anthropometric measures of obesity-their association with

Publication for Public Health M 207: 2010

Anthropometric measures of obesity-their association with type 2

diabetes and hypertension across ethnic groups

DECODA and DECODE Studies

Regzedmaa Nyamdorj

Department of Public Health, Hjelt Institute, University of Helsinki and Diabetes Prevention

Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare,

Helsinki, Finland

2010

ACADEMIC DISSERTATION

To be presented with the permission of the Faculty of Medicine of the University of

Helsinki, for public examination in the Lecture Room 1, the Institute of Dentistry,

Mannerheimintie 172, 2nd

floor, Helsinki, on the 28th

of September 2010, at 12 o’clock

ISSN 0355-7979

ISBN 978-952-10-4869-2 (paperback)

ISBN 978-952-10-4870-8 (PDF)

http://ethesis.helsinki.fi/

Helsinki University Print

Helsinki 2010

http://ethesis.helsinki.fi/

Supervised by:

Docent Qing Qiao, Academy Research Fellow, MD, PhD

Department of Public Health, University of Helsinki,

Diabetes Prevention Unit, Department of Chronic Disease Prevention, National Institute for

Health and Welfare, Helsinki, Finland

and

Professor Jaakko Tuomilehto, MD, MA (sociol), PhD

Department of Public Health, University of Helsinki,

Diabetes Prevention Unit, Department of Chronic Disease Prevention, National Institute for


Reviewed by:

Adjunct Professor Satu Männistö, Academy Research Fellow, PhD

Department of Chronic Disease Epidemiology and Prevention Unit, National Institute for


and

Professor Jaap Seidell PhD

Nutrition and Health Department, Institute of Health Sciences, Vrije University (VU)

Amsterdam, the Netherlands

Opponent:

Professor Stephen Colagiuri MD, PhD

Institute of Obesity, Nutrition, and Exercise,

University of Sydney, Sydney, Australia

Contents LIST OF ORIGINAL PUBLICATIONS .......................................................................................................... 6

ABBREVIATIONS ...................................................................................................................................... 7

TIIVISTELMÄ ...................................................................................................................................... 10

1 INTRODUCTION ............................................................................................................................. 13

2 LITERATURE REVIEW ................................................................................................................. 15

2.1 Epidemiology of obesity .......................................................................................................... 15

2.1.1 Clinical definition and classification of overweight and obesity.......................................... 15

2.1.2 Anthropometric measures of obesity .................................................................................... 16

2.1.3 Measurements of waist and hip circumference .................................................................... 16

2.1.4 Measurement error related to BMI and WC ......................................................................... 17

2.1.5 Adult prevalence, secular trend and risk factors for obesity ................................................ 18

2.2 Obesity and diabetes ............................................................................................................... 19

2.2.1 Definition, prevalence and secular trend in the prevalence of type 2 diabetes ..................... 19

2.2.2 Risk factors for type 2 diabetes ............................................................................................ 21

2.2.2.1 Obesity as a major risk factor for type 2 diabetes ............................................ 21

2.2.3 Comparison of BMI with central obesity measures in relation to type 2 diabetes ............... 22

2.2.4 Optimal cutoff values for BMI and WC in relation to diabetes ........................................... 23

2.2.5 Ethnic differences in the association of diabetes with obesity ............................................. 27

2.3 Obesity and hypertension ....................................................................................................... 27

2.3.1 Definition, prevalence and secular trend in hypertension .................................................... 27

2.3.2 Major risk factors for hypertension ...................................................................................... 31

2.3.2.1 Obesity as a major risk factor for hypertension ............................................... 31

2.3.3 Comparison of BMI with measures of central obesity in relation to hypertension .............. 32

2.4. Obesity in the pathogenesis of diabetes and hypertension .............................................. 32

2.4.1 Obesity and insulin resistance .............................................................................................. 33

2.4.2 Obesity and hypertension ..................................................................................................... 36

3. AIMS OF THE STUDY .................................................................................................................. 38

4. POPULATIONS AND METHODOLOGY ................................................................................... 39

4.1 Study population ...................................................................................................................... 39

4.2 Survey methodology and physical examination .................................................................. 43

4.2.1 Definition of clinical endpoints in the study ........................................................................ 43

4.2.2 Anthropometric measures for obesity and blood pressure measurements ............................ 44

4.2.3 Laboratory Methods ............................................................................................................. 44

4.2.4 Statistical Analysis ............................................................................................................... 45

5. RESULTS ........................................................................................................................................ 47

5.1 Comparison of BMI with central obesity measures in relation to diabetes and

hypertension, based on cross-sectional (I) and prospective study (II, III) ............................. 47

5.1.1 Characteristics of the DECODA study population ............................................................... 47

5.1.2 Comparison of BMI with central obesity measures in relation to type 2 diabetes (I, III) .... 52

5.1.3 Comparison of BMI with central obesity measures in relation to hypertension (I, II) ......... 57

5.2 Prevalence of the metabolic syndrome in populations of Asian origin---Comparison of

the IDF definition with the NCEP definition (IV) ......................................................................... 60

5.2.1 Characteristics of the DECODA study cohorts .................................................................... 60

5.2.2 Prevalence of central obesity using the 2005 IDF definition and its comparison with the

NCEP definition ............................................................................................................................ 60

5.3 Ethnic differences of the association of undiagnosed type 2 diabetes with obesity (V) 62

5.3.1 Characteristics of the DECODA and DECODE study population ....................................... 62

5.3.2 Ethnic difference in the strength of association of undiagnosed type 2 diabetes with BMI

and WC .......................................................................................................................................... 62

5.4 Assessment of change points for the presence of undiagnosed type 2 diabetes with

BMI and WC in different ethnic groups (VI) ................................................................................ 66

6 DISCUSSION ................................................................................................................................... 68

6.1 Study design and methodology ............................................................................................. 68

6.2 Comparison of BMI with central obesity measures in relation to undiagnosed diabetes

and hypertension ............................................................................................................................ 69

6.3 Role of central obesity in the metabolic syndrome ............................................................. 70

6.4 Ethnic differences in the association of diabetes with obesity .......................................... 71

6.5 Assessment of change points for the presence of undiagnosed diabetes for BMI and

WC in different ethnic groups ....................................................................................................... 72

7 IMPLICATIONS OF THE STUDY FINDINGS ............................................................................ 74

8 CONCLUSIONS .............................................................................................................................. 75

9. ACKNOWLEDGEMENTS ............................................................................................................ 76

10 REFERENCES .............................................................................................................................. 78

ORIGINAL PUBLICATIONS

6

LIST OF ORIGINAL PUBLICATIONS

This thesis is based on the following original articles.

I. Nyamdorj R, Qiao Q, Lam TH, Tuomilehto J, Ho SY, Pitkäniemi J, Nakagami T,

Mohan V, Janus ED, Ferreira SR for the DECODA Study Group. BMI Compared

With Central Obesity Indicators in Relation to Diabetes and Hypertension in

Asians. Obesity 2008; 16: 1622-1635

II. Regzedmaa Nyamdorj, Qing Qiao, Stefan Söderberg, Janne Pitkäniemi, Paul

Zimmet, Jonathan Shaw, George Alberti, Hairong Nan, Ulla Uusitalo, Vassen

Pauvaday, Pierrot Chitson, and Jaakko Tuomilehto. Comparison of body mass

index with waist circumference, waist-to-hip ratio, and waist-to-stature ratio as a

predictor of hypertension incidence in Mauritius. Journal of Hypertension 2008;

26: 866-870

III. Regzedmaa Nyamdorj, Qing Qiao, Stefan Söderberg, Janne M. Pitkäniemi, Paul Z.

Zimmet, Jonathan E. Shaw, K.G.M.M Alberti, Vassen K. Pauvaday, Pierrot

Chitson, Sudhirsen Kowlessur, and Jaakko Tuomilehto. BMI Compared With

Central Obesity Indicators as a predictor of Diabetes Incidence in Mauritius.

Obesity 2009; 17: 342-348.

IV. Nyamdorj R, Qiao Q, Tuomilehto J, Gao WG, Nakagami T, Hammar N,

Johansson S, Lam TH for the DECODA Study Group. Prevalence of the metabolic

syndrome in populations of Asian origin; Comparison of the IDF definition with

the NCEP definition. Diabetes Research and Clinical Practice 2007; 76: 57-67

V. R Nyamdorj, J Pitkäniemi, J Tuomilehto, N Hammar, CDA Stehouwer, TH Lam,

A Ramachandran, ED Janus, V Mohan, S Söderberg, T Laatikainen, R Gabriel,

and Q Qiao for the DECODA and DECODE Study Groups. Ethnic comparison of

the association of undiagnosed diabetes with obesity. Int J Obes 2010; 332-339

VI. Nyamdorj R, Pitkäniemi J, Tuomilehto J, Boyko ED, Shaw J, Lam TH, Dekker JM

and Qiao Q for the DECODA and DECODE Study Groups. Assessment of change

points of the presence of undiagnosed diabetes associated with body mass index

and waist circumference in different ethnic groups. Submitted to International

Journal of Obesity.

These articles are reproduced with permission of copyright holders.

7

ABBREVIATIONS

ADA American Diabetes Association

AUC Area under the receiver operating characteristics (ROC) curve

BMI Body mass index

CVD Cardiovascular disease

DECODA/DECODE Diabetes Epidemiology: Collaborative Analysis Of Diagnostic criteria

in Asia/Europe

FPG Fasting plasma glucose

FTO Fat mass and obesity

HDL High-density lipoprotein

HR Hazard ratio

IDF International Diabetes Federation

IL-6 Interleukin

MESA Multiethnic Study of Atherosclerosis

MONICA Monitoring of Trends and Determinants in Cardiovascular Disease

NCEP National Cholesterol Education Programme

NEFA Nonesterified fatty acid

NIH National Institutes of Health

NHANES National Health and Nutrition Examination Survey

OAC Obesity in Asia Collaboration

OGTT Oral glucose tolerance test

OR Odds ratio

SES Socio-economic status

SNS Sympathetic nervous system

TNF-α Tumour necrosis factor alpha

WC Waist circumference

WHO World Health Organization

WHO-ISH World Health Organization-International Society of Hypertension

WHR Waist-to-hip ratio

WHtR Waist-to-height ratio

WSR Waist-to-stature ratio

χ2 Chi-squared

2-h PG 2-hour plasma glucose

8

ABSTRACT

Clinical trials have shown that weight reduction with lifestyles can delay or prevent diabetes

and reduce blood pressure. An appropriate definition of obesity using anthropometric

measures is useful in predicting diabetes and hypertension at the population level. However,

there is debate on which of the measures of obesity is best or most strongly associated with

diabetes and hypertension and on what are the optimal cut-off values for body mass index

(BMI) and waist circumference (WC) in this regard.

The aims of the study were 1) to compare the strength of the association for undiagnosed or

newly diagnosed diabetes (or hypertension) with anthropometric measures of obesity in

people of Asian origin, 2) to detect ethnic differences in the association of undiagnosed

diabetes with obesity, 3) to identify ethnic- and sex-specific change point values of BMI and

WC for changes in the prevalence of diabetes and 4) to evaluate the ethnic-specific WC cutoff

values proposed by the International Diabetes Federation (IDF) in 2005 for central obesity.

The study population comprised 28 435 men and 35 198 women, ≥ 25 years of age, from 37

cohorts participating in the DECODA and DECODE studies, including 5 Asian Indian (n =

13 537), 3 Mauritian Indian (n = 4505) and Mauritian Creole (n = 1075), 6 Chinese (n

=10 801), 1 Filipino (n = 3841), 7 Japanese (n = 7934), 1 Mongolian (n = 1991) and 14

European (n = 20 979) studies. The prevalence of diabetes, hypertension and central obesity

was estimated, using descriptive statistics, and the differences were determined with the χ2

test. The odds ratios (ORs) or coefficients (from the logistic model) and hazard ratios (HRs,

from the Cox model to interval censored data) for BMI, WC, waist-to-hip ratio (WHR), and

waist-to-stature ratio (WSR) were estimated for diabetes and hypertension. The differences

between BMI and WC, WHR or WSR were compared, applying paired homogeneity tests

(Wald statistics with 1 df). Hierarchical three-level Bayesian change point analysis, adjusting

for age, was applied to identify the most likely cut-off/change point values for BMI and WC

in association with previously undiagnosed diabetes.

The ORs for diabetes in men (women) with BMI, WC, WHR and WSR were 1.52 (1.59), 1.54

(1.70), 1.53 (1.50) and 1.62 (1.70), respectively and the corresponding ORs for hypertension

were 1.68 (1.55), 1.66 (1.51), 1.45 (1.28) and 1.63 (1.50). For diabetes the OR for BMI did

9

not differ from that for WC or WHR, but was lower than that for WSR (p = 0.001) in men

while in women the ORs were higher for WC and WSR than for BMI (both p < 0.05).

Hypertension was more strongly associated with BMI than with WHR in men (p < 0.001) and

most strongly with BMI than with WHR (p < 0.001), WSR (p < 0.01) and WC (p < 0.05) in

women. The HRs for incidence of diabetes and hypertension did not differ between BMI and

the other three central obesity measures in Mauritian Indians and Mauritian Creoles during

follow-ups of 5, 6 and 11 years.

The prevalence of diabetes was highest in Asian Indians, lowest in Europeans and

intermediate in others, given the same BMI or WC category. The coefficients for diabetes in

BMI (kg/m2) were (men/women): 0.34/0.28, 0.41/0.43, 0.42/0.61, 0.36/0.59 and 0.33/0.49 for

Asian Indian, Chinese, Japanese, Mauritian Indian and European (overall homogeneity test: p

> 0.05 in men and p < 0.001 in women). Similar results were obtained in WC (cm). Asian

Indian women had lower coefficients than women of other ethnicities.

The change points for BMI were 29.5, 25.6, 24.0, 24.0 and 21.5 in men and 29.4, 25.2, 24.9,

25.3 and 22.5 (kg/m2) in women of European, Chinese, Mauritian Indian, Japanese, and Asian

Indian descent. The change points for WC were 100, 85, 79 and 82 cm in men and 91, 82, 82

and 76 cm in women of European, Chinese, Mauritian Indian, and Asian Indian. The

prevalence of central obesity using the 2005 IDF definition was higher in Japanese men but

lower in Japanese women than in their Asian counterparts. The prevalence of central obesity

was 52 times higher in Japanese men but 0.8 times lower in Japanese women compared to the

National Cholesterol Education Programme (NCEP) definition.

The findings suggest that both BMI and WC predicted diabetes and hypertension equally well

in all ethnic groups. At the same BMI or WC level, the prevalence of diabetes was highest in

Asian Indians, lowest in Europeans and intermediate in others. Ethnic- and sex-specific

change points of BMI and WC should be considered in setting diagnostic criteria for obesity

to detect undiagnosed or newly diagnosed diabetes.

10

TIIVISTELMÄ

Epidemiologiset ja kliiniset tutkimukset ovat osoittaneet, että tyypin 2 diabeteksen

kehittymistä voidaan ehkäistä ja korkeaa verenpainetta voidaan laskea terveellisen

elintapaohjauksen avulla henkilöillä, joilla on korkea riski tyypin 2 diabetekseen.

Tarkoituksenmukainen lihavuuden määritelmä pohjautuen antropometrisiin mittauksiin on

hyödyllinen väestötason tutkimuksissa. Kuitenkaan ei ole yksimielistä näkemystä siitä, onko

kehon painoindeksi vai vyötärön ympärysmitta parempi ennustamaan tyypin 2 diabetesta ja

verenpainetautia. Ei ole myöskään selvillä, mitkä ovat painoindeksin ja vyötärönympäryksen

optimaaliset raja-arvot, joita tulisi tässä yhteydessä soveltaa aasialaisissa ja eurooppalaisissa

väestöissä.

Tämän väitöskirjatyön tavoitteina oli 1) verrata painoindeksin ja vyötärön ympärysmitan

yhteyden voimakkuutta aikaisemmin toteamattomassa tai vastatodetussa diabeteksessa (ja

verenpainetaudissa) aasialaista alkuperää olevilla henkilöillä, 2) todeta etnisien ryhmien

välisiä eroja edellä mainituissa yhteyksissä, 3) identifioida ikä- ja sukupuolikohtaiset

painoindeksin ja vyötärön ympärysmitan raja-arvot ennustamaan diabeteksen vallitsevuuden

muutosta eri etnisessa ryhmissä and 4) arvioida vyötärön ympärysmitan raja-arvoja, jotka

International Diabetes Federation (IDF) on vuonna 2005 ehdottanut eri etnisille ryhmille

tarkoittamaan keskivartalolihavuutta.

Tutkimuksen aineisto koostuu 37 aasialaisesta ja eurooppalaisesta kohortista DECODA ja

DECODE tutkimuksissa, joihin osallistui yhteensä 28 435 miestä ja 35 198 naista, iältään yli

25 vuotiaita. Näistä kohorteista, 5 oli Intiasta (n =13 537), 3 Mauritukselta (n= 4505

alkuperältään intialaisia ja n= 1075 kreolejaa), 6 Kiinasta (n=10 801), yksi Filippiineiltä (n=

3841), 7 Japanista (n= 7934), yksi Mongoliasta (n= 1991) ja 14 Eurooppasta (n= 20 979).

Tyypin 2 diabeteksen, verenpainetaudin, ja vyötärölihavuuksen prevalenssit laskettiin.

Antropometristen muuttujien ja eri etnisten ryhmien välisiä eroja testattiin käyttäen useita

tilastomenetelmiä kuten χ2

testi, Waldin testi, logistinen regressioanalyysi ja Coxin

regressioanalyysi. Vedonlyöntisuhdetta (Odds ratio, OR) tai -kertoimia logistisesta mallista

ja vaarasuhteita (Hazards ratio, HR) Coxin mallista sovellettiin analyyseissä, joissa tutkittiin

painoindeksin, vyötärön ympärysmitan, vyötärö-lantio suhteen ja vyötärö-pituus suhteen

yhteyttä diabetekseen ja verenpainetautiin. Näiden antropometristen muuttujien välisiä eroja

kyseisissä analyyseissä arvioitiin parittaisilla homogeenisyystesteillä (Waldin testi, 1

11

vapausaste). Hierarkkista kolmen tason Bayesilaista ikävakioitua muutoskohta-analyysiä

sovellettiin etsittäessä todennäköisintä muutoskohtaa painoindeksille ja vyötärön

ympärysmitalle toteamaan aikaisemmin diagnosoimaton diabetes.

OR:t painoindeksille, vyötärön ympärysmitalle, vyötärö-lantio suhteelle ja vyötärö-pituus

suhteelle diabeteksen suhteen olivat miehillä (naisilla) 1.52 (1.59), 1.54 (1.70), 1.53 (1.50) ja

1.62 (1.70). Vastaavat OR:t verenpainetaudin suhteen olivat 1.68 (1.55), 1.66 (1.51), 1.45

(1.28) ja 1.63 (1.50). OR painoindeksille ei eronnut OR:sta vyötärön ympärysmitalle tai

vyötärö-lantio suhteelle, mutta oli pienempi kuin OR vyötärö-pituus suhteelle (p = 0.001)

miehillä, kun taas naisilla OR vyötärön ympärysmitalle tai vyötärö-pituus suhteelle olivat

suuremmat kuin painoindeksille (molemmat p < 0.05). Painoindeksin yhteys

verenpainetautiin oli voimakkaampi kuin vyötärö-lantio suhteen miehillä (p < 0.001), ja

naisilla painoindeksin yhteys oli voimakkaampi kuin vyötärön ympärysmitan (p < 0.05),

vyötärö-lantio suhteen (p < 0.001) ja vyötärö-pituus suhteen (p < 0.001). HR:t diabeteeksen ja

verenpainetaudin esiintyvyydelle eivät eronneet painoindeksin ja kolmen muun

antropometrisen muuttujan välillä mauritiuslaisilla intialaisilla ja kreoleilla.

Tyypin 2 diabeteksen prevalenssi oli korkein intialaisilla, matalin eurooppalaisilla ja

keskitasolla muissa Aasialaisissa väestöissä. Logistisen regressiomallin arviodut

painoindeksin (kg/m2) - kerroimet diabetekselle olivat (mies/nainen): 0.34/0.28, 0.41/0.43,

0.42/0.61, 0.36/0.59 ja 0.33/0.49 intialaisilla, kiinalaisilla, japanilaisilla, mauritiuslaisilla

intilaisilla ja eurooppalaisilla (kokonais-homogeenisuustesti: p > 0.05 miehillä ja p < 0.001

naisilla). Intialaisilla naisilla oli pienempi -kerroin kun muilla naisilla. Painoindeksin

muutoskohdaksi diabetesriskin suhteen todettiin 29.5, 25.6, 24.0, 24.0 ja 21.5 kg/m2

miehillä

ja 29.4, 25.2, 24.9, 25.3 ja 22.5 naisilla kg/m2 eurooppalaisilla, kiinalaisilla, mauritiuslaisilla

intialaisilla, japanilaisilla ja intialaisilla. Vyötärön ympärysmitan muutoskohdaksi

diabetesriskin suhteen todettiin 100, 85, 79 ja 82 cm miehillä ja 91, 82, 82 ja 76 cm naisilla

eurooppalaisilla, kiinalaisilla, mauritiuslaisilla intialaisilla ja intialaisilla. Sovellettaessa IDF:n

2005 ehdottamia vyötärön ympärysmitan raja-arvoja keskivartalolihavuudelle, sen

vallitsevuus oli japanilaisilla miehillä korkeampi kuin muissa aasialaisissa väestöissä.

Verrattuna Yhdysvaltojen National Cholesterol Education Program (NCEP):n käyttämiin raja-

arvoihin keskivartalolihavuuden vallitsevuus oli IDF:n raja-arvoja käytettäessä 52 kertaa

korkeampi japanilaisilla miehillä, mutta 0.8 kertaa matalampi japanilaisilla naisilla.

12

Nämä löydökset osoittavat sekä painoindeksin että vyötärön ympärysmitan ennustavan

diabeteksen ja verenpainetaudin esiintyvyyttä yhdenmukaisesti kaikissa etnisissä ryhmissä.

Samalla painoindeksin ja vyötärön ympärysmitan tasolla diabeteksen vallitsevuus oli korkein

intialaisilla ja matalin eurooppalaisilla. Etnis- ja sukupuoli-kohtainen painoindeksin ja

vyötärön ympärysmitan raja-arvoja tulee soveltaa ennustettaessa diabeteksen riskiä liittyen

lihavuuteen.

13

1 INTRODUCTION

Obesity is an excess fat accumulation in the body that is one of the major modifiable risk

factors for type 2 diabetes, hypertension and many other chronic conditions (Colditz et al.

1995; Huang et al. 1998; Zimmet et al. 2001). The World Health Organization (WHO)

estimates that there are more than 1 billion overweight adults worlwide and at least 300

million that are clinically obese (WHO Consultation 2000), figures that are estimated to

increase further by 2015. Increased urbanization, westernization, rapid economic development

and unhealthy lifestyles all have contributed to the rapid increase in obesity. Consequently,

the prevalence of obesity-related metabolic disorders, such as type 2 diabetes and many

others, are increasing at an alarming rate and projected to increase further.

Diabetes is the fifth or sixth leading cause of death (International Diabetes Federation 2009).

The crude prevalence of diabetes (types 1 and 2) in adults 20-79 years of age was estimated to

be 6.6% (285 million) in 216 IDF member countries in 2010 (International Diabetes

Federation 2009). By 2030, this figure is expected to rise to 7.8% (438 million), with the

largest increase in regions where economies are developing further. They further emphasized

that if the levels of obesity continue to increase, the prevalence of diabetes may be even

greater than that reported in the IDF 2009 report.

Hypertension is considered as the primary risk factor for stroke, ischemic heart disease

(Nakamura et al. 2008) and cardiovascular disease (CVD) mortality (Martiniuk et al. 2007;

He et al. 2009). Recently, it was estimated that 7.6 million premature deaths per year may be

attributed to high blood pressure (Lawes et al. 2008), that is 13.5% of the total global deaths.

About 26.4% or 972 million of the world adult population had hypertension in 2000, of which

333 million were in economically developed countries and 639 million in economically

developing countries (Kearney et al. 2005), figures predicted to increase by 60% to 1.56

billion in 2025.

Clinical intervention trials have clearly shown that weight reduction with healthy diets and

physical activity can benefit individuals at increased risk for diabetes and hypertension

(Eriksson and Lindgarde 1991; Pan et al. 1997; Tuomilehto et al. 2001; Knowler et al. 2002;

Appel et al. 2003; McGuire et al. 2004; Kosaka et al. 2005; Elmer et al. 2006; Ramachandran

et al. 2006; Bosworth et al. 2007; Cook et al. 2009). Thus, an appropriate definition of obesity

14

and its predictive value in relation to diabetes and hypertension are necessary in intervention

strategies in different populations.

Commonly used proxy or anthropometric measures such as body mass index (BMI), waist

circumference (WC), waist-to-hip ratio (WHR), hip circumference and waist-to-stature ratio

(WSR) have been proposed to define obesity in epidemiological studies. However, there is

controversy regarding which of these anthropometric measures best defines obesity and

conveys the highest risk for type 2 diabetes and hypertension (Wei et al. 1997; Folsom et al.

2000; Tulloch-Reid et al. 2003; Hayashi et al. 2004; Menke et al. 2007; Zhou et al. 2009).

Furthermore, optimal BMI and WC cut-off values for detecting diabetes, other metabolic

abnormalities, and CVD were proposed for different populations, with higher values for

Europeans and lower values for Asians (Han et al. 1995; Lean et al. 1995; Regional Office for

the Western Pacific of the World Health Organization 2000; WHO Consultation 2000; Expert

Panel on Detection 2001; WHO Expert Consultation 2004; Alberti et al. 2005, 2006).

Comparability of findings within the same ethnicity, however, is limited, which may be due to

variations in age range of the study population and the statistical methods applied.

The Diabetes Epidemiology: Collaborative Analysis Of Diagnostic criteria in Asia/Europe

(DECODA/DECODE) studies, consisting of 37 cohorts of European and Asian origin provide

an excellent opportunity for comparison of surrogate anthropometric measures for obesity

with undiagnosed diabetes and hypertension, based on both cross-sectional and prospective

data. In addition, they can be used to explore ethnic differences in the strength of association

of undiagnosed diabetes, given the same obesity level and to identify the cutoff values for

BMI or WC in different ethnic groups, using standardized statistical methods.

15

2 LITERATURE REVIEW

2.1 Epidemiology of obesity

2.1.1 Clinical definition and classification of overweight and obesity

Obesity, as an excess body fat accumulation, is increasing in both the developed and

developing world (WHO Consultation 2000). The use of different anthropometric measures

has been proposed by various organizations to classify overweight and obesity in adults

(Table 1).

Table 1 Classification of overweight and obesity by different international organizations

(WHO

Consultation

2000)

WHO (Lean et al.

1995)

NCEP (Expert Panel on

Detection 2001)

IDF (Alberti et al.

2006)

BMI (kg/m2) WC (cm) WC (cm) WC (cm)

Underweight < 18.5

Normal weight 18.5 - 24.9

Overweight 25.0 - 29.9

Obesity ≥ 30.0 ≥94/80

men/women

> 102/88 men/women ≥ 94/80 or 90/80

men/women

WC of ≥ 94/80 cm in men/women for European, Eastern Mediterranean, Middle East (Arab) and

Sub-Saharan African and ≥ 90/80 cm for Chinese, Japanese, South Asians and South and Central

American men/women, respectively

The WHO definition classified individuals into different stages of obesity using BMI (WHO

Consultation 2000) while the National Cholesterol Education Programme (NCEP) (Expert

Panel on Detection 2001) and IDF classified individuals as obese and non-obese, using

ethnic-specific WC with purpose to define the metabolic syndrome (Alberti et al. 2005, 2006).

Furthermore, in the 2005 IDF definition, central obesity was a mandatory component of the

metabolic syndrome and WC values of 85/90 cm for Japanese men/women were set as criteria

for central obesity (Alberti et al. 2005).

Overall fatness or general obesity, as measured by BMI was introduced as Quetelet’s index

(Garrow and Webster 1985) and central/abdominal obesity was first introduced by the French

physician Vague in the late 1940s (Vague 1947). Later, Vague pointed out for the first time

that central (android) obesity was more detrimental than peripheral obesity (gynoid) in

relation to diabetes, gout, atherosclerosis and urate calculus diseases (Vague 1956). Since that

16

time, a number of studies confirmed the association of android type obesity with different

morbidity outcomes. At the adipocyte level, the two different patterns of obesity, hypertrophic

(increased size alone) and hyperplastic (increased cell number with normal or increased size)

obesity (Salans et al. 1973; Bjorntorp 1991), were further recognized in human obesity with

distinct clinical consequences (Salans et al. 1968; Krotkiewski et al. 1983; Weyer et al. 2000).

People with hypertrophic obesity were seen as more likely to develop obesity-related

metabolic disturbances than those with hyperplastic obesity (Arner et al. 2010)

2.1.2 Anthropometric measures of obesity

BMI as a measure of general obesity, and WC and WHR as measures of central obesity, have

been proposed to define obesity (Seidell et al. 1989). The most common measure that has

been used is the BMI. BMI is calculated as the weight in kilograms divided by the square of

the height in metre (kg/m2) and its concept dates back to 1869 as Quetelet’s index (Garrow

and Webster 1985), which was shown as a fairly good indicator of general fatness (Seidell et

al. 1989; WHO Expert Commiittee 1995; WHO Consultation 2000). However, despite its use

in epidemiological and clinical studies, for a given BMI, the adiposity varies by age, sex and

ethnicity (Deurenberg et al. 2002).

Since the early 1980s, the waist-thigh-ratio or WHR has been considered more closely

correlated with abdominal visceral fat than the BMI and a better predictor of CVD or diabetes

incidence than the BMI (Ashwell et al. 1982; Krotkiewski et al. 1983; Lapidus et al. 1984;

Larsson et al. 1984; Ashwell et al. 1985; Ohlson et al. 1985). Since the 1990s, interest in WC

has increased because it correlates more closely with abdominal visceral fat than either the

WHR or BMI (Pouliot et al. 1994; Han et al. 1995; Lean et al. 1995; Han et al. 1998) for

identification of CVD risk factors. Other indicators, such as hip circumference (Lissner et al.

2001; Seidell et al. 2001; Snijder et al. 2003; Snijder et al. 2004), waist-to-height ratio

(WHtR) (Ashwell et al. 1996a; Ashwell et al. 1996b; Ledoux et al. 1997; Hsieh and Muto

2005) and WSR (Ho et al. 2003) may also be useful markers of obesity.

2.1.3 Measurements of waist and hip circumference

In the literature, there are as many as 14 different anatomical sites at which WC can be

measured (Wang et al. 2003). The WC measurement sites most widely used in

epidemiological studies are shown in Table 2.

17

Table 2 Anatomical sites for waist circumference measurement

Measurement sites Proposed organization Reference

Waist circumference

Below the lowest rib (Wang et al. 2003)

Minimal waist Anthropometric Standardization

Reference Manual

(Lohman 1988)

Midpoint between the

lowest rib and the iliac crest

WHO STEPS protocol (WHO 2008)

Umbilicus or navel level NIH MESA protocol (MESA Monitoring Board

2002)

Above the iliac crest NIH and NHANES III protocol (Westat 1988)

Hip circumference

The widest portions of

the buttocks

WHO STEPS, MESA,

and NHANES III protocols

(Westat 1988; MESA

Monitoring Board 2002; WHO

2008)

According to the WHO Stepwise Approach to Surveillance (STEPS) protocol, the WC should

be measured at the midpoint between the top of the iliac crest (hip bone) and the lower margin

of the last palpable rib (WHO 2008), which is the method most commonly used. A second

protocol, used by the National Institutes of Health (NIH) Multiethnic Study of Atherosclerosis

(MESA), suggests measuring the WC at the umbilicus or navel level (MESA Monitoring

Board 2002). A less frequently used method, provided in the NIH manual (National Institute

of Health 2000) and the National Health and Nutrition Examination Survey III (NHANES III)

(Westat 1988), advises measuring the WC from the top of the iliac crest.

There is generally more consensus on existing recommendations for measuring hip

circumference around the widest portion of the buttocks (Westat 1988; MESA Monitoring

Board 2002; WHO 2008).

2.1.4 Measurement error related to BMI and WC

Currently, there is no consensus regarding the optimal protocol for measurement of WC and

no scientific rationale supporting the measurement protocols recommended. Another

important consideration in choosing an anthropometric measure of BMI or WC as a screening

tool is the measurement error. Some investigators have argued that measurement of the WC is

subject to less error because only a single measurement is required, which favours the use of

the WC rather than the BMI. In a review, measurements of weight and height appeared to be

most precise among different anthropometric measures, while WC showed strong

interobserver differences (Ulijaszek and Kerr 1999). Two other studies have also shown a

18

significant interobserver difference in WC measurements, as well as higher interobserver

variability for the WC than for the BMI (Nadas et al. 2008; Panoulas et al. 2008). Training, in

the form of written instructions, eliminates the systematic error but does not reduce the

overall variation in WC measurements between observers (Panoulas et al. 2008). Although

the various measurement protocols have no substantial influence on the association between

WC and health outcomes (Ross et al. 2008), they will increase the difficulties in comparing

directly between studies. Hence the measurement of WC is recommended, due to its close

association with unfavourable health consequences, not because its measurement error is less.

In addition to the advantages of these anthropometric measures, such as low cost and less

labour, there are, potential disadvantages such as ratios difficult to interpret biologically for

the WHR or BMI, changes in body fat or visceral fat result little or no change in this ratio

(Bouchard et al. 1990), and high levels of correlation of these measures, such as WC with

BMI (Molarius and Seidell 1998).

2.1.5 Adult prevalence, secular trend and risk factors for obesity

Generally, most of the populations experienced an increase in the prevalence of obesity in the

last decade, most likely due to lifestyle changes associated with urbanization, westernization

and economic development. Similarly the increase in prevalence of obesity was reported in all

populations in the WHO MONICA study between the 1980s and 1990s, due to increased

enegy supply (Silventoinen et al. 2004). In recent years, there has been increasing recognition

that developing countries that still have a substantial problem of undernutrition are now

facing an epidemic of both obesity and undernutrition (Prentice 2006). The most recent adult

prevalence of obesity is shown in Appendix 1. The prevalence of obesity ranged from 0.3 -

3.4% in Asian Indians, Filipinos, Japanese and Chinese (Asia Pacific Cohort Study

Collaboration 2007) to 4.7 - 9.1% in Thais (Aekplakorn and Mo-Suwan 2009), Hong Kong

Chinese (Asia Pacific Cohort Study Collaboration 2007) and Singaporeans (Ministry of

Health Singapore 2005). The prevalence was between 6.0% and 9.3% in men and 12.0% and

25.0% in women from Africa (Bovet et al. 2002), Mauritius (International Obesity Task

Force), Brazil (Monteiro et al. 2007) and Mongolia (Bolormaa et al. 2008). The prevalence of

obesity ranged from 10.0 - 15.5% in the Netherlands, Spain (DORICA) and Sweden (Berg et

al. 2005; Schokker et al. 2007; Aranceta et al. 2009) to 19.3 - 27.7 % in Finland (Vartiainen et

al. 2010), Spain (Girona) (Schroder et al. 2007), Australia (Cameron et al. 2003), Canada

(Shields et al. 2010), the UK (Zaninotto et al. 2009), Italy (Berghofer et al. 2008) and Mexico

(Malina et al. 2007), with similar rates in men and women. In the USA, the prevalence of

19

obesity was over 32.0%, with higher rates in Mexican Americans and Blacks than in Whites

(Flegal et al. 2010).

As shown in Appendix 2, the increasing trend toward increase in prevalence of obesity was

observed in most of the populations, with a few exceptions; in India, Mongolia and the USA

the prevalence did not increase in the last decade. The prevalence was doubled in Brazil,

China and Thailand.

Genes, age and female sex (in Central and Eastern Europe, Latin America, Asia and Africa),

all have been considered as nonmodifiable risk factors for obesity. In 2007, Fat Mass and

Obesity (FTO) gene variants predisposed individuals to type 2 diabetes through their effect on

BMI in the European population (Frayling et al. 2007). The findings were further confirmed

in Chinese (Liu et al. 2010), Japanese (Karasawa et al. 2010), Asian Indians (Yajnik et al.

2009) and Hispanic and African Americans (Wing et al. 2009). Recently, other new gene

variants with a population-level effect on BMI and WC (or WHR) have been identified

(Lindgren et al. 2009; Willer et al. 2009). Obesity increases with age in both sexes, especially

in women (Berg et al. 2005; Wang and Beydoun 2007; Wang et al. 2007; Low et al. 2009;

Wang et al. 2009; Zaninotto et al. 2009; Flegal et al. 2010; Lahti-Koski et al. 2010) with a

peak prevalence at 50 - 60 years in developed and 40 - 50 years in developing countries (Low

et al. 2009). Individuals, particularly women with low socioeconomic status (SES), were more

obese in highly developed countries mostly (Molarius et al. 2000; Seidell 2005; McLaren

2007) but women with high SES were more obese in low- and medium-development regions,

such as in Africa (Martorell et al. 2000; Amoah 2003; McLaren 2007; Case and Menendez

2009) and India (Wang et al. 2009).

2.2 Obesity and diabetes

2.2.1 Definition, prevalence and secular trend in the prevalence of type 2 diabetes

Diabetes was defined as fasting glucose (FG) ≥ 7.00 mmol/l and/or 2-hour postchallenge

glucose (2h-PG) ≥ 11.10 mmol/l by the WHO (WHO Consultation 1999), American Diabetes

Association (American Diabetes Association 2010) and the IDF (International Diabetes

Federation 2009). The prevalence of type 2 diabetes is increasing continuously with time in

all populations and is reaching epidemic proportions in some populations, such as in Nauru

(Chan et al. 2009). In Sub-Saharan African populations, the prevalence of type 2 diabetes was

between 2.0% and 3.0% (Gill et al. 2009). The prevalence of diabetes ranged from 5.1% in

Filipinos to 8.2% in Mongolians (Bolormaa et al. 2008). In China, a recent national survey

20

revealed that 9.7% of adults had diabetes in 2007 (Yang et al. 2010), which was similar to the

prevalence in Hong Kong (Janus et al. 2000) and Japan (Ekoe et al. 2008). In India, 4.3% of

the adult population had type 2 diabetes in a national report (Sadikot et al. 2004), but a much

higher prevalence of 18.6% in Chennai city was reported (Ramachandran et al. 2008). For

European countries (Figure 1), the prevalence (types 1 and 2) ranged from 3.6% in England to

7.3 - 8.8% in Sweden, Netherlands, Finland, Spain and Italy in order of increase (International

Diabetes Federation 2009), with the highest prevalence of 12.0% in Germany and 10.4% in

Cyprus. In the USA, the prevalence was 12.9% (Cowie et al. 2009).

Figure 1 Prevalence (%) estimates of diabetes (20-79 years) in 2010.

Diabetes Atlas 4th

edition (International Diabetes Federation 2009)

In China, the prevalence of diabetes increased from 1.0% to 9.7 % between 1980 and 2007

(Yang et al. 2010). The prevalence more than doubled in Mongolia from 1999 (3.2%) to 2005

(8.2%) (Bolormaa et al. 2008). Several population-based studies from India and Mauritius

reported that the prevalence of diabetes increased (Ramachandran et al. 1992; Ramachandran

et al. 2001; Mohan et al. 2006; Ramachandran et al. 2008). In Europe, the prevalence

increased in the Netherlands (Ubink-Veltmaat et al. 2003), Sweden (Berger et al. 1999) and

the UK (Gatling et al. 1998) and doubled in Australia between 1981 and 1999 - 2000

(Dunstan et al. 2002). The continuous increase in diabetes prevalence was observed in the

21

USA as from 5.3% (1976 - 1980) to 12.9% (2005 - 2006) (Gregg et al. 2004; Cowie et al.

2009).

2.2.2 Risk factors for type 2 diabetes

Since 2000, genomewide association studies have suggested a number of gene variants that

are associated with a high risk for type 2 diabetes (Prokopenko et al. 2008; Florez 2009;

Dupuis et al. 2010). More recently, the FTO gene predisposes individuals to diabetes through

an effect of BMI in many different ethnic groups (Frayling et al. 2007; Wing et al. 2009;

Yajnik et al. 2009; Karasawa et al. 2010a; Liu et al. 2010). Some ethnic groups, e.g. Asian

Indians had a higher prevalence of diabetes than Caucasians in the USA (Abate et al. 2003;

Abate et al. 2005; Radha et al. 2006), due to genetic predisposition. Individuals with family

histories of diabetes in their parents or siblings had from 2 to 6 times higher risk for diabetes

than those without it (Knowler et al. 1981; Lin et al. 1994; Sargeant et al. 2000; Harrison et al.

2003; Valdez et al. 2007; Valdez 2009). Diabetes increased with age in both men and women

of all populations in the DECODA/DECODE studies (Qiao et al. 2003; The DECODE Study

Group 2003).

Among the lifestyle-related risk factors, Pietraszek et al. demonstrated J- or U-shaped

associations between alcohol consuption and incidence of type 2 diabetes, based on meta-

analysis and cohort studies (Pietraszek et al. 2010). Compared with abstainers, moderate

alcohol consumers had 30% reduced risk for diabetes, due to an ethanol-mediated

improvement in insulin sensitivity primarily observed in the obese, while heavy consumers

had the same or higher risk (Pietraszek et al. 2010). As for association of smoking with type 2

diabetes, the results were inconsistent. Some studies have shown that current smoking

increased the risk of diabetes incidence by 44% (Willi et al. 2007) and 31% (Yeh et al. 2010)

while in other study a reduced risk of diabetes was noted (Onat et al. 2007). Recent studies

from China demonstrated that low SES increased the risk for diabetes (Yang et al. 2010) in

urban men only, but lowered the risk in rural men of Qingdao city, which was mediated partly

by obesity (Ning et al. 2009).

2.2.2.1 Obesity as a major risk factor for type 2 diabetes

Obese women were at higher risk of developing type 2 diabetes during a 14-year follow-up,

5-fold in the BMI group of 24.0 - 24.9 kg/m2, 40-fold in 31.0 - 32.9 kg/m

2 and 93-fold in the

35.0 kg/m2 category, compared with the group with BMI of < 22.0 kg/m

2 in the large Nurse’s

Health Study (Colditz et al. 1995) as well as in the Male Health Professionals in the USA

(Chan et al. 1994) during a 7-year follow-up. A 20-year follow-up of the Nurse’s Health

22

Study further confirmed that weight increase as a major risk factor for type 2 diabetes in all,

particularly in Asians (Shai et al. 2006), which was in agreement with findings from others

(Ning et al. 2009). Prospective studies have reported a strong association between daily

physical activity and reduced risk for developing diabetes, with a relative risk reduction of 15

- 60% (Helmrich et al. 1991; Perry et al. 1995; Hu et al. 1999; Hu et al. 2004; Nakanishi et al.

2004; Meisinger et al. 2005). Furthermor, clinical intervention trials have clearly shown that

weight reduction with healthy diet and physical activity can prevent or at least delay the onset

of type 2 diabetes in individuals with impaired glucose tolerance in Swedish (Eriksson and

Lindgarde 1991), Chinese (Pan et al. 1997), Finnish (Tuomilehto et al. 2001), American

(Knowler et al. 2002), Asian Indians (Ramachandran et al. 2006) and Japanese subjects

(Kosaka et al. 2005). The relative risk reduction for diabetes ranged from 28% in Asian

Indians to 67% in Japanese during the intensive intervention period. Furthermore, these trials

demonstrated that lifestyle intervention was as effective as metformin (Knowler et al. 2002;

Ramachandran et al. 2006; Knowler et al. 2009) or pioglitazone (Ramachandran et al. 2009).

This suggests that weight reduction with a healthy lifestyle is the cornerstone in prevention of

obesity-related conditions such as diabetes.

2.2.3 Comparison of BMI with central obesity measures in relation to type 2 diabetes

Controversial opinions exist on which of these obesity measures, BMI or WC (WHR or

WSR) are more strongly associated with increased risk of type 2 diabetes and need to be

studied further, based on prospective studies with an incidence of diabetes as an outcome.

Since the 1990s, a number of epidemiological studies and meta-analyses of the comparison

between BMI and WC (or WHR) for assessing type 2 diabetes have been carried out in

different ethnic groups. A meta-analysis of 35 cohort studies that examined the association

between different anthropometric measures of obesity and incident diabetes has shown that

the pooled relative risk for diabetes incidence did not differ significantly between BMI and

WC or WHR (Vazquez et al. 2007). A stronger association with WSR than with BMI was

observed in males only and there were no differences in females between the four measures

(BMI, WC, WHR, and WSR) for presence of diabetes, based on meta-analysis (Lee et al.

2008a). WC (not for Asian men) and WHR were more strongly associated with prevalent

diabetes than with BMI in Asian and Caucasian women, but these measures did not differ in

Caucasian men in the Obesity in Asia Collaboration study (OAC) (Huxley et al. 2008).

Recently, we published a review article on studies (17 prospective and 35 cross-sectional) that

compared the performance of anthropometric measures with diabetes (Qiao and Nyamdorj

2010a). For prospective studies, in which formal statistical tests were done, inconsistent

23

findings were observed: in favour of the WC in Mexican Americans and African Americans

but in favour of the BMI in Pima Indians, and no differences were found in the Diabetes

Prevention Programme (DPP) study. Among 11 cross-sectional studies that have formally

tested the differences, most found a slightly higher odds ratio (OR) or larger area under the

receiver-operating characteristics (ROC) curve (AUC) for WC than for BMI. All studies

included in the review showed that either BMI or WC (or WHR or WSR) predicted or was

associated with type 2 diabetes independently, regardless of the controversial findings on

which of these obesity indicators is better (Qiao and Nyamdorj 2010a).

2.2.4 Optimal cutoff values for BMI and WC in relation to diabetes

Currently, different definitions for obesity, using WC has been proposed by different

organizations in various populations. Central obesity, using ethnic-specific WC values, is

used with purpose to define the metabolic syndrome. In addition, the recommended cutoff

values for WC and BMI for detecting diabetes differ among ethnic groups (Regional Office

for the Western Pacific of the World Health Organization 2000; WHO Expert Consultation

2004; Alberti et al. 2009; Qiao and Nyamdorj 2010b), with lower values for Asians and

higher for Europeans. However, the comparability of the cutoff values is limited within

populations of the same ethnicity which may be due to variation in age range of the study

participants or to the methods applied to determine the optimal cutoff values in different

studies. All studies aiming to choose BMI and WC cutoff values almost exclusively used the

ROC curve approach, in which the sum of the sensitivity and specificity was maximized, but

choosing the WC values using this approach was considered inappropriate (Cameron et al.

2009). No consensus has been reached regarding the most appropriate approach for selecting

WC cutoff values. Furthermore, no results are available that apply Bayesian change point

analysis to detect the diagnostic cutoff values. All these suggest that studies are needed for

appropriate definition of obesity, using standardized methods in different populations.

Our review (based on 4 prospective and 24 cross-sectional studies) has also shown the marked

variation in cutoff values between ethnic groups, as summarized in Table 3 (Qiao and

Nyamdorj 2010b). Tongans had the highest BMI and WC optimal cutoff values (not for

WHR), followed by studies in the USA and the UK. The BMI and WC cutoff values were

higher for ethnicities in the USA and the UK studies than in their counterparts in their original

countries. The optimal cutoff values for BMI were 27 - 28 kg/m2 in White men and women

(Australia, Germany, France (men only), the UK and the USA) but were 30 kg/m2 for men in

the NHANES III and 25 kg/m2 for women from France. The optimal WC (WHR) cutoff

24

values were 97 - 99 cm (0.95) for White men and 85 cm (0.83 - 0.85) for White women living

outside the USA and the UK. The values for BMI were 23 - 24 kg/m2 in Chinese, Japanese,

and Thai men and 22 - 23 kg/m2 in Indians. The optimal cutoff values for WC were 85 cm

(0.90) for Chinese, Japanese, Indian, and Thai men and 75 - 80 cm (0.79 - 0.85) for women in

these ethnic groups from Asia; the values for other ethnic groups were between those for

Whites and Asians. White, Chinese, Japanese, Indian and Bangladeshi men had higher values

than women of these ethnicities, but Thai, Iranian, Iraqi, Tunisian, Mexican, African and

Tongan men did not.

25

Table 3 Optimal BMI, WC, and WHR cutoff points (CP) for assessing risk of type 2 diabetes with sensitivity (Sen) (%) and specificity

(Spe) (%) (Qiao and Nyamdorj 2010b)

Ethnicity Men Women

BMI (kg/m2) WC (cm) WHR BMI (kg/m

2) WC (cm) WHR

CP Sen Spe CP Sen Spe CP Sen Spe CP Sen Spe CP Sen Spe CP Sen Spe

White

(Others)

27-

28

60-

77

64-

70

97-

99

72 74 0.95 77 65 27-

28

65-

86

63-

70

85 77 74 0.83-

0.85

77 70

White

(USA,UK)

28-

30

60 70 101-

6

61 67 0.97 69 58 27-

28

65 69 95 67 68 0.91 69 64

Turkish

(Turkey)

95 70 53 91 75 55

Chinese 24 58-

89

59-

66

85 50-

97

58-

70

0.88-

0.92

64-

76

71-

76

24 61-

81

52-

75

75-

80

58-

78

66-77 0.79-

0.83

71-

79

70-79

Chinese

(USA+UK)

25 95 24 84

Indian

(India)

22-

23

67-

78

48-

63

85-

87

64-

69

58-

67

0.92 61 66 23 67-

72

53-

54

80-

83

65-

70

56-60 0.85 66 54

Indian

(USA+UK)

27 97 25 89

Bangladeshi

(USA+UK)

24 96 27 88

Pakistani

(USA+UK)

25 93 30 101

Japanese

(Japan)

24 59 59 85 62 62 0.92 71 71 23 67 67 73 70 70 0.81 78 78

Thai

(Thailand)

23 85 0.91 25 85 0.88

Iranian

(Iran)

26

18-34 yr 86 0.88 82 0.82

35-54 yr 91 0.94 93 0.87

55-74 yr 92 0.96 95 0.91

Iraqi (Iraq) 25 66 54 90 80 49 0.92 77 61 26 66 47 91 80 47 0.91 72 63

Tunisian

(Tunisia)

85 71 63 85 76 67

Tongan

(Tonga)

32 66 68 103 63 64 0.93 69 71 35 62 61 103 65 63 0.86 69 71

Brazilian

(Brazil)

88 69 68 84 67 66

Mexican

(Mexico)

27 56 56 90-

95

47 47 0.90 57 57 28 59 59 85-

97

53 53 0.86 62 62

Mexican

(USA+UK)

28 100 30 104

African

(USA)

28 61 68 99 61 71 0.94 62 60 30 63 60 101 62 68 0.92 61 66

African 25 71 71 88 71 79 0.87 29 62 65 85-

89

62 65 0.90

Black

(USA+UK)

29-

32

109-

100

28 105-

88

27

2.2.5 Ethnic differences in the association of diabetes with obesity

The evidence shows that at any given level of BMI, WC, WHR or visceral adipose tissue

accumulation, non-Europeans have greater risk of developing diabetes than Europeans.

However, inconsistent results in the strength of the association of diabetes with obesity

measures were found in different ethnic groups, which needs further investigation.

Studies revealed ethnic differences in the prevalence or incidence of diabetes as well as in the

association of diabetes with different obesity measures, based on cross-sectional

(Ramachandran et al. 1997; McBean et al. 2004; Lee et al. 2007; Sundborn et al. 2007) and

prospective data (Shai et al. 2006; Signorello et al. 2007; Vazquez et al. 2007). However, few

studies have compared the strength of the association across ethnic groups given the same

level of obesity. In comparison to Caucasians, non-Europeans (Aboriginal people, South

Asians and Chinese in Canada), Aboriginals (Australia) and Asians from different countries

had higher levels of FG (Razak et al. 2005) or were at excess risk for diabetes (Kondalsamy-

Chennakesavan et al. 2008) or had higher prevalence of diabetes (Huxley et al. 2008) at any

given level of BMI or WC. Similarly, Filipino women living in the USA had much higher risk

of diabetes at every level of visceral adipose tissue compared with White or African American

women and the excess risk was not explained by visceral adipose tissue (Araneta and Barrett-

Connor 2005).

With regard to the strength of association of anthropometric measures with diabetes

incidence, a stronger association with BMI was observed in Asians or Chinese than in

Caucasians of Australia (Ni Mhurchu et al. 2006) and the USA, but a similar association

between Chinese and American Blacks (Stevens et al. 2008). The stronger association of

diabetes with WHR or BMI in Caucasians than in Asians was, however, observed in a meta-

analysis (Vazquez et al. 2007) and OAC (Huxley et al. 2008).

2.3 Obesity and hypertension

2.3.1 Definition, prevalence and secular trend in hypertension

High blood pressure or hypertension is defined by the presence of a chronic elevation of

arterial blood pressure as systolic and/or diastolic blood pressure of 140/90 mm Hg and/or

drug use for lowering blood pressure (WHO 1999; Giles et al. 2009). The prevalence of

hypertension was very high worldwide and varied greatly among populations: highest in

Europeans, Blacks (the USA) and North Chinese; intermediate in Whites from Australia, the

UK and the USA, Hong Kong Chinese, Filipinos, Mongolians, Africans, Mexicans, Japanese,

28

and Chinese (Taiwan); and lowest in Indians, Canadians and Thais. Generally, the prevalence

was higher in men than in women of most populations. The trend in hypertension prevalence

increased for North Chinese, Blacks and White women (the USA), slightly decreased in

Chinese (Taiwan) and was stable in Finns and Whites (the UK).

As shown in Table 4, in Africans (Steyn et al. 2001; Bovet et al. 2002; Kamadjeu et al. 2006;

Longo-Mbenza et al. 2008) the prevalence of hypertension was lower than in Blacks from the

USA (Cutler et al. 2008). The prevalence of hypertension was much higher (50.0%) in

Qingdao City of Northeast China (Ning et al. 2009) than that reported in a national survey

(18.0%) (Wu et al. 2008). The prevalence of hypertension ranged from 18.0% to 26.0% in

women and about 30.0% in men of Chinese (Hong Kong and Taiwan) (Su et al. 2008),

Mongolian, Filipino, and Japanese ethnicities (Martiniuk et al. 2007) and was 22.0% in Thais

(Aekplakorn et al. 2008). For Indians, the prevalence was estimated at 25.0% in urban and

10% in rural areas by pooling results from epidemiological studies carried out in different

regions (Gupta 2004). Approximately 22.0% of Canadians (Joffres et al. 1997), 26.0 - 33.0%

of Whites from Australia (Briganti et al. 2003) and the UK (Falaschetti et al. 2009), Mexicans

from Mexico (Barquera et al. 2008) and the USA (Cutler et al. 2008), and 35.0 - 60.0% of

Europeans (Banegas et al. 1998; Cooper et al. 2005; Gabriel et al. 2008; Kastarinen et al.

2009) and Blacks (the USA) had hypertension.

Hypertension decreased from 1972 to 2002, but from 2002 to 2007 the decline levelled off in

the FINRISK surveys (Vartiainen et al. 2010). Similarly, no increase in hypertension was

reported in the UK between 2003 and 2006 (Falaschetti et al. 2009). However, in mainland

China, the prevalence of hypertension increased from 11.0% in 1991 (Ueshima et al. 2000) to

20.0% in 2002 (Wu et al. 2008), and even more of increase was reported in Qingdao City,

China between 2002 and 2006 (Ning et al. 2009). The prevalence of hypertension increased in

the USA between 1988 - 1994 and 1999 - 2004, with a higher increase in Blacks and in

women of all three ethnic groups (Cutler et al. 2008).

29

Table 4 Adult prevalence of hypertension (%)

Ethnicity,

country, age range

Study and year Men Women Total Reference

African

Cameroon*, ≥ 15 yr

Cameroon Burden of

Diabetes Survey 2003

25.6 23.1 (Kamadjeu et al.

2006)

Congo*, ≥ 15 yr WHO STEPwise 2004 15.2 (Longo-Mbenza et

al. 2008)

Tansania*, 35-64 yr Dar es Salaam, 1998 27.1 30.2 (Bovet et al. 2002)

South Africa*,

≥ 15 yr

Demographic and Health

Survey 1998

23.5 25.0 24.4 (Steyn et al. 2001)

The USA*, ≥ 18 yr NHANES 1999-2004 39.1 40.8 (Cutler et al. 2008)

Asian

Chinese, China*,

≥ 18 yr

China National Nutrition

and Health Survey 2002

20.0 17.0 18.0 (Wu et al. 2008)

Chinese, China,

35-74 yr

Qingdao Diabetes Survey

2006 urban

61.2 49.4 (Ning et al. 2009)

Qingdao Diabetes Survey

2006 rural

56.2 50.5

Chinese, Hong Kong,

≥ 15 yr

Hong Kong Population

Health Survey 2003-2004

30.0 25.0 (Martiniuk et al.

2007)

Chinese, Taiwan*,

≥ 19 yr

Nutrition and Health

Survey in Taiwan, 1993-96

28.3 25.3 26.8 (Su et al. 2008)

Chinese, Taiwan*,

≥ 19 yr

Taiwanese Survey on

Hypertension,

Hyperglycemia, and

Hyperlipidemia 2002

27.1 20.2 23.5 (Su et al. 2008)

Japanese, Japan*,

≥ 15 yr

National Nutrition Survey

2000


2007)

Indian, India Pooled studies 25.0 10.0 (Gupta 2004)

Mongolian,

Mongolia, 15-64 yr

WHO NCD Survey 2005 30.0 26.1 28.1 (Bolormaa et al.

2008)

Filipino, Philippines*,

≥ 20 yr

5th National Nutrition

Survey 1998


2007)

Thai, Thailand*,

≥ 15 yr

Third National Health

Examination Survey 2004

23.3 20.9 22.0 (Aekplakorn et al.

2008)

European/Caucasian

Australia, ≥ 25 yr Australian Diabetes,

Obesity, and Lifestyle

28.6 (Briganti et al.

2003)

30

Study 1999-2000

The UK, ≥ 16 yr Health Survey for England

2006

32.0 29.0 30.0 (Falaschetti et al.

2009)

Finland, 25-64 yr FINRISK study 2007 (Kastarinen et al.

2009)

North Karelia 53.4 39.9

Northern Savo 55.3 35.8

South-western Finland 47.3 25.4

Germany, 35-64 yr National Health Survey

1998

60.2 50.4 55.3 (Cooper et al.

2005)

Italy, 35-64 yr National Survey 1998 48.0 35.1 41.5 (Cooper et al.

2005)

Spain*, 35-64 yr National Health Survey

1990

46.2 44.3 45.1 (Banegas et al.

1998)

Spain, ≥ 20 yr Pooled studies 1992-2001 38.0 (Gabriel et al.

2008)

Mixed, Canada*,

20-79 yr

Ontario Survey on

Prevalence and Control of

Hypertension 2006

23.8 19.0 21.3 (Leenen et al.

2008)

Canada, 18-74 yr Canadian Heart Health

Survey 1992

26.0 18.0 22.0 (Joffres et al.

1997)


Mexican

Mexico, 25-64 yr National Health Survey

(ENSA) 2000

33.0 (Barquera et al.

2008)


*age-standardized rates otherwise crude

31

2.3.2 Major risk factors for hypertension

In addition to genes, age and family history (Hunt et al. 1991; Wolf et al. 1997), there are

other lifestyle-related risk factors for hypertension such as excess salt and alcohol intake

(Dahl et al. 1958; INTERSALT Cooperative Research Group 1988; Brown et al. 2009), low

potassium (Bussemaker et al. 2010), smoking and certain dietary factors (Appel et al. 2009),

all of which probably contribute to hypertension. In a recent review, the mean sodium intake

was > 100 mmol/day in most adult populations and > 200 mmol/day in Asian populations,

both of which are far more than the recommended daily dose for salt (Brown et al. 2009). In

European and North American countries, sodium intake dominated primarily from

manufactured food, while in China and Japan, salt added in cooking and soya sauce were the

largest sources. Alcohol intake increased blood pressure in a meta-analysis, in which alcohol

dehydrogenase 2 polymorphism served as a surrogate measure of alcohol consumption (Chen

et al. 2008).

2.3.2.1 Obesity as a major risk factor for hypertension

Obesity is a well-known modifiable risk factor for hypertension (Wolf et al. 1997; Huang et

al. 1998; Mikhail et al. 1999; Brown et al. 2000; Pang et al. 2008). In the large prospective

Nurse’s Health Study, obese women were at higher risk of developing hypertension: 2-fold in

the BMI group of 24.0 - 24.9 kg/m2 and 6-fold in the 31.0 kg/m

2 category than the group with

BMI of < 20.0 kg/m2. Weight loss of 5.0 - 9.9 kg reduced the risk of developing hypertension

by 15% while weight gain of 2.1 - 4.9 kg increased the risk by 29% compared with the group

with stable weight (weight change ≤ 2 kg) (Huang et al. 1998). Similar findings were

observed in Chinese individuals (Pang et al. 2008). The prevalence of hypertension

progressively increased with increasing BMI and age in the WHO MONICA project and the

NHANES III study (Wolf et al. 1997; Brown et al. 2000). Meta-analysis of 25 randomized

intervention trials showed a net weight loss of 5 kg with energy restriction or increased

physical activity or with both systolic blood pressure reduced by 4.4 mm Hg and diastolic

blood pressure by 3.6 mm Hg (Neter et al. 2003), which was consistent with findings from

other intervention trials (Stevens et al. 2001b; Appel et al. 2003; McGuire et al. 2004; Svetkey

et al. 2005; Elmer et al. 2006; Bosworth et al. 2007; Bavikati et al. 2008; Cook et al. 2009).

Follow-up of these intervention studies has shown the sustained effect of lifestyle

interventions on hypertension (Elmer et al. 2006), coronary heart disease and stroke risk

(Cook et al. 2007; Cook et al. 2009).

32

2.3.3 Comparison of BMI with measures of central obesity in relation to hypertension

There is inconsistent evidence on which of the anthropometric measures of obesity (BMI or

central obesity measures) is more strongly associated with hypertension, based on both

prospective and cross-sectional studies. Furthermore, the prevalence of hypertension

decreased or levelled off in some populations, but at the same time obesity has increased. This

paradox needs further investigation, based on longitudinal studies.

A number of studies have compared BMI with WC or WHR in relation to the incidence or

prevalence of hypertension in adults (8 prospective and 21 cross-sectional). None of the

prospective studies used formal statistical tests for their comparisons and the results were

inconsistent: in favour of WC in Brazilian (Fuchs et al. 2005) and African Caribbean

(Nemesure et al. 2008), but in favour of BMI in Greek adults (Panagiotakos et al. 2009) and

Caucasian women from the USA (Shuger et al. 2008), while no difference was found in other

studies (Folsom et al. 2000; Woo et al. 2002; Chuang et al. 2006). Among the cross-sectional

studies, only seven formally tested the difference between BMI and measures of central

obesity in association with hypertension and the findings were also inconsistent: WC or WSR

was significantly better than BMI in Guadeloupean women (Foucan et al. 2002), multi-ethnic

population from the USA (Menke et al. 2007), Chinese men (Zhou et al. 2009) and men in a

meta-analysis (Lee et al. 2008a). However, BMI was significantly better than WHR or other

central obesity measures in Asians (Huxley et al. 2008), Chinese women (Zhou et al. 2009)

and Mexican women (Neufeld et al. 2008), but no differences between these measures were

observed for women in a meta-analysis (Lee et al. 2008a) and Caucasians in the OAC

(Huxley et al. 2008). Similar inconsistencies were also noted in studies in which formal tests

were not done (Kroke et al. 1998; Berber et al. 2001; Okosun et al. 2001; Dalton et al. 2003;

Ito et al. 2003; Esmaillzadeh et al. 2004; Grievink et al. 2004; Sung and Ryu 2004; Thomas et

al. 2004; Wildman et al. 2005; Yalcin et al. 2005; Ghosh and Bandyopadhyay 2007; Wang

2007; Abolfotouh et al. 2008; Huxley et al. 2008; Kaur et al. 2008; Uhernik and Milanovic

2009).

2.4. Obesity in the pathogenesis of diabetes and hypertension Obesity as a major contributor in the pathogenesis of diabetes and hypertension has been

studied intensively in the last decade, but the underlying mechanism is still not clear. Most

obese people are insulin-resistant (Ferrannini et al. 1997) and develop hypertension (Must et

al. 1999). The hypotheses or candidate mechanisms of obesity in the pathogenesis of

33

metabolic disorders were reviewed extensively by our group (Qiao et al. 2007) and are

summarized in Figure 2 and Figure 3.

2.4.1 Obesity and insulin resistance

Both abdominal visceral and subcutaneous fat contribute to obesity-related insulin resistance,

and the data on the relative importance of these two fat depots are conflicting and require

future investigation. A number of studies reported that visceral fat is more detrimental than

other fat depots for type 2 diabetes (Despres et al. 1989; Lebovitz and Banerji 2005; Fox et al.

2007; Kuk et al. 2008; Lee et al. 2008b; Gallagher et al. 2009; Hanley et al. 2009; Ledoux et

al. 2010), due to more metabolic activity (Smith 1985; Trayhurn and Beattie 2001) and high

rates of lipolysis (Smith 1985; Bjorntorp 1990; Arner 2002; Yang et al. 2008). An increased

release of nonesterified fatty acids (NEFAs) from visceral fat through the portal circulation to

the liver (Ferrannini et al. 1983; Bjorntorp 1990; Gastaldelli et al. 2007), as in the portal

hypothesis (Figure 2), increased release of NEFAs into the systemic circulation from

subcutaneous fat (Abate et al. 1995; Abate et al. 1996; Goodpaster et al. 1997; Frayn 2000;

Chandalia et al. 2007) or inflammatory cytokines released from visceral fat (Hyatt et al. 2009)

were considered as the link between visceral obesity and insulin resistance. A recent review

by Taylor (Taylor 2008) suggested that gastric banding (Sjostrom et al. 1999; Carroll et al.

2009) or bypass (Sjostrom et al. 1999; Dixon 2009) surgeries in extremely obese individuals

improved or reversed insulin resistance and diabetes, due to visceral fat reduction (Carroll et

al. 2009), including in dogs (Lottati et al. 2009).

Overexpression of tumour necrosis factor alpha (TNF-α) in adipose tissue of rodents

(Hotamisligil et al. 1993) and humans (Hotamisligil et al. 1996a; Hotamisligil et al. 1996b)

provided the first clear link between obesity, diabetes and chronic inflammation. In the

inflammatory hypothesis (Pickup et al. 1997; Pickup and Crook 1998; Clement et al. 2004;

Ruge et al. 2009), large adipocytes infiltrated by macrophages (Takahashi et al. 2003;

Weisberg et al. 2003; Xu et al. 2003; Lacasa et al. 2007) produced TNF-α and interleukin-6

(IL-6) in obese individuals (Weisberg et al. 2003; Xu et al. 2003). These cytokines increased

lipolysis in adipose tissue (Figure 2) (Goossens 2008), inhibited insulin receptor signalling

through different pathways (Hirosumi et al. 2002; Rask-Madsen et al. 2003; Ozcan et al.

2004; Hotamisligil 2005; Plomgaard et al. 2005; Krogh-Madsen et al. 2006; Bluher et al.

2009; Chavey et al. 2009; Gregor et al. 2009; Monroy et al. 2009), and inhibited a

differentiation of preadipocytes into mature adipocytes (Wu et al. 1999; Gustafson and Smith

2006; Hotamisligil 2006; Rosen and MacDougald 2006; Isakson et al. 2009), all of which

34

finally led to insulin resistance. The role played by these inflammatory cytokines in obesity-

related insulin resistance is still under intensive investigation.

In endocrine hypothesis, adipocytes secrete adipokines such as leptin and adiponectin, which

act at both the local and systemic (endocrine) level (Mohamed-Ali et al. 1998; Trayhurn and

Beattie 2001; Goossens 2008; Karastergiou and Mohamed-Ali 2010). Current evidence for

high leptin (or leptin resistance) and low adiponectin in obesity-related disorders is not clear

and needs further investigation. High leptin levels increased the risk of diabetes in men and

women (Schmidt et al. 2006) or only in men (McNeely et al. 1999; Soderberg et al. 2007) and

the risk was reduced once it was adjusted for body fat and inflammation (Schmidt et al. 2006).

In the endocrine hypothesis, leptin inhibits insulin secretion (Emilsson et al. 1997; Kieffer et

al. 1997; Maedler et al. 2008) or impairs insulin-mediated glucose uptake in obese individuals

(Hennige et al. 2006). Exposure of human islets to high levels of leptin and glucose resulted

in β-cell apoptosis (Maedler et al. 2008), but an experimentally induced hyperinsulinaemia

had no effects on leptin (Ruge et al. 2009).

Adiponectin levels were decreased in diabetes (Hotta et al. 2000; Weyer et al. 2001) and

obesity (Arita et al. 1999; Weyer et al. 2001), due to inhibition of their synthesis by TNF-α

and other cytokines. Low adiponectin showed decreased anti-inflammatory effect (Chandran

et al. 2003; Devaraj et al. 2008; Ouchi and Walsh 2008) and insulin sensitivity in skeletal

muscle (Stefan et al. 2002b), liver (Stefan et al. 2003), and in whole-body in Pima Indians

(Stefan et al. 2002b), and increased hepatic glucose production in mice (Yamauchi et al.

2002). It also inhibited glucose utilization and fatty acid oxidation (Yamauchi et al. 2002;

Chandran et al. 2003; Redinger 2008), but in nondiabetic Pima Indians and Whites, plasma

adiponectin was not associated with fatty acid oxidation under resting conditions (Stefan et al.

2002a).

35

Insulin sensitivity ↓ Insulin resistance Islet β-cell insulin secretion Hyperglycemia Glucose uptake and utilization by the cells ↓ Impaired glucose tolerance Liver gluconeogenesis and glycogenolysis ↑

Portal hypothesis Visceral fat ↑ Blood NFFA flux ↑ Blood triglyceride ↑ Liver VLDL production ↑

HDL-c ↓ Dyslipidemia Small and dense LDL particle ↑ NAFLD Non-adipose tissue lipid deposit ↑ Insulin resistance Adipose lipolysis ↑ FFA ↑ Inflammatory hypothesis Adipose insulin action ↓ c-JNK pathway TNF-α and IL-6 ↑ Ask1-MKK4-MAPK/JNK ↑, Ask Substrate 160 ↓, or TACE/TIMP3 dysregulation Lipid deposition Insulin resistance

CXC 5 Jak2/STAT5/SOCS2 ↑ in non-adipose Low grade inflammation

Preadipocyte differentiation↓ PPARγ and CCAAT/EBPα ↓ Wnt10b signalling ↑ MAP4K4 ↑ Other inflammation mediator factors production ↑ Endocrine hypothesis Adiponectin ↓ Anti-inflammation effect ↓ TNF-α, IL-6, and CRP ↑ Insulin sensitivity ↓ Tyrosine phosphorylation of IR ↓ Glucose utilization↓ FA oxidation ↓ Phosphorylation of IR Hepatic gluconeogenesis ↑ with AMP protein kinase↓ Insulin resistance Leptin ↑ β cells insulin secretion ↓ ATP-sensitive K+ channels ↑ or leptin resistance β cells apoptosis ↑ JNK pathway activation ↑ Glucose uptake ↓ Phosphorylation of IRS-1↓

Figure 2 Role of obesity in insulin resistance or diabetes (Qiao et al. 2007)

36

2.4.2 Obesity and hypertension

The precise role of leptin and insulin as well as the sympathetic nervous system (SNS) needs

to be clarified in obesity-associated hypertension. Activation of the SNS was increased in

obesity (Tuck 1992; Esler 2000; Alvarez et al. 2002). Studies have shown that weight loss

decreased SNS activation, which was correlated with decrease in blood pressure (Sowers et

al. 1982; Tuck et al. 1983; Tuck 1992), but not as a sufficient cause (Esler 2000). The

activation of SNS increased blood pressure by activating the renal SNS to facilitate sodium

reabsorption (Mikhail et al. 1999; Hall 2000; Bogaert and Linas 2009), as shown in Figure 3.

Na+ reabsorption in kidney↑ Antinatriuresis

Hypertension SNS ↑ Leptin secretion ↑ SNS activity ↑ in the kidney and heart Adipose tissue RAS activation ↑ AGT production ↑ Hypertension Angiotensin II ↑ Na+ retention ↑ SNS ↑ Hyperinsulinemia Insulin induced NO production ↓ vasodilatation ↓ Insulin induced endothelium ET-1 production ↑ vasoconstriction↑ AGT-angiontensinogen, ET-1 endothelin-1, NO nitric oxide, RAS-Renin angiotensin system, SNS Sympathetic nervous system

Figure 3 Role of obesity in obesity-associated hypertension (Qiao et al. 2007)

Since 2000, leptin has been considered to increase arterial pressure through activation of the

SNS in kidney and heart (Haynes 2000; Carlyle et al. 2002; Correia and Haynes 2004; Prior et

al. 2010). However, the results from association studies in humans were inconsistent. Some

investigators demonstrated that leptin levels predicted hypertension incidence in Italian males

(Galletti et al. 2008) or were positively associated with blood pressure (Aizawa-Abe et al.

2000; Ma et al. 2009; Nakamura et al. 2009), while no association was found in a Swiss

population (Suter et al. 1998).

37

Addipose tissue derived angiotensin II and angiotensiongen have been considered as possible

links between obesity and hypertension independent of the systemic renin angiontensin

system (Goossens et al. 2003), but the effect of these components on blood pressure in

humans is still not clear (Prat-Larquemin et al. 2004).

Hyperinsulinaemia was closely associated with hypertension (Lucas et al. 1985; Ferrannini et

al. 1987). Insulin has both vasodilative and vasoconstrictive effects on the endothelium (Jonk

et al. 2007), with the net effect depending on these two. Insulin stimulates activation of the

SNS (Anderson et al. 1991; Savage et al. 1998) and enhances sodium retention (DeFronzo

1981; Mikhail et al. 1999; Mikhail and Tuck 2000). However, there have been conflicting

results, since insulin’s vasodilatory effect was blunted in obesity (Laakso et al. 1990; Mikhail

2009), while others (Savage et al. 1998) did find a difference in insulin-mediated forearm

blood flow between two groups of hypertensive individuals in the upper and lower tertiles of

insulin sensitivity.

38

3. AIMS OF THE STUDY

The overall aims of the study were to compare the predictive values of various anthropometric

measures of obesity with undiagnosed or newly diagnosed type 2 diabetes and hypertension,

to identify the cut-off/change point values for BMI and WC in different populations and to

explore ethnic differences in these associations.

The specific objectives of the study were:

1. To compare BMI with central obesity measures in relation to diabetes and

hypertension in populations of Asian origin (I),

2. To compare BMI with WC, WHR and WSR as predictors of hypertension (II) and

diabetes (III) incidence in Mauritius,

3. To evaluate ethnic-specific WC cutoff values for central obesity in the metabolic

syndrome proposed by the IDF in 2005 and its comparison with the NCEP definition

(IV).

4. To explore ethnic differences in the association of undiagnosed diabetes with obesity

within the large DECODA and DECODE studies, using standardized methods (V) and

5. To identify the change point values for BMI and WC for the presence of undiagnosed

diabetes in different ethnic groups within the large DECODA and DECODE studies,

using Bayesian analysis (VI submitted to International Journal of Obesity).

39

4. POPULATIONS AND METHODOLOGY

4.1 Study population

The DECODA/DECODE

(http://ktlwww.ktl.fi/deco/decoda/index.html/http://ktlwww.ktl.fi/deco/decode/index.html)

studies were initiated by the International Diabetes Epidemiology Group and European

Diabetes Epidemiology Group in 1998 and 1997, respectively. They consist of population- or

community-based or large occupational studies from Asia, Europe, Brazil and Mauritius with

63 633 study individuals (Figure 4 and Figure 5), creating one of the largest epidemiological

databases in the world on studies of glucose intolerance, obesity and metabolic syndrome.

Researchers who had carried out epidemiological studies of diabetes and impaired glucose

regulation, using a standard 2-h 75-g oral glucose tolerance test (OGTT) were invited to

participate in the DECODA/ DECODE Studies (Qiao et al. 2000; The DECODA Study Group

2003; The DECODE Study Group 2003). Collaborative data analysis was coordinated in the

National Institute for Health and Welfare and the Department of Public Health, University of

Helsinki, Helsinki, Finland.

According to the specific aims of the articles, various inclusion criteria were set up and

described in each article. Briefly, data from 16 participating cohorts in the DECODA study

with a 9095 men and 11 732 women 35-74 years of age, from seven countries of Asia, were

included in the study population (I). The inclusion criteria for the current data analysis were

1) cohorts with all four anthropometric measures for obesity and 2) data on FPG and 2-h PG

(not for Filipino and Mongolian studies), as well as systolic and diastolic blood pressure. A

total of 14 222 nondiabetic and 1516 diabetic subjects from nine cohorts in the DECODA

study, 25 - 74 years of age, with required variables for metabolic syndrome, comprised the

study population (IV). The study population (V, VI) comprised 25 250 men and 30 788

women, ≥ 30 years of age, from 34 cohorts in the DECODA and DECODE studies of 11

countries in Asia and Europe. The inclusion criteria of the cohorts were as follows 1) cohorts

with anthropometric measures for both BMI and WC (except for Japanese cohorts), 2) data on

both FPG and 2-h PG (for subgroups of individuals in the FINRISK study), 3) individuals 30

years of age or over and 4) population-based studies with random sampling. Individuals with

prior history of diabetes and hypertension were excluded because treatment for diabetes (or

hypertension) and duration of the disease could affect weight and WC.

http://ktlwww.ktl.fi/deco/decoda/index.html

http://ktlwww.ktl.fi/deco/decode/index.html

40

Figure 4 DECODA Study Population

*occupational-study otherwise population-based, for full study names see table 5

D

E

C

O

D

A

BRAZIL

CHINA

JAPAN

INDIA

MONGOLIA

MAURITIUS

PHILIPPINES

São Paulo 1992-93, n = 346, and 86

São Paulo 1999-00, n = 546, and 80 *HKwscvdrf 1991, n = 871, and

69/50 in company/hospital

HKcvrfps 1995, n = 2439, and 38

Beijing Study 1997, n = 1401, and

98

Shunyj Study 1997, n = 1109, and

98

Qingdao 2002, n = 1796, and 83

Qingdao 2006, n = 3230, and 90

Funagata 1990, n = 2506, and 65

Funagata 1995, n = 2034, and 58

Hisayama 1988, n = 2289, and 96

Ojika 1991, 1996, n = 213, and 47

Chennai 1994, n = 1112, and 84

Mongolia 1999, n = 1991, and 94

CUPS 1997, n = 773, and 90

NUDS 2000, n = 7418, and 90

CURES 2004, n = 1600, and 90

Chennai 2006, n = 2634, and 86

Mauritius 1987, n = 2519, and 86



Philippines 2001, n = 3841, and 70

Studies, year, sample size, and response

rate (%)

Countries

41

Figure 5 DECODE Study Population

D

E

C

O

D

E

CYPRUS

FINLAND

ITALY

SWEDEN

SPAIN

NETHERLAND

THE UNITED

KINGDOM

Nicosia Study 2003, n = 946, and

76 FINRISK 1987, n = 2688, and 79

FINRISK 1992, n = 1859, and 77

FINRISK 2002, n = 3583, and 71

Savitaipale 1997, n = 1102, and

77

Cremona 1990, n = 1662, and 58

Viva Study 1996, n = 1938, and

93

MONICA 1986, n = 550, and 82

MONICA 1990, n = 696, and 81

MONICA 1994, n = 883, and 77

MONICA 2004, n = 827, and 76

Hoorn Study 1990, n = 2371, and

71

Ely Study 1990, n = 1108, and 74

Newcastle Heart, n = 766, and 96

Studies, year, sample size, and response

rate (%)

Countries

42

The Mauritius noncommunicable disease surveys

Mauritius is an island in the Indian Ocean, with a population in 1987 of about 1.2 million,

comprising about 70% Mauritian Indians, 2% Chinese and 28% Creoles (predominantly of

African and Malagasy ancestry with some European admixture, referred to as Mauritian

Creoles). Population-based surveys for prevention and control for chronic noncommunicable

disease were conducted in 1987, 1992, and 1998, respectively, using similar study protocols

and procedures (Soderberg et al. 2005; Soderberg et al. 2007). In 1987 at baseline, all adults

25 - 74 years of age living within 10 randomly selected, geographically defined areas of

Mauritius were invited to the survey. In 1992 and 1998, the same clusters were invited for re-

examination, with three new clusters added in 1992. According to the dates of entry into (free

of diabetes or hypertension) and exit (diabetes or hypertension diagnosis) from the study,

participants were divided into four cohorts with follow-up periods of 5, 6 or 11 years,

respectively, as shown in Figure 6.

Figure 6 Mauritius noncommunicable disease surveys

The inclusion criteria for hypertension incidence (II) were 1) individuals with all four

anthropometric indicators for obesity and 2) data on systolic and diastolic blood pressure,

lipids, ethnicity, alcohol use and smoking status. Individuals with known and newly

diagnosed hypertension, CVD, gout, pregnant women and missing data for age, lipids and

43

smoking status at baseline were excluded. A total of 1658 men and 1976 women of Mauritian

Indian and Mauritian Creole ethnicity were included in the analysis for anthropometric

measures of obesity as predictors of hypertension incidence. The inclusion criteria for

diabetes incidence (III) were 1) individuals with all four anthropometric indicators for obesity

and 2) data on FPG and 2-h PG, lipids, blood pressure, alcohol use, smoking status, family

history of diabetes and data on SES. Individuals with known and newly diagnosed diabetes,

CVD, gout, pregnant women and missing data for required variables at baseline were

excluded. A total of 1841 men and 2104 women of Mauritian Indian and Mauritian Creole

ethnicities were included in the analysis for anthropometric measures of obesity as predictors

of diabetes incidence.

4.2 Survey methodology and physical examination

4.2.1 Definition of clinical endpoints in the study

For individuals without previously diagnosed diabetes, undiagnosed or newly

diagnosed diabetes was defined as FPG of ≥ 7.0 mmol/l or 2-h PG of ≥ 11.1 mmol/l,

following a 75-g OGTT, except for Mongolian and Filipino in which only 2-h PG

alone was used (WHO Consultation 1999). Individuals who were free of diabetes in

the previous survey but developed diabetes during the period between the surveys, or

were tested positive in the subsequent survey, were counted as incident cases in the

Mauritius surveys.

Hypertension was defined as systolic and /or diastolic blood pressure ≥ 140/90 mmHg

or self-reported antihypertensive therapy according to the 1999 World Health

Organization-International Society of Hypertension (WHO-ISH) guidelines (WHO

1999). Individuals who were free of hypertension in the previous survey but

developed hypertension during the period between the surveys, or were tested positive

in the subsequent survey, were counted as incident cases in the Mauritius surveys.

The 2005 IDF definition requires central obesity as a mandatory component for

diagnosis of the metabolic syndrome, using ethnicity-specific values plus any two of

the four components below: 1) serum triglyceride ≥ 1.7 mmol/l 2) serum high-density

lipoprotein (HDL) cholesterol < 1.03 mmol/l in men and < 1.29 mmol/l in women 3)

systolic and/or diastolic blood pressure ≥ 130/85 mmHg, or treatment of previously

diagnosed hypertension, regardless of the current blood pressure values and 4) FPG ≥

5.6 mmol/l or previously diagnosed type 2 diabetes (Alberti et al. 2005). IDF central

obesity was defined as WC ≥ 90/80 cm for men/women of Chinese and Asian Indian

44

ethnicities ≥ 85/90 cm for Japanese men/women. The NCEP defines a person as

having metabolic syndrome when three or more of the five components above, but

using different positive cutoffs for FPG ( ≥ 6.1 mmol/l) and WC ( > 102 cm in men

and > 88 cm in women) (Expert Panel on Detection 2001).

4.2.2 Anthropometric measures for obesity and blood pressure measurements

In all DECODA/DECODE studies, anthropometric measures were taken by trained observers

and described in detail for each cohort in Appendix 3. In most of the studies, WC (cm) was

measured at the midpoint between the lower margin of the ribs and the iliac crest, except for a

few studies in which it was measured between the umbilicus and xiphoid process (two Hong

Kong studies and Mauritius87) and at the umbilicus (Cremona, São Paolo, Funagata Diabetes

studies). Hip circumference (cm) was measured over the greater trochanter in most of the

studies and around the buttocks posteriorly and the symphysis pubis anteriorly in the

Mauritius, São Paolo and Hong Kong Workforce studies. Weight and height were measured in

light clothing without shoes.

BMI was calculated as weight in kilograms divided by the square of the height in metre

(kg/m2). WHR was calculated as WC divided by hip circumference. WSR was calculated as

WC divided by the height in centimeters. Blood pressure was measured on the right arm of

the participant, using a standard mercury sphygmomanometer, after the participant had been

sitting for 5 – 10 minutes in all studies, except for São Paolo 99-00 survey, in which it was

measured using an automatic device (Omron model HEM-712C, Omron Healthcare,

Bannockburn, IL, the USA).

In the Mauritius surveys, smoking status, alcohol use, ethnicity, family history of diabetes and

data on SES as income and education levels (school years) were determined by self-reports

from questionnaires.

4.2.3 Laboratory Methods

Blood samples were collected after overnight fasting for measurements of glucose and lipids.

A 2-h 75-g OGTT was performed in all cohorts (except for Mongolians, Filipinos and for

subgroup of individuals in the FINRISK study). PG was measured in most of the studies, with

a few exceptions (Qiao et al. 2000; The DECODE Study Group 2003). Before the data were

analysed, capillary and whole blood glucose were converted into PG according to the

formulae described previously (Carstensen et al. 2008). Oxidase or dehydrogenase methods

for glucose were used in all cohorts. Enzymatic methods for triglyceride and HDL cholesterol

45

were applied in the DECODA study cohorts. Detailed information on glucose and lipid assays

for individual studies is presented in Appendix 3.

4.2.4 Statistical Analysis

The prevalence or incidence of diabetes, hypertension and central obesity was calculated,

using descriptive statistics. Significant differences between Europeans and other ethnic

groups were determined by Chi-squared (χ2)

test for categorical variables and the F test for

other continuous variables. For each obesity indicator, the squared terms were also tested to

check whether the relationship with diabetes or hypertension was curvilinear. Meta-analysis

using the method detailed by Fleiss (1993) was performed, based on the individual data of 16

studies. A fixed effect approach was chosen, since Q statistics for measuring interstudy

variation in effect size were not statistically different from zero for all obesity indicators of

either diabetes or hypertension. Standard logistic regression analysis, adjusting for studies and

age, was performed to estimate the OR for each of the obesity indicators, based on pooled

data, ethnic groups and different subgroups. The hazard ratio (HR) for the development of

diabetes and hypertension for each of the obesity indicators was estimated, applying Cox

proportional hazard models to interval-censored data (Carstensen 1996; Hosmer and

Lemeshow 1999), using age as a timescale. A series of population-based studies in Mauritius

motivated use of interval-censored survival analysis, since the exact date for incidence of

diabetes or hypertension was unknown but was known to lie between the three examinations.

A paired homogeneity test (Wald statistics with 1df) was performed to test the equality of

regression coefficients between BMI and each of the central obesity indicators of WC (BMI =

WC), WHR (BMI = WHR) and WSR (BMI = WSR), using the Car and IC packages in the R-

program in both cross-sectional and prospective data analysis.

Logistic regression analysis, adjusting for age, was also performed to estimate the

coefficient corresponding to sex- and study-specific one standard deviation (SD) increase in

BMI or WC (the slope of the regression line or the strength of the association of diabetes with

BMI or WC) for the presence of diabetes in each ethnic group. The homogeneity test with a

null hypothesis that the coefficients are the same between ethnic groups was performed,

using the method of Fleiss.

46

Hierarchical three-level Bayesian change point analysis, adjusting for age, was applied to

obtain the most likely values for the BMI or WC categories with respect to the changes in

prevalence of undiagnosed diabetes. The model parameters, such as mean change points and

prevalence before and after the change point were obtained, using OpenBugs (Thomas et al.

2006) software and packages BRugs and Coda in the R-program (Ihaka and Gentleman 1996),

with 10 000 burn-in iterations followed by 50 000 iterations. Convergence was assessed using

the Geweke-statistic. ROC curve analysis was performed to determine the optimal cutoff

values for BMI and WC with undiagnosed diabetes. The sensitivity and specificity was

calculated at the optimal cutoff values for BMI and WC. Statistical analyses were carried out,

using SPSS for Windows (versions 14 and 15) and the R-program (version 2.4.1, 2.6.0 and

2.8.1).

47

5. RESULTS

5.1 Comparison of BMI with central obesity measures in relation to diabetes and hypertension, based on cross-sectional (I) and prospective study (II, III)

5.1.1 Characteristics of the DECODA study population

In general, Mongolians had a higher mean BMI of 25 - 26 kg/m2 and WC of 87 - 88 cm than

other ethnic groups (Table 5). The mean WC was 5 cm higher for Chinese, Japanese, Asian

Indian and Mauritian Indian men than for women, but were similar in Mongolians and

Filipinos. The crude prevalence (total) of undiagnosed diabetes ranged from 9.5% to 12.6%

among Asian Indians living in India and Mauritius, from 4.7% to 10.2% in Japanese, Filipinos

and Chinese and about 2.0% in Mongolians. Hypertension was most common among Chinese

(north only), Mongolians, Filipinos and migrant Japanese.

In the Mauritius survey, a total of 787 and 628 incident cases of hypertension and diabetes

developed during the follow-up periods (Table 6). The incidence of hypertension was slightly

higher in Mauritian Creoles than in Mauritian Indians and was similar between men and

women for a given ethnic group. Individuals who developed diabetes or hypertension were

older, more obese, had a higher blood pressure (not for Creole women) and poorer lipid

profile than those who remained nondiabetic or normotensive. Smoking habits were similar

between people who did or did not develop diabetes or hypertension, but were higher in

Mauritian Creole women than in Mauritian Indian women.

48

Table 5 Characteristics of the DECODA study cohorts

Men Women

Mean

age (range)

Number

of

subject

BMI

(kg/m2)

Waist (cm)

DM

(%)

HT

(%)

Mean

age (range)

Number

of

subject

BMI

(kg/m2)

Waist (cm) DM

(%)

HT

(%)

Chinese 52 (30-89)a 4651 25.2 (0.05)

a 86.6 (0.15)

a 10.2

a 51 (30-89)

a 6195 25.4 (0.05)

a 81.5 (0.12)

a 10.2

a

HKcvrfpsb 48(30-74) 1188 24.3 (0.11) 83.5 (0.29) 7.5 15.5 46 (30-74) 1251 24.3 (0.12) 76.6 (0.31) 7.0 11.5

HKwscvdrfc 42 (35-62) 489 23.5 (0.15) 82.2 (0.35) 4.9 21.7 43 (35-63) 382 24.2 (0.20) 77.5 (0.40) 4.2 6.0

Beijing 58(40-88) 548 25.1 (0.16) 88.2 (0.44) 8.0 46.4 57 (40-89) 853 24.9 (0.15) 82.0 (0.37) 8.8 39.8

Qingdao

2002

54(30-74) 670 26.6 (0.14) 89.9 (0.40) 10.9 49.4 53 (30-74) 1126 26.3 (0.13) 83.3 (0.32) 9.1 44.4

Qingdao

2006

49 (30-87) 1311 25.7 (0.09) 87.4 (0.26) 15.2 49 (30-86) 1919 25.8 (0.08) 82.2 (0.21) 13.9

Shunyj 55(40-88) 445 24.4 (0.17) 84.6 (0.48) 4.7 57.9 53 (40-84) 664 25.5 (0.17) 83.4 (0.42) 8.6 50.0

Filipinos 48 (35-65) 1351 23.1 (0.11) 82.9 (0.25) 5.5 58.8 48 (35-65) 2490 23.8 (0.10) 81.8 (0.20) 5.9 42.6

Japanese 58 (34-89)a 3426 23.2 (0.05)

a 81.5 (0.36)

a 6.0 58 (30-86)

a 4508 23.6 (0.05)

a 74.3 (0.33)

a 4.7

Funagata

Study90-92

59 (40-87) 1102 23.5 (0.11) 4.6 60 (40-86) 1404 23.7 (0.12) 5.4

Funagata

Study95-97

58 (35-89) 863 23.6 (0.13) 81.0 (0.35) 4.3 14.2 58 (35-86) 1171 23.6 (0.51) 74.5 (0.32) 3.0 10.7

Hisayama 56 (40-79) 962 22.9 (0.12) 8.8 57 (40-79) 1327 22.8 (0.12) 5.2

Ojika 91,96 57 (34-73) 70 23.1 (0.32) 11.4 52 (30-80) 143 23.3 (0.28) 8.4

Brazil, São

Paulo 92-93

55 (40-74) 174 24.2 (0.25) 85.6 (0.60) 2.3 28.2 56 (37-74) 172 23.9 (0.24) 83.2 (0.60) 2.9 28.5

49

Brazil, São

Paulo 99-00

54 (35-73) 255 25.2 (0.20) 87.5 (0.58) 27.8 51.0 53 (35-73) 291 24.2 (0.19) 78.3 (0.54) 21.3 41.9

Mongolian 46 (35-74) 916 25.1 (0.14) 88.2 (0.30) 2.0 45.0 46 (35-74) 1075 26.4 (0.12) 86.5 (0.29) 2.0 30.3

Asian

Indian

46 (30-

102)a

6176 23.0 (0.05)a 83.7 (0.14)

a 12.6

a 45 (30-99)

a 7361 24.3 (0.06)

a 81.3 (0.14) 12.0

a

Chennai94 45 (30-84) 587 22.4 (0.15) 83.9 (0.42) 8.7 23.5 44 (30-80) 525 23.9 (0.19) 80.8 (0.47) 12.4 25.4

CUPS 1997 46 (30-82) 314 22.0 (0.21) 81.2 (0.57) 7.0 22.9 47 (30-87) 459 23.7 (0.20) 77.5 (0.51) 7.2 20.3

NUDS

2000

46 (30-96) 3431 22.9 (0.07) 82.6 (0.19) 14.4 46 (30-99) 3987 24.2 (0.08) 80.8 (0.19) 13.9

CURES 45 (30-102) 747 23.3 (0.13) 87.7 (0.37) 13.9 21.0 43 (30-80) 853 23.8 (0.15) 84.4 (0.37) 11.5 19.1

Chennai

2006

43 (30-83) 1097 23.7 (0.11) 86.1 (0.31) 9.8 42 (30-85) 1537 25.6 (0.11) 85.4 (0.28) 8.5

Mauritian

Indian

45 (30-82)a 2123 23.5 (0.08)

a 82.2 (0.22)

a 12.5

a 45 (30-79)

a 2382 24.9 (0.10)

a 78.0 (0.24)

a 9.5

a

Mauritius87 45 (30-74) 1191 22.7 (0.11) 78.4 (0.30) 11.7 25.5 45 (30-75) 1328 24.4 (0.12) 75.9 (0.30) 8.4 17.7

Mauritius92 46 (30-72) 613 24.1 (0.15) 87.6 (0.41) 13.1 19.2 46 (30-74) 657 26.4 (0.17) 86.3 (0.42) 11.6 21.4

Mauritius98 42 (30-82) 319 24.4 (0.21) 88.3 (0.57) 14.4 23.4 42 (30-79) 397 25.3 (0.22) 79.0 (0.55) 9.8 17.0

Data are age adjusted mean (SE);

DM and HT denote diabetes and hypertension prevalence; a difference compared to Europeans (p < 0.05) in table 8;

b Hong Kong Cardiovascular Risk Factor Prevalence Study;

c Hong Kong Workforce survey on CVD risk factors.

50

Table 6 Characteristics of the study population at baseline according to hypertension and diabetes status at the end of the follow-up

Ethnicity Mauritian Indian Mauritian Creole Mauritian Indian Mauritian Creole

Non-

hypertension

Hypertension Non-hypertension Hypertension Non-diabetes Diabetes Non-diabetes Diabetes

Men,

numbers

1002 249 296 111 1116 229 419 77

Age (years) 38 (37-38) 43 (41-44) 38 (37-40) 42 (40-44) 38 (37-39) 43 (41-44) 40 (39-41) 43 (40-46)

BMI (kg/m2) 22.6 (22.4-22.8) 23.8 (23.4-24.3) 22.5 (22.1-22.9) 23.5 (22.8-24.1) 22.6 (22.4-22.8) 24.9 (24.4-25.3) 22.7 (22.4-23.0) 25.5 (24.8-26.3)

WC (cm) 78.9 (78.3-79.4) 81.6 (80.5-82.7) 77.1 (76.2-78.1) 80.1 (78.5-81.8) 78.6 (78.1-79.1) 84.3 (83.2-85.4) 77.9 (77.2-78.7) 85.0 (83.2-86.8)

WHR 0.89 (0.88-0.89) 0.91 (0.90-0.91) 0.86 (0.86-0.87) 0.89 (0.88-0.90) 0.89 (0.88-0.89) 0.92 (0.91-0.93) 0.87 (0.87-0.88) 0.91 (0.90-0.92)

WSR 0.48 (0.48-0.48) 0.50 (0.49-0.51) 0.46 (0.46-0.46) 0.49 (0.48-0.50) 0.48 (0.48-0.48) 0.51 (0.51-0.52) 0.47 (0.46-0.47) 0.51 (0.50-0.52)

SBP (mm

Hg)

116 (115-116) 124 (122-125) 119 (118-120) 125 (123-126) 120 (119-121) 126 (124-128) 126 (124-127) 132 (128-135)

DBP (mm

Hg)

72 (72-73) 77 (76-78) 74 (73-75) 78 (77-80) 75 (74-76) 79 (78-81) 78 (77-79) 81 (79-83)

TC (mmol/l) 5.3 (5.2-5.4) 5.4 (5.2-5.7) 5.3 (5.1-5.4) 5.4 (5.2-5.7) 5.3 (5.2-5.4) 5.5 (5.3-5.7) 5.3 (5.2-5.5) 5.5 (5.1-5.8)

TG

(mmol/l)*

1.35 (1.30-1.40) 1.46 (1.36-1.57) 1.15 (1.08-1.23) 1.42 (1.27-1.58) 1.31 (1.26-1.35) 1.83 (1.69-1.98) 1.22 (1.16-1.29) 1.75 (1.52-2.00)

HDL

(mmol/l)

1.25 (1.23-1.27) 1.24 (1.19-1.29) 1.31 (1.27-1.36) 1.28 (1.21-1.35) 1.27 (1.25-1.29) 1.19 (1.14-1.24) 1.34 (1.30-1.38) 1.19 (1.09-1.28)

Smokers % 60.8 59.0 68.9 75.7 58.4 58.8 72.1 74.0

Women,

numbers

1174 296 375 131 1302 223 480 99

Age (years) 38 (37-38) 46 (45-47) 39 (38-40) 45 (43-47) 39 (38-40) 43 (41-44) 41 (40-42) 48 (46-50)

51

BMI (kg/m2) 23.4 (23.1-23.6) 25.9 (25.4-26.5) 24.1 (23.6-24.5) 26.2 (25.3-27.0) 23.5 (23.3-23.8) 26.0 (25.4-26.6) 24.7 (24.3-25.1) 27.0 (26.1-28.0)

WC (cm) 74.1 (73.5-74.6) 79.9 (78.6-81.1) 75.9 (74.9-76.9) 80.4 (78.5-82.3) 74.3 (73.8-74.9) 80.0 (78.4-81.1) 77.5 (76.6-78.4) 82.5 (80.4-84.5)

WHR 0.81 (0.81-0.81) 0.83 (0.83-0.84) 0.81 (0.80-0.82) 0.83 (0.82-0.84) 0.81 (0.81-0.81) 0.84 (0.83-0.84) 0.82 (0.81-0.82) 0.84 (0.83-0.85)

WSR 0.49 (0.49-0.50) 0.54 (0.53-0.55) 0.49 (0.49-0.50) 0.53 (0.52-0.54) 0.50 (0.49-0.50) 0.53 (0.52-0.54) 0.51 (0.50-0.51) 0.54 (0.53.-0.55)

SBP (mm

Hg)

112 (112-113) 123 (122-124) 115 (114-116) 124 (123-126) 117 (116-118) 121 (119-123) 123 (122-125) 126 (123-130)

DBP (mm

Hg)

69 (69-70) 75 (75-76) 71 (70-72) 77 (76-79) 72 (71-72) 73 (72-75) 75 (74-76) 77 (75-79)

TC (mmol/l) 4.9 (4.9-5.0) 5.2 (5.1-5.4) 5.2 (5.1-5.4) 5.6 (5.3-5.8) 5.0 (4.9-5.1) 5.0 (4.9-5.2) 5.4 (5.3-5.5) 5.5 (5.2-5.8)

HDL

(mmol/l)

1.34 (1.32-1.36) 1.29 (1.26-1.33) 1.34 (1.31-1.37) 1.26 (1.21-1.31) 1.35 (1.33-1.37) 1.26 (1.22-1.30) 1.36 (1.32-1.39) 1.25 (1.18-1.31)

Smokers % 2.0 1.7 22.7 11.5 1.9 1.3 17.2 19.2

*geometric mean; Data are age and cohort adjusted mean (95 % CI) and percentage.

52

5.1.2 Comparison of BMI with central obesity measures in relation to type 2 diabetes (I,

III)

The ORs (I) and HRs (III) for BMI, WC, WHR and WSR with diabetes were estimated and

compared, using paired homogeneity tests, based on the pooled data. The results of the

homogeneity tests (BMI with each of the three central obesity measures) showed that the OR

for BMI did not differ from that for WC or WHR, but was lower than that for WSR (p =

0.001) with undiagnosed diabetes in men (Figure 7a). In women the ORs were higher for WC

and WSR than for BMI (both p < 0.05) (Figure 7b). The same results were observed for

individuals < 50 years of age but the ORs for these indicators did not differ for individuals ≥

50 years of age (Figures 7a and 7b). The AUCs were slightly larger for diabetes for WSR of

0.735 (0.748) in men (women) and WC 0.749 (women only) than for BMI of 0.725 (0.742) in

Figure 8a, but their 95% CIs were all overlapped, indicating no differences were observed

between these measures.

The paired homogeneity tests were also performed, based on data in which the studies of the

same ethnic groups were pooled together (Table 7). With prevalent diabetes, the ORs for the

BMI and central obesity indicators differed neither in men nor women for any ethnic group,

except for the men of Filipino and Mauritian Indian ethnicity in which the ORs for WSR were

higher than for BMI. For Filipino women, the ORs for the central obesity indicators were

higher than for BMI for prevalent diabetes.

For diabetes incidence in the Mauritius survey, multivariable (baseline fasting glucose,

cohort, triglyceride, family history of diabetes, blood pressure, and SES) adjusted HRs

corresponding to a 1 SD increase in baseline BMI, WC, WHR and WSR for Mauritian

Indians were 1.49 (1.31 - 1.71), 1.58 (1.38 - 1.81), 1.54 (1.37 - 1.72) and 1.61 (1.41 - 1.84) in

men and 1.33 (1.17 - 1.51), 1.35 (1.19 - 1.53), 1.39 (1.24 - 1.55) and 1.38 (1.21 - 1.57) in

women, respectively. For Mauritian Creoles they were 1.86 (1.51 - 2.30), 2.07 (1.68 - 2.56),

1.92 (1.62 - 2.26) and 2.17 (1.76 - 2.69) in men and 1.29 (1.06 - 1.55), 1.27 (1.04 - 1.55), 1.24

(1.04 - 1.48) and 1.27 (1.04 - 1.55) in women (III). The paired homogeneity tests showed that

there was no difference between BMI and each of the central obesity indicators with diabetes

incidence, as shown in Table 7 (all p > 0.05).

53

Figure 7a Odds ratio (black diamond) and 95% CI (solid line) for diabetes corresponding to 1 SD increase in BMI, WC, WHR,

and WSR in men

WSR

0.2 0.5 1 2 5 1.15 (0.81, 1.64)

2.42 (1.83, 3.19) 1.71 (1.12, 2.63)

1.58 (1.24, 2.02) 0.99 (0.63, 1.56)

1.97 (1.26, 3.09) 1.10 (0.41, 2.95) 2.16 (1.57, 2.98)

1.15 (0.84, 1.58) 1.92 (1.10, 3.37)

1.63 (1.25, 2.12) 1.33 (1.09, 1.62) 1.64 (1.25, 2.15) 1.50 (1.02, 2.21)

2.05 (1.34, 3.13) 1.93 (1.54, 2.42)

1.62 (1.40, 1.87) 2.01 (1.56, 2.59)

1.45 (1.29, 1.63) 1.60 (1.44, 1.77) 1.64 (1.45, 1.84)

1.62 (1.50, 1.75)

WHR

0.5 1 2 5 China, Beijing 0.86 (0.63, 1.18)

China, Hong Kong 1.96 (1.53, 2.52) China, Hong Kong workforce 1.72 (1.18, 2.52)

China, Qing Dao 1.78 (1.36, 2.32) China, Shunyi 1.02 (0.65, 1.59)

Japan, Funagata 1.86 (1.14, 3.02) Brazil, São Paulo92-93 1.45 (0.73, 2.87) Brazil, São Paulo99-00 1.82 (1.35, 2.45)

India, Chennai94 1.12 (0.81, 1.54) India, CUPS 1.64 (0.99, 2.73)

India, CURES 1.50 (1.16, 1.95) Mauritius, Mauritius87 1.20 (0.99, 1.46) Mauritius, Mauritius92 1.66 (1.25, 2.20) Mauritius, Mauritius98 1.74 (1.17, 2.59)

Mongolian National Survey 1.83 (1.19, 2.81) Philippines Diabetes Survey 1.98 (1.56, 2.51)

All Chinese 1.50 (1.31, 1.73) All Japanese 1.78 (1.40, 2.26)

All Indian 1.38 (1.23, 1.55) Aged>= 50 years 1.49 (1.35, 1.66) Aged < 50 years 1.58 (1.40, 1.78)

All men 1.53 (1.41, 1.65)

WC

0.2 0.5 1 2 5 1.10 (0.78, 1.55)

2.33 (1.78, 3.06) 1.67 (1.11, 2.51)

1.53 (1.20, 1.96) 1.07 (0.68, 1.66)

1.73 (1.11, 2.70) 1.25 (0.46, 3.34) 1.95 (1.44, 2.66)

1.10 (0.80, 1.50) 2.24 (1.23, 4.10)

1.62 (1.24, 2.11) 1.23 (1.00, 1.50) 1.39 (1.07, 1.81) 1.48 (1.00, 2.17)

1.94 (1.28, 2.94) 1.86 (1.48, 2.33)

1.58 (1.37, 1.82) 1.83 (1.43, 2.34)

1.36 (1.21, 1.53) 1.53 (1.38, 1.70) 1.55 (1.38, 1.74)

1.54 (1.43, 1.67)

BMI

0.2 0.5 1 2 5 China, Beijing 1.46 (1.05, 2.04)









All men 1.52 (1.41, 1.64)

54

Figure 7b Odds ratio (black diamond) and 95% CI (solid line) for diabetes corresponding to 1 SD increase in BMI, WC, WHR, and WSR in

women

WSR

0.5 1 2 5 1.42 (1.12, 1.79)

2.47 (1.88, 3.25) 2.32 (1.43, 3.77)

1.34 (1.06, 1.69) 1.29 (1.01, 1.65)

2.41 (1.30, 4.46) 1.53 (0.58, 4.06) 2.05 (1.50, 2.80)

1.44 (1.07, 1.92) 1.63 (1.10, 2.43)

1.52 (1.19, 1.94) 1.77 (1.43, 2.19) 1.98 (1.47, 2.66) 2.47 (1.56, 3.91)

1.32 (0.87, 2.01) 1.91 (1.62, 2.25)

1.57 (1.39, 1.76) 2.07 (1.58, 2.70)

1.71 (1.52, 1.92) 1.60 (1.47, 1.75) 1.87 (1.67, 2.10)

1.70 (1.59, 1.83)

WHR

0.5 1 2 5 10 China, Beijing 1.16 (0.96, 1.40)








All Indian 1.49 (1.33, 1.67) Aged>=50 years 1.45 (1.32, 1.58) Aged < 50 years 1.67 (1.50, 1.87)

All women 1.50 (1.40, 1.60)

WC

0.5 1 2 5 10 1.36 (1.08, 1.70)

2.49 (1.91, 3.25) 2.00 (1.27, 3.15)

1.34 (1.08, 1.67) 1.34 (1.04, 1.72)

2.23 (1.24, 4.00) 2.28 (0.82, 6.33) 2.07 (1.51, 2.82)

1.45 (1.08, 1.93) 1.66 (1.11, 2.47)

1.49 (1.17, 1.90) 1.83 (1.48, 2.27) 1.85 (1.39, 2.46) 2.38 (1.51, 3.75)

1.31 (0.87, 1.98) 1.95 (1.65, 2.30)

1.55 (1.38, 1.74) 2.11 (1.62, 2.76)

1.70 (1.51, 1.91) 1.63 (1.49, 1.78) 1.82 (1.63, 2.04)

1.70 (1.58, 1.82)

BMI

0.5 1 2 5 10 China, Beijing 1.35 (1.07, 1.70)









All women 1.59 (1.48, 1.70)

55

a

b

Figure 8 Areas under the curves from ROC curve analysis for BMI, WC, WHR, and WSR in

relation to diabetes (a) and hypertension (b)

1,00,80,60,40,20,0

1 - Specificity

1,0

0,8

0,6

0,4

0,2

0,0

Sen

sit

ivit

y

Men

wsr 0.735 (0.717-0.753)

whr 0.729 (0.711-0.747)

wc 0.729 (0.711-0.747)

bmi 0.725 (0.706-0.743)

1,00,80,60,40,20,0

1 - Specificity

1,0

0,8

0,6

0,4

0,2

0,0

Sen

sit

ivit

y

Women

wsr 0.748 (0.733-0.764)

whr 0.742 (0.727-0.758)

wc 0.749 (0.734-0.765)

bmi 0.742 (0.726-0.756)

1,00,80,60,40,20,0

1 - Specificity

1,0

0,8

0,6

0,4

0,2

0,0

Sen

sit

ivit

y

wsr 0.759 (0.749-0.769)

whr 0.748 (0.737-0.758)

wc 0.760 (0.749-0.770)

bmi 0 760 (0.750-0.771)

Men

1,00,80,60,40,20,0

1 - Specificity

1,0

0,8

0,6

0,4

0,2

0,0

Sen

sit

ivit

y

Women

wsr 0.763 (0.754-0.772)

whr 0.751 (0.742-0.760)

wc 0.764 (0.755.0.773)

bmi 0.766 (0.757-0.775)

56

Table 7 Paired homogeneity test results (p values) between BMI and central obesity indicators

in its association with diabetes and hypertension

Ethnicity Number

of

subjects

Diabetes prevalence Hypertension prevalence

Men 9095 WC WHR WSR WC WHR WSR

Chinese 2980 0.246 0.604 0.081 0.120 < 0.001** 0.048**

Filipino 1351 0.127 0.202 0.013* 0.114 0.002** 0.374

Japanese 1040 0.620 0.623 0.377 0.182 0.046** 0.739

Native Indian 1232 0.783 0.625 0.684 0.976 0.038** 0.926

Mauritian

Indian

1576 0.520 0.615 0.035* 0.074 0.912 0.447

Mongolian 916 0.714 0.568 0.868 0.360 0.002** 0.561

Women 11732

Chinese 3850 0.493 0.123 0.496 0.480 < 0.000** 0.166

Filipino 2490 0.010* 0.011* 0.005* 0.042** 0.009** 0.002**

Japanese 1257 0.701 0.330 0.395 0.141 0.124 0.780

Native Indian 1353 0.123 0.999 0.102 0.733 0.002** 0.873

Mauritian

Indian

1707 0.513 0.274 0.525 0.620 0.240 0.903

Mongolian 1075 0.560 0.801 0.603 0.766 0.016** 0.900

Diabetes incidence Hypertension incidence

Men 1841

Mauritian

Indian

1345 0.207 0.922 0.122 0.796 0.476 0.903

Mauritian

Creole

496 0.283 0.831 0.120 0.073 0.100 0.002*

Women 2104

Mauritian

Indian

1525 0.809 0.519 0.334 0.249 0.047** 0.508

Mauritian

Creole

579 0.697 0.625 0.536 0.120 0.068 0.981

*BMI is weaker and **BMI is stronger

57

5.1.3 Comparison of BMI with central obesity measures in relation to hypertension (I,

II)

The ORs (I) and HRs (II) for BMI, WC, WHR and WSR with hypertension were estimated

and compared, using paired homogeneity tests based on the pooled data. As shown by these

homogeneity tests, the ORs for BMI did not differ from those for WC and WSR with

hypertension, but the ORs for BMI were significantly higher than those for WHR (p < 0.001)

in men (Figure 9a) and were highest for BMI than for WHR (p < 0.001), WSR (p < 0.01) and

WC (p < 0.05) in women (Figure 9b). The AUCs for hypertension were slightly larger for

BMI of 0.760 (0.766) than for WHR of 0.748 (0.751) in Figure 8b, but their 95% CIs were all

overlapped, indicating that there were no differences between these measures.

When the analysis was performed by ethnic groups (Table 7), the ORs for BMI did not differ

from those for WC and WSR, but were higher for BMI than for WHR in both men and

women, regardless of age (Figures 9a and 9b) for most of the ethnic groups, with a few

exceptions. The ORs did not differ between BMI and central obesity indicators in Mauritian

Indians (men and women) and Japanese women. For Filipino women, the OR was highest for

BMI than for the other three central obesity measures with hypertension.

For hypertension incidence, HRs adjusting for baseline systolic blood pressure, smoking, total

cholesterol and cohort, corresponding to a 1 SD increase in BMI, WC, WHR and WSR were

1.20 (1.24), 1.19 (1.21), 1.14 (1.10) and 1.20 (1.26) in Mauritian Indian men (women) and

1.23 (1.32), 1.34 (1.23), 1.41 (1.13) and 1.43 (1.33) in Mauritian Creoles, indicating that all

obesity indicators significantly predicted hypertension incidence, except for WHR in

Mauritian Creole women. The paired homogeneity tests showed that the ORs for BMI did not

differ from those for the other three measures for most of the comparisons, with two

exceptions: the OR for WSR was stronger than that for BMI (p = 0.002) in Mauritian Creole

men, but the OR for BMI was stronger than that for WHR (p = 0.047) in Mauritian Indian

women in predicting incident cases of hypertension (Table 7).

58

Figure 9a Odds ratio (black diamond) and 95% CI (solid line) for hypertension corresponding to 1 SD increase in BMI,

WC, WHR, and WSR in men

WSR

0.5 1 2 5 1.57 (1.28, 1.93)

1.81 (1.49, 2.21) 1.75 (1.37, 2.23)

1.58 (1.32, 1.88) 1.58 (1.28, 1.95)

1.34 (1.05, 1.69) 1.59 (1.11, 2.26) 1.19 (0.93, 1.53)

1.68 (1.33, 2.13) 1.44 (1.03, 2.01)

1.66 (1.32, 2.09) 1.44 (1.23, 1.69) 1.46 (1.15, 1.84) 1.92 (1.31, 2.81)

2.11 (1.81, 2.46) 1.72 (1.52, 1.94)

1.65 (1.50, 1.80) 1.32 (1.13, 1.54)

1.54 (1.40, 1.70) 1.54 (1.43, 1.65) 1.75 (1.63, 1.89)

1.63 (1.55, 1.72)

WHR

0.5 1 2 5 China, Beijing 1.21 (1.00, 1.45)









All men 1.45 (1.39, 1.52)

WC

0.5 1 2 5 1.65 (1.35, 2.01)

1.79 (1.47, 2.18) 1.66 (1.31, 2.10)

1.64 (1.37, 1.96) 1.58 (1.28, 1.93)

1.38 (1.09, 1.75) 1.63 (1.13, 2.34) 1.05 (0.82, 1.35)

1.79 (1.41, 2.28) 1.32 (0.94, 1.85)

1.63 (1.30, 2.05) 1.52 (1.29, 1.78) 1.45 (1.15, 1.83) 1.45 (1.15, 1.83)

2.22 (1.90, 2.60) 1.67 (1.48, 1.88)

1.66 (1.52, 1.82) 1.63 (1.47, 1.81)

1.54 (1.40, 1.68) 1.55 (1.45, 1.66) 1.75 (1.62, 1.88)

1.66 (1.59, 1.74)

BMI

0.5 1 2 5 China, Beijing 1.69 (1.38, 2.08)









All men 1.68 (1.60, 1.77)

59

Figure 9b Odds ratio (black diamond) and 95% CI (solid line) for hypertension corresponding to 1 SD increase in BMI,

WC, WHR, and WSR in women

WSR

0.5 1 2 5 1.93 (1.61, 2.32)

2.02 (1.61, 2.53) 2.12 (1.36, 3.32)

1.68 (1.44, 1.97) 1.62 (1.35, 1.94)

1.20 (0.93, 1.54) 1.53 (1.06, 2.21) 1.29 (1.01, 1.65)

2.19 (1.67, 2.88) 1.38 (1.05, 1.80)

1.36 (1.10, 1.69) 1.34 (1.13, 1.59) 1.67 (1.31, 2.13) 1.62 (1.13, 2.31)

1.62 (1.40, 1.86) 1.31 (1.21, 1.43)

1.79 (1.63, 1.96) 1.29 (1.10, 1.52)

1.51 (1.37, 1.66) 1.44 (1.35. 1.53) 1.60 (1.50, 1.72)

1.50 (1.44, 1.58)

WHR

0.5 1 2 5 China, Beijing 1.36 (1.11, 1.67)









All women 1.28 (1.22, 1.34)

WC

0.5 1 2 5 1.89 (1.58, 2.26)

2.08 (1.67, 2.61) 2.06 (1.34, 3.15)

1.68 (1.45, 1.95) 1.64 (1.36, 1.97)

1.15 (0.91, 1.46) 1.63 (1.13, 2.36) 1.13 (0.89, 1.44)

2.14 (1.64, 2.80) 1.41 (1.08, 1.85)

1.27 (1.03, 1.57) 1.34 (1.13, 1.59) 1.69 (1.33, 2.15) 1.73 (1.20, 2.48)

1.63 (1.42, 1.87) 1.34 (1.23, 1.46)

1.79 (1.64, 1.95) 1.22 (1.04, 1.42)

1.50 (1.36, 1.65) 1.45 (1.37, 1.54) 1.59 (1.49, 1.70)

1.51 (1.44, 1.58)

BMI

0.5 1 2 5 China, Beijing 1.67 (1.42, 1.97)









All women 1.55 (1.48, 1.63)

60

5.2 Prevalence of the metabolic syndrome in populations of Asian origin---Comparison of the IDF definition with the NCEP definition (IV)

5.2.1 Characteristics of the DECODA study cohorts

In this analysis, individuals from nine cohorts in the DECODA study were included. The

mean WC of Japanese was similar to that for Chinese and Indians (Mauritian and Asian

Indians), while Japanese women had a lower mean WC than Japanese men, as in other ethnic

groups. In general, men had higher mean blood pressure, larger mean WC, mean triglyceride

and lower mean HDL cholesterol than women, but women had higher mean 2-h PG than men.

5.2.2 Prevalence of central obesity using the 2005 IDF definition and its comparison with

the NCEP definition

The age-standardized prevalence of central obesity is shown in Figure 10. Application of the

2005 IDF criteria resulted in a higher prevalence of central obesity than when the NCEP

criteria were used, except in Japanese women who had an extremely low prevalence of central

obesity using the 2005 IDF definition. When the same criteria were applied to Japanese as to

Chinese and Asian Indians, the prevalence of central obesity in Japanese was comparable to

that of other ethnic groups in both men and women (Figure 10). The prevalence ratio of IDF

to NCEP was 1.5 (1.5), 2.7 (0.4), 1.2 (1.2) and 1.0 (1.3) in Chinese, Japanese, Mauritian

Indian and native Asian Indian men (women) respectively. The ratios were similar in all,

except for Japanese, in which the ratio was high for Japanese men and low for Japanese

women. When the same obesity criteria for Japanese as for others were used, the ratio for

Japanese was 1.5 in both genders.

61

Figure 10 The age-standardized prevalence of central obesity (%) defined by the NCEP

criteria (black) and the IDF criteria (blank). Using the same waist circumference for

Japanese as for Chinese and Indians (lined). M/Indians represent Indians from Mauritius.

N/Indians represent Indians from India.

62

5.3 Ethnic differences of the association of undiagnosed type 2 diabetes with obesity (V)

5.3.1 Characteristics of the DECODA and DECODE study population

Europeans had a higher mean BMI of 27 kg/m2 and WC of 94 cm (men only) than other

ethnic groups (Tables 5 and 8). The mean BMI did not differ between men and women for a

given ethnic group. The mean WC was higher by about 5 cm for Chinese, Japanese and

Indian men than for women, but about 10 cm higher in European men than in European

women. The crude prevalence (total) of undiagnosed diabetes ranged from 9.5% to 13.2%

among Asian Indians living in India and Mauritius, from 4.7% to 10.2% in Japanese and

Chinese and from 4.7% to 6.4% in Europeans.

5.3.2 Ethnic difference in the strength of association of undiagnosed type 2 diabetes with

BMI and WC

The prevalence of undiagnosed diabetes was highest in Asian Indians, lowest in Europeans

and intermediate in others, given the same BMI (Figure 11) or WC (Figure 12) category

across the BMI or WC ranges. The ethnic differences in prevalence of diabetes at each

category of the BMI or WC were statistically significant (p < 0.05 for all BMI or WC

categories). The coefficients corresponding to a 1 SD increase in BMI were 0.34/0.28,

0.41/0.43, 0.42/0.61, 0.36/0.59 and 0.33/0.49 for Asian Indian, Chinese, Japanese, Mauritian

Indian and European men/women (overall homogeneity test: p > 0.05 in men and p < 0.001 in

women in Figure 13a) and in WC 0.31/0.31, 0.30/0.46, 0.22/0.57 and 0.38/0.58 for Asian

Indians, Chinese, Mauritian Indians and Europeans, respectively (overall homogeneity test: p

> 0.05 in men and p < 0.001 in women in Figure 13b). Asian Indian women had lower

coefficients than women of other ethnic groups.

63

Table 8 Characteristics of the DECODE study cohorts

Men Women

Mean

age (range)

Number

of

subject

BMI

(kg/m2)

Waist (cm)

DM

(%)

Mean

age (range)

Number

of

subject

BMI

(kg/m2)

Waist (cm)

DM

(%)

European 55 (30-83) 9792 27.0 (0.04) 95.4 (0.10) 6.4 55 (30-88) 11 187 26.9 (0.04) 84.5 (0.11) 4.7

Cremona, Italy 57 (40-83) 729 26.7 (0.14) 93.8 (0.38) 3.3 59 (40-88) 933 25.6 (0.14) 84.3 (0.36) 3.4

Viva, Spain 49 (34-69) 880 27.5 (0.12) 94.2 (0.34) 4.3 49 (34-66) 1058 28.1 (0.13) 85.7 (0.33) 3.2

Nicosia, Cyprus 51 (30-80) 463 27.8 (0.17) 98.4 (0.47) 8.4 52 (30-83) 483 26.9 (0.20) 86.9 (0.49) 3.1

Ely Study, UK 54 (40-67) 483 26.2 (0.17) 91.36 (0.46) 8.5 54 (40-69) 625 25.8 (0.17) 77.6 (0.43) 6.1

National FINRISK Study

1987, Finland

54 (44-64) 1261 27.6 (0.10) 95.2 (0.29) 3.1 54 (44-64) 1427 27.7 (0.11) 82.2 (0.29) 3.7


1992, Finland

54 (44-64) 843 27.7 (0.13) 97.7 (0.35) 6.3 54 (44-64) 1016 27.0 (0.14) 82.5 (0.34) 4.1


2002, Finland

59 (45-74) 1645 27.9 (0.09) 97.5 (0.25) 11.7 57 (45-74) 1938 27.5 (0.10) 85.2 (0.25) 6.3

Savitaipale, Finland 53 (42-69) 549 26.2 (0.16) 93.9 (0.43) 7.5 54 (42-67) 553 26.3 (0.18) 84.2 (0.46) 5.6

Hoorn Study, The Netherlands 61 (49-77) 1097 26.3 (0.11) 94.6 (0.31) 7.4 62 (49-77) 1274 26.4 (0.12) 84.3 (0.31) 6.5

Newcastle Heart Project , UK 55 (30-75) 393 26.3 (0.18) 92.6 (0.51) 8.9 55 (30-76) 373 26.3 (0.22) 79.5 (0.56) 6.2

Swedish MONICA1986 47 (30-64) 280 25.5 (0.22) 93.4 (0.61) 2.5 47 (30-64) 270 25.2 (0.26) 84.6 (0.66) 2.6

Swedish MONICA1990 47 (30-64) 330 25.8 (0.20) 92.0 (0.56) 1.2 47 (30-64) 366 25.4 (0.23) 80.8 (0.57) 0.8

Swedish MONICA1994 53 (30-74) 427 26.4 (0.18) 93.7 (0.49) 4.2 52 (30-74) 456 26.0 (0.20) 84.6 (0.51) 4.8

Swedish MONICA 2004 53 (30-75) 412 27.5 (0.18) 96.9 (0.50) 4.4 53 (30-75) 415 26.6 (0.21) 85.9 (0.53) 4.1

Data are age adjusted mean (SE). DM denotes diabetes prevalence.

64

Figure 11 Crude (filled markers) prevalence and estimated (open markers with 95% CIs)

probability of undiagnosed diabetes according to BMI categories by ethnicity

Figure 12 Crude (filled markers) prevalence and estimated (open markers with 95% CIs)

probability of undiagnosed diabetes according to the waist categories by ethnicity

65

Figure 13 (a) β coefficients (95 % CI) for undiagnosed diabetes corresponding to a one SD

increase in BMI (kg/m2) and (b) in waist circumference

66

5.4 Assessment of change points for the presence of undiagnosed type 2 diabetes with BMI and WC in different ethnic groups (VI) There were marked variations in the mean change points of BMI or WC (Table 9) for

detecting undiagnosed diabetes among the ethnic groups. The mean change points were 7 - 8

units higher for BMI and 14 - 20 cm for WC in Europeans than in Asian Indians and

Mauritian Indians (not for BMI). Similar BMI of 24 - 25 kg/m2 change points were detected

in men and women of Chinese, Japanese, and Mauritian Indians. The mean WC change point

was about 15 cm higher in European men than in Chinese men, but there was no difference

among Asian men while the differences were statistically significant among ethnic groups in

all paired comparisons among women.

The change points detected, based on the age-adjusted Bayesian model, were not different

from those based on the unadjusted Bayesian model for age in most situations, except for

BMI in Japanese men (one unit lower) and for WC in European men (2 cm higher) and

European women (4 cm higher). The change points based on the unadjusted Bayesian model

differed from the cutoff values obtained based on the ROC curve approach. The difference in

mean values of BMI and WC, based on ROC and Bayesian change point method, varied from

-1 to +2 kg/m2 for BMI but from +1 to +7 cm for WC. The ROC approach, in general,

produced higher cutoff values than the Bayesian analysis.

67

Table 9 BMI and WC change points based on Bayesian model and the optimal cutoff values using ROC curve analysis and their performance for

screening of undiagnosed diabetes in different ethnic groups

Ethnicities Total

number (DM)

BMI cutoff WC cutoff

Bayesian model-mean change points (95 %

credible intervals)

ROC

curve analysis

Bayesian model-mean change points (95 %

credible Intervals)

ROC

curve analysis

Age-adjusted Un-adjusted Age-adjusted Un-adjusted

Men 25 250 (2282)

Asian Indian 6176 (779) 21.5 (20.2-21.9) 21.3 (19.7-21.9) 22.5 79 (77-82) 80 (77-82) 85

Chinese 4162 (426) 25.6 (24.0-26.9) 25.6 (24.2-26.9) 25.8 84 (82-85) 84 (82-85) 87

Japanese 2997 (181) 24.0 (21.7-29.7) 25.5 (21.6-30.9) 24.1 NA NA NA

Mauritian

Indian

2123 (265) 24.0 (23.0-25.9) 24.6 (23.0-25.9) 24.5 78 (77-80) 78 (77-83) 84

European 9792 (631) 29.5 (29.0-29.9) 29.5 (29.0-29.9) 27.0 99 (95-106) 97 (95-100) 98

Women 30 788 (2409)

Asian Indian 7361 (881) 22.5 (22.0-23.0) 22.3 (18.3-23.7) 23.1 75 (74-76) 75 (74-76) 82

Chinese 5813 (587) 25.2 (23.6-26.9) 24.6 (24.0-26.3) 25.4 81 (79-82) 81 (79-82) 82

Japanese 4045 (192) 25.3 (23.0-29.7) 25.4 (23.1-29.5) 25.3 NA NA NA

Mauritian

Indian

2382 (227) 24.9 (23.6-25.9) 24.7 (23.9-25.9) 25.7 81 (80-82) 81 (80-82) 84

European 11187 (522) 29.4 (28.3-29.9) 28.6 (27.0-29.9) 28.2 89 (86-91) 85 (83-91) 86

68

6 DISCUSSION

6.1 Study design and methodology The strengths of the DECODA and DECODE studies are that individual participant data from

each population rather than aggregate data were used. Most of the studies were population-

based or community-based with random sampling, except for the Hong Kong Workforce

(occupational study) and Hisayama (community-based study). This collaborative analysis

furthered our opportunities to explore ethnic, age and sex differences in diabetes and

hypertension with obesity, based on large sample size for a given ethnic group with more

statistical power than individual studies. Most importantly, undiagnosed or newly diagnosed

diabetes prevalence or incidence was defined by both FPG and standard 2-h PG following 75-

g OGTTs in all studies (not for all individuals in the FINRISK studies) (III, V, VI). We

previously reported that FG criteria alone underestimated the prevalence of diabetes by up to

30% (Qiao et al. 2000). Anthropometric measures were taken by trained observers in all

studies, rather than as self-reports. The sample size sufficient for a given ethnic group in the

DECODA/DECODE studies allowed exclusion of individuals with prior history of diabetes

and hypertension, because treatment and duration of these diseases could affect weight and

WC, as well as glucose and blood pressure levels. Moreover, the early detection should target

undiagnosed diabetes or hypertension.

Two of the six articles in the present study were based on the Mauritius surveys, a series of

population-based prospective studies with random sampling, covering 13 areas and using

similar study protocols in all surveys. The repeated measures of anthropometrics, blood

pressure, glucose, lipids, uric acid and other variables furthered our opportunity to study the

incidence in diabetes, hypertension, obesity and many other risk factors for Mauritian Indians

and Creoles. For statistical analysis, several important approaches were applied in the data

analysis, such as paired homogeneity test and the Bayesian change point analysis (for the first

time), which has not been used widely in this research area.

A limitation of the study was that although the laboratory assays for glucose and lipids, as

well as anthropometric measurements, were similar among most of the studies, there was no

standardization in measurement methods among the different laboratories and studies. In two

studies, WC was measured, using different measurement protocols. Thus, taking into account

these differences, study-, sex-, or ethnic-specific SD was used to standardize continuous

variables in the collaborative data analysis.

69

A further limitation was that the various studies were performed over a long time period from

1986 to 2006. The prevalence of diabetes increased with time and the higher prevalence in

Asians than in Europeans may have been due to the fact that most of the Asian studies were

carried out in the late 1990s and in early 2000. However, in comparing the contemporary

studies, we found that the prevalence of undiagnosed or newly diagnosed diabetes was still

lower in Europeans than in Asians, indicating that the study period did not affect the

prevalence substantially. Most of the Chinese studies were from North China and future

studies need to examine change point values in individuals from South China, considering the

body size differences between North and South Chinese. The small sample size for Filipinos

and Mongolians did not allow us to estimate the change point values for BMI or WC for these

ethnic groups and more studies are needed.

6.2 Comparison of BMI with central obesity measures in relation to undiagnosed diabetes and hypertension Our findings showed that all four anthropometric measures of obesity were significantly

associated with either diabetes or hypertension (I) or predicted hypertension (II) and diabetes

(III) incidence, indicating the importance of obesity in the prevention of type 2 diabetes and

hypertension. In the cross-sectional study (I), the predictive ability between BMI and WC or

WHR did not differ, but WSR (WC in women only) was a better predictor than BMI in

assessing diabetes risk in Asians. Investigators from the OAC study compared the BMI with

WC and WHR, using the homogeneity test as we used with prevalent diabetes for Asians, in

which the major ethnic groups from Asia were also pooled (Huxley et al. 2008). Limiting to

these three measures, our finding for Asian men was different from that reported for Asian

men in the OAC in which WHR was significantly better than BMI for prevalent diabetes. For

Asian women, WC was significantly better than BMI in both OAC and our studies, but with

the difference that WHR was also better than BMI in the OAC study, but not in our study.

However, these two studies differ in that diabetes was defined by both fasting and 2-h glucose

(not for Mongolians and Filipinos) in our study, but fasting glucose alone was used in the

OAC. A meta-analysis was conducted to compare pooled AUCs for BMI with that for WC,

WHR and WSR with diabetes (Lee et al. 2008a). They found that the AUC for WSR was

significantly larger than that for BMI in Asian men, with no difference in Asian women. In

our study, the AUCs for these anthropometric measures did not differ in either men or

women. Furthermore, the predictive ability of the four anthropometric measures with incident

diabetes did not differ in either Mauritian Indians or Mauritian Creoles after multivariable

adjustment (III). These findings were consistent with results of the DPP from the USA

70

(Diabetes Prevention Program Research Group 2006) and meta-analysis (Vazquez et al.

2007). However, our findings were different from that reported for Pima Indians, in which

BMI predicted the diabetes incidence better than WC or WHR (Tulloch-Reid et al. 2003) or

WC or WSR predicted better than BMI for Mexican Americans (Wei et al. 1997), African

Americans (Stevens et al. 2001a) and Iranian women (Hadaegh et al. 2009). A recent study in

British men and women, 60-79 years of age, an over 7-year follow-up, showed that WC was a

stronger predictor of diabetes incidence than BMI and WHR in elderly women, while BMI

and WC were equal predictors in elderly men (Wannamethee et al. 2010).

For assessing hypertension risk (I), there was no difference between BMI and WC or WSR in

Asian men, while in Asian women BMI was a better measure than the central obesity

measures based on pooled data in our study. Our finding that BMI was associated

significantly better than WHR with prevalent hypertension in both men and women (also by

ethnic group) was similar to that reported for Asian men and women in the OAC study

(Huxley et al. 2008). In the meta-analysis no difference was seen among the four measures in

women and WSR was better than BMI in men with prevalent hypertension (Lee et al. 2008a).

Moreover, the predictive ability between BMI and the other three obesity indicators for

hypertension incidence did not differ in Mauritian Indians and Mauritian Creoles during

follow-ups of 5,6 and 11 years (II). Few population-based prospective studies have compared

these anthropometric indicators with incident hypertension and the findings were inconsistent:

in favour of the WC in Brazilians (Fuchs et al. 2005) and African Caribbeans (Nemesure et al.

2008), but in favour of the BMI for Caucasian women from the USA (Shuger et al. 2008) and

Greek adults (Panagiotakos et al. 2009) and no difference was found in other studies (Folsom

et al. 2000; Woo et al. 2002; Chuang et al. 2006). The reasons for these inconsistent findings

are not clear and further research is needed, using an appropriate statistical analysis based on

large prospective studies in different populations at the community level with a further

emphasis on age effect.

6.3 Role of central obesity in the metabolic syndrome Asian women, particularly elderly women, had a higher prevalence of the metabolic

syndrome than Asian men (IV). This, however, did not apply to Japanese, using the 2005 IDF

definition, in which obesity for Japanese was defined differently from their Asian

counterparts. The 2005 IDF definition did not detect leaner subjects with hypertension and

dyslipidemia. It brought a dramatic rise in the prevalence of central obesity that compared

with the NCEP definition in Chinese and Indians, was 7 – 16 times higher in men and 2 - 3

71

times higher in women. However, this resulted in 52 times higher prevalence in Japanese

men, but in Japanese women the IDF criteria gave a dramatically lower prevalence. This

indicates that the cutoff values for central obesity have been set inappropriately for Japanese.

Our study, among few others strongly indicated that the 2005 IDF criteria for central obesity

for Japanese needed reconsideration. The 2005 IDF criteria for central obesity for Japanese

were corrected in 2006 (Alberti et al. 2006) and central obesity is not mandatory component

in the 2009 IDF definition of the metabolic syndrome (Alberti et al. 2009).

6.4 Ethnic differences in the association of diabetes with obesity The results showed that the prevalence of undiagnosed diabetes increased with increasing

BMI or WC to a similar degree in men but to a lesser degree in Asian Indian women than in

women of other ethnic groups (V). At the same BMI or WC level the prevalence of diabetes

was, however, highest in Asian Indians, and lowest in Europeans and intermediate in Chinese,

Japanese and Mauritian Indians.

The higher prevalence of diabetes at the same BMI or WC level in Asians (or South Asians)

than in Europeans in our study is in agreement with previous reports (McBean et al. 2004;

Yoon et al. 2006; Huxley et al. 2008; Stevens et al. 2008; Stommel and Schoenborn 2010).

The reasons for these ethnic differences remain unknown and may likely be attributed to both

genetic and nongenetic factors (Yoon et al. 2006; Wulan et al. 2010). The increase in type 2

diabetes in Asia differed from that reported in other parts of the world i.e. it has increased in a

shorter time and the onset occurs in younger age groups, and in people with a much lower

BMI (Yoon et al. 2006; Ramachandran et al. 2010).

The finding that the weaker association of undiagnosed diabetes with BMI or WC in Asian

Indian women compared with women of other ethnic groups despite their higher prevalence,

suggests that there may be other more important biological factors rather than obesity that

predispose them to higher risk for diabetes. However, these findings differed from those in

previous reports, since the strength of the association of diabetes with BMI or WC was

stronger in Chinese than in American Whites (Stevens et al. 2008) or in Asians than in

Australasians (women only) in the OAC study (Huxley et al. 2008).

Plasminogen activator inhibitor 1 is inversely associated with the glucose disposal rate in

Asian Indians but not in Caucasians (Raji et al. 2001). BMI- and age-matched apparently

healthy Asian Indians were more insulin- resistant, had higher insulin levels, poorer lipid

profiles, and significantly greater total abdominal and visceral fat than Caucasians. All these

can increase the risk of diabetes and contribute to the higher prevalence of diabetes. Surrogate

72

obesity measures, such as BMI or WC may not be sensitive measures for visceral adiposity in

this ethnic group. For migrant Asian Indians in the USA, it was demonstrated that excessive

insulin resistance is the likely mechanism for the excess prevalence of diabetes in Asian

Indians. This was independent of obesity and fat distribution (Abate and Chandalia 2007),

which were due to higher truncal subcutaneous adipose tissue and large dysfunctional

adipocytes (high NEFA and low adiponectin levels) rather than excess visceral fat in

nondiabetic South Asian men compared with Caucasian men (Chandalia et al. 2007). It was

also reported that the ectonucleotide pyrophosphatase/phosphodiesterase 1 121Q gene

explained almost entirely the ethnic difference in insulin resistance between Caucasians and

Asian Indians living in Dallas, Texas (Abate et al. 2003) or India (Abate et al. 2005). A recent

report suggests that the Pro12Ala polymorphism of the pyroxisome proliferator-activated

receptor gamma gene, which protects against type 2 diabetes and insulin resistance in

Europeans, was not protective in Asian Indians. Thus, genetic differences could, at least in

part, be the ‘Asian Indian Phenotype’ of increased susceptibility to type 2 diabetes (Radha et

al. 2006). In a recent review, the higher risk of metabolic diseases at lower degree of obesity

in Asians compared with Europeans, was partly due to unfavourable body composition

(higher body fat percentage, lower lean skeletal muscle and lower gynoid fat) that was more

pronounced in Asian Indians, followed by Southeast Asians (Malays), and East Asians as

Chinese or Japanese. The same phenomenon was already noted in Asian Indian adolescents

(Wulan et al. 2010). In general, Asians tend to store more fat in the abdominal regions.

6.5 Assessment of change points for the presence of undiagnosed diabetes for BMI and WC in different ethnic groups The change points detected for BMI and WC for undiagnosed diabetes, using Bayesian

analysis, varied among ethnicities, which confirms previous findings. The highest BMI and

WC change points were found for Europeans, followed by Chinese and Japanese, and the

lowest values for Asian and Mauritian Indians for undiagnosed diabetes with no substantial

effect of age.

Our results, using the ROC approach were similar to previous reports for a given ethnic

group. The optimal cutoff values for BMI ranged from 22 - 23 for Asian Indians (Snehalatha

et al. 2003; Mohan et al. 2007), 23 - 24 for Chinese (Zhou 2002; Li et al. 2008) and Japanese

(Ito et al. 2003) and 27 - 28 for Europeans in Germany (Stevens et al. 2001a; Diaz et al. 2007;

Schneider et al. 2007). The optimal WC cutoff values were 85/73 - 80 for Chinese (Ho et al.

2003; Li et al. 2008), Japanese (Ito et al. 2003) and Asian Indian (Snehalatha et al. 2003)

73

men/women. For White men, it ranged from 97 - 99 cm (Balkau et al. 2006; Huxley et al.

2007) to 101 - 103 cm in the USA (Stevens et al. 2001a), Germany (Schneider et al. 2007),

and the UK (Diaz et al. 2007) and 106 cm in the USA (Diaz et al. 2007), while they were 85,

91 - 94, and 95 - 96 cm, respectively, in women.

For the first time, Bayesian change point analysis was used to detect a change point that does

not attempt to maximize sensitivity and specificity, as does the ROC curve analysis. The merit

of the Bayesian analysis model is that it can be easily adjusted for other covariates and

statistical power is gained by incorporating various ethnic groups in the same hierarchical

analysis. Moreover, the Bayesian change point is determined according to the changes in

prevalence of diabetes. Nevertheless, it is worth noting that in spite of the differences in

statistical methods, the variations between ethnicities were consistent.

74

7 IMPLICATIONS OF THE STUDY FINDINGS

The present study highlights that obesity, as measured by surrogate anthropometric measures,

is a risk factor for type 2 diabetes and hypertension in the ethnic groups studied. The

beneficial effect of weight reduction with other lifestyle factors has been acknowledged in

different populations. Although, most previous studies have shown that central obesity, as

measured by WC was a better predictor for diabetes or hypertension than general obesity, our

study found no difference between BMI and WC in their predictive ability. Thus, both can be

used as screening tools at the community level in men and women of Asian Indian, Chinese,

Japanese (only BMI studied), Mauritian Indian, and Mongolian ethnicities. This finding is

important, because the measurement error is less with BMI than with WC and the measure for

BMI is easy to standardize, thus the results with BMI are easy to compare between different

populations throughout the world. But research, based on large prospective studies, is still

required to confirm the findings.

With regard to the change point values for BMI and WC, our study supports previous findings

that the use of ethnic- and sex-specific values in different populations (Lear et al. 2007) is

appropriate, although a distinct approach (Bayesian) rather than ROC curve analysis, was

used. Further research is needed to clarify which of these two methods is better for different

populations. It would be worthwhile to investigate the future risk of diabetes or hypertension

associated with the same WC or BMI level by age-group, using the Bayesian approach in

other populations. Whether the increased susceptibility to diabetes in Asians at the same BMI

or WC level, compared with Europeans in our study, is due to increased genetic

predisposition, changes in diet and lifestyle or the interplay of both remains unclear.

Therefore, longitudinal studies are needed to clarify the mechanism by which body

composition, body fat distribution, or other genetic, social, cultural, and behavioural factors

predispose Asians to metabolic diseases.

75

8 CONCLUSIONS

1. Obesity, as measured by surrogate anthropometric measures for general and central obesity,

is an important risk factor for diabetes and hypertension in all ethnic groups. This highlights

the importance of obesity in prevention of diabetes and hypertension.

2. Both BMI and WC can be used as screening tools at the community level since they behave

similarly in their predictive ability for risk assessment for undiagnosed or newly diagnosed

diabetes, based on both cross-sectional and prospective analysis.

3. For hypertension, BMI was as strong an indicator as that for central obesity measures or

was an even better measure than WHR in most of the ethnic groups studied.

4. The prevalence of newly diagnosed diabetes increased with increasing BMI or WC to a

similar degree in men, but to a lesser degree in Asian Indian women than in others. At the

same BMI or WC levels, however, the prevalence of diabetes was highest in Asian Indians,

intermediate in Chinese, Japanese, and Mauritian Indians and lowest in Europeans.

5. Ethnic- and sex-specific cutoff/change points of BMI or WC should be considered in

setting diagnostic criteria for obesity, based on the association of newly diagnosed diabetes

with BMI or WC.

76

9. ACKNOWLEDGEMENTS

This study was carried out at the Department of Public Health, Hjelt Institute, University of

Helsinki and Diabetes Prevention Unit, Department of Chronic Disease Prevention, National

Institute for Health and Welfare (THL), Finland from 2005 to 2010. I wish to thank both

institutes for providing me with excellent research facilities. A Study grant by the Doctoral

Programmes in Public Health and the Chancellor’s travel grants by the University of Helsinki

are gratefully acknowledged.

I would like to express my deepest gratitude to my principal supervisor Doc Qing Qiao,

Academy Research Fellow. Qing, without your expert guidance, crystal-clear ideas, honest

attitude to research and science, critical and quick comments, and constant interest, this work

would not have been completed. The idea of investigating obesity with chronic disorders in

diverse populations was also yours. You also taught me the basics for biostatistics and use of

different software, all of which were important at the beginning stage of my PhD study. I

would also thank you for providing me research grants throughout study period. I am proud of

being your student and friend.

I would like to express my profuse gratitude to my other supervisor Professor Jaakko

Tuomilehto, for his great knowledge, constructive guidance, enormous experience in

epidemiology and sustained interest in my work during the years. I would also thank you for

your timely help in preparing the popular abstract of this thesis in Finnish. Being your student

has been a privilege for me.

I am truly grateful to my official reviewers Adjunct Professor Satu Männistö at the

Department of Chronic Disease Epidemiology and Prevention Unit, National Institute for

Health and Welfare, Helsinki, Finland and Professor Jaap Seidell at the Nutrition and Health

Department, Institute of Health Sciences, Vrije University (VU) Amsterdam, the Netherlands,

for their time, careful evaluation and valuable comments on improving the manuscript.

I would like to thank James Thompson for the language revision of this thesis and Professor

Stephen Colagiuri for being my opponent.

77

I owe my deep gratitude to the DECODA and DECODE investigators and all the coauthors of

the manuscripts for their constant interest, significant contribution and prompt responses, all

of which improved the manuscripts enormously.

My sincere thanks are extended to my colleagues and friends Weiguo Gao, Hairong Nan, and

Lei Zhang for sharing with me teamwork in the research area. I always felt safe to travel and

spend time with all of you during conferences and postgraduate seminars.

I am grateful to Janne Pitkäniemi for his effort in advanced statistical analysis and use of

different sophisticated software to facilitate the analysis. I greatly appreciate Pirjo

Saastamoinen, Pirkko Särkijärvi, Sirkka Koskinen and other researchers from the KTTL for

their help with practical matters. I would also thank my room mates and other PhD students

Xin Song, Feng Ning, and Leonie Bogl for their help during my study.

I am deeply indebted to my Mum, Dad, little brother Yunden, sisters Byambaa, Sandui, and

Sanaa and their families for their endless love and support. My special thanks go to Battsetseg

Tseveenjav for her encouragement and efforts to study at the University of Helsinki and to

Adiya, Pochu, and Mimo for their unlimited help and companionship in our family life in

Finland. I would like to thank warmly our friends Vesa Tuovinen, Tarja Olenius, Kirsi

Lassila, Seppo Puttonen, Mika Virala, Miia Kauppinen and their children for their friendship.

I dedicate this work with my love to my dear husband Batzorig Tseveenjav for his incredible

support and to my daughters Soko and Seke for their patience during all these years of my

study.

Helsinki, September 2010

Regzedmaa Nyamdorj

78

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Appendix 1 The most recent adult prevalence (%) of obesity (BMI ≥ 30 kg/m2) measured by trained observers

Women

Men

0

10

20

30

40

10

20

30

40

50

100

Appendix 2 Secular trend in prevalence of obesity in different regions

Region Prevalence (%) Methodology Reference

Men Women

The USA

1999-2000 27.5 33.4 NHANES cross studies (Flegal et al. 2010)

2001-2002 27.8 33.3

2003-2004 31.1 33.2

2005-2006 33.3 35.3

2007-2008 32.2 35.5

Mexico

2000 19.4 29.0 Mexican National Health Survey

(South)

(Sanches-Castillo et al.

2003)

2006 24.2 34.5 National Health and Nutrition Survey (Malina et al. 2007)

Brazil

1975 2.7 7.4 National Study on Family

Expenditure

(Monteiro et al. 2007)

1989 5.1 12.4 National Survey on Health and

Nutrition

2003 8.8 13.0 National Household Budget Survey

The UK

1993 13.6 16.9 Health Survey in England annually (Zaninotto et al. 2009)

1994 14.4 17.7

1995 15.6 18.0

1996 16.9 19.2

1997 17.0 20.4

1998 17.6 21.7

1999 19.3 21.6

2000 21.5 21.8

2001 21.3 24.1

2002 22.5 23.7

2003 23.2 24.2

2004 24.0 24.4

Spain (Girona) Population-based cross-sectional

surveys

(Schroder et al. 2007)

1995 15.4 15.4

2000 21.9 21.4

Sweden WHO MONICA and INTERGENE

cohorts

(Berg et al. 2005)

1985 6.4 7.2

1990 9.1 9.8

101

1995 11.5 9.8

2002 14.8 11.0

China (Wang et al. 2007)

1992 4.9 U 7.5 U China National Nutrition Survey

1992 and 2002

1.6 R 2.5 R

2002 8.7 U 8.0 U

3.9 R 5.2 R

India

1993-1996 6.2 7.3 The Five City Study Group (urban) (Singh et al. 2007)

1992-1993 National Family Health surveys (Wang et al. 2009)

1998-1999 2.2

2005-2006 2.8

Mauritius

Mauritian

Indian

Mauritius non-communicable disease

surveys

(Hodge et al. 1996)

1987 3.1 10.1

1992 4.9 14.2

Mauritian

Creoles

1987 3.4 12.4

1992 7.5 20.2

Mongolia

1999 13.8 24.6 National Survey (Suvd et al. 2002)

2005 7.2 12.5 Mongolian NCD Risk Factor Survey (Bolormaa et al. 2008)

Thailand

1991 1.6 5.3 National Health Examination

Surveys

(Aekplakorn and Mo-Suwan

2009)

1997 3.9 7.6

2004 4.7 9.1

U and R represent urban and rural

102

Appendix 3 Methodologies used in each study Countries

and studies

Blood pressure Fasting and

2-hour glucose

Blood sample Waist circumference Hip

China Beijing Study Sitting position, on the right

arm, with a standard mercury

sphygmomanometer

Glucose oxidase

method, (Hitachi 7170

Auto analyzer)

plasma At the minimum

circumference between

the rib cage and iliac crest

Maximum circumference

over the

greater trochanters

Hong Kong

Cardiovascular Risk

Factor Prevalence Study

Sitting position, on the right

arm, after 5 min rest with

standard mercury

sphygmomanometer

Hexokinase method

(Hitachi 747 analyser

with Boehringer

Mannheim, Germany)

plasma Half way between

the xiphisternym

and the umbilicus

At the level of greater

trochanters

Hong Kong Workforce

survey on CVD

Risk Factors


arm, after 5 min rest with

standard mercury

sphygmomanometer

Glucose oxidase

method (Diagnostic Chemicals

reagent kit, Canada)

plasma Minimum circumference

between the umbilicus and

xiphoid process


of the buttocks

posteriorly

and symphysis pubis

anteriorly

Qingdao Diabetes

Survey 2002

On the right arm, sitting

position?

Glucose oxidase

method (AMS Analyser

Medical System,

SABA-18, Italy)

plasma At the minimal abdominal

girth between the rib cage

and iliac crest

Maximum horizontal

girth between the waist

and thigh

Qindao Diabetes

Survey 2006

NA Glucose oxidase

method (AMS Analyser

Medical System,

SABA-18, Italy)

plasma At the minimal abdominal

girth between the rib cage

and iliac crest

NA

Shunyi Study In the sitting position, on the

right arm, with a standard

mercury sphygmomanometer

Glucose oxidase

method, (Hitachi 7170

Auto analyzer)

plasma At the minimum


the rib cage and iliac crest


over the

greater trochanters

Philippine

The Second

Philippines National

Diabetes Survey

Mercury type

sphygmomanometer,

after 15-20 min rest, average

of two measurements were

taken

Glucose oxidase

method, Semi-automated dry

chemistry analyzer Reflotron

IV (Boehringer Mannheim,

distributed by

Roche, (Philippines) Inc

capillary glucose After at least 8

hours fasting,

midway

between the lowest

rib margin and

the iliac crest

After at least 8 hours

fasting, maximum

circumference over

the buttocks

Japan

Funagata Diabetes

Survey90-92

NA Glucose oxidase method plasma NA NA

103

Funagata Diabetes

Survey95-97

Sitting position, after 5 min

rest using a mercury

sphygmomanometer

Glucose oxidase

method, (GA1160, Arkray,

Kyoto)

plasma At umbilicus level At the level of greater

trochanters

Hisayama Study

NA Glucose oxidase method

using Glucoroder-MK2

(A & T Inc., Tokyo, Japan)

plasma NA NA

Ojika91 and 96 NA Glucokinase;

GPH/Hitachi7250

plasma NA NA

Brazil

São Paulo92-93 After 5 min sitting

with standard


Glucose oxidase method plasma At umbilicus


of the buttocks

posteriorly and the

symphysis pubis

anteriorly

São Paulo99-00 After 5 min sitting

using automatic device

(Omron model HEM-712C,

Omron Healthcare, Inc, USA)

Glucose oxidase method plasma At umbilicus Maximum circumference

of the buttocks

posteriorly and the

symphysis pubis

anteriorly

India

Chennai 94 Sitting position, on the right

arm, with


Glucose oxidase-Peroxidase

(Hitachi 704 Autoanalyser,

and Boehringer Mannheim,

Germany reagents)

plasma The smallest girth between

the costal margin and iliac

crests

Circumference

at the level of the

greater trochanters

Chennai Urban

Population

Study (CUPS) 1997


arm, with


GOD-POD method, with kit

(Boehringer Mannheim,

Germany ) and Ciba Corning

Express Plus Autoanalyser

(Corning, Medfield, MA,

USA)

plasma Midpoint between the iliac

crest and the lower margin

of the rib

Widest portion of the hip

over the greater

trochanter

NUDS 2000 NA Glucose oxidase

method, Glucometer - Johnson

& Johnson,

capillary The smallest girth between

the coastal margin

and iliac crests

NA

Chennai Urban

Rural Epidemiological

Study (CURES) 2004


arm, with


Glucose oxidase-peroxidase

method (Hitachi 912

Autoanalyser (Hitachi,

Mannheim, Germany)

plasma Smallest horizontal

girth between the

costal margins and

the iliac crests at

minimal respiration

Widest portion of the hip

over the

greater trochanter

Chennai Study 2006 NA Glucose oxidase-peroxidase plasma The smallest girth between NA

104

method (Hitachi 912

Autoanalyser (Hitachi,

Mannheim, Germany)

the coastal margin and

iliac crests

Mauritius

Mauritius 87 Sitting position, after 5 min

rest with standard


Yellow Springs Instruments

(YSI) glucose analyzer, OH,

USA

plasma Minimum horizontal


the umbilicus and

the xiphoid process


of the buttocks

posteriorly and

the symphysis

pubis anteriorly


rest with standard




USA

plasma Midpoint between

the iliac crest and

the lower rib margin


of the buttocks

posteriorly and

the symphysis

pubis anteriorly


rest with standard




USA


the iliac crest and



of the buttocks

posteriorly and

the symphysis

pubis anteriorly

Mongolia

National Survey 1999 After 10 min rest, in sitting

position, on the right

arm, using a standard mercury

sphygmomanometer

(Braunmanometer, England)

Glucose dehydrogenase

method, HemoCue

blood glucose

analyzer (HemoCue

AB, Ängelholm, Sweden)

the whole

blood

Midpoint between

the iliac crest and



over the

greater trochanters

Cyprus,

Nicosia

Diabetes Study

NA Hexokinase/Cobas

Mira Plus Roche

the whole

blood

Midway between

the lower rib margin and

the iliac crest

NA

Finland

The National FINRISK

Study 1987

NA Glucose dehydrogenase the whole

blood

Midway between


iliac crest

NA


Study 1992

NA Glucose dehydrogenase plasma Midway between


iliac crest

NA


Study 2002

NA Hexokinase assay

(Thermo Electron Oy)

plasma Midway between


and iliac crest

NA

105

Savitaipale study NA Glucose

dehydrogenase (Hemocue)

the whole

blood

Midway between the

lower rib margin and

iliac crest

NA

Italy

Cremona Study NA GOD-PAP glucose

oxidase (Boehringer

Mannheim, Milan, Italy)

plasma At umbilicus NA

The Netherlands

Hoorn Study NA Glucose dehydrogenase

(Merck,

Darmstadt, Germany)



and iliac crest

NA

Spain

Viva study NA Hexokinase method,

HITACHI



and iliac crest

NA

Sweden

Swedish MONICA

1986

NA Glucose oxidase

(Beckman analyzer)



iliac crest

NA

Swedish MONICA

1990

NA Glucose oxidase

(Beckman analyzer)



iliac crest

NA

Swedish MONICA

1994

NA Glucose oxidase

(Beckman analyzer)

plasma Midway between the

lower rib margin and

iliac crest

NA

Swedish MONICA

2004

NA Glucose oxidase

(Beckman analyzer)



iliac crest

NA

United Kingdom,

Ely Study

NA Hexokinase assay plasma Midway between


iliac crest

NA

Newcastle Study NA Glucose oxidase

(Hitachi 717 analyser)


the lower costal

margin and the

superior iliac crest

NA

NA means not included in the respective analysis.

anthropometric measures of obesity-their association with

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