Complete Thesis

University of Groningen

Epidemiology of metabolic health Slagter, Sandra Nicole

IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record

Publication date: 2017

Link to publication in University of Groningen/UMCG research database

Citation for published version (APA): Slagter, S. N. (2017). Epidemiology of metabolic health: Lifestyle determinants and health-related quality of life. Rijksuniversiteit Groningen.

Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).

Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.

Download date: 26-09-2021

Epidemiology of metabolic health

Lifestyle determinants and health-related quality of life

Sandra N. Slagter Epidemiology of metabolic health Lifestyle determinants and health-related quality of life

Thesis, University of Groningen, the Netherlands

Cover design: Mark van Wijk - markvanwijk.net Lay-out: Ridderprint BV - www.ridderprint.nl Printing: Ridderprint BV - www.ridderprint.nl ISBN: 978-90-367-9383-4 (printed) 978-90-367-9382-7 (eBook)

This work was supported by the BioSHaRE-EU project (Biobank Standardisation and Harmonisation for Research Excellence in the European Union) under grant agreement n°261433, receiving funds from the National Consortium for Healthy Ageing, and from the European Union’s Seventh Framework Program (FP7/2007-2013).

The LifeLines Cohort Study is supported by the Netherlands Organization of Scientific Research NWO (grant 175.010.2007.006), the Ministry of Economic Affairs, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the Northern Netherlands Collaboration of Provinces (SNN), the Province of Groningen, University Medical Center Groningen, the University of Groningen, Dutch Kidney Foundation and Dutch Diabetes Research Foundation.

Financial support for printing of this thesis was kindly provided by: The Endocrinology Fund (as part of the Ubbo Emmius Fund), Graduate School of Medical Sciences/University Medical Center Groningen, Univer- sity of Groningen and Novo Nordisk BV.

Financial support by the Dutch Heart Foundation for the publication of this thesis is gratefully acknowl- edged. Epidemiology of metabolic health

Lifestyle determinants and health-related quality of life

Proefschrift

ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen op gezag van de rector magnificus prof. dr. E. Sterken en volgens besluit van het College voor Promoties.

De openbare verdediging zal plaatsvinden op

woensdag 11 januari 2017 om 12.45 uur

door

Sandra Nicole Slagter

geboren op 21 augustus 1990 te Assen Promotor Prof. dr. B.H.R. Wolffenbuttel

Copromotores Dr. J.V. van Vliet-Ostaptchouk Dr. M.M. van der Klauw

Beoordelingscommissie Prof. dr. R. Sanderman Prof. dr. O.H. Franco Prof. dr. L. van Gaal Voor mijn ouders Paranimfen R.A. Slagter J.M.J. Noble ContentS

Chapter 1 General introduction 9

Chapter 2 The prevalence of metabolically healthy obesity in Europe: 21 a collaborative analysis of ten large cohort studies. BMC Endocrine Disorders 2014, 14:9

Chapter 3 Associations between smoking, components of the 47 metabolic syndrome and lipoprotein particle size. BMC Medicine 2013 11:195

Chapter 4 Combined effects of smoking and alcohol on metabolic 75 syndrome: The LifeLines Cohort Study. PLoS ONE 2014, 9(4):e96406

Chapter 5 Dietary patterns and physical activity in the (un) healthy 101 obese: The LifeLines cohort study. In preparation

Chapter 6 Health-related quality of life in relation to obesity grade, 135 type 2 diabetes, metabolic syndrome and inflammation. PLoS ONE 2015, 10(10):e0140599

Chapter 7 Sex, BMI and age differences in metabolic syndrome: 161 updated prevalence estimates in the Netherlands. In preparation

Chapter 8 Summary and general discussion 185

Nederlandse samenvatting 211

Acknowledgements / Dankwoord 217

About the author and publication list 219 1

Hoofdstukpagina-letteromtrek.indd 1 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 2 16/11/16 11:05 Chapter 1

General introduction

Hoofdstukpagina-letteromtrek.indd 1 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 2 16/11/16 11:05

General Introduction 11

An introduction of the metabolic syndrome 1

The metabolic syndrome (MetS) is a clustering of medical conditions that reflects over- nutrition, sedentary lifestyles, and resultant excess adiposity [1]. Metabolic abnormalities such as abdominal obesity, hyperglycaemia, hypertension and dyslipidaemia often are present together, suggesting that they are not independent of one another and that they may share underlying causes and mechanisms. Having MetS places a subject at a substantially increased risk to develop serious diseases like type 2 diabetes (T2D) and cardiovascular disease (CVD) [1]. Although MetS is a condition mainly seen among individuals with overweight and obesity, even lean individuals may develop features of MetS [2]. Since the 1920s, the clustering of metabolic abnormalities was under the attention of several independent scientists, but they did not address MetS as we know it today [1]. It was until 1988, when the concept of the syndrome was brought to a wider audience by Reaven. He noted that insulin resistance clustered together with glucose intolerance, dyslipidaemia and hypertension, altogether increasing the risk of CVD [3]. The collection of these medical conditions was initially designated Syndrome X, although the term insulin resistance syndrome was also commonly used [1]. Diagnostic criteria for the syndrome were developed by several health oriented organisations, such as the World Health Or- ganisation (WHO) [4], the European Group for the Study of Insulin Resistance (EGIR) [5], the National Cholesterol Education Program Third Adult Treatment Panel (NCEP ATPIII) [6] and the International Diabetes Federation (IDF) [7]. The precise definition with the contributions of the underlying MetS components is under much debate. Nowadays, researchers often use the term Metabolic Syndrome instead of Syndrome X. This term was preferred by the NCEP ATPIII, as it avoids the implication that insulin resistance is the primary or only cause of the metabolic risk factors [6]. The NCEP ATPIII definition is the most widely used definition for MetS, in both clinical medicine and in epidemiological studies, where rapid and simple assessment is important [8]. Accord- ingly, throughout this thesis the NCEP ATPIII definition was used, which classifies a person with MetS when at least three of the five risk features are present, e.g. abdominal obesity (enlarged waist circumference), elevated blood pressure, fasting plasma glucose and/or triglycerides or reduced HDL cholesterol [6, 9]. Rather than insulin resistance, abdominal obesity is one of the components of MetS. Abdominal obesity is, in contrast to insulin resistance, easily measured and has a clear link with insulin resistance, as well as with the other four metabolic abnormalities [9]. 12 Chapter 1

The epidemic of MetS

During the past years somewhat varying definitions have been used and some defining values to estimate the prevalence of MetS worldwide have been changed. Not to men- tion that the composition of the population being studied may vary by sex composition, age, race and ethnicity [1]. Regardless of such details, the obesity epidemic and the ageing population are driving the increasing prevalence of MetS around the world, as well as its consequences like T2D and CVD [10]. The presence of MetS is associated with an approximately fivefold increased risk for incident T2D [11], a twofold increased risk for CVD outcomes and a 1.5-fold increased risk for all-cause mortality [12]. Individuals with MetS are, furthermore, susceptible to other conditions such as polycystic ovary syndrome, fatty liver, gallstones, asthma, sleep disturbances, and some forms of cancer [13]. According to the National Health and Examination Survey (NHANES) 2003-2006, a program of studies among adults and children in the United States, approximately 34% of the studied adult people had MetS using the revised NCEP ATPIII criteria [14]. During the last 15 years the estimated prevalence of MetS increased up to 5% within the NHANES cohort. Grundy et al. [15] reported in his review on the Metabolic Syndrome Pandemic, that based on a series of studies on the occurrence of MetS in Europe, it would be fair to say that approximately one-quarter of the European adult population has MetS. In 2012, the Dutch National Institute for Health and Environment has estimated that among people between 30 and 70 years the prevalence of MetS is 34% in men and 24% in women1. Given the high prevalence and severe consequences, MetS is a phenomenon of high public health relevance.

How does obesity and insulin resistance contribute to MetS?

Although, MetS has received our full attention since 1988, the causative etiology of this syndrome is still not clearly understood. The causes of MetS, and each of its components, is complex since hormonal dysregulation, ageing, proinflammatory state and lifestyle interactions may be involved in the pathophysiological route [13]. Although the estimate on heritability of MetS has not been reported yet, it is clear that all components of the syndrome have a strong genetic basis [16]. Nevertheless, there are two factors which appear to be at the core of the pathophysiology of MetS and its individual components: insulin resistance and abdominal obesity. Though the focus of this dissertation lies on epidemiology, I will provide a short and basic overview.

1 http://www.rivm.nl/dsresource?objectid=rivmp:76082&type=org&disposition=inline&ns_nc=1 General Introduction 13

Insulin resistance and abdominal obesity 1 The term insulin resistance can be broadly defined as a subnormal biological response to normal insulin concentrations. As a result, a higher level of insulin is required to maintain a normal level of glucose in the blood (normoglycaemia) [17]. At normal levels insulin has vasodilator and anti-inflammatory actions [1]. However, in case of insulin resistance, the higher levels of insulin are associated with a higher chance of developing atherosclerosis, as insulin is a type of growth factor, effecting vascular smooth muscle cells, important for the maintenance of plaque stability in atherosclerosis [18]. Disturbed insulin signalling can therefore promote both atherogenesis and advanced plaque progression. While insulin resistance can develop in the absence of excess fat, it is typically seen in subjects with overweight or obesity. When body fat increases, insulin resistance increases as well [8]. Especially in visceral adipose tissue, e.g. fat surrounding internal organs, free fatty acids (FFA) are released into the circulation. There they find their way to other tissues, such as the liver and skeletal muscle [1].These tissues have a high impact on glucose use and removal of glucose from the circulation. An overload of lipids in these tissues induces insulin resistance [8]. Not only do FFA levels appear to cause insulin resistance, but insulin resistance also appear to cause elevated FFA [1]. Impaired insulin signalling increases lipolysis in adipocytes (fat cells), resulting in an increased turnover of FFA [8]. Due to the overload of FFA in the liver, and the consequent insulin resistance, triglyceride synthesis and storage is increased. The excess triglycerides are released as very low density lipoprotein (VLDL) particles. The resulting hypertriglyceridaemia is furthermore associated with reductions in high density lipoprotein (HDL) and triglyceride enriched low density lipoprotein (LDL), which are also considered factors which promote artherosclerosis [1, 8]. Adipose tissue does not only secrete FFA, but is also an active endocrine organ that releases a variety of hormones and molecules, with either pro-inflammatory or anti-inflammatory properties. In individuals with increased adipose tissue, mainly proinflammatory signaling factors are activated, such as high-sensitive C-reactive protein (hs-CRP), interleukin (IL)-6 and Tumor Necrosis Factor-α (TNF-α) [1]. Studies have linked chronic low-grade inflammation to the development of insulin resistance and MetS [19- 21].

Treatment and prevention of MetS

Although excessive adiposity is clearly linked to MetS, and both obesity and the individual MetS components might be caused by genetic defects, the high rate at which these conditions develop suggest that environmental factors, such as lifestyle, are just 14 Chapter 1

as much important (causative factors). Since the exact pathophysiological mechanism behind MetS is still not well understood and many factors may be involved, it is unclear whether MetS could be treated in itself. However, the rationale for the implementation of MetS as a diagnosis is to initiate aggressive lifestyle changes with the goal of decreas- ing T2D and CVD risk by targeting several MetS components at the same time. If lifestyle changes have no desirable result, medical therapy could be used to treat the individual components [22], for instance drugs which lower blood pressure. Similarly to western societies, the MetS prevalence is rapidly increasing in developing countries, which reflects the transition from a traditional lifestyle to a more Western-like lifestyle [23]. Physical inactivity and a diet high in fats and carbohydrates, as well as smoking, contribute to abdominal obesity and insulin resistance [10]. It is well established that weight loss is the number one treatment for MetS. It may beneficially influence all of the components of the MetS, including excessive adiposity, dyslipidaemia, hypertension, insulin resistance, and hyperglycaemia [1]. However, adherence to weight-loss programs is poor and long-term effects are modest [24, 25]. Epidemiological and clinical studies on the specific responsiveness of a certain individual to lifestyle interventions, such as smoking cessation, modification of alcohol consumption, modifying eating habits and increasing exercise, may contribute to the development of better preventive strategies and treatment of metabolic complications. In this dissertation we will, in part, focus on such lifestyles which may be associated with alterations in metabolic health.

Identification of the metabolically healthy obesity phenotype

The first step in the prevention of MetS and its related morbidities, is tackling the obesity epidemic. This is an absolute necessity since approximately 20% of the entire adult population of the world will be obese by 2030 [26]. To improve intervention and treatment strategies for obesity, we need to accept that obesity is not a uniform condition for which a ‘one size fits all’ approach might do the trick. In fact, metabolic abnormalities and cardiovascular risk may vary among obese individuals. Individuals with excess adiposity but without major obesity-associated metabolic abnormalities have been identified as metabolically healthy obesity (MHO) [27-29]. Similar to MetS, several definitions are used to define MHO. Some of them are based on the absence of MetS or only some the individual components, while others include the inflammatory status as well. This results in widely varying prevalence estimates of 10-40 % of all obese subjects being metabolically healthy within the same population [30]. Other factors that might account for this wide range of reported prevalences are differences in study design, ethnicity, age-group and sample size [30]. General Introduction 15

While the metabolically healthy obese are expected to differ from unhealthy obese 1 adults on levels of metabolic risk factors, it is of great interest to identify other physiological and behavioural factors that distinguish healthy obese adults from their unhealthy obese counterparts, as this may reveal modifiable determinants of this preferred state.

The LifeLines Cohort Study

All studies described in this thesis are based on data from the LifeLines cohort study, a large observational study carried out in the three northern provinces of the Netherlands, i.e. Groningen, Friesland and Drenthe. More than 167,000 persons participate, which is 10% of the Dutch population. With its prospective design, participants will be followed for 30 years, it aims to unravel the influence of environmental and genetic factors (including their interaction) on the development of multifactorial diseases. Between 2006 and 2013 different recruitment strategies were adopted that aimed to include three generations of participants - recruitment of an index population (25 to 49 years of age) via general practitioners, subsequent inclusion of their family members, and online self- registration – which resulted in a low risk of selection bias and a high participation rate. Individuals who were unable to read Dutch or those with limited life expectancy (due to severe illness) were excluded from participation by the general practitioner and were not invited. All participants older than 18 years completed a number of questionnaires covering topics like the occurrence of diseases, general health, medication use, diet, physical activity, personality and many more. They underwent a clinical examination, and biological samples were collected [31, 32]. A comprehensive overview of the data collection can be found in the LifeLines catalogue at www.LifeLines.net. The LifeLines adult population (91.2%, 152,915 persons) was found to be broadly representative for the adults living in the north of the Netherlands [33].

Aims and outline of the thesis

MetS is mainly a consequence of an environment that promotes overweight and obesity. However, not all obese individuals display metabolic abnormalities, and also not all lean individuals present a healthy metabolic profile. The research described in this thesis aimed to provide an update on the prevalence of MetS and MHO, to contribute to a better understanding of the associations between lifestyle factors and metabolic health, and in addition, to examine which aspect of health-related quality of life are influenced by obesity and metabolic health complications. 16 Chapter 1

Chapter 2 within the Healthy Obese Project (HOP) of the BioSHaRE-EU consortium (Biobank Standardisation and Harmonization for Research Excellence in the European Union; www.bioshare.eu), we have assessed the prevalence of MetS and MHO across participating biobanks, covering data of 163,517 people of which 17% were obese. Through a rigorous harmonization process and the use of a unified criteria, we were able to compare key characteristics defining the MHO phenotype across ten cohort studies from seven European countries. Chapter 3 examines the association between smoking and MetS. Not only MetS and the individual components are explored but also the association between smoking and levels of apolipoproteins (apoA1 and apoB) and lipoprotein particle size (HDL-C/apoA1 and LDL-C/apoB ratios). By taking the latter into account, this chapter also provides a possible patho-physiological mechanism linking smoking to increased CVD risk. Chapter 4 includes the data of a careful assessment of the combined effects of smoking and alcohol consumption on MetS and its individual components. In addition, we also used data on specific types of alcoholic beverages (beer, wine or spirits and mixed drinks) to obtain the related risk to develop MetS or having a specific component of MetS. Chapter 5 explores the sex-specific differences in diet and physical activity between the metabolically healthy- and unhealthy obese, taking into account smoking and alcohol use. To this end we have derived obesity-specific dietary patterns, based on self- reported data on 111 items from the Food Frequency Questionnaire. Chapter 6 presents the associations between obesity-related conditions and HR- QoL. These conditions were grade of obesity with and without T2D, MetS, and inflammation level. Obesity, T2D and MetS are all characterised by inflammation, which have been proposed as being part of the mechanism underlying reduced HR-QoL. Chapter 7 describes the prevalence of MetS and the individual MetS components in sex, BMI and age combined clusters. Previous studies showed that elevated blood pressure is the most common risk factor in the population. In the definition of MetS, the natural course of increasing blood pressure with ageing has not been taken into account. The strict threshold for elevated blood pressure is used irrespective of age (≥130 mmHg systolic and ≥85 mmHg diastolic). To demonstrate this illogical decision, we additionally applied age-adjusted thresholds to define elevated blood pressure based on the most recent hypertension guideline of the Joint National Committee (JNC). Chapter 8 provides a summary and discussion of the main results of the thesis, methodological considerations and future perspectives for research on risk factors for CVD and T2D. General Introduction 17

References 1 1. Cornier MA, Dabelea D, Hernandez TL, Lindstrom 10. O’Neill S, O’Driscoll L: Metabolic syndrome: a closer look RC, Steig AJ, Stob NR, Van Pelt RE, Wang H, Eckel RH: at the growing epidemic and its associated pathologies. The metabolic syndrome. Endocrine reviews 2008, Obesity reviews : an official journal of the International 29(7):777-822. Association for the Study of Obesity 2015, 16(1):1-12. 2. Karelis AD, St-Pierre DH, Conus F, Rabasa-Lhoret R, 11. Ford ES, Li C, Sattar N: Metabolic syndrome and incident Poehlman ET: Metabolic and body composition fac- diabetes: current state of the evidence. Diabetes care tors in subgroups of obesity: what do we know? The 2008, 31(9):1898-1904. Journal of clinical endocrinology and metabolism 2004, 12. Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier 89(6):2569-2575. P, Rinfret S, Schiffrin EL, Eisenberg MJ: The metabolic 3. Reaven GM: Banting Lecture 1988. Role of insulin syndrome and cardiovascular risk a systematic review resistance in human disease. 1988. Nutrition (Burbank, and meta-analysis. Journal of the American College of Los Angeles County, Calif) 1997, 13(1):65; discussion 64, Cardiology 2010, 56(14):1113-1132. 66. 13. Grundy SM, Brewer HB, Jr., Cleeman JI, Smith SC, Jr., 4. Alberti KG, Zimmet PZ: Definition, diagnosis and clas- Lenfant C: Definition of metabolic syndrome: Report of sification of diabetes mellitus and its complications. the National Heart, Lung, and Blood Institute/American Part 1: diagnosis and classification of diabetes mel- Heart Association conference on scientific issues related litus provisional report of a WHO consultation. Diabetic to definition. Circulation 2004, 109(3):433-438. medicine : a journal of the British Diabetic Association 14. Ervin RB: Prevalence of metabolic syndrome among 1998, 15(7):539-553. adults 20 years of age and over, by sex, age, race and 5. Balkau B, Charles MA: Comment on the provisional ethnicity, and body mass index: United States, 2003- report from the WHO consultation. European Group 2006. National health statistics reports 2009(13):1-7. for the Study of Insulin Resistance (EGIR). Diabetic 15. Grundy SM: Metabolic syndrome pandemic. Arterioscle- medicine : a journal of the British Diabetic Association rosis, thrombosis, and vascular biology 2008, 28(4):629- 1999, 16(5):442-443. 636. 6. Expert Panel on Detection Evaluation, and Treatment of 16. Song Q, Wang SS, Zafari AM: Genetics of the metabolic High Blood Cholesterol in Adults: Executive Summary of syndrome. Hospital Physician 2006, 52:51-61. The Third Report of The National Cholesterol Education 17. Moller DE, Flier JS: Insulin resistance--mechanisms, Program (NCEP) Expert Panel on Detection, Evaluation, syndromes, and implications. The New England journal And Treatment of High Blood Cholesterol In Adults of medicine 1991, 325(13):938-948. (Adult Treatment Panel III). JAMA 2001, 285(19):2486- 18. von der Thusen JH, Borensztajn KS, Moimas S, van 2497. Heiningen S, Teeling P, van Berkel TJ, Biessen EA: IGF-1 7. Alberti KG, Zimmet P, Shaw J: The metabolic syndrome- has plaque-stabilizing effects in atherosclerosis by -a new worldwide definition. Lancet (London, England) altering vascular smooth muscle cell phenotype. The 2005, 366(9491):1059-1062. American journal of pathology 2011, 178(2):924-934. 8. Huang PL: A comprehensive definition for metabolic 19. Bastard JP, Maachi M, Lagathu C, Kim MJ, Caron M, Vidal syndrome. Disease models & mechanisms 2009, 2(5- H, Capeau J, Feve B: Recent advances in the relationship 6):231-237. between obesity, inflammation, and insulin resistance. 9. Grundy SM, Cleeman JI, Daniels SR, Donato KA, European cytokine network 2006, 17(1):4-12. Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage 20. Elks CM, Francis J: Central adiposity, systemic inflam- PJ, Smith SC, Jr.et al: Diagnosis and management of the mation, and the metabolic syndrome. Current hyperten- metabolic syndrome: an American Heart Association/ sion reports 2010, 12(2):99-104. National Heart, Lung, and Blood Institute Scientific 21. Maury E, Brichard SM: Adipokine dysregulation, Statement. Circulation 2005, 112(17):2735-2752. adipose tissue inflammation and metabolic syndrome. Molecular and cellular endocrinology 2010, 314(1):1-16. 18 Chapter 1

22. Grundy SM: Metabolic syndrome: a multiplex cardio- the profile of obese patients who are metabolically vascular risk factor. The Journal of clinical endocrinology healthy. International journal of obesity (2005) 2011, and metabolism 2007, 92(2):399-404. 35(7):971-981. 23. Kassi E, Pervanidou P, Kaltsas G, Chrousos G: Metabolic 29. Stefan N, Kantartzis K, Machann J, Schick F, Thamer C, syndrome: definitions and controversies. BMC medicine Rittig K, Balletshofer B, Machicao F, Fritsche A, Haring 2011, 9:48. HU: Identification and characterization of metabolically 24. Douketis JD, Macie C, Thabane L, Williamson DF: benign obesity in humans. Archives of internal medicine Systematic review of long-term weight loss studies in 2008, 168(15):1609-1616. obese adults: clinical significance and applicability to 30. Phillips CM: Metabolically healthy obesity: definitions, clinical practice. International journal of obesity (2005) determinants and clinical implications. Reviews in 2005, 29(10):1153-1167. endocrine & metabolic disorders 2013, 14(3):219-227. 25. Franz MJ, VanWormer JJ, Crain AL, Boucher JL, 31. Scholtens S, Smidt N, Swertz MA, Bakker SJ, Dot- Histon T, Caplan W, Bowman JD, Pronk NP: Weight-loss inga A, Vonk JM, van Dijk F, van Zon SK, Wijmenga C, outcomes: a systematic review and meta-analysis Wolffenbuttel BHet al: Cohort Profile: LifeLines, a three- of weight-loss clinical trials with a minimum 1-year generation cohort study and biobank. International follow-up. Journal of the American Dietetic Association journal of epidemiology 2015, 44(4):1172-1180. 2007, 107(10):1755-1767. 32. Stolk RP, Rosmalen JG, Postma DS, de Boer RA, Navis 26. Kelly T, Yang W, Chen CS, Reynolds K, He J: Global burden G, Slaets JP, Ormel J, Wolffenbuttel BH: Universal risk of obesity in 2005 and projections to 2030. International factors for multifactorial diseases: LifeLines: a three- journal of obesity (2005) 2008, 32(9):1431-1437. generation population-based study. European journal 27. Karelis AD, Brochu M, Rabasa-Lhoret R: Can we identify of epidemiology 2008, 23(1):67-74. metabolically healthy but obese individuals (MHO)? 33. Klijs B, Scholtens S, Mandemakers JJ, Snieder H, Stolk Diabetes & metabolism 2004, 30(6):569-572. RP, Smidt N: Representativeness of the LifeLines Cohort 28. Primeau V, Coderre L, Karelis AD, Brochu M, Lavoie ME, Study. PloS one 2015, 10(9):e0137203. Messier V, Sladek R, Rabasa-Lhoret R: Characterizing

Hoofdstukpagina-letteromtrek.indd 3 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 4 16/11/16 11:05 Chapter 2

The prevalence of metabolic syndrome and metabolically healthy obesity in Europe: a collaborative analysis of ten large cohort studies

Jana V. van Vliet-Ostaptchouk† Jennifer R. Harris Marja-Liisa Nuotio† Hans L. Hillege Sandra N. Slagter† Jostein Holmen Dany Doiron† Antti Jula Krista Fischer Jenny E Kootstra-Ros Luisa Foco Kirsti Kvaløy Amadou Gaye Turid Lingaas Holmen Martin Gögele Satu Männistö Margit Heier Andres Metspalu Tero Hiekkalinna Kristian Midthjell Anni Joensuu Madeleine J. Murtagh Christopher Newby Annette Peters Chao Pang Peter P. Pramstaller Eemil Partinen Timo Saaristo Eva Reischl Veikko Salomaa Christine Schwienbacher Ronald P. Stolk Mari-Liis Tammesoo Matti Uusitupa Morris A. Swertz Pim van der Harst Paul Burton Melanie M. van der Klauw Vincent Ferretti Melanie Waldenberger Isabel Fortier Markus Perola§ Lisette Giepmans Bruce H.R. Wolffenbuttel§

† Equal contributors as first author § Equal contributors as last author

2 BMC Endocrine Disorders 2014, 14:9

Hoofdstukpagina-letteromtrek.indd 3 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 4 16/11/16 11:05 22 Chapter 2

Abstract

Background Not all obese subjects have an adverse metabolic profile predisposing them to developing type 2 diabetes or cardiovascular disease. The BioSHaRE-EU Healthy Obese Project aims to gain insights into the consequences of (healthy) obesity using data on risk factors and phenotypes across several large-scale cohort studies. Aim of this study was to describe the prevalence of obesity, metabolic syndrome (MetS) and metabolically healthy obesity (MHO) in ten participating studies. Methods Ten different cohorts in seven countries were combined, using data transformed into a harmonized format. All participants were of European origin, with age 18-80 years. They had participated in a clinical examination for anthropometric and blood pressure measurements. Blood samples had been drawn for analysis of lipids and glucose. Presence of MetS was assessed in those with obesity (BMI≥30 kg/m2) based on the 2001 NCEP ATP III criteria, as well as an adapted set of less strict criteria. MHO was defined as obesity, having none of the MetS components, and no previous diagnosis of cardiovascular disease. Results Data for 163,517 individuals were available; 17% were obese (11,465 men and 16,612 women). The prevalence of obesity varied from 11.6% in the Italian CHRIS cohort to 26.3% in the German KORA cohort. The age-standardized percentage of obese subjects with MetS ranged in women from 24% in CHRIS to 65% in the Finnish Health2000 cohort, and in men from 43% in CHRIS to 78% in the Finnish DILGOM cohort, with elevated blood pressure the most frequently occurring factor contributing to the prevalence of the metabolic syndrome. The age-standardized prevalence of MHO varied in women from 7% in Health2000 to 28% in NCDS, and in men from 2% in DILGOM to 19% in CHRIS. MHO was more prevalent in women than in men, and decreased with age in both sexes. Conclusion Through a rigorous harmonization process, the BioSHaRE-EU consortium was able to compare key characteristics defining the metabolically healthy obese phenotype across ten cohort studies. There is considerable variability in the prevalence of healthy obesity across the different European populations studied, even when unified criteria were used to classify this phenotype. Keywords Harmonization, Obesity, Metabolic syndrome, Cardiovascular disease, Meta- bolically healthy. Prevalence of Metabolic Syndrome and Metabolically Healthy Obesity in Europe 23

Introduction

The current obesity epidemic is one of the greatest public health concerns of our cen- 2 tury [1]. In Europe, obesity has reached epidemic proportions [2]. A study assessing data collected between 1997 and 2003 reported that the prevalence of obesity, defined as body mass index (BMI) ≥ 30 kg/m2, varied between 6% and 20%, with higher prevalence in Central and Eastern European countries and lower values in France, Italy, and some Scandinavian countries [3]. Among U.S. adults, obesity (BMI ≥ 30) prevalence has increased from 15% in the early 1970s to the most recent estimate of 34% in 2009–2010 [4, 5]. Similar patterns are seen in other countries and were shown to be comparable across different age, ethnic, educational and income groups [6]. If the observed trends of increasing prevalence of obesity persist, by 2030 the absolute number of obese individuals could rise to a total of 1.12 billion, accounting for 20% of the world’s adult population [7]. Obesity is a major contributor to the global burden of chronic diseases and dis- abilities [1]. Increased adiposity is a key risk factor for type 2 diabetes, dyslipidaemia and cardiovascular disease, and is associated with many other conditions, including osteoarthritis, certain types of cancer, mental health, and increased mortality [8-13]. However, recent evidence indicates that obesity does not always lead to adverse metabolic effects such as impaired glucose tolerance, insulin resistance, dyslipidaemia and hypertension [14], a cluster of the obesity-driven alterations also known as the metabolic syndrome (MetS) [15, 16]. A subgroup of approximately 10-30% of obese individuals is metabolically healthy despite having excessive accumulation of body fat [17-22]. This phenomenon is referred to in the current literature as metabolically healthy obesity (MHO) [23]. However, to date, little is known about the factors that delay onset of or protect obese individuals from developing metabolic disturbances [24]. Accumulating evidence indicates that the prevalence of MHO varies considerably based on the set of criteria used for its classification as well as on the cut-off values for each parameter included [19, 24, 25]. In addition, other factors such as lifestyle, ethnicity, sex, or age can largely influence the prevalence of MHO [19]. Recent observational studies show that the MHO phenotype is associated with lower risk of CVD [26] and mortality, especially in those physically active [27], although not all studies could confirm these findings [28]. This highlights the importance of investigating MHO using harmonized classification criteria and studying the extent to which MHO is associated with the risk for chronic diseases. The BioSHaRE-EU Project is an international collaborative project between European and Canadian Institutes and European cohort studies. It aims to harmonize data from clinical examinations and analytical results from biospecimens, as well as measures of life style, social circumstances and environmental exposures. Computing infrastructure 24 Chapter 2

is developed enabling the effective pooling of data and research into critical sub-components of the phenotypes associated with common complex diseases (www.bioshare. eu) [29-31]. The Healthy Obese Project (HOP) is the first scientific project in BioSHaRE to use these tools in order to gain insights into the characterization, the determinants and consequences of (healthy) obesity. We report the results of the first phase of the HOP project, in which we jointly analysed data from 163,517 individuals in ten population- based cohort studies across Europe. The objectives were to assess the potential for harmonization and collaboration, and to evaluate the prevalence of MetS in obese participants using different classification criteria and by characterizing the clinical and metabolic factors associated with MHO.

Methods

Study participants This study included participants from ten population-based cohort studies in seven European countries as listed below. Data from 163,517 individuals were available from the following cohort studies: Estonia: the population-based biobank of the Estonian Genome Project of University of Tartu (EGCUT) (n = 8,930) [32]; Finland: FINRISK2007 (DILGOM) (n = 3,685) [33] and Health 2000 (H2000) (n = 6,022) [34]; Germany: the Coop- erative Health Research in the Region of Augsburg (KORA) study (n = 2,987) [35], Italy: Collaborative Health Research in South Tyrol Study (CHRIS) (n = 1,117) and the MICROS study (n = 1,060) [36]; the Netherlands: LifeLines (n = 63,995) [37], and the Prevention of REnal and Vascular ENd stage Disease study (PREVEND) (n = 7,216) [38]; Norway: the Nord-Trøndelag health study (HUNT2 survey) (n = 61,199) [39]; and United Kingdom: the National Child Development Study birth cohort (NCDS), also known as the 1958 birth cohort (n = 7,306) [40]. A brief description of all participating studies is given in the Additional file 1: Study descriptions and methodologies. All study participants were of European origin, aged between 18 and 80 years, and had participated in a clinical examination for anthropometric and blood pressure measurements. Blood samples were taken for analysis of lipids and glucose (Additional file 1: Study descriptions and methodologies). Participants were only included if all data on clinical and metabolic measurements needed to define the status of MetS and obesity were available. All cohorts had gained approval through their local research ethics committees or institutional review board for secondary usage of data. Participants gave their written informed consent to their study of origin. The current study protocol also gained approval under the data access and ethics governance requirements of the study of origin. The data on the outcomes measured in this study have not been published before by the individual cohorts. Prevalence of Metabolic Syndrome and Metabolically Healthy Obesity in Europe 25

Data harmonization Characteristics describing each cohort study (e.g. design, sample size) are catalogued in a systematic way on the BioSHaRE website (www.bioshare.eu). BioSHaRE investiga- 2 tors met at a workshop in order to define the set of variables to be generated from the harmonization process. These ‘target’ variables determine the data information content that is required from each study to generate compatible (i.e. harmonized) variables. By evaluating study-specific questionnaires, standard operating procedures and data dic- tionaries, used by the participating cohort studies, the potential for each cohort study to generate the target variables was determined. Then researchers working with the data transformed their data locally into a common harmonized format. Parts of this process have been published recently [31], and details related to pairing decisions taken and processing algorithms are available online (https://www.bioshare.eu/content/healthy- obese-project-dataschema).

Classification of obesity, metabolic syndrome and theMHO phenotype The criteria applied for measures of weight and height required that each cohort study measured participants when dressed in lightweight clothing and no shoes. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Obesity was defined according to the current World Health Organization (WHO) classification as having a BMI ≥ 30 kg/m2 [41]. Four clinical measures were used to define the MetS phenotype in the obese subjects based on the original NCEP ATP III definition [42]: 1) elevated blood pressure, defined as systolic blood pressure (SBP) ≥130 mmHg or diastolic blood pressure (DBP) ≥85 mmHg, or antihypertensive drug treatment; 2) elevated fasting blood glucose level ≥6.1 mmol/l or use of blood glucose lowering agents or history/diagnosis of type 2 diabetes; 3) decreased HDL-cholesterol level (<1.03 mmol/l in men or <1.30 mmol/l in women) or drug treatment aimed to increase HDL-cholesterol; and 4) hypertriglyceridaemia (triglyceride level ≥ 1.70 mmol/l) or drug treatment for elevated triglycerides (Table 1). Data on waist circumference was not available in all cohorts. However, > 95% of LifeLines participants with obesity had increased waist circumference according to the NCEP ATP III definition [42], and we therefore considered the presence of ≥ 2 of the four clinical measures as diagnostic for MetS [15]. In addition, we also applied a set of less strict criteria in which the cut-off levels for elevated systolic and diastolic blood pressure were set at ≥140 mmHg and ≥90 mmHg, respectively, and the cut-off level for elevated fasting blood glucose was set at 7.0 mmol/l. As the components of MetS can be influenced by smoking, we recorded whether the participants were current smokers. 26 Chapter 2

Table 1. Criteria and the thresholds used for the definition of metabolically healthy obese individuals in each cohort study.

Strict criteria Less strict criteria Blood pressure SBP ≥ 130 mmHg or SBP ≥ 140 mmHg or DBP ≥ 85 mmHg or DBP ≥ 90 mmHg or use of antihypertensive medication use of antihypertensive medication Elevated blood glucose fasting blood glucose ≥ 6.1 mmol/l or fasting blood glucose ≥ 7.0 mmol/l non-fasting blood glucose ≥ 7.0 mmol/l or non-fasting blood glucose ≥ 7.8 or use of blood glucose lowering mmol/l medication or diagnosis of T2D or use of blood glucose lowering medication or diagnosis of T2D Decreased HDL-cholesterol < 1.03 mmol/l in men or < 1.03 mmol/l in men or < 1.30 mmol/l in women or < 1.30 mmol/l in women or medical treatment for low HDL medical treatment for low HDL Elevated triglycerides* ≥ 1.70 mmol/l or medication ≥ 1.70 mmol/l or medication for elevated triglycerides for elevated triglycerides Diagnosis for CVD yes yes Abbreviations: CVD: cardiovascular disease; DBP: diastolic blood pressure; SBP: systolic blood pressure; T2D: type 2 diabetes. The presence of ≥ 2 abnormal clinical measures (blood pressure, blood glucose, HDL-cholesterol, triglycerides) according to the strict criteria was considered diagnostic for MetS. Metabolically healthy obesity is defined as having BMI ≥ 30, none of the following criteria of the metabolic syndrome [15, 42], and no cardiovascular disease. * in case of non-fasting measurements, the cut-off value was set at 2.10 mmol/L.

The methodology for measurement of the laboratory variables in the various studies is described in the Additional file 1. As not all participating cohorts had performed measurement of triglycerides in fasting serum samples, we corrected, as part of the harmonization process, non-fasting triglycerides values based on the findings of a recent report on the associations between fasting time and serum triglycerides levels (i.e. the threshold of 2.1 mmol/l was used) [43]. For the same reason, we used a different cut-off value for non-fasting blood glucose (i.e. thresholds of 7.0 mmol/l and 7.8 mmol/l for ‘strict’ and ‘less strict’ criteria were used, respectively (Box 1)). In the NCDS study, fasting blood glucose was calculated from HbA1c based on a regression formula obtained in the LifeLines Cohort Study (see Additional file 1). We collected and analysed three types of information: (1) the presence of individual components of MetS in obese participants in each cohort study; (2) the number and percentage of MetS criteria fulfilled in obese participants in each cohort; and (3) the number and percentage of subjects fulfilling the criteria for being metabolically healthy obese in different age groups. MHO was established when subjects with obesity had none of the MetS components, and had no previous diagnosis of cardiovascular disease. As there were age differences between the cohorts, we performed age standardization against the European population, as defined by the EU-27 Member States population on January 1, 2010 (http://epp.eurostat.ec.europa.eu/portal/page/portal/ Prevalence of Metabolic Syndrome and Metabolically Healthy Obesity in Europe 27

statistics/search_database, accessed October 17, 2013). Prevalence was calculated for men and women separately based on 10-year age groups. The definition of prevalent cardiovascular disease varied slightly between cohorts (Additional file 1: The definition 2 of cardiovascular disease), but in the majority of cohort studies, it was based on self- reported history of acute myocardial infarction, stroke, angina pectoris or cardiovascular intervention (CABG or PTCA).

Statistical analyses Results are presented as means ± standard deviation, or number and percentage. Frequency of individual components of MetS were calculated, both for the whole population of obese individuals and for specific age categories. If needed, data are given for men and women separately. As this is a descriptive observational study, no formal statistical testing was performed.

Results

Overall, data for 163,517 individuals were available for the analysis, of whom 28,077 (17.2%) were obese (11,465 (15.8%) men and 16,612 (18.3%) women). Table 2 summarizes the clinical characteristics of obese participants from each cohort study. Mean age of the obese participants varied from 44.0 to 59.6 years. In all cohorts, the frequency of obesity was greater among women than among men (only statistically significant (P < 0.05) for Health2000, LifeLines, Prevend and HUNT2), while it was greater among men in the NCDS cohort (P = 0.033). The highest prevalence of obesity was found in Germany (26.3%, mean age of the participants 59.6 years), Finland (DILGOM cohort, 25.7%, 57.3 years), Estonia (23%, 52.6 years), and the United Kingdom (22.9%, 44.0 years), while the lowest prevalence of obesity was observed in the Italian studies CHRIS (11.6%, 53.6 years) and MICROS (14.8%, 54.9 years) (Figure 1). The percentage of individuals currently smoking varied between 15 and 31% (Table 2). 28 Chapter 2

A 40 Estonia (EGCUT) Finland (DILGOM) Finland (Health 2000) Germany (KORA) Italy (CHRIS) Italy (MICROS) 30 NL (LifeLines) NL (PREVEND) Norway (HUNT2) UK (NCDS)

20 % of subjects with obesity with of subjects %

0 Study Cohort

Figure 1. The prevalence of obesity in the participating cohorts given as a percentage of the total sample size of the cohort. Prevalence of Metabolic Syndrome and Metabolically Healthy Obesity in Europe 29 UK 23.9 7,306 NCDS 79/226 83 ± 10 513/269 44.0 ± 0 106 ± 10 132 ± 16 4.9 ± 1.1 33.9 ± 3.8 1.38 ± 0.32 1.30 ± 0.30 1.47 ± 0.31 2.17 ± 1.63 1,670 (22.9) 874 (52.3)/ 796 (52.3)/ 874 2 30.8 61,199 HUNT2 85 ± 13 180/553 Norway 101 ± 10 146 ± 22 5.9 ± 2.0 33.2 ± 3.1 53.5 ± 15.4 1.24 ± 0.35 1.10 ± 0.29 1.35 ± 0.36 2.35 ± 1.39 9,922 (16.2) 2,792/3,114 4,104 (41.4)/ 5,818 (41.4)/ 4,104 26.3 7,216 26/94 77 ± 10 346/335 105 ± 11 139 ± 20 5.4 ± 1.6 PREVEND 33.2 ± 3.3 53.5 ± 11.7 1.16 ± 0.34 1.01 ± 0.27 1.29 ± 0.33 1.88 ± 1.33 1,137 (15.8) 514 (45.2)/ 623 The Netherlands The 19.9 77 ± 9 63,995 108 ± 10 133 ± 15 5.4 ± 1.3 LifeLines 359/1,433 33.6 ± 3.6 47.4 ± 11.7 1.28 ± 0.33 1.13 ± 0.26 1.38 ± 0.33 1.54 ± 1.02 9,934 (15.5) 2,208/2,262 3,813 (38.4)/ 6,121 NA 4/9 28.0 1,060 34/33 85 ± 11 MICROS 143 ± 22 5.4 ± 1.5 157 (14.8) 33.6 ± 4.5 54.9 ± 15.2 1.54 ± 0.34 1.36 ± 0.25 1.65 ± 0.34 1.87 ± 1.27 57 (36.3)/ 100 Italy NA 15.4 1,117 26/26 11/12 CHRIS 83 ± 8 128 ± 14 5.6 ± 0.9 130 (11.6) 33.1 ± 3.4 53.6 ± 12.9 1.54 ± 0.45 1.31 ± 0.32 1.74 ± 0.45 1.53 ± 0.99 60 (46.2)/ 70 17.7 2,987 34/61 KORA 78 ± 10 229/216 128 ± 18 109 ± 11 5.9 ± 1.2 Germany 786 (26.3) 33.8 ± 3.7 59.6 ± 12.0 1.31 ± 0.31 1.21 ± 0.29 1.40 ± 0.30 1.77 ± 1.09 373 (47.5)/ 413 23.0 6,022 19/43 87 ± 10 425/515 142 ± 20 108 ± 10 5.9 ± 1.7 33.6 ± 3.4 54.5 ± 12.8 1.17 ± 0.32 1.05 ± 0.27 1.26 ± 0.32 2.02 ± 1.22 1,342 (22.3) HeaIth2000 573 (42.7)/ 769 Finland 15.3 7/37 3,685 83 ± 11 323/355 DILGOM 140 ± 19 110 ± 11 6.4 ± 1.3 946 (25.7) 34.2 ± 4.1 57.3 ± 11.6 1.30 ± 0.33 1.15 ± 0.26 1.42 ± 0.33 1.82 ± 1.01 399 (42.2)/ 547 30.5 8,930 EGCUT 34/166 84 ± 11 Estonia 410/606 136 ± 17 107 ± 12 4.8 ± 1.8 34.4 ± 4.1 52.6 ± 14.1 1.52 ± 0.33 1.35 ± 0.28 1.60 ± 0.32 2.10 ± 1.16 2,053 (23.0) 698 (34.0)/ 1,355 Characteristics of the obese (BMI ≥ 30) participants.

) 2 able 2. Age (yrs) Age BMI (kg/m Total number of participants (N) Total Number with BMI≥30 (%) Gender (M (%)/ F) Current smoking (%) smoking Current Number with MetS (M/F) Number with MHO (M/F) HDL cholesterol (mmol/l) HDL cholesterol Men Women (mmol/I) Triglycerides (mmol/I) Blood glucose blood pressure(mmHg) Systolic (mmHg) blood pressure Diastolic Country & study T Waist circumference (cm) circumference Waist 30 Chapter 2

The observed prevalence of MetS was mainly driven by the presence of elevated blood pressure with a range from 60% to 85% of individuals fulfilling the criterion for high BP (Table 3, Figure 2). In contrast, elevated blood glucose contributed least to MetS, although we did observe considerable diversity between the cohorts. The percentage of obese individuals with elevated blood glucose varied from 7% in the UK NCDS cohort to 52% in the Finnish DILGOM cohort. A similar difference was observed in the percentage of the obese individuals with decreased HDL-cholesterol level: the lowest prevalence was observed in the Italian studies (9% and 13% in the MICROS and CHRIS cohorts, respectively), while the highest prevalence was detected in the Dutch PREVEND cohort (57%). The percentage of the individuals with elevated triglyceride levels ranged between 31% in the Dutch LifeLines study and 55% in the UK NCDS participants. As a result, the age-standardized percentage of men with MetS according to the classic 2001 NCEP-ATPIII criteria ranged from 42.7% in the Italian CHRIS cohort to 78.2% in the Finnish DILGOM cohort, and for women from 24% in CHRIS to 64.8% in the Finnish Health2000 cohort (Figure 3A,B).

100 Estonia (EGCUT) Finland (DILGOM) Finland (Health 2000) Germany (KORA) 80 Italy (CHRIS) Italy (MICROS) NL (LifeLines) NL (PREVEND) Norway (HUNT2) 60 UK (NCDS)

% subjects of % 40

0 BP BG HDL-C TG

Figure 2. The frequency of individual components of the metabolic syndrome among obese subjects (BMI ≥ 30 kg/m2). The presence of the metabolic syndrome mainly depends on the presence of a high blood pressure followed by the level of triglycerides and HDL cholesterol and – to a lesser extent – blood glucose levels. BP = blood pressure, BG = blood glucose, HDL-C = high density lipoprotein cholesterol, TG = triglycerides. * denotes non-fasting measurement of blood glucose. Prevalence of Metabolic Syndrome and Metabolically Healthy Obesity in Europe 31 UK 1,669 NCDS 92 (5.5) 114 (6.8) 998 (59.8) 387 (23.2) 912 (54.6) 609 (36.5) 2 9,922 HUNT2 Norway 920 (9.3) 7,991 (80.5) 1,377 (13.9) 4,547 (45.8) 4,693 (47.3) 6,447 (65.0) 1,137 87 (7.7) 825 (72.6) 161 (14.2) PREVEND 646 (56.8) 496 (43.6) 660 (58.2) The Netherlands The 9,934 825 (8.3) LifeLines 6,407 (64.5) 1,524 (15.3) 3,913 (39.4) 3,028 (30.5) 4,492 (45.2) 157 14 (8.9) 12 (7.6) MICROS 25 (15.9) 68 (43.3) 98 (62.4) 123 (78.3) Italy 130 CHRIS 12 (9.2) 83 (63.9) 27 (20.8) 17 (13.1) 44 (33.9) 64 (49.2) 786 KORA Germany 573 (72.9) 251 (31.9) 281 (35.8) 317 (40.3) 498 (63.4) 135 (17.2) 1,342 329 (24.5) 750 (55.9) 710 (52.9) 916 (68.3) 148 (11.0) 1,104 (82.3) Health 2000 Health Finland 946 DILGOM 801 (84.7) 493 (52.1) 346 (36.6) 407 (43.0) 670 (70.8) 176 (18.6) 2,053 EGCUT Estonia 482 (23.4) 273 (13.3) 815 (39.7) 463 (22.5) 1,637 (79.7) 1,386 (67.5) The frequency of individual components of the metabolic syndrome in obese (BMI ≥ 30) individuals. of the metabolic syndrome frequencyThe of individual components able 3. T Total N Total Metabolic component Strict high BP (%) for criterium Strict criterium for blood glucose (%) Strict blood glucose for criterium Criterium for HDL cholesterol (%) HDL cholesterol for Criterium Criterium for triglycerides (%) triglycerides for Criterium Less strict high BP (%) for criterium Less Less strict criterium for blood glucose (%) strict blood glucose for criterium Less 32 Chapter 2

A 100 MetS MHO 80

40 % of subjects of %

B 100 MetS MHO 80

40 % of subjects of %

Figure 3. Age-standardized prevalence of metabolic syndrome (MetS) and metabolically healthy obesity (MHO) amongst obese (BMI ≥ 30 kg/m2) individuals in the participating cohorts, separately shown for men (panel A) and women (panel B). Prevalence of Metabolic Syndrome and Metabolically Healthy Obesity in Europe 33

As expected, when less strict MetS criteria were used, the percentage of obese individuals with elevated blood pressure or blood glucose was lower (Table 3). This also resulted in a lower number of subjects with MetS (Tables 4A,B ). 2 Across all ten cohorts, a total of 3,387 obese participants (12%) did not have any metabolic abnormalities according to the strict definition of MetS, as well as no previous diagnosis of cardiovascular disease, as defined by the MHO phenotype. After age standardization, the highest prevalence of MHO in men was found in the Italian CHRIS study (19%) and in the German KORA study (13.5%), and in women in UK NCDS (28.4%), Dutch LifeLines (23.1%), KORA (21.8%) and CHRIS (21.1%). The lowest prevalence was observed in the two Finnish cohorts (2.3 and 3.6% for men, 7.3 and 12.3% for women) and the Norwegian HUNT2 study (5.9% in men, 14% in women) (Figure 3A,B). The trend towards a higher percentage of MHO in women compared with men was evident in almost all studies. This sex difference was most apparent in the NCDS cohort, in which 28.4% of obese women were metabolically healthy in comparison with only 9% of obese men with the same phenotype (Figure 3). In contrast, the percentage of men and women with MHO was similar in the Italian CHRIS study (19% versus 21.1%). These findings were also independent of the definition of MHO, as we observed the same tendency with both strict and less strict criteria (data not shown). Overall, we observed a decrease in the prevalence of MHO with increasing age, independent of sex and the MetS definition criteria used (Figures 4A,B). This pattern was seen in all cohorts except the Italian CHRIS study, in which the prevalence of MHO appeared to be relatively constant until the age of 60 In all cohorts, a subset of the obese individuals remained metabolically healthy, even in the oldest age group (≥ 60 years). The highest prevalence of MHO among those 60 years and older was observed in the Dutch LifeLines study (8%). 34 Chapter 2

A 50 ≥18 – <30 years ≥30 – <40 years ≥40 – <50 years 40 ≥50 – <60 years ≥60 years

20 % of subjects %

Bl 50 ≥18 – <30 years ≥30 – <40 years ≥40 – <50 years 40 ≥50 – <60 years ≥60 years

20 % of % subjects

Figure 4. Percentage of subjects (panel A: men; panel B: women) meeting the criteria of being ‘healthy obese‘. The results are stratified for different age groups. In general, within each cohort the prevalence of healthy obesity decreases with increasing age. Note that more females are metabolically healthier than males. Prevalence of Metabolic Syndrome and Metabolically Healthy Obesity in Europe 35 UK UK 1,669 1,669 NCDS NCDS 31 (1.9) 48 (2.9) 117 (7.0) 456 (27.3) 169 (10.1) 613 (36.7) 565 (33.9) 452 (27.1) 582 (34.9) 305 (18.3) 2 9,922 9,922 HUNT2 HUNT2 Norway Norway 355 (3.6) 545 (5.5) 755 (7.6) 2,445(24.6) 2,901 (29.2) 2,027 (20.4) 3,304 (33.3) 2,916 (29.4) 1,335 (13.5) 3,261 (32.9) 1,137 1,137 37 (3.3) 72 (6.3) 355 (31.2) 229 (20.1) 286 (25.2) 344 (30.3) 323 (28.4) 172 (15.1) 336 (29.6) 120 (10.6) PREVEND PREVEND The Netherlands The The Netherlands The 9,934 9,934 211 (2.1) 410 (4.1) LifeLines LifeLines 2,290 (23.1) 1,084 (10.9) 1,456 (14.7) 3,582 (36.1) 2,604 (26.2) 2,767 (27.9) 3,656 (36.8) 1,808 (18.2) 157 157 1 (0.6) 2 (1.3) 11 (7.0) MICROS MICROS 39 (24.8) 20 (12.7) 77 (49.0) 45 (28.7) 29 (18.5) 72 (45.9) 18 (11.5) Italy Italy 130 130 3 (2.3) 6 (4.6) CHRIS CHRIS 12 (9.2) 27 (20.8) 15 (11.5) 35 (26.9) 31 (23.9) 53 (40.8) 40 (30.8) 38 (29.2) 786 786 KORA KORA 34 (4.3) 71 (9.0) 98 (12.5) Germany Germany 131 (16.7) 221 (28.1) 147 (18.7) 260 (33.1) 227 (28.9) 140 (17.8) 243 (31.0) 1,342 1,342 76 (5.7) 76 (5.7) 331 (24.7) 147 (11.0) 426 (31.7) 393 (29.3) 375 (27.9) 400 (29.8) 134 (10.0) 326 (24.3) Health 2000 Health Health 2000 Health Finland Finland 946 946 42 (4.4) 46 (4.9) DILGOM DILGOM 166 (17.6) 100 (10.6) 307 (32.5) 269 (28.4) 319 (33.7) 309 (32.7) 112 (11.8) 222 (23.5) 2,053 2,053 EGCUT EGCUT 59 (2.9) 48 (2.3) Estonia Estonia 227 (11.1) 619 (30.2) 270 (13.2) 778 (37.9) 689 (33.6) 381 (18.6) 242 (11.8) 793 (38.6) Number of components of the metabolic syndrome (waist circumference not included) present among obese participants. not included) present circumference (waist of the metabolic syndrome Number of components able 4. 3 criteria present (%) present 3 criteria 4 criteria present (%) present 4 criteria Number of MetS components (less strict B. criteria) (%) present 2 criteria (%) present 4 criteria A. Number of MetS component (strict criteria) (%) present 3 criteria N Total (%) present 1 criterium T Total N Total (%) present 2 criteria (%) present 0 criteria 0 criteria present (%) present 0 criteria (%) present 1 criterium 36 Chapter 2

Discussion

In this large-scale collaborative study, we evaluated the prevalence of metabolic syndrome and healthy obesity among obese individuals using the data of 163,517 people from ten European cohort studies from seven different countries. We found considerable variation in the prevalence of both phenotypes suggesting that the distribution of the MetS and MHO across the different populations in general is not equal. However, our analysis did reveal a consistently higher prevalence of the MHO phenotype in women compared to men. Furthermore, the percentage of obese subjects with a favourable risk profile decreases with increasing age in all cohorts. With the exception of the Italian, Norwegian and UK cohorts, the prevalence of obesity was much higher in the European populations we studied than was reported in the most recent review addressing the distribution of obesity in Europe [2]. Such differences may be due to potential underestimation of the prevalence of obesity in the systematic review because of the inclusion of studies using self-reported BMI [2]. In our study, the data on BMI were obtained through direct measurements made by trained research nurses or study assistants which provides more accurate estimation of obesity prevalence in the participating cohorts. Another explanation for the discrepancy in the prevalence patterns may be related to the difference in the time period when the studies were conducted. While the surveys included in the systematic review were performed between the mid-1980s and 2003, most of the data in our study were collected after 2000, with the earliest data available from 1995 and the most recent data from 2012. The differences in estimations of the obesity prevalence can, therefore, present different phases of an increasing trend. Although our data are obtained from large population- based cohort studies or biobanks, we have to realize that our results cannot always be generalized to the overall prevalence in the specific countries, as some cohorts have only collected data from a specific region of that country (CHRIS/MICROS/HUNT2), or from a specific age group (NCDS). Despite the detected variation, the data confirm the observations that obesity in European countries continued to rise the last decade and has reached epidemic proportions [2]. However, recent publications suggest levelling off of the obesity epidemic [44-46], although in subjects with lower socioeconomic status a steady increase in prevalence still is observed [47]. The Finnish cohorts had the highest prevalence of MetS among obese subjects and the lowest percentage of MHO. In contrast, in the Italian MICROS and the Dutch Life- Lines studies we observed a lower prevalence of MetS among obese subjects together with a higher percentage of MHO. Similar patterns in the occurrence of MetS in Europe have been reported previously [48]. MetS is a constellation of metabolic risk factors, associated with an increased risk for the development of atherosclerotic cardiovascular disease as well as type 2 diabetes mellitus [15, 16, 49]. MetS has been shown to be the Prevalence of Metabolic Syndrome and Metabolically Healthy Obesity in Europe 37

major risk determinant of heart disease, also when a population generally has low levels of HDL- and LDL-cholesterol [50]. The most frequent MetS component present in obese individuals was elevated blood pressure. In the 10 studies, obesity coincided with hy- 2 pertension in 60% to 85% cases. In contrast, we observed considerable variations in the prevalence of other components of MetS, especially blood glucose and HDL-cholesterol. A blood pressure exceeding the strict criterion for a high blood pressure can be accounted as a main contributor promoting unhealthy obesity and metabolic syndrome in the Finnish cohorts in this study. Finnish tendency for elevated blood pressure has also been detected earlier, recently by The European Heart Network and The European Society of Cardiology [51]. Our study extends previous efforts to describe the phenomenon of healthy obesity and to estimate its prevalence in different countries in several important ways, including helping to disentangle whether differences in the prevalence of MHO are due to geographic variation or differences in measurements. Using a large amount of validated information, we applied a rigorous protocol to harmonize data from multiple population- based European studies, and ensure a high level of homogeneity of the MetS definition used to calculate the MHO prevalence. Recently, the lack of a standard approach to use the same sets of criteria and cut-off values to define metabolic abnormalities has been highlighted as the major source of the high variability in the reported MHO prevalence [19, 24, 25]. Yet, our results also demonstrate a significant diversity in the prevalence of MHO across Europe using the harmonized criteria to define MetS. The highest percentage of MHO in men was found in CHRIS and KORA, and in women in NCDS, LifeLines, KORA and CHRIS, whereas the lowest prevalence was found in the Finnish cohorts and in HUNT2. In our study, we have used the established risk factors associated with the metabolic syndrome [41, 42] to identify the MHO phenotype. Our data on MetS components is consistent with the outcome of previously performed studies on the prevalence of the metabolic abnormalities in Europe [48, 52]. As age and sex are important factors in the development of MetS, we have also evaluated the age- and sex-stratified prevalence of MHO per decade. Our results indicate a higher prevalence of the MHO phenotype in women than in men as well as an age-related decline in the percentage of obese subjects with a metabolically healthy phenotype [19, 24]. Collectively, our findings raise additional questions about the underlying factors promoting the variation in the prevalence of MHO across different populations. Such variation in the distribution of metabolic phenotypes can be explained by several factors, including difference in age of the cohort participants, differences in environmental factors such as physical activity level, diet, smoking and alcohol use, and differences in the selection and inclusion of participants [52]. Also the psychosocial profile and genetic factors [19, 24] may play a role. While behavioral factors, i.e. higher levels of physical activity or moderate alcohol intake, have been shown to be associated with the MHO phenotype [18], there is no 38 Chapter 2

evidence yet whether genetic background and divergence between populations does contribute to the metabolically favorable profile in obesity [24]. Given the number of serious health problems associated with obesity including type 2 diabetes, cardiovascular disease, and an increased risk for various types of cancer, the investigation of the healthy obesity phenotype may provide novel insights into the pathophysiology of obesity-related co-morbidities and help to identify at-risk obese individuals. Furthermore, it may help in the development of better interventions for obese patients. There are strong indications that weight loss may not have a beneficial effect on certain metabolic risk factors in MHO individuals [20] and even result in a paradoxical response [53]. Therefore, the one-size-fits-all approach regarding the consequences of obesity should be revisited, and the prevailing concept in the health care system that obesity is always bad should be re-evaluated. Also, a proper classification of the at-risk and metabolically benign obese individuals should be taken into account in medical research to prevent any bias in the interpretation of the results. The main strengths of this descriptive study are the large sample size and the applica- tion of harmonized criteria to evaluate the prevalence of MetS and the degree of the MHO across different European cohort studies. Through our harmonization process [31], we have shown the possibility for collaborative research based on a careful harmonization process across multiple participating cohort studies. Several important factors may have a bearing on the results. First, we used BMI to define the obesity status. Since BMI is a measure of general obesity and cannot distinguish between fat and lean mass, other measures such as waist circumference (WC) or waist-hip-ratio (WHR) might be better indicators of visceral fat accumulation. Although a few studies reported lower fat accumulation in MHO individuals compared to the obese with metabolic abnormalities [17, 24], no difference in the prevalence of MHO was found when WC was used instead of BMI to define the MHO phenotype in the NHANES cohort [18]. Second, although our harmonized measures captured the essential information content for the MHO phenotype, there were differences between studies in the way that specific variables such as blood pressure and serum lipid levels were measured. Also, our cut-off values for non-fasting measurements of, for example, blood glucose may underestimate the actual degree of the MHO present in the corresponding studies. Third, although many participating cohort studies included several thousands of participants, their health and lifestyle habits may not always be representative of the general population in this specific country because of bias in participation or differences in recruitment of participants. We also cannot exclude that a potential participation bias could affect the results [54]. As such, higher participation rates from either healthy or unhealthy individuals can influence the outcome, and it cannot be ruled out that the high percentage of MHO in the LifeLines Cohort Study may – at least in part – be explained by a preponderance of healthy individuals willing to participate. Prevalence of Metabolic Syndrome and Metabolically Healthy Obesity in Europe 39

An important factor to discuss is the time period in which the initial screening of each individual cohort was performed. Data in some cohorts were collected in the 1990s, while, for example, the participants in the Dutch LifeLines Cohort Study were 2 recruited between 2007 and 2012, and in the Italian CHRIS study after August 2011. There have been several changes in environmental factors such as health behaviour and smoking pattern over time, which may have a bearing on the prevalence of MetS and on health in general. In many countries higher awareness of the importance of increased physical activity [55] or smoking cessation [56, 57] have been recognized, although it appears that the current epidemic of obesity is still on-going [2]. As an example, cessation of smoking is on one hand associated with weight gain [58], which may be perceived negatively by individuals [59], but it also results in improvement of the metabolic profile as smoking cessation is accompanied by an increase of HDL cholesterol and reduction of triglycerides [60]. It is important to note that the major objective of this descriptive study was to evaluate the phenomenon of healthy obesity among the participating European population-based studies. The BioSHaRE-HOP consortium is currently expanding its harmonization efforts, and assessing differences in lifestyle factors such as nutritional habits, physical activity, smoking and general awareness of health between the various participating countries in order to have a better estimate of the characterization and the determinants of (healthy) obesity.

Conclusion

In summary, we report the first scientific results of this collaborative project on the prevalence of healthy obesity within a FP7 funded consortium, BioSHaRE-EU. We have co-analysed data across the participating studies by applying careful harmonization algorithms. The present findings indicate considerable variation in the occurrence of MHO across the different European populations even when unified criteria or definitions were used to classify this phenotype. Further studies are needed to identify the underlying factors for these differences. This area of research will improve our understanding of obesity in general and possibly identify novel preventive measures for the consequences of obesity.

Authors’ information Jana V. van Vliet-Ostaptchouk, Marja-Liisa Nuotio, Sandra N. Slagter, Dany Doiron, equal contribution as first author; Markus Perola, Bruce H.R. Wolffenbuttel, equal contributors as last author. 40 Chapter 2

Competing Interests The authors declare that they have no competing interests.

Authors’ contributions MP, BHRW, RPS, PB, IF conceived the study. JVvVO, MLN, SNS, DD, MP and BHRW have been involved in integration of all analyses, data interpretation and drafting the manuscript. MLN, JVvVO, SNS, KF, LF, AJ, CN, CP, HLH, ER, KK coordinated and performed the harmonization and local analysis and interpretation of the data of all participating studies. AG, MG, MH, TH, EP, CS, MLT, MAS, PB, VF, IF, LG, JH, JEKR, TLH, SM, AM, KM, MJM, AP, PPP, TS, VS, RPS, MU, PvdH, MMvdK, MW, MP and BHRW were involved in local study design, collection of data, and / or coordination and execution of measurements and biochemical analyses. LG, JRH helped with the data interpretation and drafting the manuscript. All authors provided intellectual contributions to the manuscript and have read and approved the final version.

Acknowledgements

This work was supported by funds from the European Union’s Seventh Framework programme (FP7/2007-2013) through the BioSHaRE-EU (Biobank Standardisation and Harmonisation for Research Excellence in the European Union) project, grant agreement 261433. BioSHaRE is engaged in a Bioresource research impact factor (BRIF) policy pilot study, details of which can be found at : https://www.bioshare.eu/content/bioresource- impact-factor.

We wish to acknowledge the participating studies for providing the data and participating in the analyses and writing of the manuscript: Estonian Genome Project of University of Tartu (EGCUT); The National FINRISK Study (DILGOM, BRIF1640); Health2000 (BRIF8901); Cooperative Health Research in the Augsburg Region (Kooperative Gesundheits- forschung in der Region Augsburg, KORA, BRIF7781); Collaborative Health Research in South Tyrol Study (CHRIS, BRIF6107); Microisolates in South Tyrol Study (MICROS, BRIF2155); LifeLines Cohort Study (LifeLines, BRIF4568); Prevention of REnal and Vascular ENd stage Disease (PREVEND, BRIF2709); The Nord-Trøndelag health study (HUNT, BRIF2365); 1958 National Child Development Study (NCDS, BRIF4310). Prevalence of Metabolic Syndrome and Metabolically Healthy Obesity in Europe 41

In addition, we would like to thank K.J. Jager (PhD, ERA-EDTA Registry, Academic Medical Center, Amsterdam, The Netherlands) and A. Kramer (PhD, ERA-EDTA Registry, Academic Medical Center, Amsterdam, The Netherlands) for providing statistical advice. For ac- 2 knowledgements per cohort, see the supplementary information.

Role of the funding source

The funders of the study did not participate in study design, data collection, data analysis, data interpretation, or writing of the report. Corresponding authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. 42 Chapter 2

References

1. Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood 11. Pischon T, Boeing H, Hoffmann K, Bergmann M, Schulze DT, Moodie ML, Gortmaker SL: The global obesity MB, Overvad K, van der Schouw YT, Spencer E, Moons pandemic: shaped by global drivers and local environ- KG, Tjonneland Aet al: General and abdominal adiposity ments. Lancet 2011, 378(9793):804-814. and risk of death in Europe. The New England journal of 2. Berghofer A, Pischon T, Reinhold T, Apovian CM, Sharma medicine 2008, 359(20):2105-2120. AM, Willich SN: Obesity prevalence from a European 12. Whitlock G, Lewington S, Sherliker P, Clarke R, Ember- perspective: a systematic review. BMC Public Health son J, Halsey J, Qizilbash N, Collins R, Peto R: Body-mass 2008, 8:200. index and cause-specific mortality in 900 000 adults: 3. Rabin BA, Boehmer TK, Brownson RC: Cross-national collaborative analyses of 57 prospective studies. Lancet comparison of environmental and policy correlates of 2009, 373(9669):1083-1096. obesity in Europe. European journal of public health 13. Bijlsma JW, Berenbaum F, Lafeber FP: Osteoarthritis: an 2007, 17(1):53-61. update with relevance for clinical practice. Lancet 2011, 4. Freedman DS: Obesity - United States, 1988-2008. 377(9783):2115-2126. MMWR Surveillance summaries: Morbidity and mortality 14. Primeau V, Coderre L, Karelis AD, Brochu M, Lavoie ME, weekly report. Surveillance summaries / CDC 2011, 60 Messier V, Sladek R, Rabasa-Lhoret R: Characterizing the Suppl:73-77. profile of obese patients who are metabolically healthy. 5. Ogden CL, Carroll MD, Kit BK, Flegal KM: Prevalence of International journal of obesity 2011, 35(7):971-981. obesity in the United States, 2009-2010. NCHS Data 15. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Brief 2012(82):1-8. Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage 6. Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin PJ, Smith SC, Jr.et al: Diagnosis and management of the JK, Paciorek CJ, Singh GM, Gutierrez HR, Lu Y, Bahalim metabolic syndrome: an American Heart Association/ ANet al: National, regional, and global trends in body- National Heart, Lung, and Blood Institute Scientific mass index since 1980: systematic analysis of health Statement. Circulation 2005, 112(17):2735-2752. examination surveys and epidemiological studies with 16. Eckel RH, Alberti KG, Grundy SM, Zimmet PZ: The 960 country-years and 9.1 million participants. Lancet metabolic syndrome. Lancet 2010, 375(9710):181-183. 2011, 377(9765):557-567. 17. Stefan N, Kantartzis K, Machann J, Schick F, Thamer C, 7. Kelly T, Yang W, Chen CS, Reynolds K, He J: Global Rittig K, Balletshofer B, Machicao F, Fritsche A, Haring burden of obesity in 2005 and projections to 2030. HU: Identification and characterization of metabolically International journal of obesity 2008, 32(9):1431-1437. benign obesity in humans. Archives of internal medicine 8. Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, 2008, 168(15):1609-1616. Bales VS, Marks JS: Prevalence of obesity, diabetes, and 18. Wildman RP, Muntner P, Reynolds K, McGinn AP, obesity-related health risk factors, 2001. JAMA 2003, Rajpathak S, Wylie-Rosett J, Sowers MR: The obese 289(1):76-79. without cardiometabolic risk factor clustering and the 9. Canoy D, Boekholdt SM, Wareham N, Luben R, Welch normal weight with cardiometabolic risk factor cluster- A, Bingham S, Buchan I, Day N, Khaw KT: Body fat ing: prevalence and correlates of 2 phenotypes among distribution and risk of coronary heart disease in the US population (NHANES 1999-2004). Archives of men and women in the European Prospective Inves- internal medicine 2008, 168(15):1617-1624. tigation Into Cancer and Nutrition in Norfolk cohort: a 19. Velho S, Paccaud F, Waeber G, Vollenweider P, Marques- population-based prospective study. Circulation 2007, Vidal P: Metabolically healthy obesity: different 116(25):2933-2943. prevalences using different criteria. European journal of 10. Pischon T, Nothlings U, Boeing H: Obesity and clinical nutrition 2010, 64(10):1043-1051. cancer. The Proceedings of the Nutrition Society 2008, 20. Kantartzis K, Machann J, Schick F, Rittig K, Machicao F, 67(2):128-145. Fritsche A, Haring HU, Stefan N: Effects of a lifestyle in- Prevalence of Metabolic Syndrome and Metabolically Healthy Obesity in Europe 43

tervention in metabolically benign and malign obesity. 31. Doiron D, Ferretti V, Burton P, Marcon Y, Gaye A, Wolffen- Diabetologia 2011, 54(4):864-868. buttel BHR: Data harmonization and federated analysis 21. Pajunen P, Kotronen A, Korpi-Hyovalti E, Keinanen- of population-based studies: the BioSHaRE project. Kiukaanniemi S, Oksa H, Niskanen L, Saaristo T, Emerging Themes Epidemiology 2013. 2 Saltevo JT, Sundvall J, Vanhala Met al: Metabolically 32. Metspalu A: Estonian Genome Project--before the take- healthy and unhealthy obesity phenotypes in the off and take-off. Bioinformatics 2002, 18 Suppl 2:S152. general population: the FIN-D2D Survey. BMC public 33. Inouye M, Kettunen J, Soininen P, Silander K, Ripatti health 2011, 11:754. S, Kumpula LS, Hamalainen E, Jousilahti P, Kangas AJ, 22. Geetha L, Deepa M, Anjana RM, Mohan V: Prevalence Mannisto Set al: Metabonomic, transcriptomic, and and clinical profile of metabolic obesity and phenotypic genomic variation of a population cohort. Molecular obesity in Asian Indians. Journal of diabetes science and systems biology 2010, 6:441. technology 2011, 5(2):439-446. 34. Aromaa A, Koskinen S: Baseline Results of the Health 23. Denis GV, Obin MS: ‘Metabolically healthy obesity’: 2000 Health Examination Survey: Health and functional origins and implications. Molecular aspects of medicine capacity in Finland. Publications of the National Public 2013, 34(1):59-70. Health Institute B12/2004. Edited by Aromaa A, and 24. Pataky Z, Bobbioni-Harsch E, Golay A: Open questions Koskinen S. Helsinki: National Public Health Institute; about metabolically normal obesity. International 2004:1–148. journal of obesity 2010, 34 Suppl 2:S18-S23. 35. Wichmann HE, Gieger C, Illig T: KORA-gen--resource for 25. Phillips CM, Dillon C, Harrington JM, McCarthy VJ, Kear- population genetics, controls and a broad spectrum of ney PM, Fitzgerald AP, Perry IJ: Defining metabolically disease phenotypes. Gesundheitswesen 2005, 67 Suppl healthy obesity: role of dietary and lifestyle factors. 1:S26-S30. PLoS one 2013, 8(10):e76188. 36. Pattaro C, Marroni F, Riegler A, Mascalzoni D, Pichler 26. Hamer M, Stamatakis E: Metabolically healthy obesity I, Volpato CB, Dal CU, De GA, Egger C, Eisendle Aet al: and risk of all-cause and cardiovascular disease mortal- The genetic study of three population microisolates in ity. The Journal of clinical endocrinology and metabolism South Tyrol (MICROS): study design and epidemiologi- 2012, 97(7):2482-2488. cal perspectives. BMC medical genetics 2007, 8:29. 27. Hamer M, Stamatakis E: Low-dose physical activ- 37. Stolk RP, Rosmalen JG, Postma DS, de Boer RA, Navis ity attenuates cardiovascular disease mortality in G, Slaets JP, Ormel J, Wolffenbuttel BH: Universal risk men and women with clustered metabolic risk factors. factors for multifactorial diseases: LifeLines: a three- Circulation. Cardiovascular quality and outcomes 2012, generation population-based study. European journal 5(4):494-499. of epidemiology 2008, 23(1):67-74. 28. Kuk JL, Ardern CI: Are metabolically normal but obese 38. Pinto-Sietsma SJ, Janssen WM, Hillege HL, Navis G, de individuals at lower risk for all-cause mortality? ZD, de Jong PE: Urinary albumin excretion is associated Diabetes Care 2009, 32(12):2297-2299. with renal functional abnormalities in a nondiabetic 29. Fortier I, Burton PR, Robson PJ, Ferretti V, Little J, population. J American Society of Nephrology 2000, L’Heureux F, Deschenes M, Knoppers BM, Doiron D, Keers 11(10):1882-1888. JCet al: Quality, quantity and harmony: the DataSHaPER 39. Krokstad S, Langhammer A, Hveem K, Holmen T, approach to integrating data across bioclinical studies. Midthjell K, Stene T, Bratberg G, Heggland J, Holmen J: International journal of epidemiology 2010, 39(5):1383- Cohort Profile: The HUNT Study, Norway. International 1393. journal of epidemiology 2012. 30. Harris JR, Burton P, Knoppers BM, Lindpaintner K, 40. Power C, Elliott J: Cohort profile: 1958 British birth co- Bledsoe M, Brookes AJ, Budin-Ljosne I, Chisholm R, hort (National Child Development Study). International Cox D, Deschenes Met al: Toward a roadmap in global journal of epidemiology 2006, 35(1):34-41. biobanking for health. European journal of human 41. World Health Organization (WHO): Obesity: preventing genetics 2012, 20(11):1105-1111. and managing the global epidemic. Report of a WHO 44 Chapter 2

consultation. World Health Organisation Technical 52. Cornier MA, Dabelea D, Hernandez TL, Lindstrom Report Series 2000, 894:i-253. RC, Steig AJ, Stob NR, Van Pelt RE, Wang H, Eckel RH: 42. Expert Panel on Detection Evaluation, and Treatment of The metabolic syndrome. Endocrine reviews 2008, High Blood Cholesterol in Adults: Executive Summary of 29(7):777-822. The Third Report of The National Cholesterol Education 53. Karelis AD, Messier V, Brochu M, Rabasa-Lhoret R: Program (NCEP) Expert Panel on Detection, Evaluation, Metabolically healthy but obese women: effect of an And Treatment of High Blood Cholesterol In Adults energy-restricted diet. Diabetologia 2008, 51(9):1752- (Adult Treatment Panel III). JAMA 2001, 285(19):2486- 1754. 2497. 54. Criqui MH: Response bias and risk ratios in epidemio- 43. Sidhu D, Naugler C: Fasting time and lipid levels in a logic studies. American journal of epidemiology 1979, community-based population: a cross-sectional study. 109(4):394-399. Archives of internal medicine 2012, 172(22):1707-1710. 55. Heath GW, Parra DC, Sarmiento OL, Andersen LB, Owen 44. Sundquist J, Johansson SE, Sundquist K: Levelling off of N, Goenka S, Montes F, Brownson RC: Evidence-based prevalence of obesity in the adult population of Sweden intervention in physical activity: lessons from around between 2000/01 and 2004/05. BMC public health the world. Lancet 2012, 380(9838):272-281. 2010, 10:119. 56. Gordon T, Kannel WB, Dawber TR, McGee D: Changes 45. Lissner L, Sohlstrom A, Sundblom E, Sjoberg A: Trends associated with quitting cigarette smoking: the in overweight and obesity in Swedish schoolchildren Framingham Study. American heart journal 1975, 1999-2005: has the epidemic reached a plateau? 90(3):322-328. Obesity reviews : an official journal of the International 57. Cena H, Fonte ML, Turconi G: Relationship between Association for the Study of Obesity 2010, 11(8):553- smoking and metabolic syndrome. Nutrition reviews 559. 2011, 69(12):745-753. 46. Micciolo R, Di F, V, Fantin F, Canal L, Harris TB, Bosello 58. Williamson DF, Madans J, Anda RF, Kleinman JC, Giovino O, Zamboni M: Prevalence of overweight and obesity in GA, Byers T: Smoking cessation and severity of weight Italy (2001-2008): is there a rising obesity epidemic? gain in a national cohort. The New England journal of Annals of epidemiology 2010, 20(4):258-264. medicine 1991, 324(11):739-745. 47. Rokholm B, Baker JL, Sorensen TI: The levelling off of 59. Chiolero A, Faeh D, Paccaud F, Cornuz J: Consequences the obesity epidemic since the year 1999--a review of of smoking for body weight, body fat distribution, evidence and perspectives. Obesity reviews : an official and insulin resistance. The American journal of clinical journal of the International Association for the Study of nutrition 2008, 87(4):801-809. Obesity 2010, 11(12):835-846. 60. Sun K, Liu J, Ning G: Active smoking and risk of meta- 48. Grundy SM: Metabolic syndrome pandemic. Arterioscle- bolic syndrome: a meta-analysis of prospective studies. rosis, thrombosis, and vascular biology 2008, 28(4):629- PLoS one 2012, 7(10):e47791. 636. 49. Onat A: Metabolic syndrome: nature, therapeutic solutions and options. Expert opinion on pharmacotherapy 2011, 12(12):1887-1900. 50. Onat A, Ceyhan K, Basar O, Erer B, Toprak S, Sansoy V: Metabolic syndrome: major impact on coronary risk in a population with low cholesterol levels--a prospective and cross-sectional evaluation. Atherosclerosis 2002, 165(2):285-292. 51. Nichols M, Townsend N, Scarborough P, Rayner M: Car- diovascular disease in Europe: epidemiological update. European heart journal 2013, 34(39):3028-3034. Prevalence of Metabolic Syndrome and Metabolically Healthy Obesity in Europe 45

Additional file

Additional file 1: Study descriptions and methodologies. Can be found online: http://bmcendocrdisord.biomedcentral.com/articles/10.1186/1472-6823-14-9 2 3

Hoofdstukpagina-letteromtrek.indd 5 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 6 16/11/16 11:05 Chapter 3

Associations between smoking, components of the metabolic syndrome and lipoprotein particle size

Sandra N. Slagter Jana V. van Vliet-Ostaptchouk Judith M. Vonk H. Marike Boezen Robin P.F. Dullaart Anneke C. Muller Kobold Edith J. Feskens André P. van Beek Melanie M. van der Klauw Bruce H.R. Wolffenbuttel †Equal contributors 3 BMC Medicine 2013, 11:195

Hoofdstukpagina-letteromtrek.indd 5 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 6 16/11/16 11:05 48 Chapter 3

Abstract

Background The clustering of metabolic and cardiovascular risk factors is known as the metabolic syndrome (MetS). The risk of having MetS is strongly associated with increased adiposity and can be further modified by smoking. Apolipoproteins (apo) associated with low density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) may be altered in MetS. This study aimed to examine the association between smoking and the following parameters: MetS and its components, levels of apolipoproteins and estimated lipoprotein particle size, separately for men and women, and in different body mass index (BMI) classes. Methods We included 24,389 men and 35,078 women, aged between 18 and 80 years who participated in the LifeLines Cohort Study between December 2006 and January 2012; 5,685 men and 6,989 women were current smokers. Participants were categorized into three different BMI classes (BMI <25; BMI 25-29.9; BMI ≥30 kg/m2). MetS was defined according to the NCEP:ATPIII criteria. Blood pressure, anthropometric and lipid measurements were rigorously standardized, and the large sample size enabled a powerful estimate of quantitative changes. The association between smoking and the individual MetS components, and apoA1 and apoB, was tested with linear regression. Logistic regression was used to examine the effect of smoking and daily tobacco smoked on risk of having MetS. All models were age adjusted and stratified by gender and BMI class. Results Prevalence of MetS increased with higher BMI levels. 64% of obese men and 42% of obese women had MetS. Current smoking was associated with a higher risk of MetS in both genders and all BMI classes (odds ratio 1.7-2.4 for men, 1.8-2.3 for women, all Ps<0.001). Current smokers had lower levels of HDL cholesterol and apoA1, higher levels of triglycerides and apoB, and higher waist circumference than non-smokers (all P<0.001). Smoking had no consistent association with blood pressure or fasting blood glucose. In all 5 BMI classes, we found a dose-dependent association of daily tobacco consumption with MetS prevalence as well as with lower levels of HDL cholesterol, higher triglyceride levels and lower ratios of HDL cholesterol/apoA1 and - only in those with BMI<30 – LDL cholesterol/apoB (all P<0.001). Conclusions Smoking is associated with an increased prevalence of MetS, independent of gender and BMI class. This increased risk is mainly related to lower HDL cholesterol, and higher triglycerides and waist circumference. In addition, smoking was associated with unfavourable changes in apoA1 and apoB, and in lipoprotein particle size. Keywords Metabolic Syndrome, Smoking, HDL Cholesterol, Apolipoproteins, Triglycer- ides, Obesity, Cross-sectional. Association between Smoking, Metabolic Syndrome and Lipoprotein Particle Size 49

Introduction

Metabolic syndrome (MetS) is a combination of unfavorable health factors including abdominal obesity, dyslipidemia, hypertension and glucose intolerance [1,2] and is strongly associated with increased risk of cardiovascular disease (CVD) and type 2 dia- 3 betes [1,2]. One of the key drivers in the development of MetS is obesity [3]. In recent years, the global prevalence of obesity has increased at alarming rates, and MetS and its consequences have become a major public health burden [4,5]. This rise in MetS prevalence has also been observed in non-obese individuals [6-8] and there is strong evidence that the increase is mainly the result of unfavorable lifestyle changes, such as inactivity and poor nutrition [9]. Smoking has also been implicated as a risk factor for MetS. Earlier studies have suggested that overall tobacco use is associated with an increased risk of MetS [10,11], most likely due to its effects on waist circumference, blood lipids and blood pressure [10,12,13]. Such metabolic abnormalities may also be modulated by a direct negative effect of smoking on insulin resistance [12]. The degree to which smoking modulates the risk of developing obesity-related MetS still remains unclear, however. While the association between smoking, metabolic disturbances and the presence of MetS has been firmly established in obese individuals [7,8], with a similar trend observed in normal weight individuals [7], these findings could not be confirmed by others [8,14]. Alterations in the size and composition of low-density lipoprotein (LDL) particles and high-density lipoprotein (HDL) particles have been associated with metabolic syndrome [15], and are known to be related to CVD risk [16]. Individuals with altered HDL cholesterol (HDL-C) and triglyceride levels, two components of MetS, are more likely to also have unfavorable changes in the levels of apolipoproteins (apo) A1 and B, the apolipoproteins associated with HDL-C and LDL-C, as well as altered size and composition of these lipoprotein particles [17]. Although small-scale studies have suggested that smoking may influence the levels of apolipoproteins and the composition of lipoproteins [18-20], the extent to which this is associated with prevalent BMI and the risk of MetS is largely unknown. In addition, the latter studies have been published over two decades ago, and since then improved standardization has made apolipoprotein assays more reliable and reproducible [21,22]. The aim of the present study was to examine the association between smoking and the individual components of MetS in normal weight, overweight and obese subjects, in a very large population-based cohort study [23]. We also assessed the relationship between smoking and apolipoprotein levels, and between smoking and lipoprotein particle size, using the HDL-C/apoA1 and LDL-C/apoB ratios as a proxy. 50 Chapter 3

Methods

Study design and subjects The LifeLines Cohort Study is a multidisciplinary prospective population-based cohort study that examines the health and health-related behaviors of participants living in the northeast of The Netherlands [23]. It employs a wide range of procedures to assess the biomedical, sociodemographic, behavioral, physical and psychological factors that contribute to the health and disease of the general population, with a focus on multimorbidity. All participants filled in an extensive questionnaire about health- related items and lifestyle and underwent a clinical examination that included standard anthropometric and blood pressure measurements performed by trained technicians and collection of biological samples. All participants provided written informed consent before participating in the study. The study protocol was approved by the medical ethical review committee of the University Medical Center Groningen. For this cross-sectional study we included subjects of Western European origin (according to self-reported information in the questionnaire), aged between 18 and 80 years who participated in the LifeLines Cohort Study between December 2006 and Janu- ary 2012. Individuals who had missing data on BMI (n = 21), or on the variables needed to define MetS (n = 2,044), or whose questionnaires were incomplete with regard to smoking behavior (n = 2,202) were excluded from analysis. A total of 59,467 individuals were available for the current analysis.

Clinical examination The anthropometric measurements height, weight, waist and hip circumference, and blood pressure were conducted by trained technicians using a standardized protocol. Body weight was measured without shoes with 0.1 kg precision. Height, waist and hip circumference were measured to the nearest 0.5 cm. Waist circumference was measured in standing position with a tape measure all around the body, at the level midway between the lower rib margin and the iliac crest. Systolic and diastolic blood pressures were measured every minute for a period of 10 minutes using an automated Dinamap Monitor (GE Healthcare, Freiburg, Germany). The size of the cuff was chosen according to the arm circumference. The average of the last three readings was recorded for each blood pressure parameter.

Biochemical measurements At a second visit, blood was collected in the fasting state, between 8.00 and 10.00 a.m. The blood samples were transported under temperature-controlled conditions (at room temperature or at 4°C, depending on the sample requirements) to the LifeLines central laboratory facility. All measurements were performed the same day. Total and Association between Smoking, Metabolic Syndrome and Lipoprotein Particle Size 51

HDL cholesterol were measured using an enzymatic colorimetric method, triglycerides using a colorimetric UV method, and LDL-C using an enzymatic method, all on a Roche Modular P chemistry analyzer (Roche, Basel, Switzerland). Apolipoprotein A1 (apoA1) and apolipoprotein B (apoB) were measured by nephelometry (Siemens, Munich, Ger- many). Fasting blood glucose was measured using a hexokinase method. 3

Assessment of metabolic syndrome and lipoprotein particle size BMI was calculated as weight (kg) divided by height squared (m2). We classified the subjects into three BMI categories: normal weight (BMI <25.0), overweight (BMI 25.0 to 30) or obese (BMI ≥30). Individuals with a BMI <30 were considered to have MetS if they satisfied at least three of the five criteria named in the revised National Cholesterol Education Program’s Adult Treatment Panel III (NCEP:ATPIII, Table 1) [2]. Individuals with a BMI ≥30 were considered to have MetS if they satisfied at least two of the four MetS criteria (excluding waist circumference since a BMI ≥30 overrules the waist circumference criterion). The HDL-C/apoA1 ratio and LDL-C/apoB ratio were calculated to estimate differences in HDL-C and LDL-C particle size.

Table 1. The revised National Cholesterol Education Program’s Adult Treatment Panel III criteria (NCEP:ATP III): for a person to be defined as having metabolic syndrome (MetS) they must satisfy at least three of the five criteria below.a

Criteria Details Raised blood pressure Systolic blood pressure (SBP) ≥130 mmHg or diastolic blood pressure (DBP) ≥85 mmHg or use of blood pressure-lowering medication Elevated glucose level Fasting blood glucose ≥5.6 mmol/l or use of blood glucose-lowering medication or diagnosis of type 2 diabetes Decreased high-density lipoprotein cholesterol <1.03 mmol/l in men or <1.30 mmol/l in women or lipid-lowering medical treatment Elevated triglycerides ≥1.70 mmol/l or medication for elevated triglycerides Abdominal obesity (increased waist circumference) ≥102 cm in men or ≥88 cm in women a If body mass index (BMI) is ≥30 kg/m2, abdominal obesity can be assumed and waist circumference is not included as a criterion. A person with BMI ≥30 must satisfy at least two of the four other criteria to be defined as having MetS.

Data description Diagnosis of earlier myocardial infarction or hypertension was self-reported, as was the use of medication. Diagnosis of diabetes mellitus was based either on self-report, or on the finding of a fasting blood glucose >7.0 mmol/l. Information about smoking was collected from the self-administered questionnaires. Respondents were asked whether 52 Chapter 3

they smoked; whether they had smoked during the last month and whether they had ever smoked for an entire year; whether they had stopped smoking; which type of tobacco they currently smoked (cigarette, cigarillo, cigar, pipe tobacco or a mixture of different kinds); and the amount smoked (number of cigarettes smoked per day and/or grams tobacco per week, in the case of pipe smokers). The subjects were classified according to smoking status as non-smoker, former smoker or current smoker. Subjects were defined as a non-smoker if they had not smoked during the last month and had also never smoked for longer than a year. Former smokers were those who had not smoked during the last month but reported to have smoked for longer than a year and had stopped smoking. Current smokers were subjects who reported to have smoked during the last month or those who reported to have smoked for longer than a year and had not stopped smoking. Estimation of current smokers’ total tobacco use and their classification into light, moderate and heavy smokers were based on the following quantities: one cigarette = 1 g tobacco, one cigarillo = 3 g tobacco and one cigar = 5 g tobacco. Light smoking was defined as 10 g/day or less, moderate as 11 to 20 g/day and heavy as more than 20 g/day.

Statistical methods All analyses were conducted using IBM SPSS Statistics version 20 (IBM Corporation, Armonk, NY, USA). Data are presented as means ± SD, or geometric mean and interquartile range when they were not normally distributed. For comparisons between groups, analysis of variance was used where appropriate. Linear regression was used to examine the associations between smoking and the five components of MetS as well as between smoking and the apolipoprotein levels and the HDL-C/apoA1 and LDL-C/apoB ratios. Logistic regression was used to examine the effect of smoking and daily tobacco use on the risk of having MetS. This approach generated odds ratios that predicted the odds of having MetS for the different smoking statuses and different amounts of tobacco usage. Since distributions for triglyceride and fasting blood glucose were right skewed, before analysis we log-transformed (natural log) values to approximate normal distribution. All analyses were stratified for sex and BMI class, and were additionally adjusted for age. We applied a Bonferroni correction to account for the number of independent tests. A P value of ≤0.001 (0.05/48) was regarded as significant, given 48 independent tests (6 statistical models × 8 traits). Since the analyses were performed separately for men and women, and also for each BMI class, we used six models. The eight traits were as follows: (1) systolic and diastolic blood pressure or hypertension; (2) fasting glucose level; (3) HDL-C level; (4) triglyceride level; (5) waist circumference; (6) apoA1 and apoB; (7) HDL- C/apoA1 and LDL-C/apoB ratios; and (8) MetS. Association between Smoking, Metabolic Syndrome and Lipoprotein Particle Size 53

Results

The baseline characteristics of the participants are summarized in Table 2. Obesity prevalence was 14.4% in men and 16.1% in women. Subjects who were overweight or obese were slightly older than those with normal weight. Among normal weight men, 24.6% 3 were current smokers, while 22.3% of the overweight and 23.1% of the obese were current smokers. Among normal weight women, 21.1% were current smokers, while 19.6% of the overweight and 16.8% of the obese were current smokers. For both sexes, systolic and diastolic blood pressure, serum triglycerides, blood glucose, LDL-C and apoB, as well as the percentage of subjects with type 2 diabetes, showed a consistent increase with increasing BMI. The same trend was observed for the percentage of subjects using medication to control elevated blood pressure, triglycerides or blood glucose. HDL-C and apoA1 levels, as well as the HDL-C/apoA1 ratio, showed a consistent decrease with increasing BMI. While in subjects with BMI <25 the overall prevalence of MetS was 3.6% in men and 2.4% in women, in the overweight this figure was 21.6% in men and 16.0% in women, rising to 64.3% of obese men and 41.5% of obese women. For both sexes, former smokers were older and had higher levels of BMI, blood pressure, LDL-C, total cholesterol, waist circumference and glucose and were more frequently diagnosed with type 2 diabetes than non-smokers and current smokers (Table 3). Cur- rent smokers had the lowest levels of HDL-C and apoA1, the lowest HDL-C/apoA1 ratio, and the highest levels of triglycerides and, in women, apoB. The percentage of subjects with MetS according to smoking status and daily tobacco consumption are shown in Figure 1. In both men and women, prevalence of MetS was greater in current smokers within each BMI group. In men, smoking was associated with higher MetS prevalence, although in the normal weight and obese men there was no difference between moderate and heavy smokers. In women there was a more pronounced dosage effect, that is, the percentage of individuals with MetS increased with an increase in the amount of tobacco smoked. Former smokers had a higher prevalence of MetS than non-smokers, but it should be taken into account that they were also older. 54 Chapter 3

TG value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.0012 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 P body mass index, BMI systolic blood pressure, SBP 1.38 ± 0.33 3.26 ± 0.88 5.1 ± 1.0 211 (3.7%) 75 ± 9 267 (4.7%) 130 ± 15 8 (0.1%) 951 (16.8%) 457 (8.1%) 2,087 (36.9%) 1,351 (23.9%) 47 ± 12 2,623 (46.3%) 105 ± 10 5,661 (16.1%) 34.1 ± 3.9 1.61) 1.20 (0.86 to 1.52 ± 0.26 0.90 ± 0.49 0.97 ± 0.24 3.37 ± 0.38 5.50) 5.26 (4.80 to 2,351 (41.5%) BMI ≥30 apolipoprotein, apolipoprotein, Women Apo n = 35,078 (59.0%) ApoA1 ApoA1 and apoB results (and their ratios) were b 1.54 ± 0.36 3.25 ± 0.90 5.1 ± 1.0 118 (1.0%) 73 ± 9 150 (1.3%) 125 ± 15 11 (0.1%) 2,289 (19.6%) 608 (5.2%) 4,298 (36.8%) 1,563 (13.4%) 47 ± 12 5,080 (43.5%) 90 ± 7 11,667 (33.3%) 27.1 ± 1.4 1.33) 0.99 (0.72 to 1.60 ± 0.26 0.95 ± 0.50 0.94 ± 0.24 3.46 ± 0.36 5.20) 4.94 (4.60 to 1,871 (16.0%) BMI 25 to 30 BMI 25 to , two out of criteria. four 2 low-density lipoprotein-cholesterol, 1.69 ± 0.39 2.91 ± 0.84 4.9 ± 1.0 44 (0.2%) 70 ± 8 55 (0.3%) 119 ± 14 3 (0.0%) 3,749 (21.1%) 364 (2.1%) 5,248 (29.6%) 1,050 (5.9%) 42 ± 12 8,753 (49.3%) 79 ± 7 17,750 (50.6%) 22.4 ± 1.7 1.04) 0.81 (0.61 to 1.66 ± 0.28 1.00 ± 0.14 0.84 ± 0.22 3.45 ± 0.36 4.90) 4.70 (4.40 to 425 (2.4%) BMI <25 LDL-C value

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 P For subjects For with BMI ≥30 kg/m Data given Data as given geometric mean (interquartile range). c a 1.12 ± 0.26 3.44 ± 0.91 5.2 ± 1.0 175 (5.0%) 80 ± 9 204 (5.8%) 137 ± 14 13 (0.4%) 813 (23.1%) 473 (13.5%) 1,390 (39.6%) 843 (24.0%) 48 ± 11 1,311 (37.3%) 112 ± 9 3,514 (14.4%) 32.8 ± 2.9 2.23) 1.62 (1.15 to 1.36 ± 0.21 0.81 ± 0.11 1.05 ± 0.24 3.32 ± 0.42 5.80) 5.52 (5.00 to 2,259 (64.3%) BMI ≥30 Men high-density lipoprotein-cholesterol, n = 24,389 (41.0%) HDL-C 1.25 ± 0.29 3.47 ± 0.87 5.2 ± 1.0 175 (1.5%) 78 ± 9 203 (1.7%) 133 ± 13 32 (0.3%) 2,628 (22.3%) 1,008 (8.6%) 4,384 (37.3%) 1,516 (12.9%) 47 ± 11 4,751 (40.4%) 98 ± 6 11,763 (48.2%) 27.1 ± 1.4 1.80) 1.31 (0.91 to 1.41 ± 0.21 0.86 ± 0.12 1.01 ± 0.24 3.44 ± 0.40 5.40) 5.18 (4.80 to 2,544 (21.6%) BMI 25 to 30 BMI 25 to 1.40 ± 0.32 3.17 ± 0.86 4.9 ± 1.0 38 (0.4%) 74 ± 8 53 (0.6%) 127 ± 12 6 (0.1%) 2,244 (24.6%) 267 (2.9%) 2,401 (26.3%) 462 (5.1%) 42 ± 12 4,467 (49.0%) 87 ± 6 9,112 (37.4%) 23.0 ± 1.5 1.31) 0.98 (0.71 to 1.47 ± 0.23 0.93 ± 0.13 0.91 ± 0.23 3.51 ± 0.35 5.20) 4.94 (4.60 to 330 (3.6%) BMI <25 diastolic blood pressure, DBP c a b a b b b Characteristics of the current study population. Characteristics of the current

2 blood pressure, able 2. Triglycerides, mmol/l Triglycerides, HDL-C, mmol/l HDL-C, LDL-C, mmol/l LDL-C, fulfilling ≥3 out of 5 Percentage criteria metabolic syndrome Total cholesterol, mmol/l cholesterol, Total medication, Oral antihyperglycemic n (%) DBP, mmHg DBP, n (%) 2 diabetes, Type SBP, mmHg SBP, n (%) medication, TG-lowering Current smoker, n (%) smoker, Current n (%) use, Statin BMI, kg/m triglycerides. Former smoker, n (%) smoker, Former n (%) medication, BP-lowering Age, years Age, BP Smoking status n (%) Non-smoker, A1, g/l Apolipoprotein ratio HDL-C/apoA1 g/l B, Apolipoprotein ratio LDL-C/apoB mmol/l Blood glucose, cm circumference, Waist Characteristics n (%) Data as are presented mean or ± median SD, (interquartile range). T available in available 34,613 and 34,601 of the 59,467 subjects, respectively. Association between Smoking, Metabolic Syndrome and Lipoprotein Particle Size 55

BP TG value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.0012 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 P systolic blood systolic value P <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 NS NS <0.001 SBP body mass index, body mass index,

BMI 3 BMI systolic blood pressure, SBP 1.38 ± 0.33 3.26 ± 0.88 5.1 ± 1.0 211 (3.7%) 75 ± 9 267 (4.7%) 130 ± 15 8 (0.1%) 951 (16.8%) 457 (8.1%) 2,087 (36.9%) 1,351 (23.9%) 47 ± 12 2,623 (46.3%) 105 ± 10 5,661 (16.1%) 34.1 ± 3.9 1.61) 1.20 (0.86 to 1.52 ± 0.26 0.90 ± 0.49 0.97 ± 0.24 3.37 ± 0.38 5.50) 5.26 (4.80 to 2,351 (41.5%) BMI ≥30 not significant, significant, not Current smoker Current 951 (13.6%) 121 ± 14 25.4 ± 4.6 1.36) 1.01 (0.73 to 5.10) 4.84 (4.50 to 6,989 (19.9%) 42 ± 11 72 ± 9 1.57 ± 0.28 87 ± 12 5.0 ± 1.0 0.92 ± 0.13 621 (8.9%) 3.14 ± 0.92 0.93 ± 0.24 275 (3.9%) 1.49 ± 0.38 3.38 ± 0.36 6 (0.1%) 68 (1.0%) 55 (0.8%) NS apolipoprotein, apolipoprotein, Women Women apolipoprotein, Apo n = 35,078 (59.0%) Apo ApoA1 ApoA1 and apoB results (and their ratios) were b 1.54 ± 0.36 3.25 ± 0.90 5.1 ± 1.0 118 (1.0%) 73 ± 9 150 (1.3%) 125 ± 15 11 (0.1%) 2,289 (19.6%) 608 (5.2%) 4,298 (36.8%) 1,563 (13.4%) 47 ± 12 5,080 (43.5%) 90 ± 7 11,667 (33.3%) 27.1 ± 1.4 1.33) 0.99 (0.72 to 1.60 ± 0.26 0.95 ± 0.50 0.94 ± 0.24 3.46 ± 0.36 5.20) 4.94 (4.60 to 1,871 (16.0%) BMI 25 to 30 BMI 25 to Former smoker Former 2,087 (17.9%) 124 ± 16 26.3 ± 4.7 1.25) 0.93 (0.68 to 5.20) 4.94 (4.60 to 11,633 (33.2%) 48 ± 11 73 ± 9 1.66 ± 0.27 89 ± 12 5.1 ± 1.0 0.98 ± 0.60 1,660 (14.3%) 3.18 ± 0.89 0.91 ± 0.23 594 (5.1%) 1.65 ± 0.40 3.47 ± 0.36 10 (0.1%) 216 (1.9%) 164 (1.4%) , two out of criteria. four 2 low-density lipoprotein-cholesterol, Non-smoker 2,623 (15.9%) 122 ± 15 25.6 ± 4.8 1.16) 0.87 (0.64 to 5.10) 4.83 (4.50 to 16,456 (46.9%) 43 ± 12 72 ± 9 1.61 ± 0.27 86 ± 12 4.9 ± 1.0 0.97 ± 0.14 1,683 (10.2%) 2.99 ±0.85 0.87 ± 0.23 560 (3.4%) 1.59 ± 0.38 3.44 ± 0.37 6 (0.1%) 188 (1.1%) 154 (0.9%) 1.69 ± 0.39 2.91 ± 0.84 4.9 ± 1.0 44 (0.2%) 70 ± 8 55 (0.3%) 119 ± 14 3 (0.0%) 3,749 (21.1%) 364 (2.1%) 5,248 (29.6%) 1,050 (5.9%) 42 ± 12 8,753 (49.3%) 79 ± 7 17,750 (50.6%) 22.4 ± 1.7 1.04) 0.81 (0.61 to 1.66 ± 0.28 1.00 ± 0.14 0.84 ± 0.22 3.45 ± 0.36 4.90) 4.70 (4.40 to 425 (2.4%) BMI <25 low-density lipoprotein-cholesterol, lipoprotein-cholesterol, low-density LDL-C LDL-C value

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 P value P <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 NS <0.001 <0.001 Data Data given as geometric mean (interquartile range). For subjects For with BMI ≥30 kg/m a Data given Data as given geometric mean (interquartile range). c a 1.12 ± 0.26 3.44 ± 0.91 5.2 ± 1.0 175 (5.0%) 80 ± 9 204 (5.8%) 137 ± 14 13 (0.4%) 813 (23.1%) 473 (13.5%) 1,390 (39.6%) 843 (24.0%) 48 ± 11 1,311 (37.3%) 112 ± 9 3,514 (14.4%) 32.8 ± 2.9 2.23) 1.62 (1.15 to 1.36 ± 0.21 0.81 ± 0.11 1.05 ± 0.24 3.32 ± 0.42 5.80) 5.52 (5.00 to 2,259 (64.3%) BMI ≥30 Current smoker Current 812 (14.3%) 131 ± 13 26.2 ± 3.7 1.91) 1.35 (0.92 to 5.40) 5.11 (4.70 to 5,685 (23.3%) 43 ± 11 76 ± 9 1.40 ± 0.22 95 ± 11 5.1 ± 1.0 0.85 ± 0.13 503 (8.8%) 3.39 ± 0.92 1.00 ± 0.25 419 (7.4%) 1.21 ± 0.30 3.37 ± 0.40 14 (0.2%) 83 (1.5%) 69 (1.2%) Men Men high-density lipoprotein-cholesterol, n = 24,389 (41.0%) high-density lipoprotein-cholesterol, lipoprotein-cholesterol, high-density HDL-C 1.25 ± 0.29 3.47 ± 0.87 5.2 ± 1.0 175 (1.5%) 78 ± 9 203 (1.7%) 133 ± 13 32 (0.3%) 2,628 (22.3%) 1,008 (8.6%) 4,384 (37.3%) 1,516 (12.9%) 47 ± 11 4,751 (40.4%) 98 ± 6 11,763 (48.2%) 27.1 ± 1.4 1.80) 1.31 (0.91 to 1.41 ± 0.21 0.86 ± 0.12 1.01 ± 0.24 3.44 ± 0.40 5.40) 5.18 (4.80 to 2,544 (21.6%) BMI 25 to 30 BMI 25 to HDL-C Former smoker Former 1,390 (17.0%) 133 ± 14 27.0 ± 3.5 1.73) 1.25 (0.87 to 5.50) 5.26 (4.90 to 8,175 (33.5%) 51 ± 12 78 ± 9 1.46 ± 0.22 98 ± 10 5.2 ± 1.0 0.88 ± 0.13 1,497 (18.3%) 3.43 ± 0.89 1.00 ± 0.24 918 (11.2% 1.31 ± 0.32 3.45 ± 0.41 24 (0.3%) 264 (3.2%) 217 (2.7%) Non-smoker 1,311 (12.5%) 130 ± 13 26.1 ± 3.7 1.54) 1.12 (0.78 to 5.30) 5.05 (4.70 to 10,529 (43.2%) 42 ± 11 76 ± 9 1.43 ± 0.22 94 ± 11 5.0 ± 1.0 0.89 ± 0.13 821 (7.8%) 3.28 ± 0.86 0.94 ± 0.23 411 (3.9% 1.31 ± 0.31 3.49 ± 0.37 13 (0.1%) 113 (1.1%) 102 (1.0%) 1.40 ± 0.32 3.17 ± 0.86 4.9 ± 1.0 38 (0.4%) 74 ± 8 53 (0.6%) 127 ± 12 6 (0.1%) 2,244 (24.6%) 267 (2.9%) 2,401 (26.3%) 462 (5.1%) 42 ± 12 4,467 (49.0%) 87 ± 6 9,112 (37.4%) 23.0 ± 1.5 1.31) 0.98 (0.71 to 1.47 ± 0.23 0.93 ± 0.13 0.91 ± 0.23 3.51 ± 0.35 5.20) 4.94 (4.60 to 330 (3.6%) BMI <25 diastolic blood pressure, diastolic blood pressure, pressure, blood diastolic DBP c DBP a a b a a b triglycerides. b b , n (%) 2 TG Characteristics of the current study population. Characteristics of the current smokers. smokers and current former Baseline characteristics of non-smokers,

2 2 blood pressure, able 2. able 3. Triglycerides, mmol/l Triglycerides, HDL-C, mmol/l HDL-C, LDL-C, mmol/l LDL-C, fulfilling ≥3 out of 5 Percentage criteria metabolic syndrome Total cholesterol, mmol/l cholesterol, Total medication, Oral antihyperglycemic n (%) DBP, mmHg DBP, n (%) 2 diabetes, Type SBP, mmHg SBP, n (%) medication, TG-lowering Current smoker, n (%) smoker, Current n (%) use, Statin BMI, kg/m triglycerides. Former smoker, n (%) smoker, Former n (%) medication, BP-lowering Age, years Age, SBP, mmHg SBP, BP Smoking status n (%) Non-smoker, A1, g/l Apolipoprotein ratio HDL-C/apoA1 g/l B, Apolipoprotein ratio LDL-C/apoB mmol/l Blood glucose, cm circumference, Waist Data Data are presented as mean ± SD, or median (interquartile range). Smoking status n (%) years Age, T Characteristics n (%) Data as are presented mean or ± median SD, (interquartile range). T available in available 34,613 and 34,601 of the 59,467 subjects, respectively. DBP, mmHg DBP, pressure, pressure, BMI ≥30 kg/m A1, g/l Apolipoprotein cm circumference, Waist BMI, kg/m Total cholesterol, mmol/l cholesterol, Total blood pressure, blood HDL-C/apoA1 ratio HDL-C/apoA1 n (%) medication, BP-lowering LDL-C, mmol/l LDL-C, Apolipoprotein B, g/l B, Apolipoprotein n (%) use, Statin HDL-C, mmol/l HDL-C, LDL-C/apoB ratio LDL-C/apoB n (%) medication, TG-lowering Triglycerides, mmol/l Triglycerides, Blood glucose, mmol/l Blood glucose, n (%) 2 diabetes, Type Oral antihyperglycemic medication, medication, Oral antihyperglycemic n (%) 56 Chapter 3

100 non-smokers former smokers 0-10 g daily 10-20 g daily 80 ≥ 20 g daily

% of% subjects 40

0 BMI <25 BMI 25-30 BMI ≥30 BMI <25 BMI 25-30 BMI ≥30 Men Women

Figure 1. Prevalence of metabolic syndrome in non-smokers, former smokers and current smokers. Note that in all body mass index (BMI) classes prevalence of metabolic syndrome was higher in former smokers than in non-smokers, and that a dose-response relationship was found between prevalence of metabolic syndrome and amount of smoking, especially in women.

For all BMI classes and smoking statuses, the percentage of subjects with high blood pressure, elevated blood glucose and elevated triglyceride levels was higher in men than in women, whereas women were more likely than men to have a higher waist circumference (Figure 2). In both sexes, increasing amounts of tobacco smoked were strongly associated with an increase in the number of individuals showing abnormal HDL-C and triglyceride levels. The amount of tobacco smoked was also associated with increased waist circumference, especially in overweight individuals. There were no consistent effects of the amount of tobacco smoked on blood pressure and blood glucose, nor did the amount of tobacco smoked influence blood pressure levels following correction for use of blood pressure-lowering medication. Association between Smoking, Metabolic Syndrome and Lipoprotein Particle Size 57

Men Women 100 100 BMI <25 kg/m2 non-smokers BMI <25 kg/m2 non-smokers former smokers former smokers 0-10 g daily 0-10 g daily 80 10-20 g daily 80 10-20 g daily ≥ 20 g daily ≥ 20 g daily

60 60 3

40 40 % of subjects % % % of subjects

20 20

0 0 BP Glucose HDL-C TG Waist BP Glucose HDL-C TG Waist

100 100 BMI 25-30 kg/m2 BMI 25-30 kg/m2

80 80

60 60

40 40 % % of subjects % of subjects %

20 20

0 0 BP Glucose HDL-C TG Waist BP Glucose HDL-C TG Waist

100 100 BMI ≥30 kg/m2 BMI ≥30 kg/m2

80 80

60 60

40 40 % subjects of % % of % subjects

20 20

0 0 BP Glucose HDL-C TG Waist BP Glucose HDL-C TG Waist

Figure 2. Prevalence of the individual components of metabolic syndrome according to sex (left panel: men; right panel: women) and body mass index (BMI) class. Top: BMI <25 kg/m2; middle; BMI 25 to 30 kg/m2; bottom: BMI ≥30 kg/m2. For all BMI classes, more men met the criteria for high blood pressure, elevated blood glucose and elevated triglyceride levels than did women, while women more frequently met the criteria for high waist circumference. Prevalence of high-density lipoprotein (HDL) abnormalities was not different between men and women. Higher tobacco consumption was particularly associated with abnormalities in HDL cholesterol and triglycerides, and to a lesser extent with abnormal waist circumference. BP, blood pressure; glucose, blood glucose; HDL-C, high-density lipoprotein-cholesterol; TG, triglycerides; waist, waist circumference. 58 Chapter 3

Table 4 presents the associations between smoking and individual MetS components and between smoking and apolipoprotein levels and ratios, for the three different BMI classes, stratified by sex. There was a significant fall of HDL-C levels associated with greater amount of tobacco smoked in both sexes and all three BMI classes (P <0.001). In addition, the HDL-C/apoA1 ratio was significantly lower for higher amount of tobacco smoked in all BMI classes, and the LDL-C/apoB ratio for the lowest BMI classes (P <0.001). Former smokers had similar HDL-C levels to those of non-smokers. In all BMI classes, there was a consistent positive association between tobacco use and triglyceride levels (all P values <0.001). In all tobacco use groups, waist circumference was higher than that of non-smokers, independent of sex and BMI class, except for obese male light smokers. In obese female smokers we observed the largest rise in waist circumference: from 2.2 cm in moderate smokers to 6.4 cm in heavy smokers (both P <0.001). Moderate and heavy smoking was not associated with any strong changes in fasting blood glucose level. The age-corrected odds ratios for having MetS, for men and women separately, in the three BMI classes, are depicted in Figure 3. In all BMI classes there was a significant rise in odds ratio with increasing amount of tobacco smoked. This trend was stronger in women than in men (P <0.001). Association between Smoking, Metabolic Syndrome and Lipoprotein Particle Size 59 Heavy <0.001 <0.001 <0.001 P -0.20 (-0.26 to -0.14) -0.20 (-0.26 to P -0.15 (-0.20 to -0.09) -0.15 (-0.20 to P -0.21 (-0.26 to -0.16) -0.21 (-0.26 to NS 0.86 (-0.88 to 2.59) 0.86 (-0.88 to NS 0.66 (-0.72 to 2.04) 0.66 (-0.72 to NS 1.13 (0.06 to 2.19) 1.13 (0.06 to NS 2.04 (-0.83 to 4.91) 2.04 (-0.83 to NS -1.13 (-3.37 to 1.11) -1.13 (-3.37 to NS -0.01 (-1.73 to 1.71) -0.01 (-1.73 to 3 Women Moderate <0.001 <0.001 <0.001 P -0.17 (-0.20 to -0.13) -0.17 (-0.20 to P -0.17 (-0.20 to -0.15) -0.17 (-0.20 to P -0.17 (-0.20 to -0.15) -0.17 (-0.20 to NS -0.52 (-1.44 to 0.39) -0.52 (-1.44 to NS 0.56 (0.05 to 1.16) 0.56 (0.05 to NS 0.49 (0.04 to 0.94) 0.49 (0.04 to NS -0.74 (-2.24 to 0.77) -0.74 (-2.24 to NS -0.06 (-1.05 to 0.92) -0.06 (-1.05 to NS 0.22 (-0.51 to 0.95) 0.22 (-0.51 to Light 0.001 <0.001 <0.001 <0.001 P -0.08 (-0.12 to -0.05) -0.08 (-0.12 to P -0.06 (-0.08 to -0.04) -0.06 (-0.08 to P -0.06 (-0.08 to -0.05) -0.06 (-0.08 to NS -0.24 (-1.12 to 0.65) -0.24 (-1.12 to NS -0.27 (-0.79 to 0.25) -0.27 (-0.79 to NS -0.08 (-0.46 to 0.30) -0.08 (-0.46 to NS -0.93 (-2.39 to 0.53) -0.93 (-2.39 to P = -1.46 (-2.30 to -0.61) -1.46 (-2.30 to NS -0.64 (-1.25 to -0.03) -0.64 (-1.25 to Current smoking Current Heavy <0.001 <0.001 <0.001 P -0.10 (-0.14 to -0.06) -0.10 (-0.14 to P -0.13 (-0.16 to -0.09) -0.13 (-0.16 to P -0.14 (-0.18 to -0.09) -0.14 (-0.18 to NS 0.22 (-1.32 to 1.76) 0.22 (-1.32 to NS 0.49 (-0.51 to 1.50) 0.49 (-0.51 to NS 1.68 (0.56 to 2.81) 1.68 (0.56 to NS 2.16 (-0.24 to 4.55) 2.16 (-0.24 to NS 0.21 (-1.30 to 1.72) 0.21 (-1.30 to NS 1.46 (-0.25 to 3.18) 1.46 (-0.25 to Men Moderate 0.001 <0.001 <0.001 <0.001 <0.001 <0.001 P -0.10 (-0.13 to -0.07) -0.10 (-0.13 to P -0.10 (-0.12 to -0.08) -0.10 (-0.12 to P -0.13 (-0.15 to -0.11) -0.13 (-0.15 to NS 0.35 (-0.71 to 1.42) 0.35 (-0.71 to NS 0.66 (0.08 to 1.23) 0.66 (0.08 to P = 0.94 (0.40 to 1.49) 0.94 (0.40 to NS 0.65 (-1.02 to 2.31) 0.65 (-1.02 to P 1.59 (0.73 to 2.45) 1.59 (0.73 to P 1.93 (1.10 to 2.76) 1.93 (1.10 to Light <0.001 <0.001 NS -0.04 (-0.07 to 0.01) -0.04 (-0.07 to P -0.04 (-0.06 to -0.02) -0.04 (-0.06 to P -0.07 (-0.09 to -0.05) -0.07 (-0.09 to NS -0.54 (-1.61 to 0.54) -0.54 (-1.61 to NS -0.38 (-0.91 to 0.15) -0.38 (-0.91 to NS 0.44 (-0.07 to 0.95) 0.44 (-0.07 to NS 0.58 (-1.08 to 2.25) 0.58 (-1.08 to NS -0.64 (-1.43 to 0.15) -0.64 (-1.43 to NS 1.24 (0.46 to 2.03) 1.24 (0.46 to Effects of daily tobacco smoked on the components of MetS assessed by linear regression. by linear of components MetS assessed smoked on the tobacco Effects of daily able 4. BMI ≥30 BMI 25 to 30 BMI 25 to HDL-C, mmol/l HDL-C, BMI <25 BMI ≥30 BMI 25 to 30 BMI 25 to DBP, mmHg DBP, BMI <25 BMI ≥30 BMI 25 to 30 BMI 25 to T Component mmHg SBP, BMI <25 60 Chapter 3 Heavy <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 P 6.44 (4.37 to 8.51) 6.44 (4.37 to NS 1.61 (0.46 to 2.76) 1.61 (0.46 to NS 1.18 (0.26 to 2.09) 1.18 (0.26 to P P 0.33 (0.23 to 0.46) 0.33 (0.23 to 0.50) 0.32 (0.17 to P NS 0.33 (0.25 to 0.41) 0.33 (0.25 to 0.13) 0.06 (-0.01 to P P 0.26 (0.21 to 0.31) 0.26 (0.21 to 0.24) 0.15 (0.08 to Women Moderate <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 P 2.20 (1.10 to 3.29) 2.20 (1.10 to P 1.75 (1.24 to 2.25) 1.75 (1.24 to P 0.83 (0.45 to 1.22) 0.83 (0.45 to P NS 0.26 (0.23 to 0.31) 0.26 (0.23 to 0.13) 0.05 (-0.01 to P P 0.21 (0.18 to 0.24) 0.21 (0.18 to 0.10) 0.06 (0.03 to P NS 0.18 (0.16 to 0.19) 0.18 (0.16 to 0.08) 0.05 (0.03 to Light 0.001 <0.001 <0.001 <0.001 NS -0.04 (-1.10 to 1.01) -0.04 (-1.10 to P = 0.74 (0.30 to 1.17) 0.74 (0.30 to NS 0.30 (-0.03 to 0.621) 0.30 (-0.03 to P NS 0.11 (0.07 to 0.16) 0.11 (0.07 to -0.01) -0.07 (-0.12 to P NS 0.11 (0.09 to 0.13) 0.11 (0.09 to -0.01) -0.03 (-0.05 to P NS 0.08 (0.06 to 0.09) 0.08 (0.06 to 0.01) -0.01 (-0.02 to Current smoking Current Heavy <0.001 <0.001 <0.001 <0.001 NS 2.44 (0.89 to 3.99) 2.44 (0.89 to P 1.99 (1.28 to 2.69) 1.99 (1.28 to NS 1.20 (0.34 to 2.06) 1.20 (0.34 to P NS 0.35 (0.20 to 0.53) 0.35 (0.20 to 0.30) 0.10 (-0.05 to P NS 0.40 (0.31 to 0.50) 0.40 (0.31 to 0.16) 0.10 (0.03 to P NS 0.29 (0.22 to 0.37) 0.29 (0.22 to 0.21) 0.13 (0.05 to Men Moderate 0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 NS -0.06 (-1.14 to 1.01) -0.06 (-1.14 to P 1.53 (1.13 to 1.93) 1.53 (1.13 to P 0.81 (0.39 to 1.23) 0.81 (0.39 to P NS 0.32 (0.22 to 0.41) 0.32 (0.22 to 0.13) 0.04 (-0.05 to P P = 0.30 (0.25 to 0.34) 0.30 (0.25 to 0.11) 0.07 (0.03 to P P 0.26 (0.23 to 0.29) 0.26 (0.23 to 0.10) 0.07 (0.04 to Light 0.001 <0.001 <0.001 NS -0.97 (-2.05 to 0.11) -0.97 (-2.05 to NS 0.16 (-0.21 to 0.53) 0.16 (-0.21 to P = 0.66 (0.27 to 1.05) 0.66 (0.27 to NS NS 0.14 (0.07 to 0.22) 0.14 (0.07 to 0.10) 0.01 (-0.06 to P NS 0.16 (0.13 to 0.20) 0.16 (0.13 to 0.03) 0.01 (-0.02 to P NS 0.13 (0.10 to 0.15) 0.13 (0.10 to 0.04) 0.02 (0.01 to a a Effects of daily tobacco smoked on the components of MetS assessed by linear regression. (continued) regression. by linear of components MetS assessed smoked on the tobacco Effects of daily

able 4. BMI ≥30 BMI 25 to 30 BMI 25 to Blood glucose, mmol/l Blood glucose, cm circumference, Waist BMI <25 BMI ≥30 BMI ≥30 BMI 25 to 30 BMI 25 to 30 BMI 25 to T Component mmol/l Triglycerides, BMI <25 BMI <25 Association between Smoking, Metabolic Syndrome and Lipoprotein Particle Size 61 Heavy <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 P 0.12 (0.06 to 0.17) 0.12 (0.06 to P 0.08 (0.04 to 0.13) 0.08 (0.04 to P 0.14 (0.11 to 0.18) 0.14 (0.11 to P = -0.06 (-0.1 to -0.03) -0.06 (-0.1 to P -0.06 (-0.09 to -0.04) -0.06 (-0.09 to P -0.09 (-0.11 to -0.06) -0.09 (-0.11 to NS -0.09 (-0.16 to -0.03) -0.09 (-0.16 to NS -0.02 (-0.07 to 0.03) -0.02 (-0.07 to NS -0.07 (-0.12 to -0.02) -0.07 (-0.12 to 3 Women Moderate <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 P 0.09 (0.06 to 0.12) 0.09 (0.06 to P 0.08 (0.06 to 0.10) 0.08 (0.06 to P 0.09 (0.08 to 0.11) 0.09 (0.08 to P -0.06 (-0.08 to -0.04) -0.06 (-0.08 to P -0.06 (-0.07 to -0.05) -0.06 (-0.07 to P -0.06 (-0.07 to -0.06) -0.06 (-0.07 to P -0.09 (-0.13 to -0.06) -0.09 (-0.13 to P -0.07 (-0.09 to -0.04) -0.07 (-0.09 to P -0.06 (-0.08 to -0.04) -0.06 (-0.08 to Light <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 P 0.06 (0.03 to 0.09) 0.06 (0.03 to P 0.05 (0.03 to 0.06) 0.05 (0.03 to P 0.03 (0.02 to 0.04) 0.03 (0.02 to NS -0.01 (-0.03 to 0.01) -0.01 (-0.03 to P -0.04 (-0.05 to -0.03) -0.04 (-0.05 to P -0.03 (-0.04 to -0.03) -0.03 (-0.04 to P -0.07 (-0.10 to -0.03) -0.07 (-0.10 to NS -0.03 (-0.05 to -0.01) -0.03 (-0.05 to NS -0.00 (-0.02 to 0.02) -0.00 (-0.02 to Current smoking Current Heavy <0.001 <0.001 <0.001 <0.001 <0.001 NS 0.07 (0.02 to 0.12) 0.07 (0.02 to P 0.09 (0.06 to 0.12) 0.09 (0.06 to P 0.09 (0.05 to 0.13) 0.09 (0.05 to P -0.06 (-0.08 to -0.03) -0.06 (-0.08 to P -0.06 (-0.08 to -0.05) -0.06 (-0.08 to P -0.06 (-0.08 to -0.03) -0.06 (-0.08 to NS -0.03 (-0.07 to -0.02) -0.03 (-0.07 to NS -0.04 (-0.07 to -0.01) -0.04 (-0.07 to NS -0.04 (-0.08 to -0.00) -0.04 (-0.08 to Men Moderate <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 NS 0.06 (0.02 to 0.09) 0.06 (0.02 to P 0.08 (0.06 to 0.10) 0.08 (0.06 to P 0.09 (0.07 to 0.11) 0.09 (0.07 to P -0.04 (-0.06 to -0.03) -0.04 (-0.06 to P -0.05 (-0.06 to -0.04) -0.05 (-0.06 to P -0.06 (-0.07 to -0.05) -0.06 (-0.07 to NS -0.04 (-0.08 to -0.01) -0.04 (-0.08 to P -0.03 (-0.05 to -0.01) -0.03 (-0.05 to P -0.05 (-0.06 to -0.03) -0.05 (-0.06 to Light <0.001 <0.001 <0.001 NS 0.02 (-0.02 to 0.07) 0.02 (-0.02 to P 0.03 (0.02 to 0.05) 0.03 (0.02 to NS 0.03 (0.01 to 0.05) 0.03 (0.01 to NS -0.02 (-0.03 to 0.00) -0.02 (-0.03 to P -0.02 (-0.03 to -0.01) -0.02 (-0.03 to P -0.03 (-0.04 to -0.02) -0.03 (-0.04 to NS -0.02 (-0.05 to 0.02) -0.02 (-0.05 to NS -0.02 (-0.03 to 0.00) -0.02 (-0.03 to NS -0.02 (-0.04 to 0.00) -0.02 (-0.04 to Effects of daily tobacco smoked on the components of MetS assessed by linear regression. (continued) regression. by linear of components MetS assessed smoked on the tobacco Effects of daily able 4. BMI ≥30 BMI 25 to 30 BMI 25 to Apo B, g/l Apo B, BMI <25 BMI ≥30 BMI 25 to 30 BMI 25 to HDL-C/apoA1 ratio HDL-C/apoA1 BMI <25 BMI ≥30 BMI 25 to 30 BMI 25 to T Component Apo A1, g/l BMI <25 62 Chapter 3 Heavy low-density 0.001 LDL-C <0.001 NS -0.08 (-0.18 to 0.02) -0.08 (-0.18 to P -0.13 (-0.20 to -0.06) -0.13 (-0.20 to P = -0.11 (-0.17 to -0.05) -0.11 (-0.17 to Women Moderate 0.001 <0.001 NS -0.07 (-0.13 to -0.02) -0.07 (-0.13 to P = -0.06 (-0.09 to -0.02) -0.06 (-0.09 to P -0.07 (-0.10 to -0.05) -0.07 (-0.10 to high-density lipoprotein-cholesterol, Light HDL-C <0.001 NS -0.02 (-0.07 to 0.03) -0.02 (-0.07 to NS -0.05 (-0.07 to -0.02) -0.05 (-0.07 to P -0.06 (-0.08 to -0.04) -0.06 (-0.08 to Current smoking Current Heavy diastolic blood pressure, DBP triglycerides. <0.001 NS -0.14 (-0.23 to -0.05) -0.14 (-0.23 to P -0.19 (-0.24 to -0.13) -0.19 (-0.24 to NS -0.09 (-0.15 to -0.03) -0.09 (-0.15 to TG blood pressure, Men Data are presented as geometric mean effect size (95% confidence interval) per unit of component of metabolic of component of unit interval) per confidence (95% size effect mean geometric as presented are Data Moderate BP a <0.001 <0.001 <0.001 P = -0.13 (-0.20 to -0.07) -0.13 (-0.20 to P -0.12 (-0.15 to -0.09) -0.12 (-0.15 to P -0.13 (-0.16 to -0.10) -0.13 (-0.16 to systolic blood pressure, blood pressure, systolic SBP body mass index, Light BMI not significant, not significant, <0.001 <0.001 NS NS -0.06 (-0.12 to 0.01) -0.06 (-0.12 to P -0.08 (-0.12 to -0.06) -0.08 (-0.12 to P -0.11 (-0.14 to -0.08) -0.11 (-0.14 to values ≤0.001 are presented in bold. bold. in presented are ≤0.001 values P apolipoprotein, Apo Effects of daily tobacco smoked on the components of MetS assessed by linear regression. (continued) regression. by linear of components MetS assessed smoked on the tobacco Effects of daily

able 4. BMI ≥30 BMI 25 to 30 BMI 25 to T Component ratio LDL-C/apoB BMI <25 as mean interval) effectNon-smokers confidence (95% presented size within Data are the same per unit component of metabolicor risk syndrome associated factor. g ≥20 smoker), (moderate g 20 to 11 smoker), (light g ≤10 smoked: tobacco Daily group. subjectsper of number for 3 Table See group. reference as taken were class BMI smoker). (heavy syndrome. syndrome. lipoprotein-cholesterol, Association between Smoking, Metabolic Syndrome and Lipoprotein Particle Size 63

  3  

 

      

 

               

 

 

 

      

 

               

 

 

 

      

 

               

Figure 3. Age-corrected odds ratios for having metabolic syndrome, in men (left panels) and women (right panels) according to body mass index (BMI) class. N, non-smokers; E, former smokers; C1, smokers of 0 to 10 g tobacco daily; C2, smokers of 10 to 20 g daily; C3, smokers of ≥20 g daily. Top: BMI <25; middle: BMI 25 to 30; bottom: BMI ≥30 kg/m2. 64 Chapter 3

Discussion

In the present study, performed in a large population-based cohort of almost 60,000 individuals, we investigated the relationship between smoking and the individual components of metabolic syndrome, and the association between smoking and levels of apolipoproteins and estimated lipoprotein particle size. Such a comprehensive and large-scale analysis has not been performed to date. We demonstrated that in both men and women smoking is associated with a greater prevalence of MetS, irrespective of their BMI. The largest differences between current smokers and non-smokers were observed in the levels of HDL-C and triglycerides, and, to a lesser extent, in waist circumference. While there were no consistent associations between smoking status and either blood pressure or fasting blood glucose levels, there was a dose-dependent relationship between the amount of tobacco smoked and decreased HDL-C levels and increased triglyceride levels. We also found a clear dose-dependent association between the amount of tobacco smoked and reduced ratios of HDL-C/apoA1 and LDL-C/apoB. To our knowledge, we are the first to explore these associations between smoking and levels of apolipoproteins and lipoprotein particle size in such a large cohort of individuals, with rigorously standardized physical and laboratory measurements, while taking into account both sex and BMI levels. Our analysis revealed that in both men and women the prevalence of MetS was higher in current smokers in each BMI group, than in the non-smokers within that BMI group. Several earlier small-scale studies have reported smoking to be associated with higher prevalence of MetS [24-27]. The positive dose-response relationship between the amount of tobacco smoked and the prevalence of MetS that we observed is also consistent with previous studies [10,13,26,28]. However, when BMI was included in our analysis, the odds ratio for having MetS was higher among normal weight smoking subjects than those with higher BMI (Figure 3). This is probably related to the initial lower risk of subjects in this BMI group. Since previous studies have shown an excess of visceral fat to be a major contributor to metabolic abnormalities, overweight and obesity are known to be highly associated with MetS [29], with already a high prevalence of MetS observed in the obese non-smokers. With our approach we have been able to calculate precisely the effects of smoking on the lipid parameters. Our data unequivocally show that despite the fact that obese men and women have a lower mean HDL-C than non-obese, the effects of heavy smoking are similar in all three BMI groups, with a consistent 0.10 to 0.14 mmol/l lower HDL-C for smoking men, and 0.15 to 0.21 mmol/l lower HDL-C in smoking women, in all three BMI groups (Table 4). The fact that we found current smoking to be mainly associated with lower levels of HDL-C, higher levels of triglycerides and larger waist circumference than the non-smoking status is consistent with earlier cross-sectional studies [28,30]. Association between Smoking, Metabolic Syndrome and Lipoprotein Particle Size 65

This observation of a dose-dependent relationship between the daily amount of tobacco smoked and lower HDL-C and higher triglycerides confirms the results of previous reports [13,31-33]. In our study, the magnitude of the effects of tobacco usage on HDL-C varied between 0.04 for light smoking in men, and 0.21 mmol/l for heavy smoking in normal weight women. The study by Chen et al., comprising 1,164 men, reported a simi- 3 lar dose-response relationship with the largest effect on HDL-C and triglycerides seen in those who smoked more than 40 cigarettes per day [13]. Ishizaka et al. also reported a dose-response association between the number of cigarettes per day and prevalence of MetS in a cohort of 5,033 individuals, although they did not examine the influence of the amount of tobacco smoked on the individual MetS components [33]. A recent review summarized the effects of smoking cessation on HDL-C levels: within a few weeks after stopping smoking, HDL-C levels start to increase, resulting in an overall increase of 0.2 mmol/l [34]. Taken together, these and our data support the causal relationship between smoking and low HDL-C levels. There are indications that current smoking is associated with increased abdominal obesity [35]. In our study, although current smokers had a greater waist circumference than non-smokers, these differences were rather small. We also observed a consistent increase in the waist circumference with an increase in tobacco smoked in normal weight and overweight men, as well as in normal weight and obese women. Larger effects were especially seen among obese women, where the increase in waist circumference was 2.2 cm for moderate smokers and 6.4 cm for heavy smokers. One of the possible mechanisms that might explain these observations is a direct effect of smoking on cortisol production [12,36]. Indeed, it was demonstrated more than three decades ago that smokers have higher fasting plasma cortisol levels than non-smokers [37,38]. The increase in cortisol production leads to accumulation of abdominal fat [39], which, in turn, increases waist circumference. Although some studies have indicated that smoking is related to reduced insulin sensitivity and the development of insulin resistance [12,40] and type 2 diabetes [41,42], in our population there was no consistent association between smoking and fasting blood glucose. This confirms the results obtained in other studies [13,30,43]. Ishizaka et al. found a higher prevalence of elevated blood glucose in smoking men, but not in women [33]. Such discrepancy in the results may be due to the different cut-off values for elevated fasting glucose used in the present study (5.6 mmol/l) and that of Ishizaka et al. (6.1 mmol/l) [33]. While it is well established that acute smoking may cause a rise in blood pressure [44,45], in the chronic situation smokers’ blood pressure is similar to or even lower than that of non-smokers [33,44,46], although Primatesta et al. found higher blood pressure in male smokers older than 45 years compared to never smokers [47]. We found no association between smoking and blood pressure in any of the three BMI classes, 66 Chapter 3

even after correction for the use of blood pressure-lowering medication. In addition, we found similar blood pressure in smokers aged 45 and higher versus non-smokers (data not shown). Nevertheless, some studies have suggested that smoking may be a risk factor for developing hypertension [48] or for an increase in blood pressure during exercise [49], although in the latter study smoking cessation did not lead to reduced blood pressure. Weight changes after smoking cessation have been suggested to be involved in this paradox [48]. One of the new findings of our study is the association between smoking and alterations in levels of apolipoproteins and in the size of lipoprotein particles. Until now, only a limited number of studies have investigated the relationship between smoking and the levels of apoA1 and apoB, usually involving a small number of participants such as, for example, young adults [18,50], middle-aged men [19,51], or postmenopausal women [20]. In addition, few studies have assessed the effects of smoking on lipoprotein particle size. In the Framingham study, smoking was associated with higher levels of small LDL particles [52]. However, apoA1 and apoB measurement and standardization have considerably improved in the last decade, both because of the appearance of a legal and regulatory framework (the In Vitro Diagnostics (IVD)-directive 98/79/EC and the institution of the Joint Committee on Traceability in Laboratory Medicine (JCTLM)), technical improvements of equipment, and the availability of international reference materials [21]. An additional milestone was the preparation, evaluation and introduction of value-assigned reference materials for monitoring trueness of apolipoprotein test results [22]. ApoA1 is the main protein component of HDL-C particles, and higher levels of apoA1 are associated with lower risk of CVD [53]. We observed that in current smokers plasma apoA1 levels were lower than in non-smokers. In addition, smoking was associated with lower HDL-C/apoA1 ratio, which is a strong indication of smaller HDL particle size. Such alterations of the HDL particle have been negatively associated with heart disease [54,55]. While apoA1 is protective, apoB, the main protein component of LDL particles, reflects the atherogenic potential of LDL, and higher levels of apoB are associated with an increased risk of CVD [53]. The fact that we found higher apoB levels and lower LDL-C/apoB ratios in current smokers than in non-smokers, indicates the presence of increased numbers of small, more dense LDL particles. Such particles have been found to increase the risk not only of atherosclerosis [56,57], but also of coronary artery disease [58] and fatal myocardial infarction [59]. Furthermore, in a 3-year follow- up study among Korean men without MetS, a low LDL-C/apoB ratio was independently associated with development of MetS [60]. Taken together with our findings, the Korean study supports the conclusion that the presence of increased amounts of small, dense LDL particles can be considered both a risk factor for future cardiovascular disease and an early feature of metabolic syndrome. Association between Smoking, Metabolic Syndrome and Lipoprotein Particle Size 67

Our study has several major strengths. Considering the number of participants recruited from the general population (N >59,000), this is the largest study reporting these results. Our large dataset also enabled to carefully calculate effect sizes, and to perform sufficiently powered subgroup analyses, in subjects of both sexes and in those with normal body weight, overweight, and obesity, which to our knowledge has never 3 been performed before. All participants to the LifeLines Cohort Study have been well characterized, with rigorously standardized blood pressure and anthropometric measurements. In addition, all laboratory measurements of lipids and apolipoproteins have been carried out over a period of 5 years in fresh serum samples, in the same certified laboratory, with the same equipment, and the same rigorous quality assessment and control. This unprecedented sample size also provided us with sufficient statistical power to investigate contradictory associations reported previously. There are also some limitations to our study. Firstly, since smoking status was based on self-administered questionnaires, we cannot exclude the possibility that misreport- ing led to some individuals being misclassified with regard to their current smoking status. Considering the large number of participants, we believe that misclassification has only very limited influence on the results obtained, and earlier studies also reported low misclassification rate of smoking status [61]. We should point out that we were unable to identify individuals who had never smoked, nor could we fully take into account the duration of smoking. Secondly, apart from age we could not adjust for other possibly relevant risk factors that influence levels of HDL cholesterol and triglycerides, such as nutrition and alcohol consumption. As data collection for the LifeLines Cohort Study is still ongoing, we hope to be able to investigate the effects of such factors on MetS in the future.

Conclusions

In this very large study in individuals of western European descent, smoking was associated with an increased risk of MetS. This increased risk was observed in all BMI classes. The elevated risk of having MetS was mainly related to lower HDL cholesterol, higher triglycerides and larger waist circumference. We also found that smoking was associated with unfavorable changes in the levels of apoA1 and apoB and in estimated HDL and LDL particle size, thereby providing a new pathophysiological mechanism linking smoking to increased risk of cardiovascular disease. 68 Chapter 3

Abbreviations Apo, apolipoprotein; BMI, body mass index; CVD, cardiovascular disease; HDL, high- density lipoprotein; LDL, low-density lipoprotein; MetS, metabolic syndrome; TG, triglycerides.

Competing interests The authors declare that they have no competing interests.

Authors’ contributions SNS, JVvVO and BHRW carried out the statistical analyses and drafted the manuscript. ACMK coordinated all laboratory measurements and immunoassays. MMvdK, JMV, EJF and BHRW participated in the design of the cohort study and data collection, while JMV, HMB, SNS and MMvdK carried out the data verification and validation. RPFD and APvB participated in the data interpretation. All authors participated in drafting the manuscript, and read and approved the final version.

Acknowledgements

The LifeLines Cohort Study was supported by The Netherlands Organization for Scien- tific Research (NWO) (grant 175.010.2007.006); the Economic Structure Enhancing Fund (FES) of the Dutch government; the Ministry of Economic Affairs; the Ministry of Edu- cation, Culture and Science; the Ministry for Health, Welfare and Sports; the Northern Netherlands Collaboration of Provinces (SNN); the Province of Groningen; University Medical Center Groningen; the University of Groningen; the Dutch Kidney Foundation; and the Dutch Diabetes Research Foundation. This work was supported by the National Consortium for Healthy Ageing, and funds from the European Union’s Seventh Frame- work program (FP7/2007-2013) through the BioSHaRE-EU (Biobank Standardisation and Harmonisation for Research Excellence in the European Union) project, grant agreement 261433. LifeLines (BRIF4568) is engaged in a Bioresource research impact factor (BRIF) policy pilot study, details of which can be found at https://www.bioshare.eu/content/ bioresource-impact-factor. The authors are grateful to the study participants, the staff of the LifeLines Cohort Study and Biobank, and the participating general practitioners and pharmacists. We also thank Dr CM Cobbaert (Leiden University Medical Center) for her comments on apolipoprotein standardization. Association between Smoking, Metabolic Syndrome and Lipoprotein Particle Size 69

References

1. Grundy SM, Cleeman JI, Daniels SR, Donato KA, 9. Kemper HC, Post GB, Twisk JW, van MW: Lifestyle and Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, obesity in adolescence and young adulthood: results Smith SC, Jr. et al: Diagnosis and management of the from the Amsterdam Growth And Health Longitudinal metabolic syndrome: an American Heart Association/ Study (AGAHLS). International journal of obesity and 3 National Heart, Lung, and Blood Institute Scientific related metabolic disorders 1999, 23 Suppl 3:S34-S40. Statement. Circulation 2005, 112(17):2735-2752. 10. Nakanishi N, Takatorige T, Suzuki K: Cigarette smoking 2. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Clee- and the risk of the metabolic syndrome in middle-aged man JI, Donato KA, Fruchart JC, James WP, Loria CM, Japanese male office workers. Industrial health 2005, Smith SC, Jr.: Harmonizing the metabolic syndrome: 43(2):295-301. a joint interim statement of the International 11. Sun K, Liu J, Ning G: Active smoking and risk of meta- Diabetes Federation Task Force on Epidemiology and bolic syndrome: a meta-analysis of prospective studies. Prevention; National Heart, Lung, and Blood Institute; PLoS one 2012, 7(10):e47791. American Heart Association; World Heart Federation; 12. Chiolero A, Faeh D, Paccaud F, Cornuz J: Consequences International Atherosclerosis Society; and International of smoking for body weight, body fat distribution, Association for the Study of Obesity. Circulation 2009, and insulin resistance. The American journal of clinical 120(16):1640-1645. nutrition 2008, 87(4):801-809. 3. Primeau V, Coderre L, Karelis AD, Brochu M, Lavoie ME, 13. Chen CC, Li TC, Chang PC, Liu CS, Lin WY, Wu MT, Li CI, Messier V, Sladek R, Rabasa-Lhoret R: Characterizing the Lai MM, Lin CC: Association among cigarette smoking, profile of obese patients who are metabolically healthy. metabolic syndrome, and its individual components: International journal of obesity 2011, 35(7):971-981. the metabolic syndrome study in Taiwan. Metabolism: 4. Batsis JA, Nieto-Martinez RE, Lopez-Jimenez F: Clinical and Experimental 2008, 57(4):544-548. Metabolic syndrome: from global epidemiology to 14. Velho S, Paccaud F, Waeber G, Vollenweider P, Marques- individualized medicine. Clinical pharmacology and Vidal P: Metabolically healthy obesity: different therapeutics 2007, 82(5):509-524. prevalences using different criteria. European journal of 5. Malik VS, Willett WC, Hu FB: Global obesity: trends, clinical nutrition 2010, 64(10):1043-1051. risk factors and policy implications. Nature reviews: 15. Garin MC, Kalix B, Morabia A, James RW: Small, dense endocrinology 2013, 9(1):13-27. lipoprotein particles and reduced paraoxonase-1 6. Karelis AD, St-Pierre DH, Conus F, Rabasa-Lhoret in patients with the metabolic syndrome. The R, Poehlman ET: Metabolic and body composition Journal of clinical endocrinology and metabolism 2005, factors in subgroups of obesity: what do we know? 90(4):2264-2269. Journal of clinical endocrinology and metabolism 2004, 16. Siri PW, Krauss RM: Influence of dietary carbohydrate 89(6):2569-2575. and fat on LDL and HDL particle distributions. Current 7. Wildman RP, Muntner P, Reynolds K, McGinn AP, atherosclerosis reports 2005, 7(6):455-459. Rajpathak S, Wylie-Rosett J, Sowers MR: The obese 17. Carmena R, Duriez P, Fruchart JC: Atherogenic lipo- without cardiometabolic risk factor clustering and the protein particles in atherosclerosis. Circulation 2004, normal weight with cardiometabolic risk factor cluster- 109(23 Suppl 1):III2-III7. ing: prevalence and correlates of 2 phenotypes among 18. Donahue RP, Orchard TJ, Stein EA, Kuller LH: Apolipo- the US population (NHANES 1999-2004). Archives of proteins AI, AII and B in young adults: associations with internal medicine 2008, 168(15):1617-1624. CHD risk factors. The Beaver County experience. Journal 8. Lee K: Metabolically obese but normal weight (MONW) of chronic diseases 1986, 39(10):823-830. and metabolically healthy but obese (MHO) phenotypes 19. Periti M, Salvaggio A, Quaglia G, Di ML, Miano L: Effect in Koreans: characteristics and health behaviors. Asia of cigarette smoking and coffee consumption on apo- Pacific journal of clinical nutrition 2009, 18(2):280-284. lipoprotein B levels. European journal of epidemiology 1990, 6(1):76-79. 70 Chapter 3

20. Haarbo J, Hassager C, Schlemmer A, Christiansen 30. Berlin I, Lin S, Lima JA, Bertoni AG: Smoking Status C: Influence of smoking, body fat distribution, and and Metabolic Syndrome in the Multi-Ethnic Study alcohol consumption on serum lipids, lipoproteins, of Atherosclerosis. A cross-sectional study. Tobacco and apolipoproteins in early postmenopausal women. induced diseases 2012, 10(1):9. Atherosclerosis 1990, 84(2-3):239-244. 31. Facchini FS, Hollenbeck CB, Jeppesen J, Chen YD, Reaven 21. Marcovina SM, Albers JJ, Kennedy H, Mei JV, Henderson GM: Insulin resistance and cigarette smoking. Lancet LO, Hannon WH: International Federation of Clinical 1992, 339(8802):1128-1130. Chemistry standardization project for measurements 32. Oh SW, Yoon YS, Lee ES, Kim WK, Park C, Lee S, Jeong of apolipoproteins A-I and B. IV. Comparability of apo- EK, Yoo T: Association between cigarette smoking and lipoprotein B values by use of International Reference metabolic syndrome: the Korea National Health and Material. Clinical chemistry 1994, 40(4):586-592. Nutrition Examination Survey. Diabetes Care 2005, 22. Cobbaert C, Weykamp C, Baadenhuijsen H, Kuypers 28(8):2064-2066. A, Lindemans J, Jansen R: Selection, preparation, and 33. Ishizaka N, Ishizaka Y, Toda E, Nagai R, Yamakado M: characterization of commutable frozen human serum Association between cigarette smoking, white blood pools as potential secondary reference materials for cell count, and metabolic syndrome as defined by the lipid and apolipoprotein measurements: study within Japanese criteria. Internal medicine 2007, 46(15):1167- the framework of the Dutch project “Calibration 2000”. 1170. Clinical chemistry 2002, 48(9):1526-1538. 34. Chelland CS, Moffatt RJ, Stamford BA: Smoking and 23. Stolk RP, Rosmalen JG, Postma DS, de Boer RA, Navis smoking cessation -- the relationship between cardio- G, Slaets JP, Ormel J, Wolffenbuttel BH: Universal risk vascular disease and lipoprotein metabolism: a review. factors for multifactorial diseases: LifeLines: a three- Atherosclerosis 2008, 201(2):225-235. generation population-based study. European journal 35. Saarni SE, Pietilainen K, Kantonen S, Rissanen A, of epidemiology 2008, 23(1):67-74. Kaprio J: Association of smoking in adolescence with 24. Geslain-Biquez C, Vol S, Tichet J, Caradec A, D’Hour abdominal obesity in adulthood: a follow-up study of A, Balkau B: The metabolic syndrome in smokers. 5 birth cohorts of Finnish twins. American journal of The D.E.S.I.R. study. Diabetes and Metabolism 2003, public health 2009, 99(2):348-354. 29(3):226-234. 36. Chiodera P, Volpi R, Capretti L, Speroni G, Necchi-Ghiri S, 25. Zhu S, St-Onge MP, Heshka S, Heymsfield SB: Lifestyle Caffarri G, Colla R, Coiro V: Abnormal effect of cigarette behaviors associated with lower risk of having the smoking on pituitary hormone secretions in insulin- metabolic syndrome. Metabolism: Clinical and Experi- dependent diabetes mellitus. Clinical endocrinology mental 2004, 53(11):1503-1511. (Oxf) 1997, 46(3):351-357. 26. Weitzman M, Cook S, Auinger P, Florin TA, Daniels S, 37. Cryer PE, Haymond MW, Santiago JV, Shah SD: Nor- Nguyen M, Winickoff JP: Tobacco smoke exposure is as- epinephrine and epinephrine release and adrenergic sociated with the metabolic syndrome in adolescents. mediation of smoking-associated hemodynamic and Circulation 2005, 112(6):862-869. metabolic events. The New England journal of medicine 27. Wilsgaard T, Jacobsen BK: Lifestyle factors and incident 1976, 295(11):573-577. metabolic syndrome. The Tromso Study 1979-2001. 38. Friedman AJ, Ravnikar VA, Barbieri RL: Serum steroid Diabetes research and clinical practice 2007, 78(2):217- hormone profiles in postmenopausal smokers and 224. nonsmokers. Fertility and sterility 1987, 47(3):398-401. 28. Nakashita Y, Nakamura M, Kitamura A, Kiyama M, 39. Pasquali R, Vicennati V: Activity of the hypothalamic- Ishikawa Y, Mikami H: Relationships of cigarette smok- pituitary-adrenal axis in different obesity phenotypes. ing and alcohol consumption to metabolic syndrome International journal of obesity related metabolic in Japanese men. Journal of epidemiology 2010, disorders 2000, 24 Suppl 2:S47-S49. 20(5):391-397. 40. Houston TK, Person SD, Pletcher MJ, Liu K, Iribarren 29. Despres JP: Is visceral obesity the cause of the metabolic C, Kiefe CI: Active and passive smoking and develop- syndrome? Annals of medicine 2006, 38(1):52-63. ment of glucose intolerance among young adults Association between Smoking, Metabolic Syndrome and Lipoprotein Particle Size 71

in a prospective cohort: CARDIA study. BMJ 2006, and apolipoproteins in a male military population. 332(7549):1064-1069. Atherosclerosis 1989, 80(1):33-39. 41. Hu FB, Manson JE, Stampfer MJ, Colditz G, Liu S, 52. Shearman AM, Demissie S, Cupples LA, Peter I, Schmid Solomon CG, Willett WC: Diet, lifestyle, and the risk of CH, Ordovas JM, Mendelsohn ME, Housman DE: Tobacco type 2 diabetes mellitus in women. The New England smoking, estrogen receptor alpha gene variation and journal of medicine 2001, 345(11):790-797. small low density lipoprotein level. Human molecular 3 42. Foy CG, Bell RA, Farmer DF, Goff DC, Jr., Wagenknecht LE: genetics 2005, 14(16):2405-2413. Smoking and incidence of diabetes among U.S. adults: 53. Walldius G, Jungner I: Apolipoprotein B and apolipo- findings from the Insulin Resistance Atherosclerosis protein A-I: risk indicators of coronary heart disease Study. Diabetes Care 2005, 28(10):2501-2507. and targets for lipid-modifying therapy. Journal of 43. Hughes K, Choo M, Kuperan P, Ong CN, Aw TC: Cardio- internal medicine 2004, 255(2):188-205. vascular risk factors in relation to cigarette smoking: a 54. Arsenault BJ, Lemieux I, Despres JP, Gagnon P, Wareham population-based survey among Asians in Singapore. NJ, Stroes ES, Kastelein JJ, Khaw KT, Boekholdt SM: HDL Atherosclerosis 1998, 137(2):253-258. particle size and the risk of coronary heart disease in 44. Green MS, Jucha E, Luz Y: Blood pressure in smokers and apparently healthy men and women: the EPIC-Norfolk nonsmokers: epidemiologic findings. American heart prospective population study. Atherosclerosis 2009, journal 1986, 111(5):932-940. 206(1):276-281. 45. Barutcu I, Esen AM, Degirmenci B, Acar M, Kaya D, 55. Parish S, Peto R, Palmer A, Clarke R, Lewington S, Offer Turkmen M, Melek M, Onrat E, Esen OB, Kirma C: Acute A, Whitlock G, Clark S, Youngman L, Sleight P et al: cigarette smoking-induced hemodynamic alterations The joint effects of apolipoprotein B, apolipoprotein in the common carotid artery--a transcranial Doppler A1, LDL cholesterol, and HDL cholesterol on risk: 3510 study--. Circulation journal 2004, 68(12):1127-1131. cases of acute myocardial infarction and 9805 controls. 46. Leone A: Smoking and hypertension: independent European heart journal 2009, 30(17):2137-2146. or additive effects to determining vascular damage? 56. Berneis KK, Krauss RM: Metabolic origins and clinical Current vascular pharmacology 2011, 9(5):585-593. significance of LDL eterogeneity. Journal of lipid re- 47. Primatesta P, Falaschetti E, Gupta S, Marmot MG, search 2002, 43(9):1363-1379. Poulter NR: Association between smoking and blood 57. Sacks FM, Campos H: Clinical review 163: Cardiovas- pressure: evidence from the health survey for England. cular endocrinology: Low-density lipoprotein size and Hypertension 2001, 37(2):187-193. cardiovascular disease: a reappraisal. Journal of clinical 48. Niskanen L, Laaksonen DE, Nyyssonen K, Punnonen endocrinology and metabolism 2003, 88(10):4525- K, Valkonen VP, Fuentes R, Tuomainen TP, Salonen R, 4532. Salonen JT: Inflammation, abdominal obesity, and 58. El HK, van der Steeg WA, Stroes ES, Kuivenhoven JA, smoking as predictors of hypertension. Hypertension Otvos JD, Wareham NJ, Hutten BA, Kastelein JJ, Khaw 2004, 44(6):859-865. KT, Boekholdt SM: Value of low-density lipoprotein par- 49. Mundal R, Kjeldsen SE, Sandvik L, Erikssen G, Thaulow ticle number and size as predictors of coronary artery E, Erikssen J: Predictors of 7-year changes in exercise disease in apparently healthy men and women: the blood pressure: effects of smoking, physical fitness EPIC-Norfolk Prospective Population Study. Journal of and pulmonary function. Journal of hypertension 1997, the American College of Cardiology 2007, 49(5):547-553. 15(3):245-249. 59. Jungner I, Sniderman AD, Furberg C, Aastveit AH, 50. Chu NF, Ding YA, Wang DJ, Shieh SM: Relationship Holme I, Walldius G: Does low-density lipoprotein size between smoking status and cardiovascular disease add to atherogenic particle number in predicting the risk factors in young adult males in Taiwan. Journal of risk of fatal myocardial infarction? The American journal cardiovascular risk 1996, 3(2):205-208. of cardiology 2006, 97(7):943-946. 51. Cuesta C, Sanchez-Muniz FJ, Garcia-La CA, Garrido R, 60. Kwon CH, Kim BJ, Kim BS, Kang JH: Low-density Castro A, San-Felix B, Domingo A: Effects of age and lipoprotein cholesterol to apolipoprotein B ratio is cigarette smoking on serum concentrations of lipids independently associated with metabolic syndrome 72 Chapter 3

in Korean men. Metabolism: Clinical and Experimental 2011, 60(8):1136-1141. 61. Noonan D, Jiang Y, Duffy SA: Utility of biochemical verification of tobacco cessation in the Department of Veterans Affairs. Addictive behaviors 2013, 38(3):1792- 1795.

Hoofdstukpagina-letteromtrek.indd 7 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 8 16/11/16 11:05 Chapter 4

Combined effects of smoking and alcohol on metabolic syndrome: The LifeLines Cohort Study

PLoS ONE 2014, 9(4):e96406 4

Hoofdstukpagina-letteromtrek.indd 7 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 8 16/11/16 11:05 76 Chapter 4

Abstract

Background The development of metabolic syndrome (MetS) is influenced by environmental factors such as smoking and alcohol consumption. We determined the combined effects of smoking and alcohol on MetS and its individual components. Methods 64,046 participants aged 18-80 years from the LifeLines Cohort study were categorized into three body mass index (BMI) classes (BMI <25, normal weight; BMI 25-30, overweight; BMI ≥30 kg/m2, obese). MetS was defined according to the revised criteria of the National Cholesterol Education Program’s Adult Treatment Panel III (NCEP ATPIII). Within each BMI class and smoking subgroup (non-smoker, former smoker, <20 and ≥20 g tobacco/day), the cross-sectional association between alcohol and individual MetS components was tested using regression analysis. Results Prevalence of MetS varied greatly between the different smoking-alcohol subgroups (1.7-71.1%). HDL cholesterol levels in all alcohol drinkers were higher than in non-drinkers (0.02 to 0.29 mmol/L, P values<0.001). HDL cholesterol levels were lower when they were also a former or current smoker (<20 and ≥20 g tobacco/day). Consumption of ≤1 drink/day indicated a trend towards lower triglyceride levels (non-significant). Concurrent use alcohol (>1 drink/day) and tobacco showed higher triglycerides levels. Up to 2 drinks/day was associated with a smaller waist circumference in overweight and obese individuals. Consumption of >2 drinks/day increased blood pressure, with the strongest associations found for heavy smokers. The overall metabolic profile of wine drinkers was better than that of non-drinkers or drinkers of beer or spirits/mixed drinks. Conclusion Light alcohol consumption may moderate the negative associations of smoking with MetS. Our results suggest that the lifestyle advice that emphasizes smoking cessation and the restriction of alcohol consumption to a maximum of 1 drink/day, is a good approach to reduce the prevalence of MetS. Keywords Metabolic Syndrome, Alcohol, Smoking, BMI classes, Cross-sectional, Blood pressure, Lipids. Combined effects of Smoking and Alcohol on Metabolic Syndrome 77

Introduction

The metabolic syndrome (MetS) is present in approximately one-fourth of the adult European population [1] and mainly the result of overweight and obesity [2]. The syndrome is made up of a number of different components, namely high plasma glucose, high triglycerides, low high-density lipoprotein cholesterol, high blood pressure and enlarged waist circumference, which are all associated with excess adiposity. As a result 4 of these metabolic abnormalities, there are four main health risks attributable to MetS, namely type 2 diabetes mellitus, cardiovascular disease, some types of cancer and all- cause mortality [2, 3]. The clinical management of MetS may depend on lifestyle changes and minimizing the components that characterize MetS. However, interventions aimed at weight loss and weight management showed only to be effective in the short term [4, 5]. By contrast, clinical and public health interventions were effective in reducing blood pressure and cholesterol in whole populations [6, 7]. Controlling the metabolic components might help to tackle the adverse effects of MetS resulting from the obesity epidemic. It is therefore important to investigate how lifestyle factors influence the components of MetS within people with a different body mass index (BMI), as a measure for obesity. In this paper we focus on the two lifestyle factors smoking and alcohol consumption. Substantial evidence from epidemiological and clinical studies has shown that tobacco and alcohol are often used together, with smokers being more likely to drink than non-smokers and drinkers more likely to smoke than non-drinkers [8]. Although a dose-dependent association between tobacco use and the risk of developing MetS has been found [9, 10], the relationship between alcohol consumption and MetS is not consistent [11-18]. In addition to this, it is not well established how MetS is affected by the combination of these two lifestyle factors. The fact that tobacco and alcohol use do not affect the individual MetS components in a similar way makes the association complex. For instance, alcohol consumption is found to be positively correlated with high-density lipoprotein cholesterol (HDL-C) in a dose-dependent fashion, while smoking has the opposite effect [19]. Similarly, alcohol and smoking have opposite effects on insulin sensitivity, with alcohol having favorable effects [20, 21]. A further apparent contrast between these two factors is their effect on blood pressure. While alcohol consumption of three or more drinks per day increases blood pressure [22], the relationship between smoking and blood pressure is less clear or even non-existent [9, 23]. On the other hand, both smoking and alcohol consumption seem to have a positive association with triglyceride levels [9, 24] and abdominal obesity [9, 25, 26]. In an earlier paper, we reported on the relationship between smoking and MetS [9]. In the present study, we carefully assessed the combined effects of smoking and alcohol consumption on MetS and its individual components among normal weight, 78 Chapter 4

overweight and obese subjects from LifeLines, a very large population-based cohort study in the Netherlands (64,046 individuals). We also assessed whether the prevalence of MetS and its individual components was associated with the type of alcoholic beverage consumed. If we can identify how smoking and alcohol together influence MetS, we are better able to give tailored lifestyle advice to those at higher risk for developing MetS. To our knowledge, these lifestyle factors have not been explored directly or so extensively by other studies.

Methods

Study design and subjects The LifeLines Cohort Study is a multidisciplinary prospective population-based cohort study with a unique three-generation design that examines the health and health- related behaviours of participants living in the north-eastern region of the Netherlands. More information about the LifeLines Cohort Study can be found elsewhere [27]. Similar to our previous paper [9] we included subjects of Western European origin (according to self-reported information in the questionnaire). They were aged between 18 and 80 years and participated in the study between December 2006 and December 2012. We excluded individuals who had missing data on BMI (n = 15), or on the variables needed to define MetS (n = 480), or whose questionnaires were incomplete with regard to smoking (n = 918) or alcohol consumption (n = 2,590). The current dataset comprised 64,046 individuals available for analyses. Before participating in the study, all participants provided written informed consent. The study protocol was approved conforming to the Declaration of Helsinki by the medical ethical review committee of the University Medical Center Groningen.

Clinical measures and definitions

Clinical measures A fixed staff of well-trained technicians, who had a long experience in the clinical practice, used a standardized protocol to obtain blood pressure and anthropometric measurements: height, weight, and waist circumference. Systolic and diastolic blood pressures were measured 10 times during a period of 10 minutes, using an automated Dinamap Monitor (GE Healthcare, Freiburg, Germany). The size of the cuff was chosen according to the arm circumference. The average of the final three readings was used for each blood pressure parameter. Anthropometric measurements were measured without shoes. Body weight was measured to the nearest 0.1 kg. Height and waist circumference were measured to the nearest 0.5 cm. Height was measured with a stadiometer placing Combined effects of Smoking and Alcohol on Metabolic Syndrome 79

their heels against the rod and the head in Frankfort Plane position. Waist circumference was measured in standing position with a tape measure all around the body, at the level midway between the lower rib margin and the iliac crest.

Biochemical measures Blood was collected in the fasting state, between 8.00 and 10.00 a.m., and transported to the LifeLines central laboratory facility at room temperature or at 4°C, depending on 4 the sample requirements. On the day of collection, serum levels of total and HDL cholesterol were measured using an enzymatic colorimetric method, triglycerides using a colorimetric UV method, and LDL-C using an enzymatic method, all on a Roche Modular P chemistry analyzer (Roche, Basel, Switzerland). Fasting blood glucose was measured using a hexokinase method.

Definition of the body mass index classes (BMI) and metabolic syndrome Subjects were classified into three BMI classes: normal weight (BMI <25.0 kg/m2), overweight (BMI 25.0 to 30.0 kg/m2) or obese (BMI ≥ 30.0 kg/m2), calculated as weight (kg) divided by height squared (m2). Metabolic syndrome was defined according to the revised criteria of the National Cholesterol Education Program’s Adult Treatment Panel III (NCEP ATPIII) [28]. The NCEP ATPIII stipulates the following five criteria for MetS: (1) systolic blood pressure ≥ 130 mmHg and/or diastolic blood pressure ≥ 85 mmHg and/ or use of antihypertensive medication; (2) fasting blood glucose ≥ 5.6 mmol/L and/ or use of blood glucose-lowering medication and/or diagnosis of type 2 diabetes; (3) HDL cholesterol levels < 1.03 mmol/L in men and < 1.30 mmol/L in women and/ or use of lipid-lowering medication; (4) triglyceride levels ≥ 1.70 mmol/L and/or use of triglyceride-lowering medication; and (5) waist circumference ≥102 cm in men and ≥ 88 cm in women. Individuals were diagnosed as having MetS if at least three of the five criteria were present. Medication use was self-reported. Diagnosis of diabetes mellitus was based either on self-report, or on the finding of a fasting plasma glucose ≥ 7.0 mmol/L.

Data on smoking, alcohol consumption and medication use Information about smoking, alcohol consumption and medication use was collected from the self-administered questionnaires (http://www.p3gobservatory.org/catalogue. htm?questionnaireId=48). Non-smokers were those who had not smoked during the last month and had never smoked for longer than a year. Subjects were classified as a former smoker when they reported that they had smoked during a whole year, had not smoked during the last month and stopped smoking. Those who had smoked for longer than a year and had not stopped smoking were classified as current smoker. Total tobacco use of the current smokers was estimated by using the following quantities: 1 cigarette = 1 80 Chapter 4

g tobacco, 1 cigarillo = 3 g tobacco and 1 cigar = 5 g tobacco. Moderate smoking was defined as 20 g/day or less, and heavy as more than 20 g/day [9]. Alcohol intake was based on the response to specific questions regarding intake frequency and the average number of units consumed on a drinking day. Individuals who reported not having consumed alcohol during the past month were considered non-drinkers. The number of alcoholic drinks per week was determined by multiplying the number of drinking days per week by the average number of units consumed on a drinking day. We then divided the number of alcoholic drinks/week by 7 in order to arrive at the number of alcoholic drinks per day. Individuals were classified into four groups according to their daily alcohol intake: 0 drinks/day (non-drinker), ≤1 drink/ day (light drinker), >1 to 2 drinks/day (moderate drinker) and >2 drinks/day (heavy drinker). In the Netherlands a standard unit contains 9.9 grams of alcohol. For each type of alcoholic beverage, respondents indicated whether they consumed it never (0%), sometimes (30%), often (70%) or always (100%). We only included participants in the beer group, wine group (which included red wine, white wine, rosé, sherry, port, vermouth and madeira) or spirits/mixed drinks group (containing a spirit and a mixer) if that beverage type accounted for 70% or more of their alcohol consumption. Since very few participants consumed mainly spirits or mixed drinks, these two groups were pooled together. All medications used by participants were classified according to the Anatomical Therapeutic Chemical (ATC) classification system. Medication use was then categorized into three groups: non users, ≤5 types of medication and >5 types of medication.

Statistical analyses All analyses were conducted using IBM SPSS Statistics version 20 (IBM Corporation, Armonk, NY, USA). Continuous data are expressed as mean ± standard deviation (SD), and non-normally distributed data as geometric mean and interquartile range. For categorical variables, percentages are reported. Differences between the three BMI classes and four alcohol groups were tested using ANOVA for continuous data and chi-square test for categorical data. Multivariate linear regression models were used to examine the associations between alcohol use, smoking and the five components of MetS, within the three BMI classes. Triglycerides and fasting blood glucose were log-transformed (natural log). Measured systolic and diastolic blood pressure were corrected for blood pressure- lowering medication by adding 10 mmHg and 5 mmHg, respectively. In Genomic Wide Association Studies this method is commonly used to approximate the true blood pressure values in treated subjects for high blood pressure [29-31]. This method is a better solution than ignoring treatment or excluding treated subjects [32, 33]. Analyses were stratified according to BMI class and smoking subgroups and adjusted for age (centered Combined effects of Smoking and Alcohol on Metabolic Syndrome 81

at the mean age of the total population (45y)), sex and the number of medications used. To assess beverage-specific associations with MetS and its components, we applied multivariate logistic regression models with non-drinkers as the reference group. MetS and the individual components were defined as ‘not meeting the criteria’ and ‘meeting the criteria’, as defined by the NCEP ATPIII. These models were not stratified for BMI class and smoking subgroups due to the low number of drinkers who indicated consuming mainly beer, mainly wine or mainly spirits/mixed drinks. Models were adjusted for age, 4 sex, BMI class, alcohol consumption subgroups, smoking subgroups and the number of medications used. To account for the number of independent tests, we applied a Bonferroni correction. Given the use of 12 independent tests (three BMI classes x four smoking subgroups), a P value of ≤ 0.004 (0.05/12) was considered significant.

Results

Overweight and obese subjects were slightly older than those with normal weight (table 1). Participants with a higher BMI had higher levels of systolic and diastolic blood pressure, serum triglycerides and blood glucose, and lower levels of HDL-C. The proportion of former smokers in the overweight and obese groups was higher than in the normal weight group, whereas the proportion of current smokers was approximately the same (normal weight 22.2%, overweight 20.8% and obese 19.4%). Among obese individuals, 25.8% were non-drinkers, while this percentage was much lower in overweight (15.0%) and normal weight (14.8%) individuals. Characteristics of the study population, according to alcohol consumption groups is available as supplemental table (Table S1). 82 Chapter 4

Table 1. Characteristics of the total study population by BMI class.

Characteristics BMI <25 kg/m2 BMI 25-30 kg/m2 BMI ≥30 kg/m2 P value n (%) 29,602 (46.2) 25,436 (39.7) 9,008 (14.1) Age, yrs 42 ± 12 47 ± 12 47 ± 12 ≤0.001 Sex (m (%)/f) 11,269 (38.1) / 18,333 13,734 (54.0) / 11,702 3,721 (41.3) / 5,287 BMI, kg/m2 22.6 ± 1.7 27.1 ± 1.4 33.4 ± 3.4 ≤0.001 SBP, mmHg 121 ± 14 129 ± 15 133 ± 15 ≤0.001 DBP, mmHg 71 ± 8 76 ± 9 77 ± 9 ≤0.001 Total cholesterol, mmol/L 4.9 ± 1.0 5.2 ± 1.0 5.2 ± 1.0 ≤0.001 LDL-C, mmol/L 3.02 ± 0.86 3.39 ± 0.90 3.38 ± 0.90 ≤0.001 HDL-C, mmol/L 1.59 ± 0.40 1.40 ± 0.36 1.28 ± 0.33 ≤0.001 Triglycerides, mmol/L 0.86 (0.63-1.11) 1.13 (0.79-1.55) 1.33 (0.93-1.83) ≤0.001 Blood glucose, mmol/L 4.78 (4.50-5.00) 5.04 (4.70-5.30) 5.31 (4.90-5.60) ≤0.001 Waist circumference, cm 82 ± 8 94 ± 8 107 ± 10 ≤0.001 Smoking status Non-smoker, n (%) 14,739 (49.8) 10,951 (43.1) 3,928 (43.6) ≤0.001 Former smoker, n (%) 8,282 (28.0) 9,179 (36.1) 3,332 (37.0) ≤0.001 <20 gram tobacco/day, n (%) 5,448 (18.4) 4,157 (16.3) 1,263 (14.0) ≤0.001 ≥20 gram tobacco/day, n (%) 1,133 (3.8) 1,149 (4.5) 485 (5.4) ≤0.001 Alcohol intake Non drinker 4368 (14.8) 3,811 (15.0) 2,320 (25.8) ≤0.001 ≤1 drink/day 15,933 (53.8) 12,420 (48.8) 4,220 (46.8) ≤0.001 >1 to 2 drinks/day 6,654 (22.5) 6,164 (24.2) 1,560 (17.3) ≤0.001 > 2 drinks/day 2,647 (8.9) 3,041 (12.0) 908 (10.1) ≤0.001 Medication use No medication, n (%) 20,200 (68.2) 16,395 (64.5) 4,835 (53.7) ≤0.001 ≤5 types of medication, n (%) 9,149 (30.9) 8,507 (33.4) 3,742 (41.5) ≤0.001 >5 types of medication, n (%) 253 (0.9) 534 (2.1) 431 (4.8) ≤0.001 BP-lowering medication, n (%) 1,145 (3.9) 2,450 (9.6) 1592 (17.7) ≤0.001 Statin use, n (%) 494 (1.7) 1,300 (5.1) 683 (7.6) ≤0.001 TG-lowering medication, n (%) 6 (0.1) 31 (0.1) 17 (0.2) ≤0.001 Type 2 diabetes, n (%) 99 (0.3) 302 (1.2) 339 (3.8) ≤0.001 Oral anti-hyperglycaemic medication, 67 (0.2) 233 (0.9) 274 (3.0) ≤0.001 n (%) % fulfilling ≥ 3 out of 5 MetS criteria 792 (2.7) 4,492 (17.7) 4,388 (48.7) ≤0.001 Data are presented as mean ± SD, or geometric mean (interquartile range). Abbreviations: BMI = body mass index, SBP = systolic blood pressure, DBP = diastolic blood pressure, HDL-C = high density lipoprotein cholesterol, TG = triglycerides, BP = blood pressure, MetS = metabolic syndrome. Combined effects of Smoking and Alcohol on Metabolic Syndrome 83

Table 2. Distribution of the study population across the smoking and alcohol subgroups, according to BMI class. BMI <25 kg/m2 Non-smoker Former smoker Moderate smoker Heavy smoker (n=14,739) (n=8,282) (n=5,448) (n=1,133) All, n (%) All, n (%) All, n (%) All, n (%) Non-drinker 2791 (18.9) 864 (10.4) 554 (10.2) 159 (14.0) ≤1 drink/day 8823 (59.9) 4364 (52.7) 2373 (43.6) 373 (32.9) >1-2 drinks/day 2426 (16.5) 2299 (27.8) 1636 (30.0) 293 (25.9) 4 >2 drinks/day 699 (4.7) 755 (9.1) 885 (16.2) 308 (27.2) BMI 25-30 kg/m2 Non-smoker Former smoker Moderate smoker Heavy smoker (n=10,952) (n=9,179) (n=5,157) (n=1,149) All, n (%) All, n (%) All, n (%) All, n (%) Non-drinker 2190 (20.0) 1026 (11.2) 458 (11.0) 137 (11.9) ≤1 drink/day 5980 (54.6) 4320 (47.1) 1737 (41.8) 383 (33.3) >1-2 drinks/day 2025 (18.5) 2644 (28.8) 1218 (29.3) 277 (24.1) >2 drinks/day 756 (6.9) 1189 (13.0) 744 (17.9) 352 (30.6) BMI ≥30 kg/m2 Non-smoker Former smoker Moderate smoker Heavy smoker (n=3,928) (n=3,332) (n=1,263) (n=485) All, n (%) All, n (%) All, n (%) All, n (%) Non-drinker 1319 (33.6) 658 (19.7) 235 (18.6) 108 (22.3) ≤1 drink/day 1907 (48.5) 1607 (48.2) 539 (42.7) 167 (34.4) >1-2 drinks/day 475 (12.1) 694 (20.8) 294 (23.3) 97 (20.0) >2 drinks/day 227 (5.8) 373 (11.2) 195 (15.4) 113 (23.3)

For all three BMI classes, individuals were most frequently classified as being a non- smoker with an alcohol intake of ≤ 1 drink/day (Table 2).The prevalence of MetS is given for each of the smoking and alcohol subgroups (Table S2 and figure 1). The percentages of subjects with MetS ranged widely across the different subgroups and BMI classes (normal weight: 1.7%-8.2%, overweight: 13.0%-32.1%, obese: 39.8%-71.1%). There was a dose-dependent increase in HDL-C levels with increasing levels of alcohol consumption, in all three BMI classes (P values ≤0.001) (Figure 2). When we looked at smoking status, we found that smokers had lower HDL-C levels than non-smokers, which decreased with the amount of tobacco used. In all BMI classes, alcohol consumption of >1 drink/day showed a positive association with triglyceride levels (Figure S1a). Triglyceride levels also increased within each smoking subgroup. It should however be noted that only a few results reached statistical significance. Although alcohol consumption does appear to increase fasting glucose levels, these differences were rather small and not statistically significant (Figure S1b). The relation between alcohol consumption and systolic blood pressure (SBP) and diastolic blood pressure (DBP) showed a J-shaped curve (Figure S1c and S1d). With higher blood 84 Chapter 4

80 non drinker ≤1 drink/day 70 1 or 2 drinks/day >2 drinks/day 60

30 Prevalence of MetS, % MetS,Prevalence of 20

0 N F C1 C2 N F C1 C2 N F C1 C2 BMI <25 BMI 25-30 BMI ≥30

Figure 1. Prevalence of metabolic syndrome within the smoking and alcohol subgroups, according to BMI class. Top: BMI <25 kg/m2; middle: BMI 25-30 kg/m2; bottom: BMI ≥30 kg/m2. BMI = body mass index N: non-smokers; F: former smokers; C1: smokers of <20 g tobacco/day; C2: smokers of ≥20 g tobacco/day.

2.0

* *

* * * * * * * * * * * *

1.5 * * * * * * * * * * * * * * * * * mean HDL-C, mmol/L * *

1.0 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 F:0 F:0 F:0 N:0 N:0 N:0 C1:0 C2:0 C1:0 C2:0 C1:0 C2:0

BMI <25 BMI 25-30 BMI ³30

Figure 2. Results of the associations between the smoking-alcohol subgroups and HDL-C, according to BMI class. Adjusted for age (centered at the mean age of the total population (45y)), sex and the number of medications used. * indicates a significant difference within each smoking subgroup relative to the reference group of non-drinkers (shaded shape); P value ≤ 0.001. N: non-smokers; F: former smokers; C1: smokers of <20 g tobacco/day; C2: smokers of ≥20 g tobacco/day. 0: non-drinker; 1: ≤1 drink/day; 2: >1-2 drinks/day; 3: >2 drinks/day. BMI = body mass index; HDL-C = high-density lipoprotein cholesterol. Combined effects of Smoking and Alcohol on Metabolic Syndrome 85

pressure levels found among non-drinkers and moderate to heavy drinkers relative to light drinkers. Alcohol consumption of more than 2 drinks/day significantly increased systolic and diastolic blood pressure in normal weight and overweight individuals, in all smoking subgroups. The strongest association was within the group of heavy smokers, with increased blood pressures relative to non-drinkers by 6.1/3.1 mmHg (SBP/DBP) in normal weight individuals ( P <0.001) and by 4.3/2.2 mmHg ( P = 0.004) in overweight. The relationship between alcohol consumption and blood pressure was not significant 4 in obese individuals. Within normal weight individuals, higher amounts of alcohol consumption were associated with a larger waist circumference (Figure S1e). Among overweight individuals, alcohol consumption up to 2 drinks/day was associated with a slightly smaller waist circumference in non-smokers and former smokers compared to the non-drinkers within the same smoking class (all P ≤0.001). A stronger association was found for obese individuals, showing a smaller waist up to 2.83 cm in non-smokers and up to 2.37 cm in former smokers (all P <0.001). Obese heavy smokers with an alcohol consumption of 1 to 2 drinks/day even had a 4.13 cm lower waist ( P = 0.004). Table 3 summarizes the relationship of light, moderate or heavy alcohol consumption (relative to no alcohol consumption) and smoking on the individual MetS risk components. The analyses on the beverage-specific associations with MetS and its components (Figure 3), showed that the odds ratio of having MetS was lower for wine drinkers than for non-drinkers (adjusted OR: 0.72; 95% CI: 0.68-0.84; P <0.001). Drinkers of all types of alcoholic beverages had a lower odds ratio of meeting the HDL-C criteria (all P <0.001) and a higher odds ratio of meeting those for hypertension (all P <0.001). Wine drinking did not affect the odds ratio for increased triglycerides, enlarged waist circumference or high fasting blood glucose ( P >0.004).

Table 3. Overview of the relationships of light, moderate or heavy alcohol consumption (relative to non- consumption) and smoking on the individual MetS risk components.

MetS component Light alcohol use Moderate alcohol use Heavy alcohol use Smoking HDL-cholesterol

Triglycerides ↑↑ ↑↑ ↑↑ ↓↓ Blood glucose ↓N ↑N ↑ ↑N Blood pressure N ↑ N a a a Waist circumference ↓ ↑↑ Highlighted arrows and two arrows↕ indicate a stronger↕ association. N = neutral↕ association. ↑ a association depends on the body mass index: a larger waist circumference for BMI <25 kg/m2 and a smaller waist circumference for BMI ≥25 kg/m2. 86 Chapter 4

Odds Ratios (95% confidence interval)

Beer a

MetS Wine Spirits/Mixed drinks

Beer

Low HDL-C Wine Spirits/Mixed drinks

Beer High TG Wine Spirits/Mixed drinks

Beer

High BG Wine a Spirits/Mixed drinks

Beer

High BP Wine

Spirits/Mixed drinks

Beer a Enlarged WC Wine

Spirits/Mixed drinks

0.5 1.0 1.5 Figure 3. Odds ratios for MetS and the individual components according to type of alcoholic beverage. This analysis comprised 10,499 non-drinkers (reference group), 18,581 wine consumers, 20,894 beer consumers and 4,079 spirits/mixed drinks consumers, for all levels of alcohol consumption. Adjusted for age, sex, level of alcohol consumption, body mass index class, smoking subgroup and the number of medications used. Odds ratios were significant different from the reference group of non-drinkers at P value ≤ 0.004. a indicates a significant difference relative to the reference group of non-drinkers at P value ≤ 0.05. BG = fasting blood glucose; BP = blood pressure; HDL-C = high-density lipoprotein cholesterol; MetS = metabolic syndrome; TG = triglycerides; WC = waist circumference.

Discussion

In this large population-based cohort study of the metabolic syndrome (MetS) among normal weight, overweight and obese subjects, we found smoking and light alcohol consumption to have opposing associations with MetS. In all BMI classes light alcohol consumption was associated with lower prevalence of MetS, explained by its favorable effects on HDL-C, triglycerides and waist circumference (only in overweight and obese individuals). Heavy alcohol consumption had unfavorable associations with individual Combined effects of Smoking and Alcohol on Metabolic Syndrome 87

MetS components. When compared with non-consumption of alcohol, we found wine consumption to be associated with a lower prevalence of MetS and the separate MetS components.

Alcohol consumption, smoking and MetS As might be expected, we found a wide range of MetS prevalence across the different smoking and alcohol subgroups and across the different BMI classes. Normal weight 4 and overweight subjects with a light to moderate alcohol consumption had a lower prevalence of MetS, while for obese subjects this was the case for zero and light alcohol consumption. Compared to non-smokers, former, moderate and heavy smokers had a higher prevalence of MetS, regardless of the amount of alcohol consumed. While our study shows a possibly protective association for alcohol in some cases, the literature reports conflicting results on the relationship between alcohol consumption and the prevalence of MetS. Finding no association [15, 16] or associations in different directions [11-14, 17, 18]. The small sample size of some of these studies and the fact that they did not take into account the smoking status of the participants, might explain the discrepancy in these results. However, we reported the prevalence of MetS in a large study population and stratified by smoking and alcohol subgroups, which gives a more reliable estimation.

Effects of smoking and alcohol on metabolic risk factors We have previously reported the finding that former and current smokers have lower HDL-C levels and that this relationship is dose-dependent [9]. We now found that this negative influence of smoking on HDL-C may be suppressed by the favorable association between alcohol consumption and HDL-C. Here, we showed a dose-dependent association between alcohol consumption and higher levels of HDL-C, which is consistent with earlier studies [34-36]. The magnitude of the effects of alcohol consumption on HDL-C varied between 0.02 and 0.29 mmol/L. This means that in current smokers, moderate alcohol consumption is associated with similar mean HDL-C levels to those of their non- smoking and non-drinking counterparts within the same BMI class. With regard to triglyceride levels, a cross-sectional population study has reported a U-shaped association between alcohol and triglycerides, with triglyceride levels the lowest in people with an alcohol consumption of 4 to 30 g/day [24]. Although our results revealed only a few significant associations for alcohol consumption, we did show higher triglyceride levels among former and current smokers, especially among those who drink more than 2 alcoholic drinks per day. One cross-sectional study in 3311 subjects from a Chinese population concluded that the effect of alcohol consumption on triglycerides was substantially greater for smokers of >20 cigarettes, than for lighter smokers and non-smokers [37]. In our population this was only true for the normal weight individuals. 88 Chapter 4

For overweight and obese individuals we cannot confirm these earlier findings (Figure S1a). A dose-dependent relationship between alcohol consumption and risk of hypertension has recently been suggested in a large meta-analysis [22]. In the current study, alcohol consumption showed a ‘J-shaped’ relationship with systolic and diastolic blood pressure, within each BMI class. Alcohol intake has been found to be highly correlated with both abdominal obesity [26] and increased risk for obesity [38, 39]. However, a prospective cohort study conducted among US men over a period of nine years, found no significant associations between changes in total alcohol consumption and gain in waist circumference [40]. In our study, we found that normal weight individuals who consumed alcohol had a larger waist circumference. In contrast, among overweight and obese individuals, light and moderate drinking was associated with a smaller waist than non-drinking. The biological mechanism by which alcohol consumption may reduce the waist circumference of overweight and obese individuals remains unclear. More studies are needed to confirm the differences that we observed between the three BMI classes. We showed with our study that all metabolic parameters worsen with higher BMI. Reducing body weight would therefore be by far the best approach to reduce the prevalence of MetS. However, effective long-term successes of weight loss interventions are still missing [4, 5]. Recently, a paper has been published on the effects of metabolic mediators on coronary heart disease (CHD) and stroke within overweight and obesity [41]. They have estimated that nearly half of the excess risk for CHD and three-quarters of excess risk for stroke due to overweight and obesity were mediated through blood pressure, cholesterol and glucose. Blood pressure accounted for the highest percentage of excess risk for CHD (one-third) and stroke (two-third). Interventions that control metabolic factors might address a substantial proportion of the effect of high BMI on cardiovascular disease. However, to achieve full benefits from the interventions, reduction of body weight is recommended.

The effect of beverage type on MetS and its components The odds ratios of having MetS were lower for consumers of all types of alcoholic beverage than for non-drinkers, a finding also reported by Djoussé et al. [42]. However, in the present study, wine consumption resulted in the lowest odds ratio of having MetS and was the only significant association ( P ≤0.004). This suggests that the lowest odds ratio we observed for the wine drinkers, may be explained by other components than ethanol and/or the healthier lifestyle behavior associated with wine consumption [42, 43]. The overall metabolic profile of wine consumers was better than that of individuals who preferred other alcoholic beverages. Wine drinkers were also less likely to be current smokers (data not shown). Combined effects of Smoking and Alcohol on Metabolic Syndrome 89

When we investigated the individual components of MetS we found the odds ratio of having low HDL-C levels to be lower for all beverage types than for non-drinkers. For beer consumption the only association found was a slightly higher odds ratio of having hypertriglyceridemia. The fact that the odds ratio was higher for beer consumers can be explained by the high carbohydrate content of beer, which is a well-known risk factor for increased triglycerides [44]. The finding that the odds ratio of having hypertension was lower for wine drinkers than for consumers of the other types of beverages, is also 4 reported by another study [45]. Higher odds ratios for abdominal obesity were found for drinkers of spirits/mixed drinks and beer, although not significant for the latter ( P = 0.043). These findings are in line with those reported by Valdstrup and colleagues [46].

Strengths and limitations A major strength of our study is the nature of the study population, which is derived from the general population and both large and well characterized. We are the first to report on associations between the concurrent use of tobacco and alcohol and the various components of MetS. The sample size of 64,046 individuals allowed us to perform subgroup analyses within different smoking subgroups and BMI classes. We were even able to examine whether the presence of MetS and its components was associated with the type of alcoholic beverage consumed. However, the study still has some limitations. Firstly, we were unable to make a distinction between abstainers and former drinkers. In this respect, the ‘J-shaped’ relationships found between alcohol consumption and both blood pressure and triglycerides might be explained by the lower health status of the non-drinking group (more medication users and type 2 diabetes patients) and possible inclusion of former drinkers. However, the ‘J-shaped’ relationship remained after exclusion of individuals with medication use. Secondly, the relationship between smoking, alcohol and MetS may be confounded by levels of physical activity and food intake. Smokers are known to be less physical active and have a less healthy diet than non-smokers [47]. Light and moderate alcohol consumption, in particular wine, is usually associated with a healthier lifestyle [47]. This notion is supported by the fact that beer and wine (which have the same ethanol content) showed different associations with MetS and its components. Such differences may be explained by lifestyle-related risk factors in consumers of beer, wine and spirits/mixed drinks that we could not control. Although we were not able to account for multiple critical lifestyle factors, we are the first to report in detail the combined effect of smoking and alcohol consumption on MetS. Thirdly, our findings could not support causality, due to the cross-sectional design of this study. A final point, which might be seen as a limitation, is the possibility of misclassification, since smoking and alcohol consumption was based on self-administered questionnaires. However, earlier studies showed that self-reported smoking status, tobacco use and alcohol consump- 90 Chapter 4

tion in the general population, can be used with notable confidence and provide an accurate estimation of the actual substance use [48-50].

Conclusion

In our previous study we already showed that smoking was associated with a higher risk for MetS, explained by its negative influence on HDL-C, triglycerides and to a lesser extend waist circumference. With the current study we assessed the combined effect of smoking and alcohol consumption on MetS. In this large population-based cohort study we found that especially light alcohol consumption was associated with a favourable effect on the individual MetS components. Light alcohol consumption might therefore moderate the negative associations of smoking on MetS. Our results suggest that the lifestyle advice that emphasizes smoking cessation and the restriction of alcohol consumption to a maximum of 1 drink/day, is a good approach to reduce the prevalence of MetS. Maintaining a healthy body weight is recommended to fully benefit from this approach. These lifestyle advices may also help to prevent the onset of cardiovascular disease, since it is the main health risk attributable to MetS.

Acknowledgements

The authors are grateful to the study participants, the staff of the LifeLines Cohort Study and Biobank, and the participating general practitioners and pharmacists. The LifeLines Cohort Study (BRIF4568) is engaged in a Bioresource research impact factor (BRIF) policy pilot study, details of which can be found at https://www.bioshare. eu/content/bioresource-impact-factor. The manuscript is based on data from the Life- Lines cohort study. LifeLines adheres to standards for open data availability. The data catalogue of LifeLines is publicly accessible on www.LifeLines.net. All international researchers can apply for data at the LifeLines research office ([email protected]). The LifeLines system allows access for reproducibility of the study results. Combined effects of Smoking and Alcohol on Metabolic Syndrome 91

References

1. Grundy SM: Metabolic syndrome pandemic. Arterioscle- 10. Sun K, Liu J, Ning G: Active smoking and risk of meta- rosis, thrombosis, and vascular biology 2008, 28(4):629- bolic syndrome: a meta-analysis of prospective studies. 636. PloS one 2012, 7(10):e47791. 2. Eckel RH, Grundy SM, Zimmet PZ: The metabolic 11. Baik I, Shin C: Prospective study of alcohol consumption syndrome. Lancet 2005, 365(9468):1415-1428. and metabolic syndrome. The American journal of clini- 3. Cowey S, Hardy RW: The metabolic syndrome: A high- cal nutrition 2008, 87(5):1455-1463. 4 risk state for cancer? The American journal of pathology 12. Barrio-Lopez MT, Bes-Rastrollo M, Sayon-Orea C, 2006, 169(5):1505-1522. Garcia-Lopez M, Fernandez-Montero A, Gea A, 4. Douketis JD, Macie C, Thabane L, Williamson DF: Martinez-Gonzalez MA: Different types of alcoholic Systematic review of long-term weight loss studies in beverages and incidence of metabolic syndrome and obese adults: clinical significance and applicability to its components in a Mediterranean cohort. Clinical clinical practice. International journal of obesity (2005) nutrition (Edinburgh, Scotland) 2012. 2005, 29(10):1153-1167. 13. Gigleux I, Gagnon J, St-Pierre A, Cantin B, Dagenais 5. Franz MJ, VanWormer JJ, Crain AL, Boucher JL, GR, Meyer F, Despres JP, Lamarche B: Moderate alcohol Histon T, Caplan W, Bowman JD, Pronk NP: Weight-loss consumption is more cardioprotective in men with the outcomes: a systematic review and meta-analysis metabolic syndrome. The Journal of nutrition 2006, of weight-loss clinical trials with a minimum 1-year 136(12):3027-3032. follow-up. Journal of the American Dietetic Association 14. Park YW, Zhu S, Palaniappan L, Heshka S, Carnethon MR, 2007, 107(10):1755-1767. Heymsfield SB: The metabolic syndrome: prevalence 6. Danaei G, Finucane MM, Lin JK, Singh GM, Paciorek CJ, and associated risk factor findings in the US population Cowan MJ, Farzadfar F, Stevens GA, Lim SS, Riley LM from the Third National Health and Nutrition Examina- et al: National, regional, and global trends in systolic tion Survey, 1988-1994. Archives of internal medicine blood pressure since 1980: systematic analysis of health 2003, 163(4):427-436. examination surveys and epidemiological studies with 15. Santos AC, Ebrahim S, Barros H: Alcohol intake, smok- 786 country-years and 5.4 million participants. Lancet ing, sleeping hours, physical activity and the metabolic 2011, 377(9765):568-577. syndrome. Preventive medicine 2007, 44(4):328-334. 7. Farzadfar F, Finucane MM, Danaei G, Pelizzari PM, 16. Villegas R, Creagh D, Hinchion R, O’Halloran D, Perry IJ: Cowan MJ, Paciorek CJ, Singh GM, Lin JK, Stevens GA, Prevalence and lifestyle determinants of the metabolic Riley LM et al: National, regional, and global trends in syndrome. Irish medical journal 2004, 97(10):300-303. serum total cholesterol since 1980: systematic analysis 17. Wakabayashi I: Cross-sectional relationship between of health examination surveys and epidemiological alcohol consumption and prevalence of metabolic studies with 321 country-years and 3.0 million partici- syndrome in Japanese men and women. Journal of pants. Lancet 2011, 377(9765):578-586. atherosclerosis and thrombosis 2010, 17(7):695-704. 8. Wetzels JJ, Kremers SP, Vitoria PD, de Vries H: The 18. Yoon YS, Oh SW, Baik HW, Park HS, Kim WY: Alcohol alcohol-tobacco relationship: a prospective study consumption and the metabolic syndrome in Korean among adolescents in six European countries. Addiction adults: the 1998 Korean National Health and Nutrition (Abingdon, England) 2003, 98(12):1755-1763. Examination Survey. The American journal of clinical 9. Slagter SN, van Vliet-Ostaptchouk JV, Vonk JM, Boezen nutrition 2004, 80(1):217-224. HM, Dullaart RP, Kobold AC, Feskens EJ, van Beek AP, 19. Ellison RC, Zhang Y, Qureshi MM, Knox S, Arnett DK, van der Klauw MM, Wolffenbuttel BH: Associations be- Province MA: Lifestyle determinants of high-density tween smoking, components of m metabolic syndrome lipoprotein cholesterol: the National Heart, Lung, and and lipoprotein particle size. BMC medicine 2013, Blood Institute Family Heart Study. American heart 11:195. journal 2004, 147(3):529-535. 92 Chapter 4

20. Eliasson B: Cigarette smoking and diabetes. Progress in 29. Franceschini N, Fox E, Zhang Z, Edwards TL, Nalls MA, cardiovascular diseases 2003, 45(5):405-413. Sung YJ, Tayo BO, Sun YV, Gottesman O, Adeyemo A 21. Koppes LL, Dekker JM, Hendriks HF, Bouter LM, Heine et al: Genome-wide association analysis of blood- RJ: Moderate alcohol consumption lowers the risk of pressure traits in African-ancestry individuals reveals type 2 diabetes: a meta-analysis of prospective obser- common associated genes in African and non-African vational studies. Diabetes care 2005, 28(3):719-725. populations. American journal of human genetics 2013, 22. Taylor B, Irving HM, Baliunas D, Roerecke M, Patra J, Mo- 93(3):545-554. hapatra S, Rehm J: Alcohol and hypertension: gender 30. Kidambi S, Ghosh S, Kotchen JM, Grim CE, Krishnaswami differences in dose-response relationships determined S, Kaldunski ML, Cowley AW, Jr., Patel SB, Kotchen TA: through systematic review and meta-analysis. Addic- Non-replication study of a genome-wide association tion (Abingdon, England) 2009, 104(12):1981-1990. study for hypertension and blood pressure in African 23. Narkiewicz K, Kjeldsen SE, Hedner T: Is smoking a Americans. BMC medical genetics 2012, 13:27. causative factor of hypertension? Blood pressure 2005, 31. Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Deh- 14(2):69-71. ghan A, Glazer NL, Morrison AC, Johnson AD, Aspelund T 24. Whitfield JB, Heath AC, Madden PA, Pergadia ML, et al: Genome-wide association study of blood pressure Montgomery GW, Martin NG: Metabolic and biochemi- and hypertension. Nature genetics 2009, 41(6):677- cal effects of low-to-moderate alcohol consumption. 687. Alcoholism, clinical and experimental research 2013, 32. Navis G, Bakker SJ, van der Harst P: Dissecting the 37(4):575-586. genetics of complex traits: lessons from hypertension. 25. Saarni SE, Pietilainen K, Kantonen S, Rissanen A, Nephrology, dialysis, transplantation : official publica- Kaprio J: Association of smoking in adolescence with tion of the European Dialysis and Transplant Association abdominal obesity in adulthood: a follow-up study of - European Renal Association 2010, 25(5):1382-1385. 5 birth cohorts of Finnish twins. American journal of 33. Tobin MD, Sheehan NA, Scurrah KJ, Burton PR: Adjusting public health 2009, 99(2):348-354. for treatment effects in studies of quantitative traits: 26. Schroder H, Morales-Molina JA, Bermejo S, Barral D, antihypertensive therapy and systolic blood pressure. Mandoli ES, Grau M, Guxens M, de Jaime Gil E, Alvarez Statistics in medicine 2005, 24(19):2911-2935. MD, Marrugat J: Relationship of abdominal obesity with 34. Kloner RA, Rezkalla SH: To drink or not to drink? That is alcohol consumption at population scale. European the question. Circulation 2007, 116(11):1306-1317. journal of nutrition 2007, 46(7):369-376. 35. Park SH: Association between alcohol consumption and 27. Stolk RP, Rosmalen JG, Postma DS, de Boer RA, Navis metabolic syndrome among Korean adults: nondrinker G, Slaets JP, Ormel J, Wolffenbuttel BH: Universal risk versus lifetime abstainer as a reference group. Sub- factors for multifactorial diseases: LifeLines: a three- stance use & misuse 2012, 47(4):442-449. generation population-based study. European journal 36. Rimm EB, Williams P, Fosher K, Criqui M, Stampfer MJ: of epidemiology 2008, 23(1):67-74. Moderate alcohol intake and lower risk of coronary 28. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Clee- heart disease: meta-analysis of effects on lipids and man JI, Donato KA, Fruchart JC, James WP, Loria CM, haemostatic factors. BMJ (Clinical research ed) 1999, Smith SC, Jr.: Harmonizing the metabolic syndrome: 319(7224):1523-1528. a joint interim statement of the International 37. Wu DM PL, Sun PK, Hsu LL, Sun CA: Joint effects of alco- Diabetes Federation Task Force on Epidemiology and hol consumption and cigarette smoking on atherogenic Prevention; National Heart, Lung, and Blood Institute; lipid and lipoprotein profiles: results from a study of American Heart Association; World Heart Federation; Chinese male population in Taiwan. European journal of International Atherosclerosis Society; and International epidemiology 2001, 17(7):629-635. Association for the Study of Obesity. Circulation 2009, 38. Colditz GA, Giovannucci E, Rimm EB, Stampfer MJ, Ros- 120(16):1640-1645. ner B, Speizer FE, Gordis E, Willett WC: Alcohol intake Combined effects of Smoking and Alcohol on Metabolic Syndrome 93

in relation to diet and obesity in women and men. The 47. Rabaeus M, Salen P, de Lorgeril M: Is it smoking or American journal of clinical nutrition 1991, 54(1):49-55. related lifestyle variables that increase metabolic 39. Lukasiewicz E, Mennen LI, Bertrais S, Arnault N, Preziosi syndrome risk? BMC medicine 2013, 11(1):196. P, Galan P, Hercberg S: Alcohol intake in relation to body 48. Giovannucci E, Colditz G, Stampfer MJ, Rimm EB, mass index and waist-to-hip ratio: the importance of Litin L, Sampson L, Willett WC: The assessment of type of alcoholic beverage. Public health nutrition 2005, alcohol consumption by a simple self-administered 4 8(3):315-320. questionnaire. American journal of epidemiology 1991, 40. Koh-Banerjee P, Chu NF, Spiegelman D, Rosner B, 133(8):810-817. Colditz G, Willett W, Rimm E: Prospective study of 49. Streppel MT, de Vries JH, Meijboom S, Beekman M, de the association of changes in dietary intake, physical Craen AJ, Slagboom PE, Feskens EJ: Relative validity of activity, alcohol consumption, and smoking with 9-y the food frequency questionnaire used to assess dietary gain in waist circumference among 16 587 US men. The intake in the Leiden Longevity Study. Nutrition journal American journal of clinical nutrition 2003, 78(4):719- 2013, 12:75. 727. 50. Studts JL, Ghate SR, Gill JL, Studts CR, Barnes CN, 41. Lu Y, Hajifathalian K, Ezzati M, Woodward M, Rimm EB, LaJoie AS, Andrykowski MA, LaRocca RV: Validity of Danaei G: Metabolic mediators of the effects of body- self-reported smoking status among participants mass index, overweight, and obesity on coronary heart in a lung cancer screening trial. Cancer epidemiol- disease and stroke: a pooled analysis of 97 prospective ogy, biomarkers & prevention : a publication of the cohorts with 1.8 million participants. Lancet 2014, American Association for Cancer Research, cosponsored 383(9921):970-983. by the American Society of Preventive Oncology 2006, 42. Djousse L, Arnett DK, Eckfeldt JH, Province MA, Singer 15(10):1825-1828. MR, Ellison RC: Alcohol consumption and metabolic syndrome: does the type of beverage matter? Obesity research 2004, 12(9):1375-1385. 43. McCann SE, Sempos C, Freudenheim JL, Muti P, Russell M, Nochajski TH, Ram M, Hovey K, Trevisan M: Alcoholic beverage preference and characteristics of drinkers and nondrinkers in western New York (United States). Nutrition, metabolism, and cardiovascular diseases : NMCD 2003, 13(1):2-11. 44. Parks EJ: Effect of dietary carbohydrate on triglyceride metabolism in humans. The Journal of nutrition 2001, 131(10):2772S-2774S. 45. Puddey IB, Beilin LJ: Alcohol is bad for blood pressure. Clinical and experimental pharmacology & physiology 2006, 33(9):847-852. 46. Vadstrup ES, Petersen L, Sorensen TI, Gronbaek M: Waist circumference in relation to history of amount and type of alcohol: results from the Copenhagen City Heart Study. International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity 2003, 27(2):238-246. 94 Chapter 4

Supplemental information

Table S1. Characteristics of the total study population by alcohol subgroup.

Characteristics Non-drinker ≤1 drink/day >1 to 2 drinks/day > 2 drinks/day P value n (%) 10,499 (16.4) 32,573 (50.9) 14,378 (22.4) 6,596 (10.3) Age, yrs 46 ± 12 44 ± 12 46 ± 12 45 ± 12 ≤0.001 Sex (m (%)/f) 2,453 (23.4) / 8,046 12,636 (38.8) / 19,937 8,516 (59.2) / 5,862 5,119 (77.6) / 1,477 BMI, kg/m2 26.8 ± 5.1 25.7 ± 4.1 25.7 ± 3.6 26.1 ± 3.7 ≤0.001 SBP, mmHg 125/126 ± 16/17 125 ± 15/16 127/128 ± 15 131/132 ± 15 ≤0.001 DBP, mmHg 73/74 ± 9 73 ± 9 75 ± 9 77 ± 10 ≤0.001 Total cholesterol, mmol/L 5.0 ± 1.0 5.0 ± 1.0 5.1 ± 1.0 5.3 ± 1.0 ≤0.001 LDL-C, mmol/L 3.18 ± 0.88 3.17 ± 0.89 3.28 ± 0.91 3.38 ± 0.93 ≤0.001 HDL-C, mmol/L 1.43 ± 0.37 1.48 ± 0.39 1.50 ± 0.42 1.45 ± 0.40 ≤0.001 Triglycerides, mmol/L 1.00 (0.70-1.38) 0.97 (0.69-1.31) 1.06 (0.74-1.44) 1.22 (0.82-1.72) ≤0.001 Blood glucose, mmol/L 4.95 (4.60-5.20) 4.91 (4.60-5.20) 4.99 (4.60-5.30) 5.09 (4.70-5.40) ≤0.001 Waist circumference, cm 91 ± 14 89 ± 12 91 ± 11 94 ± 11 ≤0.001 Smoking status Non-smoker, n (%) 6,300 (60.0) 16,710 (51.3) 4,926 (34.3) 1,682 (25.5) ≤0.001 Former smoker, n (%) 2,548 (24.3) 10,291 (31.6) 5,637 (39.2) 2,317 (35.1) ≤0.001 <20 gram tobacco/day, n (%) 1,247 (11.9) 4,649 (14.3) 3,148 (21.9) 1,824 (27.7) ≤0.001 ≥20 gram tobacco/day, n (%) 404 (3.8) 923 (2.8) 667 (4.6) 773 (11.7) ≤0.001 Medication use No medication, n (%) 5,842 (55.6) 20,948 (64.3) 10,050 (69.9) 4,590 (69.6) ≤0.001 ≤5 sorts of medication, n (%) 4,242 (40.4) 11,094 (34.1) 4,150 (28.9) 1,912 (29.0) ≤0.001 >5 sorts of medication, n (%) 415 (4,0) 531 (1.6) 178 (1.2) 94 (1.4) ≤0.001 BP-lowering medication, n (%) 1,247 (11.9) 2,368 (7.3) 1,023 (7.1) 549 (8.3) ≤0.001 Statin use, n (%) 542 (5.2) 1,067 (3.3) 562 (3.9) 306 (4.6) ≤0.001 TG-lowering medication, n (%) 19 (0.2) 19 (0.1) 9 (0.1) 7 (0.1) ≤0.001 Type 2 diabetes, n (%) 231 (2.2) 329 (1.0) 120 (0.8) 60 (0.9) ≤0.001 Oral anti-hyperglycaemic 172 (1.6) 259 (0.8) 95 (0.7) 48 (0.7) ≤0.001 medication, n (%) % fulfilling ≥ 3 out of 5 MetS 2,066 (19.7) 4,249 (13.0) 2,044 (14.2) 1,313 (19.9) ≤0.001 criteria Data are presented as mean ± SD, or geometric mean (interquartile range). Abbreviations: BMI = body mass index, SBP = systolic blood pressure, DBP = diastolic blood pressure, HDL-C = high density lipoprotein cholesterol, TG = triglycerides, BP = blood pressure, MetS = metabolic syndrome. Combined effects of Smoking and Alcohol on Metabolic Syndrome 95

Table S2. Distribution of the study population across the smoking and alcohol subgroups, according to BMI class.

BMI < 25 kg/m2 Non-smoker (n=14,739) Former smoker (n=8,282) Moderate smoker (n=5,448) Heavy smoker (n=1,133) All, n (%) MetS, n (%) All, n (%) MetS, n (%) All, n (%) MetS, n (%) All, n (%) MetS, n (%) Non-drinker 2791 (18.9) 61 (2.2) 864 (10.4) 41 (4.7) 554 (10.2) 25 (4.5) 159 (14.0) 13 (8.2) ≤1 drink/day 8823 (59.9) 146 (1.7) 4364 (52.7) 110 (2.5) 2373 (43.6) 67 (2.8) 373 (32.9) 22 (5.9) >1 to 2 drinks/day 2426 (16.5) 48 (2.0) 2299 (27.8) 62 (2.7) 1636 (30.0) 75 (4.6) 293 (25.9) 16 (5.5) 4 >2 drinks/day 699 (4.7) 19 (2.7) 755 (9.1) 32 (4.2) 885 (16.2) 33 (3.7) 308 (27.2) 22 (7.1) BMI 25-30 kg/m2 Non-smoker (n=10,952) Former smoker (n=9,179) Moderate smoker (n=5,157) Heavy smoker (n=1,149) All, n (%) MetS, n (%) All, n (%) MetS, n (%) All, n (%) MetS, n (%) All, n (%) MetS, n (%) Non-drinker 2190 (20.0) 407 (18.6) 1026 (11.2) 225 (21.9) 458 (11.0) 119 (26.0) 137 (11.9) 44 (32.1) ≤1 drink/day 5980 (54.6) 798 (13.3) 4320 (47.1) 762 (17.6) 1737 (41.8) 341 (19.6) 383 (33.3) 101 (26.4) >1 to 2 drinks/day 2025 (18.5) 263 (13.0) 2644 (28.8) 465 (17.6) 1218 (29.3) 221 (18.1) 277 (24.1) 79 (28.5) >2 drinks/day 756 (6.9) 111 (14.7) 1189 (13.0) 276 (23.2) 744 (17.9) 172 (23.1) 352 (30.6) 108 (30.7) BMI ≥30 kg/m2 Non-smoker (n=3,928) Former smoker (n=3,332) Moderate smoker (n=1,263) Heavy smoker (n=485) All, n (%) MetS, n (%) All, n (%) MetS, n (%) All, n (%) MetS, n (%) All, n (%) MetS, n (%) Non-drinker 1319 (33.6) 601 (54.4) 658 (19.7) 331 (50.3) 235 (18.6) 128 (54.5) 108 (22.3) 71 (65.7) ≤1 drink/day 1907 (48.5) 759 (39.8) 1607 (48.2) 764 (47.5) 539 (42.7) 279 (51.8) 167 (34.4) 100 (59.9) >1 to 2 drinks/day 475 (12.1) 213 (44.8) 694 (20.8) 362 (52.2) 294 (23.3) 171 (58.2) 97 (20.0) 69 (71.1) >2 drinks/day 227 (5.8) 119 (52.4) 373 (11.2) 217 (58.2) 195 (15.4) 127 (65.1) 113 (23.3) 77 (68.1) Abbreviations: BMI= body mass index, MetS= metabolic syndrome. 96 Chapter 4

2.0

* *

1.5

* *

1.0 * geometric mean TG, mmol/L 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 F:0 F:0 F:0 N:0 N:0 N:0 C1:0 C2:0 C1:0 C2:0 C1:0 C2:0

BMI <25 BMI 25-30 BMI ³30

Figure S1a-e. Results of the associations between the smoking-alcohol subgroups and components of MetS, according to BMI class. Adjusted for age (centered at the mean age of the total population (45y)), sex and medication use. * indicates a significant difference within each smoking subgroup relative to the reference group of non-drinkers (shaded shape); P value ≤ 0.001. ** indicates a significant difference within each smoking subgroup relative to the reference group of non-drinkers (shaded shape); P value ≤ 0.004. N: non-smokers; F: former smokers; C1: smokers of <20g tobacco/day; C2: smokers of ≥20 g tobacco/day. 0: non-drinker; 1: ≤1 drink/day; 2: >1-2 drinks/day; 3: >2 drinks/day. BMI = body mass index; TG = triglycerides.

6.0

5.5

* * 5.0 geometric mean BG, mmol/L 4.5 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 F:0 F:0 F:0 N:0 N:0 N:0 C1:0 C2:0 C1:0 C2:0 C1:0 C2:0

BMI <25 BMI 25-30 BMI ³30

Figure S1b. Adjusted for age (centered at the mean age of the total population (45y)), sex and medication use. * indicates a significant difference within each smoking subgroup relative to the reference group of non-drinkers (shaded shape); P value ≤ 0.001. ** indicates a significant difference within each smoking subgroup relative to the reference group of non-drinkers (shaded shape); P value ≤ 0.004. N: non-smokers; F: former smokers; C1: smokers of <20g tobacco/day; C2: smokers of ≥20 g tobacco/day. 0: non-drinker; 1: ≤1 drink/day; 2: >1-2 drinks/day; 3: >2 drinks/day. BMI = body mass index; BG = fasting blood glucose. Combined effects of Smoking and Alcohol on Metabolic Syndrome 97

140

* * * 130 * *

* * * 4 mean SBP, mmHg 120 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 F:0 F:0 F:0 N:0 N:0 N:0 C1:0 C2:0 C1:0 C2:0 C1:0 C2:0

BMI <25 BMI 25-30 BMI ³30 Figure S1c. Adjusted for age (centered at the mean age of the total population (45y)), sex and medication use. * indicates a significant difference within each smoking subgroup relative to the reference group of non-drinkers (shaded shape); P value ≤ 0.001. ** indicates a significant difference within each smoking subgroup relative to the reference group of non-drinkers (shaded shape); P value ≤ 0.004. N: non-smokers; F: former smokers; C1: smokers of <20g tobacco/day; C2: smokers of ≥20 g tobacco/day. 0: non-drinker; 1: ≤1 drink/day; 2: >1-2 drinks/day; 3: >2 drinks/day. BMI = body mass index; SBP = systolic blood pressure.

* *

* * * * *

75 * * * * * mean DBP, mmHg

65 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 F:0 F:0 F:0 N:0 N:0 N:0 C1:0 C2:0 C1:0 C2:0 C1:0 C2:0

BMI <25 BMI 25-30 BMI ³30 Figure S1d. Adjusted for age (centered at the mean age of the total population (45y)), sex and medication use. * indicates a significant difference within each smoking subgroup relative to the reference group of non-drinkers (shaded shape); P value ≤ 0.001. ** indicates a significant difference within each smoking subgroup relative to the reference group of non-drinkers (shaded shape); P value ≤ 0.004. N: non-smokers; F: former smokers; C1: smokers of <20g tobacco/day; C2: smokers of ≥20 g tobacco/day. 0: non-drinker; 1: ≤1 drink/day; 2: >1-2 drinks/day; 3: >2 drinks/day. BMI = body mass index; DBP = diastolic blood pressure. 98 Chapter 4

115

* * * * * *

100

* * * * mean WC, cm

85 * * * * 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 F:0 F:0 F:0 N:0 N:0 N:0 C1:0 C2:0 C1:0 C2:0 C1:0 C2:0

BMI <25 BMI 25-30 BMI ³30

Figure S1e. Adjusted for age (centered at the mean age of the total population (45y)), sex and medication use. * indicates a significant difference within each smoking subgroup relative to the reference group of non-drinkers (shaded shape); P value ≤ 0.001. ** indicates a significant difference within each smoking subgroup relative to the reference group of non-drinkers (shaded shape); P value ≤ 0.004. N: non-smokers; F: former smokers; C1: smokers of <20g tobacco/day; C2: smokers of ≥20 g tobacco/day. 0: non-drinker; 1: ≤1 drink/day; 2: >1-2 drinks/day; 3: >2 drinks/day. BMI = body mass index; WC = waist circumference.

Hoofdstukpagina-letteromtrek.indd 9 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 10 16/11/16 11:05 Chapter 5

Dietary patterns and physical activity in the metabolically (un)healthy obese: The Dutch LifeLines Cohort Study

Sandra N. Slagter Eva Corpeleijn Melanie M. van der Klauw Anna Sijtsma Linda Swart Corine Perenboom Jeanne de Vries Edith J. Feskens Bruce H.R. Wolffenbuttel Daan Kromhout Jana V. van Vliet-Ostaptchouk

In preparation 5

Hoofdstukpagina-letteromtrek.indd 9 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 10 16/11/16 11:05 102 Chapter 5

Abstract

Introduction The diversity in the reported prevalence of metabolically healthy obesity (MHO), suggests that modifiable factors, such as diet and physical activity, may contribute to metabolic health. The aim of this study was to evaluate differences in dietary patterns and physical activity between MHO and metabolically unhealthy obesity (MUO). Methods Data was used of 9,270 obese individuals, aged 30-69 years, of the LifeLines Cohort Study. Diet, using a 111 item Food Frequency Questionnaire, and physical activity were self-reported. MHO, intermediate obesity and MUO were defined according to the presence of obesity, number of MetS risk factors and history of cardiovascular disease. Sex specific associations of dietary patterns, identified by principal component analysis, and physical activity with MHO were assessed by multivariable logistic regression (reference group: MUO), adjusted for among others demographic characteristics, smoking and alcohol use. Results Among 3,442 men and 5,828 women, respectively 10.2% and 24.4% was MHO and 56.9% and 35.3% MUO. We generated four obesity-specific dietary patterns of which two were related to MHO in women only. In the highest quartile (Q) of the ‘bread, potatoes and sweet snacks’ pattern the adjusted odds ratio (OR) (95% confidence interval) for MHO was 0.60 (0.44-0.81). A positive association with MHO was found for the more healthier pattern ‘fruit, vegetables and fish’, with OR 1.31 (1.04- 1.64) in Q3 and 1.46 (1.15-1.87) in Q4. In contrast to women, men in the highest tertile of the vigorous physical activity score had a 1.96 (1.45-2.64) OR for MHO. Non-smoking and alcohol use were positively associated with MHO, in both men and women. Conclusion Our results suggest that a healthier diet and vigorous physical activity is associated with MHO in respectively women and men. Identification of behavioural lifestyle patterns in the obese population may help in pinpointing vulnerable subgroups and to develop potential strategies improving metabolic health. Keywords Metabolically healthy obesity, Dietary patterns, Physical activity, Lifestyle. Dietary Patterns and Physical Activity in Obesity 103

Introduction

The prevalence of overweight and obesity is increasing in today’s obesogenic environment [1]. Obese individuals are more likely to develop multiple metabolic complications which increases their risk of type 2 diabetes (T2D) and cardiovascular disease (CVD) [2]. However, obesity is a complex and heterogeneous condition with phenotypic variation. Some obese individuals, called the metabolically healthy obese, show no sign of conditions associated with the metabolic syndrome (MetS), i.e. impaired glucose metabolism, hypertension and dyslipidaemia [3]. Whether metabolically healthy obesity (MHO) is a 5 truly healthy state remains controversial. Meta-analyses have indicated that adults with MHO have a risk for T2D, CVD [4] and mortality [5] that is intermediate between that of healthy normal weight and unhealthy obese adults. Meaning that even without weight loss, the (cardio)metabolic health can be improved in obese individuals. This is an important finding, given the fact that sustained weight loss is difficult [6, 7]. Interestingly, the metabolically healthy obesity (MHO) phenotype may be modifiable. Several longitudinal studies with up to 10 years of follow-up showed that 43.3-47.6% of those grouped as MHO transitioned to metabolically unhealthy obesity (MUO) [8-10]. Of course, it is partially a transient state due to ageing and the associated adverse metabolic changes. However, MHO prevalence does not only differ between age groups, but also between countries. The BioSHaRE-EU Healthy Obesity Project reported that among 28.077 obese individuals from different European countries, the MHO prevalence was lower within higher age groups and lower among men compared to women. Using the same diagnostic criteria, even the age-standardized prevalence of MHO was highly varying between countries, 2-19% among men and 7-28% among women [11]. Thus on top of age, sex and genes, this suggests that other factors are related to the transition from healthy to unhealthy, and possible, in reverse direction, from unhealthy to healthy obesity. Characterizing the metabolically healthy- and unhealthy obese is of primary importance for medical research and clinical practice, since sizable benefits may still be real- ized by promoting MHO [12]. Which determinants account for the metabolic differences observed between MHO and MUO is uncertain, and particularly data on the role of diet and physical activity are limited. Previous studies on intake of single foods and/or mi- cro- and macronutrients could not find an association with metabolic health subtypes [13-16]. However, nutrients interact with each other in food products and whole diets [17]. Examination of dietary patterns may, therefore, be more suitable to gain insight into the relation between diet and metabolic health. ‘A priori’ dietary scores are based on current knowledge about the role of foods and nutrients in the etiology of disease. They are often developed to assess diet quality based on adherence to nutritional recommendations [18]. For example, in the study of 104 Chapter 5

Cahmi et al. [19], adolescents and women with MHO (19-44 years) had higher scores on the Healthy Eating Index (HEI-2005), which assess diet quality in relation to U.S. National Dietary Guidelines (2005), compared to the MUO individuals. Less is known about existing food consumption patterns within the obese population. An ‘a posteriori’ approach of examining such dietary patterns is factor analysis. With this data-driven technique, dietary patterns are identified based upon intercorrelations between dietary items within the studied population [17]. Although they do not necessarily represent optimal diets for risk assessment, they are an expression of the way how people eat [17] and are expected to be part of a broader pattern of lifestyle factors [18]. Compared to ‘a priori’ dietary scores, dietary patterns derived from factor analysis are more likely to generate new hypotheses, and may improve our insight into possibilities for dietary changes. The aim of this study was to evaluate differences in dietary patterns and physical activity, between MHO and MUO in the large population-based LifeLines Cohort Study. More specifically, we aimed to 1a) generate obesity-specific dietary patterns, and 1b) -ex amine their associations with demographic- and other lifestyle factors; and 2) compare the dietary patterns and physical activity between MHO and MUO, taking into account among others demographic characteristics, smoking and alcohol use.

Methods

LifeLines cohort study LifeLines is a prospective population-based cohort study using a unique three-generation design to study the health and health-related behaviours of 167,729 persons living in the North of The Netherlands. The LifeLines adult population is broadly representative for the adults living in this region [20]. Detailed information on the cohort profile can be found elsewhere [21]. Before study entry, all participants signed an informed consent. The LifeLines Cohort Study is conducted according to the principles of the Declaration of Helsinki and in accordance with the research code of the University Medical Center Groningen (UMCG). The study has been approved by the medical ethics review committee of the UMCG. For this study we used a subset of the cross-sectional data, collected between 2006 and 2013. Subjects were included in the present study if they had obesity (body mass index (BMI) ≥30 kg/m2), were of western European origin, and aged between 30 and 70 years (N= 10,771). Dietary Patterns and Physical Activity in Obesity 105

Clinical measures and definitions

Clinical measurements and laboratory methods Detailed information about the physical examination and biochemical measurements has been published previously [22]. In short, during the first visit measurements of weight, waist circumference (WC), and height (to the nearest 0.5 cm) were performed in light clothing and without shoes. Body weight and height were used to calculate BMI (weight (kg)/height (m)2). Blood pressure (BP) was measured every minute during a period of 10 minutes with an automated DINAMAP Monitor (GE Healthcare, Freiburg, 5 Germany). The average of the final three readings was recorded for systolic and diastolic BP. During the second visit, on average two weeks after the first visit, blood samples were drawn after an overnight fast for measurement of plasma glucose (hexokinase method), high density lipoprotein cholesterol (HDL-C) and triglycerides (TG) (respectively, colorimetric method and colorimetric UV method, Roche Modular P chemistry analyser, Basel, Switzerland).

Definition of the metabolically healthy and unhealthy obese MHO was defined according to the criteria established by the BioSHaRE-EU Healthy Obesity Project [11], this means that subjects with obesity had none of the MetS risk factors, except for WC, according to the original NCEP ATPIII (including treatment for

Table 1. Definition for metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO).

MHO Intermediate MUO BMI ≥30 kg/m2 ≥30 kg/m2 ≥30 kg/m2 MetS risk factor none 1 risk factor ≥2 risk factors Diagnosis for CVD no MetS risk factor Threshold Elevated blood pressure SBP ≥ 130 mmHg or DBP ≥ 85 mmHg or use of antihypertensive medication Impaired fasting glucose fasting blood glucose ≥ 6.1 mmol/L or use of blood glucose lowering medication or diagnosis of type 2 diabetes a Decreased HDL-cholesterol b < 1.03 mmol/L in men or < 1.30 mmol/L in women or medical treatment for low HDL-C Elevated triglycerides b ≥ 1.70 mmol/L or medication for elevated triglycerides Abbreviations: BMI body mass index, CVD cardiovascular disease, DBP diastolic blood pressure, HDL high density lipoprotein cholesterol, SBP systolic blood pressure. a Diagnosis of type 2 diabetes was based on self-report and verified with self-reported medication use. b Subjects taking fibrates and/or nicotinic acid are presumed to have either high triglycerides and/or low HDL cholesterol. 106 Chapter 5

dyslipidaemia, hyperglycaemia or hypertension) [23], and had no previous diagnosis of CVD (defined as self-reported myocardial infarction, stroke, or vascular intervention). MUO was defined as obesity with at least two MetS risk factors, while in ‘intermediate’ obesity only one MetS risk factor was present. Detailed information can be found in table 1.

Dietary assessment

Food frequency questionnaire We used a self-administered food frequency questionnaire (FFQ) to assess the habitual intake of 111 food items during the last month (4 weeks). After the first visit the FFQ was filled in by the participant at home and handed in, approximately 2 weeks later, at the LifeLines research centre during the second visit for fasting venepuncture. An existing validated Dutch FFQ formed the basis for the FFQ used in the LifeLines study [24, 25]. The basic FFQ focused on estimates of energy intake and macronutrients, including alcohol intake. For 46 main food items, frequency of consumption was indicated as ‘not this month’ or in days per week or month; including the amount (in units or specified portion size) consumed each time. The FFQ also included 37 questions on consumption of sub-items (e.g. 20+/30+ cheese, 40+ cheese, 48+ cheese, or cream cheese) for which frequency was specified as never, sometimes, often and (almost) always. Values of nutrient contents of foods were obtained from the 2006 Dutch food composition table (NEVO) [26]. To correct for potential under- or over-reporting on the dietary questionnaire, participants in the top and bottom 2.5% of daily energy intake (kcal/day) were excluded. In total, only 0.05% of the number of servings/food items data was missing, while frequency of consumption had been filled out. To calculate the participants’ intake, the average consumption of the food was used according to the Dutch National Food Consumption Survey 1998 [27]. Food items with missing data on frequency (0.5%) could not be interpreted and were not included in the calculations.

Dietary patterns for obese adults Dietary patterns were derived on the basis of principal components analysis (PCA), a type of factor analysis. With PCA, linear combinations of the originally observed variables are formed by grouping together correlated food items/groups, thus, identifying underlying components, or dietary patterns, within the data. The coefficients defining these linear combinations are called factor loadings and represent the correlations of each food item/group with the dietary pattern [28]. Since the proportion of explained variance per component (i.e. dietary pattern) decreases with the number of variables entered, individual food items with a similar Dietary Patterns and Physical Activity in Obesity 107

nutrient profile and culinary use were combined into 58 food groups (Supplemental Table S1). To test the appropriateness of applying PCA on the study sample, the Kaiser- Meyer-Olkin (KMO) measurement was conducted for testing sampling adequacy and Bartlett’s Test of Sphericity (BTS) was used to test the homogeneity of variances. Next, the dietary patterns were derived on the basis of consumption (g/day) of each food group, unadjusted for energy intake. Within the PCA, orthogonal rotation (varimax option) was used to obtain uncorrelated patterns with greater interpretability. The decision to retain a component was based on the following grounds: component Eigenvalue >1.0 (indicating that the component explains more of the variance in the correlations 5 than is explained by a single variable), identification of an inflection point in the Scree plot, and interpretability of the pattern. Stability of the derived components was assessed by comparing the components solutions and factor loadings in two random halves of the data set and per sex group. A component was considered stable if the same major patterns were identified, meaning the food groups with significant contributions (factor loading >0.3 or <-0.3) were similar. A component score was created for each of the dietary patterns identified by multiplying the factor loadings by the corresponding standardized intake of the food (standardized for men and women separately), and summing across the food items/ groups for each pattern. These scores rank individuals according to the degree to which they adhered to the derived dietary pattern. Dietary patterns were named according to the foods with the highest loading on a component (considered as a loading >0.30).

Physical activity Physical activity was assessed by the validated SQUASH questionnaire (“Short QUes- tionnaire to ASsess Health-enhancing physical activity”) [29]. The SQUASH estimates habitual physical activities and is pre-structured in sports, commuting-, leisure time-, and household activities, and activities at work or school, referring to a normal week in the preceding months. Questions included type of activity, frequency, duration and intensity. Metabolic equivalent (MET) values were assigned to activities as defined by the Ainsworth’ compendium of Physical activities [30]. One MET unit is defined as the energy expenditure for sitting quietly. Activities with a MET value of 2 to < 4 were classified as light, 4 to < 6.5 as moderate, and ≥ 6.5 as vigorous intensity. A physical activity score was calculated by multiplying duration (minutes per week) with the MET value (taking into account the intensity at which an activity was performed). Subjects with implausible values from the SQUASH were excluded if: 1) ≥ 18 h/day was spent on activities listed in SQUASH [31], 2) separate categories exceeded plausible values, and/or 3) more than two activity categories of the questionnaire were missing. 108 Chapter 5

Demographic and lifestyle variables Based on the participants’ responses to the self-administered questionnaires, data were assessed on the presence of diabetes mellitus, history of myocardial infarction, stroke or vascular intervention, current medication use, current use of a (self-)prescribed diet (e.g. energy-, fat- or salt restricted diet, prescribed diet for diabetes or high cholesterol, or fiber rich diet), education level, alcohol use and smoking. Education level was categorized as low (no formal education, only primary school or intermediate vocational education), medium (higher secondary education) or high (higher vocational education and university). Alcohol consumption was defined as non-drinker, <1-≤2 drinks/day (light- moderate) and >2 drinks/day (heavy) [22]. Smoking status was defined as non-smoker, former smoker and current smoker (including the use of cigarettes, cigarillos, cigars and pipe tobacco) [22]. Missing values on education level (0.6%) and smoking status (0.2%) were imputed, using single point imputation, with age, sex, netto household income and postal code (as a proxy for social economic status) as predictors.

Statistical analyses All analyses were conducted using IBM SPSS Statistics version 22 (IBM Corporation, Armonk, NY,USA). All data are presented for men and women separately. Study characteristics were expressed as percentage (%), means with standard deviation (SD), or as median with interquartile range in case of non-normally distributed data. Differences between groups were tested by t-test for continuous variables or Kruskal-Wallis test when appropriate, and Chi-Square test for categorical variables. Multivariable logistic regression was used to determine the associations between MHO, dietary patterns (divided in quartiles) and physical activity scores (divided in tertiles) while adjusting for age and BMI (model 1), then smoking, alcohol use, education level, use of a diet and energy intake were added to the model (model 2). Subjects with MUO were used as the reference group. A P-value ≤0.05 was considered to be statistically significant.

Results

Study characteristics After exclusion of participants with incomplete data on dietary intake, implausible physical activity data, or missing data on clinical measures to define metabolic health, a total of 9,270 obese individuals were included in this study (86.1% from the original sample). Of those, 3,442 were men (37.1%) and 5,828 (62.9%) were women. The prevalence of MHO was 10.2% among men and 24.4% among women (Table 2). The prevalence of MUO was 56.9% in men and 35.3% in women, while the prevalence of ‘intermediate’ obesity was 32.9% in men and 40.3% in women. In general, MHO individuals had a Dietary Patterns and Physical Activity in Obesity 109 - b b MHO b b b b b b b 0.0 0.0 0.0 4.9 [4.7-5.2] 1.58 ± 0.26 0.91 [0.72-1.15] 101.4 ± 8.9 118 ± 7 70 ± 6 1,422 (24.4) 44.9 ± 8.3 32.1 [30.9-34.4] b b b b b b 5 b Women b Intermediate b b b 1.4 24.7 0.8 1.48 ± 0.33 1.06 [0.82-1.35] 32.8 [31.2-35.4] 104.0 ± 9.6 131 ± 16 75 ± 9 5.0 [4.7-5.4] 48.3 ± 9.9 2,348 (40.3) denotes a P <0.0001 compared to MUO. BMI= body denotes a mass P to MUO. <0.0001 compared b MUO 2.7 44.4 13.3 1.20 ± 0.27 1.76 [1.25-2.21] 33.6 [31.5-36.7] 108.0 ± 10.7 135 ± 15 77 ± 9 5.5 [5.0-6.3] 50.6 ± 9.4 2,058 (35.3) b denotes a P <0.01 and b b a b b b b MHO b b 0.0 74 ± 5 45.3 ± 8.6 0.0 1.08 [0.85-1.34] 0.0 122 ± 6 5.2 [4.9-5.5] 1.33 ± 0.20 31.4 [30.7-32.9] 108.9 ± 7.2 352 (10.2) b b b b b b b Men Intermediate b b 20.4 4.2 1.28 ± 0.25 1.21 [0.96-1.50] 0.6 137 ± 14 81 ± 9 5.2 [5.0-5.6] 49.6 ± 9.5 111.1 ± 8.2 31.8 [30.7-33.4] 1,131 (32.9) MUO 33.0 4.7 1.02 ± 0.21 2.04 [1.60-2.72] 10.5 139 ± 14 81 ± 9 5.5 [5.1-6.2] 49.4 ± 9.4 113.0 ± 9.0 32.2 [30.9-34.3] 1,959 (56.9) Clinical characteristics according to metabolic health group. to Clinical characteristics according

) 2 able 2. Use of BP lowering drugs of BP lowering Use index; index; BP= blood pressure; HDL= high-density lipoprotein; CVD= cardiovascular disease. * Based on known type 2 diabetes and newly-diagnosed type 2 diabetes (de ≥7.0 mmol/L). level fined as a single fasting plasma glucose CVD history (%) Data are presented as mean Data presented ± are SD or median [interquartile (%). range] or percentage HDL cholesterol (mmol/L) HDL cholesterol (mmol/L) Triglycerides * (%) 2 diabetes Type Systolic BP (mmHg) Systolic BP (mmHg) Diastolic (mmol/L) glucose Fasting Age (years) Age BMI (kg/m (cm) circumference Waist T N 110 Chapter 5

lower BMI and WC than MUO subjects (Table 2). The presence of the MHO phenotype decreased in older age groups for both men and women. Most men were defined as metabolically unhealthy obese, while most women below the age of 50 years were still defined as ‘intermediate’ (Figure 1).

   



   

        

 

Figure 1. Percentage of the metabolic health phenotype by age groups (left panel men, right panel women).

Table 3 presents the macronutrient intake, lifestyle behaviors (physical activity, smoking and alcohol use) and education level across the metabolic health subgroups. No differences in total energy intake or the energy-adjusted macronutrient intake were observed between the three metabolic health phenotypes. Obese men and women with MHO were more physically active than subjects with MUO. Furthermore, more men with MHO had a vigorous physical activity score in the highest tertile compared to men with MUO (43.8% vs 30.4%, P <0.0001). MHO women had more often a moderate physical activity score in the highest tertile compared to MUO women (36.1% vs 31.2%, P <0.01). Dietary Patterns and Physical Activity in Obesity 111

Table 3. Macronutrient intake, lifestyle factors and education level according to metabolic health phenotype.

Men Women MUO Intermediate MHO MUO Intermediate MHO N 1,959 (56.9) 1,131 (32.9) b 352 (10.2) b 2,058 (35.3) 2,348 (40.3) b 1,422 (24.4) b Energy intake (kcal/day) 2072 ± 488 2088 ± 496 2084 ± 492 1724 ± 443 1722 ± 451 1731 ± 465 Protein (% EI) 15.7 ± 2.3 15.6 ± 2.4 15.7 ± 2.5 16.4 ± 2.7 16.5 ± 2.7 16.4 ± 2.7 Plant protein (% EI) 6.1 ± 1.0 6.1 ± 1.0 6.2 ± 1.1 6.1 ± 1.0 6.1 ± 0.9 6.1 ± 1.0 Animal protein (% EI) 9.5 ± 2.5 9.4 ± 2.6 9.6 ± 2.8 10.4 ± 2.9 10.4 ± 2.8 10.3 ± 2.9 5 Carbohydrates (% EI) 45.0 ± 6.1 44.9 ± 6.4 45.2 ± 6.3 46.2 ± 6.3 46.0 ± 6.1 45.9 ± 6.2 Mono- and disaccharides 19.6 ± 5.8 19.5 ± 5.5 19.7 ± 5.3 20.6 ± 5.8 20.6 ± 5.4 20.5 ± 5.2 (% EI) Polysaccharides (% EI) 25.4 ± 4.8 25.3 ± 4.8 25.4 ± 4.7 25.5 ± 4.4 25.3 ± 4.5 25.3 ± 4.6 Fat (% EI) 36.0 ± 5.1 36.0 ± 5.1 36.0 ± 4.6 35.9 ± 5.4 35.9 ± 5.2 36.1 ± 5.1 Use of restricted/enriched 5.1 4.9 6.6 12.6 12.2 10.6 diet (%) MVPA (min/week) 285 [60-960] 360 [120-999] a 360 [140-1337] a 230 [60-600] 270 [100-646] a 242 [90-720] a Moderate PA score T1 ‘low’ 45.7 43.4 41.5 48.0 43.2 a 43.1 a T2 ‘medium’ 21.1 21.9 23.6 20.8 23.2 20.7 T3 ‘high’ 33.2 34.7 34.9 31.2 33.6 36.1 a Vigorous PA score T1 ‘low’ 36.1 31.2 a 25.9 a 36.2 33.6 32.5 T2 ‘medium’ 33.5 33.7 30.4 30.0 31.0 33.1 T3 ‘high’ 30.4 35.1 a 43.8 b 33.8 35.5 34.4 Smoking (%) Non 33.4 40.5 a 47.7 b 41.2 46.8 a 49.2 b Former 42.0 41.8 36.6 38.6 38.9 37.9 Current 24.6 17.7 b 15.6 a 20.2 14.3 b 12.9 b Use alcohol (%) Non 18.0 11.7 b 11.6 45.0 36.6 b 33.4 b ≤2 drinks 67.4 74.7 b 75.9 a 51.8 60.1 b 63.9 b >2 drinks 14.6 13.6 12.5 3.2 3.2 2.7 Education level (%) Low 39.7 39.4 36.4 46.8 40.1 b 32.2 b Medium 37.8 38.0 41.5 39.3 42.0 43.0 High 22.5 22.5 22.2 13.8 17.9 a 24.8 b Data is presented as mean ± SD or median [interquartile range]. a denotes a P <0.01 and b denotes a P <0.0001. EI = energy intake; MUO= metabolically unhealthy obese; MHO= metabolically healthy obese; MVPA= moderate-vigorous physical active; PA= physical activity. 112 Chapter 5

Since the metabolic health of a subject highly depends on age, we also checked the association between age and physical activity. Moderate physical activity scores were lower in older age groups, however, no clear association was found with the metabolic health status. Instead, vigorous physical activity scores were higher within older age groups, and higher among subjects with ‘intermediate’ obesity- and men with MHO compared to men with MUO (Figure 2).

    

       

 

                             

   

    

       

 

   

Figure 2. Moderate- and vigorous physical activity (PA) score (continuous) at the 75th percentile among age groups (left panel men, right panel women). Dietary Patterns and Physical Activity in Obesity 113

Fewer subjects with MHO were current smoker or did not use alcohol compared to subjects with MUO. The metabolically healthy obese men and women were more frequently light-moderate alcohol consumers (≤2 drinks/day). The distribution of education level was not different between the metabolic health subgroups in men, however, women with MHO were more frequent highly educated with 24.8% compared to 13.8% among women with MUO (P <0.0001).

Dietary patterns in the obese population To validate appropriateness of applying PCA on our study sample, we calculated the 5 KMO and BTS values. The observed KMO was 0.73 suggesting that the study sample is suitable for PCA (should not be lower than 0.5). The BTS was significant (P <0.0001), indicating homogeneity of variance of the foods consumed. Figure 3 shows the Scree plot of Eigenvalues for each component. The Eigenvalues of the components dropped substantially until the fourth component (2.18). After the fourth component the Eigen- values remained more consistent, 1.65 for the fifth and 1.62 for the sixth component (Figure 3). As a result, we retained the 4 components solution. These four components overall explained 18.7% (5.7%, 4.5%, 4.4% and 4.1%, respectively) of the variations in food intake. PCA conducted on the two random halves of the dataset yielded similar results (data not shown). The same four major patterns were identified for men and women, although the magnitude of the loadings differed more as compared to the derived outcomes in the random halves of the dataset (data not shown).

Scree Plot

2 Eigenvalue

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 Component Number

Figure 3. Scree plot resulting from principal component analysis.

Page 1 114 Chapter 5

The loadings of the food groups on the components (dietary patterns) are shown in Table 4. Positive loadings indicate that the subsequent food group is highly correlated with the corresponding dietary pattern, whereas negative loadings are inversely correlated. The first dietary pattern we labeled as the ‘savory snacks and sweets’ pattern, which was characterized by high intakes of salty snackfood (warm and cold), fried potatoes, sauces (warm sauces as well as mayonnaise and other non-red sauces) and sweets

Table 4. Loadings of the food groups on the dietary patterns.

Food group Savory snacks and Meat and Alcohol Bread, Potatoes and Vegetables, Fruit Sweets Sweet snacks and Fish Warm sauces ,551 ,132 -,021 ,136 Savory snacks ,500 -,007 ,030 -,107 Fried potatoes ,470 ,128 ,058 -,186 Pasta ,459 ,151 ,027 ,188 Chocolate ,431 -,210 ,093 ,092 Cold sauces ,415 ,069 ,058 -,192 Commercially prepared dishes (ready ,398 ,032 -,189 -,111 to eat meals) Pastries ,384 -,087 ,313 -,037 Candybar ,383 -,134 ,082 -,075 Pizza ,377 ,022 -,124 -,127 Composed foods ,347 ,156 -,022 -,019 Rice ,343 ,142 -,051 ,297 Candy ,342 -,089 ,111 -,023 Salad dressing ,313 ,116 -,117 ,161 Peanuts, nuts and seeds ,292 ,163 -,006 ,057 Wipped cream ,267 ,027 ,120 ,012 Ice cream ,247 -,022 ,040 ,011 Low sugar bevareges ,219 -,008 -,016 -,065 Fruit juices ,134 ,002 ,025 -,093 Processed meat ,137 ,538 ,205 -,020 Beer ,017 ,434 -,086 -,287 Red meat -,004 ,429 ,214 -,047 Coffee -,072 ,415 ,012 -,109 Spirits ,055 ,389 -,138 -,118 Wine and fortified wine -,013 ,318 -,301 ,165 Eggs ,043 ,313 -,224 ,135 Lean red meat ,092 ,304 ,018 ,130 Legumes ,021 ,271 ,061 ,039 Cheese – high fat ,152 ,261 ,013 -,014 Dietary Patterns and Physical Activity in Obesity 115

Table 4. Loadings of the food groups on the dietary patterns. (continued) Food group Savory snacks and Meat and Alcohol Bread, Potatoes and Vegetables, Fruit Sweets Sweet snacks and Fish Soup ,059 ,198 -,040 ,072 Beer – light -,002 ,118 -,039 -,048 Bread ,015 ,302 ,599 -,037 Edible fat ,010 ,299 ,591 -,045 Potatoes -,099 ,304 ,570 ,015 Sweet sandwich toppings ,094 -,185 ,498 ,077 Gravy -,049 ,340 ,486 -,076 5 Biscuits ,261 -,300 ,328 ,217 Desserts -,024 -,024 ,301 ,028 Apple sauce ,067 -,085 ,254 -,121 Nonfermented medium /low fat milk ,040 -,022 ,172 ,002 Fermented milk products - sweetened ,061 -,093 ,136 ,021 Vegetables -,002 ,238 ,118 ,543 Fruit -,182 -,073 ,091 ,454 Warm savory snacks ,417 ,183 ,056 -,434 High sugar beverages ,231 ,046 ,074 -,396 Tea ,025 -,306 ,093 ,391 Fatty fish ,005 ,187 -,293 ,357 Mayonnaise ,326 ,177 -,014 -,338 Lean fish ,057 ,154 -,267 ,336 Fermented milk products - unsweetened -,085 -,010 ,060 ,322 Added sugar ,011 ,113 ,154 -,295 Chicken ,107 ,169 -,052 ,278 Quark -,003 -,066 -,056 ,239 Cheese – low fat -,173 ,030 ,080 ,237 Cereals ,104 -,104 -,033 ,209 Chocolate milk ,107 -,096 ,124 -,164 Nonfermented whole milk -,039 ,092 ,054 -,119 Breakfast drink ,021 -,011 -,050 -,081

(chocolate, candy(bars) and pastries). The second pattern, labeled as the ‘meat and alcohol’ pattern, mainly consisted of processed meat, (lean) red meat, beer, spirits, wine/ fortified wine, eggs and coffee, but low consumption of tea and biscuits. Food groups in the third pattern, the so-called ‘bread, potatoes and sweet snacks’, had high loadings on bread, potatoes, edible fat, gravy, sweet sandwich toppings, pastries, biscuits and desserts, whereas, wine/fortified wine and fish were inversely correlated with this pattern. The fourth dietary pattern labeled as ‘fruit, vegetables and fish’ was characterized by high 116 Chapter 5                              

  

 

                                          

  

        Dietary pattern scores across age groups (left panel men, right women). age groups Dietary across scores pattern

igure 4. igure F Dietary Patterns and Physical Activity in Obesity 117

consumption of fruit, vegetables, fatty and lean fish, tea and unsweetened fermented milk products, and low consumption of high sugar beverages, mayonnaise and salty snackfood.

Dietary patterns and demographic- and lifestyle characteristics Because dietary patterns are part of a broader pattern of lifestyle factors, demographic and lifestyle characteristics are shown by quartiles of dietary pattern scores in Table 5. Di- etary pattern scores were also checked across age groups (Figure 4). Interpreting Table 5 5 and Figure 4 combined, we came to the following observations. Subjects in the highest quartile of the ‘savory snacks and sweets’ pattern, were of a younger age, more often metabolically healthy obese, higher educated and had less often a chronic condition (T2D, history of CVD, or hypertension) or used a (self-) prescribed diet. At the same time, they were less physically active and more often smokers. Scores on both the ‘meat and alcohol’ pattern and the ‘bread, potatoes and sweet snacks’ were stable across age groups, but subjects with a high score on these patterns were less often metabolically healthy obese and were less educated compared to subjects with a low score. The ‘meat and alcohol’ pattern was, furthermore, characterized by less physical activity, smoking and heavy alcohol use (17.4% consuming >2 drinks/day). In contrast, subjects in the highest quartile of the ‘bread, potatoes and sweet snacks’ pattern were more physically active, were less often a smoker and drank less alcohol. Older people were more likely to fit the ‘fruit, vegetables and fish’ pattern. The pattern was also associated with higher education levels, more physical activity, less smokers (although with more former smokers) and less alcohol use. However, subjects with a high score on the ‘ fruit, vegetables and fish’ pattern were more likely to have a chronic condition and used more often a (self-) prescribed diet.

Dietary patterns and physical activity as determinants of MHO Table 6 shows the multivariable associations between lifestyle behaviors and MHO in men and women, taking into account differences in age, BMI, education level, following a (self-) prescribed diet and energy intake. There was a gradual lower odds ratio (OR) for the MHO phenotype within higher age groups. Also, men and women with a BMI ≥35 kg/m2 were less likely MHO. Of the four obesity-specific dietary patterns, two patterns were associated with MHO in women only. Among women, a higher score on the ‘fruit, vegetables and fish’ pattern, non-smoking and alcohol consumption up to 2 drinks/day was associated with a higher OR for MHO. However, higher scores on the ‘bread, potatoes and sweet snacks’ pattern was associated with a lower OR for the MHO phenotype. Among men, higher vigorous 118 Chapter 5 b b a b b b b a b b 41.3 19.8 1813 ± 490 290 [120-680] 300 [0-1500] 960 [240-2160] 32.8 28.6 44.2 46.7 9.1 51.6 ± 9.9 32.3 [30.9-34.6] 38.5 38.9 Q4 2,317 859 (37.1) 44.8 18.5 1973 ± 511 270 [60-960] 300 [0-3780] 480 [0-1440] 47.5 11.4 39.0 30.3 30.7 44.8 ± 8.1 32.7 [31.1-35.4] 41.1 Fruit, vegetables and fish vegetables Fruit, 36.7 Q1 2,317 860 (37.1) b a a b a a b b b a a 17.7 2256 ± 404 300 [0-2700] 780 [80-1920] 46.9 13.8 46.6 37.5 15.9 48.4 ± 9.8 32.4 [30.9-34.9] 44.9 39.4 300 [100-840] 37.4 Q4 2,317 860 (37.1) 22.1 1504 ± 432 300 [0-1700] 720 [0-1800] 32.2 27.7 38.6 42.4 19.0 49.0 ± 9.5 32.4 [30.9-34.8] 39.4 40.1 Bread, potatoes and sweet snacks and sweet potatoes Bread, 250 [75-660] 38.5 Q1 2,317 860 (37.1) a b b a a a b b b 48.7 ± 8.9 32.5 [31.0-35.2] 43.8 18.3 2191 ± 456 300 [0-1700] 640 [0-1680] 41.4 40.5 18.1 31.3 42.8 25.9 280 [90-780] 37.9 Q4 2,317 860 (37.1) 47.9 ± 10.1 32.5 [30.9-34.8] 42.1 21.7 1572 ± 449 240 [0-2025] 800 [48-1920] 37.1 41.1 21.8 54.4 34.9 10.7 Meat and alcohol Meat 268 [90-720] 36.3 Q1 2,317 860 (37.1) a a b b b b b b b b b b a 40.1 255 [90-769] 645 [0-1600] 29.8 45.7 46.3 34.0 19.7 32.6 [31.1-35.3] 37.5 2279 ± 411 300 [0-2625] 24.5 Q4 22.4 2,317 860 (37.1) 44.2 ± 7.4 Savory snacks and sweets 46.4 280 [110-720] 960 [105-2160] 52.1 33.4 38.6 45.0 16.4 33.5 [30.9-34.7] 38.6 1486 ± 412 225 [0-1350] 14.5 Q1 15.0 2,317 860 (37.1) 53.9 ± 9.9 Demographic and lifestyle variables within the first and fourth variables within the first and and lifestyle Demographic quartile of the dietary scores. pattern

), median [IQR] 2 able 5. BMI (kg/m Metabolic health (%) MUO (min/week) MVPA score PA Vigorous (%) Education Low Medium (%) Smoking Non Former Current T Intermediate (kcal/day) intake Energy score PA Moderate High MHO N Males (%) Age (years), mean ± sd (years), Age Dietary Patterns and Physical Activity in Obesity 119 b b b b b b a 25.6 69.1 5.4 18.9 6.6 3.4 29.4 Q4 35.5 52.9 11.6 3.5 3.2 1.8 19.5 Fruit, vegetables and fish vegetables Fruit, Q1 5 b b b b 38.8 57.1 4.1 5.2 5.4 2.8 24.5 Q4 denotes a P <0.0001 compared to Q1 of the same dietary b 20.3 68.3 11.4 15.8 4.5 2.6 25.2 Bread, potatoes and sweet snacks and sweet potatoes Bread, Q1 denotes a P <0.01 and a b b b b 17.9 7.0 5.0 2.4 25.2 64.7 17.4 Q4 44.1 11.8 5.6 2.2 24.3 Meat and alcohol Meat 55.0 0.9 Q1 a b b b b 63.2 7.8 3.9 2.8 1.2 17.7 Q4 29.0 Savory snacks and sweets 60.2 6.8 18.2 10.3 4.4 37.3 Q1 33.0 Demographic and lifestyle variables within the first and fourth variables within the first and and lifestyle Demographic quartile (continued) of the dietary scores. pattern able 5. T ≤2 drinks >3 drinks diet (%) (Self-) prescribed (%) 2 diabetes Type CVD history (%) (%) drug of BP lowering Use Data is presented as mean ± SD or median [interquartile range] or percentage (%). pattern. MVPA= moderate–vigorous physical activity; physical moderate–vigorous disease; BP= blood pressure. CVD= cardiovascular MVPA= pattern. Use alcohol (%) alcohol Use Non 120 Chapter 5

PA, non-smoking and alcohol consumption was associated with a higher OR for MHO (model 2, Table 6). Since we have adapted the revised NCEP ATPIII by using a less strict threshold for impaired fasting glucose (≥6.1 mmol/L), in a subsequent analysis we applied the more strict cut-off of ≥5.6 mmol/L to define metabolic health (data not shown). This resulted in 196 fewer individuals classified as MHO and 566 more individuals classified as MUO. The observed associations in the multivariable regression were stronger (generally first decimal place increased), for both men and women, using the more strict threshold. No new associations were found.

Table 6. Multivariable-adjusted odds ratios for the associations of demographic- and lifestyle factors with the metabolically healthy obesity phenotype. Men Women Model 1 Model 2 Model 1 Model 2 Age group 30-39 1 1 1 1 40-49 0.75 (0.56-1.01) 0.75 (0.56-1.01) 0.45 (0.37-0.55) 0.47 (0.38-0.57) 50-59 0.36 (0.25-0.53) b 0.36 (0.24-0.53) b 0.22 (0.17-0.28) b 0.22 (0.17-0.28) b 60-69 0.33 (0.21-0.53) b 0.33 (0.21-0.54) b 0.10 (0.07-0.13) b 0.09 (0.07-0.13) b BMI group 30-34.9 1 1 1 ≥35 0.37 (0.24-0.55) b 0.34 (0.23-0.52) b 0.38 (0.32-0.45) b 0.39 (0.33-0.46) b Dietary pattern Savory snacks and sweets Q1 1 1 1 1 Q2 1.12 (0.78-1.63) 1.16 (0.79-1.71) 1.26 (1.01-1.57) 1.14 (0.90-1.44) Q3 1.46 (1.02-2.09) 1.55 (1.03-2.33) 1.13 (0.90-1.42) 0.99 (0.77-1.28) Q4 1.29 (0.88-1.90) 1.43 (0.87-2.36) 1.43 (1.13-1.81) a 1.27 (0.92-1.75) Meat and alcohol Q1 1 1 1 1 Q2 0.89 (0.64-1.23) 0.90 (0.64-1.26) 0.98 (0.80-1.21) 1.00 (0.81-1.24) Q3 0.78 (0.56-1.09) 0.83 (0.58-1.20) 0.83 (0.67-1.03) 0.81 (0.65-1.01) Q4 0.72 (0.51-1.02) 0.85 (0.56-1.29) 1.02 (0.82-1.26) 1.02 (0.80-1.31) Bread, potatoes and sweet snacks Q1 1 1 1 1 Q2 0.78 (0.56-1.09) 0.79 (0.56-1.12) 0.64 (0.52-0.80) 0.68 (0.55-0.85) a Q3 0.75 (0.54-1.06) 0.79 (0.54-1.16) 0.66 (0.53-0.81) 0.72 (0.56-0.92) a Q4 0.92 (0.66-1.28) 1.03 (0.65-1.64) 0.55 (0.44-0.68) 0.60 (0.44-0.81) a Dietary Patterns and Physical Activity in Obesity 121

Table 6. Multivariable-adjusted odds ratios for the associations of demographic- and lifestyle factors with the metabolically healthy obesity phenotype. (continued) Men Women Model 1 Model 2 Model 1 Model 2 Fruit, vegetables and fish Q1 1 1 1 1 Q2 1.03 (0.75-1.44) 1.01 (0.72-1.41) 1.24 (1.01-1.54) 1.11 (0.89-1.38) Q3 0.88 (0.62-1.24) 0.81 (0.57-1.16) 1.55 (1.25-1.92) b 1.31 (1.04-1.64) Q4 0.96 (0.68-1.36) 0.87 (0.60-1.27) 1.75 (1.40-2.19) b 1.46 (1.15-1.87) a Moderate PA score 5 T1 ‘low’ 1 1 1 1 T2 ‘medium’ 1.22 (0.90-1.65) 1.17 (0.86-1.59) 1.10 (0.90-1.34) 1.07 (0.87-1.30) T3 ‘high’ 1.03 (0.78-1.35) 0.98 (0.73-1.30) 1.15 (0.97-1.36) 1.17 (0.98-1.38) Vigorous PA score T1 ‘low’ 1 1 1 1 T2 ‘medium’ 1.21 (0.89-1.65) 1.18 (0.86-1.61) 1.17 (0.97-1.40) 1.13 (0.94-1.35) T3 ‘high’ 2.11 (1.57-2.83) b 1.96 (1.45-2.64) b 1.21 (1.01-1.45) 1.17 (0.97-1.40) Smoking Non 1 1 Former 0.73 (0.55-0.96) 0.93 (0.78-1.10) Current 0.44 (0.31-0.63) b 0.49 (0.39-0.62) b Alcohol Non 1 1 ≤ 2 drink/day 1.86 (1.28-2.70) a 1.59 (1.34-1.88) b > 2 drinks/day 1.81 (1.07-3.04) 1.33 (0.82-2.15) Education Low 1 1 Middle 1.00 (0.76-1.31) 0.94 (0.79-1.12) High 0.86 (0.61-1.21) 1.22 (0.97-1.53) Use of diet No 1 1 Yes 1.46 (0.88-2.44) 0.71 (0.56-0.92) a Energy intake* 0.81 (0.51-1.29) 0.93 (0.67-1.28) Data are expressed as odds ratios (95% confidence interval). Reference group is the metabolically unhealthy obese. Values in BOLD indicate a P <0.05, a denotes a P <0.01 and b denotes a P <0.0001. Dietary patterns are presented in quartiles, and moderate- and vigorous physical activity (PA) score are presented in tertiles. * Energy intake is given per 1.000 kcal increase. 122 Chapter 5

Discussion

In this study of 9,270 obese adults between 30-70 years of age, more than half of the obese men and more than one third of obese women were metabolically unhealthy. Only 10% of men, and 25% of women were metabolically healthy obese. We found that compared to women with MUO women with MHO had a healthier diet, rich in fruit, vegetables, fish and fermented milk products (unsweeted) while avoiding high sugar beverages and, salty- and sweet snackfood. Compared to the metabolically unhealthy obese, men with MHO were characterized by higher engagement in intensive vigorous physical activity. In addition, moderate alcohol consumption and non-smoking were positively associated with MHO in both men and women.

Dietary patterns as determinant of MHO Dietary and lifestyle factors are known to play an important role in the development of insulin resistance, obesity, metabolic syndrome and T2D [32, 33]. Consistent with previous data, in our obese population total energy intake and dietary macronutrient composition did not differ between subjects with MHO and subjects with MUO [13-16]. We identified four major obesity-specific dietary patterns, which we called the ‘savory snacks and sweets’ pattern, the ‘meat and alcohol’ pattern, the ‘bread, potatoes and sweet snacks’ pattern and the ‘fruit, vegetables and fish’ pattern. Higher scores on the ‘fruit, vegetables and fish’ pattern were positively associated with MHO in a dose-dependent way, whereas higher scores on the ‘bread, potatoes and sweet snacks’ pattern were inversely associated with MHO. These associations were only observed in women. The ‘fruit, vegetables and fish’ pattern mainly consisted of foods, which we often consider as healthy. Previous studies showed that higher intakes of vegetables and fruit were associated with a lower risk of the MetS and CVD [34], and a higher fruit consumption was associated with lower risk of T2D [35, 36]. Furthermore, higher scores on the ‘fruit, vegetables and fish’ pattern mean higher intake of fish, chicken, fermented milk products (unsweetened) and low-fat cheese, which suggests a higher overall protein intake. Both epidemiological and experimental data show that the consumption of dairy products have a beneficial effect on MetS risk factors and are associated with a lower risk of body fat gain and obesity as well as CVD [37]. Consumption of cheese and fermented dairy product were also inversely associated with T2D incidence [38, 39]. The pattern of ‘bread, potatoes and sweet snacks’ was inversely associated with MHO in our study. This pattern reflects a diet high in carbohydrates. Bread was mainly eaten in combination with sweet sandwich toppings, a typical Dutch eating habit. Furthermore, mainly sweet snacks were consumed like pastries, biscuits and desserts. These products are often labeled as foods with a high glycaemic index (GI), causing a quick, but only short-term supply of energy [40]. Although mixed results were reported for the expe- Dietary Patterns and Physical Activity in Obesity 123

rienced feeling of hunger and energy-intake after such high GI food or meal [41, 42], intervention studies have reported an inverse association between high GI diets and HDL-C [43, 44] and a positive association with triglycerides [45]. Furthermore, a higher GI was associated with the development of MetS [46]. Our observed finding is in line with data that suggest that high GI foods may deteriorate the metabolic risk profile. In practice, for those obese individuals who attempt to improve their health but experience difficulties to lose weight, shifting to the ‘fruit, vegetables and fish’-enriched diet may be more appealing or easier option to consider. To date, only a few studies examined dietary patterns and metabolic health in 5 obesity. In two studies ‘a priori’ dietary scores were applied [16, 19]. A study in obese adolescents and adults in the US used the Healthy Eating Index (HEI-2005) and found that the total score was higher in MHO adolescents and adult women compared with metabolically abnormal obese, e.g. MUO [19]. In an Irish cohort study, there was no association between dietary quality (the extent to which the eating behaviour is “healthy”), based on the DASH score (Dietary Approaches to Stop Hypertension)), and MHO [16]. The food pyramid score, representing the optimal number of servings from each basic food groups in daily food intake, was higher among MHO. However, in adjusted analysis (for sex, age, physical activity, alcohol, smoking and dietary quality) it was no longer associated with metabolic health. To the best of our knowledge there is only one study by Bell et al. [47] which used the ‘a posteriori’ approach, where actual dietary patterns were determined, which are habitual in nature [18]. The odds of having a more metabolically healthy profile was 16% greater for every standard deviation increase in the ‘healthy’ dietary pattern score (including high loadings of whole grains, fresh fruit, dried fruit, legumes and low fat dairy) [47]. Although the criteria for metabolic health between our study and the one by Bell et al. [47] were different and dietary patterns were based on a population including non-obese and obese participants, based on the outcomes of both studies we hypothesize that small changes in the diet may improve metabolic health risk. Our findings for dietary patterns and MHO were observed for women only. Such a sex difference may reflect differences in physiology, reporting of diet, or the amount consumed of the specific types of foods that contributed strongly to the pattern score [48]. For instance, women reported higher consumptions of fruit, vegetables, fish and fermented milk products (unsweetened) which contribute highly to the ‘fruit, vegetables and fish’ pattern, whereas pastries, biscuits and desserts which contribute high to the ‘bread, potatoes and sweet snacks’ pattern, were also more consumed by women than men (data not shown). Thus, higher pattern scores in women did also mean higher intakes of the specific foods likely to be associated with metabolic (un)health. We also hypothesize that men and women have a different view on improving health status. 124 Chapter 5

While women will mainly adapt their diet to lose weight, men are more focused on improving their physical fitness.

The role demographic- and lifestyle factors in dietary patterns In addition to the observed sex difference in eating behaviour, age played an important role. Subjects with a high score on the ‘savory snacks and sweets’ pattern were younger and had fewer chronic conditions, whereas subjects with a high score on the ‘fruit, vegetables and fish’ pattern were older and had more chronic conditions. While the first pattern is intuitively seen as an unhealthy dietary pattern, the latter reflects more health consciousness. Health status may, therefore, be important for the food choices made. Dietary habits may also be a proxy for other (lifestyle) variables, such as physical activity, smoking, alcohol use and educational attainment. This suggests that it is necessary to take a more holistic approach when dietary patterns are studied, otherwise incorrect conclusions could be drawn upon the possible effects of foods.

Physical activity as determinant of MHO Increasing one’s physical activity has the potential to improve adiposity profile and metabolic risk, even in the absence of weight loss [49]. A more favourable fat distribution, with less visceral fat, was associated with a long-term metabolically healthy profile in obese adults over a period of 10 year and no excess risk of T2D and CVD [50]. In line with our results, other studies on physical activity and MHO found that both objectively measured total physical activity [12] and self-reported moderate-vigorous physical activity were higher in the MHO group compared to MUO group [51-53]. In the present study, especially the level of vigorous physical activity was an important feature in the relationship between physical activity and MHO, found only in men. In general, men engage in more physical activity compared to women (data not shown). We, therefore, hypothesize that this results in a stronger positive association between vigorous physical activity and MHO in men. Another explanation may come from the potential of biased results. SQUASH is a self-reported physical activity questionnaire, where subjects are asked to specify the intensity level of each performed activity. Women may experience certain activities as more intensive than men, while actually a lower cardiorespiratory response is obtained for the same type of physical activity and the same indicated intensity level. As a result men may develop a higher cardiorespiratory fitness level than women, which has been linked to MHO [54]. Epidemiological observations provide new indications that it is important to reduce time in sedentary behaviors (e.g. activities involving low levels of metabolic energy expenditure, primarily sitting and laying down) [55]. In a recent study, metabolically healthy obese had a higher total step count and were on average less sedentary compared to metabolically unhealthy obese [56]. Studies on the interactive nature of physical activ- Dietary Patterns and Physical Activity in Obesity 125

ity and sitting time on metabolic risk factor clustering were inconclusive. While some studies suggest that the strength of the association between sitting and metabolic risk depends on the engagement in physical activity [57, 58], other studies reported that higher sitting time was associated with metabolic risk independently of physical activity [59-61]. Since the SQUASH questionnaire, used in LifeLines, is not designed to capture sedentary behavior, we were not able to compare its levels among the metabolic health phenotypes.

Strengths and limitations 5 Our study includes a representative sample of the Dutch population using extensive questionnaires to measure important lifestyle behaviors, and standardized protocols to obtain clinical and biochemical measurements. Another strength of our study is that it is the largest study on this topic to date and the use of obesity-specific dietary patterns. However, there are also some limitations. Dietary intake, physical activity and other lifestyle behaviors were based on self-reported data, and are subject to recall bias. Obese individuals tend to underestimate their dietary intake and overestimate their physical activity [62]. However, in our study we only used obese subjects to make comparisons, hence, over- and underreporting are likely to be equally distributed over the three metabolic health groups. Furthermore, we ranked individuals into categories, rather than using the estimated absolute quantities, which reduces the effect of over- or underreporting. Although PCA is extensively used in nutritional epidemiology and showed reason- able reproducibility and validity using FFQ data [63-66], more validation studies are needed. Furthermore, PCA requires several arbitrary decisions, such as the pre-selection of food groups, the number of retained patterns, the method of rotation, and the cut-off value used to define a significant contribution of the factor loadings [28]. Yet, it was found that derived dietary patterns were robust for subjective factor analytical decisions [67]. The four dietary patterns explained 18.7% of the total variance in consumption of the food groups. Although this seems low compared to other studies using PCA, the percent variance explained is a function of the number of food items included in the factor analysis [28]. We used more food items compared to others [48, 68, 69], which results in a lower explained variance, though, the derived patterns are more detailed [28]. Next, we utilized cross-sectional data from which we cannot infer causality. It is, therefore, not possible to rule out reverse causality. Individuals which experience negative health outcomes, such as having T2D, a CVD history or the knowledge of having dyslipidaemia or hypertension, may have changed their dietary intake and their physical activity. We consider it likely that dietary patterns are not isolated, but part of a broader lifestyle and subject to someone’s phase in life. 126 Chapter 5

Conclusion

Our research showed that key lifestyle behaviors differed between metabolically healthy obese and metabolically unhealthy obese adults aged 30-69 years. While non- modifiable factors like age and sex are important for determining someone’s baseline odds for MHO, our data suggests that a healthier diet in women and vigorous physical activity in men (as well as non-smoking and moderate alcohol use) may be related to this favourable obesity state. Identification of behavioural lifestyle patterns may help in pinpointing vulnerable subgroups in the obese population and to develop potential strategies improving metabolic health.

Acknowledgements

The authors wish to acknowledge the services of the LifeLines Cohort Study, the contributing research centers delivering data to LifeLines, and all the study participants. Dietary Patterns and Physical Activity in Obesity 127

References

1. Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood 11. van Vliet-Ostaptchouk JV, Nuotio ML, Slagter SN, DT, Moodie ML, Gortmaker SL: The global obesity Doiron D, Fischer K, Foco L, Gaye A, Gogele M, Heier pandemic: shaped by global drivers and local environ- M, Hiekkalinna T et al: The prevalence of metabolic ments. Lancet (London, England) 2011, 378(9793):804- syndrome and metabolically healthy obesity in Europe: 814. a collaborative analysis of ten large cohort studies. BMC 2. Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, endocrine disorders 2014, 14:9. Bales VS, Marks JS: Prevalence of obesity, diabetes, and 12. Bell JA, Hamer M, van Hees VT, Singh-Manoux A, Kivi- obesity-related health risk factors, 2001. JAMA 2003, maki M, Sabia S: Healthy obesity and objective physical 5 289(1):76-79. activity. The American journal of clinical nutrition 2015, 3. Denis GV, Obin MS: ‘Metabolically healthy obesity’: 102(2):268-275. origins and implications. Molecular aspects of medicine 13. Hankinson AL, Daviglus ML, Van Horn L, Chan Q, Brown 2013, 34(1):59-70. I, Holmes E, Elliott P, Stamler J: Diet composition and 4. Bell JA, Kivimaki M, Hamer M: Metabolically healthy activity level of at risk and metabolically healthy obese obesity and risk of incident type 2 diabetes: a meta- American adults. Obesity (Silver Spring, Md) 2013, analysis of prospective cohort studies. Obesity reviews : 21(3):637-643. an official journal of the International Association for the 14. Kimokoti RW, Judd SE, Shikany JM, Newby PK: Food Study of Obesity 2014, 15(6):504-515. intake does not differ between obese women who 5. Kramer CK, Zinman B, Retnakaran R: Are metabolically are metabolically healthy or abnormal. The Journal of healthy overweight and obesity benign conditions?: A nutrition 2014, 144(12):2018-2026. systematic review and meta-analysis. Annals of internal 15. Kimokoti RW, Judd SE, Shikany JM, Newby PK: Meta- medicine 2013, 159(11):758-769. bolically Healthy Obesity Is Not Associated with Food 6. Ebbeling CB, Swain JF, Feldman HA, Wong WW, Hachey Intake in White or Black Men. The Journal of nutrition DL, Garcia-Lago E, Ludwig DS: Effects of dietary 2015, 145(11):2551-2561. composition on energy expenditure during weight-loss 16. Phillips CM, Dillon C, Harrington JM, McCarthy VJ, Kear- maintenance. JAMA 2012, 307(24):2627-2634. ney PM, Fitzgerald AP, Perry IJ: Defining metabolically 7. Kraschnewski JL, Boan J, Esposito J, Sherwood NE, healthy obesity: role of dietary and lifestyle factors. Lehman EB, Kephart DK, Sciamanna CN: Long-term PloS one 2013, 8(10):e76188. weight loss maintenance in the United States. Inter- 17. Hu FB: Dietary pattern analysis: a new direction in national journal of obesity (2005) 2010, 34(11):1644- nutritional epidemiology. Current opinion in lipidology 1654. 2002, 13(1):3-9. 8. Achilike I, Hazuda HP, Fowler SP, Aung K, Lorenzo 18. Michels KB, Schulze MB: Can dietary patterns help us C: Predicting the development of the metabolically detect diet-disease associations? Nutrition research healthy obese phenotype. International journal of reviews 2005, 18(2):241-248. obesity (2005) 2015, 39(2):228-234. 19. Camhi SM, Whitney Evans E, Hayman LL, Lichtenstein 9. Eshtiaghi R, Keihani S, Hosseinpanah F, Barzin M, Azizi AH, Must A: Healthy eating index and metabolically F: Natural course of metabolically healthy abdominal healthy obesity in U.S. adolescents and adults. Preven- obese adults after 10 years of follow-up: the Tehran tive medicine 2015, 77:23-27. Lipid and Glucose Study. International journal of obesity 20. Klijs B, Scholtens S, Mandemakers JJ, Snieder H, Stolk (2005) 2015, 39(3):514-519. RP, Smidt N: Representativeness of the LifeLines Cohort 10. Hamer M, Bell JA, Sabia S, Batty GD, Kivimaki M: Stabil- Study. PloS one 2015, 10(9):e0137203. ity of metabolically healthy obesity over 8 years: the 21. Scholtens S, Smidt N, Swertz MA, Bakker SJ, Dotinga A, English Longitudinal Study of Ageing. European journal Vonk JM, van Dijk F, van Zon SK, Wijmenga C, Wolffen- of endocrinology / European Federation of Endocrine buttel BH et al: Cohort Profile: LifeLines, a three- Societies 2015, 173(5):703-708. 128 Chapter 5

generation cohort study and biobank. International journal of behavioral nutrition and physical activity journal of epidemiology 2015, 44(4):1172-1180. 2015, 12:132. 22. Slagter SN, van Vliet-Ostaptchouk JV, Vonk JM, Boezen 32. Harrington JM, Phillips CM: Nutrigenetics: bridging two HM, Dullaart RP, Kobold AC, Feskens EJ, van Beek AP, worlds to understand type 2 diabetes. Current diabetes van der Klauw MM, Wolffenbuttel BH: Combined effects reports 2014, 14(4):477. of smoking and alcohol on metabolic syndrome: the 33. Phillips CM: Nutrigenetics and metabolic disease: cur- LifeLines cohort study. PloS one 2014, 9(4):e96406. rent status and implications for personalised nutrition. 23. Expert Panel on Detection Evaluation, and Treatment of Nutrients 2013, 5(1):32-57. High Blood Cholesterol in Adults: Executive Summary of 34. Esmaillzadeh A, Kimiagar M, Mehrabi Y, Azadbakht The Third Report of The National Cholesterol Education L, Hu FB, Willett WC: Fruit and vegetable intakes, Program (NCEP) Expert Panel on Detection, Evaluation, C-reactive protein, and the metabolic syndrome. The And Treatment of High Blood Cholesterol In Adults American journal of clinical nutrition 2006, 84(6):1489- (Adult Treatment Panel III). JAMA 2001, 285(19):2486- 1497. 2497. 35. Bazzano LA, Li TY, Joshipura KJ, Hu FB: Intake of fruit, 24. Siebelink E, Geelen A, de Vries JH: Self-reported energy vegetables, and fruit juices and risk of diabetes in intake by FFQ compared with actual energy intake to women. Diabetes care 2008, 31(7):1311-1317. maintain body weight in 516 adults. The British journal 36. Muraki I, Imamura F, Manson JE, Hu FB, Willett WC, of nutrition 2011, 106(2):274-281. van Dam RM, Sun Q: Fruit consumption and risk of 25. Streppel MT, de Vries JH, Meijboom S, Beekman M, de type 2 diabetes: results from three prospective longi- Craen AJ, Slagboom PE, Feskens EJ: Relative validity of tudinal cohort studies. BMJ (Clinical research ed) 2013, the food frequency questionnaire used to assess dietary 347:f5001. intake in the Leiden Longevity Study. Nutrition journal 37. Astrup A: Yogurt and dairy product consumption to 2013, 12:75. prevent cardiometabolic diseases: epidemiologic and 26. Bureau stichting NEVO, NEVO-tabel 2006. In .: Den experimental studies. The American journal of clinical Haag: Voedingscentrum; 2006. nutrition 2014, 99(5 Suppl):1235s-1242s. 27. Zo eet Nederland: resultaten van de Voedselconsump- 38. O’Connor LM, Lentjes MA, Luben RN, Khaw KT, tiepeiling 1997-1998. In .: Den Haag: Voedingscentrum; Wareham NJ, Forouhi NG: Dietary dairy product intake 1998. and incident type 2 diabetes: a prospective study using 28. Newby PK, Tucker KL: Empirically derived eating pat- dietary data from a 7-day food diary. Diabetologia terns using factor or cluster analysis: a review. Nutrition 2014, 57(5):909-917. reviews 2004, 62(5):177-203. 39. Sluijs I, Forouhi NG, Beulens JW, van der Schouw YT, 29. Wendel-Vos GC, Schuit AJ, Saris WH, Kromhout Agnoli C, Arriola L, Balkau B, Barricarte A, Boeing H, D: Reproducibility and relative validity of the Bueno-de-Mesquita HB et al: The amount and type short questionnaire to assess health-enhancing of dairy product intake and incident type 2 diabetes: physical activity. Journal of clinical epidemiology 2003, results from the EPIC-InterAct Study. The American 56(12):1163-1169. journal of clinical nutrition 2012, 96(2):382-390. 30. Ainsworth BE, Haskell WL, Herrmann SD, Meckes 40. Ludwig DS: The glycemic index: physiological mecha- N, Bassett DR, Jr., Tudor-Locke C, Greer JL, Vezina J, nisms relating to obesity, diabetes, and cardiovascular Whitt-Glover MC, Leon AS: 2011 Compendium of disease. JAMA 2002, 287(18):2414-2423. Physical Activities: a second update of codes and MET 41. Bornet FR, Jardy-Gennetier AE, Jacquet N, Stowell values. Medicine and science in sports and exercise 2011, J: Glycaemic response to foods: impact on satiety 43(8):1575-1581. and long-term weight regulation. Appetite 2007, 31. Sijtsma A, Sauer PJ, Corpeleijn E: Parental correlations 49(3):535-553. of physical activity and body mass index in young 42. Sun FH, Li C, Zhang YJ, Wong SH, Wang L: Effect of children--he GECKO Drenthe cohort. The international Glycemic Index of Breakfast on Energy Intake at Sub- Dietary Patterns and Physical Activity in Obesity 129

sequent Meal among Healthy People: A Meta-Analysis. and their association with lifestyle factors in the Di@ Nutrients 2016, 8(1). bet.es study. Nutrition, metabolism, and cardiovascular 43. Jenkins DJ, Kendall CW, McKeown-Eyssen G, Josse RG, diseases : NMCD 2014, 24(9):947-955. Silverberg J, Booth GL, Vidgen E, Josse AR, Nguyen TH, 53. Kanagasabai T, Thakkar NA, Kuk JL, Churilla JR, Ardern Corrigan S et al: Effect of a low-glycemic index or a CI: Differences in physical activity domains, guideline high-cereal fiber diet on type 2 diabetes: a randomized adherence, and weight history between metabolically trial. JAMA 2008, 300(23):2742-2753. healthy and metabolically abnormal obese adults: a 44. Maki KC, Rains TM, Kaden VN, Raneri KR, Davidson MH: cross-sectional study. The international journal of Effects of a reduced-glycemic-load diet on body weight, behavioral nutrition and physical activity 2015, 12:64. body composition, and cardiovascular disease risk 54. Ortega FB, Cadenas-Sanchez C, Sui X, Blair SN, Lavie 5 markers in overweight and obese adults. The American CJ: Role of Fitness in the Metabolically Healthy but journal of clinical nutrition 2007, 85(3):724-734. Obese Phenotype: A Review and Update. Progress in 45. Ebbeling CB, Leidig MM, Feldman HA, Lovesky MM, cardiovascular diseases 2015, 58(1):76-86. Ludwig DS: Effects of a low-glycemic load vs low-fat 55. Hamilton MT, Healy GN, Dunstan DW, Zderic TW, Owen diet in obese young adults: a randomized trial. JAMA N: Too Little Exercise and Too Much Sitting: Inactivity 2007, 297(19):2092-2102. Physiology and the Need for New Recommendations on 46. Finley CE, Barlow CE, Halton TL, Haskell WL: Glycemic Sedentary Behavior. Current cardiovascular risk reports index, glycemic load, and prevalence of the meta- 2008, 2(4):292-298. bolic syndrome in the cooper center longitudinal study. 56. de Rooij BH, van der Berg JD, van der Kallen CJ, Schram Journal of the American Dietetic Association 2010, MT, Savelberg HH, Schaper NC, Dagnelie PC, Henry RM, 110(12):1820-1829. Kroon AA, Stehouwer CD et al: Physical Activity and 47. Bell LK, Edwards S, Grieger JA: The Relationship Sedentary Behavior in Metabolically Healthy versus between Dietary Patterns and Metabolic Health in a Unhealthy Obese and Non-Obese Individuals - The Representative Sample of Adult Australians. Nutrients Maastricht Study. PloS one 2016, 11(5):e0154358. 2015, 7(8):6491-6505. 57. Bankoski A, Harris TB, McClain JJ, Brychta RJ, Caserotti 48. van Dam RM, Grievink L, Ocke MC, Feskens EJ: Patterns P, Chen KY, Berrigan D, Troiano RP, Koster A: Sedentary of food consumption and risk factors for cardiovascular activity associated with metabolic syndrome indepen- disease in the general Dutch population. The American dent of physical activity. Diabetes care 2011, 34(2):497- journal of clinical nutrition 2003, 77(5):1156-1163. 503. 49. Swift DL, Johannsen NM, Lavie CJ, Earnest CP, Church TS: 58. Healy GN, Wijndaele K, Dunstan DW, Shaw JE, Salmon The role of exercise and physical activity in weight loss J, Zimmet PZ, Owen N: Objectively measured sedentary and maintenance. Progress in cardiovascular diseases time, physical activity, and metabolic risk: the Austra- 2014, 56(4):441-447. lian Diabetes, Obesity and Lifestyle Study (AusDiab). 50. Appleton SL, Seaborn CJ, Visvanathan R, Hill CL, Gill Diabetes care 2008, 31(2):369-371. TK, Taylor AW, Adams RJ: Diabetes and cardiovascular 59. Bertrais S, Beyeme-Ondoua JP, Czernichow S, Galan P, disease outcomes in the metabolically healthy obese Hercberg S, Oppert JM: Sedentary behaviors, physical phenotype: a cohort study. Diabetes care 2013, activity, and metabolic syndrome in middle-aged 36(8):2388-2394. French subjects. Obesity research 2005, 13(5):936-944. 51. Camhi SM, Waring ME, Sisson SB, Hayman LL, Must 60. Gardiner PA, Healy GN, Eakin EG, Clark BK, Dunstan DW, A: Physical activity and screen time in metabolically Shaw JE, Zimmet PZ, Owen N: Associations between healthy obese phenotypes in adolescents and adults. television viewing time and overall sitting time with Journal of obesity 2013, 2013:984613. the metabolic syndrome in older men and women: the 52. Gutierrez-Repiso C, Soriguer F, Rojo-Martinez G, Australian Diabetes, Obesity and Lifestyle study. Journal Garcia-Fuentes E, Valdes S, Goday A, Calle-Pascual of the American Geriatrics Society 2011, 59(5):788-796. A, Lopez-Alba A, Castell C, Menendez E et al: Variable 61. van der Berg JD, Stehouwer CD, Bosma H, van der Velde patterns of obesity and cardiometabolic phenotypes JH, Willems PJ, Savelberg HH, Schram MT, Sep SJ, van 130 Chapter 5

der Kallen CJ, Henry RM et al: Associations of total 66. Newby PK, Weismayer C, Akesson A, Tucker KL, Wolk A: amount and patterns of sedentary behaviour with type Long-term stability of food patterns identified by use of 2 diabetes and the metabolic syndrome: The Maastricht factor analysis among Swedish women. The Journal of Study. Diabetologia 2016, 59(4):709-718. nutrition 2006, 136(3):626-633. 62. Lichtman SW, Pisarska K, Berman ER, Pestone M, Dowl- 67. Balder HF, Virtanen M, Brants HA, Krogh V, Dixon LB, ing H, Offenbacher E, Weisel H, Heshka S, Matthews DE, Tan F, Mannisto S, Bellocco R, Pietinen P, Wolk A et al: Heymsfield SB: Discrepancy between self-reported and Common and country-specific dietary patterns in four actual caloric intake and exercise in obese subjects. The European cohort studies. The Journal of nutrition 2003, New England journal of medicine 1992, 327(27):1893- 133(12):4246-4251. 1898. 68. Chen Z, Liu L, Roebothan B, Ryan A, Colbourne J, 63. Hu FB, Rimm E, Smith-Warner SA, Feskanich D, Stamp- Baker N, Yan J, Wang PP: Four major dietary patterns fer MJ, Ascherio A, Sampson L, Willett WC: Reproduc- identified for a target-population of adults residing ibility and validity of dietary patterns assessed with a in Newfoundland and Labrador, Canada. BMC public food-frequency questionnaire. The American journal of health 2015, 15:69. clinical nutrition 1999, 69(2):243-249. 69. Wagner A, Dallongeville J, Haas B, Ruidavets JB, 64. Khani BR, Ye W, Terry P, Wolk A: Reproducibility and Amouyel P, Ferrieres J, Simon C, Arveiler D: Sedentary validity of major dietary patterns among Swedish behaviour, physical activity and dietary patterns are in- women assessed with a food-frequency questionnaire. dependently associated with the metabolic syndrome. The Journal of nutrition 2004, 134(6):1541-1545. Diabetes & metabolism 2012, 38(5):428-435. 65. Liu X, Wang X, Lin S, Song Q, Lao X, Yu IT: Reproducibility and Validity of a Food Frequency Questionnaire for As- sessing Dietary Consumption via the Dietary Pattern Method in a Chinese Rural Population. PloS one 2015, 10(7):e0134627. Dietary Patterns and Physical Activity in Obesity 131

Supplemental information

Table S1. Detailed information on the food items grouping.

Food groups Food items Bread bread, crispbread, rusk, croissants and others Rice Rice Pasta Pasta Potatoes boiled or mashed potatoes Fried potatoes fried potatoes 5 Cereals muesli, granola or cereals for the preparation of porridges Breakfast drink breakfast drink Nonfermented medium/ low-fat milk nonfermented medium/ low-fat milk Nonfermented whole milk nonfermented whole milk and coffee milk Chocolate milk chocolate milk Fermented milk products - unsweetened Butter milk, full-fat plain yogurt, semi-skimmed plain yogurt, skimmed plain yogurt Fermented milk products - sweetened yogurt drinks and flavored dairy drinks with sugar, semi-skimmed fruit or vanilla yogurt, skimmed (fruit) yogurt with sugar Cheese – low fat 20+ or 30+ cheese or spreadable cheese Cheese – high fat 40+ or 48+ cheese or spreadable cheese, cream cheese and/or foreign cheese Quark quark or fruit quark Desserts (full-fat) custard and other milk-based desserts Ice cream milk-based ice cream Whipped cream whipped cream Eggs fried and boiled eggs Processed meat luncheon meats, hamburger, minced meat (beef or mix of beef and pork), smoked sausages or frankfurters Lean red meat beef steak, steak tartare, braising steak or roast beef Red meat sirloin steak, beef bratwurst, beef blade steak, beef rib steak or steaked/marbled beef, bacon, pork bratwurst, ‘slavink’ (ground meat wrapped in bacon), pork chops (shoulder, rib, and loin chops) Chicken chicken with and without skin Fatty fish salted herring, fried herring, salmon, mackerel, eel etc. Lean fish cod, plaice, haddock, pollack, sole, deep-fried whiting in dough etc. Commercially prepared dishes Chinese/Indonesian dishes, meals from fast-food restaurants, other types of ready-to-eat meals Pizza ready-to-eat pizza’s, homemade pizza’s and pizza’s eaten at restaurants Warm savory snacks croquettes, minced meat hot dogs, sausage rolls Savory snacks potato chips or salty biscuits Composed foods salads on bread Edible fat butter, margarine, low fat margarine Gravy Gravy Warm sauces pasta sauce, mushroom sauce, sate sauce Mayonnaise Mayonnaise 132 Chapter 5

Table S1. Detailed information on the food items grouping. (continued) Food groups Food items Cold sauces Low fat mayonnaise, sauce for French fries and other non-red sauces Salad dressing salad dressing with/without oil Vegetables Vegetables Fruit fresh fruit Apple sauce apple sauce Legumes brown beans, white beans, marrowfat peas, kidney beans etc. Soup soup Peanuts, nuts and seeds peanuts, coated peanuts, nuts, seeds and peanut butter Biscuits small cookies or (nutritional) biscuits Pastries sponge cake, large cookies, cake, pie Candybar candybars (Mars, Snickers, M&M’s etc.) Chocolate chocolate, candy with chocolate and chocolates Candy liquorice, acid drops etc. Sweet sandwich toppings sweet sandwich toppings (chocolate- sprinkles, spread or flakes, honey, jam) Added sugar sugar, honey or syrups Coffee Coffee Tea Tea High sugar beverages soft drinks (coke, orange flavored soft drinks, 7-up) or lemonade with sugar Low sugar beverages diet soft drinks or lemonade without sugar Fruit juices fruit juices Beer - light non-alcoholic beer Beer Beer Wine and fortified wine white-, rosé- and red wine, sherry, port wine or vermouth Spirits distilled drinks (genever, whisky, rum, gin, cognac, vieux, liqueur)

Hoofdstukpagina-letteromtrek.indd 11 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 12 16/11/16 11:05 Chapter 6

Health-related quality of life in relation to obesity grade, type 2 diabetes, metabolic syndrome and inflammation

Sandra N. Slagter Jana V. van Vliet-Ostaptchouk André P. van Beek Joost C. Keers Helen L. Lutgers Melanie M. van der Klauw Bruce H.R. Wolffenbuttel

PLoS ONE 2015, 10(10):e0140599 6

Hoofdstukpagina-letteromtrek.indd 11 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 12 16/11/16 11:05 136 Chapter 6

Abstract

Background Health-related quality of life (HR-QoL) may be compromised in obese individuals, depending on the presence of other complications. The aim of this study is to assess the effect of obesity-related conditions on HR-QoL. These conditions are i) grade of obesity with and without type 2 diabetes (T2D), ii) metabolic syndrome (MetS), and iii) level of inflammation. Methods From the Dutch LifeLines Cohort Study we included 13,686 obese individuals, aged 18-80 years. HR-QoL was measured with the RAND 36-Item Health Survey which encompasses eight health domains. We calculated the percentage of obese individuals with poor HR-QoL, i.e. those scoring below the domain and sex specific cut-off value derived from the normal weight population. Logistic regression analysis was used to calculate the probability of having poor domain scores according to the conditions under study. Results Higher grades of obesity and the additional presence of T2D were associated with lower HR-QoL, particularly in the domains physical functioning (men: odds ratios (ORs) 1.48-11.34, P<0.005, and women: ORs 1.66-5.05, P<0.001) and general health (men: ORs 1.44-3.07, P<0.005, and women: ORs 1.36-3.73, P<0.001). A higher percentage of obese individuals with MetS had a poor HR-QoL than those without MetS. Furthermore, we observed a linear trend between inflammation and the percentage of obese individuals with poor scores on the HR-QoL domains. Individu- als with MetS were more likely to have poor scores in the domains general health, vitality, social functioning and role limitations due to emotional problems. Obese women with increased inflammation levels were more likely to have poor scores on all domains except role limitations due to emotional problems and mental health. Conclusions The impact of obesity on an individual’s quality of life is enhanced by grade of obesity, T2D, MetS and inflammation and are mainly related to reduced physical health. The mental well-being is less often impaired. Keywords Health-related quality of life, Obesity grade, Metabolic syndrome, Type 2 diabetes, Inflammation, Cross-sectional. Health-Related Quality of Life in Obesity 137

Introduction

A recent study across seven European countries estimated that obesity prevalence varies between 12% and 26% [1], confirming that obesity has become an epidemic [2]. However, the effect of obesity goes beyond obesity-related and increased morbidities and reduced life expectancy. Impaired health-related quality of life (HR-QoL) has been found in individuals with obesity. Obesity has a greater impact on domains of physical health than on domains of mental well-being [3]. However, our current knowledge is limited as studies made no distinction between the grades of obesity [4-7], had small numbers of participants in the 6 different grades of obesity [8-11] or examined only two groups of obesity [8, 11-13]. In addition, often self-reported height and weight were used to calculate the body mass index (BMI) [5, 8, 10, 12, 14]. Other issues that arise with the previously published studies is the wide use of different instruments to assess HR-QoL [4, 7, 13, 14] and taking no morbidities into account, or only a small subset of morbidities [5, 6, 13, 14]. Next, obesity often coincides with metabolic syndrome (MetS), type 2 diabetes (T2D) and chronic inflammation in varying degrees. So far, it remains unclear whether the presence of MetS, a cluster of inter-related risk factors for future cardiovascular diseases (CVD) and T2D, is affecting HR-QoL. Some studies suggest that the inflammatory state of a person may be related to HR-QoL [15-17]. However, this relationship has only been studied in elderly men [18] and in patient settings, e.g. among patients with chronic kidney disease or diabetes [15, 16, 19]. We are unaware of any evidence linking HR-QoL to the inflammatory state of individuals with obesity. In the present study, we analysed data from 13,686 obese men and women from the general Dutch population. The aim of our study is to assess the effect of obesity-related conditions on HR-QoL. These conditions are i) grade of obesity with and without type 2 diabetes (T2D), ii) metabolic syndrome (MetS), and iii) level of inflammation.

Methods

Study and participants We assessed data from the Dutch LifeLines Cohort Study. This multi-disciplinary prospective population-based cohort study examines the health and health-related behaviours of more than 167,000 persons living in the northeast region of the Netherlands [20]. We included participants of Western European origin aged 18-80 years, who enrolled in the study between 2006 and 2012. Participants were excluded if data on clinical and metabolic measurements and data from the questionnaire on HR-QoL were missing or incomplete. In total, 13,686 individuals with obesity (BMI ≥30 kg/m2) were included. Prior 138 Chapter 6

to the participation in this study, all individuals gave their written informed consent. The study protocol was in accordance with the Declaration of Helsinki and was approved by the medical ethical review committee of the University Medical Center Groningen.

Measurements and definitions A standardised protocol was used to obtain blood pressure, height, weight and waist circumference. Blood pressure was measured 10 times during a period of 10 minutes with an automated DINAMAP Monitor (GE Healthcare, Freiburg, Germany). The average of the final three readings was recorded for systolic- and diastolic blood pressure. An- thropometric measurements were done without shoes, for body weight to the nearest 0.1 kg and height and waist circumference to the nearest 0.5 cm. Height was measured with a stadiometer and waist circumference was measured in standing position with a tape measure all around the body, at the level midway between the lower rib margin and the iliac crest. For analysis of lipids, glucose and the inflammation marker high sensitivity C- reactive protein (hs-CRP), blood samples were drawn in the morning between 8:00 and 10:00 a.m. after a period of overnight fasting. High-density lipoprotein cholesterol was measured with an enzymatic colorimetric method and triglycerides with a colorimetric UV method, on a Roche Modular P chemistry analyser (Roche, Basel, Switzerland). Fast- ing blood glucose was measured with a hexokinase method. Hs-CRP was determined by nephelometry (BN II system Siemens, Marburg, Germany). The American Heart As- sociation has defined that hs-CRP levels of <1, 1 to 3, and >3 mg/L correspond to low-, moderate-, and high-risk groups for future cardiovascular events. Levels above 10 mg/L usually indicates acute inflammation [21]. BMI was calculated by dividing weight in kilograms by the squared height in meters (kg/m2). Based on their BMI, participants were classified into three grades of obesity as defined by the World Health Organisation: obesity grade 1 (BMI 30-34.9); obesity grade 2 (BMI 35.0-39.9); or obesity grade 3 (BMI ≥40.0 kg/m2) [22]. MetS was defined according to the modified guidelines of the National Cholesterol Education Program’s Adult Treat- ment Panel III (NCEP ATP III) [23]. Diagnosis of T2D was based on self-report and/or use of blood-glucose lowering medication, or an elevated fasting blood glucose ≥7.0 mmol/L at examination.

Measuring HR-QoL using RAND-36 HR-QoL was measured using the RAND 36-Item Health Survey version 1.0 (RAND-36) [24], which was self-completed by the participant. The RAND-36 include the exact same items as the 36-Item Short Form Health Survey version 1.0, however, the scoring for the two domains bodily pain and general health are slightly different. The questionnaire measures health perception across eight multi-item health domains. These include do- Health-Related Quality of Life in Obesity 139

mains mainly related to physical health (i.e. physical functioning, role limitations due to physical health problems, bodily pain and general health) and domains mainly related to mental well-being (i.e. vitality, social functioning, role limitations due to emotional problems, and general mental health). Scores on the eight domains are generated as follows: 1) numeric values are given to each answer for all items, 2) items in the same domain are averaged together to create the eight raw domain scores (the possible combinations of given answers within a domain is fixed, therefore, only fixed scores can be computed), 3) since not all HR-QoL domains include the same amount of questions in the RAND-36, the raw domain scores are transformed to scales of 0 (maximal impairment) to 100 (no impairment) [24]. Please note that the domain scores are not continuous (nor normally 6 distributed), however, based on frequencies of fixed scores.

Defining morbidities Participants were asked to indicate in a provided list from which disorders/diseases they currently or in the past suffered. The single morbidities were clustered in the following 11 subgroups: Pulmonary; Cancer; CVD; Head; Gastrointestinal & Liver; Kidney and Blad- der; Neurological diseases; Blood disorders; Musculoskeletal diseases; Dermatological diseases; and Mental disorders. If the participant had at least one morbidity within the subgroup, this subgroup was coded as being present. The formation of the subgroups are based on morbidities which affect the same organs or systems, or they share the same biological mechanism. Detailed information on the single morbidities is available in S1 Table.

Data description and statistical analyses All analyses were conducted using IBM SPSS Statistics version 20 (IBM Corporation, Armonk, NY, USA). All data are presented for men and women separately. In the characteristics table data are presented as mean ± SD, or median and interquartile range (IQR) when not normally distributed. Unfortunately, the manual of the RAND-36 questionnaire or other publications have not indicated what difference or change in HR-QoL domain scores should be considered as clinically relevant. Therefore, we generated a cut-off value to indicate a poor score based on the HR-QoL domain score at the 25th percentile of the normal weight population. This was done for each domain and separately for men and women. The individuals who were classified as normal weight (BMI ≤25.0 kg/m2) also participated in the LifeLines Cohort Study (N=39,528). To assess whether degree of obesity with and without T2D, MetS and level of inflammation were related to poor scores on the HR-QoL domains, we calculated the percentages of individuals who scored below this arbitrary cut-off value. Chi-square tests were used to analyse the differences in 140 Chapter 6

proportions between the groups and Mantel-Haenszel tests to check for a linear trend in proportions across groups. We performed per HR-QoL domain (outcome measure) three separate multivariable logistic regression analyses to assess the probability of having a poor score according to i) obesity grade with/without T2D, ii) MetS, and iii) level of inflammation. These analyses were sex-stratified and adjusted for age, the 11 subgroups of morbidities, and where appropriate for BMI and T2D. To test whether the associations varied by age, we also tested for a significant interaction between age and the condition under study i) obesity grade with/without T2D, ii) MetS; and iii) level of inflammation). In the model with the exposure variable MetS, T2D was not included as a covariate since diabetes is one of the criteria to define MetS. Since we performed our analysis separately by sex, we accounted for the number of comparisons made within one sex on one domain of HR-QoL (five comparisons within the obesity grade groups with and without T2D, one comparison in the MetS status group and three comparisons within the hs-CRP groups). As such, an arbitrary P value < 0.0055 (α=0.05/9) was considered statistically significant. All P values were two-sided.

Results

Population characteristics and percentage of obese individuals with a poor HR-QoL The study population comprised 5,210 (38%) obese men and 8,476 (62%) obese women (Table 1). Mean age was 48 in the men (SD 11 years) and 47 in the women (SD 12 years). In the male population only 3.2% had grade 3 obesity and most participants had hs-CRP levels ≤3 mg/L (68.9%). In the female population we observed a high percentage of individuals with obesity grade 3 (7.9%) and hs-CRP levels above 3 mg/L (58.2%). The prevalence of MetS was 64.1% and 39.9% in respectively, men and women. T2D was present in 9.2% of men and in 6.3% of women. Health-Related Quality of Life in Obesity 141

Table 1. Characteristics of participants with obesity.

Characteristics Men Women Number of participants 5,210 8,476 Age (yrs) 48 ± 11 47 ± 12 Weight (kg) 108.4 ± 12.6 96.3 ± 12.9 BMI (kg/m2) 31.9 (30.7-33.8) 32.9 (31.2-35.6) Obesity grade 1, n (%) 4363 (83.7) 5961 (70.3) Obesity grade 2, n (%) 681 (13.1) 1846 (21.8) Obesity grade 3, n (%) 166 (3.2) 669 (7.9) Waist circumference (cm) 110.5 (106.5-116.0) 104.0 (98.0-111.0) Blood glucose (mmol/L) 5.3 (5.0-5.8) 5.1 (4.8-5.5) 6 Total cholesterol (mmol/L) 5.2 ± 1.0 5.1 ± 1.0 Triglycerides (mmol/L) 1.58 (1.13-2.20) 1.16 (0.85-1.59) HDL-cholesterol (mmol/L) 1.13 ± 0.26 1.39 ± 0.33 LDL-cholesterol (mmol/L) 3.46 ± 0.91 3.27 ± 0.88 Systolic blood pressure (mmHg) 137 ± 14 129 ± 15 Diastolic blood pressure (mmHg) 80 ± 9 75 ± 9 hs-CRP (mg/L)a 1.8 (1.0-3.5) 3.8 (1.8-7.1) <1 mg/L, n (%) 717 (22.6) 502 (9.9) 1-3 mg/L, n (%) 1,468 (46.3) 1,621 (32.0) 3-10 mg/L , n (%) 864 (27.2) 2,337 (46.1) >10 mg/L, n (%) 124 (3.9) 612 (12.1) Metabolic syndrome, n (%) 3,339 (64.1) 3,383 (39.9) Type 2 diabetes, n (%) 480 (9.2) 533 (6.3) Data presented as mean ± SD or median (interquartile range). a hs-CRP measures were available in 3,173 men and 5,072 women. BMI= body mass index; HDL= high-density lipoprotein; LDL= low-density lipoprotein; hs-CRP= high sensitivity C-reactive protein.

Figures 1-3 represent the sex-specific percentages of obese individuals with a poor score on the HR-QoL domains. With a higher grade of obesity and/or who had T2D, higher percentages of individuals had a poor HR-QoL, especially in the domains physical functioning (PF) and general health (GH) (Figure 1). Compared to those with grade 1 obesity and no T2D, 45.0% more men and 39.0% more women with grade 3 obesity and T2D had a poor score on physical functioning. For general health these differences were 29.1% and 34.9%, respectively. For all domains, the percentage of men and women with obesity and a poor score was higher in those with MetS (Figure 2). Again, poor scores on HR-QoL was most often reported in the domains physical functioning and general health. Individuals with higher levels of inflammation scored worse on HR-QoL (Figure 3). This trend was most prominent in the group with hs-CRP >3 mg/L, and in the domains mainly related to 142 Chapter 6

100 a Normal weight Obesity grade 1: No T2D Obesity grade 1: T2D 80 Obesity grade 2: No T2D a Obesity grade 2: T2D Obesity grade 3: No T2D Obesity grade 3: T2D 60

a 40 * a Percentage (%) b

0 PF RP BP GH VT SF RE MH

100

80 a b

60 a

a * 40 a a Percentage (%) a a a

0 PF RP BP GH VT SF RE MH

Figure 1. Percentages of obese individuals with a poor HR-QoL domain score, according to obesity grade with and without T2D. (A) Men. (B) Women. Obesity grade 1: BMI 30-34.9 kg/m2 Obesity grade 2: BMI 35-39.9 kg/m2 Obesity grade 3: BMI ≥40 kg/m2 The corresponding cut-off value derived from the scores in the normal weight population for the individual domains (25th percentile men, 25th percentile women): PF = physical functioning (95.0, 90.0); RP = role limitations due to physical health problems (100.0, 100.0); BP = bodily pain (79.6, 67.4); GH = general health (65.0, 65.0); VT = vitality (60.0, 55.0); SF = social functioning (87.5, 75.0); RE = role limitations due to emotional problems (100.0, 100.0); MH = mental health (76.0, 72.0). Mantel-Haenszel tests was used to check for a linear trend in proportions across groups of obesity grade without T2D and separately for groups of obesity grade with T2D. * Indicates a linear trend at P <0.005; a P <0.001; and b P <0.002. Health-Related Quality of Life in Obesity 143

100 Normal weight Individuals with obesity: No MetS Individuals with obesity: MetS 80

40 Percentage (%) b a 6

b 20

0 PF RP BP GH VT SF RE MH

100

a 60

a 40 Percentage (%) a a a a

0 PF RP BP GH VT SF RE MH

Figure 2. Percentages of obese individuals with a poor HR-QoL domain score, according to MetS. (A) Men. (B) Women. MetS= metabolic syndrome. The corresponding cut-off value derived from the scores in the normal weight population for the individual domains (25th percentile men, 25th percentile women): PF = physical functioning (95.0, 90.0); RP = role limitations due to physical health problems (100.0, 100.0); BP = bodily pain (79.6, 67.4); GH = general health (65.0, 65.0); VT = vitality (60.0, 55.0); SF = social functioning (87.5, 75.0); RE = role limitations due to emotional problems (100.0, 100.0); MH = mental health (76.0, 72.0). Chi-square tests were used to analyse the differences in proportions between the obese without MetS and the obese with MetS. * Indicates a difference relative to the reference group (individuals with obesity in the absence of MetS) at P <0.005; aP <0.001; and bP <0.002. 144 Chapter 6



                                

 

      



        







 



       



        

Figure 3. Percentages of obese individuals with a poor HR-QoL domain score, according to level of hs-CRP. (A) Men. (B) Women. hs-CRP = high sensitivity C-reactive protein. The corresponding cut-off value derived from the scores in the normal weight population for the individual domains (25th percentile men, 25th percentile women): PF = physical functioning (95.0, 90.0); RP = role limitations due to physical health problems (100.0, 100.0); BP = bodily pain (79.6, 67.4); GH = general health (65.0, 65.0); VT = vitality (60.0, 55.0); SF = social functioning (87.5, 75.0); RE = role limitations due to emotional problems (100.0, 100.0); MH = mental health (76.0, 72.0). Mantel-Haenszel tests was used to check for a linear trend in proportions across groups of hs-CRP level within the obese population. * Indicates a linear trend for individuals with obesity and higher levels of hs- CRP at P <0.005; aP <0.001; and bP <0.002. Health-Related Quality of Life in Obesity 145

physical health and the domain vitality (VT). The percentage of individuals who had a poor score on the domain bodily pain varied between 32.2-41.9% in men and 18.5- 27.8% in women (Figure 3).

Probability of having a poor HR-QoL in the obese population Tables 2-4 show the adjusted odds ratios (ORs) for having a poor HR-QoL score on the eight domains, according to grade of obesity with and without T2D (Table 2), MetS status (Table 3) and level of inflammation (Table 4). There were no significant interactions between age and the conditions under study (data not shown). Compared with the reference group (obesity grade 1 and no T2D), both men and women with grade 2 6 or 3 obesity and/or T2D had a higher odds ratio for a poor HR-QoL score in the domains physical functioning and general health ( P <0.001) (Table 2). There were two notable results among men and women with grade 3 obesity. Firstly, the association with poor physical health was very high in men (OR [95% CI]: 4.08 [2.72-6.12] (without T2D) and 11.34 [3.39-37.89] (with T2D)) and women(OR 3.39 [2.78-4.15] (without T2D) and 5.05 [2.77-9.21] (with T2D)), with both associations highly significant ( P <0.0001). Secondly, women with grade 3 obesity, having no T2D, had a greater probability for poor role limitation due to physical health problems (RP) (OR 1.55 [1.30-1.87]) and bodily pain (BP) (OR 1.62 [1.34-1.95]) compared to the reference group, while men did not. When accompanied by T2D these associations were even stronger (OR 2.24 [1.43-3.51] for RP and OR 2.68 [1.67-4.29] for BP). Although domains of mental well-being were not affected with the same strength, men with obesity grade 2 and 3 without T2D had an OR of, respectively, 1.49 [1.22-1.81] and 2.00 [1.37-2.91] for a poor score on vitality ( P <0.001) (Table 2). 146 Chapter 6

Table 2. Adjusted odds ratios (95% confidence intervals) for having a poor score on each domain of HR- QoL, according to grade of obesity and T2D. Number of subjects Physical Role limitations Bodily Pain General Health (%) Functioning Physical health Men Obesity grade 1 - no T2D (ref.) 4,022 (77.2) 1.0 1.0 1.0 1.0 Obesity grade 1 - T2D 341 (6.5) 1.48 (1.15-1.89) b 1.13 (0.87-1.47) 1.15 (0.90-1.48) 1.61 (1.26-2.04) a Obesity grade 2 – no T2D 578 (11.1) 1.84 (1.52-2.22) a 1.16 (0.94-1.42) 1.23 (1.02-1.49) 1.44 (1.19-1.74) a Obesity grade 2 – T2D 103 (2.0) 2.79 (1.73-4.49) a 1.55 (1.00-2.40) 1.52 (1.00-2.33) 2.21 (1.45-3.36) a Obesity grade 3 – no T2D 130 (2.5) 4.08 (2.72-6.12) a 1.38 (0.91-2.07) 1.48 (1.01-2.15) 2.00 (1.39-2.89) a Obesity grade 3 – T2D 36 (0.7) 11.34 (3.39-37.89) a 1.60 (0.77-3.29) 0.97 (0.47-2.00) 3.07 (1.51-6.23) b Women Obesity grade 1 – no T2D (ref.) 5,662 (66.8) 1.0 1.0 1.0 1.0 Obesity grade 1 – T2D 299 (3.5) 1.35 (1.02-1.75) 1.31 (1.01-1.70) 1.09 (0.84-1.41) 1.74 (1.35-2.25) a Obesity grade 2 – no T2D 1,700 (20.1) 1.66 (1.47-1.87) a 1.19 (1.05-1.34) 1.14 (1.01-1.29) 1.36 (1.21-1.54) a Obesity grade 2 – T2D 146 (1.7) 1.55 (1.05-2.28) 1.10 (0.76-1.59) 1.23 (0.86-1.77) 2.06 (1.44-2.96) a Obesity grade 3 – no T2D 581 (6.9) 3.39 (2.78-4.15) a 1.55 (1.30-1.87) a 1.62 (1.34-1.95) a 2.29 (1.91-2.77) a Obesity grade 3 – T2D 88 (1.0) 5.05 (2.77-9.21) a 2.24 (1.43-3.51) a 2.68 (1.67-4.29) a 3.73 (2.32-5.99) a Number of subjects Vitality Social Functioning Role limitations Mental Health (%) Emotional problems Men Obesity grade 1 - no T2D (ref.) 4,022 (77.2) 1.0 1.0 1.0 1.0 Obesity grade 1 - T2D 341 (6.5) 1.43 (1.10-1.86) 1.53 (1.17-1.99) b 1.35 (0.98-1.87) 1.44 (1.10-1.90) Obesity grade 2 – no T2D 578 (11.1) 1.49 (1.22-1.81) a 1.25 (1.01-1.54) 1.09 (0.84-1.41) 1.22 (0.98-1.51) Obesity grade 2 – T2D 103 (2.0) 1.85 (1.20-2.85) 1.39 (0.88-2.19) 1.25 (0.72-2.17) 1.83 (1.16-2.86) Obesity grade 3 – no T2D 130 (2.5) 2.00 (1.37-2.91) a 1.87 (1.27-2.78) b 1.46 (0.90-2.38) 1.41 (0.93-2.13) Obesity grade 3 – T2D 36 (0.7) 2.10 (1.03-4.28) 1.22 (0.56-2.66) 1.70 (0.71-4.07) 0.87 (0.36-2.10) Women Obesity grade 1 – no T2D (ref.) 5,662 (66.8) 1.0 1.0 1.0 1.0 Obesity grade 1 – T2D 299 (3.5) 1.45 (1.11-1.90) 1.16 (0.86-1.57) 1.30 (0.96-1.77) 1.41 (1.07-1.86) Obesity grade 2 – no T2D 1,700 (20.1) 1.12 (0.99-1.27) 1.08 (0.94-1.24) 1.06 (0.92-1.22) 1.04 (0.91-1.18) Obesity grade 2 – T2D 146 (1.7) 1.21 (0.83-1.77) 1.37 (0.92-2.02) 1.09 (0.71-1.67) 1.18 (0.80- 1.74) Obesity grade 3 – no T2D 581 (6.9) 1.24 (1.03-1.51) 1.31 (1.07-1.61) 1.35 (1.09-1.68) 1.26 (1.03-1.54) Obesity grade 3 – T2D 88 (1.0) 1.90 (1.21-3.00) 1.45 (0.89-2.36) 1.57 (0.96-2.59) 1.60 (1.00-2.56) Adjusted for age and the following morbidities: Pulmonary; Cancer; CVD; Head; Gastrointestinal & Liver; Kidney & Bladder; Neurological diseases; Blood disorders; Musculoskeletal diseases; Dermatological diseases and Mental disorders. Ref.: reference. Odds ratios in bold indicate P <0.005; a P <0.001; and b P <0.002. Health-Related Quality of Life in Obesity 147

Table 3. Adjusted odds ratios (95% confidence intervals) for having a poor score on each domain of HR- QoL according to level of hs-CRP.

Number of subjects (%) Physical Role limitations Bodily Pain General Health Functioning Physical health Men No MetS (ref.) 4,420 (84.8) 1.0 1.0 1.0 1.0 MetS 790 (15.2) 1.25 (1.06-1.48) 1.12 (0.93-1.34) 1.12 (0.94-1.33) 1.27 (1.08-1.50) Women No MetS (ref.) 5,790 (68.3) 1.0 1.0 1.0 1.0 MetS 2,686 (31.7) 1.13 (1.02-1.26) 1.17 (1.05-1.30) 1.14 (1.02-1.27) 1.35 (1.22-1.51) a Number of subjects (%) Vitality Social Functioning Role limitations Mental Health Emotional 6 problems Men No MetS (ref.) 4,420 (84.8) 1.0 1.0 1.0 1.0 MetS 790 (15.2) 1.31 (1.10-1.57) 1.31 (1.09-1.57) 1.40 (1.12-1.75) 1.16 (0.96-1.41) Women No MetS (ref.) 5,790 (68.3) 1.0 1.0 1.0 1.0 MetS 2,686 (31.7) 1.27 (1.14-1.42) a 1.24 (1.10-1.40) a 1.26 (1.11-1.43) a 1.17 (1.04-1.31) Adjusted for age, BMI and the following morbidities: Pulmonary; Cancer; CVD; Head; Gastrointestinal & Liv- er; Kidney & Bladder; Neurological diseases; Blood disorders; Musculoskeletal diseases; Dermatological diseases and Mental disorders. Ref.: reference. Odds ratios in bold indicate P <0.005; aP <0.001; and bP <0.002.

Table 4. Adjusted odds ratios (95% confidence intervals) for having a poor score on each domain of HR- QoL according to level of hs-CRP.

Number of subjects Physical Role limitations Bodily Pain General Health (%) Functioning Physical health Men hs-CRP <1 mg/L (ref.) 717 (22.6) 1.0 1.0 1.0 1.0 hs-CRP 1-3 mg/L 1,468 (46.3) 1.12 (0.93-1.37) 1.08 (0.86-1.35) 1.04 (0.84-1.27) 1.09 (0.89-1.33) hs-CRP 3-10 mg/L 864 (27.2) 1.29 (1.04-1.61) 1.31 (1.02-1.67) 1.18 (0.94-1.48) 1.25 (1.00-1.57) hs-CRP >10 mg/L 124 (3.9) 1.26 (0.83-1.92) 1.32 (0.84-2.08) 1.35 (0.89-2.06) 1.50 (0.99-2.28) Women hs-CRP <1 mg/L (ref.) 502 (9.9) 1.0 1.0 1.0 1.0 hs-CRP 1-3 mg/L 1,621 (32.0) 1.13 (0.90-1.42) 1.16 (0.92-1.47) 1.24 (0.99-1.55) 1.16 (0.92-1.46) hs-CRP 3-10 mg/L 2,337 (46.1) 1.41 (1.13-1.76) b 1.17 (0.92-1.47) 1.25 (1.00-1.55) 1.50 (1.19-1.88) b hs-CRP >10 mg/L 612 (12.1) 1.85 (1.41-2.44) a 1.55 (1.17-2.05) b 1.56 (1.19-2.05) b 1.65 (1.25-2.18) a 148 Chapter 6

Table 4. Adjusted odds ratios (95% confidence intervals) for having a poor score on each domain of HR- QoL according to level of hs-CRP. (continued) Number of subjects Vitality Social Functioning Role limitations Mental Health (%) Emotional problems Men hs-CRP <1 mg/L (ref.) 717 (22.6) 1.0 1.0 1.0 1.0 hs-CRP 1-3 mg/L 1,468 (46.3) 0.86 (0.70-1.07) 0.81 (0.65-1.02) 0.75 (0.57-0.99) 0.95 (0.76-1.20) hs-CRP 3-10 mg/L 864 (27.2) 1.05 (0.83-1.33) 1.17 (0.92-1.48) 1.21 (0.90-1.63) 1.11 (0.86-1.43) hs-CRP >10 mg/L 124 (3.9) 1.26 (0.82-1.94) 1.07 (0.67-1.69) 1.16 (0.66-2.02) 1.25 (0.78-1.99) Women hs-CRP <1 mg/L (ref.) 502 (9.9) 1.0 1.0 1.0 1.0 hs-CRP 1-3 mg/L 1,621 (32.0) 1.19 (0.93-1.52) 1.37 (1.04-1.80) 1.31 (0.99-1.75) 1.17 (0.91-1.51) hs-CRP 3-10 mg/L 2,337 (46.1) 1.41 (1.11-1.79) 1.50 (1.15-1.96) 1.47 (1.11-1.96) 1.33 (1.04-1.71) hs-CRP >10 mg/L 612 (12.1) 1.70 (1.27-2.26) a 1.70 (1.23-2.34) b 1.28 (0.91-1.80) 1.32 (0.98-1.78) Adjusted for age, BMI and the following morbidities: T2D; Pulmonary; Cancer; CVD; Head; Gastrointestinal & Liver; Kidney & Bladder; Neurological diseases; Blood disorders; Musculoskeletal diseases; Dermatologi- cal diseases and Mental disorders. Ref.: reference. Odds ratios in bold indicate P <0.005; aP <0.001; and bP <0.002.

For both men and women, the odds ratios for a poor HR-QoL were higher in the presence of MetS for the domains general health, vitality, social functioning and role limitations due to emotional problems (among men all Ps <0.005 and among women all Ps <0.001) (Table 3). In women, elevated hs-CRP levels (>3 mg/L) were associated with poor scores on the HR-QoL domains. The associations were especially strong in women with hs-CRP above 10 mg/L for the domains physical functioning ( P <0.001), role limitations due to emotional problems ( P <0.002), bodily pain ( P <0.002), general health ( P <0.001), vitality ( P <0.001), and social functioning ( P <0.002). Since BMI, T2D and MetS are not completely independent of inflammation level, we also performed an analysis where in the multivariable logistic regression model hs-CRP was modelled together with obesity grade with and without T2D (S2 Table), and also modelled together with MetS (S3 Table).

Discussion

To the best of our knowledge, this is the first study to report in a very large population with obesity (13,686 individuals) the sex-specific effect of grade of obesity with and without T2D, the effect of MetS and the effect of level of inflammation on HR-QoL. A systematic review and meta-analysis was published in 2013 on the association of all-cause mortality with overweight and obesity [25]. Results showed that relative to normal weight individuals, only people with grade 2 and 3 obesity have a significantly Health-Related Quality of Life in Obesity 149

higher all-cause mortality rate. Studies among individuals with obesity often focus on obesity-related morbidities and mortality, and rarely on quality of life. Rather than simply focusing on the long-term effects of obesity on health and survival, it is also important to understand the influence of obesity and related conditions on an individual’s daily life. We used data of normal weight individuals from the same cohort study to indicate poor scores on eight domains of HR-QoL. The results showed that individuals with grade 1 obesity already had more often a diminished HR-QoL compared to the normal weight individuals. The reduced HR-QoL already visible among individuals with grade 1 obesity seems to be related to their greater BMI. Although, the presence of co-morbidities might also be partly responsible for this observation, earlier studies have shown that a higher 6 BMI is associated with lower HR-QoL [3]. In the obese population the effect of obesity grade and T2D was particularly evident for the domains of physical functioning and general health, even after adjustment for age and morbidities. Since obese individuals carry more weight, and have to deal with a larger body mass, it is not surprising that the daily performance of physical activities is a daunting task for them. As we observed, it becomes even more difficult in the presence of T2D. These results support the previously reported association between T2D and a reduced quality of life, being already evident at the early stage of the disease, especially in relation to the ability to perform physical activities [26]. Obesity often leads to MetS, a cluster of inter-related risk factors for atherosclerosis, CVD and T2D [23]. Results of data on the impact of MetS on HR-QoL are inconsistent. A study conducted in a sample of nondiabetic Iranian adults showed that, only in women, MetS was associated with lower scores on the domains physical functioning, bodily pain and social functioning [27]. However, this study did not address the influence of BMI or obesity. In contrast, BMI and obesity were a key factor in our study, and we found that even after adjusting for BMI, individuals who had both obesity and MetS were more likely to have a poor score on HR-QoL domains than individuals with obesity alone. Both obese men and women had in the presence of MetS a higher probability for poor scores on the domains general health, vitality, social functioning and role limitations due to emotional problems. Previously, Vetter et al. could not establish a relationship between MetS and HR-QoL in individuals with obesity. This may well be accounted for by the small sample size (390 obese participants, 85% female), the study design (inclusion in a weight reduction program) and their use of summary scores for physical health and mental well-being [28]. Tsai et al. also examined HR-QoL among obese individuals aiming for weight reduction [29]. The association between MetS and the summary score of physical HR-QoL was eliminated when adjusted for BMI. This study did also not investigate the association of MetS with the individual domains of HR-QoL. The validity of the summary scores, which are aggregated from the eight health domains, has been the subject of debate [30, 31]. 150 Chapter 6

For this reason, the scores from the individual health domains may be more informative than the summary scores. Levinger et al. reported that in individuals with an increased number of metabolic risk factors a common characteristic is low aerobic fitness and muscle strength, leading to an impaired capacity to perform activities of daily living (ADLs) and impaired quality of life [32]. Resistance training increased the muscle strength and capacity to perform ADLs in individuals with at least two metabolic risk factors and were followed by improvements in the domains physical functioning, general health, and social functioning, despite no changes in body fat content or aerobic power [32]. In a second study from the same research group of Levinger et al., it was found that among a small group of 55 middle-aged adults, women, but not men, with higher numbers of MetS components were more likely to have impaired physical functioning and to experience bodily pain [33]. However, the findings of this second study were unadjusted for BMI. In our study there was also a strong association between MetS and physical functioning, in both obese men (OR 1.37 [1.16-1.61], P <0.001) and obese women (OR 1.31 [1.18-1.46], P <0.001), when we did not take BMI into account. Also women experienced more bodily pain (OR 1.22 [1.10-1.35], P <0.001). These findings indicate that BMI, rather than MetS, seems to be associated with lower scores on physical functioning and bodily pain. The conditions under study here – obesity, T2D and MetS – are not only known to be associated with the progression of CVD but are also all characterised by inflammation [34]. Inflammatory factors have been proposed as being part of the mechanism underlying reduced HR-QoL [15-17]. Since the accumulation of intra-abdominal fat is an important risk factor for inflammation, leading to the elevation of circulating hs-CRP, measurement of this widely used marker provides an indication of whether or not inflammation is likely to impair HR-QoL in individuals with obesity [35, 36]. We found that with increasing levels of inflammation there was an increasing number of obese individuals who reported impaired HR-QoL. In our study, women had higher hs-CRP levels than men ( P <0.0001). Despite this gender difference, women are in general at lower risk for CVD events than men, which is explained by the fact that women have a greater accumulation of subcutaneous fat, while men carry more visceral fat [37]. Nevertheless, we found that obese women with hs-CRP levels above 3 mg/L had a higher probability for poor HR-QoL domain scores, while obese men did not. Others have also looked at the link between inflammation and HR-QoL. For example, among chronic kidney disease patients with anaemia, only inflammatory markers such as interleukin (IL)-6, IL-8 and tumour necrosis factor (TNF)-α were correlated with some of the HR-QoL domains, but CRP was not [19]. In such group of non-obese patients, lower levels of hs-CRP might not be strong enough for predicting HR-QoL. However, our additional analysis showed that the found association between obesity grade with and without T2D and HR-QoL (S2 Table), and the association between MetS and HR-QoL, Health-Related Quality of Life in Obesity 151

is likely to be partially attributable to elevated hs-CRP levels (S3 Table). For example, the OR for poor quality of life in the domain physical functioning was dropped in for individuals with grade 3 obesity and T2D from 11.34 to 7.27 in men and from 5.05 to 3.98 in women when we also included hs-CRP levels in the model. While our data certainly support the hypothesis that hs-CRP is adversely associated with HR-QoL, future studies should also include measurements of other inflammatory markers. In our study, the impact of obesity on domains of mental well-being was not as strong as that on domains of physical health. Of the four domains mainly related to mental well-being, vitality was more often affected in obese individuals. Previously published studies also suggest that the impact of obesity on mental well-being is weak. One pos- 6 sible explanation for such a relationship is that obesity only affects mental well-being in individuals whose obesity is accompanied by binge eating [38, 39], or diseases, chronic or otherwise [8]. Recently Jagielski et al. reported a high prevalence of psychological co- morbidity, such as symptoms of anxiety (70.3%) and depression (66.2%) among extreme obese individuals (BMI ≥35 kg/m2) who sought assistance in weight-management. However, the authors did not find a significant association between these conditions and adiposity. These findings suggest that the treatment-seeking obese individuals may suffer more from the psychological co-morbidities of extreme obesity than the rest of the obese population [40]. In our crude logistic model (adjusted for age only, data not shown), women with grade 3 obesity had higher odds of having a poor score on all four domains related to mental well-being, compared to those with grade 1 obesity ( P <0.0001). Men with grade 3 obesity only had a poor outcome on vitality ( P <0.0001). However, after adjusting for the presence of the various morbidities, most associations disappeared, which suggests that the relationship was attributable to the presence of other morbidities or obesity-related conditions. For instance, MetS was especially related to the domains of mental well-being (vitality, social functioning and role limitations due to emotional problems) and elevated hs-CRP levels in women was besides the domains of physical health, also associated with vitality and social functioning.

Strengths and limitations The main strength of our study is the large, are the well-characterised obese individuals, derived from the general population. Such large sample size provided us with sufficient statistical power to thoroughly investigate all associations between different obesity- related conditions. We even had data on 835 individuals with morbidly obesity. The normal weight individuals from the same cohort study provided us with a large and suitable reference group to define cut-off values of poor HR-QoL. A further strength of our study is the adjustment for a wide range of physical and mental morbidities, which enabled us to examine the effect of obesity and related conditions on HR-QoL as profound as possible. 152 Chapter 6

A limitation of this study is the cross-sectional design. As prospective data collection for the LifeLines Cohort Study is still ongoing, we might conclude about causality in the follow-up studies. A second limitation is that although the RAND-36 has proven to be highly valid for assessing HR-QoL and is a practical tool for epidemiological research, it remains a generic health status questionnaire. Furthermore, it is possible that our results are subject to volunteer bias. Individuals with obesity who elected to participate in the LifeLines Cohort Study may have had a better physical and mental health than those who did not volunteer. Finally, when assessing morbidity, our study relied on data from a self-reported questionnaire, which may have caused under- or over-reporting.

Conclusions

A substantial portion of obese individuals in the general population experience physical or mental consequences of their weight, which are reflected in the low scores on domains of health-related quality of life. The impact of obesity on an individual’s quality of life is enhanced by grade of obesity, T2D, MetS and inflammation and are mainly related to reduced physical health, such as in the domain of physical functioning and general health. Although mental well-being is less frequently impaired among the general obese population, they still have a higher probability to experience consequences in the related domains.

Acknowledgements

We wish to thank Eric van Sonderen (University of Groningen, University Medical Center Groningen) for his advice on the use of the RAND-36 and Sally Hill (Zwolle, The Netherlands) for critical reading and editing of the manuscript. The authors are grateful to the study participants, the staff of the LifeLines Cohort Study and Biobank, and the participating general practitioners and pharmacists. The LifeLines Cohort Study (BRIF4568) is engaged in a Bioresource research impact factor (BRIF) policy pilot study, details of which can be found at https://www.bioshare. eu/content/bioresource-impact-factor. The manuscript is based on data from the LifeLines cohort study. LifeLines adheres to standards for open data availability. The data catalogue of LifeLines is publicly accessible on www.LifeLines.net. All international researchers can apply for data at the LifeLines research office ([email protected]). The LifeLines system allows access for reproducibility of the study results. Health-Related Quality of Life in Obesity 153

References

1. van Vliet-Ostaptchouk JV, Nuotio ML, Slagter SN, international journal of quality of life aspects of treat- Doiron D, Fischer K, Foco L, Gaye A, Gogele M, Heier ment, care and rehabilitation 2010, 19(4):515-520. M, Hiekkalinna T et al: The prevalence of metabolic 11. Korhonen PE, Seppala T, Jarvenpaa S, Kautiainen H: syndrome and metabolically healthy obesity in Europe: Body mass index and health-related quality of life in a collaborative analysis of ten large cohort studies. BMC apparently healthy individuals. Quality of life research endocrine disorders 2014, 14:9. : an international journal of quality of life aspects of 2. Formiguera X, Canton A: Obesity: epidemiology and treatment, care and rehabilitation 2014, 23(1):67-74. clinical aspects. Best practice & research Clinical gastro- 12. Jia H, Lubetkin EI: The impact of obesity on health- enterology 2004, 18(6):1125-1146. related quality-of-life in the general adult US popula- 3. Fontaine KR, Barofsky I: Obesity and health-related tion. Journal of public health 2005, 27(2):156-164. quality of life. Obesity reviews : an official journal of the 13. Soltoft F, Hammer M, Kragh N: The association of body 6 International Association for the Study of Obesity 2001, mass index and health-related quality of life in the 2(3):173-182. general population: data from the 2003 Health Survey 4. Kolotkin RL, Crosby RD, Williams GR: Health-related of England. Quality of life research : an international quality of life varies among obese subgroups. Obesity journal of quality of life aspects of treatment, care and research 2002, 10(8):748-756. rehabilitation 2009, 18(10):1293-1299. 5. Bentley TG, Palta M, Paulsen AJ, Cherepanov D, Dunham 14. Serrano-Aguilar P, Munoz-Navarro SR, Ramallo-Farina NC, Feeny D, Kaplan RM, Fryback DG: Race and gender Y, Trujillo-Martin MM: Obesity and health related qual- associations between obesity and nine health-related ity of life in the general adult population of the Canary quality-of-life measures. Quality of life research : an Islands. Quality of life research : an international journal international journal of quality of life aspects of treat- of quality of life aspects of treatment, care and rehabili- ment, care and rehabilitation 2011, 20(5):665-674. tation 2009, 18(2):171-177. 6. Cameron AJ, Magliano DJ, Dunstan DW, Zimmet PZ, 15. Cummings DM, King DE, Mainous AG, 3rd: C-reactive Hesketh K, Peeters A, Shaw JE: A bi-directional rela- protein, antiinflammatory drugs, and quality of life tionship between obesity and health-related quality in diabetes. The Annals of pharmacotherapy 2003, of life: evidence from the longitudinal AusDiab study. 37(11):1593-1597. International journal of obesity 2012, 36(2):295-303. 16. Kalender B, Ozdemir AC, Dervisoglu E, Ozdemir O: 7. Choo J, Jeon S, Lee J: Gender differences in health-relat- Quality of life in chronic kidney disease: effects of ed quality of life associated with abdominal obesity in a treatment modality, depression, malnutrition and Korean population. BMJ open 2014, 4(1):e003954. inflammation. International journal of clinical practice 8. Doll HA, Petersen SE, Stewart-Brown SL: Obesity and 2007, 61(4):569-576. physical and emotional well-being: associations be- 17. Mommersteeg PM, Pelle AJ, Ramakers C, Szabo BM, tween body mass index, chronic illness, and the physi- Denollet J, Kupper N: Type D personality and course of cal and mental components of the SF-36 questionnaire. health status over 18 months in outpatients with heart Obesity research 2000, 8(2):160-170. failure: multiple mediating inflammatory biomarkers. 9. de Zwaan M, Petersen I, Kaerber M, Burgmer R, Nolting Brain, behavior, and immunity 2012, 26(2):301-310. B, Legenbauer T, Benecke A, Herpertz S: Obesity and 18. Wong YY, Almeida OP, McCaul KA, Yeap BB, Hankey GJ, Quality of Life: A Controlled Study of Normal-Weight van Bockxmeer FM, Flicker L: Elevated homocysteine is and Obese Individuals. Psychosomatics 2009, associated with poorer self-perceived physical health in 50(5):474-482. older men: the Health in Men Study. Maturitas 2012, 10. Renzaho A, Wooden M, Houng B: Associations between 73(2):158-163. body mass index and health-related quality of life 19. Farag YMK, Keithi-Reddy SR, Mittal BV, Surana SP, among Australian adults. Quality of life research : an Addabbo F, Goligorsky MS, Singh AK: Anemia, inflammation and health-related quality of life in chronic 154 Chapter 6

kidney disease patients. Clinical Nephrology 2011, individuals seeking weight reduction. Obesity 2008, 75(06):524-533. 16(1):59-63. 20. Stolk RP, Rosmalen JG, Postma DS, de Boer RA, Navis 30. Taft C, Karlsson J, Sullivan M: Do SF-36 summary com- G, Slaets JP, Ormel J, Wolffenbuttel BH: Universal risk ponent scores accurately summarize subscale scores? factors for multifactorial diseases: LifeLines: a three- Quality of life research : an international journal of qual- generation population-based study. European journal ity of life aspects of treatment, care and rehabilitation of epidemiology 2008, 23(1):67-74. 2001, 10(5):395-404. 21. Ridker PM: Cardiology Patient Page. C-reactive protein: 31. Farivar SS, Cunningham WE, Hays RD: Correlated physi- a simple test to help predict risk of heart attack and cal and mental health summary scores for the SF-36 stroke. Circulation 2003, 108(12):e81-85. and SF-12 Health Survey, V.I. Health and quality of life 22. Obesity: preventing and managing the global epidemic. outcomes 2007, 5:54. Report of a WHO consultation. World Health Organiza- 32. Levinger I, Goodman C, Hare DL, Jerums G, Selig S: tion technical report series 2000, 894:i-xii, 1-253. The effect of resistance training on functional capac- 23. Grundy SM, Cleeman JI, Daniels SR, Donato KA, ity and quality of life in individuals with high and low Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, numbers of metabolic risk factors. Diabetes care 2007, Smith SC, Jr. et al: Diagnosis and management of the 30(9):2205-2210. metabolic syndrome: an American Heart Association/ 33. Levinger I, Goodman C, Hare DL, Jerums G, Selig S: National Heart, Lung, and Blood Institute Scientific Functional capacity and quality of life in middle-age Statement. Circulation 2005, 112(17):2735-2752. men and women with high and low number of meta- 24. VanderZee KI, Sanderman R, Heyink JW, de Haes H: Psy- bolic risk factors. International journal of cardiology chometric qualities of the RAND 36-Item Health Survey 2009, 133(2):281-283. 1.0: a multidimensional measure of general health 34. Motie M, Evangelista LS, Horwich T, Lombardo D, Zaldivar status. International journal of behavioral medicine F, Hamilton M, Fonarow GC: Association between inflam- 1996, 3(2):104-122. matory biomarkers and adiposity in obese patients with 25. Flegal KM, Kit BK, Orpana H, Graubard BI: Association of heart failure and metabolic syndrome. Experimental and all-cause mortality with overweight and obesity using therapeutic medicine 2014, 8(1):181-186. standard body mass index categories: a systematic 35. Ford ES: Body mass index, diabetes, and C- review and meta-analysis. JAMA 2013, 309(1):71-82. reactive protein among U.S. adults. Diabetes care 1999, 26. Tapp RJ, Dunstan DW, Phillips P, Tonkin A, Zimmet PZ, 22(12):1971-1977. Shaw JE: Association between impaired glucose me- 36. Visser M, Bouter LM, McQuillan GM, Wener MH, Harris tabolism and quality of life: results from the Australian TB: Elevated C-reactive protein levels in overweight and diabetes obesity and lifestyle study. Diabetes research obese adults. JAMA 1999, 282(22):2131-2135. and clinical practice 2006, 74(2):154-161. 37. Lakoski SG, Cushman M, Criqui M, Rundek T, Blumen- 27. Amiri P, Hosseinpanah F, Rambod M, Montazeri A, Azizi thal RS, D’Agostino RB, Jr., Herrington DM: Gender and F: Metabolic syndrome predicts poor health-related C-reactive protein: data from the Multiethnic Study of quality of life in women but not in men: Tehran Lipid Atherosclerosis (MESA) cohort. American heart journal and Glucose Study. Journal of women’s health (2002) 2006, 152(3):593-598. 2010, 19(6):1201-1207. 38. Rieger E, Wilfley DE, Stein RI, Marino V, Crow SJ: A 28. Vetter ML, Wadden TA, Lavenberg J, Moore RH, Volger S, comparison of quality of life in obese individuals with Perez JL, Sarwer DB, Tsai AG: Relation of health-related and without binge eating disorder. The International quality of life to metabolic syndrome, obesity, depres- journal of eating disorders 2005, 37(3):234-240. sion and comorbid illnesses. International journal of 39. Baiano M, Salvo P, Righetti P, Cereser L, Baldissera E, obesity 2011, 35(8):1087-1094. Camponogara I, Balestrieri M: Exploring health-related 29. Tsai AG, Wadden TA, Sarwer DB, Berkowitz RI, Womble quality of life in eating disorders by a cross-sectional LG, Hesson LA, Phelan S, Rothman R: Metabolic study and a comprehensive review. BMC psychiatry syndrome and health-related quality of life in obese 2014, 14:165. Health-Related Quality of Life in Obesity 155

40. Jagielski AC, Brown A, Hosseini-Araghi M, Thomas GN, Taheri S: The association between adiposity, mental well-being, and quality of life in extreme obesity. PloS one 2014, 9(3):e92859.

6 156 Chapter 6

Supplemental Information

Table S1. Detailed overview of the single morbidities, clustered in 11 subgroups.

Subgroup Morbidities Pulmonary asthma and COPD Cancer Cardiovascular disease myocardial infarction, stroke, heart valve problems, atherosclerosis, thrombosis and pulmonary embolism Head migraine, cataracts and chronic throat/sinus infections Gastrointestinal & Liver hepatitis, cirrhosis of the liver, coeliac disease and gallstones Kidney and Bladder kidney stones, chronic bladder infection and incontinence Neurological diseases epilepsy, multiple sclerosis, spasticity, Parkinson’s disease and dementia Blood disorders anaemia and clotting disorders Musculoskeletal diseases fibromyalgia, arthrosis, rheumatic disease, osteoporosis, back or neck hernia, and repetitive strain injury Dermatological diseases serious acne, eczema and psoriasis Mental disorders chronic fatigue syndrome, burnout, depression, panic disorder, social phobia, agoraphobia, other anxiety syndromes, manic depressive syndrome, schizophrenia, eating problems, obsessive/compulsive disorders and ADHD Health-Related Quality of Life in Obesity 157

Table S2. Adjusted odds ratios (95% confidence intervals) for having a poor score on each domain of HR- QoL, according to obesity grade with/without T2D, and hs-CRP.

Number of Physical Role limitations Bodily Pain General Health subjects (%) Functioning Physical health Men Obesity grade 1 - no T2D (ref.) 2,437 (76.8) 1.0 1.0 1.0 1.0 Obesity grade 1 - T2D 212 (6.7) 1.59 (1.16-2.18) 1.08 (0.77-1.51) 1.27 (0.93-1.74) 1.53 (1.13-2.09) Obesity grade 2 - no T2D 359 (11.3) 1.82 (1.43-2.33) a 0.97 (0.74-1.27) 1.11 (0.87-1.43) 1.31 (1.03-1.66) Obesity grade 2 - T2D 67 (2.1) 1.79 (1.02-3.15) 1.38 (0.80-2.37) 1.18 (0.70-2.01) 2.16 (1.29-3.64) Obesity grade 3 - no T2D 76 (2.4) 3.70 (2.16-6.34) a 1.04 (0.59-1.82) 1.26 (0.76-2.09) 1.58 (0.96-2.58) Obesity grade 3 - T2D 22 (0.7) 7.27 (1.61-32.80) 1.78 (0.72-4.40) 0.74 (0.29-1.87) 2.83 (1.09-7.37) 6 Women Obesity grade 1 - no T2D (ref.) 3,333 (65.7) 1.0 1.0 1.0 1.0 Obesity grade 1 - T2D 179 (3.5) 1.82 (1.26-2.64) b 1.50 (1.07-2.09) 1.14 (0.82-1.60) 1.76 (1.26-2.45) b Obesity grade 2 - no T2D 1,053 (20.8) 1.53 (1.31-1.79) a 1.02 (0.87-1.20) 1.00 (0.85-1.17) 1.21 (1.04-1.42) Obesity grade 2 - T2D 95 (1.9) 1.81 (1.08-3.07) 1.21 (0.76-1.90) 1.17 (0.74-1.86) 2.17 (1.37-3.44) b Obesity grade 3 - no T2D 349 (6.9) 2.66 (2.05-3.45) a 1.38 (1.07-1.76) 1.28 (1.00-1.64) 1.98 (1.55-2.53) a Obesity grade 3 - T2D 63 (1.2) 3.98 (1.99-7.98) a 1.65 (0.96-2.85) 3.10 (1.72-5.56) a 2.90 (1.65-5.10) a Men hs-CRP <1 mg/L (ref.) 717 (22.6) 1.0 1.0 1.0 1.0 hs-CRP 1-3 mg/L 1,468 (46.3) 1.16 (0.96-1.41) 1.08 (0.87-1.35) 1.04 (0.85-1.28) 1.11 (0.90-1.36) hs-CRP 3-10 mg/L 864 (27.2) 1.36 (1.09-1.69) 1.32 (1.04-1.68) 1.19 (0.95-1.50) 1.30 (1.04-1.63) hs-CRP >10 mg/L 124 (3.9) 1.33 (0.87-2.03) 1.33 (0.85-2.09) 1.38 (0.91-2.11) 1.56 (1.03-2.36) Women hs-CRP <1 mg/L (ref.) 502 (9.9) 1.0 1.0 1.0 1.0 hs-CRP 1-3 mg/L 1,621 (32.0) 1.15 (0.92-1.45) 1.18 (0.93-1.49) 1.26 (1.00-1.57) 1.17 (0.92-1.48) hs-CRP 3-10 mg/L 2,337 (46.1) 1.47 (1.18-1.83) b 1.19 (0.95-1.50) 1.28 (1.03-1.60) 1.52 (1.21-1.91) a hs-CRP >10 mg/L 612 (12.1) 1.98 (1.51-2.61) a 1.60 (1.21-2.11) b 1.62 (1.23-2.12) a 1.70 (1.29-2.24) a 158 Chapter 6

Table S2. Adjusted odds ratios (95% confidence intervals) for having a poor score on each domain of HR-QoL, according to obesity grade with/without T2D, and hs-CRP. (continued) Number of Vitality Social Functioning Role limitations Mental Health subjects (%) Emotional problems Men Obesity grade 1 - no T2D (ref.) 2,437 (76.8) 1.0 1.0 1.0 1.0 Obesity grade 1 - T2D 212 (6.7) 1.48 (1.06-2.07) 1.64 (1.18-2.29) 1.47 (0.98-2.19) 1.51 (1.07-2.13) Obesity grade 2 - no T2D 359 (11.3) 1.38 (1.07-1.77) 1.03 (0.79-1.35) 0.98 (0.71-1.37) 1.12 (0.86-1.47) Obesity grade 2 - T2D 67 (2.1) 1.61 (0.94-2.75) 1.08 (0.60-1.92) 1.07 (0.53-2.14) 1.56 (0.89-2.75) Obesity grade 3 - no T2D 76 (2.4) 1.78 (1.08-2.94) 1.70 (1.01-2.86) 0.99 (0.49-1.99) 1.13 (0.64-1.99) Obesity grade 3 - T2D 22 (0.7) 3.23 (1.30-8.06) a 1.64 (0.65-4.16) 1.41 (0.48-4.10) 0.79 (0.28-2.29) Women Obesity grade 1 - no T2D (ref.) 3,333 (65.7) 1.0 1.0 1.0 1.0 Obesity grade 1 - T2D 179 (3.5) 1.73 (1.23-2.44) b 1.46 (1.01-2.12) 1.44 (0.98-2.10) 1.65 (1.16-2.35) Obesity grade 2 - no T2D 1,053 (20.8) 1.09 (0.92-1.28) 1.08 (0.90-1.29) 0.98 (0.81-1.19) 1.08 (0.91-1.28) Obesity grade 2 - T2D 95 (1.9) 1.40 (0.88-2.24) 1.43 (0.87-2.33) 1.12 (0.67-1.89) 1.46 (0.91-2.36) Obesity grade 3 - no T2D 349 (6.9) 1.13 (0.88-1.46) 1.30 (0.99-1.71) 1.51 (1.15-1.99) 1.20 (0.92-1.56) Obesity grade 3 - T2D 63 (1.2) 1.52 (0.87-2.65) 1.36 (0.75-2.44) 1.65 (0.92-2.95) 1.42 (0.81-2.52) Men hs-CRP <1 mg/L (ref.) 717 (22.6) 1.0 1.0 1.0 1.0 hs-CRP 1-3 mg/L 1,468 (46.3) 0.87 (0.71-1.08) 0.83 (0.67-1.04) 0.75 (0.57-1.00) 0.96 (0.76-1.21) hs-CRP 3-10 mg/L 864 (27.2) 1.08 (0.85-1.36) 1.22 (0.95-1.55) 1.22 (0.91-1.63) 1.14 (0.88-1.46) hs-CRP >10 mg/L 124 (3.9) 1.29 (0.84-1.98) 1.12 (0.71-1.77) 1.17 (0.67-2.03) 1.28 (0.80-2.03) Women hs-CRP <1 mg/L (ref.) 502 (9.9) 1.0 1.0 1.0 1.0 hs-CRP 1-3 mg/L 1,621 (32.0) 1.19 (0.93-1.53) 1.37 (1.04-1.81) 1.32 (0.99-1.76) 1.17 (0.91-1.50) hs-CRP 3-10 mg/L 2,337 (46.1) 1.42 (1.11-1.80) 1.51 (1.15-1.97) 1.49 (1.13-1.97) 1.32 (1.04-1.69) hs-CRP >10 mg/L 612 (12.1) 1.72 (1.29-2.29) a 1.72 (1.25-2.37) b 1.29 (0.91-1.81) 1.31 (0.98-1.78) Adjusted for age and the following morbidities: Pulmonary; Cancer; CVD; Head; Gastrointestinal & Liver; Kidney & Bladder; Neurological diseases; Blood disorders; Musculoskeletal diseases; Dermatological diseases and Mental disorders. Ref.: reference. Odds ratios in bold indicate P <0.005; aP <0.001; and bP <0.002. Health-Related Quality of Life in Obesity 159

Table S3. Adjusted odds ratios (95% confidence intervals) for having a poor score on each domain of HR- QoL, according to MetS and hs-CRP. Number of subjects Physical Role limitations Bodily Pain General Health (%) Functioning Physical health Men No MetS (ref.) 2,654 (83.6) 1.0 1.0 1.0 1.0 MetS 519 (16.4) 1.22 (0.99-1.51) 1.05 (0.84-1.32) 1.11 (0.90-1.37) 1.26 (1.03-1.56) Women No MetS (ref.) 3,356 (66.2) 1.0 1.0 1.0 1.0 MetS 1,716 (33.8) 1.09 (0.95-1.25) 1.16 (1.01-1.33) 1.11 (0.99-1.01) 1.30 (1.13-1.49) a Men hs-CRP <1 mg/L (ref.) 717 (22.6) 1.0 1.0 1.0 1.0 6 hs-CRP 1-3 mg/L 1,468 (46.3) 1.12 (0.92-1.36) 1.08 (0.86-1.35) 1.03 (0.84-1.26) 1.08 (0.88-1.32) hs-CRP 3-10 mg/L 864 (27.2) 1.29 (1.03-1.61) 1.31 (1.02-1.67) 1.18 (0.94-1.48) 1.25 (1.00-1.57) hs-CRP >10 mg/L 124 (3.9) 1.27 (0.83-1.94) 1.34 (0.85-2.10) 1.36 (0.89-2.07) 1.53 (1.01-2.32) Women hs-CRP <1 mg/L (ref.) 502 (9.9) 1.0 1.0 1.0 1.0 hs-CRP 1-3 mg/L 1,621 (32.0) 1.12 (0.90-1.41) 1.16 (0.92-1.46) 1.23 (0.98-1.55) 1.14 (0.90-1.45) hs-CRP 3-10 mg/L 2,337 (46.1) 1.41 (1.13-1.75) 1.16 (0.92-1.45) 1.24 (0.99-1.54) 1.47 (1.17-1.84) b hs-CRP >10 mg/L 612 (12.1) 1.85 (1.40-2.43) a 1.53 (1.16-2.03) 1.55 (1.18-2.04) b 1.61 (1.22-2.13) b Number of subjects Vitality Social Functioning Role limitations Mental Health (%) Emotional problems Men No MetS (ref.) 2,654 (83.6) 1.0 1.0 1.0 1.0 MetS 519 (16.4) 1.26 (1.01-1.57) 1.21 (0.96-1.52) 1.24 (0.94-1.64) 1.10 (0.87-1.40) Women No MetS (ref.) 3,356 (66.2) 1.0 1.0 1.0 1.0 MetS 1,716 (33.8) 1.28 (1.11-1.47) b 1.27 (1.09-1.49) 1.22 (0.99-1.01) a 1.19 (1.02-1.38) Men hs-CRP <1 mg/L (ref.) 717 (22.6) 1.0 1.0 1.0 1.0 hs-CRP 1-3 mg/L 1,468 (46.3) 0.85 (0.69-1.06) 0.81 (0.64-1.01) 0.74 (0.56-0.99) 0.94 (0.75-1.19) hs-CRP 3-10 mg/L 864 (27.2) 1.05 (0.83-1.33) 1.17 (0.92-1.49) 1.21 (0.90-1.62) 1.11 (0.86-1.43) hs-CRP >10 mg/L 124 (3.9) 1.28 (0.83-1.97) 1.09 (0.69-1.72) 1.17 (0.67-2.04) 1.27 (0.80-2.03) Women hs-CRP <1 mg/L (ref.) 502 (9.9) 1.0 1.0 1.0 1.0 hs-CRP 1-3 mg/L 1,621 (32.0) 1.18 (0.92-1.51) 1.36 (1.03-1.79) 1.31 (0.98-1.74) 1.16 (0.90-1.50) hs-CRP 3-10 mg/L 2,337 (46.1) 1.38 (1.09-1.76) 1.47 (1.13-1.93) 1.45 (1.10-1.92) 1.32 (1.03-1.69) hs-CRP >10 mg/L 612 (12.1) 1.66 (1.24-2.22) b 1.66 (1.20-2.29) 1.25 (0.89-1.77) 1.31 (0.97-1.77) Adjusted for age, BMI and the following morbidities: Pulmonary; Cancer; CVD; Head; Gastrointestinal & Liv- er; Kidney & Bladder; Neurological diseases; Blood disorders; Musculoskeletal diseases; Dermatological diseases and Mental disorders. Ref.: reference. Odds ratios in bold indicate P <0.005; aP <0.001; and bP <0.002. 7

Hoofdstukpagina-letteromtrek.indd 13 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 14 16/11/16 11:05 Chapter 7

Sex, BMI and age differences in metabolic syndrome: updated prevalence estimates of the Netherlands

Sandra N. Slagter Robert P. van Waateringe André P. van Beek Melanie M. van der Klauw Bruce H.R. Wolffenbuttel Jana V. van Vliet-Ostaptchouk

In preparation 7

Hoofdstukpagina-letteromtrek.indd 13 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 14 16/11/16 11:05 162 Chapter 7

Abstract

Objective In this study we evaluated the prevalence of metabolic syndrome (MetS) and its individual components, according to the revised NCEP ATPIII definition within sex- , body mass index (BMI)- and age combined clusters of a representative population sample. In addition, we used age-adjusted blood pressure thresholds to evaluate the effect on MetS prevalence and the prevalence of an elevated blood pressure. Methods Cross-sectional data of 74,531 western European participants, aged 18–79 years, were used from the Dutch LifeLines Cohort Study. MetS was defined according to the revised NCEP ATPIII definition. Furthermore, we also applied age-adjusted blood pressure thresholds as recommended by the eight report of the Joint National Committee (≥140/90 mmHg for those aged <60 years, and ≥150/90 mmHg for those aged ≥60 years). Results According to the revised NCEP ATPIII, 19.2% men and 12.1% women fulfilled the criteria for MetS. We observed a linear increase in the prevalence of MetS with BMI and up to the seventh age decade, associated with the age-related increase of blood pressure, waist circumference and glucose. Elevated blood pressure and abdominal obesity were the most common components of MetS in our population. While abdominal obesity dominated the MetS prevalence especially in women, an elevated blood pressure was already highly prevalent among young men, both independent of BMI. Applying age-adjusted blood pressure thresholds resulted in a 0.2-11.9% drop in the prevalence of MetS and a 6.0-36.3% drop in elevated blood pressure, within the different sex-, BMI- and age combined clusters. Conclusion We observed a gender disparity with age and BMI for the prevalence of MetS and, especially, abdominal obesity and elevated blood pressure. Our data indicate that compared to the other MetS components, an elevated blood pressure is highly prevalent in the (elderly) population due to the strict selected threshold level resulting in the overestimation of the MetS prevalence. Keywords Metabolic syndrome, Blood pressure, Age-adjusted, Population-based. Sex, BMI and Age differences in Metabolic Syndrome 163

Introduction

The metabolic syndrome (MetS) is nowadays frequently used to identify individuals at higher risk for future type 2 diabetes (T2D) and cardiovascular disease (CVD) [1]. Recog- nized metabolic risk components are abdominal obesity, dyslipidaemia, elevated blood pressure and elevated fasting glucose. However, the estimated prevalence of MetS differs between various populations, because variations exist in the frequencies of metabolic risk components [2]. It has also been reported that the prevalence of each metabolic risk component differs with sex [3-5]. Especially abdominal obesity is more common in women [2, 4, 5]. Whether the sex differences in the MetS features persist within different body mass index (BMI) classes and across different age groups, is unclear. Previously, we observed that besides abdominal obesity, elevated blood pressure 7 was the most common abnormality contributing to the prevalence of MetS, within all BMI classes [6, 7]. Elevated blood pressure is also very common among the elderly, and many studies have described a gradual increase of blood pressure with increasing age [8-10]. The rise in systolic blood pressure continues throughout life in contrast to diastolic blood pressure, which shows a reversed U-shaped trend with age [11]. It can, therefore, be argued that the defining value for elevated blood pressure used in the revised NCEP ATPIII definition for MetS (systolic blood pressure ≥130 or diastolic blood pressure ≥85 mmHg) is too low for an elderly population, and may lead to overestimation of the MetS prevalence. An earlier paper has suggested that the blood pressure level used in the definition of MetS should be adjusted to age [8]. In addition, recent guidelines on the treatment of elevated blood pressure indicate higher and age-adjusted blood pressure levels to start either a lifestyle or medical intervention [12, 13]. Harmonization of diagnostic criteria would greatly benefit the implementation of MetS in clinical practice. Despite the prevalence of MetS is well-known in various populations, there is no in-depth information available about the prevalence of MetS and the individual components within particular combined subgroups of sex, BMI and age. The LifeLines cohort is the largest population-based study in the Netherlands and therefore particularly suitable to evaluate these detailed prevalence estimates in the Dutch population. Our second aim was to evaluate the influence of age-adjusted blood pressure thresholds on the prevalence estimates of MetS and elevated blood pressure.

Methods

The LifeLines Cohort Study LifeLines is a population-based cohort study examining in a unique three-generation design the health and health-related behaviours of persons living in the North of the 164 Chapter 7

Netherlands. The adult population participating in LifeLines was found to be broadly representative for the adults living in the three northern provinces of the Netherlands [14]. Between 2006 and 2013 different recruitment strategies were adopted - recruitment of an index population (aged 25-49 years) via general practitioners, subsequent inclusion of their family members, and online self-registration – which resulted in a low risk of selection bias [15]. The LifeLines Cohort Study is conducted according to the principles of the Declaration of Helsinki and in accordance with the research code of the University Medical Center Groningen (UMCG). Before study entrance, all participants signed an informed consent. The study was approved by the medical ethics review committee of the UMCG. For this study we used cross-sectional data, collected between 2006 and 2013, of subjects from western European descendent (according to self-reported information in the questionnaire) and aged ≥18 and <80 years (N= 92.409). We excluded individuals who had no verified data on medication use or missing data on variables needed to calculate the body mass index or on the variables used to diagnose MetS. A total of 74,531 individuals were included in the study.

Clinical measurements A standardized protocol was used to obtain blood pressure and anthropometric measurements: height, weight, and waist circumference. Blood pressure was measured every minute during a period of 10 minutes with an automated DINAMAP Monitor (GE Healthcare, Freiburg, Germany). The average of the final three readings was recorded for systolic- and diastolic blood pressure. Anthropometric measurements were measured in light clothing and without shoes. Body weight was measured to the nearest 0.1 kg. Height and waist circumference were measured to the nearest 0.5 cm. Waist circumference was measured in standing position with a tape measure all around the body, at the level midway between the lower rib margin and the iliac crest. Body weight and height were used to calculate BMI (weight (kg)/height (m)2), which was categorized as normal weight (< 25 kg/m2), overweight (25-30 kg/m2) and obesity (≥ 30 kg/m2). Blood was collected in the fasting state, between 8.00 and 10.00 in the morning. On the same day, serum levels of HDL-cholesterol were measured, using an enzymatic colorimetric method, and triglycerides, using a colorimetric UV method on a Roche Modular P chemistry analyzer (Roche, Basel, Switzerland). Fasting blood glucose was measured using a hexokinase method.

Definitions of metabolic syndrome and metabolic risk components According to the revised NCEP ATPIII (R-ATPIII), at least three out of the five metabolic risk components need to be present to diagnose MetS. These metabolic risk components include: (1) systolic blood pressure ≥130 mmHg and/or diastolic blood pressure Sex, BMI and Age differences in Metabolic Syndrome 165

≥85 mmHg and/or use of antihypertensive drugs; (2) fasting blood glucose ≥5.6 mmol/L and/or use of blood glucose-lowering medication and/or diagnosis of T2D; (3) HDL cholesterol levels <1.03 mmol/L in men, and <1.30 mmol/L in women and/or use of lipid- lowering medication influencing these parameters; (4) triglyceride levels ≥1.70 mmol/L and/or use of triglyceride-lowering medication; and (5) waist circumference ≥102 cm in men and ≥88 cm in women. According to the most recent hypertension guideline from the eighth report of the Joint National Committee (JNC 8, 2014), non-diabetic individuals between 18 and 60 years should be treated to a target blood pressure <140/90 mmHg and individuals ≥60 years to a target blood pressure of <150/90 mmHg. Accordingly, age-adjusted thresholds for elevated blood pressure were considered at: (1) systolic blood pressure ≥140 and/or diastolic blood pressure ≥90 mmHg for those aged <60 years, and (2) systolic 7 blood pressure ≥150 and/or diastolic blood pressure ≥90 mmHg for those aged ≥60 years [12]. MetS defined by the age-adjusted thresholds for blood pressure are referred to as ‘revised NCEP ATPIII updated’ (R-ATPIII updated). All medications used by participants were self-reported and classified according to the Anatomical Therapeutic Chemical (ATC) classification system. Diagnosis of T2D was based on self-report and verified with self-reported medication use. Newly-diagnosed T2D was based on a single fasting blood glucose level ≥7.0 mmol/L. A CVD history was defined as self-reported previously sustained myocardial infarction, stroke, or vascular intervention.

Data analysis The prevalence of MetS (according to the R-ATPIII and R-ATPIII updated) and each metabolic risk factor are reported in subgroups that were defined by sex, BMI (normal weight, overweight and obese) and age decades (18-29 years, 30-39 years, 40-49 years, 50-59 years, 60-69 years and 70-79 years). Results are expressed as counts and/or proportions (%). All data analyses were conducted using IBM SPSS Statistics version 20 (IBM Corpora- tion, Armonk, NY, USA). In our analysis we chose to focus on absolute differences and not on statistical significance, because the large study sample of LifeLines may produce low p-values even when absolute differences are small.

Results

In the present study, data of 74,531 individuals were used, including 32,731 (43.9%) men (mean age 45±13 years) and 41,800 (56.1%) women (mean age 45±12 years). Of Among the male participants, 12,691 (38.8%) were normal weight, 15,677 (47.9%) overweight and 4,363 (13.3%) obese. Among female participants these numbers were 166 Chapter 7

21,460 (51.3%), 13,893 (33.2%) and 6,447 (15.5%), respectively. Clinical characteristics of the study population can be found in table 1. In supplemental table 1A-C, clinical characteristics are depicted for the sex, age and BMI stratified samples. The prevalence of T2D and CVD history increased with age and BMI. Among older adults (≥60 years), the prevalence of T2D and CVD history were respectively, 9.4% and 9.1% in men and 7.5% and 3.1% in women.

Table 1. Clinical characteristics of the study population

Men Women (N= 32,731) (N=41,800) Age (years) 45.2 ± 12.7 44.9 ± 12.5 Weight (kg) 87.6 ± 13.1 73.6 ± 13.5 BMI (m/kg2) 26.3 ± 3.6 25.7 ± 4.6 Waist circumference (cm) 94.9 ± 10.6 86.6 ± 12.0 Systolic BP (mmHg) 131 ± 14 122 ± 15 Diastolic BP (mmHg) 76 ± 9 72 ± 9 HDL-cholesterol (mmol/L) 1.30 ± 0.32 1.61 ± 0.39 Triglycerides (mmol/L) 1.39 (0.83-1.65) 1.01 (0.65-1.20) Fasting blood glucose (mmol/L) 5.2 ± 0.8 4.9 ± 0.7 Use of antihypertensive drugs (%) 9.0 9.1 Type 2 diabetes (%) 2.7 1.8 CVD history (%) 2.1 0.8 Abbreviations: body mass index, BMI; blood pressure, BP; high density lipoprotein cholesterol, HDL-cholesterol; cardiovascular disease, CVD.

The prevalence of MetS, according to the operating definitions The age-, sex- and BMI-specific prevalence of MetS according to the R-ATPIII and R-ATPIII updated criteria are shown in Table 2. In both men and women, the prevalence of MetS increased with age in all BMI classes, irrespective of the used cut-offs for blood pressure. Also the number of MetS components increased with age (Table 3). In general, MetS was more common in men than in women. Only in normal weight women ≥60 years and overweight women ≥70 years, MetS prevalence exceeded that of men (Table 2). When the age-adjusted blood pressure thresholds were used to define MetS (R-ATPIII updated), the percentage of subjects with MetS decreased with 0.9-11.9% in men and 0.2-8.6% in women (Table 2). Sex, BMI and Age differences in Metabolic Syndrome 167

Table 2. Percentage of subjects with metabolic syndrome, according to clustered subgroups of sex, BMI and age. Age, years Men Women No. of subjects R-ATPIII R-ATPIII No. of subjects R-ATPIII R-ATPIII updated updated Normal weight 18-29 2,507 1.6 0.7 3,474 0.5 0.3 30-39 3,244 2.9 1.5 5,260 1.1 0.7 40-49 4,002 4.2 2.6 7,833 1.8 1.4 50-59 1,600 4.8 2.5 3,006 3.6 2.3 60-69 976 5.2 3.8 1,453 6.3 4.8 70-79 362 7.7 6.1 434 11.8 10.4 Overweight 18-29 1,154 8.9 5.2 1,142 6.2 3.2 30-39 3,262 16.2 10.6 2,684 6.9 4.6 7 40-49 5,974 20.8 15.7 5,014 13.1 10.1 50-59 2,636 22.3 15.6 2,519 17.0 12.1 60-69 1,893 27.0 21.0 1,808 26.2 21.6 70-79 758 31.5 28.5 726 35.5 30.3 Obese 18-29 241 47.7 38.6 493 22.7 14.6 30-39 857 54.1 42.2 1,323 25.5 19.0 40-49 1,734 59.9 51.5 2,427 37.5 31.0 50-59 780 60.8 50.6 1,009 48.0 39.4 60-69 578 68.5 57.8 823 55.5 46.9 70-79 173 68.8 63.6 372 59.4 54.0 R-ATPIII updated is based on the age-adjusted blood pressure cut-offs, i.e. ≥140 mmHg (systolic) and/or ≥90 mmHg (diastolic) for those aged <60 years, and ≥150 mmHg (systolic) and/or ≥90 mmHg (diastolic) for those aged ≥60 years.

Table 3. Prevalence of having one to five MetS components by sex and age groups according to the R- ATPIII. Men 18-29 30-39 40-49 50-59 60-69 70-79 Number of subjects 3,902 7,363 11,710 5,016 3,447 1,293 None 47.3 33.9 26.7 22.9 14.7 7.8 One 34.0 33.3 31.3 30.9 32.2 34.3 Two 12.1 18.1 21.1 23.6 25.3 28.0 Three 4.9 9.9 12.6 13.7 18.2 18.4 Four 1.4 4.0 6.5 6.8 6.8 8.2 All five 0.3 0.9 1.9 2.2 2.8 3.2 Women 18-29 30-39 40-49 50-59 60-69 70-79 Number of subjects 5,109 9,267 15,274 6,534 4,084 1,532 None 54.1 47.9 40.5 30.2 14.9 6.9 One 29.0 30.7 30.5 33.3 30.3 22.7 Two 12.9 15.1 17.9 20.9 29.7 35.8 Three 3.3 4.9 7.6 10.0 14.9 20.4 Four 0.5 1.1 2.9 4.2 7.7 10.3 All five 0.1 0.2 0.7 1.5 2.5 3.9 168 Chapter 7

The prevalence of the individual metabolic risk factors in the total population Figure 1 illustrates the prevalence of the individual MetS components, applying the cut-offs for the individual metabolic risk factors as recommended by the R-ATPIII. Exact numbers of the prevalence estimates can be found in supplemental tables 2A-C. In men below the age of 60 years, the most common MetS component was elevated blood pressure (49.6%), followed by increased triglycerides (24.1%) and decreased HDL-cholesterol (22.1%). In women below the age of 60 years, abdominal obesity (39.0%), elevated blood pressure (25.2%) and decreased HDL-cholesterol (18.5%) were the most common MetS components. However, in older adults (≥60 years) the sex differences in the various MetS components were diminished. In both sexes elevated blood pressure (75.9% in men and 69.2% in women), abdominal obesity (35.6% in men and 60.9% in women) and impaired fasting glucose (32.2% in men and 23.8% in women) were the most prevalent.

Elevated blood pressure The MetS component ‘elevated blood pressure’ showed the most pronounced increase with age. Across the entire cohort, the prevalence of elevated blood pressure (≥130/85 mmHg, including participants receiving antihypertensive drugs) increased from 23.3% in the youngest age group (18-29 years) to 84.4% in the oldest age group (70-79 years). In men below the age of 60 years, elevated blood pressure was present in a much higher percentage compared to women (independent of BMI). Among individuals ≥60 years, the percentages of men and women with elevated blood pressure were roughly similar (Figure 1 and 2). In figure 2 the prevalence of elevated blood pressure is displayed for the age- adjusted blood pressure thresholds, together with the strict threshold of the R-ATPIII for comparison. The prevalence estimates also include those using antihypertensive drugs. Age-adjustment of the blood pressure threshold resulted in a large reduction in the number of subjects fulfilling the criteria elevated blood pressure compared to the standard strict threshold. This was most pronounced among younger men (<60 years), where the prevalence of elevated blood pressure dropped with 20.4-36.3%, depending on the age and BMI group. Supplemental Tables 1A-1C depict the absolute blood pressure levels in the various age and BMI groups, and the percentage of participants using antihypertensive drugs. With increasing age and higher BMI, the use of antihypertensive drugs increased, and this was comparable between men and women. The age-adjusted prevalence estimates of elevated blood pressure were closer to the estimates for antihypertensive drug use. In other words, the ratio of those having an elevated blood pressure based on their measured blood pressure values vs. the use of antihypertensive drugs decreased. Sex, BMI and Age differences in Metabolic Syndrome 169

         

    



  7





  











  





           

   

Figure 1. Prevalence of the metabolic syndrome components in the total population. Left panel A: men, and right panel B: women. Abbreviations: waist circumference, WC; blood pressure, BP; high density lipoprotein cholesterol, HDL-C; triglycerides, TG; fasting glucose, FG. 170 Chapter 7

        



    











  









   





               

Figure 2. Prevalence of elevated blood pressure, according to the strict and age-adjusted thresholds, including antihypertensive drug use. Left panel A: men, and right panel B: women. Abbreviations: blood pressure, BP. Strict blood pressure values are ≥130 mmHg (systolic) or ≥85 mmHg (diastolic) (including those using antihypertensive drugs). Age-adjusted blood pressure values are ≥140 mmHg (systolic) or ≥90 mmHg (diastolic) for those aged <60 years, and ≥150 mmHg (systolic) or ≥90 mmHg (diastolic) for those aged ≥60 years (including those using antihypertensive drugs). Sex, BMI and Age differences in Metabolic Syndrome 171

Abdominal obesity Prevalence of abdominal obesity became higher with increasing age and was higher among women than men. In normal weight women, the prevalence of abdominal obesity increased from 6.0% to 24.9% with age. This percentage was much lower among normal weight men, namely 0.1-3.0%. The sex difference was also present in overweight (9.5%-39.6% in men and 51.0%-77.8% in women) and obese individuals, although among obese individuals essentially all had a waist circumference above the defined cut-offs for abdominal obesity.

Dyslipidaemia and impaired fasting glucose The prevalence of decreased HDL-cholesterol gradually fell with increasing age in both men and women (Figure 1). In contrast, the prevalence of elevated triglycerides became 7 higher with increasing age among women, while in men there was a reversed U-shaped trend. In both men and women, the prevalence of impaired fasting glucose increased with age as well, being most pronounced in overweight and obese individuals. Only from the age of 60 years and onwards, impaired fasting glucose became one of the three most prevalent MetS components (Figure 1).

Discussion

In western-European individuals living in the Netherlands, the prevalence of MetS risk factors differed by sex, age and BMI. Elevated blood pressure and abdominal obesity were the two most frequently present risk factors, and their contribution to the diagnosis of MetS greatly overrides the other three components. Furthermore, the age-adjusted thresholds for elevated blood pressure better approximated the treatment of hypertension in clinical practice.

Prevalence of MetS (components) This is one of the largest studies in the Netherlands, in which the prevalence of MetS was meticulously assessed. Similar to other observations, we found that the prevalence of MetS increases with age, up to the seventh age decade [1, 16, 17]. In our study, this trend was observed both when the strict and age-adjusted thresholds for blood pressure were used. In line with other population studies, we also found that prevalence of MetS is higher in men than in women [2, 4, 5]. Data from NHANES III (1988-1994) showed, however, that prevalence of MetS in women exceeded that of men, when individuals older than 50 years of age were evaluated [4]. In our dataset, we observed a higher prevalence of MetS only in normal weight women ≥60 years and overweight women ≥70 years compared with the women and men from the same age and BMI 172 Chapter 7

group. The difference in prevalence of MetS between men and women may be related to differences in body fat distribution: men have more visceral- and hepatic fat, whereas women have more total body fat [18]. Difference in total fat and visceral fat with age and the cardiometabolic effects of menopause may explain the diminished sex difference in MetS prevalence seen with older age [18, 19]. In some studies, prevalence estimates for MetS are found to plateau or drop off after the sixth or seventh age decade in both sexes [20, 21], or only in men [22-25]. This observation might be due to a survival effect or participation bias, as individuals prone to obesity-related morbidity and mortality have already died or decline to participate in a study [26]. While it may also depend on the definition used for MetS [1, 20], even if the same definition was used, different trends were observed between countries [7, 17, 24]. This underpins the importance of estimating the country-specific prevalence of MetS. The observed trend of increasing in MetS prevalence with age can be explained by the large number of people developing MetS conditions by the time they are aged ≥60 years (i.e. more than 85% of the individuals have at least one metabolic risk factor). Due to the age-related rises of blood pressure, abdominal obesity and glucose a more similar make-up of MetS was seen in the elderly, whereas in younger people the MetS profile was more heterogeneous and differed more by sex. As reported previously, abdominal obesity was already highly prevalent in younger women and much more common than in men [2, 4, 5]. However, we found that a large proportion of young men had an elevated blood pressure (42.3% below the age of 40 years). This is much higher than the 24.1% found in 20-39 year old men from the NHANES 2003-2006 study [3]. This finding may suggest that, across the entire lifespan, blood pressure has a greater relative importance in the development of MetS in men than in women. Further research is needed to clarify why already a large group of young men have a blood pressure above the ‘normal high’ range. MetS is used to define individuals with a higher lifetime risk for cardiovascular events, and still widely used [27]. However, the clinical utility of MetS has been criticized for quit some years [28, 29]. Criticism is related to the predictive value of MetS for CVD. MetS is found to have no greater predictive value for CVD compared to the individual components [30]. Furthermore, all MetS components are weighted equally while it is clear that some risk factors are more important for risk prediction. Also, continuous variables are dichotomized and MetS is operationalized as a combination of three or more of the five components. While already this dichotomization at two levels results in a loss of predictive power, it is not clear which thresholds optimise the positive predictive value of the definition [28]. Furthermore, in the current R-ATPIII definition, only blood pressure and fasting glucose are variables used for targeted risk factor interventions in clinical practice. Though, interventions are seldom started at the levels proposed by the R-ATPIII. Because elevated blood pressure was the most common metabolic risk factors Sex, BMI and Age differences in Metabolic Syndrome 173

in our population, we will discuss the suggested threshold for this feature in the MetS definition.

‘Elevated’ blood pressure in different age groups Compared to the other MetS components, the contribution of elevated blood pressure to the prevalence of MetS was remarkably high in our study. In this respect, a threshold of ≥130/85 mmHg seems very strict, especially for the older subjects where the natural course of blood pressure changes with ageing is not taken into account. The observation of increasing blood pressure with age can, in part, be explained by the effects of increasing arterial stiffness [31]. Since approximately half of the deaths from stroke or CVD is attributable to hypertension [32], early screening and diagnosis of hypertension is important. However, the optimal threshold of blood pressure for intervention remains 7 disputable, especially in the elderly [33, 34]. Several long-term follow-up studies have shown that cardiovascular risk gradually increases with rising blood pressure [35-38]. Over the last decades, several guidelines have tried to define the optimal cut-off levels for treatment of elevated blood pressure with lifestyle adjustment and medication. In the most recent JNC 8 treatment guideline for hypertension, it is advised to aim for a blood pressure <140/90 mmHg in nondiabetic adults (<60 years), whereas blood pressure values <150/90 mmHg were advised for elderly (≥60 years) [12]. In the current study, we applied both the very strict blood pressure threshold from the R-ATPIII as well as these age-adjusted thresholds. Apply- ing the age-adjusted thresholds resulted in a considerable reduction, varying between 6.0-36.3%, of subjects fulfilling the blood pressure criteria. Especially (younger) men were now less frequent classified as having an elevated blood pressure. Meaning that there is a large group of men with a blood pressure range of 130-140 systolic and 85-90 diastolic. Whether this group of men face severe long-term implications needs further investigation. Intervention studies using a variety of blood pressure-lowering medications have clearly shown the benefit of such treatments in reducing the incidence of cardiovascular events [39]. For instance, in the HOPE study, drug treatment of hypertension compared to placebo reduced the incidence of death, myocardial infarction, stroke and death from cardiovascular causes by 22% [40]. Lowering systolic blood pressure below 140 mmHg or even below 130 mmHg to reduce cardiovascular risk is supported by data from respectively, the HOT study and the INVEST study [41, 42]. Although the elderly people may benefit from antihypertensive treatment as well, it was shown previously that among placebo-controlled trials only one Japanese trial achieved an average systolic blood pressure value <140 mmHg in the elderly [43]. In our study we observed that the prevalence of subjects meeting the age-adjusted blood pressure thresholds were closer to the prevalence estimates of subjects treated for hypertension. However, still 174 Chapter 7

some under-treatment was observed within all BMI- and age groups, especially among young men compared to young women. The cross-sectional character of LifeLines does not allow to establish a cause for this, however it may be that fewer men are checked for elevated blood pressure. Indeed, it has been reported that men are less likely than women to receive certain preventive services [44-46]. The “elderly” is a difficult definition, because this subgroup is not a simple age range, but includes groups with a different level of overall health. Treatment of hypertension is therefore more complex in the elderly compared to the general population. In a study among 1.25 million people, with a median follow-up time of 5.2 years, the relative risks for nearly all CVD decreased with age when systolic- and diastolic blood pressure increased with respectively, 20/10 mmHg [47]. This indicates that a very strict blood pressure target seems less useful in older subjects compared to applying a strict blood pressure target in younger subjects. A finding supported by the SHEP study [48], where in patients aged ≥60 years, risk reduction for stroke was higher in those who achieved a systolic blood pressure <150 mmHg (decreased by 33%) than in those who achieved a systolic blood pressure <140 mmHg (decreased by 22%) [48]. Unfortunately, two Japanese trials in older patients (one placebo-controlled study and one multicenter parallel-group study) were underpowered to observe benefits from more- (<140 mmHg) vs. less- (<150 mmHg) intensive blood pressure lowering on composites of cardiovascular events [49, 50]. While at first sight the decision of the JNC 8 to recommend age-specific treatment targets is in line with the available evidence, there is some criticism as well. The JNC 8 used mainly the data from randomized clinical trials, while evidence from observational studies, systematic reviews or meta-analyses were excluded [34]. Still, well-conducted trials are needed to investigate the size of benefits of treating the ‘hypertensive’ elderly with mild hypertension (140-159 systolic and 90-99 diastolic).

Strengths and limitations There are several strong points, which characterize our study. We used data of 74,531 Dutch participants, of only western European descent, from whom high quality data on anthropometric and clinical measurements were obtained. The large number of participants allowed us to explore trends within detailed clusters of sex, BMI and age, which has not been done before. However, the findings of our study are limited by the cross-sectional data, and therefore, no trends in the development of clinically significant endpoints, such as T2D and cardiovascular morbidity and mortality, could yet be established. Although LifeLines is a relatively young cohort, it is one of the largest cohort studies to date, which is prospectively collecting follow-up data on a wide range of subjects. The LifeLines Cohort Study will therefore add a valuable contribution to strengthen evidence upon complex research questions. Sex, BMI and Age differences in Metabolic Syndrome 175

Conclusion

In this representative sample of a Dutch adult population we observed a gender disparity with age and BMI for the prevalence of MetS and, especially, the blood pressure and waist circumference component. The observed sex differences tended to diminish in older adults. Due to the strict selected threshold level, the blood pressure component is much higher in the (elderly) population compared to the other MetS components. This update of the MetS prevalence and its individual components in the Dutch population show that there is an ongoing burden of risk factors associated with development of T2D and CVD.

7 Acknowledgements

The authors wish to acknowledge the services of the LifeLines Cohort Study, the contributing research centers delivering data to LifeLines, and all the study participants. 176 Chapter 7

References

1. Cornier MA, Dabelea D, Hernandez TL, Lindstrom adults in the US population. Journal of clinical hyperten- RC, Steig AJ, Stob NR, Van Pelt RE, Wang H, Eckel RH: sion 2012, 14(8):502-506. The metabolic syndrome. Endocrine reviews 2008, 11. Burt VL, Whelton P, Roccella EJ, Brown C, Cutler JA, 29(7):777-822. Higgins M, Horan MJ, Labarthe D: Prevalence of 2. Scuteri A, Laurent S, Cucca F, Cockcroft J, Cunha PG, hypertension in the US adult population. Results from Manas LR, Raso FU, Muiesan ML, Ryliskyte L, Rietzschel the Third National Health and Nutrition Examination E et al: Metabolic syndrome across Europe: different Survey, 1988-1991. Hypertension 1995, 25(3):305-313. clusters of risk factors. European journal of preventive 12. James PA, Oparil S, Carter BL, Cushman WC, Dennison- cardiology 2015, 22(4):486-491. Himmelfarb C, Handler J, Lackland DT, LeFevre ML, 3. Ervin RB: Prevalence of metabolic syndrome among MacKenzie TD, Ogedegbe O et al: 2014 evidence-based adults 20 years of age and over, by sex, age, race and guideline for the management of high blood pressure ethnicity, and body mass index: United States, 2003- in adults: report from the panel members appointed 2006. National health statistics reports 2009(13):1-7. to the Eighth Joint National Committee (JNC 8). JAMA 4. Ford ES, Giles WH, Dietz WH: Prevalence of the 2014, 311(5):507-520. metabolic syndrome among US adults: findings from 13. Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, the third National Health and Nutrition Examination Bohm M, Christiaens T, Cifkova R, De Backer G, Domi- Survey. JAMA 2002, 287(3):356-359. niczak A et al: 2013 ESH/ESC Practice Guidelines for the 5. Kuk JL, Ardern CI: Age and sex differences in the cluster- Management of Arterial Hypertension. Blood pressure ing of metabolic syndrome factors: association with 2014, 23(1):3-16. mortality risk. Diabetes care 2010, 33(11):2457-2461. 14. Scholtens S, Smidt N, Swertz MA, Bakker SJ, Dotinga A, 6. Slagter SN, van Vliet-Ostaptchouk JV, Vonk JM, Boezen Vonk JM, van Dijk F, van Zon SK, Wijmenga C, Wolffen- HM, Dullaart RP, Kobold AC, Feskens EJ, van Beek AP, buttel BH et al: Cohort Profile: LifeLines, a three- van der Klauw MM, Wolffenbuttel BH: Associations generation cohort study and biobank. International between smoking, components of metabolic syndrome journal of epidemiology 2015, 44(4):1172-1180. and lipoprotein particle size. BMC medicine 2013, 15. Klijs B, Scholtens S, Mandemakers JJ, Snieder H, Stolk 11:195. RP, Smidt N: Representativeness of the LifeLines Cohort 7. van Vliet-Ostaptchouk JV, Nuotio ML, Slagter SN, Study. PloS one 2015, 10(9):e0137203. Doiron D, Fischer K, Foco L, Gaye A, Gogele M, Heier 16. Alkerwi A, Donneau AF, Sauvageot N, Lair ML, Scheen M, Hiekkalinna T et al: The prevalence of metabolic A, Albert A, Guillaume M: Prevalence of the metabolic syndrome and metabolically healthy obesity in Europe: syndrome in Luxembourg according to the Joint Interim a collaborative analysis of ten large cohort studies. BMC Statement definition estimated from the ORISCAV-LUX endocrine disorders 2014, 14:9. study. BMC public health 2011, 11(1):4. 8. Gause-Nilsson I, Gherman S, Kumar Dey D, Kennerfalk 17. Hildrum B, Mykletun A, Hole T, Midthjell K, Dahl AA: A, Steen B: Prevalence of metabolic syndrome in an Age-specific prevalence of the metabolic syndrome elderly Swedish population. Acta diabetologica 2006, defined by the International Diabetes Federation and 43(4):120-126. the National Cholesterol Education Program: the Nor- 9. Saukkonen T, Jokelainen J, Timonen M, Cederberg H, wegian HUNT 2 study. BMC public health 2007, 7:220. Laakso M, Harkonen P, Keinanen-Kiukaanniemi S, Ra- 18. Pradhan AD: Sex differences in the metabolic syndrome: jala U: Prevalence of metabolic syndrome components implications for cardiovascular health in women. Clini- among the elderly using three different definitions: a cal chemistry 2014, 60(1):44-52. cohort study in Finland. Scandinavian journal of primary 19. Rosano GM, Vitale C, Marazzi G, Volterrani M: health care 2012, 30(1):29-34. Menopause and cardiovascular disease: the evidence. 10. Sumner AD, Sardi GL, Reed JF, 3rd: Components of the Climacteric : the journal of the International Menopause metabolic syndrome differ between young and old Society 2007, 10 Suppl 1:19-24. Sex, BMI and Age differences in Metabolic Syndrome 177

20. Cameron AJ, Magliano DJ, Zimmet PZ, Welborn T, Shaw 29. Reaven GM: The metabolic syndrome: is this diagnosis JE: The metabolic syndrome in Australia: prevalence necessary? The American journal of clinical nutrition using four definitions. Diabetes research and clinical 2006, 83(6):1237-1247. practice 2007, 77(3):471-478. 30. Ding EL, Smit LA, Hu FB: The metabolic syndrome as a 21. Deepa M, Farooq S, Datta M, Deepa R, Mohan V: Preva- cluster of risk factors: is the whole greater than the sum lence of metabolic syndrome using WHO, ATPIII and IDF of its parts?: comment on “The metabolic syndrome, its definitions in Asian Indians: the Chennai Urban Rural component risk factors, and progression of coronary Epidemiology Study (CURES-34). Diabetes/metabolism atherosclerosis”. Archives of internal medicine 2010, research and reviews 2007, 23(2):127-134. 170(5):484-485. 22. Adams RJ, Appleton S, Wilson DH, Taylor AW, Dal 31. Mitchell GF, Parise H, Benjamin EJ, Larson MG, Keyes Grande E, Chittleborough C, Gill T, Ruffin R: Population MJ, Vita JA, Vasan RS, Levy D: Changes in arterial stiff- comparison of two clinical approaches to the metabolic ness and wave reflection with advancing age in healthy syndrome: implications of the new International Diabe- men and women: the Framingham Heart Study. 7 tes Federation consensus definition.Diabetes care 2005, Hypertension 2004, 43(6):1239-1245. 28(11):2777-2779. 32. Laslett LJ, Alagona P, Jr., Clark BA, 3rd, Drozda JP, Jr., 23. Ford ES, Li C, Zhao G: Prevalence and correlates of Saldivar F, Wilson SR, Poe C, Hart M: The worldwide metabolic syndrome based on a harmonious definition environment of cardiovascular disease: prevalence, among adults in the US. Journal of diabetes 2010, diagnosis, therapy, and policy issues: a report from the 2(3):180-193. American College of Cardiology. Journal of the American 24. Gavrila D, Salmeron D, Egea-Caparros JM, Huerta JM, College of Cardiology 2012, 60(25 Suppl):S1-49. Perez-Martinez A, Navarro C, Tormo MJ: Prevalence 33. Andersson C, Vasan RS: Lower is not always better? of metabolic syndrome in Murcia Region, a southern Blood pressure treatment targets revisited. Journal of European Mediterranean area with low cardiovascular the American College of Cardiology 2014, 64(6):598-600. risk and high obesity. BMC public health 2011, 11:562. 34. Schwartz CL, McManus RJ: What is the evidence base 25. Park YW, Zhu S, Palaniappan L, Heshka S, Carnethon MR, for diagnosing hypertension and for subsequent Heymsfield SB: The metabolic syndrome: prevalence blood pressure treatment targets in the prevention of and associated risk factor findings in the US population cardiovascular disease? BMC medicine 2015, 13:256. from the Third National Health and Nutrition Examina- 35. Menotti A, Jacobs DR, Jr., Blackburn H, Kromhout D, Nis- tion Survey, 1988-1994. Archives of internal medicine sinen A, Nedeljkovic S, Buzina R, Mohacek I, Seccareccia 2003, 163(4):427-436. F, Giampaoli S et al: Twenty-five-year prediction of 26. Zamboni M, Mazzali G, Zoico E, Harris TB, Meigs JB, stroke deaths in the seven countries study: the role Di Francesco V, Fantin F, Bissoli L, Bosello O: Health of blood pressure and its changes. Stroke; a journal of consequences of obesity in the elderly: a review of four cerebral circulation 1996, 27(3):381-387. unresolved questions. International journal of obesity 36. Selmer R: Blood pressure and twenty-year mortal- 2005, 29(9):1011-1029. ity in the city of Bergen, Norway. American journal of 27. Grundy SM: Metabolic syndrome: a multiplex cardio- epidemiology 1992, 136(4):428-440. vascular risk factor. The Journal of clinical endocrinology 37. Stamler J, Neaton JD, Wentworth DN: Blood pressure and metabolism 2007, 92(2):399-404. (systolic and diastolic) and risk of fatal coronary heart 28. Kahn R, Buse J, Ferrannini E, Stern M: The metabolic disease. Hypertension 1989, 13(5 Suppl):I2-12. syndrome: time for a critical appraisal. Joint statement 38. van den Hoogen PC, Feskens EJ, Nagelkerke NJ, Menotti from the American Diabetes Association and the Euro- A, Nissinen A, Kromhout D: The relation between blood pean Association for the Study of Diabetes. Diabetologia pressure and mortality due to coronary heart disease 2005, 48(9):1684-1699. among men in different parts of the world. Seven Coun- 178 Chapter 7

tries Study Research Group. The New England journal of 45. Stewart SH, Silverstein MD: Racial and ethnic disparity medicine 2000, 342(1):1-8. in blood pressure and cholesterol measurement. Jour- 39. Turnbull F, Neal B, Ninomiya T, Algert C, Arima H, Barzi nal of general internal medicine 2002, 17(6):405-411. F, Bulpitt C, Chalmers J, Fagard R, Gleason A et al: Effects 46. Viera AJ, Thorpe JM, Garrett JM: Effects of sex, age, and of different regimens to lower blood pressure on major visits on receipt of preventive healthcare services: a cardiovascular events in older and younger adults: secondary analysis of national data. BMC health services meta-analysis of randomised trials. BMJ (Clinical research 2006, 6:15. research ed) 2008, 336(7653):1121-1123. 47. Rapsomaniki E, Timmis A, George J, Pujades-Rodriguez 40. Yusuf S, Sleight P, Pogue J, Bosch J, Davies R, Dagenais M, Shah AD, Denaxas S, White IR, Caulfield MJ, Dean- G: Effects of an angiotensin-converting-enzyme field JE, Smeeth L et al: Blood pressure and incidence inhibitor, ramipril, on cardiovascular events in high-risk of twelve cardiovascular diseases: lifetime risks, patients. The Heart Outcomes Prevention Evaluation healthy life-years lost, and age-specific associations Study Investigators. The New England journal of medi- in 1.25 million people. Lancet (London, England) 2014, cine 2000, 342(3):145-153. 383(9932):1899-1911. 41. Hansson L, Zanchetti A, Carruthers SG, Dahlof B, 48. Perry HM, Jr., Davis BR, Price TR, Applegate WB, Fields Elmfeldt D, Julius S, Menard J, Rahn KH, Wedel H, WS, Guralnik JM, Kuller L, Pressel S, Stamler J, Probst- Westerling S: Effects of intensive blood-pressure lower- field JL: Effect of treating isolated systolic hypertension ing and low-dose aspirin in patients with hypertension: on the risk of developing various types and subtypes of principal results of the Hypertension Optimal Treat- stroke: the Systolic Hypertension in the Elderly Program ment (HOT) randomised trial. HOT Study Group. Lancet (SHEP). JAMA 2000, 284(4):465-471. (London, England) 1998, 351(9118):1755-1762. 49. Ogihara T, Saruta T, Rakugi H, Matsuoka H, Shimamoto 42. Messerli FH, Mancia G, Conti CR, Hewkin AC, Kupfer S, K, Shimada K, Imai Y, Kikuchi K, Ito S, Eto T et al: Target Champion A, Kolloch R, Benetos A, Pepine CJ: Dogma blood pressure for treatment of isolated systolic disputed: can aggressively lowering blood pressure hypertension in the elderly: valsartan in elderly iso- in hypertensive patients with coronary artery disease lated systolic hypertension study. Hypertension 2010, be dangerous? Annals of internal medicine 2006, 56(2):196-202. 144(12):884-893. 50. Principal results of the Japanese trial to assess optimal 43. Zanchetti A, Grassi G, Mancia G: When should systolic blood pressure in elderly hypertensive patients antihypertensive drug treatment be initiated and to (JATOS). Hypertension research : official journal of the what levels should systolic blood pressure be lowered? Japanese Society of Hypertension 2008, 31(12):2115- A critical reappraisal. Journal of hypertension 2009, 2127. 27(5):923-934. 44. Baker P, Dworkin SL, Tong S, Banks I, Shand T, Yamey G: The men’s health gap: men must be included in the global health equity agenda. Bulletin of the World Health Organization 2014, 92(8):618-620. Sex, BMI and Age differences in Metabolic Syndrome 179

Supplemental information

Supplemental table 1A. Clinical characteristics of the normal weight population.

Men 18-29 30-39 40-49 50-59 60-69 70-79 Number of subjects 2,507 3,244 4,002 1,600 976 362 Systolic BP, mmHg 124 ± 11 125 ± 11 126 ± 12 127 ± 14 132 ± 16 137 ± 19 Diastolic BP, mmHg 68 ± 7 72 ± 7 76 ± 8 77 ± 9 77 ± 9 76 ± 9 Waist circumference, cm 82.2 ± 6.0 85.6 ± 5.9 87.4 ± 5.9 88.5 ± 6.0 89.1 ± 6.0 90.8 ± 5.8 HDL-C, mmol/L 1.37 ± 0.28 1.37 ± 0.31 1.42 ± 0.33 1.47 ± 0.34 1.51 ± 0.35 1.48 ± 0.35 Triglycerides, mmol/L 0.85 (0.65-1.14) 0.94 (0.69-1.31) 0.98 (0.73-1.37) 1.01 (0.75-1.39) 0.98 (0.75-1.29) 0.94 (0.73-1.23) Fasting blood glucose, 4.8 ± 0.6 4.9 ± 0.6 5.0 ± 0.7 5.1 ± 0.8 5.1 ± 0.8 5.3 ± 0.8 mmol/L 7 Use of anti-hypertensive 0.4 1.0 2.6 4.9 17.2 35.1 drugs (%) Type 2 diabetes (%) 0.3 0.4 0.7 1.3 3.6 4.9 CVD history (%) 0.4 0.2 0.5 1.2 4.6 12.4 Women 18-29 30-39 40-49 50-59 60-69 70-79 Number of subjects 3,474 5,260 7,833 3,006 1,453 434 Systolic BP, mmHg 114 ± 11 114 ± 12 118 ± 13 122 ± 15 130 ± 18 138 ± 19 Diastolic BP, mmHg 67 ± 7 69 ± 8 71 ± 8 72 ± 9 72 ± 9 72 ± 9 Waist circumference, cm 76.2 ± 7.0 78.1 ± 6.8 79.3 ± 6.8 80.7 ± 7.0 81.4 ± 7.0 82.7 ± 7.3 HDL-C, mmol/L 1.57 ± 0.35 1.62 ± 0.35 1.73 ± 0.38 1.85 ± 0.43 1.85 ± 0.44 1.85 ± 0.45 Triglycerides, mmol/L 0.78 (0.60-1.03) 0.70 (0.55-0.93) 0.75 (0.59-0.98) 0.84 (0.65-1.09) 0.92 (0.73-1.23) 0.96 (0.74-1.25) Fasting blood glucose, 4.6 ± 0.4 4.6 ± 0.4 4.7 ± 0.5 4.8 ± 0.5 5.0 ± 0.7 5.1 ± 1.0 mmol/L Use of anti-hypertensive 0.6 1.4 3.5 7.0 16.9 33.6 drugs (%) Type 2 diabetes (%) <0.1 0.1 0.3 0.3 2.3 5.6 CVD history (%) 0.2 0.2 0.4 0.7 1.2 3.0 Abbreviations: blood pressure, BP; high density lipoprotein cholesterol, HDL-C; cardiovascular disease, CVD. 180 Chapter 7

Supplemental table 1B. Clinical characteristics of the overweight population.

Men 18-29 30-39 40-49 50-59 60-69 70-79 Number of subjects 1,154 3,262 5,974 2,636 1,893 758 Systolic BP, mmHg 129 ± 11 130 ± 11 131 ± 13 133 ± 14 137 ± 16 141 ± 17 Diastolic BP, mmHg 70 ± 7 75 ± 8 79 ± 9 80 ± 9 80 ± 9 78 ± 9 Waist circumference, cm 93.2 ± 6.2 95.7 ± 5.9 97.3 ± 6.0 98.4 ± 6.0 99.3 ± 6.1 100.2 ± 6.2 HDL-C, mmol/L 1.23 ± 0.27 1.21 ± 0.26 1.25 ± 0.28 1.30 ± 0.31 1.34 ± 0.32 1.32 ± 0.31 Triglycerides, mmol/L 1.09 (0.80-1.56) 1.24 (0.89-1.81) 1.30 (0.93-1.88) 1.25 (0.94-1.77) 1.18 (0.89-1.62) 1.15 (0.87-1.57) Fasting blood glucose, 4.9 ± 0.7 5.1 ± 0.7 5.2 ± 0.7 5.3 ± 0.8 5.4 ± 0.9 5.6 ± 1.1 mmol/L Use of anti-hypertensive 0.4 2.0 5.3 10.3 30.5 51.3 drugs (%) Type 2 diabetes (%) 0.3 0.9 1.4 1.5 7.9 11.8 CVD history (%) <0.1 0.3 1.0 2.0 7.3 14.8 Women 18-29 30-39 40-49 50-59 60-69 70-79 Number of subjects 1,142 2,684 5,014 2,519 1,808 726 Systolic BP, mmHg 119 ± 11 119 ± 11 123 ± 13 127 ± 16 133 ± 17 140 ± 18 Diastolic BP, mmHg 69 ± 7 71 ± 8 74 ± 9 74 ± 9 74 ± 9 74 ± 9 Waist circumference, cm 88.0 ± 7.4 89.1 ± 7.1 89.9 ± 7.0 90.9 ± 7.0 92.0 ± 6.9 93.2 ± 6.9 HDL-C, mmol/L 1.42 ± 0.33 1.47 ± 0.32 1.54 ± 0.35 1.64 ± 0.38 1.64 ± 0.38 1.63 ± 0.39 Triglycerides, mmol/L 0.87 (0.67-1.16) 0.82 (0.62-1.12) 0.90 (0.69-1.24) 1.00 (0.76-1.38) 1.12 (0.85-1.53) 1.19 (0.91-1.58) Fasting blood glucose, 4.7 ± 0.5 4.8 ± 0.7 4.9 ± 0.7 5.0 ± 0.6 5.2 ±0.9 5.4 ±1.0 mmol/L Use of anti-hypertensive 1.0 2.5 6.4 9.9 28.4 50.6 drugs (%) Type 2 diabetes (%) 0.3 0.2 0.2 0.8 5.1 9.5 CVD history (%) 0.2 0.3 0.3 0.5 3.0 4.4 Abbreviations: blood pressure, BP; high density lipoprotein cholesterol, HDL-C; cardiovascular disease, CVD. Sex, BMI and Age differences in Metabolic Syndrome 181

Supplemental table 1C. Clinical characteristics of the obese population.

Men 18-29 30-39 40-49 50-59 60-69 70-79 Number of subjects 241 857 1,734 780 578 173 Systolic BP, mmHg 135 ± 13 135 ± 12 137 ± 14 137 ± 14 141 ± 17 141 ± 19 Diastolic BP, mmHg 73 ± 8 78 ± 8 81 ± 9 82 ± 9 81 ± 9 76 ± 8 Waist circumference, cm 109.7 ± 9.0 110.0 ± 9.0 111.6 ± 8.8 111.5 ± 8.6 113.0 ± 8.1 112.4 ± 7.6 HDL-C, mmol/L 1.07 ± 0.24 1.09 ± 0.24 1.12 ± 0.26 1.14 ± 0.25 1.21 ± 0.27 1.21 ± 0.31 Triglycerides, mmol/L 1.43 (1.02-1.90) 1.62 (1.15-2.26) 1.60 (1.16-2.30) 1.60 (1.17-2.25) 1.44 (1.09-1.98) 1.36 (1.04-1.78) Fasting blood glucose, 5.1 ± 4.9 5.2 ± 0.7 5.5 ± 1.1 5.7 ± 1.1 5.9 ± 1.3 6.1 ± 1.4 mmol/L Use of anti-hypertensive 1.7 4.6 12.3 21.8 45.0 68.2 drugs (%) Type 2 diabetes (%) 0.8 1.6 6.2 9.7 18.5 28.9 CVD history (%) 0.0 1.2 1.7 4.0 9.2 22.0 Women 18-29 30-39 40-49 50-59 60-69 70-79 7 Number of subjects 493 1,323 2,427 1,009 823 372 Systolic BP, mmHg 123 ± 11 123 ± 12 129 ± 15 132 ± 15 136 ± 17 139 ± 17 Diastolic BP, mmHg 70 ± 7 73 ± 8 76 ± 9 76 ± 9 75 ± 9 73 ± 8 Waist circumference, cm 102.2 ± 10.5 103.6 ± 10.5 105.0 ± 10.5 104.9 ± 10.1 105.2 ± 9.3 105.4 ± 9.4 HDL-C, mmol/L 1.29 ± 0.30 1.32 ± 0.31 1.38 ± 0.32 1.45 ± 0.35 1.50 ± 0.35 1.47 ± 0.35 Triglycerides, mmol/L 1.03 (0.78-1.33) 1.00 (0.76-1.37) 1.14 (0.84-1.57) 1.27 (0.95-1.81) 1.31 (0.97-1.75) 1.39 (1.10-1.76) Fasting blood glucose, 4.9 ± 0.7 5.0 ± 0.8 5.2 ± 0.9 5.5 ± 1.1 5.7 ± 1.2 5.9 ± 1.6 mmol/L Use of anti-hypertensive 2.4 4.2 13.5 26.5 48.0 68.5 drugs (%) Type 2 diabetes (%) 0.6 1.4 3.9 7.8 15.7 20.7 CVD history (%) 0.0 0.4 0.7 1.5 3.3 7.5 Abbreviations: blood pressure, BP; high density lipoprotein cholesterol, HDL-C; cardiovascular disease, CVD.

Supplemental table 2A. The prevalence of the individual MetS components among the normal weight population.

Men 18-29 30-39 40-49 50-59 60-69 70-79 Number of subjects 2,507 3,244 4,002 1,600 976 362 Waist circumference 0.1 0.4 0.5 1.5 1.4 3.0 Blood pressure 29.3 33.9 38.8 43.4 60.8 76.0 HDL-cholesterol 11.3 14.8 11.6 8.8 7.2 7.2 Triglycerides 7.1 12.0 14.2 15.2 9.8 9.7 Fasting blood glucose 3.4 5.2 9.7 13.7 17.7 23.8 Women 18-29 30-39 40-49 50-59 60-69 70-79 Number of subjects 3,474 5,260 7,833 3,006 1,453 434 Waist circumference 6.0 9.3 11.9 17.1 19.6 24.9 Blood pressure 8.7 10.0 19.3 32.6 55.1 73.7 HDL-cholesterol 16.8 13.4 8.5 6.5 6.8 9.9 Triglycerides 3.3 2.7 3.4 6.2 8.4 10.1 Fasting blood glucose 1.1 1.8 3.5 5.1 11.5 17.7 182 Chapter 7

Supplemental table 2B. The prevalence of the individual MetS components among the overweight population.

Men 18-29 30-39 40-49 50-59 60-69 70-79 Number of subjects 1,154 3,262 5,974 2,636 1,893 758 Waist circumference 9.5 16.6 24.2 28.0 35.5 39.6 Blood pressure 48.4 50.1 56.1 62.9 76.9 87.6 HDL-cholesterol 25.9 27.6 25.2 20.4 17.8 19.8 Triglycerides 18.8 28.6 31.1 27.9 22.1 21.2 Fasting blood glucose 4.8 11.4 17.1 23.4 31.3 37.6 Women 18-29 30-39 40-49 50-59 60-69 70-79 Number of subjects 1,142 2,684 5,014 2,519 1,808 726 Waist circumference 51.0 57.0 62.4 67.7 73.3 77.8 Blood pressure 17.2 18.3 32.7 43.9 66.4 85.0 HDL-cholesterol 31.8 26.1 20.4 14.3 13.2 14.0 Triglycerides 7.1 5.7 9.8 13.7 18.2 19.3 Fasting blood glucose 2.3 3.9 8.3 12.3 21.5 27.7

Supplemental table 2C. The prevalence of the individual MetS components among the obese population.

Men 18-29 30-39 40-49 50-59 60-69 70-79 Number of subjects 241 857 1,734 780 578 173 Waist circumference 85.5 84.9 91.2 91.0 95.5 95.4 Blood pressure 68.5 66.4 74.7 78.5 87.7 92.5 HDL-cholesterol 52.3 46.6 41.5 38.8 29.2 28.3 Triglycerides 31.5 47.3 46.5 44.4 35.5 31.8 Fasting blood glucose 10.8 19.1 33.6 40.5 52.9 54.3 Women 18-29 30-39 40-49 50-59 60-69 70-79 Number of subjects 493 1,323 2,427 1,009 823 372 Waist circumference 92.7 96.1 97.8 97.5 98.5 98.9 Blood pressure 29.8 30.9 51.7 65.6 83.0 93.5 HDL-cholesterol 49.9 45.9 37.6 30.7 26.4 25.0 Triglycerides 12.8 13.2 20.0 28.6 27.7 28.2 Fasting blood glucose 5.7 10.1 21.3 33.6 42.9 47.3

Hoofdstukpagina-letteromtrek.indd 15 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 16 16/11/16 11:05 Chapter 8

Summary and general discussion 8

Hoofdstukpagina-letteromtrek.indd 15 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 16 16/11/16 11:05

Summary and General Discussion 187

“May you enjoy the horn of plenty without blowing it.” Bill Copeland

The obesity epidemic in developed countries, while the genetic background has not changed, is due to our obesogenic environment [1]. Everywhere around us there are a lot of responsible factors that provoke unhealthy lifestyle choices, such as physical inactivity and increased consumption of high-calorie foods [2]. Visceral obesity does, however, increase the risk of metabolic complications. Approximately one in four Europeans have the metabolic syndrome (MetS), which places them at higher risk for several chronic diseases, with the most common type 2 diabetes (T2D) and cardiovascular disease (CVD) [3]. MetS even threatens developing countries where the traditional lifestyle is replaced by a more Western-like lifestyle [4]. However, it appears that not all obese individuals develop metabolic complications, despite the fact that they are exposed to the same obesity-promoting environment. This is the so-called metabolically healthy obese [5]. The research described in this thesis aimed to provide an update on the prevalence 8 of MetS and metabolically healthy obesity (MHO), to contribute to a better understanding of the associations between lifestyle factors and metabolic health, and in addition, to examine which aspects of health-related quality of life are influenced by obesity and metabolic health complications. To this end, we used data from the large Dutch LifeLines Cohort Study.

Variation in the prevalence of MetS and MHO

The prevalence of obesity has continued to rise the last decades in European countries [6]. In our collaborative Healthy Obesity Project (HOP), among ten large European population-based cohort studies (chapter 2), prevalence of obesity varied between 11.6% in Italy to 26.3% in Germany. The earliest data came from 1995 and the most recent data from 2012. We also calculated the prevalence of MetS and MHO among obese individuals. Such an extensive comparison has never been done before. We used the well-established clinical risk factors associated with MetS to identify the metabolically healthy obese. MHO was defined as the presence of a BMI ≥30 kg/m2, none of the MetS components except for waist circumference, and no previous diagnosis of CVD. MetS prevalence increased with ageing, while the MHO prevalence decreased with ageing. When corrected for age, MetS was more common among men (43-78%, 56% in Life- Lines) than among women (24-65%, 37% in LifeLines), while MHO was more frequent in women (7-28%, 23% in LifeLines) compared to men (2-19%, 10% in LifeLines). Our results demonstrated a significant diversity in the prevalence of MetS and MHO across the European cohort studies, despite the use of a uniform definition and age-standardized prevalence estimates. Although, we obtained our data from large population-based 188 Chapter 8

cohort studies, the results cannot always be generalized to the overall prevalence in the specific countries, as some cohorts have only collected data from a specific region (Italy, Norway and the Dutch PREVEND study), or from a specific age group (United Kingdom). Another finding of the study was that elevated blood pressure was the most frequently occurring factor contributing to the prevalence of MetS. This has also been confirmed in the Dutch normal weight and overweight population, where high blood pressure and increased waist circumference were most frequently present components of MetS (chapter 7). MetS was not only seen among obese (51%) and overweight individuals (19%), but also in a small subset of the normal weight population (4%) (Figure 1A). Within all these BMI groups, MetS was more common among men. There are several explanations for this observation. First, men have more elevated visceral and hepatic fat, making them more susceptible to insulin resistance and the development of other MetS features, while women do have more total body fat [7]. Second, elevated blood pressure is already often present among young men. They will, therefore, meet the criteria for MetS sooner than women. Last, there are studies suggesting that the female hormone estrogen may protect against the development of cardiometabolic risk factors [8-10]. This may explain why after the age of 50 years, when in general the menopause starts, the difference in MetS prevalence between men and women becomes smaller (Figure 1B). We found that in the entire population prevalence of MetS increased with age (Figure 1B), which can be explained by the age-related increase in blood pressure, waist circumference and fasting glucose (chapter 7). The detected variation in metabolic health prevalence between countries, suggest that on top of age, sex and genes our environment is accountable [11, 12]. Indeed, a high

A B 70 70 Men Men 60 Women 60 Women

50 50

40 40

30 30 Prevalence, Prevalence, % Prevalence, Prevalence, % 20 20

10 10

0 0 Normal Overweight Obese 18-29 30-39 40-49 50-59 60-69 70-79 weight BMI class Age group

Figure 1. Prevalence of MetS in the LifeLines Cohort Study. Summary and General Discussion 189

blood pressure (to some extent, depending on age) and waist circumference can be modified by adapting a more healthy lifestyle. Therefore, targeting lifestyle may play an important role in reducing the development of MetS as well as becoming or remaining metabolically healthy. However, first we need to understand how lifestyle is associated with MetS and its individual components.

The influence of lifestyle on MetS

There is extensive evidence that many health benefits can be gained through favourable lifestyle modifications. Smoking, too much alcohol, poor diet and lack of physical activity are common risk factors. It has been estimated that approximately 50% of cases of coronary heart disease, stroke and diabetes are attributable to these lifestyle factors [13, 14]. In the following section the results of the studies in this thesis regarding lifestyle 8 factors will be summarized and discussed.

Smoking is bad for lipids In the Netherlands, an important risk factor for health problems is still smoking, despite the fact that the prevalence of smoking decreased from 60.0% in 1958 to 18.4% in 2012 [15]. It has been suggested by several small-scale studies [16-19] and one meta-analysis [20] that smoking contributes to MetS. In this meta-analysis data from 13 prospective studies, involving 56,000 individuals, a positive association was found between smoking and risk of MetS, although the increased risk was only significant for heavy smokers and not among light and former smokers [20]. Our study in chapter 3 is the first very large study, including almost 60,000 individuals, supporting the association between smoking and MetS in both men and women, in all BMI classes, and in a dose-related manner. In the LifeLines study population, 21.3% of participants were current smokers. Light-, moderate- and heavy smokers had lower levels of HDL cholesterol (HDL-C), higher levels of triglycerides, and a larger waist circumference compared to non-smokers. While this relationship was found for both men and women, irrespective of their BMI, there was no clear association between smoking and either blood pressure or fasting blood glucose. Furthermore, the amount of smoking was also associated with unfavourable changes in the levels of apoA1 and apoB and the HDL and LDL particle size. The latter finding may provide a new pathophysiological mechanism that links smoking to increased risk of CVD. There are some biologically plausible explanations linking smoking to increased risk of MetS. Smoking leads to acute and chronic changes in the balance of the autonomic nervous system, resulting in sympathetic predominance, which increase the risk of cardiovascular events [21, 22]. Also, smoking increase the levels of circulating insulin- 190 Chapter 8

antagonistic hormones levels, such as cortisol and growth hormone [22]. As a result the glucose and lipid metabolism is affected. Although in our study fasting blood glucose levels were only marginally increased in smokers, there are some studies showing that compared to non-smokers, active smokers have more serious insulin resistance and hyperinsulinaemia, which increase their risk for T2D [23]. Furthermore, data exist that smokers (especially heavy smokers) have a higher BMI than non-smokers [24] and greater risk of abdominal fat accumulation [25]. The increased plasma cortisol concentrations seen in smokers are in part responsible for the accumulation of visceral fat, which, in turn, increases waist circumference [26]. While our study suggests that smoking may unfavourably change certain MetS components, it can be debated whether smoking by itself may increase the prevalence of MetS, or whether it is related to other unhealthy lifestyle factors. For instance, smoking is associated with higher alcohol consumption, lower consumption of fruit and vegetables, and less leisure time physical activity [27]. Therefore, the next step was to evaluate the effect of the combined use of tobacco and alcohol on MetS.

Light alcohol consumption may partly counteract effects of smoking Compared to other European countries, the consumption of alcohol is rather moderate in The Netherlands. Dutch people consumed an average of 9.9 liters of pure alcohol per capita (15+ years of age) per year in the period 2008-2010, while the European average is 10.9 liters a year1. However, compared to 50 years ago alcohol consumption has increased in the Netherlands, and since 1990 the level of alcohol consumption has remained stable1. Moderate alcohol consumption has consistently been associated with a decreased risk of T2D and CVD in prospective cohort studies compared with absten- tion or excessive consumption [28, 29]. Alcohol consumption often clusters together with smoking [30]. In chapter 4 we carefully assessed the combined effects of smoking and alcohol consumption on MetS and its individual components. MetS was least prevalent among subjects with light to moderate alcohol consumption (≤1-2 drinks/day), in normal-weight and overweight subjects, irrespective of their smoking status. Among obese former and current smokers, non-drinkers or those with light alcohol consumption (≤1 drink/day) showed the lowest MetS prevalence. In figure 2 the observed associations of smoking and light alcohol consumption (relative to not drinking) with the individual MetS components are summarized. We found that especially light alcohol consumption was associated with a favourable effect or no effect on the individual MetS components. Light alcohol consumption might therefore partly compensate for the unfavourable associations of smoking with Summary and General Discussion 191

HDL-cholesterol -- ++ + - Triglycerides

+ +/- Waist circumference N + Blood pressure N N Blood glucose

+ = a favourable association, - = an unfavourable association, N = neutral. Figure 2. Overview of the associations of smoking and light alcohol consumption (relative to no alcohol consumption) with the individual MetS components.

8 MetS. An interesting finding of chapter 3 is that in people with normal weight, heavy alcohol consumption showed a trend towards a larger waist circumference in non-smokers and light-moderate smokers. In contrast, light-moderate alcohol consumption was associated with a smaller waist circumference in overweight non-smokers and former smokers, and obese non-, former and heavy smokers. In chapter 2 we did, however, report that smokers had a larger waist circumference, especially heavy smoking obese women. A possible explanation for the results described in chapter 3, is that in general light-moderate drinking, especially wine consumption, is associated with healthier overall dietary and lifestyle choices, which may have led to less abdominal obesity [31, 32]. The most popular alcoholic drink in the Netherlands is beer, which accounts for more than half of all alcohol consumed. Next is wine with 36% and spirits with 17%1. We found that wine drinking was characterized by an overall better metabolic profile and showed a protective association for MetS, compared to that of non-drinkers and drinkers of mainly beer or spirits (chapter 3). Both alcohol and non-alcohol components of wine, such as polyphenols, may be responsible for the lower prevalence of MetS and some of the MetS components. In general, alcohol consumption may increase HDL-cholesterol and reduce insulin resistance [33, 34]. However, non-alcohol components of wine have also been shown to increase HDL-cholesterol and lower triglycerides [35, 36]. The healthier lifestyle of wine consumers is an important factor as well [31, 32]. Sluik et al. suggested that alcoholic beverage preference is merely a proxy for socio-demographic and other lifestyle factors, rather than independently related to health status [32]. Occasional alcohol drinking is well accepted as a favourable lifestyle factor for cardiovascular health. Although we would not encourage alcohol consumption to abstain-

1 http://www.who.int/substance_abuse/publications/global_alcohol_report/profiles/nld.pdf 192 Chapter 8

ers, our results suggest that light alcohol consumption will not negatively influence the development of MetS. Smoking cessation and reduction of excessive alcohol consumption seem promising steps in the management of MetS, and prevention of its sequelae.

Food choices and physical activity may improve metabolic health It is well known, that the first step in improving metabolic health is weight reduction. The best way to lose the extra pounds is by restricting energy intake and increasing physical activity. In practice, weight loss by energy-restricted diets are only successful for a few months [37]. Only one in six overweight and obese adults reported to ever have maintained weight loss of at least 10% for one year in the National Health and Nutrition Examination Survey (1999-2006) [38]. One of the reasons for the poor long-term outcome of weight-loss diets is that the dietary advises are too different from the existing patterns of food consumption in a (sub-)population. Adherence to the strict regimens of the diet is therefore difficult and the motivation typically diminishes with time. As described in chapter 2, there is a subgroup of obese individuals that, despite their excessive weight, has not (yet) developed any metabolic abnormalities except abdominal obesity. Between countries there is still a diversity in the reported prevalence of obese subjects with MetS and MHO, when taking age and sex into account (chapter 2). This have led to the hypothesis that independently of obesity, modifiable factors, such as diet and physical activity, still contribute to the differences in metabolic health. In chapter 5, we assessed obesity-specific dietary patterns using principal component analysis (e.g. a form of factor analysis), and compared these dietary patterns and physical activity between MHO and metabolically unhealthy obesity (MUO). While moderate alcohol consumption and not smoking were positively associated with MHO, in both men and women, we observed sex-specific differences in dietary patterns and physical activity associated with MHO. Our data showed that only two of the four obesity-specific dietary patterns were associated with metabolic health in women only. A higher score on the ‘fruit, vegetables and fish’ pattern, which reflected high consumption of mainly fruit, vegetables, fish and fermented milk products, was positively associated with MHO. The ‘bread, potatoes and sweet snacks’ pattern reflected a diet of mainly foods with a high glycaemic index (GI), e.g. bread, potatoes, sweet sandwich toppings, pastries, biscuits and desserts. A higher score of this pattern was inversely associated with MHO. These associations were not found for men. One possible explanation for this may be that women reported higher consumption of the specific types of foods that contributed high to the‘fruit, vegetables and fish’ pattern compared to men. Also pastries, biscuits and desserts were foods more consumed by women. We also suggested that men and women may have a different view on improving health status. Women might prefer dietary changes to lose weight, while men might prefer changes in physical activity to improve their fitness. Summary and General Discussion 193

Only one recent study from Australia used the factorial method, i.e. principal component analyses, to link dietary patterns to MHO [39]. The authors found that a higher score on the ‘healthy’ dietary pattern (with high intakes of whole grains, fresh fruit, dried fruit, legumes and low fat dairy) was associated with a more healthy metabolic profile. In contrast to our study, the authors derived dietary patterns from a population including obese and non-obese people instead of obesity-specific patterns. Furthermore, the authors used different criteria to define MHO than used in our study [39]. Still, both the results of the Australian and our study increase the confidence that dietary pattern analysis can be meaningful in understanding the metabolic health. Metabolically healthy obese men were characterized by higher engagement in intensive vigorous physical activity, while among women there was no association of physical activity with MHO. The reason for this finding needs to be explored further. We did, however, hypothesize that the higher level of physical activity in men compared to women, may have strengthened the association between vigorous physical activity and 8 MHO. Furthermore, it is possible that women experience and, therefore, report certain activities as more intensive than men, but actually a lower cardiorespiratory response is obtained. Hence, as a result men may develop a higher cardiorespiratory fitness level than women, which has been linked to MHO [40]. The results described in chapter 5 generates the hypothesis that the preferred MHO phenotype may be maintained or even transition from unhealthy to healthy obesity can be achieved through diet and physical activity, without aiming for weight loss per se. However, we should keep in mind that lifestyle factors are interrelated and no exclusive lifestyle factor could independently affect health status.

Health-related quality of life compromised in obesity Research often focuses on the long-term effects of obesity on health and mortality. While people overestimate health risks which they cannot (or hardly) influence, people have in general an optimistic view about lifestyle-related health risks [41, 42]. Adaption of lifestyle changes are more successful if there is a reward within a short period of time [43]. To motivate obese individuals to change their lifestyle, therapeutic strategies should, therefore, also consider (existing) decrements in the physical and mental health of the individual. In chapter 6 we assessed with the RAND 36-Item Health Survey (RAND-36) [44], which domains of health-related quality of life (HR-QoL) were affected by obesity together with obesity-related conditions, e.g. T2D, MetS and inflammation. Previously, the association between obesity and HR-QoL has been investigated in a variety of settings, including centers for weight loss, general medical practices, and the general population [45-50]. Physical, and to a lesser extent, psychosocial impairments were found to be more severe in obese women than in men [47, 49-51]. In chapter 6, we showed that low scores on several domains of HR-QoL were enhanced by the grade 194 Chapter 8

of obesity, T2D, MetS, and level of inflammation. Significant associations were found between these conditions and lower HR-QoL scores related to general health and physical functioning in both obese men and women. However, in men, obesity grade and T2D were associated with more frequent impairment of physical functioning than in women. In contrast, inflammation level assessed by high sensitive C-reactive protein measurements was associated with more frequent impairment of physical functioning in women. These results suggest that in men and women, the presence of morbidity may operate differently in the pathway linking obesity to reduced HR-QoL. In both men and women, mentally-oriented domains (especially vitality and social functioning) were marginally more often impaired among subjects with a higher obesity grade, T2D, MetS or with higher levels of inflammation. Although the RAND-36 does include questions regarding pleasant (happy, lot of energy, full of pep, peaceful and calm) and unpleasant feelings (worn out, nervous, tired, downhearted and blue, felt so down that nothing could cheer you up), the questions are not necessarily aimed at psychological problems often present in obese individuals. Such psychological problems might include depression, eating disorders, distorted body image, and a low self-esteem [52]. The negative associations found between obesity and obesity-related conditions and HR-QoL suggest that obese people already experience adverse effects of obesity before they develop more severe conditions such as CVD. Assessment of HR-QoL in obese individuals can help caregivers to offer a more personalized approach in obesity treatment. Placing specific attention to the affected domains of HR-QoL may help in setting personal goals.

The use of MetS and MHO in epidemiology

The prevalence of MetS and MHO, as well as their individual components, are entirely dependent on the choice of cut-off values used to define an impaired metabolic profile. In the existing definition of MetS and MHO a dichotomization has been made at two levels: 1) first, every single risk factor is dichotomized using a threshold; and 2) then, the diagnosis of MetS or MHO is made if a certain number of risk factors is present or absent.

Used definition for MetS and MHO Throughout this thesis, MetS was defined according to the revised version of the National Cholesterol Education Program’s Adult Treatment Panel III (NCEP ATPIII) [53]. Compared to the original NCEP ATPIII, not only treatment for dyslipidaemia, hyperglycaemia or hypertension has been included in the revised version, but also the threshold for impaired fasting glucose has been lowered from ≥6.1 mmol/L to ≥5.6 mmol/L [54]. Although this new threshold for fasting glucose is generally accepted as part of the MetS definition, Summary and General Discussion 195

we chose to use the old threshold of ≥6.1 mmol/L in the definition for MHO. The lenient threshold for impaired fasting glucose has the potential to label one third of the general population as ‘high risk’, while the majority of them will never develop diabetes [55]. In addition, subjects with fasting glucose 5.6–6.0 mmol/L do not have an increased risk of CVD in contrast to subjects with a fasting glucose 6.1–6.9 mmol/L [56, 57]. So instead of using the strict cut-off for glucose, we were more strict on the number of MetS components needed to be absent to define MHO. Our definition of MHO was considered as the absence of all MetS components, as defined by the original NCEP ATPIII (with additionally including treatment for dyslipidaemia, hyperglycaemia or hypertension) [53], a BMI ≥30 kg/m2, and no history of CVD. Waist circumference was excluded from these criteria because it highly correlates with BMI ≥30 kg/m2 [58]. Defining MHO as having none of the components may have greater utility as well, to show a greater difference in risk for CVD and T2D. 8 Cut-off points for identification of abdominal obesity Abdominal obesity is an important feature of insulin resistance and MetS [53]. Waist circumference, as a measure for abdominal obesity, is therefore one variable of MetS. As reported in our study (chapter 7) and previous ones, abdominal obesity is more common among women than among men [59-61]. This appears to be in contrast with our knowledge that women have more total body fat and men have more visceral and hepatic fat [7]. The purpose of defining abdominal obesity is to identify those individuals at increased risk for obesity-related cardiomatabolic disease. Because this risk may depend on ethnicity, it was suggested that ethnicity specific cut-off values should be used [62]. There are two cut-offs to define abdominal obesity in Europeans: i.e. the high cut-offs in the widely used NCEP ATPIII definition (≥102 cm in men and ≥88 cm in women), and the lower cut-offs (≥94 cm and ≥80 cm in respectively, men and women). Since MetS can even develop in non-obese individuals, it is important that the cut- offs for abdominal obesity are set at values that are highly predictive for CVD. However, it is interesting to know that the currently used low- and high levels of abdominal obesity are based on correlations with BMI, respectively ≥25 kg/m2 (overweight) and ≥30 kg/m2 (obesity), instead of resembling an ‘optimal’ point at which CVD risk increases [63, 64]. Moreover, in 2014, a cross-sectional study among German Caucasians showed that even the ≥88 cm (high) cut-off was too low for capturing CVD risk in women, while the ≥94 cm (low) cut-off seemed to be appropriate in men [65]. To our knowledge, no longitudinal studies have assessed the performance of the low- and high cut-offs of abdominal obesity for cardiovascular risk prediction. We believe that re-evaluation of the waist circumference thresholds for Europeans, at which decisions are made relating to health care provision and screening, is still required. Large cohorts with follow-up data such as PREVEND, HUNT and FINRISK are suitable studies 196 Chapter 8

to select the most appropriate waist circumference values for CVD risk prediction. The anticipated long-term follow-up of this LifeLines Cohort Study can undoubtedly add to these prospective assessments.

Cut-off points for identification of ‘elevated’ blood pressure In chapter 2 and 3, we already observed that the prevalence of an elevated blood pressure was remarkably high in the population compared to the other MetS components. This is the result of the strict threshold of ≥130/85 mmHg. For individuals with blood pressure levels ranging from 120–139 mmHg systolic and/or 80–89 mmHg diastolic, lifestyle modifications, such as salt restriction, moderation of alcohol consumption, weight reduction and more physical activity, could reduce blood pressure, decrease the progression of blood pressure to hypertensive levels with age, or prevent hypertension entirely [66]. Lifestyle changes often can only control mild elevations of blood pressure and therefore drug therapy might still be necessary. The natural course of increasing blood pressure with ageing has, however, not been taken into account in the definition of MetS. Therefore, more than half of the elderly people (≥60 yrs) will already be assigned one risk factor of MetS (chapter 7). The threshold for elevated blood pressure in MetS is different from the blood pressure levels which warrant (medical) treatment according to current international guidelines [67]. In the eight report of the Joint National Committee (JNC 8) age-specific thresholds are advised, endorsing a blood pressure goal of <150/90 mmHg starting at age 60 years and a blood pressure goal of <140/90 mmHg for those below the age of 60 years [68]. In chapter 7, we applied these age-adjusted blood pressure thresholds to emphasize the illogical choice of not taking age into account for the blood pressure component. Accordingly, the ‘elevated’ blood pressure component was 6.0-36.3% less frequent present in different groups of the population, compared to when the strict threshold was used. Especially among men aged <60 years the prevalence of elevated blood pressure decreased when the age-adjusted thresholds were used. Another interesting finding was that, although, the prevalence of elevated blood pressure (with the age-adjusted thresholds) matched better with the prevalence of those treated for hypertension, under-treatment of ‘elevated’ blood pressure was still high among young men. Compared to the other MetS components, the particular high prevalence of elevated blood pressure in our study population suggests that the threshold of ≥130/85 mmHg is too strict. Even a systolic blood pressure target of <140 mmHg, is too optimistic in the elderly as has been shown by placebo-controlled trials [67]. Although LifeLines is a well-designed cohort study, which collects longitudinal data, at this moment only cross- sectional data are available. We urge other large cohorts with follow-up data to establish better evidence on age-related blood pressure thresholds associated with increased risk for T2D and CVD. Summary and General Discussion 197

The predictive ability of MetS and MHO Prevalence statistics of MetS are useful in providing an estimate of the current risk factor burden and the likely burden of CVD and T2D that will result. Yet, it is suggested that the value of MetS beyond that of its individual components or traditional risk factors as a predictor of both all-cause mortality (relative risk ~1.5) and CVD (relative risk ~2.0) is modest at best [69]. However, all available definitions of MetS showed to be a stronger predictor of T2D (estimated relative risk 3.5 to 5.1) [70]. This is likely due to inclusion of components such as fasting glucose and abdominal obesity, which are more strongly associated with diabetes. Unfortunately, MetS does not comprise the full range of clinically valid CVD risk factors. Even the godfather of MetS, Gerald M. Reaven, is concerned about its clinical value, and he bids farewell to this syndrome with the words requiescat in pace (rest in peace) [71]. To improve the predictive ability of MetS for T2D and CVD, other features could be added to the MetS definition, such as C-reactive protein, non-HDL cholesterol or apoB [72, 73], or we could use a more gradual or continuous approach of 8 MetS [74]. This is because dichotomizing continuous variables results in a loss of predictive power. Also interventions are seldom started at the levels proposed by the revised NCEP ATPIII. So, individuals will soon be labelled as ‘unhealthy’, but will not receive an intervention either medical or aimed at improving lifestyle. Nowadays in Europe, treatment decisions are based on the validated tool the Systematic COronary Risk Evaluation (SCORE) risk chart, estimating the 10-year absolute risk for fatal CVD in patients [75]. For the Netherlands the SCORE risk chart has been calibrated with Dutch CVD mortality data. The scoring system is gender-specific and includes age, smoking status, systolic blood pressure and the ratio total cholesterol/HDL cholesterol. An important target for risk reduction of CVD is blood pressure and low-density lipoprotein (LDL) cholesterol. When a patient has a medium- or high 10-year risk for CVD, LDL-lowering therapy is initiated to reduce LDL cholesterol below 2.5 mmol/L [75]. However, despite achieving the recommended level for LDL cholesterol, many patients retain a high CVD risk. This ‘residual risk’ is mainly due to elevated triglyceride and low HDL-cholesterol levels [76]. It is well known that more risk factors and higher levels of these risk factors increase the absolute CVD risk in a continuous manner. It would therefore be worthwhile to consider an aggregation of the MetS components (i.e. triglycerides, HDL cholesterol, waist circumference and fasting glucose) with SCORE to improve the primary prevention of CVD . As obesity will remain a public health concern for the coming decades, the MHO phenotype will become increasingly important. Meta-analyses have indicated that adults who are metabolically healthy obese have a risk of T2D [77], CVD and mortality [78] that is intermediate between that of healthy normal weight and unhealthy obese adults. However, MHO is a temporary state for a sizeable proportion of obese adult population, and many of these individuals will shift to become metabolically unhealthy obese [79-81]. While ageing has a large responsibility in this, poor health behaviours 198 Chapter 8

(chapter 6) and changes in fat distribution (accumulation of visceral fat) may also play an important role in the progression towards less healthy phenotypes and disease [82]. We must, therefore, be cautious in using the term ‘healthy’ obesity. Examination of the MHO phenotype has gained interest the last years, but many questions remain regarding the MHO definition and its determinants, the stability of the condition over time, and the long-term health outcomes (not only CVD and T2D) [83].

Methodological considerations

In this section methodological issues will be discussed, as they are important for a correct interpretation of our results. The studies described in this thesis are largely based on data from the LifeLines cohort study. Although follow-up data will be collected for the next 30 years, at the time of conducting our studies, only cross-sectional data were available from participants enrolled in the study between 2006-2013. Since cross-sectional data are subject to certain limitations, several general, but iterative aspects will be briefly commented on to put the observed findings into perspective.

Cross-sectional design Cross-sectional means that the derived data comes from a single time point, and therefore one cannot be sure that the development of the outcome of interest (MetS and its individuals components), was preceded by the presence of putative risk factors (lifestyle). This being so, it is not possible to infer causality. Nevertheless, the observed associations in our studies are particularly useful for generating hypotheses and providing directions for future studies [84].

Sample selection The criteria used to recruit participants and the response rate, determine how well results of the conducted studies can be generalized to the population as a whole. In total, 49% of the included participants in the LifeLines cohort study, i.e. those in the age 25-50 years, were originally invited through their general practitioner, while 38% volunteered to participate via participating family members and 13% self-registered via the LifeLines website. Almost 25% of the invited persons agreed to participate, which was comparable to the response rate in several other large-scale population studies [85]. A major concern in population-based cohort studies is selection bias, a systematic error induced if the association between exposure and outcome differs for those participating and those who do not participate [86]. The presence of selection bias in the LifeLines cohort study could, therefore, lead to results only representative for the cohort sampled, rather than for the entire adult Dutch population from the northern part of the Netherlands. Summary and General Discussion 199

Compared with the population of the north of the Netherlands, LifeLines participants were more often female, middle aged, married, living in a semi-urban place and Dutch native [87]. However, adjusted for differences in demographic composition, LifeLines was found to be broadly representative on socioeconomic characteristics, weight status, smoking, the prevalence of major chronic diseases and general health. Although it is no strict guarantee against selection on other variables, the recruitment strategy had no substantial effect on the representativeness of the LifeLines population [87]. This suggests that risk estimates are likely to represent real associations in the general adult population from the northern part of the Netherlands. However, we still have to deal with the fact that MetS is a very heterogeneous phenotype. This makes it more difficult to find associations between the exposure of interest (environmental factors) and MetS if any exist. Subtypes of MetS, i.e. the pre-dominant clustering of separate MetS components, may depend on sex, age and BMI (chapter 7), but may also differ between populations [61]. Therefore, if an association between a 8 feature and MetS has been found in LifeLines, it is possible that it may not be present in a population with a different sex, age and BMI distribution or in a non-Dutch population. Genetic and ethnic background may partially account for the differences seen between countries as well [88, 89]. A way to get around the heterogeneity problem of MetS is by providing results by more detailed subgroups. In our studies we included only subjects with a Western-European descent, and stratified the population sample as much as possible by sex and BMI, adjusting for age. In that way the findings are more group-specific and does not depend on the distribution of sex and BMI in the study population. In addition, there is a better chance of finding potential differences in the association between the exposure and outcome measure.

Data availability A particular strength of the LifeLines cohort study is the large sample size and bioma- terial collection, the extensive physical examination, and the range of topics covered in the baseline questionnaire (demographics, health status, lifestyle and psychosocial aspects). Despite this, the researcher is strongly dependent on the data that is available at the stage of conducting the study. An limitation of our studies is that the presented results could be in part influenced by other important (lifestyle) factors, that are both associated with the exposure and the outcome but does not lie in the causal pathway, i.e. confounding [86]. Gradually we were able to account for multiple important lifestyle factors: chapter 3 – smoking, chapter 4 – smoking and alcohol, and chapter 5 - nutrition, physical activity, smoking and alcohol. Nevertheless, the findings in this thesis should be interpreted with caution. The overall lifestyle of smokers is generally considered different from the lifestyle of non-smokers. Smoking is associated with lower levels of physical activity, higher alcohol consumption, and specific eating habits [27]. But then, light or 200 Chapter 8

moderate drinking –especially of wine- is usually associated with a healthier lifestyle [31, 32]. In future studies multiple lifestyle factors should be analysed, including the possibility of multiple interactions between lifestyle factors.

Questionnaires Data on demographics, health status, lifestyle and psychosocial aspects were assessed by a self-reported questionnaire. The quality of the data therefore, is determined to a large extent on the patient’s ability to accurately recall past exposures. Recall bias may result in either an underestimate or overestimate of the association between exposure and outcome [86]. There are, however, two types of recall bias: differential and non- differential. Differential recall bias means that cases have a different recall than controls, while with non-differential recall bias both cases and controls have the same recall. In prospective cohort studies, non-differential recall bias is more likely to occur, since there are no prior hypotheses regarding the exposure-outcome associations [86, 90]. Hence, individuals do not know if they are a ‘case’. In our studies, most of the participants are not aware that they have a metabolic risk factor or even MetS. Therefore, the risk of a selective memory is reduced. However, behaviours or habits which are socially less desirable such as smoking, alcohol abuse, or overconsumption of foods are more often prone to underestimation, while physical activity is often overestimated [90]. Therefore, we ranked individuals into categories, rather than using the estimated absolute quantities, which reduces the effect of over- or underreporting. Thus, recall bias is expected to have only a minor effect on the presented results in this thesis. Summary and General Discussion 201

Conclusion and future perspectives

In this thesis we had a closer look at the epidemiology of metabolic health, its associated lifestyle risk factors and the health-related quality of life in obese individuals. We confirmed that, together with (overweight and) obesity, MetS is very common in different European countries and the Netherlands (the Dutch population living in the Northern part). Although not so prevalent, in the Dutch LifeLines cohort still almost 1 out of 4 obese women and 1 out of 10 obese men are metabolically healthy obese, depending on their age. Lifestyle factors, such as smoking, alcohol consumption, diet and physical activity, may play an important role in understanding why some people do have metabolic abnormalities, while others remain healthy or develop MetS at a slower pace. However, the obese without (multiple) metabolic dysregulations may still experience a lower health-related quality of life. Therefore, it is recommended to implement assessment of health-related quality of life in the treatment of obesity. Follow-up data and 8 more in-depth measurements of lifestyle factors can optimize the prediction of people who will progress from metabolically healthy to metabolically unhealthy, and whether reversal of the process is possible. This will offer a promising window of opportunity for the prevention of MetS and hence, T2D and CVD. MetS is still a concept which is especially used in research settings rather than in clinical practice. A famous quote of Albert Einstein is: “Everything should be made as simple as possible, but not simpler”. Perhaps the current definition of MetS is made too simple. Prevention can only be effective if we identify persons at real risk for developing MetS and understand the interactive background of metabolic health. To do so, future research may include:

– Improvement of the definition of MetS and MHO, by adding biochemical measurements more predictive for CVD. – Investigating the use of a continuous metabolic risk score, which might be an integration of the MetS components with the SCORE risk chart. – Identification of metabolic health across racial/ethnic and socioeconomic lines, which may help to set priorities for interventions. – Examination of lifestyle interactions with metabolic health. – Gain insight in the role of genes to better understand why some (obese) individuals do not develop or experience delayed development of metabolic abnormalities. – Evaluation of possible gene-environment interactions in metabolic health. 202 Chapter 8

References

1. Bouchard C: The biological predisposition to obesity: 14. The four domains of chronic disease prevention beyond the thrifty genotype scenario. International 15. OECD: Health at a Glance: Europe 2014. In: OECD journal of obesity (2005) 2007, 31(9):1337-1339. Publishing. 2014. 2. Wells JC: The evolution of human adiposity and 16. Geslain-Biquez C, Vol S, Tichet J, Caradec A, D’Hour obesity: where did it all go wrong? Disease models & A, Balkau B: The metabolic syndrome in smokers. mechanisms 2012, 5(5):595-607. The D.E.S.I.R. study. Diabetes & metabolism 2003, 3. Grundy SM: Metabolic syndrome pandemic. Arterioscle- 29(3):226-234. rosis, thrombosis, and vascular biology 2008, 28(4):629- 17. Weitzman M, Cook S, Auinger P, Florin TA, Daniels S, 636. Nguyen M, Winickoff JP: Tobacco smoke exposure is as- 4. Kassi E, Pervanidou P, Kaltsas G, Chrousos G: Metabolic sociated with the metabolic syndrome in adolescents. syndrome: definitions and controversies. BMC medicine Circulation 2005, 112(6):862-869. 2011, 9:48. 18. Wilsgaard T, Jacobsen BK: Lifestyle factors and incident 5. Denis GV, Obin MS: ‘Metabolically healthy obesity’: metabolic syndrome. The Tromso Study 1979-2001. origins and implications. Molecular aspects of medicine Diabetes research and clinical practice 2007, 78(2):217- 2013, 34(1):59-70. 224. 6. Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin 19. Zhu S, St-Onge MP, Heshka S, Heymsfield SB: Lifestyle JK, Paciorek CJ, Singh GM, Gutierrez HR, Lu Y, Bahalim behaviors associated with lower risk of having the AN et al: National, regional, and global trends in body- metabolic syndrome. Metabolism: clinical and experi- mass index since 1980: systematic analysis of health mental 2004, 53(11):1503-1511. examination surveys and epidemiological studies with 20. Sun K, Liu J, Ning G: Active smoking and risk of meta- 960 country-years and 9.1 million participants. Lancet bolic syndrome: a meta-analysis of prospective studies. (London, England) 2011, 377(9765):557-567. PloS one 2012, 7(10):e47791. 7. Geer EB, Shen W: Gender differences in insulin resis- 21. Curtis BM, O’Keefe JH, Jr.: Autonomic tone as a tance, body composition, and energy balance. Gender cardiovascular risk factor: the dangers of chronic fight medicine 2009, 6 Suppl 1:60-75. or flight. Mayo Clinic proceedings 2002, 77(1):45-54. 8. Cignarella A, Kratz M, Bolego C: Emerging role of estro- 22. Middlekauff HR, Park J, Moheimani RS: Adverse effects gen in the control of cardiometabolic disease. Trends in of cigarette and noncigarette smoke exposure on the pharmacological sciences 2010, 31(4):183-189. autonomic nervous system: mechanisms and implica- 9. Munoz J, Derstine A, Gower BA: Fat distribution and tions for cardiovascular risk. Journal of the American insulin sensitivity in postmenopausal women: influ- College of Cardiology 2014, 64(16):1740-1750. ence of hormone replacement. Obesity research 2002, 23. Willi C, Bodenmann P, Ghali WA, Faris PD, Cornuz J: 10(6):424-431. Active smoking and the risk of type 2 diabetes: a 10. Rosano GM, Vitale C, Mercuro G: The metabolic syn- systematic review and meta-analysis. JAMA 2007, drome in women. Women’s health (London, England) 298(22):2654-2664. 2006, 2(6):889-898. 24. Chiolero A, Jacot-Sadowski I, Faeh D, Paccaud F, Cornuz 11. Andreassi MG: Metabolic syndrome, diabetes and J: Association of cigarettes smoked daily with obesity in atherosclerosis: influence of gene-environment inter- a general adult population. Obesity (Silver Spring, Md) action. Mutation research 2009, 667(1-2):35-43. 2007, 15(5):1311-1318. 12. Pataky Z, Bobbioni-Harsch E, Golay A: Open questions 25. Clair C, Chiolero A, Faeh D, Cornuz J, Marques-Vidal P, about metabolically normal obesity. International Paccaud F, Mooser V, Waeber G, Vollenweider P: Dose- journal of obesity (2005) 2010, 34 Suppl 2:S18-23. dependent positive association between cigarette 13. Raad voor de Volksgezondheid en Zorg: Preventie van smoking, abdominal obesity and body fat: cross- welvaartsziekten: effectief en efficiënt georganiseerd. sectional data from a population-based survey. BMC In: Den Haag, december. 2011. public health 2011, 11:23. Summary and General Discussion 203

26. Pasquali R, Vicennati V: Activity of the hypothalamic- tioxidant, hypolipidemic, and antiinflammatory effects pituitary-adrenal axis in different obesity phenotypes. in both hemodialysis patients and healthy subjects. The International journal of obesity and related metabolic American journal of clinical nutrition 2006, 84(1):252- disorders : journal of the International Association for the 262. Study of Obesity 2000, 24 Suppl 2:S47-49. 36. Zern TL, Wood RJ, Greene C, West KL, Liu Y, Aggarwal 27. Chiolero A, Wietlisbach V, Ruffieux C, Paccaud F, Cornuz D, Shachter NS, Fernandez ML: Grape polyphenols exert J: Clustering of risk behaviors with cigarette consump- a cardioprotective effect in pre- and postmenopausal tion: A population-based survey. Preventive medicine women by lowering plasma lipids and reducing oxida- 2006, 42(5):348-353. tive stress. The Journal of nutrition 2005, 135(8):1911- 28. Baliunas DO, Taylor BJ, Irving H, Roerecke M, Patra J, 1917. Mohapatra S, Rehm J: Alcohol as a risk factor for type 37. Ebbeling CB, Swain JF, Feldman HA, Wong WW, Hachey 2 diabetes: A systematic review and meta-analysis. DL, Garcia-Lago E, Ludwig DS: Effects of dietary Diabetes care 2009, 32(11):2123-2132. composition on energy expenditure during weight-loss 29. Ronksley PE, Brien SE, Turner BJ, Mukamal KJ, Ghali maintenance. JAMA 2012, 307(24):2627-2634. WA: Association of alcohol consumption with selected 38. Kraschnewski JL, Boan J, Esposito J, Sherwood NE, 8 cardiovascular disease outcomes: a systematic review Lehman EB, Kephart DK, Sciamanna CN: Long-term and meta-analysis. BMJ (Clinical research ed) 2011, weight loss maintenance in the United States. Inter- 342:d671. national journal of obesity (2005) 2010, 34(11):1644- 30. Schuit AJ, van Loon AJ, Tijhuis M, Ocke M: Clustering 1654. of lifestyle risk factors in a general adult population. 39. Bell LK, Edwards S, Grieger JA: The Relationship Preventive medicine 2002, 35(3):219-224. between Dietary Patterns and Metabolic Health in a 31. Barefoot JC, Gronbaek M, Feaganes JR, McPherson RS, Representative Sample of Adult Australians. Nutrients Williams RB, Siegler IC: Alcoholic beverage preference, 2015, 7(8):6491-6505. diet, and health habits in the UNC Alumni Heart 40. Ortega FB, Cadenas-Sanchez C, Sui X, Blair SN, Lavie Study. The American journal of clinical nutrition 2002, CJ: Role of Fitness in the Metabolically Healthy but 76(2):466-472. Obese Phenotype: A Review and Update. Progress in 32. Sluik D, van Lee L, Geelen A, Feskens EJ: Alcoholic cardiovascular diseases 2015, 58(1):76-86. beverage preference and diet in a representative Dutch 41. van Steenkiste B, van der Weijden T, Timmermans population: the Dutch national food consumption D, Vaes J, Stoffers J, Grol R: Patients’ ideas, fears and survey 2007-2010. European journal of clinical nutrition expectations of their coronary risk: barriers for primary 2014, 68(3):287-294. prevention. Patient education and counseling 2004, 33. Ellison RC, Zhang Y, Qureshi MM, Knox S, Arnett DK, 55(2):301-307. Province MA: Lifestyle determinants of high-density 42. Weijden T, Timmermans D, Wensing M: “Dus alles is lipoprotein cholesterol: the National Heart, Lung, and goed dokter” Hoe informeer ik mijn patiënten over Blood Institute Family Heart Study. American heart grote en kleine risico’s. 2006. journal 2004, 147(3):529-535. 43. Lechner L, Mesters I, Bolman C: Gezondheidspsycholo- 34. Schrieks IC, Heil AL, Hendriks HF, Mukamal KJ, Beulens gie bij patiënten. Assen: Koninklijke Van Gorcum; 2010. JW: The effect of alcohol consumption on insulin 44. VanderZee KI, Sanderman R, Heyink JW, de Haes H: Psy- sensitivity and glycemic status: a systematic review chometric qualities of the RAND 36-Item Health Survey and meta-analysis of intervention studies. Diabetes 1.0: a multidimensional measure of general health care 2015, 38(4):723-732. status. International journal of behavioral medicine 35. Castilla P, Echarri R, Davalos A, Cerrato F, Ortega H, 1996, 3(2):104-122. Teruel JL, Lucas MF, Gomez-Coronado D, Ortuno J, 45. Fontaine KR, Barofsky I: Obesity and health-related Lasuncion MA: Concentrated red grape juice exerts an- quality of life. Obesity reviews : an official journal of the 204 Chapter 8

International Association for the Study of Obesity 2001, 56. Dekker JM, Balkau B: Counterpoint: impaired fasting 2(3):173-182. glucose: The case against the new American Diabetes 46. Heo M, Allison DB, Faith MS, Zhu S, Fontaine KR: Obesity Association guidelines. Diabetes care 2006, 29(5):1173- and quality of life: mediating effects of pain and comor- 1175. bidities. Obesity research 2003, 11(2):209-216. 57. Forouhi NG, Balkau B, Borch-Johnsen K, Dekker J, 47. Katz DA, McHorney CA, Atkinson RL: Impact of obesity Glumer C, Qiao Q, Spijkerman A, Stolk R, Tabac A, Ware- on health-related quality of life in patients with chronic ham NJ: The threshold for diagnosing impaired fasting illness. Journal of general internal medicine 2000, glucose: a position statement by the European Diabetes 15(11):789-796. Epidemiology Group. Diabetologia 2006, 49(5):822- 48. Kolotkin RL, Crosby RD, Williams GR: Health-related 827. quality of life varies among obese subgroups. Obesity 58. Flegal KM, Shepherd JA, Looker AC, Graubard BI, Borrud research 2002, 10(8):748-756. LG, Ogden CL, Harris TB, Everhart JE, Schenker N: Com- 49. Larsson U, Karlsson J, Sullivan M: Impact of overweight parisons of percentage body fat, body mass index, waist and obesity on health-related quality of life--a Swedish circumference, and waist-stature ratio in adults. The population study. International journal of obesity and American journal of clinical nutrition 2009, 89(2):500- related metabolic disorders : journal of the International 508. Association for the Study of Obesity 2002, 26(3):417- 59. Ford ES, Giles WH, Dietz WH: Prevalence of the 424. metabolic syndrome among US adults: findings from 50. Soltoft F, Hammer M, Kragh N: The association of body the third National Health and Nutrition Examination mass index and health-related quality of life in the Survey. JAMA 2002, 287(3):356-359. general population: data from the 2003 Health Survey 60. Kuk JL, Ardern CI: Age and sex differences in the cluster- of England. Quality of life research : an international ing of metabolic syndrome factors: association with journal of quality of life aspects of treatment, care and mortality risk. Diabetes care 2010, 33(11):2457-2461. rehabilitation 2009, 18(10):1293-1299. 61. Scuteri A, Laurent S, Cucca F, Cockcroft J, Cunha PG, 51. Choo J, Jeon S, Lee J: Gender differences in health-relat- Manas LR, Raso FU, Muiesan ML, Ryliskyte L, Rietzschel ed quality of life associated with abdominal obesity in a E et al: Metabolic syndrome across Europe: different Korean population. BMJ open 2014, 4(1):e003954. clusters of risk factors. European journal of preventive 52. Fabricatore A, Wadden T: Psychological functioning of cardiology 2015, 22(4):486-491. obese individuals. Diabetes Spectrum 2003, 16(4):245- 62. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Clee- 252. man JI, Donato KA, Fruchart JC, James WP, Loria CM, 53. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Smith SC, Jr.: Harmonizing the metabolic syndrome: Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, a joint interim statement of the International Smith SC, Jr. et al: Diagnosis and management of the Diabetes Federation Task Force on Epidemiology and metabolic syndrome: an American Heart Association/ Prevention; National Heart, Lung, and Blood Institute; National Heart, Lung, and Blood Institute Scientific American Heart Association; World Heart Federation; Statement. Circulation 2005, 112(17):2735-2752. International Atherosclerosis Society; and International 54. Expert Panel on Detection Evaluation, and Treatment of Association for the Study of Obesity. Circulation 2009, High Blood Cholesterol in Adults: Executive Summary of 120(16):1640-1645. The Third Report of The National Cholesterol Education 63. Han TS, van Leer EM, Seidell JC, Lean ME: Waist circum- Program (NCEP) Expert Panel on Detection, Evaluation, ference action levels in the identification of cardiovas- And Treatment of High Blood Cholesterol In Adults cular risk factors: prevalence study in a random sample. (Adult Treatment Panel III). JAMA 2001, 285(19):2486- BMJ (Clinical research ed) 1995, 311(7017):1401-1405. 2497. 64. Lean ME, Han TS, Morrison CE: Waist circumference as a 55. WHO: Definition and diagnosis of diabetes mellitus measure for indicating need for weight management. and intermediate hyperglycemia: report of a WHO/IDF BMJ (Clinical research ed) 1995, 311(6998):158-161. consultation. In .: World Health Organisation; 2006. Summary and General Discussion 205

65. Florath I, Brandt S, Weck MN, Moss A, Gottmann P, ity for epidemiological analyses. Diabetes care 2006, Rothenbacher D, Wabitsch M, Brenner H: Evidence 29(10):2329. of inappropriate cardiovascular risk assessment in 75. Smulders YM, Burgers JS, Scheltens T, van Hout BA, middle-age women based on recommended cut-points Wiersma T, Simoons ML: Clinical practice guideline for for waist circumference. Nutrition, metabolism, and car- cardiovascular risk management in the Netherlands. diovascular diseases : NMCD 2014, 24(10):1112-1119. The Netherlands journal of medicine 2008, 66(4):169- 66. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green 174. LA, Izzo JL, Jr., Jones DW, Materson BJ, Oparil S, Wright 76. Joshi PH, Martin SS, Blumenthal RS: The remnants of JT, Jr. et al: The Seventh Report of the Joint National residual risk. Journal of the American College of Cardiol- Committee on Prevention, Detection, Evaluation, and ogy 2015, 65(21):2276-2278. Treatment of High Blood Pressure: the JNC 7 report. 77. Bell JA, Kivimaki M, Hamer M: Metabolically healthy JAMA 2003, 289(19):2560-2572. obesity and risk of incident type 2 diabetes: a meta- 67. Zanchetti A, Grassi G, Mancia G: When should analysis of prospective cohort studies. Obesity reviews : antihypertensive drug treatment be initiated and to an official journal of the International Association for the what levels should systolic blood pressure be lowered? Study of Obesity 2014, 15(6):504-515. A critical reappraisal. Journal of hypertension 2009, 78. Kramer CK, Zinman B, Retnakaran R: Are metabolically 27(5):923-934. healthy overweight and obesity benign conditions?: A 8 68. James PA, Oparil S, Carter BL, Cushman WC, Dennison- systematic review and meta-analysis. Annals of internal Himmelfarb C, Handler J, Lackland DT, LeFevre ML, medicine 2013, 159(11):758-769. MacKenzie TD, Ogedegbe O et al: 2014 evidence-based 79. Achilike I, Hazuda HP, Fowler SP, Aung K, Lorenzo guideline for the management of high blood pressure C: Predicting the development of the metabolically in adults: report from the panel members appointed healthy obese phenotype. International journal of to the Eighth Joint National Committee (JNC 8). JAMA obesity (2005) 2015, 39(2):228-234. 2014, 311(5):507-520. 80. Eshtiaghi R, Keihani S, Hosseinpanah F, Barzin M, Azizi 69. Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier F: Natural course of metabolically healthy abdominal P, Rinfret S, Schiffrin EL, Eisenberg MJ: The metabolic obese adults after 10 years of follow-up: the Tehran syndrome and cardiovascular risk a systematic review Lipid and Glucose Study. International journal of obesity and meta-analysis. Journal of the American College of (2005) 2015, 39(3):514-519. Cardiology 2010, 56(14):1113-1132. 81. Hamer M, Bell JA, Sabia S, Batty GD, Kivimaki M: Stabil- 70. Ford ES, Li C, Sattar N: Metabolic syndrome and incident ity of metabolically healthy obesity over 8 years: the diabetes: current state of the evidence. Diabetes care English Longitudinal Study of Ageing. European journal 2008, 31(9):1898-1904. of endocrinology / European Federation of Endocrine 71. Reaven GM: The metabolic syndrome: requiescat in Societies 2015, 173(5):703-708. pace. Clinical chemistry 2005, 51(6):931-938. 82. Appleton SL, Seaborn CJ, Visvanathan R, Hill CL, Gill 72. Alberti KG, Zimmet P, Shaw J: Metabolic syndrome--a TK, Taylor AW, Adams RJ: Diabetes and cardiovascular new world-wide definition. A Consensus Statement disease outcomes in the metabolically healthy obese from the International Diabetes Federation. Diabetic phenotype: a cohort study. Diabetes care 2013, medicine : a journal of the British Diabetic Association 36(8):2388-2394. 2006, 23(5):469-480. 83. Phillips CM: Metabolically Healthy Obesity: Personalised 73. Eckel RH, Cornier MA: Update on the NCEP ATP-III and Public Health Implications. Trends in endocrinology emerging cardiometabolic risk factors. BMC medicine and metabolism: TEM 2016, 27(4):189-191. 2014, 12:115. 84. Mann CJ: Observational research methods. Research 74. Wijndaele K, Beunen G, Duvigneaud N, Matton L, design II: cohort, cross sectional, and case-control stud- Duquet W, Thomis M, Lefevre J, Philippaerts RM: ies. Emergency medicine journal : EMJ 2003, 20(1):54- A continuous metabolic syndrome risk score: util- 60. 206 Chapter 8

85. Scholtens S, Smidt N, Swertz MA, Bakker SJ, Dotinga A, 88. Orho-Melander M: The metabolic syndrome: genetics, Vonk JM, van Dijk F, van Zon SK, Wijmenga C, Wolffen- lifestyle and ethnicity. Diabetes Voice 2006, 51. buttel BH et al: Cohort Profile: LifeLines, a three- 89. Song Q, Wang SS, Zafari AM: Genetics of the metabolic generation cohort study and biobank. International syndrome. Hospital Physician 2006, 52:51-61. journal of epidemiology 2015, 44(4):1172-1180. 90. Hassan E: Recall bias can be a threat to retrospective 86. Bouter LM, van Dongen MCJM, Zielhuis GA: Epidemi- and prospective research designs. The internet Journal ologisch onderzoek: opzet en interpretatie, vol. 5: Bohn of Epidemiology 2005, 3(2). Stafleu van Loghum; 2008. 87. Klijs B, Scholtens S, Mandemakers JJ, Snieder H, Stolk RP, Smidt N: Representativeness of the LifeLines Cohort Study. PloS one 2015, 10(9):e0137203.

Hoofdstukpagina-letteromtrek.indd 17 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 18 16/11/16 11:05 Nederlandse samenvatting

Acknowledgements / Dankwoord

About the author and publication list

Hoofdstukpagina-letteromtrek.indd 17 16/11/16 11:05 Hoofdstukpagina-letteromtrek.indd 18 16/11/16 11:05

Nederlandse samenvatting 211

Nederlandse samenvatting

Epidemiologie van de metabole gezondheid - Leefstijl determinanten en gezondheid gerelateerde kwaliteit van leven Overgewicht en obesitas leidt vaak tot de ontwikkeling van een verstoord glucose me- tabolisme, een verhoogde bloeddruk en dyslipidemie (te lage waarden van het ‘goede’ HDL-cholesterol en te hoge triglyceriden waarden). De combinatie van deze metabole (stofwisselings) complicaties, wordt het metabool syndroom genoemd (zie box 1). Het metabool syndroom heeft een negatief effect op de gezondheid. Zo is er een verhoogd risico op type 2 diabetes (suikerziekte) en hart- en vaatziekten. Ongeveer één op de vier Europeanen is belast met het metabool syndroom. Hoewel het metabool syndroom meestal veroorzaakt wordt door overgewicht, lijkt een subgroep van zwaarlijvige mensen minder vatbaar te zijn voor de metabole gezondheidsrisico’s. Ze hebben een even gezonde stofwisseling als slanke mensen. In de literatuur wordt dit metabool gezonde obesitas genoemd. In dit proefschrift is gekeken naar de prevalentie van het metabool syndroom en metabool gezonde obesitas, en er is gekeken naar de invloed van leefstijlfactoren op de metabole gezondheid. Daarnaast is onderzocht welke aspecten van kwaliteit van leven beïnvloed worden door obesitas en obesitas-gerelateerde complicaties. Voor het onderzoek beschreven in dit proefschrift hebben we de data van de grote populatie studie LifeLines gebruikt. Alle resultaten zijn gebaseerd op volwassen deelnemers van West-Europese afkomst.

Box 1. Definitie metabool syndroom en metabool gezonde obesitas

Er is sprake van het metabool syndroom als ten minste 3 van de 5 criteria aanwezig zijn: - Abdominale obesitas, gekenmerkt door een vergroot middelomtrek. - Verhoogde bloeddruk (of wordt ervoor behandeld). - Te hoge nuchtere bloedglucose waarde of type 2 diabetes. - Lage HDL-cholesterol waarde (of krijgt medicatie die deze beïnvloed). - Verhoogde triglyceriden (of krijgt medicatie die deze beïnvloed). Er is sprake van metabool gezonde obesitas als een persoon met obesitas geen van bovenstaande metabole risicofactoren heeft (behalve abdominale obesitas) en hart- en vaatziekten. 212 Nederlandse samenvatting

Prevalentie van het metabool syndroom en metabool gezonde obesitas varieert sterk tussen Europese studies. In hoofdstuk 2 staan de resultaten beschreven van een Europese samenwerking, BioSHaRE-EU Healthy Obesity Project. Voor deze studie hadden we 163.517 deelnemers tot onze beschikking, afkomstig uit tien grote populatie onderzoeken in Europa. De prevalentie van obesitas varieerde van 11,6% in Italië tot 26,3% in Duitsland. In de subpopulatie met obesitas (28.077 deelnemers) nam de prevalentie van het metabool syndroom toe met toenemende leeftijd, terwijl de prevalentie van metabool gezonde obesitas afnam. Omdat de leeftijdsverdeling binnen de Europese studies varieerde ten opzichte van de leeftijdssamenstelling in het desbetreffende land, hebben we leeftijd gecorrigeerde cijfers gebruikt. Echter, de prevalentie van het metabool syndroom en metabool gezonde obesitas varieerde alsnog sterk tussen de verschillende Europese studies. Het metabool syndroom kwam vaker voor bij mannen (43-78%) dan bij vrouwen (24-65%), terwijl metabool gezonde obesitas vaker voorkwam bij vrouwen (7-28%) dan bij mannen (2-19%).

Roken is slecht voor je vet profiel In hoofdstuk 3 hebben we onderzocht in welke mate roken het risico op het ontstaan van het metabool syndroom beïnvloedt. Dit hebben we gedaan door te kijken naar de individuele componenten van het metabool syndroom, door te kijken naar apolipopro- teine (apo) A1 en apoB waarden (eiwitten die zich binden aan vetten om deze door het bloedbaan te vervoeren) en de samenstelling van de lipoproteïne (de combinatie van apolipoproteïne met vetten). Dit laatste aspect is ook een risico factor voor het ontstaan van hart- en vaatziekten. Data van 59.467 LifeLines deelnemers is gebruikt. We vonden dat het metabool syndroom vaker voorkwam bij mensen met overgewicht en obesitas, mannen, ex-rokers, en naar mate men meer tabak gebruikte. Zowel bij mannen als vrouwen in elke body mass index (BMI) categorie zorgde roken voor een hoger risico op het metabool syndroom. Ook vonden we dat de hoeveelheid roken geassocieerd was met ongezondere waardes voor HDL-cholesterol, triglyceriden, apoA1, apoB, de samenstelling van de lipoproteïne en middelomtrek. Aan de hand van dit onderzoek concludeerden wij dat roken verband houdt met een verhoogd risico op het ontstaan van het metabool syndroom, onafhankelijk van geslacht of BMI categorie.

Licht alcohol gebruik zou deels de negatieve invloed van roken kunnen tegenwerken Roken gaat vaak samen met het drinken van alcohol. Daarom hebben we in hoofdstuk 4 bekeken hoe groot de invloed is van roken en alcohol samen op het hebben van het metabool syndroom. Daarvoor hebben we het rookgedrag en alcoholgebruik Nederlandse samenvatting 213

van 64.046 LifeLines deelnemers bestudeerd. Het metabool syndroom kwam met name voor bij rokers en bij mensen die geen of juist veel alcohol drinken. We vonden tevens dat licht alcohol gebruik (minder dan 1 glas alcohol per dag) geassocieerd was met een gezonder metabool profiel, onafhankelijk van de rookstatus. Licht alcohol gebruik zou daarom deels de negatieve invloed van roken kunnen tegenwerken. Het mogelijk gunstige effect van alcohol op het ontwikkelen van het metabool syndroom was groter bij wijndrinkers dan bij bierdrinkers of drinkers van sterke alcohol. We concludeerden in deze studie dat naast afvallen, het stoppen met roken en te- gelijkertijd het reduceren van het alcoholgebruik het aantal gevallen van het metabool syndroom zou kunnen verminderen.

Voedingskeuzes en lichamelijke activiteit zouden de metabole gezondheid kunnen verbeteren In hoofdstuk 5 is bekeken wat de verschillen zijn in voedingspatroon en lichamelijke activiteit bij mensen met metabool gezonde obesitas en obese mensen met metabool ongezonde obesitas (obese mensen met het metabool syndroom). Hiervoor hadden we data tot onze beschikking van 9.270 obese mensen. Op basis van de antwoorden die mensen hadden gegeven op de voedselvragenlijst, konden we vier voedingspatronen onderscheiden. Daarvan waren twee voedingspatronen gerelateerd aan metabool gezonde obesitas in vrouwen. Een hogere score op het dieet patroon dat vooral gekenmerkt werd door consumptie van fruit, groente, vis en gefermenteerde melkproducten verhoogde de kans op metabool gezonde obesitas. Echter, een verlaagde kans op metabool gezonde obesitas werd gezien bij hogere scores op het dieet patroon dat gekenmerkt werd door consumptie van brood, aardappelen en zoete snacks (zoet brood beleg, koek(jes), taart en toetjes). In tegenstelling tot de vrouwen, werd bij mannen niet een dieet patroon maar zwaar lichamelijke activiteit geassocieerd met een hogere kans op metabool gezonde obesitas. De resultaten van deze studie vormen de hypothese dat zelfs zonder gewichtsaf- name aanpassingen in de voeding en lichamelijke activiteit de metabole gezondheid van obese mensen positief kunnen beïnvloeden.

De gezondheid gerelateerde kwaliteit van leven is verminderd in obesitas In een groep van 13.686 LifeLines deelnemers met obesitas hebben we gekeken naar de kwaliteit van leven, zowel op lichamelijk als mentaal gebied, als maat voor de beperkingen die ze ervaren door de ernst van hun obesitas en de daaraan gerelateerde aandoeningen (hoofdstuk 6). Zowel mensen met een hogere graad van obesitas, met of zonder type 2 diabetes, als mensen met het metabool syndroom of met hogere ontstekingswaarden in het lichaam, hadden een lagere kwaliteit van leven. Ze ervoeren met name problemen op het gebied van algemene gezondheid en fysiek functioneren. 214 Nederlandse samenvatting

We zagen echter dat met name de ernst van de obesitas en type 2 diabetes mannen beperkte in hun fysieke functioneren, terwijl bij vrouwen vooral de mate van ontsteking in het lichaam de boosdoener was. Deze bevinding suggereert dat bij mannen en vrouwen andere mechanismen een rol spelen die de kwaliteit van leven beïnvloeden. Ten opzichte van het lichamelijke aspect van kwaliteit van leven, werd op mentaal gebied minder beperkingen gevonden in zowel mannen en vrouwen. Het uitvragen van de kwaliteit van leven in mensen met obesitas zou zorgverleners kunnen helpen om een meer gepersonaliseerde behandeling aan te bieden. Door de probleemgebieden te betrekken in de behandeling, kunnen persoonlijke doelen ge- steld worden.

Diagnose van het metabool syndroom wordt vooral gedreven door een verhoogde bloeddruk en buikvet In hoofdstuk 7 hebben we heel gedetailleerd gekeken naar de prevalentie van het metabool syndroom en de individuele metabool syndroom risico factoren. Daarvoor hebben we 74.531 LifeLines deelnemers opgesplitst naar geslacht, BMI categorie en leeftijdsgroep. Zo’n 19% van de mannen en 12% van de vrouwen had het metabool syndroom. De kans op het metabool syndroom nam toe bij hogere BMI categorieën en bij hogere leeftijd. Een verhoogde bloeddruk en een verhoogde middelomtrek kwam het vaakst voor in de populatie. Met het ouder worden neemt de bloeddruk toe door- dat slagaders met de jaren stijver worden. Echter, de definitie voor een verhoogde bloeddruk als risicofactor voor het metabool syndroom, is erg strikt voor een oudere populatie (systolische bloeddruk ≥130 mmHg en/of een diastolische bloeddruk ≥85 mmHg). Daarom hebben we ook leeftijdsspecifieke afkapwaarden gebruikt om een verhoogde bloeddruk te definiëren (mensen <60 jaar: ≥140/90 mmHg; en mensen ≥60 jaar: ≥150/90mmHg). Niet alleen daalde de prevalentie van een verhoogde bloeddruk met 6-36%, ook de prevalentie van het metabool syndroom daalde met 0,2-12%. Daar- naast zagen wij dat de prevalentie voor een verhoogde bloeddruk - op basis van op de leeftijd aangepaste afkapwaarden- beter paste bij het percentage van mensen die ook daadwerkelijk behandeld wordt voor een verhoogde bloeddruk. De prevalentie cijfers van het metabool syndroom en de individuele metabole risico factoren zijn nog steeds erg hoog. Nu en in de toekomst moeten we actief (blijven) inzetten op het verlagen van deze risicofactoren zodat het aantal nieuwe gevallen met type 2 diabetes en hart- en vaatziekten zal dalen. Nederlandse samenvatting 215

Conclusie In dit proefschrift hebben we laten zien dat het metabool syndroom in Europa en Neder- land vaak voorkomt. Hoewel metabool gezonde obesitas veel minder vaak voorkomt, is toch nog bijna 1 op de 4 obese vrouwen en 1 op de 10 obese mannen in de LifeLines studie metabool gezien gezond, afhankelijk van de leeftijd. Uit de resultaten lijkt verder het rook-, drink-, eet- en beweeg gedrag bij te dragen aan de ontwikkeling van het metabool syndroom. Het intensief aanpakken van deze leefstijl factoren kan helpen om het aantal mensen met het metabool syndroom terug te dringen en draagt daarmee bij aan het minder voorkomen van type 2 diabetes en hart- en vaatziektes in de toekomst. Echter, nog voordat type 2 diabetes of hart- en vaatziekten ontstaan, hebben mensen met obesitas (ook zonder metabole complicaties) een verminderde kwaliteit van leven. Het dient daarom aanbeveling om aspecten van de kwaliteit van leven mee te laten wegen in de behandeling van obesitas.

Acknowledgements / Dankwoord 217

Acknowledgements / Dankwoord

Voor het schrijven van dit proefschrift (in drie jaar tijd!) ben ik veel dank verschuldigd aan de mensen die direct en indirect betrokken zijn geweest bij het promotietraject. Waar ik tijdens het schrijven van dit proefschrift vaak moeite had om bondig te zijn, heb ik er nu moeite mee om onder woorden te krijgen hoezeer ik de geleverde bijdrage waardeer.

Allereerst wil ik alle (inmiddels) 167,729 deelnemers van LifeLines en alle LifeLines medewerkers bijzonder bedanken voor hun inzet. Zij hebben het fundament van dit proefschrift gevormd en hebben voor indrukwekkende studie groottes gezorgd die schaars zijn in de wetenschap.

Mijn promotor prof. dr. Bruce H.R. Wolffenbuttel en mijn co-promotores dr. Jana V. van Vliet-Ostaptchouk en dr. Melanie M. van der Klauw:

Beste Bruce, niet te veel ‘poeha’ was je hint. En dat past misschien ook niet bij jou. Toch wil ik zeggen dat ik heel dankbaar ben dat je mij de kans hebt gegeven om mijzelf als onderzoeker te ontwikkelen. Ik heb veel van je wetenschappelijke inzicht en expertise kunnen leren. Daar waar ik nog wat onzeker was, heb je meerdere malen in daden laten zien hoeveel vertrouwen je in mij hebt. Bedankt!

Beste Jana, de vele uren overleg in combinatie met ons aanhoudende nauwkeurigheid hebben geleid tot een heel mooi eindresultaat! Met die nauwkeurigheid (lees ook wel perfectionisme) hield ik mezelf soms langer bezig dan wellicht nodig was. Om het grotere plaatje te blijven zien stelde je mij de vraag of het bijschaven nog wel significant zou bijdragen, of dat ik bezig was met een verbetering van 2%. Met jouw kritische blik en scherpe feedback heb je het proefschrift naar een hoger niveau weten te tillen. Bedankt voor de coaching op zowel wetenschappelijk als persoonlijk gebied!

Beste Melanie, jij wist altijd feilloos de hobbels te identificeren die eerst nog glad gestreken moesten worden. Jouw klinische blik was onmisbaar in dit geheel. Bedankt voor je constructieve commentaar, adviezen en niet te vergeten je vrolijke lach.

Dear prof. dr. O.H. Franco, prof. dr. L. van Gaal and prof. dr. R. Sanderman of the reading committee, thank you for the critical reading and evaluation of my thesis. 218 Acknowledgements / Dankwoord

Ook wil ik de leden die hebben plaatsgenomen in de corona hartelijk bedanken voor hun bereidwilligheid om met mij van gedachten te wisselen over de inhoud van het proefschrift.

Dear BioSHaRE colleagues, thank you for the good collaboration and the nice work that we have delivered. I enjoyed and learned a lot from the inspiring talks we had during our HOP meetings.

Naast het superviserende team wil ik ook de co-auteurs van verschillende hoofdstuk- ken in dit proefschrift bedanken voor de ideeën, scherpe feedback en bemoedigende woorden. In het bijzonder wil ik dr. André van Beek, dr. Eva Corpeleijn en prof. dr. Daan Kromhout bedanken. Jullie manier van werken heb ik als erg prettig ervaren.

Verder gaat mijn dank uit naar de endocrinologie afdeling, met in het bijzonder de (ex-) promovendi Jorien, Sarah, Dineke, Robert, Edward, Karin, Thamara, Mariëlle en Marloes. Maar ook alle stage wetenschap studenten. Het was fijn om met elkaar de voor ons herkenbare ups en downs te kunnen delen en om zoveel van elkaar te mogen leren. En als we weer iets te vieren hadden, deden jullie er nooit moeilijk over dat ik van alle taarten een stukje wilde proberen, bedankt!

Lieve Jennifer, onze tijd samen gaat helemaal terug naar de basisschool. Daar stonden we als kleine meisjes al zij aan zij. Ik vind het bijzonder waardevol dat je tijdens mijn promotie weer aan mijn zijde zal staan.

Mijn lieve zussen Sarah en Scarlett, wat ben ik trots op jullie! Het is altijd een feest om weer met z’n drieën te zijn. Bedankt dat jullie altijd voor mij klaar staan.

Lieve mam en pap, jullie staan aan de wieg van wat ik heb bereikt. Altijd hebben jullie mij gesteund en het vertrouwen gegeven dat ik het kan (als je het maar zelf wilt!). Door alles wat ik van jullie heb meegekregen en geleerd ben ik een “rijk” mens geworden. Bedankt lieve ouders!

Lieve Rolf, bedankt voor zo’n fijn (schoon) thuis. Voor jou is naast een dankwoord ook een ‘sorry’ wel op zijn plaats. Sorry voor alle keren dat ik zei al onderweg naar huis te zijn, maar in werkelijkheid nog naar mijn computerscherm zat te staren. About the author and publication list 219

About the author

Sandra Nicole Slagter was born on August 21th 1990 in Assen, the Netherlands. After completing pre-university education (Fivelcollege, Delfzijl), she moved in 2009 to Amsterdam to study Health Sciences at the VU University. She received her Bachelor’s degree in 2012, and her Master’s degree in 2013 in the two specializations Prevention & Public Health and Nutrition & Health. From 2013 till 2016, she worked as a PhD student under the supervision of prof. dr. B.H.R. Wolffenbuttel, dr. J.V. van Vliet-Ostaptchouk and dr. M.M. van der Klauw at the department of Endocrinology of the University Medi- cal Center Groningen (UMCG). During her PhD she was involved in the BioSHaRE-EU consortium (Biobank Standardisation and Harmonisation for Research Excellence in the European Union). In the final year of her PhD, she started to work as research coordinator on a part-time basis. In collaboration with dr. H. Aanstoot from Diabeter (Rotterdam), a cohort study was set-up, investigating biomarkers of heterogeneity in type 1 diabetes. This collaboration is still on-going. Sandra further developed her skills as a researcher by presenting at national and international conferences and by completing her training for the Graduate School of Medical Sciences GUIDE and her training for Epidemiologist B by the Netherlands Epi- demiological Society.

List of publications

1. Wouters HJ, van Loon HC, Van der Klauw MM, Elderson MF, Slagter SN, Muller Kobold AC, Kema IP, Links TP, van Vliet-Ostaptchouk JV, Wolffenbuttel BH: No effect of the Thr92Ala polymorphism of deiodinase-2 on thyroid hormone parameters, health- related quality of life and cognitive functioning in a large population-based cohort study. Thyroid 2016, Epub ahead of print.

2. van Waateringe RP, Slagter SN, van der Klauw MM, van Vliet-Ostaptchouk JV, Graaff R, Paterson AD, Lutgers HL, Wolffenbuttel BH: Lifestyle and clinical determinants of skin autofluorescence in a population-based cohort study. European journal of clinical investigation 2016, 46(5):481-490.

3. Slagter SN, van Vliet-Ostaptchouk JV, van Beek AP, Keers JC, Lutgers HL, van der Klauw MM, Wolffenbuttel BH: Health-Related Quality of Life in Relation to Obe- sity Grade, Type 2 Diabetes, Metabolic Syndrome and Inflammation. PLoS one 2015, 10(10):e0140599. 220 About the author and publication list

4. Bousquet J, Malva J, Nogues M, Manas LR, Vellas B, Farrell J, the MACVIA Research Group*: Operational Definition of Active and Healthy Aging (AHA): The European Innovation Partnership (EIP) on AHA Reference Site Questionnaire: Montpellier Oc- tober 20-21, 2014, Lisbon July 2, 2015. Journal of the American Medical Directors Association 2015, 16(12):1020-1026.

5. Bousquet J, Kuh D, Bewick M, Standberg T, Farrell J, Pengelly R, Joel ME, Rodriguez Manas L, Mercier J, Bringer J, …, Slagter S, …., Villalba-Mora E, Wilson N, Wouters E, Zins M: Operational Definition of Active and Healthy Ageing (AHA): A Conceptual Framework. The journal of nutrition, health & aging 2015, 19(9):955-960.

6. van Vliet-Ostaptchouk JV†, Nuotio ML†, Slagter SN†, Doiron D†, Fischer K, Foco L, Gaye A, Gogele M, Heier M, Hiekkalinna T, …, van der Klauw MM, Waldenberger M, Perola M§, Wolffenbuttel BH§: The prevalence of metabolic syndrome and metabolically healthy obesity in Europe: a collaborative analysis of ten large cohort studies. BMC endocrine disorders 2014, 14:9.

7. Slagter SN, van Vliet-Ostaptchouk JV, Vonk JM, Boezen HM, Dullaart RP, Kobold AC, Feskens EJ, van Beek AP, van der Klauw MM, Wolffenbuttel BH: Combined effects of smoking and alcohol on metabolic syndrome: the LifeLines cohort study. PLoS one 2014, 9(4):e96406.

8. Slagter SN†, van Vliet-Ostaptchouk JV†, Vonk JM, Boezen HM, Dullaart RP, Kobold AC, Feskens EJ, van Beek AP, van der Klauw MM, Wolffenbuttel BH: Associations between smoking, components of metabolic syndrome and lipoprotein particle size. BMC medicine 2013, 11:195.

* Slagter S as part of the MACVIA Research Group. † Equal contributors as first author. § Equal contributors as last author.