anthropometric measures of obesity-their association with
TRANSCRIPT
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
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
Health and Welfare, Helsinki, Finland
Reviewed by:
Adjunct Professor Satu Männistö, Academy Research Fellow, PhD
Department of Chronic Disease Epidemiology and Prevention Unit, National Institute for
Health and Welfare, Helsinki, Finland
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
28.4 18.0 (Martiniuk et al.
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
30.0 24.0 (Martiniuk et al.
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)
The USA*, ≥ 18 yr NHANES 1999-2004 27.5 26.9 (Cutler et al. 2008)
Mexican
Mexico, 25-64 yr National Health Survey
(ENSA) 2000
33.0 (Barquera et al.
2008)
The USA*, ≥ 18 yr NHANES 1999-2004 26.2 27.5 (Cutler 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.
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
Mauritius 1992, n = 1270, and 90
Mauritius 1998, n = 716, and 87
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)
China, Hong Kong 2.12 (1.63, 2.76) China, Hong Kong workforce 1.54 (1.04, 2.28)
China, Qing Dao 1.34 (1.05, 1.70) China, Shunyi 0.96 (0.60, 1.53)
Japan, Funagata 1.70 (1.10, 2.63) Brazil, São Paulo92-93 0.93 (0.31, 2.72) Brazil, São Paulo99-00 2.18 (1.57, 3.03)
India, Chennai94 1.21 (0.88, 1.64) India, CUPS 1.65 (0.99, 2.76)
India, CURES 1.54 (1.20, 1.99) Mauritius, Mauritius87 1.30 (1.06, 1.58) Mauritius, Mauritius92 1.39 (1.07, 1.81) Mauritius, Mauritius98 1.39 (0.95, 2.04)
Mongolian National Survey 1.95 (1.31, 2.89) Philippines Diabetes Survey 1.71 (1.37, 2.12)
All Chinese 1.53 (1.33, 1.75) All Japanese 1.91 (1.48, 2.47)
All Indian 1.37 (1.23, 1.54) Aged>= 50 years 1.52 (1.37, 1.69) Aged < 50 years 1.47 (1.32, 1.65)
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)
China, Hong Kong 1.72 (1.39, 2.13) China, Hong Kong workforce 1.99 (1.24, 3.20)
China, Qing Dao 1.34 (1.09, 1.65) China, Shunyi 1.09 (0.88, 1.35)
Japan, Funagata 2.25 (1.26, 4.01) Brazil, São Paulo92-93 2.46 (0.77, 7.82) Brazil, São Paulo99-00 1.73 (1.27, 2.35)
India, Chennai94 1.61 (1.19, 2.17) India, CUPS 1.18 (0.82, 1.70)
India, CURES 1.30 (1.04, 1.62) Mauritius, Mauritius87 1.80 (1.44, 2.24) Mauritius, Mauritius92 1.52 (1.14, 2.03) Mauritius, Mauritius98 1.46 (1.01, 2.11)
Mongolian National Survey 1.33 (0.88, 2.02) Philippines Diabetes Survey 2.14 (1.78, 2.58)
All Chinese 1.32 (1.19, 1.46) All Japanese 1.86 (1.43, 2.42)
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)
China, Hong Kong 2.07 (1.64, 2.60) China, Hong Kong workforce 1.86 (1.20, 2.88)
China, Qing Dao 1.19 (0.97, 1.46) China, Shunyi 1.53 (1.18, 1.99)
Japan, Funagata 2.13 (1.29, 3.51) Brazil, São Paulo92-93 2.21 (0.86, 5.65) Brazil, São Paulo99-00 2.10 (1.54, 2.84)
India, Chennai94 1.21 (0.91, 1.60) India, CUPS 1.52 (1.05, 2.21)
India, CURES 1.46 (1.14, 1.86) Mauritius, Mauritius87 1.72 (1.39, 2.13) Mauritius, Mauritius92 1.81 (1.40, 2.34) Mauritius, Mauritius98 2.24 (1.49, 3.37)
Mongolian National Survey 1.38 (0.94, 2.04) Philippines Diabetes Survey 1.60 (1.37, 1.86)
All Chinese 1.50 (1.34, 1.67) All Japanese 2.11 (1.64, 2.71)
All Indian 1.60 (1.43, 1.79) Aged>=50 years 1.55 (1.42, 1.69) Aged < 50 years 1.62 (1.46, 1.80)
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)
China, Hong Kong 1.67 (1.41, 1.97) China, Hong Kong workforce 1.43 (1.15, 1.79)
China, Qing Dao 1.36 (1.15, 1.60) China, Shunyi 1.34 (1.10, 1.63)
Japan, Funagata 1.33 (1.14, 1.57) Brazil, São Paulo92-93 1.34 (0.94, 1.91) Brazil, São Paulo99-00 1.04 (0.81, 1.33)
India, Chennai94 1.53 (1.23, 1.92) India, CUPS 1.42 (1.04, 1.93)
India, CURES 1.35 (1.10, 1.66) Mauritius, Mauritius87 1.40 (1.21, 1.62) Mauritius, Mauritius92 1.46 (1.18, 1.80) Mauritius, Mauritius98 1.96 (1.38, 2.80)
Mongolian National Survey 1.81 (1.57, 2.08) Philippines Diabetes Survey 1.53 (1.36, 1.71)
All Chinese 1.40 (1.29, 1.52) All Japanese 1.25 (1.10, 1.42)
All Indian 1.45 (1.33, 1.58) Aged>= 50 years 1.29 (1.21, 1.38) Aged < 50 years 1.59 (1.48, 1.71)
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)
China, Hong Kong 1.74 (1.43, 2.12) China, Hong Kong workforce 1.87 (1.46, 2.38)
China, Qing Dao 1.80 (1.49, 2.17) China, Shunyi 1.79 (1.41, 2.28)
Japan, Funagata 1.43 (1.12, 1.82) Brazil, São Paulo92-93 1.61 (1.12, 2.31) Brazil, São Paulo99-00 1.20 (0.93, 1.54)
India, Chennai94 1.63 (1.30, 2.04) India, CUPS 1.46 (1.05, 2.04)
India, CURES 1.67 (1.33, 2.10) Mauritius, Mauritius87 1.40 (1.20, 1.64) Mauritius, Mauritius92 1.49 (1.18, 1.88) Mauritius, Mauritius98 1.89 (1.30, 2.75)
Mongolian National Survey 2.15 (1.84, 2.51) Philippines Diabetes Survey 1.77 (1.57, 2.01)
All Chinese 1.77 (1.61, 1.95) All Japanese 1.36 (1.17, 1.60)
All Indian 1.53 (1.39, 1.68) Aged>= 50 years 1.63 (1.52, 1.76) Aged < 50 years 1.74 (1.61, 1.86)
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)
China, Hong Kong 1.36 (1.11, 1.67) China, Hong Kong workforce 2.52 (1.57, 4.04)
China, Qing Dao 1.30 (1.13, 1.49) China, Shunyi 1.15 (0.95, 1.38)
Japan, Funagata 0.95 (0.75, 1.22) Brazil, São Paulo92-93 1.40 (0.97, 2.02) Brazil, São Paulo99-00 1.26 (0.99, 1.61)
India, Chennai94 1.42 (1.11, 1.81) India, CUPS 1.15 (0.89, 1.48)
India, CURES 1.09 (0.89, 1.33) Mauritius, Mauritius87 1.27 (1.07, 1.51) Mauritius, Mauritius92 1.42 (1.12, 1.79) Mauritius, Mauritius98 1.49 (1.07, 2.05)
Mongolian National Survey 1.38 (1.20, 1.59) Philippines Diabetes Survey 1.26 (1.16, 1.38)
All Chinese 1.31 (1.21, 1.43) All Japanese 1.15 (0.98, 1.34)
All Indian 1.27 (1.16, 1.39) Aged>=50 years 1.26 (1.19, 1.34) Aged < 50 years 1.35 (1.26, 1.45)
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)
China, Hong Kong 1.92 (1.57, 2.34) China, Hong Kong workforce 1.76 (1.17, 2.66)
China, Qing Dao 1.86 (1.60, 2.16) China, Shunyi 1.78 (1.49, 2.13)
Japan, Funagata 1.34 (1.07, 1.68) Brazil, São Paulo92-93 1.69 (1.18, 2.42) Brazil, São Paulo99-00 1.13 (0.89, 1.42)
India, Chennai94 1.99 (1.53, 2.58) India, CUPS 1.61 (1.23, 2.11)
India, CURES 1.34 (1.09, 1.64) Mauritius, Mauritius87 1.30 (1.10, 1.54) Mauritius, Mauritius92 1.60 (1.28, 2.00) Mauritius, Mauritius98 1.79 (1.27, 2.53)
Mongolian National Survey 1.60 (1.40, 1.84) Philippines Diabetes Survey 1.43 (1.31, 1.56)
All Chinese 1.80 (1.65, 1.95) All Japanese 1.30 (1.12, 1.51)
All Indian 1.50 (1.37, 1.65) Aged>=50 years 1.49 (1.40, 1.58) Aged < 50 years 1.62 (1.52, 1.73)
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
National FINRISK Study
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
National FINRISK Study
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
Sitting position, on the right
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
Maximum circumference
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
circumference between
the rib cage and iliac crest
Maximum circumference
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
mercury sphygmomanometer
Glucose oxidase method plasma At umbilicus
Maximum circumference
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
mercury sphygmomanometer
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
Sitting position, on the right
arm, with
mercury sphygmomanometer
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
Sitting position, on the right
arm, with
mercury sphygmomanometer
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
mercury sphygmomanometer
Yellow Springs Instruments
(YSI) glucose analyzer, OH,
USA
plasma Minimum horizontal
circumference between
the umbilicus and
the xiphoid process
Maximum circumference
of the buttocks
posteriorly and
the symphysis
pubis anteriorly
Mauritius 92 Sitting position, after 5 min
rest with standard
mercury sphygmomanometer
Yellow Springs Instruments
(YSI) glucose analyzer, OH,
USA
plasma Midpoint between
the iliac crest and
the lower rib margin
Maximum circumference
of the buttocks
posteriorly and
the symphysis
pubis anteriorly
Mauritius 98 Sitting position, after 5 min
rest with standard
mercury sphygmomanometer
Yellow Springs Instruments
(YSI) glucose analyzer, OH,
USA
plasma Midpoint between
the iliac crest and
the lower rib margin
Maximum circumference
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
the lower rib margin
Maximum circumference
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
the lower rib margin and
iliac crest
NA
The National FINRISK
Study 1992
NA Glucose dehydrogenase plasma Midway between
the lower rib margin and
iliac crest
NA
The National FINRISK
Study 2002
NA Hexokinase assay
(Thermo Electron Oy)
plasma Midway between
the lower rib margin
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)
plasma Midway between
the lower rib margin
and iliac crest
NA
Spain
Viva study NA Hexokinase method,
HITACHI
plasma Midway between
the lower rib margin
and iliac crest
NA
Sweden
Swedish MONICA
1986
NA Glucose oxidase
(Beckman analyzer)
plasma Midway between
the lower rib margin and
iliac crest
NA
Swedish MONICA
1990
NA Glucose oxidase
(Beckman analyzer)
plasma Midway between
the lower rib margin and
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)
plasma Midway between
the lower rib margin and
iliac crest
NA
United Kingdom,
Ely Study
NA Hexokinase assay plasma Midway between
the lower rib margin and
iliac crest
NA
Newcastle Study NA Glucose oxidase
(Hitachi 717 analyser)
plasma Midpoint between
the lower costal
margin and the
superior iliac crest
NA
NA means not included in the respective analysis.