-Trondelag

Health Study (HUNT), Norway. American Journal of Kidney Diseases 42: 466-473.

http://www.r-project.org/

http://www.fda.gov/Drugs/DrugSafety/PostmarketDrugSafetyInformationforPatientsandProviders/DrugSafetyInformationforHeathcareProfessionals/PublicHealthAdvisories/UCM051756

http://www.fda.gov/Drugs/DrugSafety/PostmarketDrugSafetyInformationforPatientsandProviders/DrugSafetyInformationforHeathcareProfessionals/PublicHealthAdvisories/UCM051756

http://www.kidney.org/professionals/kdoqi/guidelines_ckd/p5_lab_g5.htm

CHAPTER 2: Results

114

36. Van der Tol A, Van Biesen W, De Groote G, Verbeke P, Vermeiren F, et al. (2011) Mislabeling

Cardiovascular Risk by One Single Measurement of Microalbuminuria. Acta Clinica Belgica 66: 171-

171.

CHAPTER 3: GENERAL DISCUSSION AND

CONCLUSIONS

CHAPTER 3: General discussion and conclusions

117

In this thesis we discuss whether cardiovascular risk factors and kidney damage could be

screened by measuring albuminuria. Our results are based on two prospective studies, one study

was performed in workers (URI study) and another study was performed in hypertensive subjects

(ERICABEL study). In what follows we will discuss the main questions raised at the start of this

thesis.


118

3.1. The prevalence of microalbuminuria and the associations with

traditional cardiovascular risk factors in a general population.

In the URI study (n = 1,398), the prevalence of microalbuminuria was 5.2 and 0.5%. Only a

minority of the workers with microalbuminuria (14/73 = 19%) had known diabetes and/or

hypertension. In a Dutch population cohort (PREVEND), also a minority of the subjects with

microalbuminuria (644/2,918 = 22%) had self-reported diabetes and/or hypertension (1). After

exclusion of these known risk factors, the PREVEND trial reported a higher prevalence of

microalbuminuria than the URI study (6.6 vs. 4.2%, p < 0.001), conceivably as a consequence of

higher mean age (49.5 ± 12.9 y vs. 38.3 ± 9.7 y, p< 0.001), higher prevalence of smokers (42 vs.

31%, p < 0.001) and differences in methods to define microalbuminuria; urinary albumin

concentration (UAC): 20-200 mg/l in the PREVEND study vs. urinary albumin creatinin ratio

(ACR): 17-249 in men 25-354 mg/g in women in the URI study (2). The PREVEND study

concluded that the presence of microalbuminuria predicts cardiovascular morbidity and mortality,

even in subjects without recognized cardiovascular risk factors (2, 3). However, this association

might be confounded by inflammation, unrecognized cardiovascular risk factors and ‘borderline’

levels of cardiovascular risk factors, such as prehypertension or impaired glucose tolerance (4-6).

In this thesis, we described in chapter 2.1.1 based on data from the URI study, that ACR in

apparently healthy workers was positively associated with unrecognized hypertension (≥ 140/90

mmHg), plasma glucose (≥100 mg/dl) and resting heart rate (≥ 85 bpm). The prevalence of these

risk factors was high (36%) in this presumably healthy population. Moreover, as described in

chapter 2.1.3, one third of the workers of the overall URI population had at least one traditional

cardiovascular risk factor (hypertension, diabetes and/or dyslipidemia) of whom only one third

was known and treated. In the PREVEND study (n = 40,856) cardiovascular risk factors were

only measured in selected subjects with UAC > 10 mg/l (n= 6,000) and in a randomly selected

control group of the total study population with UAC < 10 mg/l (n = 2,592). Microalbuminuria as

defined by UAE > 30 mg/24h was only confirmed in 933 subjects (7). Two thirds of these

subjects (633/933) had hypertension, diabetes and/or a cardiovascular history, and one third

(300/933) was considered to have isolated microalbuminuria. Nevertheless, the subjects with

isolated microalbuminuria had associated additional cardiovascular risk factors, such as age, male

gender, smoking, obesity and dyslipidemia, ‘borderline’ levels of cardiovascular risk factors, and


119

also an increased risk to develop diabetes, hypertension and cardiovascular diseases (7). Thus,

persistent microalbuminuria is rather a reflection of widespread vascular damage that is linked to

associated risk factors than an independent risk factor (8). In the URI study (chapter 2.1.1), two

thirds of the workers with microalbuminuria had associated cardiovascular risk factors. We

hypothesized that a subject with microalbuminuria in absence of any cardiovascular risk factor

could be considered as a false positive. The prevalence of these false positive results was 2.4%.

Similar figures were found in a New Zealand study (2.0%) and in the Copenhagen City Heart

Study (2%) (9, 10). In subjects without any feature of the metabolic syndrome, the presence of

microalbuminuria was not associated with increased risk for cardiovascular disease and death,

suggesting that microalbuminuria by itself might not be an independent determinant of outcome

(10).


120

3.2. Should we use microalbuminuria as a screening tool to identify subjects

with unrecognized cardiovascular risk factors in a presumably healthy

population?

The authors of the PREVEND trial suggest that screening for albuminuria improves the detection

of unrecognized renal and cardiovascular risk factors (1, 11, 12). When this approach had been

implemented in the URI cohort, only 9% (37/431) of the workers with at least one unrecognized

risk factor would have been identified (chapter 2.1.1). The sensitivity of microalbuminuria to

detect a subject with at least one cardiovascular risk factor was low (12%) in the PREVEND

study as well, leaving most subjects (88%) with modifiable cardiovascular risk factors untreated

(12). According to the authors of the PREVEND trial, hypertensive patients without

microalbuminuria would not benefit from blood pressure lowering agents as the risk reduction for

cardiovascular events would be too low. It was estimated that more hypertensive patients without

vs. with microalbuminuria need to be treated (111 vs. 8) to prevent one cardiovascular event (13).

Yet, no RCT exists to test the hypothesis that medical management of microalbuminuria reduces

cardiovascular or renal events independent of blood pressure reduction (14). According to us, the

‘PREVEND’ approach might offer prospects for secondary prevention in high risk patients with

microalbuminuria while more subjects would be (earlier) identified by screening for

cardiovascular risk factors with more potential possibilities to reduce cardiovascular risk (chapter

2.1.1). The efficiency of interventions in patients with diabetes and/or hypertension is also

practically more easy to assess by measuring the plasma glucose and blood pressure levels than it

would be by controlling urinary albumin. Additionally, the interpretation of microalbuminuria is

hampered by the fluctuations which commonly occur.


121

3.3. Conditions which could lead to false positive test results of

microalbuminuria

Another problem of using microalbuminuria as a screening tool is the high number of false

positive results. Biological variation and temporary conditions such as changes in blood pressure,

plasma glucose, fever, urinary tract infections, hematuria, exposure to cold and physical exercise

might result in false positive results (chapter 1). Microalbuminuria fluctuates more in random

urine samples than in early morning urine samples (15). Conceivably, the URI trial included a

substantial number of false positive test results as urine was collected randomly. The exact

number of false positive subjects in our trial is unknown as we did not perform an overnight or

24-h urine collection in all subjects.

In the URI study, we used gender specific cut-off values (ACR: 17-249 for males and 25-354

mg/g for females) in an urine spot to indicate microalbuminuria, because men have a higher

urinary excretion of creatinine than women due to higher muscle mass (16). According to the

PREVEND trial, the sensitivity, specificity and positive predictive value of these gender specific

cut-off values in an urinary spot, to predict microalbuminuria in subsequent 24-hours urine

collection was 65, 98 and 65%, respectively (17). However, the PREVEND trial used a lower

threshold to define microalbuminuria UAC > 10 mg/l during the prescreening, consequently

more subjects with microalbuminuria were identified (increased sensitivity) but simultaneously

also more subjects with a false positive result were included.

As described in chapter 2.1.2, within the URI study, a prospective analysis was designed to

evaluate the reproducibility of microalbuminuria. Of the overall URI population, 341 subjects

underwent consecutive occupational check-ups. Only 60% of the initially microalbuminuric

subjects tested positive for microalbuminuria at a subsequent visit. The reproducibility of initial

microalbuminuria was higher in subjects with vs. without traditional risk factors (89 vs. 36%, p =

0.03). In addition, subjects with persistent microalbuminuria had a higher cardiovascular risk

score than subjects with intermittent or without microalbuminuria. These findings corroborate the

data from the HUNT study that showed that intermittent microalbuminuria in presumably healthy

subjects might simply reflect biological variation, whereas persistent microalbuminuria more

likely explains a cardiovascular risk and/or disease (18). We assumed in chapter 2.1.1 that


122

approximately 1 of 3 detected workers had a false positive test result, as one third of the workers

with microalbuminuria had no cardiovascular risk factor. In workers younger than 40 years, the

presence of microalbuminuria was not associated with cardiovascular risk factors (chapter 2.1.3).

In this group, the presence of microalbuminuria was associated with low BMI or with

physicochemical exposure risk (PCER). Conceivably, a substantial number of young workers had

postural proteinuria and might be mislabeled as having a risk for cardiovascular and/or renal

disease (19-21). The meaning of microalbuminuria in workers with PCER is unknown.

According to the literature, exposure to some heavy metals is associated to reversible tubular

overflow albuminuria by increased liver synthesis of metallothionein (22-24).

Another condition that affects urinary albumin excretion is use of statins. In chapter 2.2.1 we

showed that statin use is associated with microalbuminuria, even after adjusting for bias by

indication to receive a statin. Other studies confirmed that statin use generates tubular

albuminuria hence affecting the total excreted albumin (25-27). The effect of statins was maximal

after 3 hours and disappears after 24 hours in healthy subjects (27). The ERICABEL study did

not evaluate the timing of statin intake. The finding of an increased urinary α1-

microglobulin/albumin ratio might help to distinguish statin induced tubular albuminuria from

glomerular albuminuria (27). This might help to anticipate incorrect labeling of persons with an

increased risk for a cardiovascular or renal disease. In the ERICABEL study no marker for

tubular albuminuria was measured. Thus, whether microalbuminuria could be implemented in a

mass screening program is uncertain as it might have different underlying causes, and prognostic

meanings.


123

3.4. Can we prevent ESRD by screening for microalbuminuria in a general

population?

The central idea of screening for albuminuria is early disease detection followed by adequate

treatment to reduce the risk of the disease or progression. KDIGO recommends to screen for

albuminuria in selected patients with hypertension, diabetes, and/or cardiovascular disease (28).

However, some authors advocate to screen for albuminuria in the general population as many

persons are not aware of the presence of cardiovascular risk factors (1). The criteria to select a

disease for screening have been reported by Wilson and Jungner and the Council of Europe and

are summarized in table 1 (29, 30). We discuss by means of these criteria whether a population

wide albuminuria screening followed by appropriate treatment might lead to benefits in terms of

prevention of end stage renal disease (ESRD).

Table 1. The Wilson-Jungner criteria (29) and recommendations of the Council of Europe

(30)

1) The disease should be an obvious burden for the individual and the community in terms of

death, poor quality of life and socio-economic factors including health care costs.

2) The natural course of the disease should be well known and the disease should go through an

initial latent stage or be determined by risk factors, which can be detected by appropriate tests.

3) A suitable test is highly sensitive and specific for the disease as well as being acceptable to the

person screened.

4) Screening followed by diagnosis and interventions in an early stage of the disease should

provide a better prognosis than intervention after spontaneously sought treatment.

5) Adequate treatment or other intervention possibilities are indispensable. Adequacy is

determined both by proven medical effect and ethical and legal acceptability.

6) The cost of case-finding should be economically balanced in relation to possible expenditure

on medical care as a whole.

7) Case-finding should be a continuing process and the interval of testing should be known.


124

3.4.1 ‘The disease should be an obvious burden for the individual and the community in

terms of death, poor quality of life and socio-economic factors including health care costs’.

Although this condition seems to be fulfilled for ESRD (31-33), the incidence of ESRD might be

too low to justify a population screening for albuminuria. In the PREVEND study (n= 40,854)

only 13 subjects reached ESRD while 568 subjects experienced a CVD event in a follow-up

period of 7 years (34). The burden of disease for the community can be measured by disability

adjusted life year (DALY). DALYs are the sum of years of life lost due to premature death and

years lived with disability (prevalence of a disease sequel multiplied by the disability weight for

that sequel), compared to the standard life expectancy in full health (35). In the developed

countries, the major part of the DALYs in the general population is lost by cardiovascular

diseases whereas a negligible part is lost by ESRD (36). Conceivably, population-based screening

for albuminuria is only useful if CVD would be detected early and prevented.


125

3.4.2 ‘The natural course of disease should be well-known and the disease should go through

an initial latent stage or be determined by risk factors, which can be detected by appropriate

tests’.

The precursor stage of diabetic kidney disease is detected by a sustained presence of

microalbuminuria, a long preclinical phase before the development of overt nephropathy (37, 38).

Consequently, one third of the diabetic patients have microalbuminuria (39). Prospective long

term studies observed that 40-60% of the diabetic patients with microalbuminuria regressed to

lower level of albuminuria spontaneously, or by improvement of glycaemia and blood pressure

control, while only 20-30% of microalbuminuric diabetic patients progressed to a higher level of

albuminuria (40-42). A major concern of using microalbuminuria as an early indicator for

nephropathy is that this potentially causal relation is confounded by hematuria, age, gender,

essential hypertension, hyperglycemia, obesity, inflammation, atherosclerosis and drugs (43-46).

The presence of microalbuminuria is also frequently found in hypertensive subjects, even in the

absence of diabetes (39). The prevalence of microalbuminuria was 16% in our Belgian

hypertensive cohort (ERICABEL). Large epidemiological studies in general and high risk

populations showed that microalbuminuria was independently associated with an increased

likelihood of renal events and mortality (47, 48). However, it seems unlikely that

microalbuminuria is part of the causal pathway for overt nephropathy in non-diabetic subjects, as

the presence of microalbuminuria might be rather a sign of systemic endothelial dysfunction or

underlying systemic disease (atherosclerosis, cancer, CVD...) than a (first) manifestation of

kidney disease (49). The positive predictive value of microalbuminuria to find a subject with a

primary kidney disease will be low as primary kidney diseases are rare. Moreover, in patients

with a primary kidney disease the occurrence of microalbuminuria will progress rapidly to

macroalbuminuria whereas diabetic patients with microalbuminuria slowly progress to

macroalbuminuria. Additionally, the causes of renal failure in RRT patients differ widely:

diabetes (18.3%), glomerulosclerosis (17.8%), polycystic kidney disease (9.6%), pyelonephritis

(7.6%), renovascular disease (7.6%), hypertension (6.8%), miscellaneous (23%) and unknown

(9%) in Belgium (50). Thus, unselected screening for microalbuminuria does not fulfill the

second request, as the natural course of diabetic microalbuminuria could not extrapolated to

other kidney diseases, where the initial latent stage is too short or absent.


126

3.4.3 ‘A suitable test is highly sensitive and specific for the disease as well as being

acceptable to the person screened’.

Subjects with microalbuminuria have approximately a twofold relative risk for CVD or ESRD,

but the absolute risk is 40 fold higher for CVD than for ESRD (34, 51, 52). According to the

literature, the diagnostic performance of microalbuminuria to predict RRT is not satisfied,

especially the positive predictive value is too low (0.8%) (1). According to the results of the

PREVEND study, subjects with microalbuminuria in absence of cardiovascular risk factors had a

normal decline of renal function over time whereas subjects with macroalbuminuria had a rapid

decline of renal function (1). Collecting urine samples to measure albuminuria is clinically

acceptable for the general population (1, 53). However, a person with a positive test result should

be screened for a second time to confirm the diagnosis of microalbuminuria, whereas in subjects

with macroalbuminuria a second urine sample is not necessary as fluctuations in this range of

albuminuria are less prominent. Thus, screening for macroalbuminuria is more appropriate than

screening for microalbuminuria to predict ESRD.


127

3.4.4 ‘Screening followed by diagnosis and interventions in an early stage of the disease

should provide a better prognosis than intervention after spontaneously sought treatment’.

There is no robust evidence to suggest that interventions that directly target microalbuminuria

result in a reduction of ESRD and CVD (14, 54). A randomized controlled trial (PREVEND-IT)

showed that detecting subjects with microalbuminuria in the general population, followed by the

initiation of an ACE-I vs. placebo was associated with a reduction in baseline albuminuria but

this was not translated in a significant reduction of renal and cardiovascular events (55). In

contrast, trials in patients with diabetes and/or hypertension who were treated with ACE-I vs.

placebo notably showed a reduction of albuminuria, and a decrease of cardiovascular events and

mortality (42, 56-62), although these trials are confounded by a reduction of blood pressure.

Only in post hoc analyses, a reduction in baseline albuminuria has been associated to a reduction

in cardiovascular morbidity and mortality, independent of blood pressure (63-66). In addition ,

reducing albuminuria is not always associated with improvement of clinical outcome. The

ONTARGET trial showed that ARB + ACE-I vs. ACE-I or ARB reduce blood pressure and

albuminuria but not the clinical outcome in patients with cardiovascular risk factors (67). The

ROADMAP trial showed that an ARB on top of antihypertensive medication in 4,447 diabetes

patients reduced blood pressure and albuminuria, but was associated with a higher cardiovascular

mortality (15 vs. 3 patients, p = 0.01) (60). No evidence exists that protective treatment in

patients with microalbuminuria avoids new cases of ESRD whereas ACE-I or ARB in patients

with macroalbuminuria showed a 25% reduction of ESRD (68-75). All these data suggest that

mainly screening for macroalbuminuria (and not microalbuminuria) followed by treatment could

be effective to reduce ESRD.


128

3.4.5 ‘Adequate treatment or other intervention possibilities are indispensable. Adequacy is

determined both by proven medical effect and ethical and legal acceptability’.

Despite the use of ACE-I or ARB, some patients might develop macroalbuminuria and/or CKD

stage 4 or 5. These patients should be timely referred to renal care to delay the progress to ESRD

and to detect and manage associated conditions, such as anemia, hypertension, dyslipidaemia,

hyperkaliemia, hyperphophatemia, bone mineral disorders, cardiovascular disease and mental

health disturbances. The availability of renal care (including RRT) depends on macro-economic

parameters, such as gross domestic product (GDP) per capita, the percentage of GDP spent on

health care and dialysis reimbursement (76). In countries spending less on health care the need

for renal care is not being met, whereas in countries spending more on health care the need for

renal care is being created by patients who survive long enough to develop ESRD (76).

Paradoxically, a rise of RRT patients is expected by introducing interventions that reduce

cardiovascular mortality (77). Each 1-year increase in life expectancy at 60 years is associated

with a 12% increase in RRT incidence (76). In Belgium, half of the patients who start with RRT

is older than 75 (50). In the elderly population, the life expectancy is quite limited after the

initiation of RRT, many experience loss of functional status and independence, much of the time

gained will be spent in a health care setting, and one third of these elderly discontinue

maintenance dialysis prior to death (78). This shows that chronic dialysis should not be proposed

to patients with a short life expectancy as a necessity but as a choice, and that conservative

(nondialytic) care is a legimate option. In clinical practice, initiation and maintenance of chronic

dialysis is encouraged in patients even when the treatment burden outweights the medical and

psychosocial benefits (79). The most important reason for this malpractice is that many

nephrologists do not feel comfortable talking with patients about end-of-life treatment

preferences (78). The real challenge in nephrology with regards to the ageing problem is to force

back the increasing incidence of RRT in adults above the age of 75. We need more therapeutic

options and/or better guidelines to delay RRT in CKD patients, particularly in elderly.


129

3.4.6 ‘The cost of case-finding should be economically balanced in relation to possible

expenditure on medical care as a whole’.

Screening followed by diagnosis and intervention at an early reversible stage of disease should be

cost-effective, which means that screening plus intervention should result in a more positive

socioeconomic balance than not screening and following the traditional approach. The costs of

screening contain not only the expenditure for the screening test, but also those of treating

because of false positive screening results and not treating because of false negatives (80). False

positive screening tests lead to increased medical visits with further tests, increased health care

costs and unnecessary treatment with in some cases adverse effects that further increase cost (80).

False negative test results provide false reassurance about the absence of disease and possibly

longer waiting before installing appropriate treatment which again may increase costs. Even, a

“truly positive test” has a psychological cost for the patient that comes from being labeled earlier

in the natural history of the process than would have occurred without screening (80). According

to the literature, screening for albuminuria (microalbuminuria and macroalbuminuria) in the

general population is not cost-effective, unless selectively directed toward high risk groups or

conducted at a ten-year interval (81-83). In addition, the beneficial effect of screening is to large

extent based on a delay in onset of ESRD or death. This delay is clearly illustrated in a modeling

analysis in patients with diabetes and hypertension that showed an increase of ESRD-free

survival from 10.8 to 11.2 years per patient (5 months) and a life expectancy from 10.7 to 11.0

years per patient (4 months) by screening for albuminuria (microalbuminuria and

macroalbuminuria) followed by optimized treatment versus no screening (84). However, these

modeling studies need to be confirmed in an RCT, whereby the outcome of subjects who are

invited for albuminuria screening should be compared to subjects who are not screened. In

addition, an RCT obviates a potentially prognostic selection bias that occurs in a population-

based screening program as volunteers are more likely to improve their health than those who

refuse to be screened (80). However, an RCT to jusitify screening might fail by contamination.

Contamination is the effect of opportunistic case-finding that commonly occurs in those who

were randomized not to be screened (the control group). The latter effect was one of the main

reasons why screening programs for diabetes and prostate cancer have failed (85-90). We suggest

that an RCT to justify screening for CKD might fail as well, as nowadays more and more patients

are already treated with ACE-I or ARB, irrespective of the presence of microalbuminuria.


130

3.4.7 ‘Case-finding should be a continuing process and the interval of testing should be

known’.

A benchmark of US$50,000/QALY is often used for judging the cost-effectiveness of

interventions (91). Figure 1 shows that only in diabetic patients annual screening vs. no screening

is cost-effective. Decreasing the frequency of screening for microalbuminuria in patients with

diabetes or hypertension has little impact on QALYs or ESRD incidence while simultaneously

reducing the costs (82). As the lowest cost-effectiveness ratios were achieved when screening

was started in persons between 50 and 60 years old (figure 1), it was hypothesized that screening

for albuminuria (microalbuminuria and macroalbuminuria) beginning at the age of 50 years with

an interval of 2 years, was cost-effective in hypertensive patients (82). Screening for albuminuria

in subjects with neither diabetes nor hypertension is supposed to be cost-effective if it is started at

the age of 50 years only with an interval of 10 years (81, 82). By using such long interval for

testing albuminuria, patients with rapidly progressive kidney disease (such as glomerulonephritis)

would be hard to identify while subjects with slow disease progression (such as diabetes) would

be detected more frequently (length-bias sampling).


131

Figure 1. Cost-effectiveness of universal annual albuminuria screening beginning at different

ages relative to no screening for the full population and subgroups with diabetes or hypertension,

adapted from (82).

Whether population screening for microalbuminuria to detect and treat new cases of

hypertension, diabetes and CVD might improve outcome is uncertain.


132

3.5 Conclusions

This thesis explores the pitfalls of screening for microalbuminuria to detect persons with

cardiovascular and renal risk in an apparently healthy working (URI) and in a hypertensive

(ERICABEL) cohort.

A conducted microalbuminuria screening to detect subjects with cardiovascular and renal risk in

the URI population was not effective for two reasons. Firstly, only a small proportion (9%) of the

unrecognized cardiovascular risk factors was detected. When traditional cardiovascular risk

factors are taken into account to select candidates for screening, more subjects (36%) were

detected with possibilities to improve cardiovascular and renal outcome than was the case from a

population-based screening for microalbuminuria. Second, as the causes of microalbuminuria are

widely divergent, not all subjects with microalbuminuria had at least one cardiovascular risk

factor. In addition, the underlying condition of microalbuminuria determines the clinical outcome.

This is confirmed in the literature; subjects with orthostatic microalbuminuria have an excellent

prognosis, whereas diabetic patients with microalbuminuria have a high risk for overt

nephropathy (92, 93). In the cross-sectional URI study, healthy young workers with

microalbuminuria had a low BMI, suggesting orthostatic proteinuria. The presence of

microalbuminuria was also associated in workers with physicochemical exposure risk. A

longitudinal study is necessary to investigate the prognostic value of microalbuminuria in

workers with physicochemical exposure risk. Furthermore, a longitudinal study is lacking that

observes the outcomes in microalbuminuric subjects with different underlying conditions.

Conceivably, interventions in persons with microalbuminuria in absence of cardiovascular risk

factors engender medical frustration, unnecessary anxiety, harm, worries and lead to excess of

costs, whereas interventions in persons with microalbuminuria in presence of modifiable

cardiovascular risk factors may improve clinical outcome.

Microalbuminuria was associated with low BMI and physicochemical exposure risk in workers

under the age of 40, whereas microalbuminuria was associated with cardiovascular risk factors in

workers above the age of 40. Thus, if a mass screening for microalbuminuria to detect new

cardiovascular or renal risk factors is conducted, it should start above the age of 40. Moreover,

there is no direct evidence that treatment of microalbuminuria reduces cardiovascular disease.


133

Screening for microalbuminurie might become useful as new therapeutic options become

available to prevent CVD.

According to the literature, the absolute risk to develop ESRD in patients with microalbuminuria

seems to be too low to justify a population-based screening for albuminuria (48).

The predictive value of micro- or macroalbuminuria to develop ESRD increases dramatically by

selecting subjects with an estimated GFR lower than 60 ml/min/1.73m² (94). It was estimated that

1.4% of a population (with an estimated GFR lower than 60 ml/min/1.73m² and micro- or

macroalbuminuria) had an increased risk for ESRD (94). As the prevalence of reduced GFR

increases with age, screening of renal markers to identify those with increased risk for ESRD has

been suggested to be performed in subjects who are older than 55 years (94). However, the URI

population was relatively young and we identified only one subject with microalbuminuria in

presence of an estimated GFR lower than 60 ml/min/1.73m². Thus, screening for

microalbuminuria in a relatively young population might not be useful to identify subjects with

increased risk for ESRD, as almost all subjects had an estimated GFR higher than 60

ml/min/1.73m².

According to the URI study, screening for microalbuminuria is more useful in adults with vs.

without cardiovascular risk factors to detect CKD. Screening for albuminuria in diabetic patients

is considered to be effective as treatment of an ACE-I or ARB reduces the level of albuminuria

and the number of ESRD in those with macroalbuminuria. However, screening is less effective as

more and more patients are already treated with ACE-I or ARB, irrespectively of the presence of

albuminuria (95). In addition, false positive test results might also occur in presence of

cardiovascular risk factors. In the ERICABEL cohort we observed that statin use was associated

with microalbuminuria in hypertensive patients, very likely related to the characteristics of statins

to induce tubular albuminuria by inhibition of albumin reabsorption. The presence of

microalbuminuria in statin users can lead to incorrect labeling of subjects as having an increased

risk for overt nephropathy. We need more prospective studies to investigate the prognostic value

of microalbuminuria in patients who are treated with statins.

The URI study showed that only persistent microalbuminuria is associated with increased

cardiovascular risk. Microalbuminuria should be confirmed in a subsequent urine sample, as a


134

single measurement could be false positive due to a temporary condition. According to KDIGO,

all subjects with microalbuminuria for longer than 3 months have CKD stage A2. Consequently,

the prevalence of CKD (A2) is overestimated in most cohorts as mostly one single measurement

of microalbuminuria is performed. Moreover, microalbuminuria in half of diabetic patients

disappears according longitudinal studies. We showed that fluctuations of microalbuminuria

commonly occur in persons without cardiovascular risk factors. The fluctuations of

microalbuminuria generate a substantial bias if microalbuminuria is used as a screening tool or as

a surrogate for renal or cardiovascular outcome. In the ERICABEL cohort, the presence of

microalbuminuria fluctuated in hypertensive patients during a follow-up of 5 years. Thus,

doubts could also arise about the diagnostic performance of using microalbuminuria in this

population.

A rapid decline of renal function is more frequently observed in subjects with macroalbuminuria

than in presence of no albuminuria or microalbuminuria (1, 75). Thus, screening for progressive

kidney disease might be more effective by identifying subjects with macoralbuminuria (late or

overt nephropathy) than by identifying subjects with microalbuminuria (early or potential

nephropathy). Currently, no hard evidence exists whether screening for albuminuria is cost-

effective in the general population, even by using a ten-year screening interval. Such mass

screening might not be feasible as a substantial number of subjects may develop overt

nephropathy over a short period of time. Whether a population screening program that measures

albuminuria and estimates GFR (by serum creatinine and/or cystatin C) in subjects who are older

than 55 years could reduce the incidence of ESRD should be studied in a RCT.


135

3.6 References


Screening for Albuminuria Identifies Individuals at Increased Renal Risk. Journal of the American Society of Nephrology 2009; 20: 852-862. Available at <Go to ISI>://000264831300023.








<Go to ISI>://000178385700010.

4. Brantsma AH, Bakker SJ, Hillege HL, de Zeeuw D, de Jong PE, Gansevoort RT. Urinary albumin

excretion and its relation with C-reactive protein and the metabolic syndrome in the prediction of type

2 diabetes. Diabetes Care 2005; 28: 2525-2530. Available at


=16186291.

5. Jorgensen L, Jenssen T, Heuch I, Jacobsen BK. The combined effect of albuminuria and inflammation

on all-cause and cardiovascular mortality in nondiabetic persons. J Intern Med 2008; 264: 493-501.

Available at


=18624904.

6. Pedrinelli R, Dell'Omo G, Di Bello V, Pellegrini G, Pucci L, Del Prato S, Penno G. Low-grade

inflammation and microalbuminuria in hypertension. Arteriosclerosis, thrombosis, and vascular biology 2004; 24: 2414-2419. Available at http://www.ncbi.nlm.nih.gov/pubmed/15486313.

7. Scheven L, Van der Velde M, Lambers Heerspink HJ, De Jong PE, Gansevoort RT. Isolated

microalbuminuria indicates a poor medical prognosis. Nephrol Dial Transplant 2013; 28: 1794-1801.


8. Jensen JS, Borchjohnsen K, Jensen G, Feldtrasmussen B. Atherosclerotic Risk-Factors Are Increased

in Clinically Healthy-Subjects with Microalbuminuria. Atherosclerosis 1995; 112: 245-252. Available

at <Go to ISI>://A1995QH02000011.

9. Metcalf PA, Scragg RK, Dryson E. Associations between body morphology and microalbuminuria in

healthy middle-aged European, Maori and Pacific Island New Zealanders. Int J Obes Relat Metab

Disord 1997; 21: 203-210. Available at


=9080259.




ISI>://000249947500010.

11. Brantsma AH, Bakker SJ, de Zeeuw D, de Jong PE, Gansevoort RT. Urinary albumin excretion as a

predictor of the development of hypertension in the general population. J Am Soc Nephrol 2006; 17:



=16434504.


the efficacy of screening for cardiovascular risk factors. Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association 2010;


13. Boersma C, Postma MJ, Visser ST, Atthobari J, de Jong PE, de Jong-van den Berg LT, Gansevoort RT, Group PS. Baseline albuminuria predicts the efficacy of blood pressure-lowering drugs in preventing













136

cardiovascular events. Br J Clin Pharmacol 2008; 65: 723-732. Available at




ISI>://000277757300014.





=19092125.




17. Gansevoort RT, Verhave JC, Hillege HL, Burgerhof JGM, Bakker SJL, de Zeeuw D, de Jong PE, Grp

PS. The validity of screening based on spot morning urine samples to detect subjects with

microalbuminuria in the general population. Kidney International 2005; 67: S28-S35. Available at <Go

to ISI>://WOS:000227251000007.




ISI>://000185518500004.

19. Miller WG, Bruns DE, Hortin GL, Sandberg S, Aakre KM, McQueen MJ, Itoh Y, Lieske JC,

Seccombe DW, Jones G, Bunk DM, Curhan GC, Narva AS, Urine NKDEP-IWGSA. Current Issues in

Measurement and Reporting of Urinary Albumin Excretion. Clinical Chemistry 2009; 55: 24-38.

Available at <Go to ISI>://WOS:000262303900008.

20. Robinson RR, Coggins CH, Shapiro K, Cohen JJ, Harrington JT, Kassirer JP. Isolated Proteinuria in

Asymptomatic Patients. Kidney International 1980; 18: 395-406. Available at <Go to

ISI>://WOS:A1980KK18700014.







23. Bernard A, Lauwerys RR. Proteinuria: changes and mechanisms in toxic nephropathies. Critical

reviews in toxicology 1991; 21: 373-405. Available at http://www.ncbi.nlm.nih.gov/pubmed/1741950.



25. Deslypere JP, Delanghe J, Vermeulen A. Proteinuria as Complication of Simvastatin Treatment.

Lancet 1990; 336: 1453-1453. Available at <Go to ISI>://A1990EL85100061.

26. Kostapanos MS, Milionis HJ, Saougos VG, Lagos KG, Christine K, Bairaktari ET, Elisaf MS. Dose-

dependent effect of rosuvastatin treatment on urinary protein excretion. J Cardiovasc Pharmacol Ther


27. Wehlou CMJ, Speeckaert MM, Fiers T, De Buyzere ML, Delanghe JR. alpha(1)-

Microglobulin/albumin ratio may improve interpretation of albuminuria in statin-treated patients.

Clinical Chemistry and Laboratory Medicine 2013; 51: 1529-1534. Available at <Go to

ISI>://WOS:000321104900031.

28. Levey AS, Atkins R, Coresh J, Cohen EP, Collins AJ, Eckardt KU, Nahas ME, Jaber BL, Jadoul M,

Levin A, Powe NR, Rossert J, Wheeler DC, Lameire N, Eknoyan G. Chronic kidney disease as a

global public health problem: Approaches and initiatives - a position statement from Kidney Disease

Improving Global Outcomes. Kidney International 2007; 72: 247-259. Available at <Go to

ISI>://000248394300007.









137

29. Wilson JMG, Jungner G. principles and practice of screening for disease. public health papers, Geneva,

World Health Organization 1968; 34: . Available at http://whqlibdoc.who.int/php/WHO_PHP_34.pdf.

30. Council of Europe. Recommendation no R(94))11 on screening as a tool of preventive medicine. 1994.

Available at

https://wcd.coe.int/com.instranet.InstraServlet?command=com.instranet.CmdBlobGet&InstranetImage

=534532&SecMode=1&DocId=514336&Usage=2.

31. de Jong PE, Curhan GC. Screening, monitoring, and treatment of albuminuria: Public health

perspectives. J Am Soc Nephrol 2006; 17: 2120-2126. Available at






33. Mahmoodi BK, Matsushita K, Woodward M, Blankestijn PJ, Cirillo M, Ohkubo T, Rossing P, Sarnak

MJ, Stengel B, Yamagishi K, Yamashita K, Zhang L, Coresh J, de Jong PE, Astor BC, Chronic Kidney

Disease Prognosis C. Associations of kidney disease measures with mortality and end-stage renal

disease in individuals with and without hypertension: a meta-analysis. Lancet 2012; 380: 1649-1661.


34. de Jong PE, van der Velde M, Gansevoort RT, Zoccali C. Screening for chronic kidney disease: Where

does Europe go? Clinical Journal of the American Society of Nephrology 2008; 3: 616-623. Available

at <Go to ISI>://000253709800041.

35. Collaborators USBoD. The state of US health, 1990-2010: burden of diseases, injuries, and risk factors.

JAMA 2013; 310: 591-608. Available at http://www.ncbi.nlm.nih.gov/pubmed/23842577.

36. Murray CJ, Richards MA, Newton JN, Fenton KA, Anderson HR, Atkinson C, Bennett D, Bernabe E,

Blencowe H, Bourne R, Braithwaite T, Brayne C, Bruce NG, Brugha TS, Burney P, Dherani M, Dolk

H, Edmond K, Ezzati M, Flaxman AD, Fleming TD, Freedman G, Gunnell D, Hay RJ, Hutchings SJ,

Ohno SL, Lozano R, Lyons RA, Marcenes W, Naghavi M, Newton CR, Pearce N, Pope D, Rushton L,

Salomon JA, Shibuya K, Vos T, Wang H, Williams HC, Woolf AD, Lopez AD, Davis A. UK health

performance: findings of the Global Burden of Disease Study 2010. Lancet 2013; 381: 997-1020.


37. Jerums G, Premaratne E, Panagiotopoulos S, MacIsaac RJ. The clinical significance of hyperfiltration

in diabetes. Diabetologia 2010; 53: 2093-2104. Available at


38. Mogensen CE. Prediction of clinical diabetic nephropathy in IDDM patients. Alternatives to

microalbuminuria? Diabetes 1990; 39: 761-767. Available at


39. Jones CA, Francis ME, Eberhardt MS, Chavers B, Coresh J, Engelgau M, Kusek JW, Byrd-Holt D,

Narayan KM, Herman WH, Jones CP, Salive M, Agodoa LY. Microalbuminuria in the US population:

third National Health and Nutrition Examination Survey. Am J Kidney Dis 2002; 39: 445-459.

Available at


=11877563.




41. Hovind P, Tarnow L, Rossing P, Jensen BR, Graae M, Torp I, Binder C, Parving HH. Predictors for

the development of microalbuminuria and macroalbuminuria in patients with type 1 diabetes: inception

cohort study. Bmj 2004; 328: 1105. Available at http://www.ncbi.nlm.nih.gov/pubmed/15096438.

42. de Boer IH, Rue TC, Cleary PA, Lachin JM, Molitch ME, Steffes MW, Sun W, Zinman B, Brunzell

JD, Diabetes C, Complications Trial/Epidemiology of Diabetes I, Complications Study Research G,

White NH, Danis RP, Davis MD, Hainsworth D, Hubbard LD, Nathan DM. Long-term renal outcomes

of patients with type 1 diabetes mellitus and microalbuminuria: an analysis of the Diabetes Control and

http://whqlibdoc.who.int/php/WHO_PHP_34.pdf













138

Complications Trial/Epidemiology of Diabetes Interventions and Complications cohort. Arch Intern

Med 2011; 171: 412-420. Available at http://www.ncbi.nlm.nih.gov/pubmed/21403038.

43. Glassock RJ. Is the Presence of Microalbuminuria a Relevant Marker of Kidney Disease? Curr Hypertens Rep 2010; 12: 364-368. Available at <Go to ISI>://WOS:000283145900009.

44. Hallan SI, Orth SR. The conundrum of chronic kidney disease classification and end-stage renal risk

prediction in the elderly--what is the right approach? Nephron Clin Pract 2010; 116: c307-316.






46. Mogensen CE. Microalbuminuria and hypertension with focus on type 1 and type 2 diabetes. Journal

of Internal Medicine 2003; 254: 45-66. Available at <Go to ISI>://000183583600006.






48. van der Velde M, Matsushita K, Coresh J, Astor BC, Woodward M, Levey A, de Jong P, Gansevoort

RT, Chronic Kidney Disease Prognosis C, van der Velde M, Matsushita K, Coresh J, Astor BC,

Woodward M, Levey AS, de Jong PE, Gansevoort RT, Levey A, El-Nahas M, Eckardt KU, Kasiske

BL, Ninomiya T, Chalmers J, Macmahon S, Tonelli M, Hemmelgarn B, Sacks F, Curhan G, Collins AJ,

Li S, Chen SC, Hawaii Cohort KP, Lee BJ, Ishani A, Neaton J, Svendsen K, Mann JF, Yusuf S, Teo

KK, Gao P, Nelson RG, Knowler WC, Bilo HJ, Joosten H, Kleefstra N, Groenier KH, Auguste P,

Veldhuis K, Wang Y, Camarata L, Thomas B, Manley T. Lower estimated glomerular filtration rate

and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative

meta-analysis of high-risk population cohorts. Kidney Int 2011; 79: 1341-1352. Available at


49. Weir MR. Microalbuminuria and cardiovascular disease. Clin J Am Soc Nephrol 2007; 2: 581-590.





51. Hui X, Matsushita K, Sang Y, Ballew SH, Fulop T, Coresh J. CKD and cardiovascular disease in the

Atherosclerosis Risk in Communities (ARIC) study: interactions with age, sex, and race. Am J Kidney

Dis 2013; 62: 691-702. Available at http://www.ncbi.nlm.nih.gov/pubmed/23769137.

52. Hallan SI, Matsushita K, Sang Y, Mahmoodi BK, Black C, Ishani A, Kleefstra N, Naimark D,

Roderick P, Tonelli M, Wetzels JF, Astor BC, Gansevoort RT, Levin A, Wen CP, Coresh J, Chronic

Kidney Disease Prognosis C. Age and association of kidney measures with mortality and end-stage

renal disease. JAMA 2012; 308: 2349-2360. Available at


53. de Jong PE, Hillege HL, Pinto-Sietsma SJ, de Zeeuw D. Screening for microalbuminuria in the general

population: a tool to detect subjects at risk for progressive renal failure in an early phase? Nephrology

Dialysis Transplantation 2003; 18: 10-13. Available at <Go to ISI>://000180293800002.

54. Karalliedde J, Viberti G. Proteinuria in diabetes: bystander or pathway to cardiorenal disease? J Am Soc Nephrol 2010; 21: 2020-2027. Available at http://www.ncbi.nlm.nih.gov/pubmed/21051738.





=15492322.














139

56. Gaede P, Vedel P, Larsen N, Jensen GVH, Parving H, Pedersen O. Multifactorial intervention and

cardiovascular disease in patients with type 2 diabetes. New Engl J Med 2003; 348: 383-393. Available

at <Go to ISI>://WOS:000180643800003.

57. Group AS, Cushman WC, Evans GW, Byington RP, Goff DC, Jr., Grimm RH, Jr., Cutler JA, Simons-

Morton DG, Basile JN, Corson MA, Probstfield JL, Katz L, Peterson KA, Friedewald WT, Buse JB,

Bigger JT, Gerstein HC, Ismail-Beigi F. Effects of intensive blood-pressure control in type 2 diabetes

mellitus. N Engl J Med 2010; 362: 1575-1585. Available at


58. Patel A, MacMahon S, Chalmers J, Neal B, Woodward M, Billot L, Harrap S, Poulter N, Marre M,

Cooper M, Glasziou P, Grobbee DE, Hamet P, Heller S, Liu LS, Mancia G, Mogensen CE, Pan CY,

Rodgers A, Williams B. Effects of a fixed combination of perindopril and indapamide on

macrovascular and microvascular outcomes in patients with type 2 diabetes mellitus (the ADVANCE

trial): a randomised controlled trial. Lancet 2007; 370: 829-840. Available at <Go to

ISI>://000249733300028.

59. Gaede P, Vedel P, Parving HH, Pedersen O. Intensified multifactorial intervention in patients with type

2 diabetes mellitus and microalbuminuria: the Steno type 2 randomised study. Lancet 1999; 353: 617-


60. Haller H, Ito S, Izzo JL, Jr., Januszewicz A, Katayama S, Menne J, Mimran A, Rabelink TJ, Ritz E,

Ruilope LM, Rump LC, Viberti G, Investigators RT. Olmesartan for the delay or prevention of

microalbuminuria in type 2 diabetes. N Engl J Med 2011; 364: 907-917. Available at


61. de Galan BE, Perkovic V, Ninomiya T, Pillai A, Patel A, Cass A, Neal B, Poulter N, Harrap S,

Mogensen CE, Cooper M, Marre M, Williams B, Hamet P, Mancia G, Woodward M, Glasziou P,

Grobbee DE, MacMahon S, Chalmers J, Group AC. Lowering blood pressure reduces renal events in

type 2 diabetes. Journal of the American Society of Nephrology 2009; 20: 883-892. Available at






=10675071.

63. Schmieder RE, Mann JF, Schumacher H, Gao P, Mancia G, Weber MA, McQueen M, Koon T, Yusuf

S, Investigators O. Changes in albuminuria predict mortality and morbidity in patients with vascular

disease. J Am Soc Nephrol 2011; 22: 1353-1364. Available at


64. Ibsen H, Olsen MH, Wachtell K, Borch-Johnsen K, Lindholm LH, Mogensen CE, Dahlof B, Devereux

RB, de Faire U, Fyhrquist F, Julius S, Kjeldsen SE, Lederballe-Pedersen O, Nieminen MS, Omvik P,

Oparil S, Wan Y. Reduction in albuminuria translates to reduction in cardiovascular events in

hypertensive patients: losartan intervention for endpoint reduction in hypertension study. Hypertension


65. Estacio RO, Dale RA, Schrier R, Krantz MJ. Relation of reduction in urinary albumin excretion to ten-

year cardiovascular mortality in patients with type 2 diabetes and systemic hypertension. Am J Cardiol 2012; 109: 1743-1748. Available at http://www.ncbi.nlm.nih.gov/pubmed/22440125.

66. Schrader J, Luders S, Kulschewski A, Hammersen F, Zuchner C, Venneklaas U, Schrandt G,

Schnieders M, Rangoonwala B, Berger J, Dominiak P, Zidek W, Group MS. Microalbuminuria and

tubular proteinuria as risk predictors of cardiovascular morbidity and mortality in essential

hypertension: final results of a prospective long-term study (MARPLE Study)*. J Hypertens 2006; 24:


67. Yusuf S, Teo KK, Pogue J, Dyal L, Copland I, Schumacher H, Ingelheim B, Dagenais G, Sleight P,

Anderson C, Investigators O. Telmisartan, ramipril, or both in patients at high risk for vascular events.

New Engl J Med 2008; 358: 1547-1559. Available at <Go to ISI>://WOS:000254783300004.

68. Jafar TH, Schmid CH, Landa M, Giatras I, Toto R, Remuzzi G, Maschio G, Brenner BM, Kamper A,

Zucchelli P, Becker G, Himmelmann A, Bannister K, Landais P, Shahinfar S, de Jong PE, de Zeeuw D,












140

Lau J, Levey AS. Angiotensin-converting enzyme inhibitors and progression of nondiabetic renal

disease. A meta-analysis of patient-level data. Ann Intern Med 2001; 135: 73-87. Available at


69. Randomised placebo-controlled trial of effect of ramipril on decline in glomerular filtration rate and

risk of terminal renal failure in proteinuric, non-diabetic nephropathy. The GISEN Group (Gruppo

Italiano di Studi Epidemiologici in Nefrologia). Lancet 1997; 349: 1857-1863. Available at


70. Jerums G, Panagiotopoulos S, Premaratne E, Power DA, Maclsaac RJ. Lowering of proteinuria in

response to antihypertensive therapy predicts improved renal function in late but not in early diabetic

nephropathy: A pooled analysis. American Journal of Nephrology 2008; 28: 614-627. Available at

<Go to ISI>://000255896200011.

71. Brenner BM, Cooper ME, de Zeeuw D, Keane WF, Mitch WE, Parving HH, Remuzzi G, Snapinn SM,

Zhang Z, Shahinfar S, Investigators RS. Effects of losartan on renal and cardiovascular outcomes in

patients with type 2 diabetes and nephropathy. N Engl J Med 2001; 345: 861-869. Available at


72. Lewis EJ, Hunsicker LG, Bain RP, Rohde RD. The effect of angiotensin-converting-enzyme inhibition

on diabetic nephropathy. The Collaborative Study Group. N Engl J Med 1993; 329: 1456-1462.


73. Lewis EJ, Hunsicker LG, Clarke WR, Berl T, Pohl MA, Lewis JB, Ritz E, Atkins RC, Rohde R, Raz I,

Collaborative Study G. Renoprotective effect of the angiotensin-receptor antagonist irbesartan in

patients with nephropathy due to type 2 diabetes. N Engl J Med 2001; 345: 851-860. Available at


74. Ruggenenti P, Perna A, Mosconi L, Matalone M, Garini G, Salvadori M, Zoccali C, Scolari F,

Maggiore Q, Tognoni G, Remuzzi G, Migone L, Marubini E, DelFavero A, Ideo G, Geraci E, Loi U,

Bracchi M, Costantino E, Scolari F, Maiorca R, Cofano F, Fellin G, DAmico G, Dissegna D,

Brendolan A, LaGreca G, Feriozzi A, Ancarani E, Gandini E, dAmato I, Giangrande A, Garneri G,

Giacchino F, Giannico G, Giotta N, Vitale O, Manno C, Mazzi A, Garini G, Borghetti A, Pisoni R,

Bertani T, Novelli R, Scanferla F, Bazzato G, Oliva E, Zoccali C, Pieri F, Sisca S, Maggiore Q,

Pignone E, Boero R, Piccoli G, Piperno R, Rosati A, Salvadori M, Arnoldi F, Gaspari F, Signorini O,

Ferrari S, Guerini E. Randomised placebo-controlled trial of effect of ramipril on decline in glomerular

filtration rate and risk of terminal renal failure in proteinuric, non-diabetic nephropathy. Lancet 1997;

349: 1857-1863. Available at <Go to ISI>://WOS:A1997XG41700007.

75. Bakris GL, Williams M, Dworkin L, Elliott WJ, Epstein M, Toto R, Tuttle K, Douglas J, Hsueh W,

Sowers J, Diab NKFH. Preserving renal function in adults with hypertension and diabetes: A

consensus approach. American Journal of Kidney Diseases 2000; 36: 646-661. Available at <Go to

ISI>://000089227300027.

76. Caskey FJ, Kramer A, Elliott RF, Stel VS, Covic A, Cusumano A, Geue C, Macleod AM, Zwinderman

AH, Stengel B, Jager KJ. Global variation in renal replacement therapy for end-stage renal disease.

Nephrol Dial Transplant 2011; 26: 2604-2610. Available at


77. Orlando LA, Belasco EJ, Patel UD, Matchar DB. The chronic kidney disease model: a general purpose

model of disease progression and treatment. BMC medical informatics and decision making 2011; 11:


78. Williams AW, Dwyer AC, Eddy AA, Fink JC, Jaber BL, Linas SL, Michael B, O'Hare AM, Schaefer

HM, Shaffer RN, Trachtman H, Weiner DE, Falk RJ, Quality ASN. Critical and Honest Conversations:

The Evidence Behind the "Choosing Wisely" Campaign Recommendations by the American Society

of Nephrology. Clinical Journal of the American Society of Nephrology 2012; 7: 1664-1672. Available

at <Go to ISI>://WOS:000310036800018.

79. Tong A, Cheung KL, Nair SS, Kurella Tamura M, Craig JC, Winkelmayer WC. Thematic Synthesis of

Qualitative Studies on Patient and Caregiver Perspectives on End-of-Life Care in CKD. Am J Kidney Dis 2014. Available at http://www.ncbi.nlm.nih.gov/pubmed/24411716.

80. Rothman KJ. Epidemiology an introduction. Oxford university press 2002: 202-203.










141

81. Boulware LE, Jaar BG, Tarver-Carr ME, Brancati FL, Powe NR. Screening for proteinuria in US

adults - A cost-effectiveness analysis. Jama-Journal of the American Medical Association 2003; 290:

3101-3114. Available at <Go to ISI>://WOS:000187277300029.

82. Hoerger TJ, Wittenborn JS, Segel JE, Burrows NR, Imai K, Eggers P, Pavkov ME, Jordan R, Hailpern

SM, Schoolwerth AC, Williams DE, Centers for Disease C, Prevention CKDI. A health policy model

of CKD: 2. The cost-effectiveness of microalbuminuria screening. Am J Kidney Dis 2010; 55: 463-473.


83. Kessler R, Keusch G, Szucs TD, Wittenborn JS, Hoerger TJ, Brugger U, Wieser S. Health economic

modelling of the cost-effectiveness of microalbuminuria screening in Switzerland. Swiss medical

weekly 2012; 142: w13508. Available at http://www.ncbi.nlm.nih.gov/pubmed/22307760.

84. Palmer AJ, Valentine WJ, Chen R, Mehin N, Gabriel S, Bregman B, Rodby RA. A health economic

analysis of screening and optimal treatment of nephropathy in patients with type 2 diabetes and

hypertension in the USA. Nephrology Dialysis Transplantation 2008; 23: 1216-1223. Available at <Go

to ISI>://WOS:000254472400024.

85. Simmons RK, Echouffo-Tcheugui JB, Sharp SJ, Sargeant LA, Williams KM, Prevost AT, Kinmonth

AL, Wareham NJ, Griffin SJ. Screening for type 2 diabetes and population mortality over 10 years

(ADDITION-Cambridge): a cluster-randomised controlled trial. Lancet 2012; 380: 1741-1748.


86. Griffin SJ, Borch-Johnsen K, Davies MJ, Khunti K, Rutten GE, Sandbaek A, Sharp SJ, Simmons RK,

van den Donk M, Wareham NJ, Lauritzen T. Effect of early intensive multifactorial therapy on 5-year

cardiovascular outcomes in individuals with type 2 diabetes detected by screening (ADDITION-

Europe): a cluster-randomised trial. Lancet 2011; 378: 156-167. Available at


87. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, Nathan DM,

Diabetes Prevention Program Research G. Reduction in the incidence of type 2 diabetes with lifestyle

intervention or metformin. N Engl J Med 2002; 346: 393-403. Available at


88. Tuomilehto J, Lindstrom J, Eriksson JG, Valle TT, Hamalainen H, Ilanne-Parikka P, Keinanen-

Kiukaanniemi S, Laakso M, Louheranta A, Rastas M, Salminen V, Uusitupa M, Finnish Diabetes

Prevention Study G. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with

impaired glucose tolerance. N Engl J Med 2001; 344: 1343-1350. Available at


89. Chamnan P, Simmons RK, Khaw KT, Wareham NJ, Griffin SJ. Estimating the potential population

impact of stepwise screening strategies for identifying and treating individuals at high risk of Type 2

diabetes: a modelling study. Diabet Med 2012; 29: 893-904. Available at


90. Glumer C, Carstensen B, Sandbaek A, Lauritzen T, Jorgensen T, Borch-Johnsen K, inter s. A Danish

diabetes risk score for targeted screening: the Inter99 study. Diabetes Care 2004; 27: 727-733.


91. Grosse SD. Assessing cost-effectiveness in healthcare: history of the $50,000 per QALY threshold.

Expert review of pharmacoeconomics & outcomes research 2008; 8: 165-178. Available at


92. Springberg PD, Garrett LE, Jr., Thompson AL, Jr., Collins NF, Lordon RE, Robinson RR. Fixed and

reproducible orthostatic proteinuria: results of a 20-year follow-up study. Ann Intern Med 1982; 97:


93. Parving HH, Chaturvedi N, Viberti G, Mogensen CE. Does microalbuminuria predict diabetic

nephropathy? Diabetes Care 2002; 25: 406-407. Available at


94. Hallan SI, Ritz E, Lydersen S, Romundstad S, Kvenild K, Orth SR. Combining GFR and Albuminuria

to Classify CKD Improves Prediction of ESRD. Journal of the American Society of Nephrology 2009;

20: 1069-1077. Available at <Go to ISI>://000265959800020

http://jasn.asnjournals.org/cgi/reprint/20/5/1069.pdf.












http://jasn.asnjournals.org/cgi/reprint/20/5/1069.pdf


142








143

3.7 Conclusies

Deze thesis omvat twee studies waarbij het opsporen van cardiovasculaire en renale

risicofactoren met behulp van microalbuminurie ter discussie staat. In de URI studie werden

arbeiders geïncludeerd door arbeidsgeneeskundigen en in de ERICABEL studie werden

hypertensie patiënten geïncludeerd door huisartsen.

Een screeningsprogramma om arbeiders op te sporen met cardiovasculaire en renale

risicofactoren met behulp van een bepaling van albumine in een urinestaal leek niet zinvol

wegens de volgende redenen. Ten eerste, slechts een klein deel (9%) van de personen met

ongekende cardiovasculaire risicofactoren werd opgespoord met behulp van detectie van

microalbuminurie. Door traditionele cardiovasculaire risicofactoren (bloeddruk, hartslag,

bloedglucosespiegel) direct op te sporen, konden veel meer personen (36%) worden gedetecteerd

met meer mogelijkheden om nier-, hart- en vaatziekten te voorkomen dan door het opsporen van

microalbuminurie alleen. Ten tweede, zijn er ook andere onderliggende oorzaken van

microalbuminurie dan louter cardiovasculair risicofactoren. Bijvoorbeeld, de aanwezigheid van

orthostatische albuminurie heeft een goede prognose, daar waar de aanwezigheid van

albuminurie bij diabetici een hoog risico voor macroalbuminurie en vervolgens nierfalen heeft (1,

2). Arbeiders die blootgesteld werden aan fysisch-chemische agentia hadden een hogere

prevalentie van microalbuminuria, onafhankelijk van hun cardiovasculaire risicofactoren. Om de

prognostische waarde van microalbuminurie bij arbeiders met fysisch-chemische blootstelling te

investigeren is een longitudinale studie nodig. Eveneens ontbreekt er een studie waarbij de

prognose van personen met de verschillende onderliggende oorzaken van microalbuminurie met

elkaar worden vergeleken. Mogelijks zullen interventies bij personen met microalbuminurie in

afwezigheid van cardiovasculaire risicofactoren aanleiding geven tot frustratie, angst,

psychologische schade, neveneffecten van onnodig behandelingen en toename van medische

kosten, terwijl interventies bij patiënten met microalbuminurie in aanwezigheid van behandelbare

risicofactoren aanleiding zouden kunnen geven tot een verbetering van de klinische prognose.

De aanwezigheid van microalbuminurie was geassocieerd met een mager gestalte (orthostatische

albuminurie) en aan fysisch-chemische blootstelling in arbeiders jonger dan 40 jaar, terwijl de

aanwezigheid van microalbuminurie was geassocieerd met cardiovasculaire risicofactoren in

arbeiders ouder dan 40 jaar. Een populatiescreening voor microalbuminurie om cardiovasculaire


144

en renale risicofactoren op te sporen lijkt dus pas zinvol vanaf het 40ste

levensjaar. Doch, er is

geen rechtstreeks bewijs dat het opsporen en vervolgens behandelen van microalbuminurie

geassocieerd is met een significante daling van hart- en vaatziekten (4, 5). Het opsporen van

microalbuminurie zou zinvol kunnen worden als nieuwe behandelingen ter beschikking worden

gesteld om hart- en vaatziekten te voorkomen. Alhoewel, personen met microalbuminurie een

verhoogd relatief risico vertonen om nierfalen te ontwikkelen, is het absolute risico te laag om

een populatiescreening te verantwoorden. De predictieve waarde van microalbuminurie of

macroalbuminurie om nierfalen te ontwikkelen stijgt aanzienlijk bij personen met een geschatte

eGFR < 60 ml/min/1.73m² (3). Omdat de prevalentie van een geschatte GFR < 60 ml/min/1.73m²

toeneemt met de leeftijd, zou een screening om personen met een verhoogd risico op ESRD te

identificeren zinvol kunnen zijn vanaf een bepaalde leeftijd (bv. 55 jaar). In de URI populatie,

waar bijna iedereen een geschatte GFR > 60 ml/min/1.73m² had (jonge arbeiders populatie) is de

predictieve waarde van microalbuminurie om nierfalen te ontwikkelen dus veel te zwak.

Volgens de URI studie is het opsporen van microalbuminurie effectiever bij arbeiders met versus

zonder cardiovasculaire risicofactoren om bijvoorbeeld nierschade te kunnen detecteren. Het

opsporen van microalbuminuria in aanwezigheid van risicofactoren kan invloed hebben op de

behandeling. Bij patiënten met diabetes is het opsporen van microalbuminurie zinvol omdat een

behandeling met ACE-I of ARB, de incidentie van macroalbuminurie en vervolgens nierfalen

reduceert (1). Alhoewel, het opsporen van microalbuminurie momenteel minder efficiënt is

omdat meer en meer patiënten reeds worden behandeld met een ACE-I of ARB, onafhankelijk

van de aanwezigheid van albuminurie (5). De afwezigheid van microalbuminurie bij patiënten die

behandeld worden met een ACE-I sluit nierschade niet uit, terwijl de aanwezigheid van

microalbuminurie in patiënten die behandeld worden met een statine niet altijd geassocieerd is

met nierschade. De ERICABEL cohorte observeerde dat het gebruik van een statine geassocieerd

was met microalbuminurie. Mogelijks doordat statines tubulaire proteinurie kunnen genereren

door inhibitie van tubulaire reabsorptie. De aanwezigheid van microalbuminurie bij patiënten die

een statine gebruiken zou aanleiding kunnen geven tot het incorrect vaststellen van een nierziekte.

Meer prospectieve studies zijn nodig om de prognostische waarde van microalbuminurie in

patiënten die worden behandeld met een statine in kaart te brengen.


145

De URI studie toonde dat enkel personen met persisterend microalbuminurie geassocieerd waren

aan een verhoogd cardiovasculair risico. De aanwezigheid van microalbuminurie dient dus te

worden bevestigd in een nieuw urinestaal, omdat tijdelijke omstandigheden (zoals fysische

inspanning, koorts, stress e.a.) aanleiding kunnen geven tot een vals positieve meting. Volgens

KDIGO, heeft iedereen met microalbuminurie dat langer dan 3 maanden persisteert CKD stadium

A2. Hieruit volgt dat de prevalentie van CKD stadium A2 in cohorten overschat wordt wanneer

slechts éénmalig microalbuminurie wordt gemeten. Bovendien, zal bij de helft van de diabetici

met microalbuminurie “deze aandoening” verdwijnen in functie van de tijd (6). In de URI studie

werd bij personen zonder risicofactoren frequent fluctuerende microalbuminurie waargenomen.

Het fluctuerende karakter van microalbuminurie werd ook geobserveerd in de ERICABEL

cohorte (nog niet gepubliceerd).

Een vermindering van de nierfunctie wordt voornamelijk gezien bij personen met

macroalbuminurie terwijl dit minder het geval is bij personen met microalbuminurie. Hieruit, zou

men kunnen concluderen dat het opsporen van personen met macroalbuminurie effectiever is om

nierinsufficiëntie te detecteren dan het opsporen van personen met microalbuminurie. Echter de

kosten van een massascreening voor albuminurie worden hoger geschat dan de baten. De

screeningskosten kunnen worden gereduceerd door een screeningsinterval van 10 jaar te hanteren.

Echter, hierdoor zullen personen die macroalbuminurie ontwikkelen op enkele jaren niet

gedetecteerd kunnen worden. Of een screeningsprogramma aanleiding zou kunnen geven om de

incidentie van ESRD te reduceren door bepaling van albuminurie en een schatting van de GFR

(door serumcreatinine en/of Cystatine C te meten) in personen ouder dan bv. 55 jaar dient in de

toekomst onderzocht te worden.


146

3.8 Referenties

1. Parving HH, Chaturvedi N, Viberti G, Mogensen CE. Does microalbuminuria predict diabetic

nephropathy? Diabetes Care 2002; 25: 406-407. Available at


2. Springberg PD, Garrett LE, Jr., Thompson AL, Jr., Collins NF, Lordon RE, Robinson RR. Fixed and

reproducible orthostatic proteinuria: results of a 20-year follow-up study. Ann Intern Med 1982; 97:


3. Hallan SI, Ritz E, Lydersen S, Romundstad S, Kvenild K, Orth SR. Combining GFR and Albuminuria

to Classify CKD Improves Prediction of ESRD. Journal of the American Society of Nephrology 2009;

20: 1069-1077. Available at <Go to ISI>://000265959800020

http://jasn.asnjournals.org/cgi/reprint/20/5/1069.pdf.





=15492322.











http://jasn.asnjournals.org/cgi/reprint/20/5/1069.pdf





149

Dankwoord

Dit proefschrift is grotendeels gebaseerd op epidemiologisch onderzoek dat dankzij de volgende

enthousiaste protagonisten tot stand is gekomen.

Prof. Dr. Em. Raymond Vanholder en Prof. Dr. Van Biesen van de dienst nefrologie UZG, wil ik

bijzonder bedanken voor de mogelijkheden om de URI studie te initiëren. Dank voor jullie

engelengeduld en de investering die nodig bleek om mij tot het niveau te krijgen om A1

publicaties te schrijven. Het is jullie verdiensten geweest om de teksten van mijn chaotisch brein

te structuren tot een leesbaar en verstaanbaar geheel.

Dr. Frans Vermeiren, directeur van Adhesia en schoonpapa, wil ik graag bedanken voor de

opportuniteit om de URI studie uit te voeren in zijn bedrijf. Ook dient vermeld te worden dat veel

studiepersonen door hem zelf werden geïncludeerd. Zijn interesse in de geneeskunde uitte zich

door bespreking van individuele afwijkende resultaten die door de URI studie aan het licht

kwamen. Dr. Kathleen Eeckhaut, wil ik graag bedanken voor haar praktische inzicht, ijverige

inzet en enthousiaste betrokkenheid om de URI studie te initiëren en de arbeiders ook

daadwerkelijk te includeren. Eveneens, dank ik alle andere dokters en verpleegkundigen die

geholpen hebben om de arbeiders te includeren. Dr. Guy De Groote, directeur van CRI labo, wil

ik bedanken voor zijn wetenschappelijke kennis en inzicht bij het initiëren van de URI studie.

Prof. Dr. Kris Bogaerts, statisticus verbonden aan KULeuven, voor zijn bijdrage aan het

manuscript ‘microalbuminuria and statins”. Dr. Hans Warrinnier, medical director bij Hoffmann-

La Roche voor de samenwerking in kader van de ERICABEL studie.

De leden van examencommissie, Prof. Dr. Joris R. Delange, Prof. Dr. Jan De Maeseneer,

Prof. Dr. Johannes Ruige, Dr. Johan De Meester, Dr. Marijn Speeckaert en Dr. Kitty Jager, voor

het kritisch nalezen en hun constructieve suggesties.

Graag richt ik ook het woord aan mijn collega’s: Prof. Dr. Nic Veys, Katrien Blanckaert,

Prof. Dr. Annemie Dhondt, Dr. Patrick Peeters, Prof. Dr. Francis Verbeke en Prof. in spe

Dr. Van Laecke en Dr. Marijn Speeckaert, zijn ieder op zich een groot voorbeeld voor mij en

verrijking van de dienst nefrologie UZG. Idem dito voor Jill Vanmassenhove, Nathalie Neirynck

en Evi Nagler die binnenkort hun proefschrift succesvol zullen voltooien.

150

Een speciale dank voor Christel voor mijn vragen en te helpen met “WORD” en Chantal om er

een echt feest van te maken.

Last but not least, wil ik Vanessa, Dr., Prof., mama en mijn levensgezel bedanken voor je morele

steun. Aanvankelijk was ik voor jou een voorbeeld en met onze tijd keerde dit om, doch met

blijvend wederzijds respect. Ik dank je om mijn chaotische denkpaden te simplificeren tot een

beter verstaanbaar persoon, al blijven mijn gedachtenkronkels zoals de jouwe bestaan. Ik dank

mijn familie, voor hun steun aan hun ontwortelde zoon en broer. Ik dank tante Sonja om al die

jaren voor het huishouden te zorgen. Tenslotte is dit werk speciaal opgedragen aan mijn

belangrijkste creaturen: Laura en Arthur, zolang ik voor hen als voorbeeld kan dienen.

Mijn excuses aan hen die ik in bovenstaand dankwoord vergat; ik vraag hen om begrip voor de

vergetelheid.

Arjan 2014

151

CURRICULUM VITAE

Arjan van der Tol

1 PERSONALIA

SURNAME Van der Tol

FIRST NAME Arjan

PLACE of BIRTH Alphen a/d Rijn, The Netherlands

DATE of BIRTH July 22th, 1971

PROFESSIONAL ADRESS Renal Division, Department: Internal Medicine

Gent University Hospital

De Pintelaan 185

B-9000 Gent

Tel: +32-(0)9-3326665

2 QUALIFICATIONS

ACADEMIC STUDIES IN THE MEDICAL SCIENCES

Legal degree of Bachelor of Medicine, cum laude, granted by the State University Antwerp,

Belgium, 1995

Legal degree of Doctor of Medicine, Surgery and Obstetrics, cum laude, granted by the State

University of Antwerp, Belgium, 1999

Legal degree of Specialist in Internal Medicine recognized as such by the Department of Social

Affairs, Public Health and Environment of Belgium, 2004

Legal degree of Specialist in Nephrology recognized as such by the Department of Social Affairs,

Public Health and Environment of Belgium, 2005

Working in Renal Division, Ghent University Hospital, Belgium, since 2003

152

3 MEMBERSHIP

Renal Disaster Relief Task Force (RDRTF) as volunteer doctor in:

Kashmir earthquake (Islamabad October 2005)

Yogyakarta earthquake (Java May 2006)

Chicuan earthquake (Chengdu May 2008)

Padang earthquake (Sumatra October 2009)

EDTA-ERA: member

4 SCIENTIFIC PUBLICATIONS

ARTICLES IN INTERNATIONAL JOURNALS WITH REFEREE SYSTEM

Van der Tol A, Hussain A, Sever MS, Claus S, Van Biesen W, Hoste E, Khan S, Vanholder R.

Impact of local circumstances on outcome of renal casualties in major disasters. Nephrol Dial

Transplant. 2009 Mar;24(3):907-12. Epub 2008 Oct 8. Available at:

http://ndt.oxfordjournals.org/content/24/3/907.full.pdf+html

Van der Tol A, Van Biesen W, Verbeke F, De Groote G, Vermeiren F, Eeckhaut K, Vanholder R.

Towards a rational screening strategy for albuminuria: results from the unreferred renal

insufficiency trial. PLoS ONE. 13 Oct 2010. Available at:


Van der Tol A, Van Biesen W, Bogaerts K, Van Laecke S, De Lombaert K, Warrinnier H and

Vanholder R (2012) Statin use and the presence of microalbuminuria. Results from the

ERICABEL trial: a non interventional epidemiological cohort study. PLoS ONE: 16 Feb 2012.


Van der Tol A, Van Biesen W, De Groote G, Verbeke P, Vermeiren F, Eeckhaut K, Vanholder R.

Microalbuminuria is more consistent in presence of cardiovascular risk factors: Results from the

Unreferred Renal Insufficiency Trial..J Nephrol, May-Jun 2013, 26(3): 580-5. Available at:



http://ndt.oxfordjournals.org/content/24/3/907.full.pdf+html

http://clinicaltrials.gov/ct2/bye/zQoPWw4lZX-i-iSxuBcyeXNxvdDxuQ7Ju6c9cXcHuioyzTp9ai7HSTDxNBciescgm64LD61PSQ7Hc6D65B0LVi7yg67VN6h9Ei4L3BUgWwNG0iY6vQ1gW1-He6oR9R0txg0wSgCV.






153

Van der Tol A, Van Biesen W, De Groote G, Verbeke P, Vermeiren F, Eeckhaut K, Vanholder R.

Should screening of renal markers be recommended in a working population? Int. Urology

Nephrology 2014 Aug 46(8): 1601-8. Available at:


ARTICLES IN NATIONAL JOURNALS

Van der Tol A, E pelzers, G Wyffels, J Mareels, H Verbraeken. Non β-cel tumour induced

hypoglycemia. Tijdschrift voor Geneeskunde 2006; 62, p 299-305.

Van der Tol A, Dhondt A diabetische nefropathie. Tijdschrift voor Geneeskunde 2012; 68, p

1089-1094.

5 ABSTRACTS

Van der Tol A, Van Biesen W, De Groote G, Vermeiren F, Eeckhaut K, Vanholder R. High

prevalence of cardiovascular risk factors but not of CKD stage 3-5 in healthy worker. M302.

World Congress of Nephrology 2009, Milan, Italy

Van der Tol A, Van Biesen W, De Groote G, Vermeiren F, Eeckhaut K, Vanholder R. Do women

with CKD stage 3 as estimated by MDRD really have CKD? M272. World Congress of

Nephrology 2009, Milan, Italy

Van der Tol A, Van Biesen W, De Groote G, Paul Verbeke, Eeckhaut K, Vanholder R.

Mislabeling cardiovascular risk by one single measurement of microalbuminuria

ERA-EDTA, 2011, Praag, Czech.

Van der Tol, A, Van Biesen W, De Groote G, Vermeiren F, Verbeke P, Vanholder R.

Albuminuria is strongly associated with low BMI and only weakly to cardiovascular risk factors

in apparently healthy subjects younger than forty. ASN 2011, Philadelphia, VS.

Van der Tol A, Van Biesen W, De Groote G, Vermeiren F, Eeckhaut K, Vanholder R. Is Cystatin

C an independent biomarker to predict renal function in a general population? Annual Scientific

Meeting of the BVN/SBN, UCL Brussel, 23/04/2009


Pitfalls of using microalbuminuria as a screening tool to ...

Documents

Transcript of Pitfalls of using microalbuminuria as a screening tool to ...