The role of polycystic ovary syndrome (PCOS) and overweight ...

UNIVERSITY OF OULU P .O. Box 8000 F I -90014 UNIVERSITY OF OULU FINLAND

A C T A U N I V E R S I T A T I S O U L U E N S I S

University Lecturer Tuomo Glumoff

University Lecturer Santeri Palviainen

Senior research fellow Jari Juuti

Professor Olli Vuolteenaho

University Lecturer Veli-Matti Ulvinen

Planning Director Pertti Tikkanen

Professor Jari Juga

University Lecturer Anu Soikkeli


Publications Editor Kirsti Nurkkala

ISBN 978-952-62-2258-5 (Paperback)ISBN 978-952-62-2259-2 (PDF)ISSN 0355-3221 (Print)ISSN 1796-2234 (Online)

U N I V E R S I TAT I S O U L U E N S I S

MEDICA

ACTAD

D 1513

ACTA

Meri-M

aija Ollila

OULU 2019

D 1513

Meri-Maija Ollila

THE ROLE OF POLYCYSTIC OVARY SYNDROME (PCOS) AND OVERWEIGHT/OBESITY IN WOMEN’S METABOLIC AND CARDIOVASCULAR RISK FACTORS AND RELATED MORBIDITIES

UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU, FACULTY OF MEDICINE;OULU UNIVERSITY HOSPITAL

ACTA UNIVERS ITAT I S OULUENS I SD M e d i c a 1 5 1 3

MERI-MAIJA OLLILA


Academic dissertation to be presented with the assent ofthe Doctoral Training Committee of Health andBiosciences of the University of Oulu for public defence inAuditorium 4 of Oulu University Hospital, on 7 June2019, at 12 noon

UNIVERSITY OF OULU, OULU 2019

Copyright © 2019Acta Univ. Oul. D 1513, 2019

ISSN 0355-3221 (Printed)ISSN 1796-2234 (Online)

Cover DesignRaimo Ahonen

JUVENES PRINTTAMPERE 2019

Supervised byDocent Laure Morin-Papunen Docent Terhi Piltonen

Reviewed byProfessor Risto KaajaDocent Oskari Heikinheimo

OpponentProfessor Joop S. E. Laven

ISBN 978-952-62-2258-5 (Paperback) ISBN 978-952-62-2259-2 (PDF)

Ollila, Meri-Maija, The role of polycystic ovary syndrome (PCOS) and overweight/obesity in women’s metabolic and cardiovascular risk factors and relatedmorbidities. University of Oulu Graduate School; University of Oulu, Faculty of Medicine; Oulu UniversityHospitalActa Univ. Oul. D 1513, 2019University of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland

Abstract

Polycystic ovary syndrome (PCOS) is the most common endocrine disorder affecting reproductiveaged women, with reproductive, metabolic and cardiovascular implications across the life span.The typical features of PCOS include irregular menstruation, androgen excess and polycysticovaries in ultrasonography. The majority of women with PCOS are overweight or obese, and, atleast partly, obesity-driven metabolic abnormalities often coexist with PCOS. Despite intensiveresearch, it has remained unclear whether PCOS per se is a risk factor of metabolic abnormalities,and cardiovascular disease and events.

The main aim of the current work was to investigate whether PCOS is an independent riskfactor of metabolic abnormalities and cardiovascular diseases. The study population consisted ofthe prospective population-based Northern Finland Birth Cohort 1966, and we used data collectedat ages 14, 31 and 46. The definition of PCOS was based on self-reported PCOS symptoms at age31 and/or PCOS diagnosis by age 46.

The results revealed that weight gain in early life was a risk factor for the development ofPCOS. As for metabolic outcomes, at age 46, normal-weight women with PCOS did not displayincreased odds of abnormal glucose metabolism. However, weight gain during early adulthoodwas significantly associated with abnormal glucose metabolism in women with PCOS by age 46.Interestingly, PCOS per se was already associated with elevated blood pressure at age 31 andhypertension at age 46, independently of obesity. Women with PCOS also displayed reducedcardiac vagal activity, which was associated with metabolic abnormalities and hypertension.Furthermore, even though no major anatomical or functional impairments were observed inechocardiography, women with PCOS displayed a significantly greater prevalence of myocardialinfarction and a two-fold higher prevalence of cardiovascular events than controls.

In conclusion, our findings indicate that even though PCOS is an independent risk factor ofmetabolic derangements, related obesity is a major metabolic risk factor in these women. The roleof PCOS in cardiovascular events per se remains controversial and requires follow-up of thiscohort. Given all this, maintaining normal weight and preventing weight gain, especially duringearly adulthood, should be the main priority in the prevention of adverse metabolic changes inwomen with PCOS.

Keywords: autonomic nervous system, body mass index, cardiovascular diseases,hyperandrogenism, hypertension, metabolism, polycystic ovary syndrome, prediabetes,type 2 diabetes

Ollila, Meri-Maija, Munasarjojen monirakkulaoireyhtymän ja ylipainon merkitysnaisten metabolisiin ja kardiovaskulaarisiin riskitekijöihin ja tauteihin. Oulun yliopiston tutkijakoulu; Oulun yliopisto, Lääketieteellinen tiedekunta; Oulunyliopistollinen sairaalaActa Univ. Oul. D 1513, 2019Oulun yliopisto, PL 8000, 90014 Oulun yliopisto

Tiivistelmä

Munasarjojen monirakkulaoireyhtymä (polycystic ovary syndrome, PCOS) on lisääntymisikäis-ten naisten yleisin hormonaalinen häiriö aiheuttaen runsaasti sairastavuutta ja terveydenhuollonkustannuksia. PCOS:n diagnostisiin kriteereihin kuuluvat epäsäännöllinen kuukautiskierto,lisääntynyt miessukupuoli-hormonivaikutus sekä monirakkulaiset munasarjat. Merkittävä osaoireyhtymää sairastavista naisista on ylipainoisia tai lihavia ja oireyhtymän kanssa yhtä aikaaesiintyykin useita, ainakin osittain ylipainosta johtuvia, metabolisia häiriöitä. Lukuisista tutki-muksista huolimatta on kuitenkin epäselvää, altistaako PCOS itsessään metabolisille häiriöillesekä sydän- ja verisuonisairauksille.

Väitöskirjatutkimuksen tavoitteena oli selvittää, onko PCOS itsenäinen metabolisten jasydän- ja verisuonisairauksien riskiä lisäävä tekijä. Tutkimus pohjautui Pohjois-Suomen synty-mäkohortti 1966 tutkimuksen 14-, 31- ja 46-vuotisseurantoihin. PCOS luokittelu perustui 31- ja46-vuotiskyselyissä itse ilmoitettuihin tyypillisiin PCOS oireisiin ja/tai diagnoosiin.

Tutkimuksessa havaittiin, että 14- ja 31-ikävuoden välillä tapahtuva painonnousu oli yhtey-dessä PCOS diagnoosiin myöhemmällä iällä. 46-vuotiaana normaalipainoisilla PCOS naisilla eiollut suurentunut tyypin 2 diabetes riski, mutta painonnousu varhaisaikuisuudessa oli merkittä-västi yhteydessä sokeriaineenvaihdunnan häiriöön PCOS naisilla. PCOS oli yhteydessä kohon-neeseen verenpaineeseen 31-vuotiaana ja hypertensioon 46-vuotiaana ylipainosta riippumatta.Oireyhtymään liittyvät metaboliset häiriöt olivat tärkein sydämen autonomisen hermoston sääte-lyyn vaikuttava tekijää, kun taas PCOS itsessään ei vaikuttanut autonomisen hermoston toimin-taan. PCOS:ään sairastavien naisten sydämen rakenne ja funktio eivät merkitsevästi poikenneetkontrolloiden vastaavista muuttujista. Kuitenkin suhteellisen nuoresta iästä huolimatta PCOSnaisilla esiintyi enemmän sydäninfarkteja ja kaksi kertaa enemmän sydän- ja verisuonitapahtu-mia, kuin kontrolleilla.

Tutkimuksen tulokset osoittavat, että vaikkakin PCOS on itsenäinen riskitekijä metabolisillehäiriöille, oireyhtymään liittyvä ylipaino vaikuttaa merkittävästi metabolisten häiriöiden esiinty-miseen. PCOS:n ja sydän- ja verisuonitautitapahtumien yhteyden tarkempi tutkiminen vaatiikohortin jatkoseurantaa. Painonhallinnan tukemisen tulisi olla PCOS:ää sairastavien naisten hoi-don kulmakivi.

Asiasanat: aineenvaihdunta, autonominen hermosto, hyperandrogenismi, munasarjojenmonirakkulatauti, painoindeksi, prediabetes, sydän- ja verisuonitaudit, tyypin 2diabetes, verenpainetauti

To my family

9

Acknowledgements The present study was carried out at the Department of Obstetrics and Gynaecology,

PEDEGO research unit, Oulu University Hospital, Medical Research Center Oulu

and the Faculty of Medicine, University of Oulu in 2015–2019. The study is based

on data from the Northern Finland Birth Cohort 1966 and I wish to honor and thank

the founders and developers of this unique cohort data, Professor Paula Rantakallio,

Professor Marjo-Riitta Järvelin and Professor Anna-Liisa Hartikainen. It has been

a privilege to work with this excellent data.

I wish to express my deepest gratitude and respect to Docent Laure Morin-

Papunen for all the unconditional support, time and guidance during these years. I

have been most privileged to work with her and to get to know her. In the same

way, I am greatly thankful to Docent Terhi Piltonen for all the energetic and

inspiring guidance, support and help during these years.

I wish to thank Professor Juha Tapanainen, who has always shared his great

knowledge and found time to help. I also wish to thank Professor Stephen Franks

for important constructive criticism, which has improved the manuscripts.

I wish to thank the reviewers of this thesis, Professor Risto Kaaja and Docent

Oskari Heikinheimo. I also want to express warm thanks to my follow-up group

members Docent Tytti Raudaskoski, Professor Ulla Puistola and Docent Marja

Ojaniemi for their warm support and constructive comments. I wish to thank our

important collaborators Professor Sirkka Keinänen-Kiukaanniemi, PhD Juha

Auvinen, PhD Kari Kaikkonen and PhD Antti Kiviniemi. I am also greatly thankful

to PhD Tanja Nordström and MSc Jari Jokelainen who have patiently helped me

with the cohort data.

Special thanks go to PhD Sammeli West for working with me in these projects.

I would also like to thank post-graduates Pekka Pinola and Johanna Puurunen, and

peer PhD students Marika Kangasniemi, Salla Karjula, Maria-Elina Mosorin,

Johanna Laru and Masuma Khatum for many memorable moments and the great

atmosphere in our group.

I also want to thank all my dear friends and my family for priceless support

and never-ending kindness. Thank you for being in my life.

This study was financially supported by Oulu University Hospital, the Finnish

Medical Foundation, the 1.3milj club and the Paulo Foundation, all of which are

gratefully acknowledged.

Oulu, April 2019 Meri-Maija Ollila

11

Abbreviations AGM Abnormal glucose metabolism

AMH Anti-Müllerian hormone

AMI Acute myocardial infarction

ART Assisted reproductive technology

ASRM American Society of Reproductive Medicine

BMI Body mass index

BP Blood pressure

BRS Baroreflex sensitivity

cFT Calculated free testosterone

CVD Cardiovascular disease

DHEA Dehydroepiandrosterone

DHEAS Dehydroepiandrosterone Sulphate

E Peak velocity of early diastolic transmitral flow

e’ Peak velocity of early diastolic mitral annulus motion

ECG Electrocardiogram

EF Ejection fraction

ESHRE European Society of Human Reproduction and Embryology

FAI Free androgen index

GnRH Gonadotrophin releasing hormone

HA Hyperandrogenism

HbA1c Glycated haemoglobin

HDL High density lipoprotein

HF High frequency

HOMA-IR Homeostasis model assessment of insulin resistance

HR Heart rate

HRV Heart rate variability

hsCRP High-sensitivity C-reactive protein

HTA Hypertension

ICD International Classification of Diseases

IR Insulin resistance

IFG Impaired fasting glucose

IGT Impaired glucose tolerance

IVSd Interventricular septal end diastole

LAESV Left atrial end systolic volume

LC-MS/MS Liquid chromatography-tandem mass spectometry

12

LF Low frequency

LH Luteinizing hormone

LV Left ventricle/ventricular

LVIDd Left ventricular internal diameter end diastole

LVMI Left ventricular mass index

MSNA Muscle sympathetic nerve activity

nAGM New abnormal glucose metabolism

NFBC66 Northern Finland Birth Cohort 1966

NGT Normal glucose tolerance

NIH National Institutes of Health

nT2DM New T2DM

nu Normalised unit

OA Oligo-anovulation

OGTT Oral glucose tolerance test

OR Odds ratio

PCOM Polycystic ovarian morphology

PCOS Polycystic ovary syndrome

pNN50 Percentage of successive differences in RRi > 50 ms

Pre-DM Pre-diabetes

pT2DM Previously diagnosed T2DM

QUICKI Quantitative insulin sensitivity check index

RAS Renin-angiotensin system

RRi R-R interval

RMSSD Root mean square of successive R-R differences

SDANN Standard deviation of average normal-to-normal intervals

SDNN Standard deviation of all RRi(s)

SHBG Sex hormone-binding globulin

SNS Sympathetic nervous system

T Testosterone

T2DM Type 2 diabetes mellitus

ULF Ultra-low frequency

VLF Very low frequency

WHO World Heath Organisation

WHR Waist-to-hip ratio

13

List of original publications This thesis is based on the following publications, which are referred to throughout

the text by their Roman numerals:

I Ollila, MM., Piltonen, T., Puukka, K., Ruokonen, A., Järvelin, M., Tapanainen, JS., Franks, S., Morin-Papunen, L. (2016). Weight Gain and Dyslipidemia in Early Adulthood Associate With Polycystic Ovary Syndrome: Prospective Cohort Study. The Journal of Clinical Endocrinology & Metabolism, 101(2), 739-747. doi://dx.doi.org/10.1210/jc.2015-3543.

II Ollila, MM., West, S., Keinänen-Kiukaaniemi, S., Jokelainen, J., Auvinen, J., Puukka, K., Ruokonen, A., Järvelin, MR., Tapanainen, JS., Franks, S., Piltonen, TT., Morin-Papunen, L. (2017). Overweight and obese but not normal weight women with PCOS are at increased risk of Type 2 diabetes mellitus – a prospective population-based cohort study. Human Reproduction, 32(2), 423-431. doi: 10.1093/humrep/dew329

III Ollila, MM., Kaikkonen, K., Järvelin, M., Huikuri, HV., Tapanainen, JS., Franks, S., Piltonen TT., Morin-Papunen, L. (2019). Self-reported Polycystic Ovary Syndrome is Associated with Hypertension: A Northern Finland Birth Cohort 1966 Study. The Journal of Clinical Endocrinology and Metabolism, 104(4), 1221-1231. doi:10.1210/jc.2018-00570.

IV Ollila, MM., Kiviniemi, A., Stener-Victorin, E., Tulppo, M., Puukka, K., Ruokonen, A., Tapanainen, JS., Franks, S., Morin-Papunen L., Piltonen, TT. Cardiac Autonomic Balance at age 46 in Women with PCOS - A Cohort Study of 1856 women. Manuscript.

15

Contents Abstract

Tiivistelmä

Acknowledgements 9 Abbreviations 11 List of original publications 13 Contents 15 1 Introduction 17 2 Review of the literature 19

2.1 Polycystic ovary syndrome ..................................................................... 19 2.1.1 Definition and prevalence ............................................................. 19 2.1.2 Aetiology of PCOS ....................................................................... 21 2.1.3 Clinical features ............................................................................ 23

2.2 Overweight and obesity .......................................................................... 27 2.2.1 Classification of overweight and obesity ...................................... 27 2.2.2 Overweight and obesity in PCOS ................................................. 27 2.2.3 Hormonal and metabolic effects of overweight and obesity ........ 28

2.3 Glucose metabolism in PCOS ................................................................. 30 2.3.1 Classification of glucose metabolism ........................................... 30 2.3.2 Insulin resistance in PCOS ........................................................... 31 2.3.3 Abnormal glucose metabolism in PCOS ...................................... 33

2.4 Blood pressure in PCOS ......................................................................... 34 2.4.1 Definition of hypertension ............................................................ 34 2.4.2 PCOS and hypertension ................................................................ 34 2.4.3 Effect of blood pressure on cardiac structure and function .......... 36

2.5 Cardiac autonomic function in PCOS ..................................................... 38 2.5.1 Assessment of cardiac autonomic function .................................. 39 2.5.2 Autonomic function in women with PCOS .................................. 41

2.6 Cardiovascular diseases in PCOS ........................................................... 44 3 Purpose of the present study 47 4 Study subjects and methods 49

4.1 Study population ..................................................................................... 49 4.2 Definition of PCOS and control groups .................................................. 50 4.3 Methods ................................................................................................... 51

4.3.1 Statistical analysis ........................................................................ 55

16

5 Results and Discussion 59 5.1 Association of weight gain with the development of PCOS by

age 46 (Study I) ....................................................................................... 59 5.2 Abnormal glucose metabolism in women with PCOS (Study II) ........... 63 5.3 Hypertension and cardiac structure and function in PCOS (Study

III) ........................................................................................................... 66 5.4 PCOS and cardiac autonomic function (Study IV) ................................. 73 5.5 PCOS and cardiovascular disease morbidity (Study III) ......................... 77 5.6 Strengths and limitations ......................................................................... 78

6 Conclusions 81 References 83 Original publications 101

17

1 Introduction Polycystic ovary syndrome (PCOS) presents with widely ranging health

implications in women, such as reproductive, metabolic, and psychological issues.

The syndrome affects 8 to 13% of the reproductive aged female population and is

thus the most common endocrine disorder of fertile aged women (Teede et al.,

2018). In the United States, the estimated annual total cost of evaluating and

providing care to women with PCOS was approximately 4.36 billion U.S. dollars

in the early 2000s (Azziz, Marin, Hoq, Badamgarav, & Song, 2005).

According to Rotterdam criteria, PCOS is defined as the presence of two of the

following three features: polycystic ovarian morphology in ultrasonography, oligo-

and/or anovulation, or biochemical or clinical hyperandrogenism (Rotterdam

ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group, 2004; Teede et al.,

2018). The exact aetiology of PCOS is unknown but it is thought to be a

multifactorial disorder stemming from intrinsic/genetic and environmental

predispositions. The majority of women with PCOS are overweight or obese, but it

has remained unclear whether weight gain and high body mass index (BMI)

predispose women to the development of PCOS or whether PCOS per se

predisposes them to obesity (Hoeger & Oberfield, 2012). Insulin resistance (IR)

often coincides with PCOS independently of BMI, exposing women with PCOS

to glucose metabolism disorders (pre-diabetes and type 2 diabetes mellitus [T2DM])

(Diamanti-Kandarakis & Papavassiliou, 2006). Several studies have shown that

PCOS is a risk factor of T2DM independently of BMI (Moran, Misso, Wild, &

Norman, 2010), but it is unclear whether normal-weight women with PCOS are at

an increased risk of T2DM. Similarly, women with PCOS often present with

elevated blood pressure (BP) (Joham, Boyle, Zoungas, & Teede, 2015), but it has

not been elucidated whether PCOS is an independent risk factor of hypertension,

or if women with PCOS present with elevated BP as a result of confounding factors

such as obesity and hyperandrogenism.

Metabolic abnormalities, such as obesity, dyslipidaemia and hypertension are

also often associated with impaired cardiac autonomic function; over-activity of

the sympathetic autonomic nervous system and decreased parasympathetic activity.

These derangements have also been shown to be risk factors of cardiovascular

morbidity (Wulsin, Horn, Perry, Massaro, & D'Agostino, 2015). The results of

previous studies have also suggested impaired cardiac autonomic balance in

women with PCOS (Gui & Wang, 2017), although again, it is unclear if this is due

to PCOS per se or due to other co-existing metabolic abnormalities.

18

As many studies have shown that women with PCOS display a clustering of

cardiovascular disease risk factors (such as obesity, hyperandrogenism, abnormal

glucose metabolism, dyslipidaemia, hypertension, and altered cardiac autonomic

function), it has been postulated that such women might also have an increased risk

of cardiovascular disease events, such as acute myocardial infarction and stroke,

but the evidence is still controversial and further studies are awaited (Dokras, 2013).

19

2 Review of the literature

2.1 Polycystic ovary syndrome

2.1.1 Definition and prevalence

Polycystic ovary syndrome (PCOS) was first described by Chereau in 1844

(Chereau, 1844) and later by Stein and Leventhal in 1935 (Stein & Leventhal, 1935).

Since then, the syndrome has been defined by various criteria. In 1990, the National

Institutes of Health (NIH) established the first diagnostic criteria for PCOS: the

presence of hyperandrogenism (HA) and chronic oligo-anovulation (OA), after

exclusion of other diseases such as congenital adrenal hyperplasia,

hyperprolactinaemia and androgen-secreting neoplasms (Zawadski & Dunaif,

1992). Since then, the combination of OA+HA has been referred to as classic PCOS.

The diagnostic criteria for PCOS were updated in 2003 in Rotterdam by the

European Society of Human Reproduction and Embryology/The American Society

for Reproductive Medicine (ESHRE/ASRM) expert conference, which defined

PCOS according to the presence of at least two of the following three features:

menstrual irregularities (oligo- or anovulation), HA (clinical and/or biochemical)

or polycystic ovarian morphology (PCOM), after excluding other aetiologies. The

Rotterdam PCOS diagnostic criteria expanded the PCOS definition by adding two

new PCOS phenotypes: PCOM+HA and PCOM+OA (Rotterdam ESHRE/ASRM-

Sponsored PCOS Consensus Workshop Group, 2004).

In 2006, The Androgen Excess and PCOS (AE-PCOS) Society published a

third recommendation for the diagnosis of PCOS (Azziz et al., 2006). This

recommendation included the presence of hyperandrogenism (hirsutism or

hyperandrogenaemia) as a mandatory criterion for PCOS. Other features for PCOS

diagnosis in that recommendation were the presence of ovarian dysfunction (OA

or PCOM) and exclusion of other androgen excess or related disorders, such as 21-

hydroxylase-deficient nonclassic adrenal hyperplasia, androgen-secreting

neoplasms, androgenic/anabolic drug use or abuse, Cushing’s syndrome, and the

syndromes of severe insulin resistance, thyroid dysfunction, and

hyperprolactinaemia. The aforementioned three recommendations for the diagnosis

of PCOS are described in Table 1.

20

Table 1. Different diagnostic criteria for PCOS.

Criteria Hyperandrogenism Oligo-anovulation PCOM

NIH 1990 + + -

Rotterdam 2003 + + -

+ + +

+ - +

- + +

AE-PCOS Society 2006 + + +

+ + -

+ - +

PCOM: Polycystic ovarian morphology, NIH: National Institutes of Heath, AE: Androgen Excess

Recently, in 2018, the first international evidence-based guidelines for the

assessment and management of PCOS were published, including updated

recommendations for the diagnosis of PCOS (Teede et al., 2018). The guidelines

endorsed the Rotterdam criteria for PCOS diagnosis, i.e. the presence of two of the

following criteria: OA, HA, or PCOM. In addition, the guidelines stressed that, in

line with Rotterdam criteria, in the presence of both OA and HA, ultrasonographic

assessment of the ovaries was not necessary for diagnosis, and that when using

ultrasonography, different criteria for PCOM (Figure 1) should be used when using

older and newer ultrasonographic technologies (Teede et al., 2018).

Fig. 1. PCOM in ultrasonography. Copyright Laure Morin-Papunen.

21

In adolescence and during puberty many features of PCOS (irregular menstruation,

acne, PCOM in ultrasonography) overlap with normal features, making PCOS

diagnosis in adolescence challenging. Of note, ultrasonographic assessment of the

ovaries should not be performed for those with a gynaecological age of less than

eight years (Teede et al., 2018). However, as early as in puberty overweight and

obese girls demonstrate consistent hyperandrogenaemia and hyperinsulinaemia

compared with their normal-weight peers (McCartney et al., 2007), which might

predict the development of PCOS (Nader, 2013). Moreover, in a previous study

involving the Northern Finland Birth Cohort 1986 it was found that menstrual

irregularities at age 16 were a good marker of hyperandrogenaemia, and that there

was a significant linear trend towards higher free androgen index (FAI) values in

the higher BMI quartiles (Pinola et al., 2012). Another study concerning the same

cohort revealed that irregular menstruation and hyperandrogenaemia in

adolescence were associated with PCOS and infertility in later life (West et al.,

2014a).

The generally accepted prevalence of PCOS is between 8 and 13% (Teede et

al., 2018), but the prevalence greatly varies according to the diagnostic criteria used.

Indeed, use of the Rotterdam criteria results in a three-fold increased prevalence

compared with the prevalence when using NIH criteria (19.9% vs. 6.1%) (Yildiz,

Bozdag, Yapici, Esinler, & Yarali, 2012). In addition, population characteristics

such as ethnicity affect the prevalence of PCOS. A systematic review showed that

the prevalence of PCOS was lowest among Chinese women, intermediate among

Caucasian women and highest among Black women (Ding et al., 2017).

2.1.2 Aetiology of PCOS

The aetiology of PCOS remains under debate, but it is thought to be a complex,

multifactorial developmental condition. Previous studies have suggested that

PCOS could be a consequence of exposure to androgen excess in the intrauterine

environment, which could lead to altered programming of the hypothalamic-

pituitary-ovarian axis, with increased gonadotrophin pulsatility and subsequent

excess of androgen synthesis (Abbott, Dumesic, & Franks, 2002). The resulting

phenotype is suggested to be further modified by environmental and genetic factors,

which would explain the heterogeneous nature of the syndrome. PCOS clusters in

families and although genome-wide association studies have identified a number

of candidate regions, their role in contributing to PCOS is still largely unknown

(Dumesic et al., 2015).

22

Recently, new data has also emerged on early-life growth and the prevalence

of PCOS. Reduced foetal growth followed by infantile catch-up growth has been

suggested to predispose girls to PCOS (de Zegher & Ibáñez, 2006) as well as early

childhood weight gain (Koivuaho et al., 2019). Moreover, it has been proposed that

normal-weight women with PCOS display the most severe defects in ovarian

steroidogenesis, as they do not require any additional factors to develop the

syndrome, whereas women with mild defects would need the contribution of other

factors, such as obesity and IR, to develop the syndrome (Escobar-Morreale & San

Millan, 2007; Homburg, 2009). On the other hand, the different phenotypes

together with BMI manifestations may also represent different aetiologies behind

the syndrome.

Fig. 2. Roles of androgen excess and triggering factors during life for the development of PCOS.

Recently, the role of anti-Müllerian hormone (AMH), a glycoprotein secreted by

the ovarian granulosa cells, has been under intense investigation in the aetiology of

PCOS. Interestingly, it was found that in mice, AMH was able to modulate

luteinizing hormone (LH) pulsatility, resulting in a PCOS-like phenotype

characterized as ovulatory dysfunction and HA (Cimino et al., 2016). Given that

pregnant women with PCOS show significantly higher AMH levels than controls,

it has been postulated that AMH could possibly trigger HA during pregnancy (Tata

et al., 2018). Indeed, the same group showed that in mice, elevated AMH levels

were able to result in testosterone (T) excess in pregnant animals and consequent

PCOS with reproductive and neuroendocrine phenotypes in the offspring (Tata et

al., 2018). This publication was ground-breaking, as it was the first work to

introduce a mechanism for PCOS aetiology. However, the causal relationship

between AMH levels and development of PCOS is still under debate.

23

2.1.3 Clinical features

Women with PCOS usually seek medical help for hirsutism, irregular menstruation

or infertility. In addition to these main clinical features, 50 to 80% of women with

PCOS are overweight or obese, and the majority of them present with IR (Ovalle

& Azziz, 2002).

Hyperandrogenism

Hyperandrogenism can be either clinical (i.e. expressed as hirsutism) or

biochemical. Hirsutism, excessive male-type terminal hair growth, is a common

clinical manifestation of HA, together with acne, and less commonly, female-

pattern hair loss. Of note, acne and female-pattern hair loss are not diagnostic

features of HA. The clinical evaluation of hirsutism is based on visual inspection

of nine skin areas (scores 1 to 4) and hirsutism is defined as a modified Ferriman–

Gallwey score of ≥ 4–6, depending on ethnicity (Ferriman & Gallwey, 1961; Teede

et al., 2018; Yildiz, Bolour, Woods, Moore, & Azziz, 2010).

Fig. 3. Modified Ferriman & Gallwey scoring sheet for the clinical evaluation of hirsutism. Published by permission of Elsevier.

Biochemical HA can be defined as elevated levels of total serum T, calculated free

testosterone (cFT), FAI, androstenedione, or dehydroepiandrosterone sulphate

24

(DHEAS). The evidence-based guideline recommends that in PCOS,

hyperandrogenaemia should be assessed using cFT, FAI or calculated bioavailable

T (Teede et al., 2018). Liquid chromatography-mass spectrometry (LC–MS/MS)

and extraction/chromatography immunoassay are the most accurate assessment

methods for total or free testosterone in PCOS (Teede et al., 2018). However, the

gold standard techniques for T measurement are mass spectrometry and

equilibrium analysis, of which LC–MS/MS is the method of choice for routine

steroid hormone determination in the clinical laboratory (Taylor, Keevil, &

Huhtaniemi, 2015). The FAI is widely used, but its role as an indicator of

hyperandrogenaemia, especially in women with PCOS, is affected by IR, as IR is

associated with lower circulating concentrations of sex hormone-binding globulin

(SHBG) and thus increased FAI values (Wallace, McKinley, Bell, & Hunter, 2013).

Calculated FT correlates well with free T concentrations measured by equilibrium

dialysis (Vermeulen, Verdonck, & Kaufman, 1999).

The origin of HA in PCOS is complex. In women with PCOS, gonadotrophin-

releasing hormone (GnRH) pulse frequency from the hypothalamus is increased,

possibly as a consequence of neuroendocrine impairment related to kisspeptin and

GABA neurons (Katulski, Podfigurna, Czyzyk, Meczekalski, & Genazzani, 2018;

Moore & Campbell, 2016). Increased GnRH pulse frequency leads to increased LH

pulse frequency and amplitude in the anterior pituitary and thus increased serum

LH concentrations (Blank, McCartney, Helm, & Marshall, 2007). This leads to

increased T secretion in the ovarian theca cells, which are under LH regulation. The

GnRH pulse generator is relatively resistant to negative feedback of progesterone

and androgens, and an excess of circulating androgens does not have efficient

negative feedback control in females (Nisenblat & Norman, 2009). Furthermore,

both in vivo and in vitro studies have revealed that the ovarian theca cells of women

with PCOS display intrinsic steroidogenic dysfunction, as they convert androgen

precursors more effectively to T than normal theca cells, further enhancing the T

excess (Rosenfield & Ehrmann, 2016). Furthermore, IR and compensatory

hyperinsulinaemia, which commonly exist in women with PCOS, also contribute

to hyperandrogenaemia. Insulin acts synergistically with LH, increasing T

production from theca cells and inhibiting hepatic synthesis of SHBG, which leads

to increased amounts of unbound, biologically active T (Ehrmann, 2005). In

addition to ovarian androgen excess, 20–36% of women with PCOS also

demonstrate adrenal androgen excess, the role and mechanism of which are still

unclear (Nisenblat & Norman, 2009).

25

Fig. 4. Effects of insulin and LH on hyperandrogenaemia.

Menstrual irregularities and reproductive disturbances

Chronic anovulation often manifests as oligoamenorrhoea (menstrual cycles of >

35 days or < 8 cycles per year) or amenorrhoea (Ehrmann, 2005; Teede et al., 2018).

The main factors leading to anovulation in women with PCOS are suggested to be

associated with the accumulation of small follicles in the polycystic ovary

(Homburg, 2009). HA contributes to this by accelerating the progression of pre-

antral and small antral follicles from their precursors (Homburg, 2009), but on the

other hand the small follicles are not recruited forward, resulting in disrupted

folliculogenesis, and anovulation (Franks, Stark, & Hardy, 2008). Anovulatory

cycles may lead to dysfunctional uterine bleeding and decreased fertility, but they

also cause progesterone deficiency in the endometrium (Piltonen, 2016). Moreover,

women with PCOS display an increased risk of endometrial cancer (Barry, Azizia,

& Hardiman, 2014; Piltonen, 2016).

PCOS is the most common cause of anovulatory infertility, and even though

pregnancies occurring without assisted reproductive technology (ART) are

relatively frequent, many women with PCOS need medical help to conceive (Joham,

Teede, Ranasinha, Zoungas, & Boyle, 2015). On the other hand, women with PCOS

have been reported to have at least one child as often as non-symptomatic women,

although non-symptomatic women more commonly have two deliveries and larger

family sizes (West et al., 2014b). Furthermore, women with PCOS may be at an

increased risk of pregnancy complications such as pre-eclampsia, pregnancy-

26

induced hypertension and gestational diabetes (Palomba et al., 2015). A recent

study, however, revealed that perinatal complications did not differ between PCOS

and control women after adjusting for gestational weight gain (Kent et al., 2018).

Life-long effects

PCOS is a syndrome with life-long effects on women’s health. The results of some

studies have suggested that a predisposition to PCOS begins as early as in

childhood, as children born small for gestational age have been reported to be at an

increased risk of PCOS (Koivuaho et al., 2019). The cardinal features of PCOS

become evident after puberty, and usually remain stable during reproductive years,

when women with PCOS suffer from HA, irregular menstruation, infertility,

obesity and metabolic as well as psychological problems (Karjula et al., 2017;

Pinola et al., 2015; Teede et al., 2018). However, with aging the clinical symptoms

of PCOS often fade (Brown et al., 2011). Both ovarian and adrenal androgen

secretion decrease with time and usually older women with PCOS display lower

levels of total and free T, androstenedione and DHEAS than younger PCOS women,

even though T and androstenedione levels remain higher than in non-PCOS women

(Piltonen et al., 2004; Pinola et al., 2015; Welt & Carmina, 2013). Similarly, PCOM

normalizes with aging as both ovarian volume and follicle numbers decrease

(Koivunen et al., 1999; Piltonen et al., 2005; Welt & Carmina, 2013). As a result of

this, women with PCOS may achieve regular menstrual cycles (Elting, Kwee,

Korsen, Rekers-Mombarg, & Schoemaker, 2003), but whether this improvement

results in a prolonged fertility period in women with PCOS compared with other

women remains elusive. It has been reported that women with PCOS reach

menopause significantly later than control women (Forslund et al., 2018; Li,

Eriksson, Czene, Hall, & Rodriguez-Wallberg, 2016). The appearance of ovulatory

cycles with aging also seems to impact on metabolic and cardiovascular disease

risk factors, as a 20-year follow-up of hyperandrogenic women with PCOS showed

that those recovering ovulatory cycles had less disturbed glucose and lipid

metabolism than women with PCOS and anovulatory cycles (Carmina, Campagna,

& Lobo, 2013).

27

Fig. 5. The changing characteristics of PCOS during life.

2.2 Overweight and obesity

2.2.1 Classification of overweight and obesity

The prevalence of overweight and obesity is dramatically increasing worldwide,

driven by western lifestyle, including reduced physical activity and excess food

consumption. However, the rising prevalence seems to plateau in children and

adolescents in high-income countries (Worldwide trends in body-mass index,

underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416

population-based measurement studies in 128.9 million children, adolescents, and

adults. 2017). BMI and waist circumference are often used in clinical practice to

evaluate overweight and obesity. BMI is a simple index of weight-to-height ratio

(kg/m2) that is commonly used to classify underweight (BMI < 18.50 kg/m2),

normal weight (BMI 18.50–24.99 kg/m2), overweight (BMI ≥ 25.00 kg/m2) and

obesity (BMI ≥ 30.00 kg/m2) in adults (Obesity: preventing and managing the

global epidemic. Report of a WHO consultation. 2000). Waist circumference,

measured at the level of the approximate midpoint between the lower margin of the

last palpable rib and the top of the iliac crest, is also often used to estimate the

distribution of excess adipose tissue. In females, waist circumference over 80 cm

increases the risk of metabolic complications (World Health Organization, 2011).

2.2.2 Overweight and obesity in PCOS

The majority of women with PCOS are overweight or obese, although the

prevalence of overweight and obesity is widely variable in reported series of cases.

In the United States, as many as 74% of women with PCOS have been reported to

be obese (Yildiz, Knochenhauer, & Azziz, 2008) and in an Italian study, more than

50% of women with PCOS were overweight or obese (Gambineri, Pelusi, Vicennati,

Adolescence• Obesity• Hirsutism• Irregular menses• Acne• Psychological stress?

Reproductive age• Obesity• Hirsutism• Metabolic abnormalities• Infertility• Pregnancy complications• Psychological stress

Pre and postmenopause• Obesity• Hirsutism• Metabolic abnormalities• Endometrial cancer• Cardiovascular diseases?• Psychological stress

28

Pagotto, & Pasquali, 2002). In contrast, in a Greek study it was reported that BMI

was similar in PCOS and reference groups (Diamanti-Kandarakis et al., 1999).

Besides the major variation of the prevalence of overweight and obesity between

different countries and ethnicities, these results also reflect differences in diagnostic

criteria, PCOS phenotypes, and study populations. Moreover, it has remained

unclear whether women are obese because of the syndrome or whether obesity

predisposes women to the development of PCOS (Hoeger & Oberfield, 2012).

Weight change during life in cases of PCOS

Weight gain has been proposed to predispose women to the development of PCOS,

as the condition frequently becomes manifest after significant weight gain

(Escobar-Morreale & San Millan, 2007). On the other hand, weight reduction

diminishes PCOS symptoms, as it reduces HA and IR as well as restoring ovulation

(Escobar-Morreale, Botella-Carretero, Alvarez-Blasco, Sancho, & San Milla n,

2005). Interestingly, in a longitudinal general-population-based study from

Australia three BMI trajectories (low-stable, moderately-rising and high-rising)

were identified, and it was found that, compared with control women, women with

PCOS were 1.6 times more likely to belong to the moderately-rising trajectory and

4.7 times more likely to belong to the high-rising trajectory (Kakoly, Earnest,

Moran, Teede, & Joham, 2017). Another longitudinal general-population-based

follow-up study revealed that obesity and greater weight gain between ages 20 and

30 were associated with the prevalence of PCOS at age 27–30 (Teede et al., 2013).

That study also showed that every BMI unit increase elevated the risk of PCOS by

9.2% (Teede et al., 2013)

2.2.3 Hormonal and metabolic effects of overweight and obesity

Adipose tissue, particularly visceral tissue, is an active metabolic and endocrine

organ, secreting numerous molecules such as leptin, cytokines, adiponectin,

complement components, and proteins of the renin-angiotensin system (Kershaw

& Flier, 2004). It is also an important site for sex-steroid metabolism. Indeed,

adipose tissue is an important site for peripheral conversion of androgen precursors

(DHEA and DHEAS) to T, and its aromatase activity enables the conversion of

androgens to oestrogens (Payne & Hales, 2004). Excess weight and adipose tissue

are associated with IR and compensatory hyperinsulinaemia via increased secretion

of proinflammatory cytokines, such as tumour necrosis factor alpha and leukotriene

29

B4, as well as free fatty acids, and worsening IR in muscle and liver tissues, further

promoting hyperinsulinaemia. Furthermore, obesity significantly exacerbates all

metabolic abnormalities, such as pre-diabetes (pre-DM), diabetes and metabolic

syndrome, as well as the reproductive disturbances associated with the syndrome

(Lim, Norman, Davies, & Moran, 2013).

Some studies have suggested that women with PCOS more often display

visceral/abdominal fat accumulation than control women (Carmina et al., 2007;

Yildirim, Sabir, & Kaleli, 2003), whereas other studies have not shown any

differences between women with PCOS and control women (Barber et al., 2008;

Mannerås-Holm et al., 2011). Women with PCOS also have larger adipocytes,

lower adiponectin levels, and lower adipose tissue lipoprotein-lipase activity when

compared with BMI- and age-matched controls. Moreover, in a multivariate

regression analysis, these adipose tissue-related disturbances played a more

important role than hyperandrogenaemia to explain the presence of insulin

resistance in women with PCOS (Mannerås-Holm et al., 2011).

Fig. 6. Vicious cycle of metabolic disturbances in women with PCOS.

30

2.3 Glucose metabolism in PCOS

2.3.1 Classification of glucose metabolism

According to WHO classification, glucose metabolism can be classified as normal,

pre-DM, including impaired fasting glucose (IFG) or impaired glucose tolerance

(IGT), or T2DM (Table 2) (Alberti & Zimmet, 1998).

Table 2. Classification of glucose metabolism based on 2-hour oral glucose tolerance test (2h-OGTT) results according to the WHO.

Categories Fasting plasma glucose 2-hour oral glucose tolerance test

(75 g)

Normal glucose tolerance ≤ 6.0 mmol/l and < 7.8 mmol/l

Impaired fasting glucose 6.1–6.9 mmol/l and < 7.8 mmol/l

Impaired glucose tolerance ≤ 6.0 mmol/l and 7.8–11.0 mmol/l

Type 2 diabetes mellitus ≥ 7.0 mmol/l or ≥11.0 mmol/l

Glucose metabolism can be evaluated in various ways, such as assay of fasting

plasma glucose, use of the 2h-OGTT, assay of glycated haemoglobin (HbA1c),

performance of the frequently sampled intravenous glucose tolerance test, or the

euglycaemic-hyperinsulinaemic clamp. Of these, the last two also give a measure

of insulin sensitivity/resistance, as do many mathematical indexes, such as

homeostasis model assessment for insulin resistance (HOMA-IR), the Matsuda

index and the quantitative insulin sensitivity check index (QUICKI), which are

based on fasting glucose and insulin or their values in the 2h-OGTT. The

euglycaemic-hyperinsulinaemic clamp is considered to be the gold standard for

assessment of IR, but it is a demanding, invasive and time-consuming method, and

not feasible in everyday clinical practice. Instead, HOMA-IR and the 2h-OGTT are

less time consuming and thus in wider use. However, the mathematical indexes

(such as HOMA-IR, Matsuda, QUICKI etc.) are not fully accurate for the

identification of IR; a large study of women with PCOS revealed that these

surrogate indexes erroneously diagnose many subjects with PCOS as insulin

sensitive, especially in the normal-weight group of women (Tosi, Bonora, &

Moghetti, 2017). Moreover, HbA1c and fasting plasma glucose assays and the 2h-

OGTT also have their limitations. Assay of HbA1c is neither sensitive nor

sufficiently specific to detect pre-diabetes, fasting glucose is specific but not

sensitive, and the 2h glucose values in OGTTs are so variable that the test should

31

always be performed twice before deciding on a diagnosis of pre-diabetes or T2DM

(Barry et al., 2017). The detection of glucose metabolism abnormalities is of great

importance, as T2DM is a major risk factor of cardiovascular diseases (Booth,

Kapral, Fung, & Tu, 2006).

2.3.2 Insulin resistance in PCOS

Insulin resistance (IR) is a pathological condition in which the ability to transfer

glucose from the circulation into the cells in response to activation of the insulin

signalling pathway is reduced. This leads to compensatory hyperinsulinaemia, as

the pancreas increases insulin production to compensate for reduced insulin action

and glucose uptake. In women with PCOS, IR is due to an intrinsic, unique defect

in post-receptor insulin signalling in its classical metabolic target tissues, muscle

and adipose tissue (Diamanti-Kandarakis & Papavassiliou, 2006). However, as

normal insulin signalling is maintained in the ovaries, the compensatory

hyperinsulinaemia promotes hyperandrogenaemia by activating the

steroidogenesis pathways in the ovaries (Diamanti-Kandarakis & Papavassiliou,

2006). Moreover, overweight and obesity, particularly visceral obesity, together

with PCOS, synergistically further impair glucose tolerance and increase IR

(Dunaif, Segal, Futterweit, & Dobrjansky, 1989), though IR also occurs in many

normal-weight women with PCOS (Stepto et al., 2013). The prevalence of IR in

women with PCOS varies between 50 to 95% (Ovalle & Azziz, 2002; Stepto et al.,

2013) as a consequence of different IR assessment techniques, PCOS definitions

and BMIs of the study populations.

IR is a progressive condition that increases the risks of pre-DM, T2DM, and

metabolic syndrome. In women with PCOS, lifestyle interventions and treatments

reducing IR and hyperinsulinaemia, such as metformin treatment, significantly

decrease weight and may improve hyperandrogenaemia, menstrual pattern and

ovulation (Morin-Papunen, Koivunen, Ruokonen, & Martikainen, 1998;

Naderpoor et al., 2015; Rosenfield & Ehrmann, 2016).

32

Fig. 7. Interplay between excess adipose tissue, IR and androgen excess. Modified from Escobar-Morreale & San Millan, 2007.

Testosterone and pancreatic beta cell function

Besides defective insulin-signalling in pancreatic beta cells, T excess also seems to

play a role in the development of abnormal glucose metabolism in women with

PCOS. Pancreatic beta cells exhibit androgen receptors and long-term androgen

excess causes chronic activation of these receptors, leading to insulin

hypersecretion and secondary beta cell failure in females (Xu, Morford, &

Mauvais-Jarvis, 2019). Interestingly, prenatal androgen exposure could alter the

development and, later on, the function of pancreatic beta cells (Mauvais-Jarvis,

2016). Accordingly, it has been found in murine and ovine models that maternal

testosterone exposure alters the female offsprings’ beta cell function by causing

basal hyperinsulinaemia, even in the absence of IR, and reduced insulin secretion

33

(Hogg, Wood, McNeilly, & Duncan, 2011; Roland, Nunemaker, Keller, & Moenter,

2010).

2.3.3 Abnormal glucose metabolism in PCOS

It is generally accepted that women with PCOS have an increased prevalence of

glucose metabolism abnormalities. In most studies, the prevalence of T2DM in

women with PCOS has been around 10% (Ehrmann, Barnes, Rosenfield, Cavaghan,

& Imperial, 1999; Hudecova et al., 2011; Joham, Ranasinha, Zoungas, Moran, &

Teede, 2014). In a meta-analysis it was concluded that PCOS is associated with an

increased risk of pre-DM and T2DM, independently of BMI (Moran et al., 2010).

Furthermore, in a more recent meta-analysis and systematic review including only

good- or fair-quality studies it was found that women with PCOS (based on NIH

criteria) had an increased prevalence of IGT and T2DM. Importantly, this

prevalence differed by ethnicity and increased with obesity (Kakoly et al., 2018).

Indeed, the results of several other previous studies have suggested that PCOS itself

increases the risk of glucose metabolism abnormalities (Joham et al., 2014; Legro,

Kunselman, Dodson, & Dunaif, 1999; Wang et al., 2011), but some studies have

linked the risk mainly to overweight and obesity, but not to PCOS per se

(Boudreaux, Talbott, Kip, Brooks, & Witchel, 2006; Espinos-Gomez, Corcoy, &

Calaf, 2009; Gambineri et al., 2012).

Follow-up studies have highlighted the importance of excess weight, and

especially weight gain, in the development of abnormal glucose metabolism in

women with PCOS. Norman et al. found that PCOS subjects converting from

normal to abnormal glucose metabolism displayed greater BMI, abdominal obesity,

and weight gain compared with those remaining normoglycaemic (Norman,

Masters, Milner, Wang, & Davies, 2001) and Morgan et al. showed that a 1%

increase of BMI increases the risk of T2DM by 2% (Morgan, Jenkins-Jones, Currie,

& Rees, 2012).

The recently published evidence-based guidelines for the assessment and

management of PCOS (Teede et al., 2018) recommend the screening of glucose

metabolism by way of fasting plasma glucose, HbA1c, or the 2h-OGTT, of which

the last one is particularly recommended for high-risk individuals, i.e., women with

overweight, a history of impaired fasting glucose or gestational diabetes, a family

history of T2DM, hypertension or high-risk ethnicity. The detection of abnormal

glucose metabolism is important to prevent the development of the morbidity and

mortality associated with diabetes (Booth et al., 2006).

34

2.4 Blood pressure in PCOS

2.4.1 Definition of hypertension

Hypertension is a major global health issue affecting approximately 40% of adults

worldwide (WHO | Global status report on noncommunicable diseases. 2011). The

definitions of elevated BP are variable, but according to guidelines from the

European Society of Hypertension (ESH) and the European Society of Cardiology

(ECS), hypertension is defined as systolic BP ≥ 140 mmHg and/or diastolic BP ≥

90 mmHg (Williams et al., 2018). Elevated BP levels can damage blood vessels

and cause adverse changes in important target organs such as the heart, the kidneys

and the brain. In a meta-analysis of 61 prospective studies it was concluded that in

the general population increased BP levels are strongly and directly associated with

vascular and overall mortality (Lewington, Clarke, Qizilbash, Peto, & Collins,

2002).

2.4.2 PCOS and hypertension

The prevalence of hypertension has been reported to be higher in women with

PCOS compared with controls (Joham et al., 2015; Schmidt, Landin-Wilhelmsen,

Brannstrom, & Dahlgren, 2011). The results of some studies have suggested that

this is mainly due to excess weight (Luque-Ramirez, Alvarez-Blasco, Mendieta-

Azcona, Botella-Carretero, & Escobar-Morreale, 2007), although others have

suggested that PCOS per se increases the prevalence of hypertension (Zachurzok-

Buczynska, Szydlowski, Gawlik, Wilk, & Malecka-Tendera, 2011) (Table 3).

Blood pressure is under constant regulation. Previous studies have suggested

that women with PCOS might display changes in the BP regulation system,

predisposing them to hypertension. Activation of the renin-angiotensin system

(RAS) has a major role in the pathophysiology of hypertension. Women with PCOS

have been shown to display elevated renin levels, which were significantly

correlated with androgen levels (Jaatinen et al., 1995). It has been reported that in

a rat in vivo model, androgens might upregulate the RAS in proximal tubules in the

kidneys, and thus increase the volume reabsorption rate and consequently blood

pressure levels (Quan et al., 2004). Moreover, 3-month treatment of female rats

with dihydrotestosterone resulted in elevation of BP and IR, and these changes were

associated with significant upregulation of intrarenal angiotensinogen and the

sympathetic nervous system, suggesting that hyperandrogenaemia could have an

35

effect on the RAS and BP level (Yanes et al., 2011), and also that it could have a

direct effect on the sympathetic nervous system (Maranon et al., 2015). It could be

speculated, based on the above-mentioned possible RAS activation in women with

PCOS, that angiotensin-converting enzyme inhibitors might be an optimal choice

for first-line antihypertensive medication in women with PCOS, but this issue

needs to be further studied.

Table 3. Previous studies concerning hypertension and blood pressure in women with PCOS.

Study Number Age (years) Findings

Zachurzok-Buczynska et

al., 2011

N=43 Mean: 16 Obese groups: sig. ↑ 24-hour

mean BP

Elting, Korsen, Bezemer, &

Schoemaker, 2001

N=346 Mean: 39 HTA prevalence: sig. ↑

Holte, Gennarelli, Berne,

Bergh, & Lithell, 1996

N=36 Mean: 26 Day-time systolic and mean

arterial BP sig. ↑

Joham et al., 2015 N=26 (with HTA) Range: 28–33 Normal-weight group: HTA

prevalence sig. ↑;

Overweight/obese group: HTA

prevalence ↔

Multivariate regression analysis:

PCOS was not associated with

HTA.

Schmidt et al., 2011 N=35 Range: 61–79 HTA prevalence sig. ↑

Office BP level: ↔

Luque-Ramirez et al., 2007 N=36 Mean: 24 HTA prevalence: ↔

Ambulatory or office BP levels: ↔

Chen et al., 2007 N=151 Mean: 24 FAI sig. associated with BP

Meyer, McGrath, & Teede,

2005

N=100 Mean: 33 Ambulatory BP: ↔

Zimmermann et al., 1992 N=14 Range: 30 Ambulatory BP: ↔

HTA = hypertension, BP = Blood pressure, sig. = significantly, ↑ = higher, ↔ = comparable.

Two groups have also reported that circulating levels of copeptin, a surrogate

marker of antidiuretic hormone, are significantly higher in women with PCOS

(Karbek et al., 2014; Taskin, Bulbul, Adali, Hismiogulları, & Inceboz, 2015).

Furthermore, in the presence of IR, which often coexists with PCOS, the

vasodilating effect of insulin can be lost (Eckel, Grundy, & Zimmet, 2005),

promoting elevation of BP. Women with PCOS may also display changes in

function of the autonomic nervous system (Saranya, Pal, Habeebullah, & Pal, 2014),

36

which is an important fast-acting regulator of BP. In addition, as the majority of

women with PCOS are overweight/obese, they are also at a risk of obstructive sleep

apnoea, which is a major risk factor of hypertension (Gonzaga, Bertolami,

Bertolami, Amodeo, & Calhoun, 2015). However, despite all this, it has remained

unclear whether PCOS per se is a risk factor of hypertension.

2.4.3 Effect of blood pressure on cardiac structure and function

Long-lasting elevation of BP can cause changes in cardiac structure and function,

as elevated BP increases the workload of the heart, leading to compensatory

myocardial hypertrophy, impaired left ventricular (LV) relaxation, and increased

left ventricle pressure, which in turn cause enlargement of the left atrium (Santos

& Shah, 2014).

Echocardiographic examination of the heart is easy to perform and widely

available and generates several parameters that describe the heart’s structure and

function. Both LV ejection fraction (LVEF) and global strain describe the heart’s

systolic function. Of these, LVEF has been in clinical use for a long time, whereas

global strain is a novel indicator of the overall systolic function of the LV and may

reveal heart disease at an early stage, even when the LVEF is still normal (Smiseth,

Torp, Opdahl, Haugaa, & Urheim, 2016). The diastolic function of the heart can

also be impaired, which is usually due to impaired relaxation of the LV, reduced

restoring forces, and/or increased LV chamber stiffness (Nagueh et al., 2016). The

diastolic function of the heart can be evaluated by using several two-dimensional

or Doppler parameters. According to a recent recommendation, the following

parameters should be used for identifying diastolic dysfunction: left atrium volume

index (describes cumulative effects of increased LV filling pressures over time),

annular e’ velocity (septal e’ and lateral e’), average E/e’ ratio, and peak tricuspid

regurgitation velocity (Nagueh et al., 2016).

37

Table 4. Echocardiographic parameters.

Abbreviation Parameter’s full name

Systolic function

LVEF Left ventricular ejection fraction

Global strain Global longitudinal strain

Cardiac structure

IVSd Interventricular septal end diastole

LVIDd Left ventricular internal diameter end diastole

LVMI Left ventricular mass index

Diastolic function

LAESV Left atrial end systolic volume

E Peak velocity of early diastolic transmitral flow

e’ Peak velocity of early diastolic mitral annulus motion

E/e’ Ratio of E/e’

TR Tricuspid regurgitation

Cardiac structure and function in women with PCOS

Only a few studies have concerned evaluation of cardiac structure and function in

women with PCOS. A study of 18–30-year old women recruited from an academic

centre revealed that women with PCOS displayed higher LAESV and LVMI as well

as lower LVEF and early to late mitral flow velocity ratio, suggesting impairment

of both systolic and diastolic function in PCOS, although the role of hypertension

was not assessed in that study (Orio et al., 2004). An American prospective cohort

study of 42 women with PCOS in their 30s showed that PCOS was significantly

associated with higher LVMI and LAESV compared with a reference population,

even though women with PCOS had significantly lower systolic and diastolic BP

(Wang et al., 2012). A Turkish study on 35 women with PCOS revealed that they

displayed diastolic dysfunction (Tíras et al., 1999). In contrast, some studies have

not shown any significant differences in echocardiographic parameters between

PCOS and control women. Selcoki et al. performed echocardiographic

examinations in 48 age- and BMI-matched Turkish women with PCOS and 21

healthy controls and found comparable left ventricular and atrial diameters, and

similar ejection fractions as well as indices of diastolic function (Selcoki et al.,

2010). In addition, a third Turkish study on echocardiographic features in 26

women with PCOS and 24 controls did not show differences in cardiac structure or

function (Tekin et al., 2009). Interestingly, a case-control study of 150 women

38

divided into three groups according to PCOS and IR status (with both PCOS and

IR, without PCOS and with IR, and without either PCOS or IR) revealed that LV

function was impaired only in the groups with IR (Kosmala et al., 2008).

2.5 Cardiac autonomic function in PCOS

The autonomic nervous system includes the sympathetic and parasympathetic

nervous systems. As the parasympathetic nerve branches follow the vagus nerve

(the 10th cranial nerve) parasympathetic activity is often referred to as vagal activity.

The parasympathetic nervous system is dominant in “rest and digest” situations

whereas the sympathetic nervous system is active in so-called “fight or flight”

situations.

Sympathetic activity can be assessed by using many different methods, such as

measurement of muscle sympathetic nerve activity (MSNA), plasma levels of

noradrenaline and adrenaline, and noradrenaline spillover (Lansdown & Rees,

2012). Of these, MSNA and noradrenaline spillover are very accurate estimates of

sympathetic activity, but they are also invasive and time demanding and thus not

suitable for studying large populations. Noradrenaline spillover describes the

sympathetic activity of a specific organ, whereas the plasma levels of noradrenaline

and adrenaline describe the body’s “overall” sympathetic tone.

The function of the heart is under precise control of the autonomic nervous

system, hormones, the surrounding temperature and mechanical stress from

breathing. In normal situations in healthy individuals, there are small constant

changes in the time intervals between consecutive heart beats (or the time interval

between consecutive R peaks in the electrocardiogram, Figure 8), and this variation

is called heart rate variability (HRV).

39

Fig. 8. An illustration of the RRi. When parasympathetic activity is increased (above), the pulse is slow and there are variations in the RRi, whereas when sympathetic activity is increased (below), the pulse is fast and variations in the RRi are small.

Changes in autonomic activity are reflected in HRV so that parasympathetic

activity increases it, whereas sympathetic activity decreases it. Moreover, high

HRV is a marker of “general good health”, as it indicates that our bodies can react

to changes in the internal and external environments. For example, respiratory

arrhythmia, often seen in the electrocardiograms of young fit individuals is a

consequence of the decrease in parasympathetic activity during inhalation leading

to an increase in the heart rate during inhalation, whereas the opposite is observed

during exhalation (Yasuma & Hayano, 2004).

The clinical relevance of HRV was recognised in 1965, when it was found that

when the foetal heart was exposed to distress, changes in HRV occurred before

abnormalities in the heart rate itself (Hon & Lee, 1965). Later in life, decreased

HRV has been associated with increased risks of many major global public-health

problems, such as depression (Sgoifo, Carnevali, Alfonso, Maria de los Angeles

Pico, & Amore, 2015), hypertension, diabetes, cardiovascular diseases and

increased mortality (Wulsin et al., 2015).

2.5.1 Assessment of cardiac autonomic function

The function of the cardiac autonomic nervous system can be investigated non-

invasively using HRV from electrocardiograms with the aid of mathematical

methods, such as time-domain and frequency-domain methods (Table 5) (Heart

rate variability: standards of measurement, physiological interpretation and

clinical use. Task Force of the European Society of Cardiology and the North

40

American Society of Pacing and Electrophysiology. 1996). Thus measurement of

HRV is an optimal choice for assessment of cardiac autonomic function in large

study populations.

Table 5. Heart rate variability parameters.

Parameter abbreviations Full name Modifiers/Describes

Time-domain

RMSSD Root mean square of successive

R-R differences

If decreased HRV is ↓

SDNN Standard deviation of all RRi(s) If decreased HRV is ↓

pNN50 Percentage of successive

differences in RRi > 50 ms

If decreased HRV (and vagal

activity) is ↓ Frequency-domain

ULF Ultra-low frequency (< 0.003 Hz) Mainly modified by diurnal rhythm

VLF Very low frequency (0.003–0.05

Hz)

Mainly modified by temperature

and hormones (adrenaline, renin)

LF Low frequency (0.05–0.15 Hz) Describes mainly sympathetic

activity

HF High frequency (0.15–0.4 Hz) Describes mainly parasympathetic

activity

LF/HF ratio Autonomic balance

HRV = Heart rate variability. ↓ = decreased. Hz = Hertz.

It is also important to note that besides surrounding temperature, hormone activity

and the autonomic nervous system, many other factors also have an effect on HRV.

Increasing age, BMI, and stress, as well as decreased physical activity, and the

presence of chronic diseases (such as hypertension, coronary artery diseases, and

diabetes), and acute diseases (such as myocardial infarction, and acute psychosis)

decrease HRV (Heart rate variability: standards of measurement, physiological

interpretation and clinical use. Task Force of the European Society of Cardiology

and the North American Society of Pacing and Electrophysiology. 1996; Almeida-

Santos et al., 2016; Föhr et al., 2016; Istenes et al., 2014; Valkonen-Korhonen et

al., 2003).

Women with PCOS have significantly higher leptin levels (Zheng, Du, & Li,

2017) and elevated leptin levels increase the activity of the sympathetic nervous

system (Quilliot, Böhme, Zannad, & Ziegler, 2008), which might predispose

women with PCOS to sympathetic overactivity and thus decreased HRV. The

function of the autonomic nervous system is also related to inflammation, as higher

levels of high-sensitivity C-reactive protein (hsCRP) and interleukin-6 are

41

associated with impaired HRV (Haensel, Mills, Nelesen, Ziegler, & Dimsdale,

2008). This is important, as women with PCOS are often reported to have low-

grade inflammation (Spritzer, Lecke, Satler, & Morsch, 2015).

2.5.2 Autonomic function in women with PCOS

Women with PCOS typically present with many factors that are associated with

autonomic dysfunction in the general population, such as weight excess, IR,

hyperinsulinaemia, hypertension, dyslipidaemia, and obstructive sleep apnoea

(Lambert, Straznicky, Lambert, Dixon, & Schlaich, 2010; Stuckey, Tulppo,

Kiviniemi, & Petrella, 2014), and therefore could be at increased risk of impaired

cardiac autonomic function. Previous studies have suggested that women with

PCOS might display reduced parasympathetic (vagal) (Saranya et al., 2014; Tekin

et al., 2008; Yildirir, Aybar, Kabakci, Yarali, & Oto, 2006) and increased

sympathetic activity (Lambert et al., 2015). In a recent systematic review and meta-

analysis including eight studies it was reported that women with PCOS (n = 243)

might display cardiovascular autonomic dysfunction with reduced parasympathetic

and increased sympathetic activity. Unfortunately, the meta-analysis did not cover

assessment of the effect of confounding factors, even though women with PCOS

had significantly higher BMIs, WHRs, as well as significantly higher levels of

glucose, triglycerides, and total cholesterol compared with the control group (n =

211 women) (Gui & Wang, 2017).

Previous studies among women with PCOS have involved the use of various

methods, such as microneurography, measurement of sympathetic skin responses,

heart rate variability, heart rate recovery and noradrenaline spillover measurement,

and concerned only women with PCOS in their 20s and 30s (Table 6). In addition,

the duration of HRV recordings has varied, which may bias the results, as HRV

guidelines state that it is not appropriate to compare time-domain parameters

derived from recordings of different durations (Heart rate variability: standards of

measurement, physiological interpretation and clinical use. Task Force of the

European Society of Cardiology and the North American Society of Pacing and

Electrophysiology. 1996).

42

Tabl

e 6.

Pre

viou

s st

udie

s on

aut

onom

ic fu

nctio

n in

wom

en w

ith P

CO

S.

Stu

dy

Des

ign

Stu

dy p

opul

atio

n R

esul

ts

Ji e

t al.,

201

8 R

etro

spec

tive

char

t rev

iew

W

omen

with

PC

OS

(n=3

5) h

ad h

ighe

r BM

I

and

BP

than

con

trols

(n=3

2)

SD

NN

and

RM

SS

D ↔

; LF,

LFn

u, a

nd

LF/H

F ra

tio ↑

; HFn

u ↓

Kili

t & P

aşal

ı Kili

t, 20

17

HR

V

60 n

orm

al-w

eigh

t wom

en w

ith P

CO

S a

nd 6

0

age-

mat

ched

con

trols

SD

NN

, RM

SS

D, H

F, L

F, H

Fnu,

LFn

u, a

nd

LF/H

F ra

tio ↔

Özk

eçec

i et a

l., 2

016

24 h

Hol

ter

Met

abol

ical

ly h

ealth

y P

CO

S (n

=23)

and

cont

rols

(n=2

5)

SD

NN

, SD

AN

N, a

nd R

MS

SD

↔

Lam

bert

et a

l., 2

015

Mic

rone

urog

raph

y an

d H

RV

19

ove

rwei

ght/o

bese

PC

OS

, 21

over

wei

ght/o

bese

con

trols

Mul

tiuni

t mus

cle

SN

S1 a

ctiv

ity ↑

; HR

V ↔

Has

him

, Ham

dan,

& A

l-

Sal

ihi,

2015

Pla

sma

epin

ephr

ine,

SS

K2 ,

HR

V,

Val

salv

a ra

tio

64 P

CO

S, 4

0 co

ntro

ls

Sym

path

oexc

itatio

n m

ore

pron

ounc

ed in

obes

e th

an in

non

-obe

se w

omen

with

PC

OS

Sar

anya

et a

l., 2

014

HR

V, H

R a

nd B

P re

spon

se to

stan

ding

(30:

15 ra

tio),

deep

brea

thin

g (E

:I ra

tio)

31 P

CO

S, 3

0 co

ntro

ls

Dec

reas

ed p

aras

ympa

thet

ic a

nd in

crea

sed

sym

path

etic

act

ivity

Di D

omen

ico

et a

l., 2

013

Res

t and

pos

t men

tal-s

tress

HR

V

32 a

novu

lato

ry c

lass

ic P

CO

S (C

-PC

OS

), 16

ovul

ator

y P

CO

S, 2

3 co

ntro

ls

Res

t-HR

V ↔

. C-P

CO

S: m

ean

RR

i, P

NN

50

and

RM

SS

D ↓

dur

ing

men

tal s

tress

.

Ovu

lato

ry-P

CO

S ↔

de S

á, J

ocel

ine

Cás

sia

Fere

zini

et a

l., 2

011

HR

V

Wom

en w

ith P

CO

S (n

=23)

had

hig

her w

eigh

t,

gluc

ose,

insu

lin, c

hole

ster

ol a

nd tr

igly

cerid

es

than

con

trols

(n=2

3)

SD

NN

, RM

SS

D, L

F, a

nd H

F ↓,

whi

ch

corr

elat

ed w

ith B

MI

43

Stu

dy

Des

ign

Stu

dy p

opul

atio

n R

esul

ts

Sve

rris

dótti

r, M

ogre

n,

Kat

aoka

, Jan

son,

& S

tene

r-

Vic

torin

, 200

8

MS

NA

3 20

PC

OS

, 18

cont

rols

M

SN

A b

urst

freq

uenc

y ↑

Yild

irir e

t al.,

200

6 H

RV

30

PC

OS

, 30

cont

rols

LF

nu a

nd L

F/H

F ra

tio ↑

, HF

↓ 1 S

NS

= S

ympa

thet

ic n

ervo

us s

yste

m, 2 S

SK

= S

ympa

thet

ic s

kin

resp

onse

, 3 MS

NA

= M

uscl

e sy

mpa

thet

ic n

erve

act

ivity

44

In conclusion, previous studies have been very heterogeneous regarding study

populations and designs, and thus it remains unclear whether the autonomic

dysfunction detected in women with PCOS is independent of overweight/obesity,

IR, hyperandrogenaemia and metabolic abnormalities and whether the dysfunction

persists during late reproductive years in affected women.

2.6 Cardiovascular diseases in PCOS

Women with PCOS present with an increased prevalence of cardiovascular disease

risk factors, but it has remained unclear whether they have increased morbidity and

mortality due to cardiovascular diseases (Dokras, 2013). Previous studies have

shown inconsistent findings regarding the risk of cardiovascular morbidity in

women with PCOS, as some have shown an increased prevalence of events

(Glintborg, Hass Rubin, Nybo, Abrahamsen, & Andersen, 2015; Glintborg, Rubin,

Nybo, Abrahamsen, & Andersen, 2018; Mani et al., 2013), whereas others have not

(Iftikhar et al., 2012; Lunde & Tanbo, 2007; Meun et al., 2018; Morgan et al., 2012;

Schmidt et al., 2011), possibly because of variable study designs and populations.

In addition, previous studies have concerned relatively young women when

considering CVD events (Glintborg et al., 2018; Mani et al., 2013; Morgan et al.,

2012).

Table 7. Cardiovascular morbidity in women with PCOS in previous studies.

Study Study design and

country of origin

Age of study

population (years)

Number of PCOS

patients

Results

Meun et al., 2018 A prospective

population-based

cohort study from

the Netherlands

Mean: 70 N=106 Incident CVD,

Atherosclerosis ↔

Glintborg et al.,

2018

National register-

based study from

Denmark

Mean: 29 N=18 112 CVD event rate ↑

Merz et al., 2016

Cross-sectional

study from the

United States

Mean: 62 N=25 CVD ↔

45

Study Study design and

country of origin

Age of study

population (years)

Number of PCOS

patients

Results

Glintborg et al.,

2015

Nationwide register

study from Denmark

Mean: 31 N=1217–19 199 Stroke and

thrombosis ↑

Mani et al., 2013 20-yr retrospective

cohort study from

the UK

Mean: 30 N=2301 Myocardial

infarction ↑

Iftikhar et al., 2012 Retrospective

cohort study from

the United States

Mean: 45 N=309 Myocardial

infarction, coronary

artery bypass graft

surgery, stroke,

death ↔

Morgan et al., 2012 Retrospective

observational study

from the UK

Mean: 27 N=21 740 Large vessel

disease1 ↔

Schmidt et al., 2011 A 21-yr follow-up

study from Sweden

Range: 61–79 N=35 Myocardial

infarction and stroke

↔

Lunde & Tanbo,

2007

Follow-up study

from Norway

Range: 37–57 N=149 CVD ↔

Lo et al., 2006 Register-based

study from the

United States

Mean: 31 N=11 035 CVD ↔

Cibula et al., 2000 A cross-sectional

study from the

Czech Republic

Range: 45–59 N=28 Coronary artery

disease ↑

Wild, Pierpoint,

McKeigue, &

Jacobs, 2000

Retrospective

cohort study from

the UK

Mean: 57 (range:

38–98)

N=319 Coronary artery

disease ↔,

Cerebrovascular

disease ↑

Pierpoint,

McKeigue, Isaacs,

Wild, & Jacobs,

1998

A 30-yr follow-up

study from the UK

Mean: 56 N=786 CVD ↔

Dahlgren, Janson,

Johansson,

Lapidus, & Oden,

1992

A prospective study

from Sweden

Mean: 55 N=33 AMI or ischaemic

cerebral disease ↔

1Large vessel disease: myocardial infarction, stroke, angina, or central or peripheral revascularization.

AMI: Acute myocardial infarction.

46

The overall risk of cardiovascular diseases is influenced by multiple factors, such

as age, family history, lifestyle habits, and the presence of many metabolic changes.

In clinical practice the role of individual risk factors and the prevention of future

cardiovascular events must be evaluated in relation to the overall risk profile.

Moreover, the independent role of hyperandrogenaemia as a metabolic risk

factor is still controversial. A previous study revealed that women with PCOS and

hyperandrogenaemia seemed to have a more adverse cardiometabolic profile and a

greater number of CVD risk factors than non-hyperandrogenic women with PCOS,

indicating that hyperandrogenaemia might play a role as an independent CVD risk

factor (Daan et al., 2014). Interestingly, in the same study it was reported that this

may not apply to women of Northern European descent. Moreover, the phenotype

of PCOS also influenced the prevalence of CVD risk factors (Daan et al., 2014),

and might therefore have an impact on the occurrence of later CVD events.

However, in a recent study it was reported that postmenopausal women with high

androgen levels and retrospectively diagnosed PCOS did not have an increased risk

of CVD (Meun et al., 2018).

It has been speculated that despite the high CVD risk profiles commonly seen

in women with PCOS, later age at menopause and, consequently, more prolonged

oestrogen exposure could alleviate the the CVD burden in PCOS. Later age at

menopause (defined as menopause occurring at age 55 or later) was recently

reported to be associated with significantly longer life expectancy compared with

early-onset menopause (defined as menopause occurring at age 44 or earlier) in a

general female population (Asllanaj et al., 2018). All in all, data on the risks of

CVD events and mortality are still under debate. Whether these conflicting results

are due to a lack of reliable data, variable study designs and/or to the fact that the

women in most studies have been too young to display CVD events, remains to be

resolved in future longitudinal studies.

47

3 Purpose of the present study The main purpose of the present study was to investigate the effects of PCOS on

women’s metabolic and cardiovascular health by using the Northern Finland Birth

Cohort 1966 study population. The prospective, longitudinal study design, with

follow-up from birth to late adulthood in this large cohort offered a unique

opportunity to investigate the impact of PCOS on glucose metabolism and

cardiometabolic morbidity. The study design also allowed us to investigate the

effects of confounding factors, such as weight gain, weight excess, and lifestyle

habits on the development of PCOS itself as well as on the development of

abnormal glucose metabolism and adverse cardiometabolic outcomes.

The specific aims were:

1. To investigate the association between weight gain during life and the

development of PCOS, and to identify parameters associated with PCOS

diagnosis by age 46 (Study I).

2. To study whether the prevalence of abnormal glucose metabolism is increased

in women with PCOS by age 46, independently of BMI (Study II).

3. To explore whether women with PCOS have a higher risk of hypertension at

the ages of 31 and 46, independently of BMI (Study III).

4. To gain insight into whether or not women with PCOS exhibit changes in the

function of the cardiac autonomic nervous system (Study IV).

5. To investigate whether women with PCOS have an increased prevalence of

cardiovascular morbidity and mortality (Study III).

49

4 Study subjects and methods

4.1 Study population

The study is based on the Northern Finland Birth Cohort 1966 (NFBC1966), which

is a large prospective general-population-based longitudinal birth cohort. All

individuals with expected term in 1966 in the two northernmost provinces in

Finland (Oulu and Lapland) were included in this birth cohort. The study

population comprised all individuals born alive in 1966 (12,231 births, 5889

females). Enrolment in this database began at the 24th gestational week and, so far,

this cohort population has been followed at birth, and at ages 1, 14, 31 and 46.

Postal questionnaires were sent at ages 14, 31 and 46, and clinical examinations

were performed at ages 31 and 46. The study followed the principles of the

Declaration of Helsinki. The Ethics Committee of the Northern Ostrobothnia

Hospital District approved the research. All participants took part on a voluntary

basis and signed an informed consent document.

In 1980, at age 14, a postal questionnaire was sent to 5764 females and 95% of

them (n = 5455) responded, with help from their parents. In 1997, at age 31, a

comprehensive postal questionnaire was sent to 5608 women and 4523 (81%) of

them responded. In addition, those living in Northern Finland or in the Helsinki

metropolitan area (n = 4074 women) were invited to a clinical examination. Of

these, 77% (n = 3127) participated in a clinical examination including

anthropometric measurements and collection of blood samples for assessment of

hormonal and metabolic parameters.

Again, in 2012, at age 46, a comprehensive health investigation was carried

out. A postal questionnaire and an invitation to a clinical examination were sent to

all individuals alive and with known addresses (n = 5123 women). Of these, 3706

(72%) women responded to the questionnaire and 3280 women (64%) participated

in the clinical examination.

50

Fig. 9. Data collection from the Northern Finland Birth Cohort 1966 included postal questionnaires and clinical examinations.

4.2 Definition of PCOS and control groups

At age 31, the postal questionnaire included questions on oligo/amenorrhoea: “Is

your menstrual cycle often (more than twice a year) longer than 35 days?” and

excessive body hair: “Do you have bothersome, excessive body hair growth?” Of

the women who responded to these questions, 4.1% (n = 125) reported both

oligo/amenorrhoea and hirsutism (OA+H), after excluding pregnant women,

women using hormonal preparations (n = 1459) or those not permitting the use of

their data for analysis (n = 41). The validity of this questionnaire to distinguish

PCOS cases has already been shown in our previous studies of the same cohort, as

the women with both OA+H present the typical hormonal, metabolic and

psychological characteristics of the syndrome (Karjula et al., 2017; Taponen et al.,

2003; Taponen, Ahonkallio et al., 2004). At age 46, the postal questionnaire

included the question: “Have you ever been diagnosed as having polycystic ovaries

and/or polycystic ovary syndrome (PCOS)?”, to which 181 responded “yes”.

Consequently, women who reported both OA+H at age 31 and/or diagnosis of

PCOS by age 46 were classified as women with PCOS (n = 279). Women without

14 years

Postal questionnaire

Weight and height

31 years


Weight and heightWaist circumference

Blood pressure

Laboratory measurements

46 years


Weight and heightWaist circumference

Blood pressure

Laboratory measurements

Oral glucose tolerancetest

EchocardiographyHeart rate variability

51

any PCOS symptoms at age 31 and without diagnosis of PCOS by age 46 were

classified as controls (n = 1577).

4.3 Methods

Anthropometrics

At ages 14, 31 and 46 the study subjects reported their weight and height in the

postal questionnaires. In addition, weight and height were measured by an

experienced research nurse in the clinical examinations at ages 31 and 46. BMI was

calculated as the ratio of weight (kg) to height squared (m2). Women with BMI <

25.0 kg/m2 were classified as normal weight and those with BMI ≥ 25.0 kg/m2 as

overweight/obese (Obesity: preventing and managing the global epidemic. Report

of a WHO consultation. 2000). Waist circumference was measured at the level

midway between the lowest rib margin and the iliac crest in the clinical

examinations.

Glucose metabolism

Fasting plasma glucose and insulin were measured at ages 31 and 46. In addition,

at age 46, a 2h-OGTT was performed in 2780 women after overnight (12-hour)

fasting. Exclusion criteria for the OGTT were medication for diabetes or fasting

capillary blood glucose level over 8.0 mmol/l. Both serum insulin and plasma

glucose levels were measured at baseline and at 30, 60 and 120 minutes after a 75-

g glucose intake. Glucose levels in fasting plasma (fP) and 2-hour OGTT samples

at each time point were analysed and categorized according to WHO standards

(Alberti & Zimmet, 1998): Normal glucose tolerance (NGT), IFG, IGT, Pre-DM:

IFG or IGT, and T2DM. Furthermore, glucose metabolism disorders were classified

more accurately: previously diagnosed T2DM (pT2DM), new T2DM diagnosis

revealed by a 2h-OGTT at age 46 (nT2DM: fP-glucose ≥ 7.0 mmol/l and/or 2h-

glucose in a 2h-OGTT ≥ 11.1 mmol/l), all type-2 diabetes mellitus diagnoses

(T2DM: pT2DM or nT2DM), abnormal glucose metabolism (AGM: Pre-DM or

T2DM) and new abnormal glucose metabolism disturbance in a 2h-OGTT at age

46 (nAGM: Pre-DM or nT2DM). All the pT2DM cases reported in postal

questionnaires were verified and completed in hospital discharge and national drug

registers from the Social Insurance Institution of Finland.

52

Table 8. Abbreviations and definitions of glucose metabolism status.

Abbreviation Definition

NGT Normal glucose tolerance

IFG Impaired fasting glucose in 2h-OGTT

IGT Impaired glucose tolerance in 2h-OGTT

Pre-DM IFG or IGT

pT2DM Previously diagnosed T2DM

nT2DM New T2DM diagnosis revealed by 2h-OGTT at age

46

T2DM pT2DM or nT2DM

AGM Abnormal glucose metabolism: pre-DM or T2DM

nAGM New AGM: pre-DM or nT2DM

The classification of glucose metabolism is based on WHO criteria (Alberti & Zimmet, 1998).

Blood pressure

In the clinical examination, brachial systolic and diastolic BP were measured twice

at age 31 and three times at age 46 (with a one-minute interval and after 15 minutes’

rest) from the right arm of the seated study subject, using a manual mercury

sphygmomanometer at age 31 and an automated, oscillometric BP device with an

appropriately sized cuff (Omron Digital Automatic Blood Pressure Monitor Model

M10-IT) at age 46. Mean systolic and diastolic BP values were calculated. In

addition, information concerning hypertension diagnosis and use of

antihypertensive medication was gathered from postal questionnaires at ages 31

and 46.

Cardiac structure and function

At age 46, a sub-population was enrolled for echocardiographic examination at the

Oulu research unit. As a rule, every second individual attending the clinical

examination was randomly enrolled for echocardiographic examination without

any preselection criteria, and without the participant’s own wish influencing the

enrolment. All participants agreed to take part in echocardiography. In total,

echocardiography was performed in 645 women, including 37 women with PCOS

and 283 control women. The transthoracic 2D echocardiography was performed

on-line by an experienced cardiologist, using a General Electric Vivid E9 device

with an M5S-D 1.5/4.6 MHz sector transducer for cardiovascular imaging (GE

Health Medical, Horten, Norway). All measurements followed the American

53

Society of Echocardiography guidelines (Lang et al., 2005). In addition, global

longitudinal strain, a novel indicator of the overall systolic function of the LV, was

assessed off-line using EchoPac 7 software (automated function imaging)

(Patrianakos et al., 2015).

Cardiac autonomic function

The clinical examination at age 46 included assessments of HRV and baroreflex

sensitivity as part of the broad cardiovascular health status evaluation. The study

subjects were informed about the measurement protocol (shown below, Figure 10).

Fig. 10. Flow chart illustrating the HRV measurement protocol.

A heart rate (HR) monitor (RS800CX, Polar Electro Oy, Kempele, Finland) was

used to record RRi. In addition, standard lead-II ECG (Cardiolife, Nihon Kohden,

Tokyo, Japan), breathing frequency (MLT415/D, Nasal Temperature Probe,

ADInstruments, Bella Vista, New South Wales, Australia), and BP by finger

photoplethysmography (Nexfin, BMEYE Medical Systems, Amsterdam, the

Netherlands) were recorded, with a sampling frequency of 1,000 Hz (PowerLab

8/35, ADInstruments). The preparations were followed by at least a 1-minute

stabilization period before the beginning of the 3-minute recording period in a

seated position. After that recording period, the participants stood up and remained

still in a standing position for another three minutes while breathing normally. The

first 150 seconds of recording in a seated position and the last 150 seconds in

standing position were used in the analysis. The RRi data were edited on the basis

54

of visual inspection, artefacts and ectopic beats were removed and replaced with

the local average (Hearts 1.2, University of Oulu, Oulu, Finland). Sequences with

≥ 10 consecutive beats of noise, and ectopic beats were deleted. RRi series with ≥

80% accepted data were included in the analyses. The final study population

included 1029 controls and 160 women with PCOS. Mean HR, RMSSD, spectral

power densities (fast Fourier transformation, length 512 beats) including LF and

HF components of HRV, and their ratio (LF/HF), were analysed.

Spontaneous baroreflex sensitivity (BRS) was assessed in participants studied

at the Oulu laboratory unit. Continuous ECG, BP and respiration signals were

imported to custom-made stand-alone Matlab-based software (Biosignal

Processing Team, University of Oulu, Oulu, Finland), where RRi and systolic BP

values were extracted. Artefacts and ectopic beats were replaced using linear

interpolation (< 5% for accepted recording) and thereafter, resampled at 2 Hz and

detrended (< 0.04 Hz, Savitzky–Golay method). A fast Fourier transform (Welch

method, segments of 128 samples with 50% overlap) was performed to analyse low

frequency (LF: 0.04–0.15 Hz) power of RRi and systolic BP oscillations for

subsequent analysis of BRS by the alpha method, if sufficient coherence (≥ 0.5)

between LF oscillations in RRi and systolic BP was verified. BRS was successfully

calculated in 609 controls and 105 women with PCOS.

Cardiovascular morbidity and mortality

In Finland, all individuals have a unique personal identification number.

Individuals with serious acute illnesses are treated in public specialised health care

centres, which report the diagnoses to the hospital discharge and hospital outpatient

clinic registers. An individual’s data can be retraced from these national registers

using the unique personal identification number. ICD-8, ICD-9 and ICD-10

diagnostic codes for cardiovascular morbidity and events (I20, I21-25, I50, I60-I63,

I63-I64, I65-I68, I69, I70, G45, G46) were identified from hospital discharge,

hospital outpatient clinic and basic health care registers, with data covering 1972–

2015, 1998–2015 and 2011–2015, respectively. Cardiovascular mortality data was

retrieved from the mortality register of Statistics Finland, which covers the

information about cause and time of death. According to Finnish law, it is

mandatory to report an occurrence of death and cause of death to this register and

thus the coverage is practically 100%.

55

Laboratory methods

At age 31, plasma glucose, and serum concentrations of total cholesterol, high-

density lipoprotein cholesterol (HDL-cholesterol), low-density lipoprotein

cholesterol (LDL-cholesterol), triglycerides, insulin, hsCRP and SHBG were all

assayed as previously described (Taponen et al., 2003).

Serum T concentrations at the ages of 31 and 46 were assayed using Agilent

triple quadrupole 6410 LC/MS equipment (Agilent Technologies, Wilmington, DE,

USA). The normal upper limit for T was 2.3 nmol/l at age 31 and 1.70 nmol/l at

age 46, based on 97.5% percentiles calculated in this study population.

At age 31 SHBG was assayed as previously described (Taponen et al., 2003),

and at age 46 by chemiluminometric immunoassay (Immulite 2000, Siemens

Healthcare, Llanberis, UK). As the SHBG analysis method had changed, serum

SHBG concentrations analysed by the Immulite method were compared with

concentrations analysed by the former method (fluoroimmunoassay, Wallac, Inc.

Ltd., Turku, Finland) by using linear regression analysis (y = 0.7615x + 0.7088)

and coefficient of determination (0.9915). Therefore, the SHBG values at age 31

were transformed to be comparable with the values analysed at age 46 by using the

formula 0.7615 × old method 31yr SHBG + 0.7088, and the results are reported

accordingly. The FAI was calculated by using the equation 100 × T (nmol/l)/SHBG

(nmol/l).

Concentrations of HbA1c and total haemoglobin were measured by an

immunochemical assay method (Advia 1800; Siemens Healthcare Diagnostics Inc.,

Tarrytown, Ny, USA) and their ratios were reported as mmol/mol. All samples were

analysed at NordLab Oulu, a testing laboratory (T113) accredited by the Finnish

Accreditation Service (FINAS) (EN ISO 15189).

4.3.1 Statistical analysis

BMI values at the ages of 31 and 46 were obtained mainly at the clinical

examination and supplemented with self-reported BMI if clinically measured BMI

was not available. There were no differences between the self-reported and

clinically measured BMI values. Continuous data were presented as means ±

standard deviation (SD) or as medians with 25% and 75% quartiles and differences

in continuous parameters were analysed by Student’s t-test or by the Mann–

Whitney U-test, as appropriate. Categorial data were analysed using cross-

tabulation and Pearson’s Chi-squared (χ²) test or Fisher’s exact test. Binary logistic

56

regression models as well as linear regression models were also used, with

adjustment for appropriate confounding factors. Statistical analyses were

performed using IBM SPSS Statistics 22.0 and 24.0 (SPSS, Inc., 1989, 2013, IBM

Corp.). Values of p < 0.05 were considered as statistically significant.

Specific methods in different studies

Study I. Multivariate binary logistic regression models included the parameters

significantly associated (p < 0.15) with the diagnosis of PCOS in the univariate

regression analysis (Akaike, 1973). The FAI was used in the models as it is

generally considered as a good indicator of biochemical hyperandrogenism in

women with PCOS. HOMA-IR was selected to estimate insulin resistance, and

serum levels of LDL and triglycerides were included in the models as they represent

the most typical lipid disorders observed in women with PCOS. The maximum

number of variables was limited to five in each model.

Studies II and III. Binary logistic regression models were adjusted for

consumption of alcohol (no use, light [> 0–150 g/week], moderate [> 150–210

g/week] and heavy use [> 210 g/week]), smoking (never a smoker, former smoker

for > 6 months, former smoker for < 6 months and current smoker), education

(basic, secondary, tertiary), current use of cholesterol-lowering drugs, and of

combined contraceptive pills at age 46. In Study III effect sizes were reported as

Cohen’s d-values.

Study IV. Women using beta-blockers were excluded from the HRV analysis

(104 controls [7.7%] and 30 women with PCOS [13.3%]). Continuous variables

with skewed distributions were transformed into natural logarithms (ln) for the

analysis of difference between the PCOS and control groups. Mean arterial pressure

(MAP) was calculated as follows: DBP + 1/3 (SBP - DBP).

Correlation analysis as well as univariate and multivariate linear regression

analyses were used to study factors associated with HRV parameters. First,

univariate linear regression models were used to reveal the parameters significantly

associated with the outcome variable. Then, step-wise multivariate models were

used to identify the most important explanatory variables. The final multivariate

model included the following as explanatory variables: PCOS, BMI, MAP, FAI,

HOMA-IR and triglycerides. The number of explanatory variables included in the

model had to be limited to avoid multicollinearity. BMI was selected for the model,

as it significantly differs between PCOS and control women, and obesity is

suggested to effect HRV. MAP was selected because it combines information from

57

both systolic and diastolic BP, and FAI because it is considered as a good indicator

of hyperandrogenism in women with PCOS, and hyperandrogenism has been

suggested to alter HRV in cases of PCOS. HOMA-IR was used as an estimate of

insulin resistance. Anxiety was not included in the final multivariate model,

because in the preliminary models using the step-wise method, anxiety was always

the first variable to be excluded (indicating that anxiety was not significantly

associated with RMSSD).

The results of linear regression models were reported as unstandardized

coefficients (B), the 95% confidence interval for B, p-values and the R2 value for

the model. The multicollinearity assumptions in the multivariate linear regression

model were investigated using VIF, tolerance and eigenvalue indexes. In addition,

histograms of regression standardized residual frequency, normal P-P plots of

regression-standardized residuals and scatterplot figures were visually inspected to

ensure that the model met the analysis assumption.

58

Tabl

e 9.

Cha

ract

eris

tics

and

resu

lts o

f Stu

dies

I–IV

.

Stu

dy

char

acte

ristic

s

Stu

dy I

Stu

dy II

S

tudy

III

Stu

dy IV

Num

ber o

f

subj

ects

125

wom

en w

ith P

CO

S

sym

ptom

s (O

A+H

) at a

ge 3

1,

181

wom

en w

ith s

elf-r

epor

ted

PC

OS

dia

gnos

is b

y ag

e 46

,

1577

con

trols

279

PC

OS

(i.e

. OA

+H a

t age

31

and/

or s

elf-

repo

rted

PC

OS

dia

gnos

is b

y ag

e 46

), 15

77

cont

rols

279

PC

OS

, 157

7 co

ntro

ls

279

PC

OS

, 157

7 co

ntro

ls

Out

com

e

mea

sure

men

t

Fact

ors

asso

ciat

ed w

ith

sym

ptom

s or

dia

gnos

is o

f

PC

OS

Glu

cose

met

abol

ism

abn

orm

aliti

es

Hyp

erte

nsio

n, c

ardi

ac s

truct

ure

and

func

tion,

CV

D

Car

diac

aut

onom

ic

func

tion

Mai

n re

sults

B

MI w

as s

igni

fican

tly g

reat

er

at a

ges

14, 3

1 an

d 46

in

PC

OS

wom

en.

Wei

ght g

ain

in e

arly

adul

thoo

d w

as s

igni

fican

tly

asso

ciat

ed w

ith la

ter P

CO

S

diag

nosi

s.

Nor

mal

-wei

ght w

omen

with

PC

OS

did

not

have

incr

ease

d od

ds fo

r T2D

M.

Ove

rwei

ght/o

bese

wom

en w

ith P

CO

S h

ad

sign

ifica

ntly

hig

her o

dds

for T

2DM

than

over

wei

ght/o

bese

con

trols

.

PC

OS

was

ass

ocia

ted

with

hype

rtens

ion,

but

BM

I had

hig

her

odds

for h

yper

tens

ion

than

PC

OS

.

Pre

vale

nce

of C

VD

was

sig

. hig

her

in w

omen

with

PC

OS

.

Wom

en w

ith P

CO

S

disp

laye

d im

paire

d H

RV

,

whi

ch w

as d

epen

dent

on

met

abol

ic a

bnor

mal

ities

.

59

5 Results and Discussion

5.1 Association of weight gain with the development of PCOS by age 46 (Study I)

The aim of Study I was to clarify the change of BMI from age 14 to age 46 in

women with PCOS compared with controls and to identify the factors associated

with the presence of PCOS diagnosis by age 46. The most important finding was

that women with typical PCOS symptoms (OA+H at age 31) or diagnosis of PCOS

by age 46 had significantly greater BMI values (Figure 11) and greater prevalence

of obesity compared with the control women at ages of 14, 31 and 46 years.

Fig. 11. BMI values at ages 14, 31 and 46 in controls, in women with OA+H at age 31, and in women with self-reported PCOS diagnosis by age 46. ***p < 0.001 and **p < 0.01.

In addition, women with self-reported symptoms at age 31 or self-reported

diagnosis of PCOS by age 46 had experienced significantly greater weight gain in

early adulthood (between ages 14 and 31) compared with control women, whereas

weight gain in later adulthood (between ages 31 and 46) was comparable between

these groups. Women with self-reported symptoms at age 31 or self-reported

diagnosis of PCOS by age 46 also exhibited significantly greater abdominal obesity,

measured by waist circumference, both at ages 31 and 46, compared with control

women (Figure 12).

60

Fig. 12. Waist circumference at ages 31 and 46 in controls, in women with OA+H at age 31 and in women with self-reported PCOS diagnosis by age 46. ***p < 0.001.

In univariate binary logistic regression analysis, high BMI in adolescence, early

adulthood and late adulthood, as well as waist circumference at ages 31 and 46,

were significantly associated with self-reported diagnosis of PCOS by age 46

(Figure 13). Of these, BMI at age 31 was the strongest predictor (highest R2 value

0.028). Moreover, the change in BMI between 14 and 31 years (R2 value = 0.013),

but not between 31 and 46 years, was a significant risk factor of PCOS.

61

Fig. 13. Univariate binary regression analysis for self-reported PCOS diagnosis by age 46, Part I.

Additionally, at age 31, HOMA-IR, the FAI and serum concentrations of T, insulin,

LDL, hsCRP, total cholesterol and triglycerides were significantly positively

associated and serum levels of SHBG negatively associated with a diagnosis of

PCOS by age 46 in univariate regression analysis (Figure 14).

62

Fig. 14. Univariate binary regression analysis for self-reported diagnosis of PCOS by age 46, Part II.

In multivariate regression analysis, the FAI (OR = 1.08, 95% CI: 1.03–1.14) and

serum levels of triglycerides (OR = 1.48, 95% CI: 1.08–2.03), insulin (OR = 1.05,

95% CI: 1.00–1.09), SHBG (OR = 1.01, 95% CI: 1.00–1.01) and T (OR = 1.44,

95% CI: 1.15–1.81) at age 31 were associated with PCOS by age 46, after

adjustment for BMI at age 31. However, when BMI, the FAI, HOMA-IR and serum

levels of LDL and triglycerides were included in the same multivariate binary

regression analysis for explaining PCOS by age 46, only BMI at age 31 (OR = 1.05,

95% CI: 1.00–1.10) and the FAI at age 31 (OR = 1.08, 95% CI: 1.02–1.14)

remained significantly associated with diagnosis of PCOS by age 46.

The present data analysis completed and strengthened previous findings in this

same cohort (Laitinen et al., 2003) by extending follow-up until late adulthood. The

results showed that weight gain in early adulthood, but not later, was significantly

associated with self-reported diagnosis of PCOS by age 46. Our results are also in

line with those of an Australian longitudinal general-population-based study (Teede

et al., 2013), showing that obesity and greater weight gain between the ages of 20

and 30 were associated with PCOS status at age 27–30. Similarly, in a longitudinal

general-population-based study it was also reported that women with PCOS were

1.6 times more likely to experience a moderate increase of weight and were 4.7

63

times more likely to experience a large increase of weight than healthy control

women (Kakoly et al., 2017). In the light of a previous developmental hypothesis

of the pathophysiology of PCOS, these results suggest strongly that major weight

gain in early adulthood as well as and obesity are among the major triggering

factors for the development of overt PCOS (Abbott et al., 2002; Escobar-Morreale

& San Millan, 2007; Homburg, 2009). In line with this, an early childhood BMI

rise has been associated with PCOS diagnosis (Koivuaho et al., 2019). A high

prevalence of overweight and obesity might also predispose women with PCOS to

cardiovascular diseases and a shorter life span. Accordingly, in a large cohort study

from the United Kingdom it was reported that individuals of normal weight had a

3.5-year longer life expectancy compared with obese women, and that BMI had a

J-shaped association with cardiovascular diseases independently of confounding

factors (Bhaskaran, Dos-Santos-Silva, Leon, Douglas, & Smeeth, 2018).

5.2 Abnormal glucose metabolism in women with PCOS (Study II)

Study II was focused on glucose metabolism abnormalities in women with PCOS

and we aimed to investigate the respective relationships between PCOS, long-term

weight gain and obesity with the development of pre-DM and T2DM by age 46.

The main finding was that only overweight and obese women, but not normal-

weight women with PCOS were at an increased risk of T2DM by age 46, suggesting

that the risk of T2DM in women with PCOS is mainly due to excess weight, which,

as shown in Study I as well as in numerous previous studies (Gambineri et al., 2002;

Targher et al., 2009; Yildiz et al., 2008), is a very common disorder in women with

PCOS.

64

Fig. 15. Prevalence of T2DM and pre-diabetes in normal-weight controls and in women with PCOS at age 46.

Fig. 16. Prevalence of T2DM and pre-diabetes in overweight/obese controls and women with PCOS at age 46.

Most of the T2DM cases among women with PCOS had already been diagnosed

by age 46, as only four nT2DM cases were identified on the basis of the 2h-OGTT

results in this study. Of note, the mean BMI of these nT2DM cases was 36.0 ± 5.3

(SD) kg/m2. Women with PCOS and T2DM also had significantly higher levels of

HbA1c compared with women with PCOS and NGT, but HbA1c was ≥ 48

mmol/mol (the cut-off value for T2DM) in only four of 23 women with PCOS and

T2DM. Our findings suggest that it is not necessary to perform 2h-OGTTs routinely

in normal-weight women with PCOS, whereas 2h-OGTT screening should be

focused on overweight/obese women with PCOS, and HbA1c seems not to be an

accurate tool to detect T2DM in women with PCOS. Our findings are well in line

with the recent international evidence-based PCOS guidelines (Teede et al., 2018),

65

which recommend the use of 2h-OGTTs in the evaluation of glucose metabolism

in overweight/obese women with PCOS.

Polycystic ovary syndrome per se was associated with an increased risk of

T2DM in overweight/obese women compared with overweight/obese controls (OR

2.45, 95% CI: 1.28–4.67). However, we were unable to statistically assess the

interaction between PCOS and BMI, due to a lack of normal-weight women with

both PCOS and T2DM. In addition, we found that weight gain in early adulthood,

between ages 14 and 31, was significantly greater in women with both PCOS and

T2DM at age 46 than in women with PCOS and NGT (Figure 17).

Fig. 17. Development of BMI in women with PCOS according to glucose metabolism status at age 46. ***p < 0.001, **p < 0.01 and *p < 0.05.

Our findings were recently confirmed in a cross-sectional Nordic multicentre study

of 876 women with PCOS from Norway, Sweden, Denmark and Finland who

underwent 2h-OGTTs. The results indicated that none of the normal-weight women

with PCOS had T2DM, and that elevated BMI was the most important risk factor

of abnormal glucose metabolism in PCOS (Pelanis et al., 2017). Likewise, in a

nationwide study of Danish women it was found that BMI and fasting blood

glucose levels were the best predictors of the development of T2DM in women

with PCOS (Rubin, Glintborg, Nybo, Abrahamsen, & Andersen, 2017). In the same

study it was also reported that the risk of development of T2DM in normal-weight

66

women with PCOS vs. age-matched controls was not elevated (hazard ratio 1.22

[95% CI: 0.58–2.55], p = 0.60), whereas in their overweight/obese group, the

hazard ratio was as high as 6.57 (95% CI: 4.53–9.54, p < 0.001) compared with

age-matched controls (Glintborg, Rubin, Abrahamsen, & Andersen, 2018).

Recently, an updated version (Kakoly et al., 2018) of a previous meta-analysis

and systematic review of glucose metabolism in women with PCOS (Moran et al.,

2010) was published. Interestingly, this recent update, focusing only on high-

quality studies, revealed that women with PCOS had a significantly higher

prevalence of T2DM compared with non-PCOS controls. However, in subgroup

analysis there were no significant differences in the prevalence of T2DM among

European women with and without PCOS, and the same was found in BMI-

matched studies (Kakoly et al., 2018).

We also found that women with both PCOS and T2DM had comparable levels

of serum T at the ages of 31 and 46 as the women with PCOS and NGT, suggesting

that hyperandrogenaemia is not associated with abnormal glucose metabolism. This

is in line with the results of a previous retrospective cohort study (Mani et al., 2013).

In conclusion, our results together with the above-mentioned recent meta-

analysis suggest that in Nordic populations, normal-weight women with PCOS do

not have an increased prevalence of glucose metabolism abnormalities. However,

overweight and obese women with PCOS have a significantly higher risk of T2DM

when compared with overweight and obese control women. Weight gain during

early adulthood emerged as a significant risk factor of later development of T2DM,

emphasizing the important role of weight management, especially during early

reproductive years. Our results are best generalised to European populations, as in

the aforementioned recent meta-analysis it was reported that ethnicity has a marked

impact on the prevalence of abnormal glucose metabolism (Kakoly et al., 2018).

5.3 Hypertension and cardiac structure and function in PCOS (Study III)

The main aim of Study III was to investigate the prevalence of hypertension at age

46 in women with PCOS, based on BP levels at clinical examination, hypertension

diagnosis set by a physician, and self-reported use of antihypertensive medication.

Secondly, we investigated whether the heart’s structure and function are altered in

women with PCOS at age 46. Our main finding was that women with PCOS were

significantly more often hypertensive (and diagnosed as such) and more often used

antihypertensive medication compared with controls. Moreover, we found that in

67

hypertensive women with PCOS, elevated BP was associated with adverse changes

in heart structure and function compared with normotensive women.

Hypertension

At age 31, women with PCOS (both normal-weight and overweight/obese groups)

displayed significantly higher systolic and diastolic BP levels when compared with

control women of similar weight (Figure 18). At age 46, normal-weight women

with PCOS displayed significantly higher diastolic, but not systolic BP compared

with controls, whereas there were no significant differences between

overweight/obese PCOS women and overweight/obese controls (Figure 18). Of

note, at age 46, the prevalence of self-reported obstructive sleep apnoea was

comparable in the PCOS and control groups (1.3% [n = 3/238] vs. 1.2% [n =

19/1543]), indicating that obstructive sleep apnoea did not affect the results.

However, the possibility of underdiagnosis of obstructive sleep apnoea should be

kept in mind.

Our findings are in accordance with the results of two previous (cross-sectional)

studies including adolescents (Zachurzok-Buczynska et al., 2011) and young

reproductive-aged women (Joham et al., 2015). In these studies it was found that

normal-weight women with PCOS had significantly higher BP levels compared

with normal-weight controls, whereas overweight/obese PCOS and control groups

had similar prevalence rates of hypertension (Joham et al., 2015; Zachurzok-

Buczynska et al., 2011).

68

Fig. 18. Blood pressure levels in controls and in women with PCOS according to BMI group at ages 31 and 46. **p < 0.01 and *p < 0.05.

Self-reported use of antihypertensive medication was significantly more common

among women with PCOS when compared with controls both at ages 31 and 46

(Figure 19), as was the prevalence of self-reported hypertension diagnosis at age

46 (30.1% [n = 72/239] vs. 18.2% [n = 283/1556], p < 0.001). Again, these

findings are in line with the results of the Danish nationwide register study, in which

it was reported that hypertension diagnosis (based on ICD codes) and use of

antihypertensive medication were significantly more common among women with

PCOS (mean age 29 years) when compared with the controls (Glintborg et al.,

2015).

69

Fig. 19. Prevalence of use of antihypertensive medication (%). ***p < 0.001 and *p < 0.05.

In binary logistic regression analysis, PCOS was significantly associated with

hypertension at age 46 independently of overweight/obesity (Figure 20). This

difference persisted after adjustment for other potential confounding factors such

as smoking, education level, alcohol consumption, physical activity and T2DM. In

line with our findings, in a large American register study it was reported that PCOS

was significantly associated with hypertension and elevated BP in adjusted

regression analysis (Lo et al., 2006).

Fig. 20. Binary logistic regression analysis of hypertension (HTA) at age 46.

70

Taken together, women with PCOS were significantly more often hypertensive

(and diagnosed as such) and more often used antihypertensive medication

compared with controls. Women with PCOS also already had significantly higher

BP levels in their 30s when compared with control women in a similar BMI group.

Our results, together with those in the aforementioned previous studies, indicate

that PCOS is an independent risk factor of hypertension, although weight excess

emerged as a major determinant of hypertension in regression analysis.

Long-term epidemiological studies have demonstrated a clear relationship

between BP and long-term risk of CVD events and mortality in young adults with

BP > 130/80 mmHg (Williams et al., 2018). In previous studies it has also been

reported that an increase of BP of 1.5–2 mmHg has a marked impact on CVD risk

at population level (Staessen et al., 1997). Conversely, a reduction of diastolic BP

by as little as 2 mmHg can result in a 6% reduction in the risk of coronary heart

disease and a 15% reduction in the risk of stroke and transient ischaemic attacks

(Cook, Cohen, Hebert, Taylor, & Hennekens, 1995) at a population level. In the

same study it was also pointed out that these small changes are also important when

BP is at a so-called high-normal level, because a large proportion of the general

population displays BP levels in that range, therefore having a marked impact on

the attributable risks of stroke and coronary heart disease in general (Cook et al.,

1995). In that context, the relatively modest absolute BP difference in our study

between PCOS and control women may be of major importance at a population

level.

An elevated morning BP surge is a predictor of adverse cardiovascular events

and, interestingly, in a recent study it was reported that young women with PCOS

(mean age 27 years) displayed a significantly greater morning BP surge than control

women (Kadi, Avci, Usta, & Demirtaş, 2018). The authors suggested that an

exacerbated morning BP surge might be explained by increased IR and increased

sympathetic and renin-angiotensin-aldosterone activities in PCOS. Blood pressure

and glucose metabolism might also be connected to each other, as vasopressin

influences gluconeogenesis and glucogenolysis in the liver as well as insulin

release from the pancreas (Bankir, Bichet, & Morgenthaler, 2017). In a study of

BMI- and age-matched reproductive-aged controls and women with PCOS it was

also reported that copeptin, a surrogate marker of vasopressin, was significantly

associated with HOMA-IR, fasting insulin, and free T (Karbek et al., 2014).

In conclusion, women with PCOS displayed higher BP than control women,

and excess weight seemed to be a major determinant of elevated BP in women with

PCOS, although other compex mechanisms may also be implicated. The present

71

findings strengthen the need for early systematic screening and efficient treatment

of hypertension in women with PCOS.

Cardiac structure and function

At age 46, women with PCOS displayed small, adverse changes in cardiac structure,

such as increases in interventricular septal thickness at diastole (IVSd: 0.91cm ±

0.1 vs. 0.87 cm ± 0.2, p = 0.154), left ventricular mass index (LVMI: 157.51 ± 32.0

vs. 150.43 ± 38.3, p = 0.283) and left atrial systolic volume (LAESV: 53.09 ml ±

15.3 vs. 50.67 ml ± 15.1 p = 0.376), compared with the control women. There were

no significant differences in systolic (evaluated by left ventricle ejection fraction

and global strain) or diastolic function between PCOS and control women.

Compared with normotensive control women, hypertensive women with

PCOS had significantly higher values of IVSd, LVMI and LAESV (Figure 21).

Estimated left ventricular filling pressure (E/e’) was also increased in hypertensive

PCOS women, suggesting modest alterations in diastolic function in this PCOS

subgroup (Figure 21). Left ventricular systolic function was mildly decreased in

hypertensive PCOS women (assessed by Global strain) even though the difference

in LVEF was not statistically significant.

72

Fig. 21. Echocardiographic parameters in normotensive controls and in hypertensive PCOS women.

Increased interventricular septal thickness, left ventricular mass and left atrial

systolic volume are typical adaptive changes of the heart in response to elevated

BP. These changes are thought to predispose individuals later to heart failure with

preserved ejection fraction (Ponikowski et al., 2016). Our findings suggest that

elevated BP is the main determinant of adverse cardiac structure in women with

PCOS, as normotensive PCOS and control groups showed comparable cardiac

structure and function. The relatively small number and young age of the women

explain most likely why there were only a few statistically significant differences

in the echocardiographic parameters. Our findings are in line with those in three

previous studies (Orio et al., 2004; Tíras et al., 1999; Wang et al., 2012), which also

revealed changes in left atrial size, left ventricular mass and in the parameters

73

describing systolic and diastolic function. However, conflicting results have also

been reported (Kosmala et al., 2008; Selcoki et al., 2010), and overall, the roles of

confounding factors, such as hypertension, IR and hyperandrogenaemia, are still

unclear and need to be investigated in future studies with adequate sample sizes.

Moreover, the controversial findings in previous studies might also be explained

by differences in age and ethnicity of the study groups as well as by the criteria

used for the diagnosis of PCOS.

5.4 PCOS and cardiac autonomic function (Study IV)

The main aim of Study IV was to investigate whether premenopausal women with

PCOS display impaired HRV as an indicator of impaired cardiac autonomic

function, independently of confounding metabolic abnormalities, such as excess

weight, abdominal obesity, hyperandrogenaemia, increased BP, dyslipidaemia, and

IR. The main finding was that women with PCOS displayed significantly decreased

vagal activity, but this seemed to be mainly associated with the metabolic

abnormalities linked to the syndrome and not to PCOS itself.

At age 46, women with PCOS had significantly higher mean HR values and

significantly lower values of RMSSD, LFRRi, HFRRi and BRS, when compared with

control women (Figure 22). However, when the analyses were adjusted for BMI,

women with PCOS showed significantly smaller values only for RMSSD (p =

0.033) and HFRRi (p = 0.016), when compared with the controls.

74

Fig. 22. HRV and baroreflex sensitivity in controls and in women with PCOS in a seated position. HR: Heart rate, RMSSD: The square root of the mean squared differences of successive normal-to-normal RR intervals (RRi), LFRRi: low frequency power, HFRRi: high frequency power, α1: short-term fractal-like scaling exponent in detrended fluctuation analysis, SBP: systolic blood pressure, BRS: baroreflex sensitivity.

75

The confounding effect of metabolic abnormalities was analysed in multivariate

linear regression analysis. The multivariate linear regression model included

PCOS, BMI, MAP, FAI, HOMA-IR and triglycerides at age 46 as explanatory

variables for RMSSD. The model showed that PCOS, BMI and the FAI were not

significantly associated with RMSSD, whereas MAP, HOMA-IR and triglycerides

were. Anxiety (assessed by Hopkins’ Symptom Check List-25, a well-known and

widely used symptom inventory) was not included in the final multivariate model,

because in the preliminary models, using a step-wise method, anxiety was not

significantly associated with RMSSD.

Total and calculated free T at age 46 was not significantly associated with

RMSSD in linear regression analysis. Serum levels of SHBG were positively

associated with RMSSD (B = 0.001, 95% CI: 0.001–0.002, p < 0.001) and the FAI

was negatively associated with it (B = -0.121, 95% CI: -0.199– -0.042, p = 0.003),

but both associations lost their significance after adjustment for BMI.

In line with our findings, in a study of 60 normal-weight women with PCOS

(mean age 25 yr) and 60 age-matched normal-weight controls, with comparable

HOMA-IR indices, it was reported that there was no difference between the groups

in HRV (Kilit & Paşalı Kilit, 2017). These investigators also reported that serum T

did not correlate with HRV among the study population, which is also in line with

our findings. In another study, including 19 overweight/obese women with PCOS

(mean BMI 31 kg/m2) and 19 similar-weight controls it was reported that HRV did

not differ between the groups, whereas women with PCOS had elevated MSNA

activity (Lambert et al., 2015). In that study, IR and glucose metabolism status

seemed to be the main mediators of sympathetic tone, regardless of PCOS status,

and T was not associated with MSNA (Lambert et al., 2015). In another cross-

sectional study, concerning 31 women with PCOS and 30 age-matched controls it

was reported that women with PCOS had significantly increased sympathetic

activity as well as decreased parasympathetic activity, and they had a significantly

more adverse metabolic profile than the control women (Saranya et al., 2014). In a

study of 23 young women with PCOS and 25 healthy controls with a similar

metabolic profile and normal BMI, comparable HRV parameters between the

groups were reported (Özkeçeci et al., 2016).

In accordance with our findings, in another study it was found that normal-

weight PCOS and control groups had comparable HRV parameters, whereas in the

overweight/obese group, women with PCOS had significantly impaired HRV

compared with those in other groups (Hashim et al., 2015). It is well known that

metabolic profiles differ between PCOS phenotypes, suggesting that autonomic

76

function might also differ across PCOS phenotypes. Accordingly, women with

classic PCOS, i.e. hyperandrogenism and anovulation displayed altered HRV

responses to a mental stress test, whereas ovulatory women with PCOS did not

significantly differ from controls (Di Domenico et al., 2013). This finding may be

due to the fact that women with classic PCOS have a significantly more adverse

metabolic and hormonal profile than ovulatory women with PCOS and controls.

Last, another study showed that the adverse HRV in women with PCOS was

strongly related to BMI, dyslipidaemia, inflammation and IR (de Sá, Joceline

Cássia Ferezini et al., 2011).

Our findings together with previous reports indicate that women with PCOS

display impaired HRV mainly because of the metabolic abnormalities often co-

existing with PCOS. In line with ourselves, most investigators have come to the

conclusion that obesity, IR, hyperinsulinaemia, and elevated BP are the main

determinants of altered autonomic function in women with PCOS. Obesity, and

especially excess visceral adipose tissue, correlates with decreased HRV both in the

general population (Windham et al., 2012) and in women with PCOS (Hashim et

al., 2015). However, obesity seems to increase sympathetic activity in the kidneys

and in skeletal muscle vascularity, but reduces it in the heart (Vaz et al., 1997),

which might partly explain conflicting results between studies involving HRV and

MSNA techniques. There also seems to be a complex relationship between excess

adipose tissue, chronic inflammation, IR and sympathetic action in PCOS, but the

exact mechanisms among these factors are still unclear (Lansdown & Rees, 2012;

Shorakae et al., 2018).

Impaired autonomic function has been suggested to be associated with

hyperandrogenaemia and elevated BP in PCOS (Perciaccante, Fiorentini, Valente,

& Tubani, 2007). The role of hyperandrogenaemia seems to be controversial, as

some have found that it was significantly associated with autonomic function

(Sverrisdóttir et al., 2008), whereas others (Kilit & Paşalı Kilit, 2017; Lambert et

al., 2015; Yildirir et al., 2006), including ourselves, have not. These conflicting

results might be due to differences in the assessment and definition of

hyperandrogenaemia, different T assays and overall differences in study

populations. However, weight loss has been reported to improve HR (and thus

vagal activation) and to reduce waist circumference, BP, IR and

hyperandrogenaemia in a 10-week prospective clinical intervention including 57

overweight/obese women with PCOS (Thomson et al., 2010).

77

5.5 PCOS and cardiovascular disease morbidity (Study III)

An important aim of Study III was to investigate whether or not premenopausal

women with PCOS have a greater prevalence of CVD events than control women.

We found that the prevalence of myocardial infarction was already significantly

higher in premenopausal women with PCOS (1.8% vs. 0.5%, respectively, p =

0.034), as was the prevalence of any cardiovascular disease (6.8% vs. 3.4%, p =

0.011) (Figure 23). In addition, by age 46, two women with PCOS, but no woman

in the control group, had died as a result of ischaemic heart disease.

Fig. 23. Prevalence of acute myocardial infarction and any CVD event in controls and women with PCOS by age 46.

Our findings suggest that women with PCOS have an increased prevalence of

cardiovascular morbidity. Importantly, we were able to study data on

cardiovascular morbidity from high-quality registers that have been shown to have

very high coverage and accuracy (88–98%) as regards vascular diseases in Finland

(Sund, 2012), and the CVD event diagnoses were also verified by patient chart

review. However, these results need to be interpreted with caution, as the number

of affected women with overt CVD was very small, and we need to further follow

these women to clarify how the prevalence will develop with aging.

Our findings are in line with the results of some (Glintborg et al., 2015;

Glintborg et al., 2018; Mani et al., 2013) but not all previous studies (Iftikhar et al.,

2012; Lo et al., 2006; Merz et al., 2016; Meun et al., 2018; Morgan et al., 2012;

Schmidt et al., 2011). These divergent results might be explained by differences in

study designs, definitions of PCOS and CVD events, PCOS phenotypes, ages of

78

study populations, and different ethnicities. In addition, the overall CVD event risk

is a combination of multiple different factors, of which PCOS might be merely one

possible contributing factor. In future studies, the definition of CVD events should

be harmonized according to recent recommendations (Hicks et al., 2018).

Neverthless, our results suggest strongly that CVD risk factors should be efficiently

managed in women with PCOS, even though it is still unclear whether women with

PCOS are at a higher risk of CVD events than non-PCOS women.

5.6 Strengths and limitations

The main strength of the present study is that it is a prospective general-population-

based cohort study, with a long follow-up period. Moreover, the study concerns a

research population with exceptionally high participation and response rates and

low dropout rates. Furthermore, anthropometric parameters as well as BP were not

merely self-reported, and glucose metabolism status was assessed by 2h-OGTTs in

the great majority of women. We were also able to combine information on

physician-made diagnoses and register data on types of medication to provide the

most accurate estimates of the prevalence of hypertension and T2DM. In addition,

the study population consisted of similar-aged subjects and we were able to adjust

the analysis for a wide variety of important confounding factors.

We were also able to analyse the prevalence of CVD events and CVD-related

deaths from high-quality registers (hospital discharge and hospital outpatient clinic

registers, and the death register). Since 1969, according to Finnish law, it has been

mandatory to report all diagnoses to the hospital discharge and hospital outpatient

clinic registers. These registers are among the oldest individual-level hospital

discharge registers in the world, containing nationwide linkable data on all inpatient

hospital discharges. Hospital discharge and hospital outpatient clinic registers are

maintained by the National Institute of Health of Finland and the information in

these registers is used in WHO registers. In Finland, all individuals have the right

to use low-cost and easy-access health care services and people can access public-

health-care providers for both acute and non-acute illnesses. A previous systematic

review (Sund, 2012) showed that these registers are extremely reliable, and that the

coverage of vascular diseases was as high as 88% to 98%. Of note, the review by

Sund (2012) included many large studies that had validated the accuracy of the

registers, especially for cardiovascular diseases.

The main limitation of this study is that the definition of PCOS was based on

self-reported symptoms (OA+H), as for its diagnosis. However, we have previously

79

shown that self-reported OA+H can identify women with the typical endocrine,

metabolic and psychological profiles of PCOS (Karjula et al., 2017; Taponen et al.,

2003; Taponen, Martikainen et al., 2004; Taponen et al., 2004). Of note,

oligo/amenorrhoea is always self-reported in clinical practice, and the presence of

OA+H fulfils both Rotterdam and NIH criteria for PCOS. Furthermore, according

to the two latest guidelines, it is not necessary to perform ovarian ultrasonographic

assessment in the presence of both OA+H (Rotterdam ESHRE/ASRM-Sponsored

PCOS Consensus Workshop Group, 2004; Teede et al., 2018). Another limitation

of the PCOS definition is that the questionnaire at age 46 did not distinguish

between women with only PCOM and those with established PCOS. However, as

women with PCOM have been reported to show similar metabolic status as control

women (Catteau-Jonard et al., 2012), we would have expected that the differences

between the PCOS and control groups would have been even greater if we would

have been able to exclude women with PCOM only.

Other limitations of this study include the relatively small numbers of women

in the echocardiographic analysis and those who had a diagnosis of CVD. Indeed,

the women assessed were still relatively young to have manifest CVD, explaining

the small number of women with CVD events. Follow-up of these women will

reveal whether the cardiovascular risk actually increases further in women with

PCOS.

81

6 Conclusions 1. Women with the two typical PCOS symptoms (OA+H) at age 31 and women

with self-reported PCOS diagnosis by age 46 had significantly greater BMI

values at ages 14, 31 and 46. In addition, weight gain between ages 14 and 31

was significantly associated with PCOS diagnosis by age 46, whereas weight

gain in later adulthood (between ages 31 and 46) was comparable in the PCOS

and control groups. Besides BMI, the FAI at age 31 was significantly

associated with PCOS diagnosis by age 46. Our results indicate that early

weight gain and hyperandrogenaemia might be predisposing factors for the

development of overt PCOS.

2. Normal-weight women with PCOS, i.e. women with typical PCOS symptoms

(OA+H) and/or diagnosis of PCOS did not have an increased prevalence of

T2DM by age 46 compared with controls, whereas the prevalence of T2DM

was significantly increased in overweight/obese women with PCOS compared

with overweight/obese control women. These results emphasize the role of

weight management during adolescence and early adulthood to prevent the

development of pre-diabetes and T2DM in women with PCOS.

3. PCOS was already associated with significantly higher blood pressure levels

in early adulthood (at age 31), even in the normal-weight group. Moreover,

PCOS was also associated with hypertension at age 46, independently of BMI,

although overweight/obesity showed markedly higher odds ratios in

connection with hypertension than did PCOS. In addition, hypertensive women

with PCOS displayed consistent, unfavourable changes in cardiac structure and

function compared with normotensive controls.

4. Women with PCOS displayed decreased vagal activity, which was associated

with metabolic abnormalities. This indicated that metabolic abnormalities

should be efficiently managed in these women.

5. Women with PCOS already had significantly higher prevalence rates of acute

myocardial infarction and CVD by age 46 when compared with control women.

However, as the absolute number of affected women was very small, further

follow-up is needed to evaluate the risk of CVD later, after menopause.

All in all, the results show that BMI is a major determinant of overall metabolic

health in women with PCOS, and the effect of BMI on health implications should

be considered when treating women with PCOS. Weight management is proposed

as the first-line treatment for women with PCOS and more resources should be

82

targeted to early lifestyle interventions (Moran, Brown, McNaughton, Joham, &

Teede, 2017). It should, however, be remembered that our results are best

generalised to European populations, as ethnicity is well known to have a marked

impact on the prevalence of metabolic risks and events in PCOS.

In summary, regarding the clinical implications, our results suggest that regular

screening for glucose metabolism disturbances should be targeted to overweight

and obese women with PCOS, at least in Nordic populations. Our findings also

indicate that hypertension should be efficiently screened and managed, starting

early in life in women with PCOS.

Fig. 24. A simplified diagram concerning the interrelated roles of different abnormalities present in women with PCOS.

83

References Abbott, D. H., Dumesic, D. A., & Franks, S. (2002). Developmental origin of polycystic

ovary syndrome - a hypothesis. Journal of Endocrinology, 174(1), 1-5. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle.

In B. N. Petrov, & F. Caski (Eds.), Proceedings of the Second International Symposium on Information Theory. (pp. 267-281). Budabest: Akadémiai Kiado.

Alberti, K. G., & Zimmet, P. Z. (1998). Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabetic Medicine, 15(7), 539-553.

Almeida-Santos, M. A., Barreto-Filho, J. A., Oliveira, J. L. M., Reis, F. P., da Cunha Oliveira, Cristiane Costa, & Sousa, A. C. S. (2016). Aging, heart rate variability and patterns of autonomic regulation of the heart. Archives of Gerontology and Geriatrics, 63, 1-8. doi:10.1016/j.archger.2015.11.011

Asllanaj, E., Bano, A., Glisic, M., Jaspers, L., Ikram, M. A., Laven, J. S. E., . . . Franco, O. H. (2018). Age at natural menopause and life expectancy with and without type 2 diabetes. Menopause (New York, N.Y.), doi:10.1097/GME.0000000000001246

Azziz, R., Carmina, E., Dewailly, D., Diamanti-Kandarakis, E., Escobar-Morreale, H. F., Futterweit, W., . . . Witchel, S. F. (2006). Positions statement: criteria for defining polycystic ovary syndrome as a predominantly hyperandrogenic syndrome: an Androgen Excess Society guideline. The Journal of Clinical Endocrinology and Metabolism, 91(11), 4237-4245. doi:10.1210/jc.2006-0178

Azziz, R., Marin, C., Hoq, L., Badamgarav, E., & Song, P. (2005). Health care-related economic burden of the polycystic ovary syndrome during the reproductive life span. The Journal of Clinical Endocrinology and Metabolism, 90(8), 4650-4658. doi:10.1210/jc.2005-0628

Bankir, L., Bichet, D. G., & Morgenthaler, N. G. (2017). Vasopressin: physiology, assessment and osmosensation. Journal of Internal Medicine, 282(4), 284-297. doi:10.1111/joim.12645

Barber, T. M., Golding, S. J., Alvey, C., Wass, J. A., Karpe, F., Franks, S., & McCarthy, M. I. (2008). Global adiposity rather than abnormal regional fat distribution characterizes women with polycystic ovary syndrome. The Journal of Clinical Endocrinology & Metabolism, 93(3), 999-1004.

Barry, E., Roberts, S., Oke, J., Vijayaraghavan, S., Normansell, R., & Greenhalgh, T. (2017). Efficacy and effectiveness of screen and treat policies in prevention of type 2 diabetes: systematic review and meta-analysis of screening tests and interventions. BMJ (Clinical Research Ed.), 356, i6538.

Barry, J. A., Azizia, M. M., & Hardiman, P. J. (2014). Risk of endometrial, ovarian and breast cancer in women with polycystic ovary syndrome: a systematic review and meta-analysis. Human Reproduction Update, 20(5), 748-758. doi:10.1093/humupd/dmu012

84

Bhaskaran, K., Dos-Santos-Silva, I., Leon, D. A., Douglas, I. J., & Smeeth, L. (2018). Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK. The Lancet. Diabetes & Endocrinology, doi:10.1016/S2213-8587(18)30288-2

Blank, S. K., McCartney, C. R., Helm, K. D., & Marshall, J. C. (2007). Neuroendocrine effects of androgens in adult polycystic ovary syndrome and female puberty. Seminars in Reproductive Medicine, 25(5), 352-359. doi:10.1055/s-2007-984741

Booth, G. L., Kapral, M. K., Fung, K., & Tu, J. V. (2006). Relation between age and cardiovascular disease in men and women with diabetes compared with non-diabetic people: a population-based retrospective cohort study. Lancet (London, England), 368(9529), 29-36. doi:10.1016/S0140-6736(06)68967-8

Boudreaux, M. Y., Talbott, E. O., Kip, K. E., Brooks, M. M., & Witchel, S. F. (2006). Risk of T2DM and impaired fasting glucose among PCOS subjects: results of an 8-year follow-up. Current Diabetes Reports, 6(1), 77-83.

Brown, Z. A., Louwers, Y. V., Fong, S. L., Valkenburg, O., Birnie, E., de Jong, F. H., . . . Laven, J. S. E. (2011). The phenotype of polycystic ovary syndrome ameliorates with aging. Fertility and Sterility, 96(5), 1259-1265. doi:10.1016/j.fertnstert.2011.09.002

Carmina, E., Bucchieri, S., Esposito, A., Del Puente, A., Mansueto, P., Orio, F., . . . Rini, G. (2007). Abdominal fat quantity and distribution in women with polycystic ovary syndrome and extent of its relation to insulin resistance. The Journal of Clinical Endocrinology & Metabolism, 92(7), 2500-2505.

Carmina, E., Campagna, A. M., & Lobo, R. A. (2013). Emergence of ovulatory cycles with aging in women with polycystic ovary syndrome (PCOS) alters the trajectory of cardiovascular and metabolic risk factors. Human Reproduction, 28(8), 2245-2252.

Catteau-Jonard, S., Bancquart, J., Poncelet, E., Lefebvre-Maunoury, C., Robin, G., & Dewailly, D. (2012). Polycystic ovaries at ultrasound: normal variant or silent polycystic ovary syndrome? Ultrasound in Obstetrics & Gynecology: The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology, 40(2), 223-229. doi:10.1002/uog.11202

Chen, M., Yang, W., Yang, J., Chen, C., Ho, H., & Yang, Y. (2007). Relationship between androgen levels and blood pressure in young women with polycystic ovary syndrome. Hypertension (Dallas, Tex.: 1979), 49(6), 1442-1447. doi: 10.1161/HYPERTENSIONAHA.106.083972

Chereau, A. (1844). Mémoires pour servir à l'étude des maladies des ovaires. Paris: Fortin, Masson, Cie:

Cibula, D., Cifkova, R., Fanta, M., Poledne, R., Zivny, J., & Skibova, J. (2000). Increased risk of non-insulin dependent diabetes mellitus, arterial hypertension and coronary artery disease in perimenopausal women with a history of the polycystic ovary syndrome. Human Reproduction, 15(4), 785-789.

Cimino, I., Casoni, F., Liu, X., Messina, A., Parkash, J., Jamin, S. P., . . . Giacobini, P. (2016). Novel role for anti-Müllerian hormone in the regulation of GnRH neuron excitability and hormone secretion. Nature Communications, 7, 10055. doi:10.1038/ncomms10055

85

Consensus on infertility treatment related to polycystic ovary syndrome. (2008). Fertility and Sterility, 89(3), 505-522. doi:10.1016/j.fertnstert.2007.09.041

Cook, N. R., Cohen, J., Hebert, P. R., Taylor, J. O., & Hennekens, C. H. (1995). Implications of small reductions in diastolic blood pressure for primary prevention. Archives of Internal Medicine, 155(7), 701-709.

Daan, N. M. P., Louwers, Y. V., Koster, M. P. H., Eijkemans, M. J. C., de Rijke, Y. B., Lentjes, E. W. G., . . . Laven, J. S. E. (2014). Cardiovascular and metabolic profiles amongst different polycystic ovary syndrome phenotypes: who is really at risk? Fertility and Sterility, 102(5), 1451.e3. doi:10.1016/j.fertnstert.2014.08.001

Dahlgren, E., Janson, P. O., Johansson, S., Lapidus, L., & Oden, A. (1992). Polycystic ovary syndrome and risk for myocardial infarction. Evaluated from a risk factor model based on a prospective population study of women. Acta Obstetricia Et Gynecologica Scandinavica, 71(8), 599-604.

de Sá, Joceline Cássia Ferezini, Costa, E. C., da Silva, E., Zuttin, R. S., da Silva, E. P., Lemos, Telma Maria Araújo Moura, & De Azevedo, G. D. (2011). Analysis of heart rate variability in polycystic ovary syndrome. Gynecological Endocrinology: The Official Journal of the International Society of Gynecological Endocrinology, 27(6), 443-447. doi:10.3109/09513590.2010.501881

de Zegher, F., & Ibáñez, L. (2006). Prenatal growth restraint followed by catch-up of weight: a hyperinsulinemic pathway to polycystic ovary syndrome. Fertility and Sterility, 86 Suppl 1, 4. doi:10.1016/j.fertnstert.2006.03.013

Di Domenico, K., Wiltgen, D., Nickel, F. J., Magalhães, J. A., Moraes, R. S., & Spritzer, P. M. (2013). Cardiac autonomic modulation in polycystic ovary syndrome: does the phenotype matter? Fertility and Sterility, 99(1), 286-292. doi: 10.1016/j.fertnstert.2012.08.049

Diamanti-Kandarakis, E., Kouli, C. R., Bergiele, A. T., Filandra, F. A., Tsianateli, T. C., Spina, G. G., . . . Bartzis, M. I. (1999). A survey of the polycystic ovary syndrome in the Greek island of Lesbos: hormonal and metabolic profile. The Journal of Clinical Endocrinology & Metabolism, 84(11), 4006-4011.

Diamanti-Kandarakis, E., & Papavassiliou, A. G. (2006). Molecular mechanisms of insulin resistance in polycystic ovary syndrome. Trends in Molecular Medicine, 12(7), 324-332.

Ding, T., Hardiman, P. J., Petersen, I., Wang, F., Qu, F., & Baio, G. (2017). The prevalence of polycystic ovary syndrome in reproductive-aged women of different ethnicity: a systematic review and meta-analysis. Oncotarget, 8(56), 96351-96358. doi:10.18632/oncotarget.19180

Dokras, A. (2013). Cardiovascular disease risk in women with PCOS. Steroids, 78(8), 773-776.

Dumesic, D. A., Oberfield, S. E., Stener-Victorin, E., Marshall, J. C., Laven, J. S., & Legro, R. S. (2015). Scientific Statement on the Diagnostic Criteria, Epidemiology, Pathophysiology, and Molecular Genetics of Polycystic Ovary Syndrome. Endocrine Reviews, 36(5), 487-525. doi:10.1210/er.2015-1018

86

Dunaif, A., Segal, K. R., Futterweit, W., & Dobrjansky, A. (1989). Profound peripheral insulin resistance, independent of obesity, in polycystic ovary syndrome. Diabetes, 38(9), 1165-1174.

Eckel, R. H., Grundy, S. M., & Zimmet, P. Z. (2005). The metabolic syndrome. Lancet (London, England), 365(9468), 1415-1428. doi:S0140-6736(05)66378-7 [pii]

Ehrmann, D. A. (2005). Polycystic ovary syndrome. New England Journal of Medicine, 352(12), 1223-1236.

Ehrmann, D. A., Barnes, R. B., Rosenfield, R. L., Cavaghan, M. K., & Imperial, J. (1999). Prevalence of impaired glucose tolerance and diabetes in women with polycystic ovary syndrome. Diabetes Care, 22(1), 141-146.

Elting, M. W., Korsen, T. J., Bezemer, P. D., & Schoemaker, J. (2001). Prevalence of diabetes mellitus, hypertension and cardiac complaints in a follow-up study of a Dutch PCOS population. Human Reproduction, 16(3), 556-560.

Elting, M. W., Kwee, J., Korsen, T. J., Rekers-Mombarg, L. T., & Schoemaker, J. (2003). Aging women with polycystic ovary syndrome who achieve regular menstrual cycles have a smaller follicle cohort than those who continue to have irregular cycles. Fertility & Sterility, 79(5), 1154-1160.

Escobar-Morreale, H. F., & San Millan, J. L. (2007). Abdominal adiposity and the polycystic ovary syndrome. Trends in Endocrinology & Metabolism, 18(7), 266-272.

Escobar-Morreale, H. F., Botella-Carretero, J. I., A lvarez-Blasco, F., Sancho, J., & San Milla n, J. L. (2005). The Polycystic Ovary Syndrome Associated with Morbid Obesity May Resolve after Weight Loss Induced by Bariatric Surgery. The Journal of Clinical Endocrinology & Metabolism, 90(12), 6364-6369. doi:10.1210/jc.2005-1490

Espinos-Gomez, J. J., Corcoy, R., & Calaf, J. (2009). Prevalence and predictors of abnormal glucose metabolism in Mediterranean women with polycystic ovary syndrome. Gynecological Endocrinology, 25(3), 199-204. doi://dx.doi.org/ 10.1080/09513590802585597

Ferriman, D., & Gallwey, J. D. (1961). Clinical assessment of body hair growth in women. The Journal of Clinical Endocrinology and Metabolism, 21, 1440-1447. doi:10.1210/jcem-21-11-1440

Föhr, T., Pietilä, J., Helander, E., Myllymäki, T., Lindholm, H., Rusko, H., & Kujala, U. M. (2016). Physical activity, body mass index and heart rate variability-based stress and recovery in 16 275 Finnish employees: a cross-sectional study. BMC Public Health, 16, 701. doi:10.1186/s12889-016-3391-4

Forslund, M., Landin-Wilhelmsen, K., Schmidt, J., Brännström, M., Trimpou, P., & Dahlgren, E. (2018). Higher menopausal age but no differences in parity in women with polycystic ovary syndrome compared with controls. Acta Obstetricia Et Gynecologica Scandinavica, doi:10.1111/aogs.13489

Franks, S., Stark, J., & Hardy, K. (2008). Follicle dynamics and anovulation in polycystic ovary syndrome. Human Reproduction Update, 14(4), 367-378. doi:10.1093/ humupd/dmn015

87

Gambineri, A., Patton, L., Altieri, P., Pagotto, U., Pizzi, C., Manzoli, L., & Pasquali, R. (2012). Polycystic ovary syndrome is a risk factor for type 2 diabetes: results from a long-term prospective study. Diabetes, 61(9), 2369-2374. doi://dx.doi.org/ 10.2337/db11-1360

Gambineri, A., Pelusi, C., Vicennati, V., Pagotto, U., & Pasquali, R. (2002). Obesity and the polycystic ovary syndrome. International Journal of Obesity & Related Metabolic Disorders: Journal of the International Association for the Study of Obesity, 26(7), 883-896.

Glintborg, D., Hass Rubin, K., Nybo, M., Abrahamsen, B., & Andersen, M. (2015). Morbidity and medicine prescriptions in a nationwide Danish population of patients diagnosed with polycystic ovary syndrome. European Journal of Endocrinology, 172(5), 627-638. doi:10.1530/EJE-14-1108

Glintborg, D., Rubin, K. H., Abrahamsen, B., & Andersen, M. (2018). Response to Letter to the Editor: "Development and Risk Factors of Type 2 Diabetes in a Nationwide Population of Women With Polycystic Ovary Syndrome". The Journal of Clinical Endocrinology and Metabolism, 103(1), 362-363. doi:10.1210/jc.2017-02123

Glintborg, D., Rubin, K. H., Nybo, M., Abrahamsen, B., & Andersen, M. (2018). Cardiovascular disease in a nationwide population of Danish women with polycystic ovary syndrome. Cardiovascular Diabetology, 17(1), 37. doi:10.1186/s12933-018-0680-5

Gonzaga, C., Bertolami, A., Bertolami, M., Amodeo, C., & Calhoun, D. (2015). Obstructive sleep apnea, hypertension and cardiovascular diseases. Journal of Human Hypertension, 29(12), 705-712. doi:10.1038/jhh.2015.15

Gui, J., & Wang, R. (2017). Cardiovascular autonomic dysfunction in women with polycystic ovary syndrome: a systematic review and meta-analysis. Reproductive Biomedicine Online, 35(1), 113-120. doi:10.1016/j.rbmo.2017.03.018

Haensel, A., Mills, P. J., Nelesen, R. A., Ziegler, M. G., & Dimsdale, J. E. (2008). The relationship between heart rate variability and inflammatory markers in cardiovascular diseases. Psychoneuroendocrinology, 33(10), 1305-1312. doi:10.1016/j.psyneuen. 2008.08.007

Hashim, Z. H., Hamdan, F. B., & Al-Salihi, A. R. (2015). Autonomic dysfunction in women with polycystic ovary syndrome. Iranian Journal of Reproductive Medicine, 13(1), 27-34.

Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. (1996). Circulation, 93(5), 1043-1065.

Hicks, K. A., Mahaffey, K. W., Mehran, R., Nissen, S. E., Wiviott, S. D., Dunn, B., . . . Temple, R. J. (2018). 2017 Cardiovascular and Stroke Endpoint Definitions for Clinical Trials. Journal of the American College of Cardiology, 71(9), 1021-1034. doi: 10.1016/j.jacc.2017.12.048

Hoeger, K. M., & Oberfield, S. E. (2012). Do women with PCOS have a unique predisposition to obesity? Fertility and Sterility, 97(1), 13-17. doi:10.1016/ j.fertnstert.2011.11.026

88

Hogg, K., Wood, C., McNeilly, A. S., & Duncan, W. C. (2011). The in utero programming effect of increased maternal androgens and a direct fetal intervention on liver and metabolic function in adult sheep. PloS One, 6(9), e24877. doi:10.1371/ journal.pone.0024877

Holte, J., Gennarelli, G., Berne, C., Bergh, T., & Lithell, H. (1996). Elevated ambulatory day-time blood pressure in women with polycystic ovary syndrome: a sign of a pre-hypertensive state? Human Reproduction (Oxford, England), 11(1), 23-28.

Homburg, R. (2009). Androgen circle of polycystic ovary syndrome. Human Reproduction, 24(7), 1548-1555.

Hon, E. H., & Lee, S. T. (1965). THE FETAL ELECTROCARDIOGRAM. 3. DISPLAY TECHNIQUES. American Journal of Obstetrics and Gynecology, 91, 56-60.

Hudecova, M., Holte, J., Olovsson, M., Larsson, A., Berne, C., & Poromaa, I. S. (2011). Diabetes and impaired glucose tolerance in patients with polycystic ovary syndrome--a long term follow-up. Human Reproduction, 26(6), 1462-1468. doi:// dx.doi.org/10.1093/humrep/der065

Iftikhar, S., Collazo-Clavell, M. L., Roger, V. L., St Sauver, J., Brown, R. D., Cha, S., & Rhodes, D. J. (2012). Risk of cardiovascular events in patients with polycystic ovary syndrome. The Netherlands Journal of Medicine, 70(2), 74-80.

Istenes, I., Körei, A. E., Putz, Z., Németh, N., Martos, T., Keresztes, K., . . . Kempler, P. (2014). Heart rate variability is severely impaired among type 2 diabetic patients with hypertension. Diabetes/Metabolism Research and Reviews, 30(4), 305-312.

Jaatinen, T. A., Matinlauri, I., Anttila, L., Koskinen, P., Erkkola, R., & Irjala, K. (1995). Serum total renin is elevated in women with polycystic ovarian syndrome. Fertility and Sterility, 63(5), 1000-1004.

Ji, H. R., Woo, H. L., Park, Y. J., Hwang, D. S., Lee, J. M., Lee, C. H., . . . Park, K. S. (2018). Characteristics of heart rate variability in women with polycystic ovary syndrome: A retrospective cross-sectional study. Medicine, 97(38), e12510. doi:10.1097/ MD.0000000000012510

Joham, A. E., Ranasinha, S., Zoungas, S., Moran, L., & Teede, H. J. (2014). Gestational diabetes and type 2 diabetes in reproductive-aged women with polycystic ovary syndrome. The Journal of Clinical Endocrinology & Metabolism, 99(3), 447. doi://dx.doi.org/10.1210/jc.2013-2007

Joham, A. E., Boyle, J. A., Zoungas, S., & Teede, H. J. (2015). Hypertension in Reproductive-Aged Women With Polycystic Ovary Syndrome and Association With Obesity. American Journal of Hypertension, 28(7), 847-851. doi://dx.doi.org/ 10.1093/ajh/hpu251

Joham, A. E., Teede, H. J., Ranasinha, S., Zoungas, S., & Boyle, J. (2015). Prevalence of infertility and use of fertility treatment in women with polycystic ovary syndrome: data from a large community-based cohort study. Journal of Women's Health (2002), 24(4), 299-307. doi:10.1089/jwh.2014.5000

89

Kadi, H., Avci, E., Usta, A., & Demirtaş, A. O. (2018). Morning blood pressure surge and its relation to insulin resistance in patients of reproductive age with polycystic ovary syndrome. Reproductive Biology and Endocrinology: RB&E, 16(1), 75. doi:10.1186/ s12958-018-0394-2

Kakoly, N. S., Khomami, M. B., Joham, A. E., Cooray, S. D., Misso, M. L., Norman, R. J., . . . Moran, L. J. (2018). Ethnicity, obesity and the prevalence of impaired glucose tolerance and type 2 diabetes in PCOS: a systematic review and meta-regression. Human Reproduction Update, 24(4), 455-467. doi:10.1093/humupd/ dmy007

Kakoly, N. S., Earnest, A., Moran, L. J., Teede, H. J., & Joham, A. E. (2017). Group-based developmental BMI trajectories, polycystic ovary syndrome, and gestational diabetes: a community-based longitudinal study. BMC Medicine, 15(1), 195. doi:10.1186/ s12916-017-0957-7

Karbek, B., Ozbek, M., Karakose, M., Topaloglu, O., Bozkurt, N. C., Cakır, E., . . . Delibasi, T. (2014). Copeptin, a surrogate marker for arginine vasopressin, is associated with cardiovascular risk in patients with polycystic ovary syndrome. Journal of Ovarian Research, 7, 31. doi:10.1186/1757-2215-7-31

Karjula, S., Morin-Papunen, L., Auvinen, J., Ruokonen, A., Puukka, K., Franks, S., . . . Piltonen, T. T. (2017). Psychological Distress Is More Prevalent in Fertile Age and Premenopausal Women With PCOS Symptoms: 15-Year Follow-Up. The Journal of Clinical Endocrinology & Metabolism, 102(6), 1861-1869. doi://dx.doi.org/ 10.1210/jc.2016-3863

Katulski, K., Podfigurna, A., Czyzyk, A., Meczekalski, B., & Genazzani, A. D. (2018). Kisspeptin and LH pulsatile temporal coupling in PCOS patients. Endocrine, 61(1), 149-157. doi:10.1007/s12020-018-1609-1

Kent, J., Dodson, W. C., Kunselman, A., Pauli, J., Stone, A., Diamond, M. P., . . . Legro, R. S. (2018). Gestational Weight Gain in Women with Polycystic Ovary Syndrome: A Controlled Study. The Journal of Clinical Endocrinology and Metabolism, doi: 10.1210/jc.2017-02764

Kershaw, E. E., & Flier, J. S. (2004). Adipose tissue as an endocrine organ. The Journal of Clinical Endocrinology & Metabolism, 89(6), 2548-2556.

Kilit, C., & Paşalı Kilit, T. (2017). Heart rate variability in normal-weight patients with polycystic ovary syndrome. Anatolian Journal of Cardiology, 17(5), 404-409. doi:10.14744/AnatolJCardiol.2017.7473

Koivuaho, E., Laru, J., Ojaniemi, M., Puukka, K., Kettunen, J., Tapanainen, J., . . . Piltonen, T. (2019). Age at adiposity rebound in childhood is associated with PCOS diagnosis and obesity in adulthood – longitudinal analysis of BMI data from birth to age 46 in cases of PCOS. International Journal of Obesity.

Koivunen, R., Laatikainen, T., Tomás, C., Huhtaniemi, I., Tapanainen, J., & Martikainen, H. (1999). The prevalence of polycystic ovaries in healthy women. Acta Obstetricia Et Gynecologica Scandinavica, 78(2), 137-141.

90

Kosmala, W., O'Moore-Sullivan, T. M., Plaksej, R., Kuliczkowska-Plaksej, J., Przewlocka-Kosmala, M., & Marwick, T. H. (2008). Subclinical impairment of left ventricular function in young obese women: contributions of polycystic ovary disease and insulin resistance. The Journal of Clinical Endocrinology and Metabolism, 93(10), 3748-3754. doi:10.1210/jc.2008-1017

Laitinen, J., Taponen, S., Martikainen, H., Pouta, A., Millwood, I., Hartikainen, A. L., . . . Jarvelin, M. R. (2003). Body size from birth to adulthood as a predictor of self-reported polycystic ovary syndrome symptoms. International Journal of Obesity & Related Metabolic Disorders: Journal of the International Association for the Study of Obesity, 27(6), 710-715.

Lambert, E. A., Teede, H., Sari, C. I., Jona, E., Shorakae, S., Woodington, K., . . . Lambert, G. W. (2015). Sympathetic activation and endothelial dysfunction in polycystic ovary syndrome are not explained by either obesity or insulin resistance. Clinical Endocrinology, 83(6), 812-819. doi:10.1111/cen.12803

Lambert, G. W., Straznicky, N. E., Lambert, E. A., Dixon, J. B., & Schlaich, M. P. (2010). Sympathetic nervous activation in obesity and the metabolic syndrome--causes, consequences and therapeutic implications. Pharmacology & Therapeutics, 126(2), 159-172. doi:10.1016/j.pharmthera.2010.02.002

Lang, R. M., Bierig, M., Devereux, R. B., Flachskampf, F. A., Foster, E., Pellikka, P. A., . . . Stewart, W. J. (2005). Recommendations for chamber quantification: a report from the American Society of Echocardiography's Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardiology. Journal of the American Society of Echocardiography: Official Publication of the American Society of Echocardiography, 18(12), 1440-1463. doi:10.1016/j.echo.2005.10.005

Lansdown, A., & Rees, D. A. (2012). The sympathetic nervous system in polycystic ovary syndrome: a novel therapeutic target? Clinical Endocrinology, 77(6), 791-801. doi:10.1111/cen.12003

Legro, R. S., Kunselman, A. R., Dodson, W. C., & Dunaif, A. (1999). Prevalence and predictors of risk for type 2 diabetes mellitus and impaired glucose tolerance in polycystic ovary syndrome: a prospective, controlled study in 254 affected women. The Journal of Clinical Endocrinology & Metabolism, 84(1), 165-169.

Lewington, S., Clarke, R., Qizilbash, N., Peto, R., & Collins, R. (2002). Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet (London, England), 360(9349), 1903-1913.

Li, J., Eriksson, M., Czene, K., Hall, P., & Rodriguez-Wallberg, K. A. (2016). Common diseases as determinants of menopausal age. Human Reproduction (Oxford, England), 31(12), 2856-2864. doi:10.1093/humrep/dew264

Lim, S. S., Norman, R. J., Davies, M. J., & Moran, L. J. (2013). The effect of obesity on polycystic ovary syndrome: a systematic review and meta-analysis. Obesity Reviews, 14(2), 95-109.

91

Lo, J. C., Feigenbaum, S. L., Yang, J., Pressman, A. R., Selby, J. V., & Go, A. S. (2006). Epidemiology and adverse cardiovascular risk profile of diagnosed polycystic ovary syndrome. The Journal of Clinical Endocrinology and Metabolism, 91(4), 1357-1363. doi:10.1210/jc.2005-2430

Lunde, O., & Tanbo, T. (2007). Polycystic ovary syndrome: a follow-up study on diabetes mellitus, cardiovascular disease and malignancy 15-25 years after ovarian wedge resection. Gynecological Endocrinology: The Official Journal of the International Society of Gynecological Endocrinology, 23(12), 704-709. doi:10.1080/ 09513590701705189

Luque-Ramirez, M., Alvarez-Blasco, F., Mendieta-Azcona, C., Botella-Carretero, J. I., & Escobar-Morreale, H. F. (2007). Obesity is the major determinant of the abnormalities in blood pressure found in young women with the polycystic ovary syndrome. The Journal of Clinical Endocrinology & Metabolism, 92(6), 2141-2148. doi:// dx.doi.org/10.1210/jc.2007-0190

Mani, H., Levy, M. J., Davies, M. J., Morris, D. H., Gray, L. J., Bankart, J., . . . Howlett, T. A. (2013). Diabetes and cardiovascular events in women with polycystic ovary syndrome: a 20-year retrospective cohort study. Clinical Endocrinology, 78(6), 926-934.

Mannerås-Holm, L., Leonhardt, H., Kullberg, J., Jennische, E., Odén, A., Holm, G., . . . Lönn, M. (2011). Adipose tissue has aberrant morphology and function in PCOS: enlarged adipocytes and low serum adiponectin, but not circulating sex steroids, are strongly associated with insulin resistance. The Journal of Clinical Endocrinology and Metabolism, 96(2), 304. doi:10.1210/jc.2010-1290

Maranon, R., Lima, R., Spradley, F. T., do Carmo, J. M., Zhang, H., Smith, A. D., . . . Reckelhoff, J. F. (2015). Roles for the sympathetic nervous system, renal nerves, and CNS melanocortin-4 receptor in the elevated blood pressure in hyperandrogenemic female rats. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 308(8), 708. doi:10.1152/ajpregu.00411.2014

Mauvais-Jarvis, F. (2016). Role of Sex Steroids in β Cell Function, Growth, and Survival. Trends in Endocrinology and Metabolism: TEM, 27(12), 844-855. doi:10.1016/j.tem.2016.08.008

McCartney, C. R., Blank, S. K., Prendergast, K. A., Chhabra, S., Eagleson, C. A., Helm, K. D., . . . Marshall, J. C. (2007). Obesity and sex steroid changes across puberty: evidence for marked hyperandrogenemia in pre- and early pubertal obese girls. The Journal of Clinical Endocrinology & Metabolism, 92(2), 430-436.

Merz, C. N. B., Shaw, L. J., Azziz, R., Stanczyk, F. Z., Sopko, G., Braunstein, G. D., . . . Pepine, C. J. (2016). Cardiovascular Disease and 10-Year Mortality in Postmenopausal Women with Clinical Features of Polycystic Ovary Syndrome. Journal of Women's Health (2002), 25(9), 875-881. doi:10.1089/jwh.2015.5441

Meun, C., Franco, O. H., Dhana, K., Jaspers, L., Muka, T., Louwers, Y., . . . Laven, J. S. E. (2018). High Androgens in Postmenopausal Women and the Risk for Atherosclerosis and Cardiovascular Disease: The Rotterdam Study. The Journal of Clinical Endocrinology and Metabolism, 103(4), 1622-1630. doi:10.1210/jc.2017-02421

92

Meyer, C., McGrath, B. P., & Teede, H. J. (2005). Overweight women with polycystic ovary syndrome have evidence of subclinical cardiovascular disease. The Journal of Clinical Endocrinology & Metabolism, 90(10), 5711-5716. doi://dx.doi.org/10.1210/jc.2005-0011

Moore, A. M., & Campbell, R. E. (2016). The neuroendocrine genesis of polycystic ovary syndrome: A role for arcuate nucleus GABA neurons. The Journal of Steroid Biochemistry and Molecular Biology, 160, 106-117. doi:10.1016/j.jsbmb.2015.10.002

Moran, L. J., Misso, M. L., Wild, R. A., & Norman, R. J. (2010). Impaired glucose tolerance, type 2 diabetes and metabolic syndrome in polycystic ovary syndrome: a systematic review and meta-analysis. Human Reproduction Update, 16(4), 347-363. doi://dx.doi.org/10.1093/humupd/dmq001

Moran, L. J., Brown, W. J., McNaughton, S. A., Joham, A. E., & Teede, H. J. (2017). Weight management practices associated with PCOS and their relationships with diet and physical activity. Human Reproduction (Oxford, England), 32(3), 669-678. doi:10.1093/humrep/dew348

Morgan, C. L., Jenkins-Jones, S., Currie, C. J., & Rees, D. A. (2012). Evaluation of adverse outcome in young women with polycystic ovary syndrome versus matched, reference controls: a retrospective, observational study. The Journal of Clinical Endocrinology & Metabolism, 97(9), 3251-3260. doi://dx.doi.org/10.1210/jc.2012-1690

Morin-Papunen, L. C., Koivunen, R. M., Ruokonen, A., & Martikainen, H. K. (1998). Metformin therapy improves the menstrual pattern with minimal endocrine and metabolic effects in women with polycystic ovary syndrome. Fertility and Sterility, 69(4), 691-696.

Nader, S. (2013). Hyperandrogenism during puberty in the development of polycystic ovary syndrome. Fertility and Sterility, 100(1), 39-42. doi:10.1016/j.fertnstert.2013.03.013

Naderpoor, N., Shorakae, S., de Courten, B., Misso, M. L., Moran, L. J., & Teede, H. J. (2015). Metformin and lifestyle modification in polycystic ovary syndrome: systematic review and meta-analysis. Human Reproduction Update, 21(5), 560-574. doi:10.1093/humupd/dmv025

Nagueh, S. F., Smiseth, O. A., Appleton, C. P., Byrd, B. F., Dokainish, H., Edvardsen, T., . . . Waggoner, A. D. (2016). Recommendations for the Evaluation of Left Ventricular Diastolic Function by Echocardiography: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Journal of the American Society of Echocardiography: Official Publication of the American Society of Echocardiography, 29(4), 277-314. doi:10.1016/j.echo.2016.01.011

Nisenblat, V., & Norman, R. J. (2009). Androgens and polycystic ovary syndrome. Current Opinion in Endocrinology, Diabetes, and Obesity, 16(3), 224-231. doi:10.1097/MED.0b013e32832afd4d

Norman, R. J., Masters, L., Milner, C. R., Wang, J. X., & Davies, M. J. (2001). Relative risk of conversion from normoglycaemia to impaired glucose tolerance or non-insulin dependent diabetes mellitus in polycystic ovarian syndrome. Human Reproduction, 16(9), 1995-1998.

93

Obesity: preventing and managing the global epidemic. Report of a WHO consultation. (2000). World Health Organization Technical Report Series, 894, 253.

Orio, F. J., Palomba, S., Spinelli, L., Cascella, T., Tauchmanova, L., Zullo, F., . . . Colao, A. (2004). The cardiovascular risk of young women with polycystic ovary syndrome: an observational, analytical, prospective case-control study. The Journal of Clinical Endocrinology & Metabolism, 89(8), 3696-3701.

Ovalle, F., & Azziz, R. (2002). Insulin resistance, polycystic ovary syndrome, and type 2 diabetes mellitus. Fertility and Sterility, 77(6), 1095-1105.

Özkeçeci, G., Ünlü, B. S., Dursun, H., Akçi, Ö, Köken, G., Onrat, E., & Avşar, A. (2016). Heart rate variability and heart rate turbulence in patients with polycystic ovary syndrome. Anatolian Journal of Cardiology, 16(5), 323-327. doi:10.5152/ AnatolJCardiol.2015.6307

Palomba, S., de Wilde, M. A., Falbo, A., Koster, M. P. H., La Sala, G. B., & Fauser, Bart C J M. (2015). Pregnancy complications in women with polycystic ovary syndrome. Human Reproduction Update, 21(5), 575-592. doi:10.1093/ humupd/dmv029

Patrianakos, A. P., Zacharaki, A. A., Kalogerakis, A., Solidakis, G., Parthenakis, F. I., & Vardas, P. E. (2015). Two-dimensional global and segmental longitudinal strain: are the results from software in different high-end ultrasound systems comparable? Echo Research and Practice, 2(1), 29-39. doi:10.1530/ERP-14-0070

Payne, A. H., & Hales, D. B. (2004). Overview of steroidogenic enzymes in the pathway from cholesterol to active steroid hormones. Endocrine Reviews, 25(6), 947-970. doi:10.1210/er.2003-0030

Pelanis, R., Mellembakken, J. R., Sundström-Poromaa, I., Ravn, P., Morin-Papunen, L., Tapanainen, J. S., . . . Glintborg, D. (2017). The prevalence of Type 2 diabetes is not increased in normal-weight women with PCOS. Human Reproduction (Oxford, England), 32(11), 2279-2286. doi:10.1093/humrep/dex294

Perciaccante, A., Fiorentini, A., Valente, R., & Tubani, L. (2007). Polycystic ovary syndrome: androgens, autonomic nervous system, and hypertension. Hypertension (Dallas, Tex.: 1979), 50(1), e7; author reply e8. doi:10.1161/ HYPERTENSIONAHA.107.091710

Pierpoint, T., McKeigue, P. M., Isaacs, A. J., Wild, S. H., & Jacobs, H. S. (1998). Mortality of women with polycystic ovary syndrome at long-term follow-up. Journal of Clinical Epidemiology, 51(7), 581-586. doi:S0895435698000353

Piltonen, T. T. (2016). Polycystic ovary syndrome: Endometrial markers. Best Practice & Research. Clinical Obstetrics & Gynaecology, 37, 66-79. doi:10.1016/ j.bpobgyn.2016.03.008

Piltonen, T., Koivunen, R., Perheentupa, A., Morin-Papunen, L., Ruokonen, A., & Tapanainen, J. S. (2004). Ovarian age-related responsiveness to human chorionic gonadotropin in women with polycystic ovary syndrome. The Journal of Clinical Endocrinology and Metabolism, 89(8), 3769-3775. doi:10.1210/jc.2003-031851

94

Piltonen, T., Morin-Papunen, L., Koivunen, R., Perheentupa, A., Ruokonen, A., & Tapanainen, J. S. (2005). Serum anti-Müllerian hormone levels remain high until late reproductive age and decrease during metformin therapy in women with polycystic ovary syndrome. Human Reproduction (Oxford, England), 20(7), 1820-1826. doi:10.1093/humrep/deh850

Pinola, P., Lashen, H., Bloigu, A., Puukka, K., Ulmanen, M., Ruokonen, A., . . . Morin-Papunen, L. (2012). Menstrual disorders in adolescence: a marker for hyperandrogenaemia and increased metabolic risks in later life? Finnish general population-based birth cohort study. Human Reproduction (Oxford, England), 27(11), 3279-3286. doi:10.1093/humrep/des309

Pinola, P., Piltonen, T. T., Puurunen, J., Vanky, E., Sundström-Poromaa, I., Stener-Victorin, E., . . . Morin-Papunen, L. C. (2015). Androgen Profile Through Life in Women With Polycystic Ovary Syndrome: A Nordic Multicenter Collaboration Study. The Journal of Clinical Endocrinology and Metabolism, 100(9), 3400-3407. doi:10.1210/jc.2015-2123

Ponikowski, P., Voors, A. A., Anker, S. D., Bueno, H., Cleland, J. G., Coats, A. J., . . . Authors/Task Force Members. (2016). 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. European Heart Journal, 37(27), 2129-2200. doi:10.1093/ eurheartj/ehw128 [doi]

Quan, A., Chakravarty, S., Chen, J., Chen, J., Loleh, S., Saini, N., . . . Quigley, R. (2004). Androgens augment proximal tubule transport. American Journal of Physiology. Renal Physiology, 287(3), 452. doi:10.1152/ajprenal.00188.2003

Quilliot, D., Böhme, P., Zannad, F., & Ziegler, O. (2008). Sympathetic-leptin relationship in obesity: effect of weight loss. Metabolism: Clinical and Experimental, 57(4), 555-562. doi:10.1016/j.metabol.2007.11.020

Roland, A. V., Nunemaker, C. S., Keller, S. R., & Moenter, S. M. (2010). Prenatal androgen exposure programs metabolic dysfunction in female mice. The Journal of Endocrinology, 207(2), 213-223. doi:10.1677/JOE-10-0217

Rosenfield, R. L., & Ehrmann, D. A. (2016). The Pathogenesis of Polycystic Ovary Syndrome (PCOS): The Hypothesis of PCOS as Functional Ovarian Hyperandrogenism Revisited. Endocrine Reviews, 37(5), 467-520. doi:10.1210/er.2015-1104

Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group. (2004). Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome. Fertility & Sterility, 81(1), 19-25.

Rubin, K. H., Glintborg, D., Nybo, M., Abrahamsen, B., & Andersen, M. (2017). Development and Risk Factors of Type 2 Diabetes in a Nationwide Population of Women With Polycystic Ovary Syndrome. The Journal of Clinical Endocrinology and Metabolism, 102(10), 3848-3857. doi:10.1210/jc.2017-01354

95

Santos, M., & Shah, A. M. (2014). Alterations in cardiac structure and function in hypertension. Current Hypertension Reports, 16(5), 428. doi://dx.doi.org/ 10.1007/s11906-014-0428-x

Saranya, K., Pal, G. K., Habeebullah, S., & Pal, P. (2014). Assessment of cardiovascular autonomic function in patients with polycystic ovary syndrome. The Journal of Obstetrics and Gynaecology Research, 40(1), 192-199. doi:10.1111/jog.12154

Schmidt, J., Landin-Wilhelmsen, K., Brannstrom, M., & Dahlgren, E. (2011). Cardiovascular disease and risk factors in PCOS women of postmenopausal age: a 21-year controlled follow-up study. The Journal of Clinical Endocrinology & Metabolism, 96(12), 3794-3803.

Selcoki, Y., Yilmaz, O. C., Carlioglu, A., Onaran, Y., Kankilic, M. N., Karakurt, F., & Eryonucu, B. (2010). Cardiac flow parameters with conventional and pulsed tissue Doppler echocardiography imaging in patients with polycystic ovary syndrome. Gynecological Endocrinology, 26(11), 815-818.

Sgoifo, A., Carnevali, L., Alfonso, Maria de los Angeles Pico, & Amore, M. (2015). Autonomic dysfunction and heart rate variability in depression. Stress (Amsterdam, Netherlands), 18(3), 343-352. doi:10.3109/10253890.2015.1045868

Shorakae, S., Ranasinha, S., Abell, S., Lambert, G., Lambert, E., de Courten, B., & Teede, H. (2018). Inter-related effects of insulin resistance, hyperandrogenism, sympathetic dysfunction and chronic inflammation in PCOS. Clinical Endocrinology, 89(5), 628-633. doi:10.1111/cen.13808

Smiseth, O. A., Torp, H., Opdahl, A., Haugaa, K. H., & Urheim, S. (2016). Myocardial strain imaging: how useful is it in clinical decision making? European Heart Journal, 37(15), 1196-1207. doi:10.1093/eurheartj/ehv529

Spritzer, P. M., Lecke, S. B., Satler, F., & Morsch, D. M. (2015). Adipose tissue dysfunction, adipokines, and low-grade chronic inflammation in polycystic ovary syndrome. Reproduction (Cambridge, England), 149(5), 219. doi:10.1530/REP-14-0435

Staessen, J. A., Fagard, R., Thijs, L., Celis, H., Arabidze, G. G., Birkenhäger, W. H., . . . Zanchetti, A. (1997). Randomised double-blind comparison of placebo and active treatment for older patients with isolated systolic hypertension. The Systolic Hypertension in Europe (Syst-Eur) Trial Investigators. Lancet (London, England), 350(9080), 757-764.

Stein, I. F., & Leventhal, M. L. (1935). Amenorrhea associated with bilateral polycystic ovaries. American Journal of Obstetrics and Gynecology, 29(2), 181-191. doi: 10.1016/S0002-9378(15)30642-6

Stepto, N. K., Cassar, S., Joham, A. E., Hutchison, S. K., Harrison, C. L., Goldstein, R. F., & Teede, H. J. (2013). Women with polycystic ovary syndrome have intrinsic insulin resistance on euglycaemic-hyperinsulaemic clamp. Human Reproduction, 28(3), 777-784. doi://dx.doi.org/10.1093/humrep/des463

96

Stuckey, M. I., Tulppo, M. P., Kiviniemi, A. M., & Petrella, R. J. (2014). Heart rate variability and the metabolic syndrome: a systematic review of the literature. Diabetes/Metabolism Research and Reviews, 30(8), 784-793. doi: 10.1002/dmrr.2555

Sund, R. (2012). Quality of the Finnish Hospital Discharge Register: a systematic review. Scandinavian Journal of Public Health, 40(6), 505-515. doi:10.1177/1403494812456637

Sverrisdóttir, Y. B., Mogren, T., Kataoka, J., Janson, P. O., & Stener-Victorin, E. (2008). Is polycystic ovary syndrome associated with high sympathetic nerve activity and size at birth? American Journal of Physiology. Endocrinology and Metabolism, 294(3), 576. doi:10.1152/ajpendo.00725.2007

Taponen, S., Ahonkallio, S., Martikainen, H., Koivunen, R., Ruokonen, A., Sovio, U., . . . Jarvelin, M. R. (2004). Prevalence of polycystic ovaries in women with self-reported symptoms of oligomenorrhoea and/or hirsutism: Northern Finland Birth Cohort 1966 Study. Human Reproduction, 19(5), 1083-1088.

Taponen, S., Martikainen, H., Jarvelin, M. R., Laitinen, J., Pouta, A., Hartikainen, A. L., . . . Ruokonen, A. (2003). Hormonal profile of women with self-reported symptoms of oligomenorrhea and/or hirsutism: Northern Finland birth cohort 1966 study. The Journal of Clinical Endocrinology & Metabolism, 88(1), 141-147.

Taponen, S., Martikainen, H., Jarvelin, M. R., Sovio, U., Laitinen, J., Pouta, A., . . . Northern Finland Birth Cohort 1966, Study. (2004). Metabolic cardiovascular disease risk factors in women with self-reported symptoms of oligomenorrhea and/or hirsutism: Northern Finland Birth Cohort 1966 Study. The Journal of Clinical Endocrinology & Metabolism, 89(5), 2114-2118.

Targher, G., Solagna, E., Tosi, F., Castello, R., Spiazzi, G., Zoppini, G., . . . Moghetti, P. (2009). Abnormal serum alanine aminotransferase levels are associated with impaired insulin sensitivity in young women with polycystic ovary syndrome. Journal of Endocrinological Investigation, 32(8), 695-700.

Taskin, M. I., Bulbul, E., Adali, E., Hismiogulları, A. A., & Inceboz, U. (2015). Circulating levels of obestatin and copeptin in obese and nonobese women with polycystic ovary syndrome. European Journal of Obstetrics, Gynecology, and Reproductive Biology, 189, 19-23. doi:10.1016/j.ejogrb.2015.03.006

Tata, B., Mimouni, N. E. H., Barbotin, A., Malone, S. A., Loyens, A., Pigny, P., . . . Giacobini, P. (2018). Elevated prenatal anti-Müllerian hormone reprograms the fetus and induces polycystic ovary syndrome in adulthood. Nature Medicine, doi:10.1038/s41591-018-0035-5

Taylor, A. E., Keevil, B., & Huhtaniemi, I. T. (2015). Mass spectrometry and immunoassay: how to measure steroid hormones today and tomorrow. European Journal of Endocrinology, 173(2), 1. doi:10.1530/EJE-15-0338

Teede, H. J., Joham, A. E., Paul, E., Moran, L. J., Loxton, D., Jolley, D., & Lombard, C. (2013). Longitudinal weight gain in women identified with polycystic ovary syndrome: results of an observational study in young women. Obesity, 21(8), 1526-1532.

97

Teede, H. J., Misso, M. L., Costello, M. F., Dokras, A., Laven, J., Moran, L., . . . International PCOS Network. (2018). Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Clinical Endocrinology, doi:10.1111/cen.13795

Tekin, A., Tekin, G., Colkesen, Y., Kilicdag, E. B., Bashan, I., Sezgin, A. T., & Muderrisoglu, H. (2009). Left ventricular function in patients with polycystic ovary syndrome: a Doppler echocardiographic study. Experimental & Clinical Endocrinology & Diabetes, 117(4), 165-169.

Tekin, G., Tekin, A., Kiliçarslan, E. B., Haydardedeoğlu, B., Katircibaşi, T., Koçum, T., . . . Müderrisoğlu, H. (2008). Altered autonomic neural control of the cardiovascular system in patients with polycystic ovary syndrome. International Journal of Cardiology, 130(1), 49-55. doi:10.1016/j.ijcard.2007.08.037

Thomson, R. L., Buckley, J. D., Noakes, M., Clifton, P. M., Norman, R. J., & Brinkworth, G. D. (2010). Heart rate recovery improves after weight loss in overweight and obese women with polycystic ovary syndrome. Fertility and Sterility, 93(4), 1173-1178. doi:10.1016/j.fertnstert.2008.12.003

Tíras, M. B., Yalcìn, R., Noyan, V., Maral, I., Yìldìrìm, M., Dörtlemez, O., & Daya, S. (1999). Alterations in cardiac flow parameters in patients with polycystic ovarian syndrome. Human Reproduction (Oxford, England), 14(8), 1949-1952.

Tosi, F., Bonora, E., & Moghetti, P. (2017). Insulin resistance in a large cohort of women with polycystic ovary syndrome: a comparison between euglycaemic-hyperinsulinaemic clamp and surrogate indexes. Human Reproduction (Oxford, England), 32(12), 2515-2521. doi:10.1093/humrep/dex308

Valkonen-Korhonen, M., Tarvainen, M. P., Ranta-Aho, P., Karjalainen, P. A., Partanen, J., Karhu, J., & Lehtonen, J. (2003). Heart rate variability in acute psychosis. Psychophysiology, 40(5), 716-726.

Vaz, M., Jennings, G., Turner, A., Cox, H., Lambert, G., & Esler, M. (1997). Regional sympathetic nervous activity and oxygen consumption in obese normotensive human subjects. Circulation, 96(10), 3423-3429.

Vermeulen, A., Verdonck, L., & Kaufman, J. M. (1999). A critical evaluation of simple methods for the estimation of free testosterone in serum. The Journal of Clinical Endocrinology and Metabolism, 84(10), 3666-3672. doi:10.1210/jcem.84.10.6079

Wallace, I. R., McKinley, M. C., Bell, P. M., & Hunter, S. J. (2013). Sex hormone binding globulin and insulin resistance. Clinical Endocrinology, 78(3), 321-329. doi:10.1111/cen.12086

Wang, E. T., Ku, I. A., Shah, S. J., Daviglus, M. L., Schreiner, P. J., Konety, S. H., . . . Bibbins-Domingo, K. (2012). Polycystic ovary syndrome is associated with higher left ventricular mass index: the CARDIA women's study. The Journal of Clinical Endocrinology & Metabolism, 97(12), 4656-4662.

Welt, C. K., & Carmina, E. (2013). Clinical review: Lifecycle of polycystic ovary syndrome (PCOS): from in utero to menopause. The Journal of Clinical Endocrinology and Metabolism, 98(12), 4629-4638. doi:10.1210/jc.2013-2375

98

West, S., Lashen, H., Bloigu, A., Franks, S., Puukka, K., Ruokonen, A., . . . Morin-Papunen, L. (2014a). Irregular menstruation and hyperandrogenaemia in adolescence are associated with polycystic ovary syndrome and infertility in later life: Northern Finland Birth Cohort 1986 study. Human Reproduction (Oxford, England), 29(10), 2339-2351. doi:10.1093/humrep/deu200

West, S., Vähäsarja, M., Bloigu, A., Pouta, A., Franks, S., Hartikainen, A. -., . . . Morin-Papunen, L. (2014b). The impact of self-reported oligo-amenorrhea and hirsutism on fertility and lifetime reproductive success: results from the Northern Finland Birth Cohort 1966. Human Reproduction (Oxford, England), 29(3), 628-633. doi:10.1093/humrep/det437

WHO | Global status report on noncommunicable diseases 2010. (2011). World Health Organization. Retrieved from http://www.who.int/nmh/publications/ncd_report 2010/en/

Wild, S., Pierpoint, T., McKeigue, P., & Jacobs, H. (2000). Cardiovascular disease in women with polycystic ovary syndrome at long-term follow-up: a retrospective cohort study. Clinical Endocrinology, 52(5), 595-600.

Williams, B., Mancia, G., Spiering, W., Agabiti Rosei, E., Azizi, M., Burnier, M., . . . Desormais, I. (2018). 2018 ESC/ESH Guidelines for the management of arterial hypertension. European Heart Journal, 39(33), 3021-3104. doi:10.1093/ eurheartj/ehy339

Windham, B. G., Fumagalli, S., Ble, A., Sollers, J. J., Thayer, J. F., Najjar, S. S., . . . Ferrucci, L. (2012). The Relationship between Heart Rate Variability and Adiposity Differs for Central and Overall Adiposity. Journal of Obesity, 2012, 149516. doi:10.1155/ 2012/149516

World Health Organization. (2011). Waist circumference and waist-hip ratio: report of a WHO expert consultation, Geneva, 8-11 December 2008 Geneva: World Health Organization.

Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. (2017). Lancet (London, England), 390(10113), 2627-2642. doi:10.1016/S0140-6736(17)32129-3

Wulsin, L. R., Horn, P. S., Perry, J. L., Massaro, J. M., & D'Agostino, R. B. (2015). Autonomic Imbalance as a Predictor of Metabolic Risks, Cardiovascular Disease, Diabetes, and Mortality. The Journal of Clinical Endocrinology and Metabolism, 100(6), 2443-2448. doi:10.1210/jc.2015-1748

Xu, W., Morford, J., & Mauvais-Jarvis, F. (2019). Emerging role of testosterone in pancreatic β-cell function and insulin secretion. The Journal of Endocrinology, doi:10.1530/JOE-18-0573

Yanes, L. L., Romero, D. G., Moulana, M., Lima, R., Davis, D. D., Zhang, H., . . . Reckelhoff, J. F. (2011). Cardiovascular-renal and metabolic characterization of a rat model of polycystic ovary syndrome. Gender Medicine, 8(2), 103-115. doi:10.1016/ j.genm.2010.11.013

99

Yasuma, F., & Hayano, J. (2004). Respiratory sinus arrhythmia: why does the heartbeat synchronize with respiratory rhythm? Chest, 125(2), 683-690.

Yildirim, B., Sabir, N., & Kaleli, B. (2003). Relation of intra-abdominal fat distribution to metabolic disorders in nonobese patients with polycystic ovary syndrome. Fertility & Sterility, 79(6), 1358-1364.

Yildirir, A., Aybar, F., Kabakci, G., Yarali, H., & Oto, A. (2006). Heart rate variability in young women with polycystic ovary syndrome. Annals of Noninvasive Electrocardiology: The Official Journal of the International Society for Holter and Noninvasive Electrocardiology, Inc, 11(4), 306-312. doi:10.1111/j.1542-474X.2006.00122.x

Yildiz, B. O., Bolour, S., Woods, K., Moore, A., & Azziz, R. (2010). Visually scoring hirsutism. Human Reproduction Update, 16(1), 51-64. doi:10.1093/humupd/dmp024

Yildiz, B. O., Knochenhauer, E. S., & Azziz, R. (2008). Impact of obesity on the risk for polycystic ovary syndrome. The Journal of Clinical Endocrinology and Metabolism, 93(1), 162-168. doi:10.1210/jc.2007-1834

Yildiz, B. O., Bozdag, G., Yapici, Z., Esinler, I., & Yarali, H. (2012). Prevalence, phenotype and cardiometabolic risk of polycystic ovary syndrome under different diagnostic criteria. Human Reproduction (Oxford, England), 27(10), 3067-3073. doi:10.1093/ humrep/des232

Zachurzok-Buczynska, A., Szydlowski, L., Gawlik, A., Wilk, K., & Malecka-Tendera, E. (2011). Blood pressure regulation and resting heart rate abnormalities in adolescent girls with polycystic ovary syndrome. Fertility & Sterility, 96(6), 1519-1525. doi:// dx.doi.org/10.1016/j.fertnstert.2011.09.043

Zawadski, J. K., & Dunaif, A. (1992). Diagnostic criteria for polycystic ovary syndrome; towards a rational approach. Boston: Blackwell Scientific.

Zheng, S., Du, D., & Li, X. (2017). Leptin Levels in Women With Polycystic Ovary Syndrome: A Systematic Review and a Meta-Analysis. Reproductive Sciences (Thousand Oaks, Calif.), 24(5), 656-670. doi:10.1177/1933719116670265

Zimmermann, S., Phillips, R. A., Dunaif, A., Finegood, D. T., Wilkenfeld, C., Ardeljan, M., . . . Krakoff, L. R. (1992). Polycystic ovary syndrome: lack of hypertension despite profound insulin resistance. The Journal of Clinical Endocrinology & Metabolism, 75(2), 508-513. doi://dx.doi.org/10.1210/jcem.75.2.1639952

101

Original publications I Ollila, MM., Piltonen, T., Puukka, K., Ruokonen, A., Järvelin, M., Tapanainen, JS.,

Franks, S., Morin-Papunen, L. (2016). Weight Gain and Dyslipidemia in Early Adulthood Associate With Polycystic Ovary Syndrome: Prospective Cohort Study. The Journal of Clinical Endocrinology & Metabolism, 101(2), 739-747. doi://dx.doi.org/10.1210/jc.2015-3543

II Ollila, MM., West, S., Keinänen-Kiukaaniemi, S., Jokelainen, J., Auvinen, J., Puukka, K., Ruokonen, A., Järvelin, MR., Tapanainen, JS., Franks, S., Piltonen, TT., Morin-Papunen, L. (2017). Overweight and obese but not normal weight women with PCOS are at increased risk of Type 2 diabetes mellitus – a prospective population-based cohort study. Human Reproduction, 32(2), 423-431. doi: 10.1093/humrep/dew329

III Ollila, MM., Kaikkonen, K., Järvelin, M., Huikuri, HV., Tapanainen, JS., Franks, S., Piltonen, TT., Morin-Papunen, L. (2019). Self-reported Polycystic Ovary Syndrome is Associated with Hypertension: A Northern Finland Birth Cohort 1966 Study. The Journal of Clinical Endocrinology and Metabolism, 104(4), 1221-1231. doi:10.1210/jc.2018-00570.

IV Ollila, MM., Kiviniemi, A., Stener-Victorin, E., Tulppo, M., Puukka, K., Ruokonen, A., Tapanainen, JS., Franks, S., Morin-Papunen, L., Piltonen TT. Cardiac Autonomic Balance at age 46 in Women with PCOS - A Cohort Study of 1856 women. Manuscript.

Reprinted with permission from Oxford University press (Studies I, II, III).

Original publications are not included in the electronic version of the dissertation.


Book orders:Granum: Virtual book storehttp://granum.uta.fi/granum/

S E R I E S D M E D I C A

1496. Lackman, Jarkko (2018) Glycosylation and dimerization of the human ?-opioidreceptor polymorphic variants

1497. Hoikka, Marko (2018) Prehospital risk assessment and patient outcome : apopulation based study in Northern Finland

1498. Wiens, Varpu (2018) Pohjoissuomalaisten nuorten tyttöjen hyvinvointi :hypoteettinen malli

1499. Piispala, Johanna (2019) Atypical electrical brain activity related to attention andinhibitory control in children who stutter

1500. Leppänen, Joni (2019) The role of hypoxia, innate immunity receptors andstromal response in pancreatic cancer

1501. Lahtinen, Sanna (2019) Complications, quality of life and outcome after free flapsurgery for cancer of the head and neck

1502. Rajavaara, Päivi (2019) Children’s dental general anaesthesia : reasons andassociated factors

1503. Varpuluoma, Outi (2019) Drugs, dermatitis herpetiformis and celiac disease asrisk factors for bullous pemphigoid in Finland

1504. Mäkelä-Kaikkonen, Johanna (2019) Robotic-assisted and laparoscopic ventralrectopexy in the treatment of posterior pelvic floor procidentia

1505. Lahtinen, Antti (2019) Rehabilitation after hip fracture : Comparison of physical,geriatric and conventional treatment

1506. Alakärppä, Antti (2019) Primary sinonasal surgery and health-related quality of lifein adults

1507. Komulainen-Ebrahim, Jonna (2019) Genetic aetiologies and phenotypic variationsof childhood-onset epileptic encephalopathies and movement disorders

1508. Jussila, Päivi (2019) Prevalence and associated risk factors of temporomandibulardisorders (TMD) in the Northern Finland Birth Cohort (NFBC) 1966

1509. Määttä, Jenni (2019) Effects of the hypoxia response on metabolism inatherosclerosis and pregnancy

1510. Taka-Eilola, Tiina (2019) Mental Health Problems in the Adult Offspring ofAntenatally Depressed mothers in the Northern Finland 1966 Birth Cohort;Relationship with Parental Severe Mental Disorder

1511. Korhonen, Tommi (2019) Bone flap survival and resorption after autologouscranioplasty

UNIVERSITY OF OULU P .O. Box 8000 F I -90014 UNIVERSITY OF OULU FINLAND


University Lecturer Tuomo Glumoff

University Lecturer Santeri Palviainen

Senior research fellow Jari Juuti


University Lecturer Veli-Matti Ulvinen

Planning Director Pertti Tikkanen

Professor Jari Juga

University Lecturer Anu Soikkeli


Publications Editor Kirsti Nurkkala

ISBN 978-952-62-2258-5 (Paperback)ISBN 978-952-62-2259-2 (PDF)ISSN 0355-3221 (Print)ISSN 1796-2234 (Online)

U N I V E R S I TAT I S O U L U E N S I S

MEDICA

ACTAD

D 1513

ACTA

Meri-M

aija Ollila

OULU 2019

D 1513

Meri-Maija Ollila


UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU, FACULTY OF MEDICINE;OULU UNIVERSITY HOSPITAL

The role of polycystic ovary syndrome (PCOS) and overweight ...

Documents

Transcript of The role of polycystic ovary syndrome (PCOS) and overweight ...