ANTHROPOMETRIC MEASURES

AND MORTALITY IN

SINGAPORE

WANG XIN

A THESIS SUBMITTED

FOR THE DEGREE OF MASTER OF SCIENCE

SAW SWEE HOCK SCHOOL OF PUBLIC HEALTH

NATIONAL UNIVERSITY OF SINGAPORE

2013

i

DECLARATION

I hereby declare that this thesis is my original work and it has been written by me in

its entirety. I have duly acknowledged all the sources of information which have been

used in the thesis.

This thesis has also not been submitted for any degree in any university previously.

Wang Xin

25 July 2013

.

ii

ACKNOWLEDGEMENTS

First of all, I would like to express my gratitude to my supervisor Dr Teo Yik Ying.

His encouragement and support with his valuable advice lead me to the right direction

along the progress of this thesis.

I also would like to thank to Dr Jeannette Lee, Dr Tai E Shyong and Dr Agus Salim

for their encouragement, support and guidance throughout this research.

Finally, I also would like to appreciate the Biostatistics domain to inspire the

wonderful research in biostatistics. I would like to thank to National University of

Singapore, for granting me the Graduate Scholarship which enables me to study

without financial constraints.

iii

TABLE OF CONTENTS

DECLARATION……………………………………………………………………ⅰ

ACKNOWLEDGEMENTS………………………………………………………...ⅱ

TABLE OF CONTENTS…………………………………………………………...ⅲ

SUMMARY………………………………………………………………………….ⅴ

LIST OF TABLES………………………………………………………………….ⅵ

LIST OF FIGURES………………………………………………………………...ⅶ

LIST OF ABBREVIATIONS AND SYMBOLS……………………………….....ⅷ

LIST OF APPENDICES……………………………………………………………ⅸ

Chapter 1 Introduction………………………………………………………………1

Chapter 2 Literature Review ………………………………………………………..3

2.1 Prevalence of Obesity…………………………………………………………...3

2.2 Impact of Obesity…………………………………………………………….....4

2.3 Assessment of Obesity …………………………………………………………6

2.3.1 Body Mass Index (BMI)…………..……………………………………….6

2.3.2 Waist Circumference (WC) and Waist-to-Hip Ratio (WHR)………….…..7

2.4 Previous Studies on Anthropometric Measurements of Obesity

and Mortality ………………………………………………………….……………8

2.4.1 Search Strategy and Pitfalls of Literature Review…………………….…...9

2.4.2 Studies comparing BMI, WC, WHR and Mortality in Adults……….…...10

2.4.3 Discussion……………………………………………………………..….14

2.4.3.1 Methods……………………………………………………….….14

2.4.3.2 Results……………………………………………………………14

iv

2.4.3.3 Limitation………………………………………………………...16

2.4.4 Studies in Asian Populations..................................................................….16

2.4.5 Section Conclusion………………………………………………………18

Chapter 3 Methodology…………………………………………………………….19

3.1 Aims……………………………………………………..…………………….19

3.2 Study Design..............................................................................................…....19

3.3 Data Collection………………………………………………….........…….….21

3.4 Data Entry…………………………………………………………….....…….21

3.5 Data Analysis.........................................................................................…........22

3.5.1 Univariate Analysis…………………………………….………………..22

3.5.2 Waist Residual (WR) Score……………………………..………………22

3.5.3 Cox Proportional Hazards Model………………………….……………23

Chapter 4 Results…………………………………………………………………...26

4.1 Baseline Characteristics of the Study Population..................................…........26

4.2 Anthropometric Variables and All-cause and Cardiovascular

Diseases (CVD) Mortality in Men…………………………………….……..........28

4.3 Anthropometric Variables and All-cause and CVD Mortality

in Women………………………………………………………………..…...........33

Chapter 5 Discussion………………………………………………………………..38

Chapter 6 Future Work…………………………………………………………….43

6.1 More Accurate Measures of Fat Composition………………….………..........43

6.2 Prediction Equation...........................................................................….............44

6.3 Body Composition and Cardiometabolic Risk Factors……………..........……46

6.4 Body Composition and Obesity Prevention......................................….............47

Chapter 7 Conclusion……………………………………………………………….49

References…………………………………………………………………………...50

Appendices…………………………………………………………………………..64

Appendix 1..............................................................................................….............64

Appendix 2......................................................................................….....................73

v

SUMMARY

Objective:

Our goal was to examine anthropometric measures of central and overall adiposity as

predictors of all-cause and cardiovascular disease (CVD) mortality.

Methods:

Subjects included 2091 men and 2227 women in the Singapore Cardiovascular Cohort

Study. Over a mean follow up of 12.0 years, there were 202 deaths of which 70 were

due to CVD. Body mass index (BMI), waist circumference (WC) and waist-to-hip

ratio (WHR) were obtained from direct anthropometric measurements. Waist residual

(WR) was the residual after regressing WC on BMI in each gender group.

Results:

The associations between BMI, WC, WHR and all-cause mortality in men were

U-shaped and persisted for BMI after adjusting for central obesity indicators. A

U-shaped association was also found between WC and CVD mortality in men.

However, a linear association between WHR and CVD mortality was found in women

after adjusting for BMI. WR was marginally associated with all-cause mortality in

women independently of BMI.

Conclusions:

In this cohort general adiposity appears to be a significant predictor of all-cause

mortality in men, more so than central adiposity. Although measures of central

adiposity were better predictors of CVD mortality in both men and women as

compared with measures of general adiposity, there was a difference in that the

association was U-shaped for men and linear for women.

vi

LIST OF TABLES

Table 1 Associations between anthropometric measures and mortality

across countries…………………………………………………………………...….13

Table 2 Characteristics of the study population by gender…………………………..27

Table 3 Partial correlation adjusted for age between anthropometric

variables at baseline…………………………………………………………………..27

Table 4 Associations between anthropometric variables and mortality

in men………………………………………………………………………………...29

Table 5 Models for the prediction of mortality from indicators of overall

adiposity and adipose distribution in men……………………………………………31

Table 6 Associations between anthropometric variables and mortality

in women……………………………………………………………………………..34

Table 7 Models for the prediction of mortality from indicators of overall

adiposity and adipose distribution in women………………………………………...36

vii

LIST OF FIGURES

Figure 1 Waist residual score = (Fitted waist value) (Observed waist)…...............23

viii

LIST OF ABBREVIATIONS AND SYMBOLS

WHO World Health Organization

CVD Cardiovascular disease

BMI Body mass index

WC Waist circumference

WHR Wait-to-hip ratio

WR Waist residual

DXA Dual X-ray absorptiometry

CT Computed tomography

MRI Magnetic resonance imaging

BIA Bioelectrical impedance analysis

EPIC European Prospective Investigation on Cancer

NHANES III The Third National Health and Nutrition Examination Survey

RR Relative risk

HR Hazard ratio

SD Standard deviation

CI Confidence Interval

Z-score Standardized score

BF% Body fat percentage

NRIC National Registry Identity Card

ICD-9 The ninth revision of the International Classification of Diseases

IHD Ischaemic heart disease

ix

LIST OF APPENDICES

Appendix 1: National University of Singapore Heart

Study Questionnaires…………………………………………………………………64

Appendix 2: National Health Survey Questionnaires………………………………..73

1

Chapter 1

1. Introduction

According to the World Health Organization (WHO), obesity is defined as a condition

with excessive fat accumulation in the body to the extent that health and well-being

are adversely affected [1]. The current view of fatness is that fat collectively

constitutes an endocrine organ which plays a wide-ranging role in metabolic

regulation and physiological homeostasis [2]. In the past few decades, obesity is

becoming more common, and is becoming the most significant cause of ill-health and

threat of health [3, 4].

The prevalence of obesity in Asia has increased at an alarming rate, in conjunction

with an increase in obesity-related diseases [5, 6]. The causes of this rapid increase

within the region are likely to be complex. Although studies indicate a possible

genetic susceptibility to obesity in some minority groups, environmental factors also

play a significant role. Increasing economic developments of Asian countries

contribute to the increasing prevalence of obesity [7]. Our current „obesogenic‟

environment facilitates the development of obesity by providing virtually unlimited

access to inexpensive, energy-dense food while decreasing the need for prolonged

periods of physical activity [3, 8]. Whereas many recognize the significant risk of

cardiovascular disease (CVD) and diabetes mellitus associated with excess body fat, a

myriad of other health problems can accompany overweight and obesity, potentially

leading to early morbidity and mortality [9].

The health impact of fatness is particularly troubling because obesity prevalence in

Singapore has increased dramatically and effective strategies to alleviate the societal

burden of obesity are needed [7]. Given the link between fatness and morbidity and

2

mortality, excessive fatness is now recognized as one of the most serious public health

challenges [10-12]. Prevention, prompt diagnosis and management of obesity in

Singapore are crucial. Better knowledge on the association between obesity and

mortality could aid better disease prevention and early detection of diseases among

individuals [5, 13].

To date, it is unclear which measure of obesity is the most appropriate for risk

stratification and death prediction. In light of the growing epidemic of obesity, it is

increasingly important to identify individuals that are at particularly high risk of

obesity-related mortality. In general, Body mass index (BMI) is still used as the main

criterion to prompt behavioral, medical or surgical interventions against obesity [14,

15]. However, BMI does not distinguish between overweight due to muscle or fat

accumulation [16]. Moreover, visceral rather than subcutaneous fat accumulation is

associated with increased secretion of free fatty acids, hyperinsulinemia, insulin

resistance, hypertension and dyslipidemia [17]. There is an agreement that abdominal

obesity is a better indicator of cardiovascular risk than BMI [18-20]. However, the

studies available to date have not given a conclusive answer as to which

anthropometric measure better predicts CVD and all-cause mortality.

In this paper, the association between obesity and mortality among Singaporeans will

be explored. In the following chapter, I will give a throughout review of the literature

on obesity and CVD and all-cause mortality. In Chapter 3 to 4, I will discuss the aim,

methodology and results of the study. In Chapter 5, I will give a detail account on the

discussion on the findings and limitations of the study. In Chapter 6, I will discuss the

further work. In chapter 7, I will the end this paper with an overall conclusion of the

results.

3

Chapter 2

2. Literature Review

2.1 Prevalence of Obesity

Obesity has reached epidemic proportions globally [21, 22] (In the studies cited below,

unless otherwise mentioned, overweight refers to a BMI between 25.0 and 29.9, and

obesity as BMI 30.0 kg/m2). According to WHO, between 1980 and 2008, the

prevalence of obesity has nearly doubled. Between 1980 and 2008, obesity prevalence

rose from 4.8% to 9.8% in men and from 7.9% to 13.8% in women [22]. In 2008,

more than 1.4 billion adults were overweight and more than half a billion were obese

[22]. In the United States in 2009-2010, 35.5% of men and 35.8% of women had

obesity [23].

Though Asia is home to some of the leanest populations on the globe, obesity has

become a serious and growing problem across the region over the past two decades

[24, 25]. In Asia, many countries are dealing with a rise in obesity [24-26]. China and

India are the most populous nations on the planet, hence a small percentage increase

in obesity rate would translate into millions more cases of chronic diseases. In China,

from 1993 to 2009, obesity (defined as BMI of 27.5 or higher) increased from about 3

percent to 11 percent in men and from about 5 percent to 10 percent in women.

Abdominal obesity (defined as waist circumference [WC] of 90 centimeters or higher

in men, and 80 centimeters or higher in women) also increased during this time period,

from 8 percent to 28 percent in men and 28 percent to 46 percent in women [27]. In

India, recent data in 2005 reported 14 percent of women aged 18 to 49 were

overweight or obese. The rate of overweight and obesity in women, overall, increased

4

by 3.5 percent a year from 1998 to 2005 [26].

As part of a worldwide phenomenon, obesity is increasing in prevalence in Singapore

[28]. The latest National Health Survey shows the obesity rate has increased from 6.9

percent in 2004 to 10.8 percent in 2010 [29]. Singapore needs to “act now” to prevent

obesity from becoming a diabetes epidemic.

2.2 Impact of Obesity

Obesity is a complex, multifactorial condition [2, 30]. The pathogenic link between

increased adipose tissue mass and higher risk for obesity-related disorders is related to

adipose tissue dysfunction and ectopic fat accumulation [31]. Ectopic fat

accumulation including visceral obesity is characterized by changes in the cellular

composition, increased lipid storage and impaired insulin sensitivity in adipocytes and

secretion of a proinflammatory adipokine pattern [9, 31, 32]. Increase in body fat

alters the body‟s response to insulin, potentially leading to insulin resistance and the

risk of thrombosis [33]. Many endogenous genetic, endocrine and inflammatory

pathways and environmental factors are involved in the development of

obesity-related diseases [9, 31].

Obesity carries substantial health implications for both chronic diseases and mortality.

Obese individuals have an increased risk of developing some of the most prevalent,

yet costly diseases. Because of its maladaptive effects on various cardiovascular risk

factors and its adverse effects on cardiovascular structure and function, obesity has a

major impact on cardiovascular diseases, such as heart failure, coronary heart disease,

sudden cardiac death, and atrial fibrillation [34]. A myriad of other health problems

can accompany overweight and obesity, including type 2 diabetes, hypertension,

several forms of cancer (endometrial, postmenopausal breast, kidney and colon),

musculoskeletal disorders, sleep apnea and gallbladder disease [30]. In addition,

obesity may contribute to debilitating health problems such as osteoarthritis and

5

pulmonary diseases and is related to stress, anxiety and depression [35]. In light of the

overwhelming evidence linking obesity to disease risk, it is no surprise that obesity

has been shown to increase the risk of all-cause mortality [36]. Overweight and

obesity rank fifth as worldwide causes of death among risk factors [37]. At least 2.8

million people each year die from complications as a result of being overweight or

obese [38]. Epidemiological studies suggest that obesity is an important predictor of

longevity [39-41]. In the Framingham Heart Study, the risk of death within 26 years

increased by 1% for each extra pound gained between the ages of 30 years and 42

years and by 2% between the ages of 50 years and 62 years [39]. A meta-analysis

based on person-level data from twenty-six observational studies also documented

excess mortality associated with obesity [40]. The Prospective Studies Collaboration

in Western Europe and North American reported BMI is a strong predictor of overall

mortality [42]. In pooled analyses among more than 1 million Asians, the excess risk

of death associated with a high BMI was seen among East Asians [41].

The cost of obesity and its associated comorbidities are staggering, both in terms of

quality of life and health care expenditure [21]. Obese individuals report impaired

quality of life. In the Unites States, obese men and women lost 1.9 million and 3.4

million quality-adjusted life years, respectively, per year relative to their normal

weight counterparts [43]. Worldwide, an estimated 35.8 million (2.3%) of global

disability-adjusted life years are caused by overweight or obesity [38]. The costs from

health care and lost productivity to the individual and society are also substantial. A

recent study in US estimated that medical expenditures of health complications

attributed to overweight and obesity may have reached 78.5 billion dollars [44].

Taken together, obesity has taken a toll on the health and quality of life of people, and

the global economy. This makes obesity one of the biggest public health challenges of

the 21st century. Today, cancer, CVD and diabetes are among the top ten disease

conditions affecting Singaporeans and they account for more than 60 percent of all

deaths [45]. These facts and the increasing prevalence of obesity make it an important

6

health problem. In spite of the discovery of new mechanisms of these diseases, the

prevention and treatment of obesity remains an open problem.

2.3 Assessment of Obesity

Body fat can be measured in several ways. Some are simple, requiring only a tape

measure, such as anthropometric measures. Others use expensive equipments to

precisely estimate fat mass, muscle mass, and bone density, such as dual X-ray

absorptiometry (DXA), computed tomography (CT) and magnetic resonance imaging

(MRI) [46-48]. Each body fat assessment method has its pros and cons. Imaging

techniques such as MRI or CT are now considered to be the most accurate methods

[48]. MRI, CT or DXA scans are typically used in research settings since it is

expensive and immobile [49]. Simple anthropometric measurements such as BMI,

WC and WHR have more practical value in both clinical practice and large-scale

epidemiological studies and are the most widely used methods to measure body fat

and fat distribution [14]. The distinct advantages of anthropometric methods are that

they are portable, non-invasive, inexpensive, making them useful in field studies [14,

47, 50].

2.3.1 BMI

BMI is a simple marker to reflect total body fat amount [51]. It is commonly accepted

as a general measure of overweight and obesity. It is calculated by dividing the

patient‟s weight in kilograms by the square of the individual‟s height in meters.

According to WHO, adults with a BMI in the range of 25 to 29.9 are classified as

overweight, and those with a BMI of more than 30 are classified as obese. For Asian

populations, including Singapore, lower BMI cut offs are used: Low risk BMI (kg/m2)

= 18.5 to 22.9; Moderate risk BMI (kg/m2) = 23.0 to 27.4; High risk BMI (kg/m

2) =

equal or more than 27.5 [28].

7

BMI is the most frequently used measure of obesity because of the robust nature of

the measurements of weight and height [14]. BMI forms the backbone of the obesity

classification system [52]. It is an important screening tool to assess patients with

excess body weight and stratify treatments according to the likelihood of underlying

disease risk [53]. The determination of BMI may provide a determination of global

disease risk. Because BMI is relatively highly correlated with body fat, it is often used

in epidemiologic studies to assess adiposity and is frequently used to estimate the

prevalence of obesity within a population [15, 53]. However, BMI does have some

limitations. As compared to weight and height, BMI is just an index of weight excess,

rather than body fatness composition [51]. BMI does not take into account the

variation in body fat distribution and abdominal fat mass, which can differ greatly

among populations and can vary substantially within a narrow range of BMI [32]. In

addition, BMI is a limited diagnostic tool in very muscular individuals and those with

little muscle mass, such as elderly patients [13].

2.3.2 WC and WHR

One important category of obesity not captured by BMI is “abdominal obesity” the

extra fat found around the middle that is an important factor in health [17, 24].

Regional obesity measures, including WC and wait-to-hip ratio (WHR), provide

estimates of abdominal adiposity, which is related to the amount of visceral adipose

tissue [14, 32].

WC is commonly used to complement BMI when characterizing obesity. WC could

provide important additional prognostic information, especially when BMI is not

substantially increased but an unhealthy level of excessive adiposity is still suspected

[54, 55]. A recent WHO report summarized evidence for WC as an indicator of

disease risk [56]. WC correlates with abdominal obesity, and the presence of

abdominal obesity confers a higher absolute disease risk [56, 57]. WC is an important

surrogate measure of abdominal obesity and disease risk.

8

WHR is the ratio of the circumference of the waist to that of the hips. WHR is more

complicated to measure and more prone to measurement error because it requires two

measurements [14]. In general, obesity can be classified into central or peripheral

obesity [30]. In central obesity, the distribution of fat is commonly on the upper part

of the trunk. However, in the peripheral type of obesity, the distribution of fat is

mainly on the hip and thighs. WHR is a measure of body fat distribution or body

shape. WHR was shown to be a good predictor of health risk [29]. However, WHR is

more complex to interpret than WC, since increased WHR can be caused by increased

abdominal fat or decrease in lean muscle mass around the hips [14].

2.4 Previous Studies on Anthropometric Measurements of Obesity

and Mortality

BMI has been routinely used in clinical and public health practice for decades to

identify individuals and populations at risk of diseases and death [58]. Many studies

have evaluated the relationship between BMI and mortality [40, 59-62]. In recent

years, BMI has been criticized as a measure of risk because it reflects both fat and

lean mass [63]. Multiple studies worldwide have shown that overweight subjects have

similar or better outcomes for survival and cardiovascular events when compared to

people classified as having normal body weight [12, 64]. Results of these studies

suggest intrinsic limitations of BMI to differentiate adipose tissue from lean mass in

intermediate BMI range [63, 64].

An increasing amount of knowledge has been gathered about the metabolic

consequences of central fat distribution [65, 66]. Greater abdominal adiposity is

strongly associated with insulin resistance, dyslipidemia and systematic inflammation,

factors that play essential roles in the pathogenesis of CVD [66]. WC or WHR as

indicators of abdominal obesity may be better predictors of the risk of death than BMI,

an indicator of overall obesity [55, 67-71]. Although a number of epidemiological

9

studies have demonstrated that measures of abdominal adiposity significantly predict

chronic diseases such as CVD and diabetes mellitus independently of overall body

adiposity, the associations of these measures with premature death have not been

widely studied and previous findings have been inconsistent [19, 55, 67-73]. The

inconsistencies may be due to differences in study populations, sampling, measures

and analytic approaches [55].

Given the inconsistency of prior results and the potential impact of central obesity on

mortality outcomes, we performed a review of the current evidence for the association

between anthropometric measures of adiposity and the risk of mortality.

2.4.1 Search Strategy and Pitfalls of Literature Review

Pubmed was used to identify relevant articles published from 1990 to October 1, 2012,

by using a combination of keywords: “anthropometry”, “obesity”, “body mass index”,

“waist circumference”, “waist-to-hip ratio” and “mortality”. One hundred and twenty

three articles were indentified. A first selection of articles was made based on title.

Only articles with titles relevant to the topic of our study were selected. Of the 123

articles, 22 had appropriate titles and we read their abstracts to evaluate their

relevance reducing the number of articles to 13. After that, the full text articles were

read and 7 articles were selected since these studies are more relevant to our research

questions.

There are some limitations in the search strategy. First, we only searched for relevant

studies in Pubmed. Other databases were not searched. Second, the articles included

are all from publications in peer-reviewed journals. Non-English language journals

were not included. Third, reference lists from relevant publications were not included

in our review. Fourth, the included studies are all epidemiologic studies. Non-human

studies, reviews, meta-analyses, letters to the editor and editorials were not included.

10

2.4.2 Studies comparing BMI, WC, WHR and Mortality in Adults

The largest study in this respect is the European Prospective Investigation on Cancer

(EPIC) study in 359,387 participants from nine European countries with 14,723

deaths during a follow-up of 9.7 years on average [67]. For all-cause mortality, there

was a strong relationship between increased WC and WHR in both men and women.

Relative risks (RRs) among men and women in the highest quintile of WC as

compared with the lowest quintile were 2.05 (95% confidence interval [CI], 1.80 to

2.33) and 1.78 (95% CI 1.56 to 2.04), respectively, and in the highest quintile of

WHR as compared with the lowest quintile, the RRs were 1.68 (95% CI, 1.53 to 1.84)

and 1.51 (95% CI, 1.37 to 1.66), respectively. The study suggested that both general

adiposity and abdominal adiposity are associated with the risk of death and support

the use of WC or WHR in addition to BMI in assessing the risk of all-cause mortality.

Welborn and Dhaliwal showed in a study that followed 9309 Australian urban adults

aged 20–69 years for 11 years that WHR was superior to BMI and WC in predicting

all-cause mortality (male hazard ratio [HR]: 1.25, P=0.003; female HR: 1.24, P=0.003

for an increase in 1 standard deviation [SD]) and CVD mortality (male HR: 1.62,

P<0.001; female HR: 1.59, P<0.001 for an increase in 1 SD ) [74].

Based on 22,426 adults from a nationally representative sample of the Scottish

population, Hotchkiss and Leyland found that BMI-defined obesity (≥ 30.0 kg/m2)

was not associated with increased risk of mortality (HR=0.93; 95% CI: 0.80-1.08),

whereas the overweight category was associated with a decreased risk (HR=0.80;

95%CI: 0.70-0.91). A low BMI (<18.5 kg/m2) was associated with elevated HR for

all-cause mortality (HR=2.66; 95% CI: 1.97-3.60). The reference group is the normal

BMI category (18.5-25 kg/m2). In contrast, the HR for a high WC (men>102 cm,

women>88 cm) was 1.17 (95% CI: 1.02-1.34) as compared with the reference WC

group (men: 79-94 cm, women: 68-80 cm) and a high WHR (men>1, women>0.85)

was 1.34 (95% CI: 1.16-1.55) as compared with the reference WHR group (men:

11

0.85-0.95, women: 0.7-0.8). There was an increased risk of CVD mortality associated

with BMI-defined obesity, a higher WC and a higher WHR categories. The HR

estimates for these were 1.36 (1.05-1.77), 1.41(1.11-1.79), 1.44(1.12-1.85),

respectively [75].

Simpson and colleagues followed 16,969 men and 24,344 women for 11 years who

were participants in the Melbourne Collaborative Cohort Study and aged 27–75 years

at baseline [76]. Comparing the top quintile to the second quintile, for men there was

an increased risk of between 20 and 30% for all-cause mortality for all anthropometric

measures (BMI, WC and WHR). Comparing the top quintile to the second quintile,

for women, there was an increased relative risk for WC (RR: 1.3; 95% CI: 1.1–1.6)

and WHR (RR: 1.5; 95% CI: 1.2–1.8). Measures of central obesity were better

predictors of mortality in women in this cohort study compared with measures of

overall adiposity.

In the Nurse‟s Health Study, a prospective cohort study of 44,636 women,

associations of abdominal adiposity with all-cause and CVD mortality were examined

[55]. During 16 years of follow-up, 3507 deaths were identified. After adjustment for

BMI and potential confounders, the RRs across the lowest to the highest WC quintiles

were 1.00, 1.11, 1.17, 1.31 and 1.71 (95% CI, 1.47 to 1.98) for all-cause mortality;

1.00, 1.04, 1.04, 1.28, and 1.99 (95% CI 1.44 to 2.73) for CVD mortality (all P<0.001

for trend); the RRs across the lowest to the highest WHR quintiles were 1.00, 1.09,

1.14, 1.33, 1.59 (95% CI 1.41 to 1.79) for all-cause mortality; 1.00, 0.99, 0.93, 1.05,

1.63 (95% CI 1.27 to 2.09) for CVD mortality (all P<0.001 for trend). This study

concludes that anthropometric measures of abdominal adiposity were strongly and

positively associated with all-cause and CVD mortality independently of BMI in

women.

In US, the Third National Health and Nutrition Examination Survey (NHANES III)

provided a set of standardized measurements of body size and composition in a

12

representative sample of the US population. Based on this survey, Jared and

colleagues conducted a study for comparison of overall and body fat distribution in

predicting risk of mortality [77]. WHR in women (P<0.001 for trend) was positively

associated with mortality in middle-aged adults (30–64 years), while BMI and WC

exhibited U- or J-shaped associations. Among middle-aged men and women, J-shaped

associations of BMI with CVD mortality were observed. CVD mortality was 2.8- and

3.2- fold higher across quintiles of WC in middle-aged men and women respectively;

5.4- and 4.1- fold higher across quintiles of WHR. The reference groups are the

lowest WC or WHR quintile categories. The authors concluded that ratio measures of

body fat distribution were strongly and positively associated with mortality and

offered additional prognostic information beyond BMI and WC in middle-aged adults.

Jared and colleagues also investigated the association of overall obesity and

abdominal adiposity in predicting risk of all-cause mortality in white and black adults

[78]. This prospective study included a national sample of 3219 non-Hispanic white

and 2561 non-Hispanic black adults 30 to 64 years of age enrolled in NHANES III.

During 12 years of follow-up (51,133 person-years), 188 white and 222 black adults

died. After adjustment for confounders, positive dose-response associations between

WHR and mortality in white and black women were observed (all P<0.05 for trend).

These results were unchanged after additional adjustment for BMI. In contrast, BMI

and WC alone exhibited curvilinear-shaped associations with mortality.

13

Table1. Associations between anthropometric measures and mortality across

countries

First

Author

and Year

Sample

Size

Country Men

(%)

Age

range

(years)

follow-

up

(years)

Results

T.

Pischon

2008[67]

359, 387 EU 34.6 25-70 9.7 All-cause mortality:

BMI nonlinear

WC linear

WHR linear

TA

Welborn

2007[74]

9206 Australia 49.0 20-69 11 All-cause mortality

BMI not significant

WC

Men not significant

Women linear

WHR linear

CVD mortality

BMI

Men linear

Women not significant

WC linear

WHR linear

Julie A.

Simpson

2007[76]

41, 313 Australia 41.1 27-75 11 All-cause mortality

BMI U shaped

WC U shaped

WHR linear

Cuilin

Zhang

2008[55]

44, 636 U.S. 0 30-55 16 All-cause and CVD mortality

WC

women linear

WHR

women linear

JW

Hotchkiss

2011[75]

22, 426 UK 44.3 18-86 12.9 All-cause mortality

BMI U shaped

WC U shaped

WHR linear

CVD mortality

WC linear

WHR linear

Jared P.

REIS

2009[77]

8480 U.S. 46.8 30-64 12 All-cause mortality

BMI U or J shaped

WC U or J shaped

WHR

Women linear

CVD mortality

BMI J shaped

14

First

Author

and Year

Sample

Size

Country Men

(%)

Age

range

(years)

follow-

up

(years)

Results

Jared P.

REIS

2008[78]

5780 U.S. 45.6 30-64 12 All-cause mortality

BMI curvilinear-shaped

WC curvilinear-shaped

WHR

Men not significant

Women linear

2.4.3 Discussion

2.4.3.1 Methods

The study designs of the seven studies are cohort studies. They have used Cox

proportional hazards models to estimate the HR or RR for mortality. In five out of

seven papers, the analyses were conducted in females and males separately and they

reported gender differences in the associations between adiposity and mortality. In

these seven papers, the usually adjusted covariates are age, smoking status, alcohol

use, physical activity and education. Two studies used age instead of follow-up time

as the underlying time variable. Using age as the time axis allows the baseline hazard

to change as a function of age, which is a better method for controlling the potential

confounding due to age. Most of the studies grouped the subjects into quintiles or

quartiles categories of WC, WHR or BMI at baseline. Three studies conducted

sensitivity analysis excluding subjects with comorbidities and those experiencing

early death during follow-up. Several studies conducted subgroup analysis for age

groups and smoking status.

2.4.3.2 Results

Most studies observed a nonlinear association between BMI and all-cause mortality.

In the EPIC study, there was a significant nonlinear association of BMI with the risk

15

of death [67]. In the Melbourne Collaborative study, the associations between BMI

and all-cause mortality were U shaped for both men and women [76]. In the study

conducted on white and black adults in the US, BMI exhibited curvilinear-shaped

associations with mortality [78]. In another US study based on NHANES III, U and J

shaped associations of BMI with mortality in men and women were observed

respectively [77].

Most studies suggest a linear association between WHR and mortality, especially in

women. In the Nurses‟ Health study, the researchers reported after adjustment for age,

smoking and other covariates, increasing WHR was strongly associated with a graded

increase in all-cause mortality. In stratified analysis, the associations of WHR with

mortality were not appreciably different between never and ever smokers, between

older and younger women and between premenopausal and postmenopausal women

[55]. In the EPIC study, after adjustment for BMI, WHR was strongly associated with

the risk of death [67]. In the Australia Heart study, WHR was superior by magnitude

and significance in predicting all-cause mortality [74]. In the Melbourne

Collaborative study, the researchers found a linear trend of all-cause mortality across

incremental quintiles of WHR in women [76]. In the study conducted on white and

black adults in the US, WHR in women were strongly and positively associated with

mortality in a dose-response fashion. The association was independent of overall

obesity (reflected by BMI) [78]. In another US study based on NHANES III, graded

hazard ratios of mortality across incremental quintiles of WHR in middle-aged

women were noted [77]. In the seven papers, most findings support an independent

contribution of body fat distribution to mortality and the importance of abdominal

adiposity in predicting mortality in women.

Table 1 summarizes the empirical evidences reported from the seven studies. It is

difficult to compare the magnitude of RR or HR across studies due to differences in

the categories and reference category chosen for the modeling of the anthropometric

measures. Variations in these risk estimates are likely to reflect differences in the

16

measurement, the populations examined, baseline age of participants in the study,

follow-up time, and confounders adjusted for in multivariable Cox regression models.

2.4.3.3 Limitations

There were some limitations in these studies. First, all the seven studies had only a

single baseline assessment of adiposity measurements and other exposures; therefore,

they could not examine whether the changes in these variables during the follow-up

period had any influence on the outcome. Second, there may be residual cofounding

in some studies. For example, in the EPIC study, although people who had a history

of cancer, heart disease, or stroke were excluded, the analysis may have included a

number of participants who had other serious diseases that could potentially confound

the observed associations [67]. Third, some studies in the seven papers suggest that

smoking may change the pattern of association between adiposity and mortality. This

association should be investigated in healthy persons who have never smoked.

However, several studies have limited power to perform subgroup analysis by

smoking status. Fourth, because the study population was predominantly Caucasians,

the results may not be generalized. The findings require confirmation in other ethnic

groups.

2.4.4 Studies in Asian Populations

Most studies on overweight, obesity and fat distribution and mortality are based on

studies from the North America and Europe. Studies have shown that the relationships

among BMI, body fat percentage (BF%) and body fat distribution differ across

populations [79]. Asians generally have a higher percentage of body fat than

Caucasians of the same age, sex and BMI [15]. For a given amount of total body fat,

Chinese and most South Asians had more visceral fat than the Europeans [80].

Moreover, not all Asians will have similar body fat composition [81]. Even between

Chinese groups living in Beijing, Hong Kong and Singapore, there seemed to be

17

differences in the BMI/BF% relationship [82]. In addition, Asian populations have

different associations between BMI, BF% and health risks than American and

European populations [83, 84]. Obesity-associated metabolic risks are greater in

Asian people than in European populations since Asian tend to have lower BMI but

higher fat volume. Lean individuals can show increased risk of CVD and other

metabolic and inflammatory disorders if they present accumulated fat in the

abdominal region [84, 85].

Studies examining the role of various anthropometric variables and mortality in Asia

are relatively sparse [59, 60, 86, 87], and few have examined whether the distribution

of body fat contributes to the prediction of death. In Singapore, we have also shown

overweight (BMI 25kg/m2) to be associated with all-cause mortality in women [88].

Increased risk of mortality was also apparent for individuals in the underweight

(<18.5 kg/m2) and obese BMI categories (27.5 kg/m

2) independent of age and

smoking [89]. However, the above mentioned and previous studies in Asia have relied

predominantly on BMI to assess the association between adiposity and mortality [41,

60, 87-90]. In Asia, only two studies investigated the relationships between central

obesity and mortality. In the Japanese Community-Based Studies, the authors reported

WC was associated inversely with increased risk of all-cause death in men, but not in

women. CVD mortality risk was increased in men aged≤65 years with a higher WC.

This relationship was U-shaped. WC was not associated with all-cause or CVD

mortality in women [91]. Only the investigators of the Shanghai Women‟s Health

Study compared the effect of body fat distribution (WHR, WC and waist-to-height

ratio) on mortality after adjustment for BMI in relatively lean Chinese women [92].

Data from this prospective cohort study with the follow-up period extended to the end

of 2007 suggested the associations between BMI, WC and WHR with mortality in

Chinese women [93]. In addition, results from published studies to date that have tried

to compare different measurements of general and regional adiposity have not been

consistent [94]. At the present time, there is a lack of prospective epidemiological

studies in Asian populations where different anthropometric measures as predictors of

18

mortality are compared.

2.4.5 Section Conclusions

Although there is no agreement on which anthropometric measures could be better

predictors of mortality, our review suggests that anthropometric measures of

abdominal obesity may be stronger predictors of mortality than BMI, especially in

women. However, the unexplained heterogeneity highlights the need for additional

well designed observational studies to evaluate the effects of adiposity on mortality.

There is a lack of studies in Asian populations. Hence, a comparison of

anthropometric measures as predictors of all-cause and CVD mortality in Asian

countries merits further research.

19

Chapter 3

3. Methodology

3.1 Aims

In this study, we will investigate the relationship between several anthropometric

measures of adiposity (BMI, WHR, WC and waist residual [WR]) with all-cause and

CVD mortality and also compare, where possible, different anthropometric measures

as predictors of mortality in an Asian cohort.

3.2 Study Design

This will be a prospective study using data from subjects who participated in the 1992

National Health Survey or the National University of Singapore Heart Study

(1992-1995). The two studies were nationwide surveys undertaken to determine the

risk factors for the major non-communicable diseases in Singapore.

The 1992 National Health Survey was conducted between September and November

1992 at six community centers distributed around Singapore Island. The survey team

moved systematically each fortnight, through Silat, Toa Payoh, Fengshan, Ang Mo

Kio, Ulu Pandan, and Chong Pang community centers over the 3-month period, to

provide island-wide coverage of survey sites and proximity to those selected. A total

of 4915 individuals were randomly selected from a sample of all household units in

Singapore, obtained from the Department of Statistics‟ National database on

dwellings in Singapore. The characteristics of the selected sample conformed with

that of the resident population. Systematic sampling, followed by disproportionate

20

stratified sampling by ethnic groups, was used to select the sample for the survey. The

two minority groups, Malays and Asian Indians, were oversampled to give an ethnic

distribution of 60% Chinese, 20% Malays, and 20% Asian Indians. This was to ensure

sufficient numbers for statistical analysis, and representative results were weighted

back during the analysis of findings. From the 4915 eligible individuals randomly

selected from a sample of all household units in Singapore, 3568 Singapore residents

aged between 18 and 69 years finally participated in the survey. The response rate

was 72.6%. Although response rates did differ between ethnic groups, the

nonresponders were contacted and information was sought regarding their

demographic and socioeconomic profile and diabetes and hypertension status. This

was to ensure that the prevalence of these diseases would not be underestimated

during the survey. Characteristics of the nonresponders were similar to those of the

survey respondents [95].

The National University of Singapore Heart Study was a random sample of people

aged 30 to 69 years from the general population of Singapore. The sample was

obtained from electoral registers of five divisions, each in a different part of the island

(north, south, east, west, and centre). There was disproportionate sampling in relation

to ethnic groups to obtain equal numbers of subjects in each of the six gender-ethnic

groups. The required sample was 180 subjects in each gender-ethnic group giving a

total of 1080 subjects. Assuming that 20% of the subjects would not be recruitable

because of death, migration, infirmity, or relocation (which is high in Singapore due

to massive urban redevelopment), and assuming a response rate of 75%, a total of

1800 persons was selected. Of these, 419 (23.3%) were not recruitable and 983

responded, giving a response rate of 71.2%. Of the 983 subjects, 22 were 70 years or

over and were excluded, leaving 961 persons aged between 30 and 69 years [96].

Of the 4529 participants from the two cross-sectional surveys, individuals with

pre-existing coronary heart disease or prior cerebrovascular accident and belonging to

an ethnic group other than Chinese, Malay or Asian Indian were excluded. Individuals

21

with missing data for anthropometric variables or other covariates were also excluded.

Thus a total of 4318 participants were included in the cohort.

3.3 Data Collection

Weight, height, waist and hip circumference were measured at baseline according to

standardized protocols. Weight (in kilograms) was measured in subjects in light

clothing using the electronic weighing scales. Height (to the nearest millimeter) was

recorded in all subjects without shoes. BMI was calculated as weight in kilograms

divided by height in meters squared. Waist (defined as the narrowest part of the body

below the costal margin) and hip (defined as the widest part of the body below the

waist) measurements were also taken, and the WHR was computed. Interviewer

administered questionnaires captured age, gender, ethnic group and the use of alcohol

and cigarettes.

All-cause mortality and CVD mortality were obtained by linking individual records

(using the National Registry Identity Card [NRIC] number that is unique to all

Singaporean citizens) to the Singapore Registry of Births and Deaths. The registration

of deaths is a compulsory requirement in Singapore and certification is only done by

medical practitioners. All outcomes were in coded form using the ninth revision of the

International Classification of Diseases (ICD-9). All-cause mortality included all

deaths that occurred in the cohort up till 31 December 2004. The primary cause of

death was used. CVD mortality included all deaths due to ischaemic heart disease

(IHD) (ICD-9410–414) and cerebrovascular accidents (ICD-9430–438). All

individuals were followed up till death or censored at 31 December 2004, whichever

occurred first.

3.4 Data Entry

22

All questionnaires and physical examination/blood test forms were checked for

completeness. Double data entry was done which allowed quality control checks.

3.5 Data Analysis

Type I error for all statistical tests is set at 0.05 (5%). All statistical analyses are

conducted using STATA version 11, unless otherwise stated.

3.5.1 Univariate Analysis

Continuous and categorical variables are summarized using appropriate summary

measures, depending on the distributions of the variables. Differences in baseline

characteristics between men and women were tested by Student‟s t-test for continuous

variables and by Chi-square tests for categorical variables.

3.5.2 Waist Residual Score

Waist residual score was calculated as WC minus the value predicted by the

regression of WC on BMI in each gender group. The residuals method has been

introduced in nutritional epidemiology by Willett and Stampfer and has been

previously utilized in the evaluation of independent associations of waist and hip

circumferences with cardiovascular risk factors [97]. Residual scores are often used to

dissociate specific effects among highly correlated variables.

23

Figure 1. Waist residual score = (Fitted waist value) – (Observed waist)

3.5.3 Cox Proportional Hazards Model

Cox, in 1972, developed a modeling procedure termed Cox‟s proportional hazards

model for analyzing survival data when the number of prognostic factors is large. The

model helps to assess the effect of various prognostic factors on survival after

adjusting each for the other factors. Cox‟s proportional hazards model is analogous to

a multiple regression model and enables the difference between survival times of

particular groups of patients to be tested while controlling for other factors [98, 99].

The model can be written as:

Where and are the hazard and baseline hazard at time t, is the

coefficient for the i-th covariate. The exponential of these coefficients are the hazard

waist residual

40

60

80

100

120

10 20 30 40 50

BMI (kg/m2)waist Fitted values

WC

(cm

)

24

ratios (HRs). The estimate of , denoted , is obtained from the data of the

particular study concerned.

In this model, the response variable is the „hazard‟. The hazard is the instantaneous

probability of death given that an individual has survived up to a given point in time, t.

In Cox‟s model, no assumption is made about the probability distribution of the

hazard. However, it is assumed that the HR does not depend on time. In other words,

the HR is constant over time. The strength of the model developed by Cox is not only

that it allows survival data arising from a non-constant hazard rate to be modeled, but

it does so without making any assumption about the underlying distribution of the

hazards in different groups, except that the hazard in one group remains proportional

to the other group over time [98-100].

In our study, person-years of follow-up were calculated from the date of the baseline

examination until the date of death or 31 December 2004 for each study participant.

Sex-specific Cox proportional hazards regression models were used to evaluate the

relationship between standardized scores (Z-scores) of BMI, WC, WR and WHR with

all-cause and CVD mortality with HR and 95% CI. When the event of interest was

CVD mortality, non-CVD events were treated as being censored. Z-score

transformation allows the comparison of effect of different anthropometric variables.

The HR indicates the increased risk for 1 SD increase of each anthropometric variable.

The proportional hazards assumption was checked for all outcomes studied.

Second-order polynomial terms were used to evaluate nonlinear relationships between

anthropometric measures and mortality. Models were all adjusted for age (continuous),

ethnic group (Chinese, Malay, India), smoking status (current, former or never) and

alcohol consumption (<1, ≥ 1 drinks per month). Presence of diabetes, hypertension,

blood glucose, blood pressure and lipids were not adjusted for as they were

considered as potential mediators. Partial correlations between all of the

anthropometric variables were computed, adjusting for age. Anthropometric variables

that were not highly correlated (r < 0.7) [101] were subsequently included together in

25

the same Cox proportional hazards model in order to determine the effect of these

variables adjusted for each other.

26

Chapter 4

4. Results

4.1 Baseline Characteristics of the Study Population

Table 2 provides the descriptive characteristics of the sample at the baseline whereas

Table 3 provides the partial correlations of anthropometric variables. There are 2091

males and 2227 females. Women and men differed significantly with respect to the

anthropometric variables. Women had lower height, weight, WC and WHR compared

with men. A greater proportion of men were current smokers and drank alcohol

compared with women, although they were not significantly different in age, race,

BMI and WR. After adjusting for age, there is very strong correlation between BMI

and WC (r=0.826) in women; very strong correlation between BMI and WC (r=0.867),

between WHR and WC (r=0.841) in men.

27

Table 2. Characteristics of the study population by gender

Man Woman P-value

N 2091 2227

Age(years) 40.0 (13.6) 39.8 (12.6) 0.623

Race＊

0.855

Chinese 1221(58.4) 1315(59.1)

Malay 446 (21.3) 460(20.6)

Indian 424 (20.3) 452(20.3)

Height (cm) 167.8(6.6) 155.4 (6.0) <0.001

Weight (kg) 66.0 (12.0) 56.9 (11.2) <0.001

BMI (kg/m2) 23.3 (3.8) 23.4 (4.6) 0.190

Waist circumference(cm) 81.3 (10.8) 73.2 (11.1) <0.001

Waist-to-hip ratio 0.85 (0.07) 0.75 (0.07) <0.001

Waist Residuals (cm) 0 (5.5) 0 (6.0) 1.000

Current smoker ＊

739 (35.3) 63(2.8) <0.001

Alcohol consumption＊ <0.001

< once per month 1732 (82.8) 2167(97.3)

>= once per month 359(17.2) 60(2.7)

＊Categorical variables: numbers and column percentages

Continuous variables: means and standard deviation

Table 3. Partial correlation adjusted for age between anthropometric variable at

baseline

BMI WC WR WHR

Women

BMI 0.826 -0.125 0.439

WC 0.867 0.456 0.786

WR -0.082 0.426 0.690

WHR 0.608 0.841 0.577

Men

P<0.001

BMI, body mass index; WC, waist circumference; WR, waist residual; WHR, waist-to-hip ratio

Correlation in women are shown above the diagonal and correlations in men are shown below the diagonal

28

4.2 Anthropometric Variables and All-cause and CVD Mortality in

men

Assessment of each of the anthropometric variables showed that there were nonlinear

significant associations for BMI, WC and WHR with all-cause mortality (Table 4). To

assess the independent contribution of the various anthropometric measures to

all-cause mortality, we included different anthropometric variables in the same model.

Of note BMI and WC are highly correlated and were not included in the same model.

The quadratic term for BMI (BMI2) was significantly associated with all-cause

mortality (Table 5) when either WR or WHR were included in the model. The

association between WHR and all-cause mortality was attenuated slightly, but

achieved borderline significance when BMI was included in the model. Only WC was

associated with CVD mortality. WR was not associated with all-cause or CVD

mortality.

29

Table 4. Associations between anthropometric variables and mortality in men

Model Terms HR (95%CI) per SD increase P-value

All death

Model1

BMI 0.99 (0.82 - 1.20) 0.953

BMI2 1.08 (1.02 - 1.15) 0.013

Model2

WC 0.90 (0.75 - 1.08) 0.243

WC2 1.22 (1.12 - 1.32) <0.001

Model3

WR 1.00 (0.84 - 1.19) 0.990

WR2 1.02 (0.97 - 1.06) 0.454

Model4

WHR 0.98 (0.79 - 1.23) 0.890

WHR2 1.11 (1.01 - 1.22) 0.030

CVD death

Model1

BMI 1.16 (0.83 - 1.63) 0.376

BMI2 1.04 (0.93 - 1.17) 0.481

Model2

WC 0.97 (0.70 - 1.34) 0.862

WC2 1.16 (1.00 - 1.36) 0.050

Model3

WR 0.94 (0.64 - 1.38) 0.753

WR2 0.96 (0.77 - 1.19) 0.688

Model4

WHR 1.10 (0.72 - 1.69) 0.650

WHR2 1.00 (0.81 - 1.24) 0.997

Model 1, Model 2, Model 3 and Model 4 include variables listed and also age, race,

smoking and alcohol consumption.

Regression equations for models in Table 4:

All death in men

Model1

Model2

30

Model 3

Model 4

CVD death in men:

Model 1

Model 2

Model 3

Model 4

31

Table 5. Models for the prediction of mortality from indicators

of overall adiposity and adipose distribution in men

Model Terms HR (95%CI) per SD increase P-value

All death

Model1

BMI 0.99 (0.82-1.20) 0.926

BMI2 1.09(1.02-1.16) 0.010

WR 1.06 (0.88-1.27) 0.543

Model2

BMI 0.94(0.73-1.22) 0.656

BMI2 1.09(1.01-1.16) 0.019

WHR 1.03(0.77-1.38) 0.843

WHR2 1.09(0.99-1.21) 0.083

CVD death

Model1

BMI 1.27 (0.90 - 1.80) 0.178

WHR 0.94 (0.63 - 1.43) 0.787

Model2

BMI 1.23 (0.92 - 1.64) 0.161

WR 0.92 (0.68 - 1.24) 0.568

Model 1 and Model 2 were models adjusted for age, race, smoking group and

alcohol consumption group.

Regression equations for models in Table 5:

All death in men

Model 1

Model 2

CVD death in men

Model 1

32

Model 2

33

4.3 Anthropometric Variables and All-cause and CVD Mortality in

Women

All four measures of adiposity were associated with all-cause and CVD mortality in

women without adjusting for confounders. Quadratic terms in the models were not

statistically significant. After adjustment for age, ethnicity, smoking and alcohol

intake, only WR (HR 1.21; 95%CI: 0.98-1.49) remained marginally significant for

all-cause mortality. For CVD mortality, only WHR (HR 1.55; 95%CI: 0.98-2.43) was

found to be marginally significant (Table 6).

To further assess the effect of different anthropometric variables, BMI was combined

with WR or WHR, as similar to men; since WC was highly correlated with BMI. WC

was not combined with BMI. After adjustment for BMI, WR remained a marginally

significant predictor of all-cause mortality in women (HR 1.21; 95%CI: 0.98-1.49);

However for CVD mortality, when BMI and WHR was included in a model, WHR

remained a significant predictor (HR 1.58; 95%CI: 1.00-2.49) (Table 7).

34

Table 6. Associations between anthropometric variables and mortality in women

Model Terms HR (95%CI) per SD increase P-value

All death

Model 1

BMI 1.00 (0.78 - 1.27) 0.973

Model 2

WC 1.13 (0.89 - 1.44) 0.325

Model 3

WR 1.21 (0.98 - 1.49) 0.079

Model 4

WHR 1.20 (0.92 - 1.57) 0.181

CVD death

Model 1

BMI 0.98 (0.66 - 1.45) 0.909

Model 2

WC 1.16 (0.78 - 1.73) 0.459

Model 3

WR 1.21 (0.90 – 1.62) 0.206

Model 4

WHR 1.55 (0.98 - 2.43) 0.059

Model 1, Model 2, Model 3 and Model 4 include variables listed and also age, race,

smoking and alcohol consumption.

Regression equations for models in Table 6:

All death in women

Model 1

Model 2

Model 3

35

Model 4

CVD death in women

Model 1

Model 2

Model 3

Model 4

36

Table 7. Models for the prediction of mortality from indicators

of overall adiposity and adipose distribution in women

Model Terms HR (95%CI) per SD increase P-value

All death

Model 1

BMI 1.01 (0.79 - 1.30) 0.915

WR 1.21 (0.98 - 1.49) 0.079

Model 2

BMI 0.93 (0.71 - 1.21) 0.572

WHR 1.24 (0.93 - 1.65) 0.144

CVD death

Model 1

BMI 1.03 (0.69 - 1.53) 0.90

WR 1.21 (0.90 – 1.64) 0.210

Model 2

BMI 0.89 (0.59 - 1.35) 0.59

WHR 1.58 (1.00 – 2.49) 0.048

Model 1 and Model 2 include variables listed and also age, race, smoking and

alcohol consumption.

Regression equations for models in Table 7:

All death in women

Model 1

Model 2

CVD death in women

Model 1

37

Model 2

38

Chapter 5

5. Discussion

In this study, men showed a statistically significant U-shaped relationship between

BMI, WC and WHR and all-cause mortality. Most of the effects of obesity on

all-cause mortality were captured by BMI. Of the measures of central obesity, only

WHR showed a borderline association with all-cause mortality that was independent

of BMI. WC was not an independent predictor of all-cause mortality, as suggested by

the lack of association between WR and mortality. In contrast, in women, all four

measures of adiposity showed a linear relationship with all-cause mortality. However,

after adjusting for age, ethnicity, smoking and alcohol intake, only WR showed a

borderline association with all-cause mortality. WHR showed a borderline association

with CVD mortality which was statistically significant after including BMI in the

model.

Our study contributes to the literature in several ways. Firstly, it confirms the

U-shaped association between BMI and all-cause mortality in men, that has been

demonstrated in China [87] and Korea [59]. In this study, we extend this U-shaped

relationship to include measures of central obesity, WC and WHR. In the Canada

Fitness Survey, significant J shaped relationships in men were observed between BMI

and WC and all-cause mortality rate [102]. A U-shaped relationship between WC and

mortality was also observed in the NHANES III study [77], although the quadratic

term was not statistically significant in that study. The frequently observed U-shaped

relationship between BMI and mortality rate may be due to the opposing effects of fat

mass and fat-free mass components of BMI on mortality rate [103]. However, the

U-shaped association between WC and WHR, which are not affected by fat free mass,

39

and mortality in men suggest that this is not the case. Another possible explanation is

that BMI is a poor anthropometric measure to diagnose obesity. The percentage of

body fat for a given BMI is highly variable [104] and if so, our study suggests that

this applies to WC and WHR as well.

Secondly, our study suggests, at least in men, that the association between WC and

mortality is largely mediated through its co-linearity with BMI. No independent

association was observed between WC and mortality. In recent years, there has been

increasing speculation over which measure of overweight and obesity is best able to

discriminate those individuals who are at increased mortality risk. The most

appropriate adiposity marker for assessing the risk of death is debated [76, 77, 95, 102,

105-108]. BMI is the most frequently used measure of obesity because of the robust

nature of the measurement of weight and height [14]. However, BMI cannot

differentiate between lean and fat mass nor can it account for differences in relative

fat distribution.

The metabolic effects of abdominal adiposity are well established. Greater abdominal

adiposity is closely associated with adverse metabolic profiles such as insulin

resistance, dislipidemia, and systematic inflammation, which play essential roles in

the pathogenesis of CVD and diabetes mellitus [109]. Adipose tissue from visceral fat

deposits secretes potential mediators in the development of chronic diseases [13].

Paradoxically, increased lower body fat does not confer the same risk of CVD and

insulin resistance as the same amount of fat in the upper body; lower body fat appears

to exert a protective effect [110]. A number of epidemiological studies have

demonstrated that measures of abdominal adiposity significantly predict chronic

diseases such as CVD and diabetes mellitus independently of overall adiposity [32]. A

meta-analysis shows that indices of abdominal obesity are better discriminators of

cardiovascular risk factors than BMI in both men and women [19]. The use of WC

has been proposed because it is more strongly related to visceral fat and may therefore

be a risk indicator of mortality caused by visceral fat. However, enlarged WC

40

generally reflects increased fat deposition either in the subcutaneous or visceral layers.

As a consequence, WC and BMI are highly correlated. In our study, we were not able

to detect any independent association between WR and mortality despite previous

findings that waist circumference residual scores after adjustment for BMI was more

closely associated with increased total body fat mass than fat free mass [111] and can

provide specific information about regional fat distribution. WHR is less strongly

related to BMI than is WC and is therefore a more specific surrogate for fat

distribution. An increased WHR may reflect a relative abundance of visceral fat,

decreased peripheral fat or muscle, or both. Alternatively, a low WHR may reflect a

relative lack of visceral fat, increased muscle or peripheral fat, or some combination

[77]. Thus, the combination of waist circumference with hip circumference may

provide a unique estimation of body shape or fat distribution. Some researchers have

shown that WHR is the most useful measure of obesity, and is the best and simple

anthropometric index in predicting a wide range of risk factors and related health

conditions [14]. Our findings showing a borderline association between WHR and

all-cause mortality in men, and between WHR and CVD mortality in women are in

line with these previous findings.

Finally, our findings suggest the possibility that the relationship between various

anthropometric measures of adiposity and mortality are gender dependent in Asians.

The U-shaped relationship between obesity and mortality observed in men was not

seen in women. Furthermore, only WHR (and not BMI, WC or WR) showed a

statistically significant association with mortality (specifically CVD mortality) after

adjustment for age, ethnic group, smoking and alcohol intake. However, it seems

important to consider BMI when assessing the relationship between WHR and CVD

mortality. We would point out that these associations are very marginal in terms of

statistical significance, and these findings must be taken with that in mind.

Nonetheless, in the Hong Kong Cardiovascular Risk Factor Study, the authors

reported the impact of sex-specific body composition on cardiovascular risk factors.

After adjustment for BMI, hip circumference independently and inversely contributed

41

to CVD risk in women but not in men, unlike in Caucasians where the relationship

was identified in both sexes. Increasing adjusted waist circumference was associated

with increased risk of hypertension, diabetes and dyslipidemia in Chinese women

only [112]. In another study comprising of Chinese women, WHR was positively and

significantly associated with risk of death from CVD in a dose-response fashion

(P<0.01 for trend) [92]. In the Nurses‟ Health Study, increasing WHR were associated

with a graded increase in CVD mortality independent of BMI (P<0.001 for trend).

Spline regression also showed a linear association of WHR with CVD mortality [55].

In the Norwegian HUNT 2 study, WHR predicted CVD mortality in women (P<0.001

for trend) [71]. In an Australian study, WHR was the dominant risk factor predicting

cardiovascular death in women [113].

The strength of our prospective study includes its high follow-up via data linkage,

relatively long duration of follow-up and direct measure of the anthropometric

measures. Our anthropometric measures were made by direct physical examination

according to standard protocols as opposed to self-reported data. This eliminated the

possibility of any bias from self-reporting.

Our study does have some potential limitations. The principle limitation of this study

was the relatively small sample size and modest numbers of events. In studies

assessing the association of obesity with mortality, two sources of biases are likely to

be particularly important [114]. One of them is the problem of reverse causality.

Weight loss can be the result, rather than the cause, of underlying illness. To avoid the

impact of reverse causality, exclusion of deaths in the early period of follow-up,

exclusion of participants with existing disease or recent weight loss are required.

Second, smoking is an important confounder that might mask true effect of obesity.

Smoking is associated with reduced weight and is also an important determinant of

mortality. Simple adjustment for smoking categories in multivariable models may be

insufficient to fully control for its effect and lead to residual confounding. One way to

get around this problem is to examine the effect of obesity on mortality only among

42

never-smokers. However, due to the relatively young age of the cohort, the number of

deaths is not large and this limits the statistical power of the study and the possibility

to address the two types of biases in the statistical analyses. Moreover, for public

health and clinical recommendations, it is not sufficient to know whether an

anthropometric measure is a significant predictor or not, but it is also important to

define the most favorable range for interventions. Grouping participants according to

categories of anthropometric measures would allow for more detailed examination of

the associations. However, the sample size and the number of deaths are too limited

for such detailed analyses. In addition, although anthropometric variables are reliable,

quick and inexpensive to administer, these measures could not distinguish fat from fat

free mass. Hence, it is important to explore these issues with sophisticated imaging

techniques that incorporate direct measurements of body composition. Further

research to carefully assess body composition may yield novel insights into the

relationship between body composition and mortality.

43

Chapter 6

6. Future Work

There are several areas in which future research could be undertaken.

6.1 More Accurate Measures of Fat Composition

The traditional approaches for measurements of body fat are anthropometric measures.

The advantages of anthropometric measures such as BMI and WHR are inexpensive,

widely available and portable, qualities that make it attractive for field studies or

population based studies [49]. However, these measurements also include lean tissue

and bone and therefore are not direct measures of fat content.

BMI and other surrogate measures of fatness are widely used in community studies

and adopted by international organizations for a few basic applications, such as

surveillance of secular trends or regional variations [15, 52]. However, there are

certain important circumstances where there is a mismatch between the surrogate

measures (especially BMI) and true body fatness, which results in the surrogate

measures giving misleading information. In a large representative sample of the US

population, bioelectrical impedance analysis (BIA) was used to estimate BF%. Abel

and colleagues found that in men BMI correlated significantly better with lean mass

than with BF% [58]. Okorodudu and colleagues have performed a systematic review

and meta-analysis on the diagnostic performance of BMI to indentify obesity.

Commonly used BMI cutoff values have high specificity but low sensitivity to

identify adiposity (Included studies used a body composition technique as the

reference standard for BF% measurement) [115]. Frankenfield and colleagues

44

reported that measurement of body fat (using impedance-derived body fat mass as the

criterion) is a more appropriate way to assess obesity in people with a BMI below 30

kg/m2 [116]. In addition, in recent years there has been an increasing awareness that

certain ethnic groups display a very different relationship between BMI and body fat

to that described for Caucasians [32]. Many Asian races tend to carry a

proportionately higher fat mass for a given BMI than Caucasians. In Chinese

population, Chen and colleagues concluded that BMI was correlated with BF% (body

fat was estimated from Body Composition Analyzer). However, BMI had its

limitations in the interpretation of subjects with BMI between 24 and 27.9 kg/m2 [51].

The above examples illustrate that the simple anthropometric measures of body fat

can give misleading information concerning the true body composition of an

individual or group of individuals owing to a mismatch in the usual relationship

between lean and fat tissue [52].

In future, greater attention should be paid to the development of standards based on

the direct measurement of body fat in population, rather than on surrogate measures.

It is rather remarkable that there is little data on body fat content and no normative

standards that can be used as clinical or surveillance tools. We should be making

greater efforts to actually assess body fat and to develop population standards against

which individuals could be compared.

6.2 Prediction Equation

BF% and visceral fat are appealing direct measures of fatness because the medical

literature suggests that it is fat that causes morbidity and mortality [32]. And it is not

just the amount of fat that matters, but also the location or distribution of the fat [17,

32]. Simple anthropometric methods, such as WHR, WC are widely used. However,

these methods cannot differentiate between visceral and subcutaneous fat and are less

accurate [47]. Techniques for direct measurement of visceral fat such as CT are

45

expensive, time-consuming or require a relatively high radiation dose [117]. Effective

methods for assessing BF% or visceral fat are important to investigate the role of

body fat for the increased health risks. In future work, we can generate prediction

equations for BF% or visceral fat, so researchers using a dataset that include

anthropometric measures could estimate these more accurate measures of fatness.

Equations that predict body fat from anthropometric measurements allow the

estimation of body composition without complex and costly techniques, and they can

be used easily for on-field assessment, to provide adequate information that is

required in clinical and research practice, especially in low budget and field settings

[118]. In Europe, the age- and sex- specific equations of Durnin & Rahaman and

Durnin & Womersley relating skinfold thickness at four sites to BF% are widely used

[119]. The anthropometric prediction equation can provide useful information about

the percentage of body fat, thus eliminating the need to scan participants with

technologies such as DXA. In Asians, there are a limited number of

anthropometry-based prediction equations for the estimation of BF% in adults. Using

DXA as the reference method, future study could construct and validate prediction

equations for BF% from anthropometric measures. In addition, there are also

relatively few studies which investigate the relationships between anthropometric

variables and visceral fat. Janssen and colleagues found that BMI and WC

independently contribute to the prediction of visceral fat in Caucasian men and

women [54]. Snijder and colleagues reported that for the prediction of visceral fat, the

sagittal diameter, which has a practical advantage compared to DXA, is just as

effective [120]. In Japanese adults, Demura and colleagues proposed a prediction

equation for visceral fat area at the umbilical level from WHR and internal fat mass

based on three skinfolds [121]. They also developed equations to predict visceral fat

area at the umbilicus level using fat mass of the trunk measured by DXA and BIA

[122]. Kuk and colleagues found that hip circumference, thigh circumference and

BMI are positively associated with total, lower-body and abdominal subcutaneous

adipose tissue and skeletal muscle mass but negatively associated with visceral

46

adipose tissue after adjustment for WC [123].

The use of anthropometric measures as surrogates of body fat differs by ethnicity and

equations that have been derived in western population are often not suited for other

populations [79]. At the present time, there is little systematic data available in

Singapore regarding the comparison of anthropometric measures of adiposity to a

standard reference method such as DXA or CT. Therefore, it is meaningful to develop

prediction equations to estimate body composition in Singapore in future work.

6.3 Body Composition and Cardiometabolic Risk Factors.

Studies indicate that obesity closely associated with a number of metabolic and

cardiovascular risk factors including high-fasting glucose, hypertension, dislipidemia

and high-sensitively C-reactive protein levels [20, 124-132]. However, most studies in

the literature focused on the associations between anthropometric measures and

cardiometabolic risk factors [20, 124-128]. The use of BMI, WHR and WC as a single

anthropometric index of cardiovascular and metabolic risk is limited by the fact that

for a given measurement value, there may be large variations in the level of total body

fat and in the level of abdominal visceral adipose tissue that are most likely to be

associated with important variations in the metabolic profile [63, 133]. Jennifer and

colleagues also found that when using BMI to define obesity, approximately one third

of obese individuals were metabolically healthy [134]. Studies in the literature have

evaluated the ability of commonly used anthropometric indices in identifying

cardiovascular risk factors [20, 124-132]. The best adiposity measurement for

identifying cardiovascular risk factors remains controversial [127]. Moreover,

although the overall correlations between anthropometric measures and

cardiometabolic risk factors appear to be significant, the magnitude of associations

were small [20]. The prediction of cardiovascular risk factors by anthropometric

indices has limitations. Reasonable explanations are anthropometric indices as

47

indicators of obesity are inadequate and are not reliable in individuals [135].

The investigations into the roles of fat and lean body mass are not only highly

appropriate, but timely. First, fat and lean body mass are more accurate measurements

of body composition than anthropometric methods [46]. Second, to our knowledge,

the role of fat mass and lean mass in relation to cardiovascular risk factors has not

been studied in a population based study in Singapore. Knowledge regarding direct

indices of adiposity in relation to cardiometabolic risk factors can have significant

impact on the prevention of obesity, type 2 diabetes and CVD. Although

anthropometric measurements have the advantage of being simple and inexpensive

parameters, direct assessment of body fat mass and lean mass may be better indexes

of obesity because they correlate better with cardiometabolic risk factors. In further

study, gender-specific cutoffs of direct indices of fat mass and lean body mass could

be used for clinical and research purpose to detect cardiovascular risk factor and for

categorizing obesity in Singapore. Greater understanding about the associations

between fat and lean body mass and cardiovascular risk factors will result in the

development of innovative and more effective therapeutic strategies and better quality

of life for the patients.

6.4 Body Composition and Obesity Prevention

Accurate determination of body fat could provide clinically useful guidance for

physicians to assess disease risks in patients with obesity and optimize the preventive

or therapeutic remedies for these patients. In addition, for a given BMI, Asian

populations were noted to have higher BF% and higher cardiovascular risk factors

than Western populations [81, 82]. We estimate that in Singapore, many people with

normal BMI but higher body fat mass may be unaware of their heightened

cardiometabolic risk. Because self-awareness of a condition is the initial step in

behavioral modification and incorporation of therapeutic lifestyle changes, it might be

48

relevant to incorporate fat mass and lean mass measurement in the regular physical

exam. This can help us to focus on early recognition and treatment of risk factors and

symptoms instead of end-stage treatment of chronic diseases. From a clinical

perspective, assessment of indicators of body composition may contribute to a better

evaluation of an individual‟s risk for CVD. Measures of body composition and body

fat distribution that are both simple and direct could help stratify individuals

according to their level of body fat and preserved lean mass. The significance of

differentiating between lean mass and fat mass are not only for adequate risk

assessment and accurate prognosis but also to determine appropriate interventions.

For example, weight loss without preservation of lean mass might be harmful and

could be associated with increased overall mortality and adverse cardiovascular

events.

49

Chapter 7

7. Conclusion

In summary, we confirmed the U-shaped association between BMI and all-cause

mortality and extended these findings to include WC and WHR in men. WC (either as

directly measured or calculated as a residual) did not provide additional information

that helped predict mortality in men or in women. In contrast, WHR appeared a better

predictor of mortality. Our study also suggests that the relationships between

measures of adiposity and mortality may differ between genders. General adiposity

appears to be a significant predictor of all-cause mortality in men, more so than

central adiposity. Although measures of central adiposity were better predictors of

CVD mortality in both men and women as compared with measures of general

adiposity, there was a difference in that the association was U-shaped for men and

linear for women. This type of heterogeneity may result in the differing findings from

studies that used different study populations, sampling, measures and analytic

approaches [55].

50

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64

APPENDIX 1

NATIONAL UNIVERSITY OF SINGAPORE HEART STUDY

This is a medical survey.

The information is confidential and available only to the doctors in the survey.

If you require any treatment, you will be informed.

If you have any questions, please ask the attending doctor.

The study findings will help us improve the health of the people of Singapore.

Thank you for your co-operation.

Blood taken

Interviewed

Measurements

Doctor

Tea

65

Source 1. Thyroid and Heart Study 2. Corolipid Study 3 General population

Name:______________________ Serial Number

Address_____________________ Date___/ ____/______(day/month/year)

___________________________________

________________________________ Interviewer __________________

Tel. No. ___________________________ Interview language/dialect _________

I/C No. ___________________________ Time of blood collection ___________

1. DEMOGRAPHIC

1 When did you last take food or drink (except water)?____________Hrs fasting

1.2 Sex 1. Male 2. Female

1.3 What is your date of birth? _____/______/_______ (day/month/year)

1.4 What is your marital status?

1. Single

2. Married

3. Widowed

4. Separated/Divorced

1.5 What is your ethnic group?

1. Chinese

2. Malay

3. Indian

4. Others/specify________

2. OCCUPATION

2.1 What is your present working status?

66

1. Working/employer

2. Working/employee

3. Unemployed/seeking work

4. Unemployed/sick or disabled

5. Student

6. National Service

7. Housewife

8. Retired

2.2 What is (was) your present (last) job? ___________________________________

2.3 If Female, What is (was) your husband’s present (last) job? __________________

3. EXERCISE

3.1 How much physical activity do (did) you have in your present ( last ) job?

1. Mostly sitting

2. Moderate activity

3. Much activity

3.2 (i) Do you take active exercise, e.g. jogging, sports? 1. No 2. Yes

If No. go to 4.1

(ii) What type of activity is this?_______________________________________

(iii) How many times a month (on average) do you do this activity? __________

(iv) Do you exercise for at least 15 minutes fairly continuously during each session?

1.No 2. Yes

(v) Do you exercise till you perspire and breathe deeply during each session?

1. No 2. Yes

4. ALCOHOL

4.1 (i) Would you describe your present alcohol intake as :

1. None/Special occasions only

2. Once or twice a month

3. Once or twice a week

4. Daily/most days

If None/Special occasions only, go to 5.1

67

(ii) How many drinks do you usually take on those days that you drink?

( a drink is a single spirit, glass of wine, or small beer) ___________________

5. CIGARETTE SMOKING

5.1 (i) Do you smoke cigarettes now? 1. No 2. Yes

If No, go to 5.2

(ii) On average how many cigarettes do you smoke a day?____________________

(iii) How old were you when you began to smoke cigarettes?_________________

5.2 (i) Did you ever smoke cigarettes? 1. No 2. Yes

If No, go to 6.1

(ii) On average how many cigarettes did you smoke a day?__________________

(iii) How old were you when you began to smoke cigarettes?________________

(v) How old were you when you stopped smoking cigarettes?_______________

(vi) Why did you stop ?_______________________________________________

6. CHEST PAIN ON EFFORT

6.1 ( i ) Have you ever had any pain or discomfort in your chest ? 1. No 2. Yes

If No, go to 7.1

(ii) How old were you when you first had the pain or discomfort? _____________

6.2 Do you get it when you walk uphill or hurry? 1. No 2. Yes

6.3 Do you get it when you walk at an ordinary pace on the level? 1. No 2. Yes

If No to 6.2 and 6.3, go to 7.1

6.4 What do you do if you get it while you are walking?

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1. Carry on

2. Stop, slow down or take medicine

6.5 If you stand still, does it go away? 1. No 2. Yes

If No, go to 7.1

6.6 How long does it take to go way?

1. More than 10 minutes

2. 10 minutes or less

6.7 Where do you get this pain or discomfort?

(record all areas indicated)

1. Upper or middle sternum

2. Lower sternum

3. Left anterior chest

4. Left arm

5. Others (specify)___________

7. POSSIBLE INFARCTION

7.1 Have you ever had a severe pain in your chest lasting for half an hour or more? 1. No 2. Yes

If No, go to 8.1

7.2 How many of these attacks have you had?________________________________________

7.3 How old were you when you had the first attack?_________________________________

8. CARDIOVASCULAR DISEASES AND DIABETES

8.1 (i) Has a doctor ever told you that you hd angina? 1. No 2. Yes

If Yes: (ii) What was your age at that time?__________________________________________

8.2 (i) Has a doctor ever told you that you had a heart attack? 1.No 2. Yes

If Yes: (ii) What was your age at that time?__________________________________________

8.3 (i) Has a doctor ever told you that you had high blood pressure? 1. No 2. Yes

If Yes: (ii) What was your age at that time?__________________________________________

69

8.4 (i) Has a doctor ever told you that you had diabetes mellitus? 1.No 2.Yes

If Yes: (ii) What was your age at that time?__________________________________________

8.5 Has any member of your immediate family ( father, mother, brother, sisters) ever suffered

from?

Angina or a heart attack: 1. No 2. Yes Relatives________________________________

High blood pressure: 1. No 2. Yes Relatives________________________________

Diabetes mellitus: 1. No 2. Yes Relatives________________________________

9. DIET

9.1 Are you on any of the following special diets?

Weight reducing: 1. No 2. Yes

Diabetic: 1.No 2. Yes

Low fat: 1. No 2. Yes

Low salt: 1.No 2. Yes

Vegetarian: 1.No 2. Yes

10. PREGNANCY AND PILL ( For all females below 50 years)

10.1 Are you pregnant? 1. No 2. Yes

10.2 Are you on the contraceptive pill? 1. No 2. Yes Specify_____________________

11. ADVICE FROM DOCTOR ( For “Thyroid and Heart Study” subjects)

11.1 Did you consult your doctor after your check-up about 10 years ago?

1. No 2. Yes

11.2 If Yes: What advice did he give you? __________________________________________

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12. DEATH ( if applicable)

12.1 Date of death: __/___/____(day/month/year)

12.2 Cause of death:____________________________________________________________

12.3 Source of information on cause of death:________________________________________

13. EXAMINATION DOCTOR___________________

13.1 BLOOD PRESSURE Blood Pressure 1 S1____________

D5____________

Blood Pressure 2 S1_____________

D5____________

13.2 HEART

History (consult previous questions) _________________________________________________

Examination_____________________________________________________________________

Diagnosis_______________________________________________________________________

Angina: 1. No 2. Yes

Myocardial Infarction: 1. No 2. Yes

Other heart disease: 1.No 2.Yes Specify______

13.3 MEDICATIONS

What medicines are you now taking?_____________________________________

Complete below if relevant

Antihypertensive 1. No 2. Yes Specify:_______________ Have you taken this drug in

the last 48 hrs? 1. No 2. Yes

Diuretic 1. No 2. Yes Specify:_______________ Have you taken this drug in

71

the last 48 hrs? 1. No 2. Yes

Lipid lowering 1. No 2. Yes Specify:_______________ Have you taken this drug in

the last 48 hrs? 1. No 2. Yes

Insulin 1. No 2. Yes Specify:_______________ Have you taken this drug in

the last 48 hrs? 1. No 2. Yes

14. MEASUREMENTS

14.1 Height ( to nearest 0.5 cm)_________________

14.2 Weight ( to nearest 0.1 kg)___________________

14.3 Wait ( to nearest 0.1 cm)____________________

14.4 Hips( to nearest 0.1 cm)____________________

14.5 Electrocardiogram ( Minnesota Code)____________

14.6 Angiogram______________________

15. INVESTGATIONS

15.1 Lipoproteins

Triglyceride ( mmol/l)

Total cholesterol ( mmol/l)

HDL cholesterol ( mmol/l)

LDL cholesterol ( mmol/l)

VLDL cholesterol ( mmol/l)

Apo A-1 (mg/dl)

Apo B (mg/dl)

Lp (a) (mg/dl)

15.2 Haematology

Fibrinogen (g/l)

72

Factor VIIc (Iu/ml)

tPA (ng/ml)

PAI-1 (Iu/ml)

Prothrombin Fragment F1+2 ( nmol/l)

15.3 Diabetes

Glucose ( mmol/l)

Insulin ( miu/l)

OGTT: 0 hrs Glucose ( mmol/l)

Insulin ( miu/l)

OGTT: 1 hrs Glucose ( mmol/l)

Insulin ( miu/l)

OGTT: 2 hrs Glucose ( mmol/l)

Insulin ( miu/l)

Diabetes: 1. No 2. Yes ( diagnosed lst survey)

3. Yes ( diagnosed since lst survey) 4. Yes ( diagnosed this survey)

15.4 Vitamins

A Beta-carotene (ug/ml)

Retinoid (ug/ml)

Trans-retionol (ug/ml)

E Alpha-tocopherol (ug/ml)

Gamma-tocoperol (gu/ml)

C Vitamin C (ug/ml)

15.5 Minerals Selenium (ug/l)

Feerritin (ug/l)

73

APPENDIX 2

NATIONAL HEALTH SURVEY, 1992

(Please record data/code(s) in the boxes provided)

1. Name: ___________________________________________________________ 2. NRIC No.: ___________________________________________________________ 3. Sex

1. Male 2. Female

___________________________________________________________ 4. Ethnic Group :

1. Chinese 2. Malay 3. Indian

___________________________________________________________ 5. Date of Birth: ___________________________________________________________ 6. Marital Status: 1. Single 2. Married 3. Divorced/widowed/separated

7. At what time did you last eat or drink (except plain water only)? 8.a) Have you ever been told by a doctor (Western-trained) that you have Diabetes? 1. Yes 2. NO b) if “ YES”: Are you currently on regular medication from your doctor (Western-trained)?

1. Yes (Exempted from OGTT) 2. No (Proceed with OGTT)

_____________________________________________________________________

A- PERSONAL PARTICULARS

B- FASTING STATUS

C- DIABETIC STATUS

Registration Clerk No. Date of Registration :_______(day/mth/Yr)

SURVEY NO.

74

9. Height/Weight Reading 1 Weight (kg) Height (cm) Recorder No. Reading 2 Weight (kg) Height (cm) 10.a) Waist/Hip Girth Reading 1 Waist (cm) Hip (cm) Recorder No. Reading 2 Waist (kg) Hip (cm) Reading 3* Waist (kg) Hip (cm) (* Only to be done if there is a difference of more than 2cm between Readings 1 & 2 in either waist &/or hip measurements) b) Triceps Skin Fold Thickness (mm)

Reading 1 Reading 2 Reading 3*

(* Only to be done if there is a difference of more than 0.4mm between Readings 1 & 2 ) _____________________________________________________________________ 11. Blood Pressure Reading 1 Systolic (mmHg) Diastolic (mmHg) Recorder No. Reading 2 Systolic (mmHg) Diastolic (mmHg) Reading 3* Systolic (mmHg) Diastolic (mmHg) (* Only to be done if there is a difference of more than 20% between Readings 1 & 2 in either Systolic &/or Diastolic blood pressure) 12. Plasma glucose (venous, fasting) (mmol/L)

D- PHYSICAL EXAMINATION

E- BLOOD CHEMISTRY RESULTS (For Official Use Only)

75

13. OGTT Results (2hr plasma glucose (venous)) (mmol/L) 14. Plasma lipids (fasting) (mmol/L) Total Cholesterol LDL HDL Triglycerides Apolipoprotein A1 Apolipoprotein B 15. Serum insulin (fasting) (mU/L) Serum insulin (2 hr post glucose load (mU/L) Serum uric acid (µmol/L) Serum creatinine (µmol/L) 16. HbA1C(%) (For known diabetics only) 17. a) Occupational status : 1. Working (record occupation below) 2. Unemployed (record previous occupational below) 3. Retired (record previous occupational below) 4. Housewife 5. Student b) Occupation:_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ___________________________________________________________________ 18. Highest level of Education Attained: 1. No formal education 2. PSLE 3. GCE ‘O’ Level 4. GCE ‘A’ Level/diploma 5. Degree/professional qualification 19. Level of occupational physical activity (Relate to occupational status and occupation indicated in Q.17 above and probe respondent if in doubt):

F- SOCIO-ECONOMIC PROFILE

G- PHYSICAL ACTIVITY

76

1. Sedentary (eg. office worker, Unemployed, retired, student) 2. Light (eg. Sales, housewives) 3. Moderate (eg. Craftsman) 4. Heavy (eg. Construction) _____________________________________________________________________ 20. a) In the past month, did you participate in any sports or exercises? 1. Yes 2. No (Go To Q.21) b) if “YES”:

(i) What were the sports or exercises that you participated in (each session should have lasted for at least 30 minutes); and

(ii) How many times in the past month did you go for each of those activities? 01. Running/jogging 02. Brisk walking 03. Swimming Activity No. of times in past month 04. Cycling (each time lasting at least 30 mins) 05. Aerobic exercise 06. Basketball (i) 07. Netball 08. Sepak takraw (ii) 09. Soccer 10. Squash (iii) 11. Badminton 12. Table tennis (iv) 13. Tennis 14. Social dancing (V) 15. Taichi/yoga 16. Others (specify): _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

21. Have you ever smoked cigarettes before?

1. Yes 2. No, never (Go to Q.25)

_____________________________________________________________________ 22. Which of the following categories do you belong to now?

1. Previously experimented with smoking (Go to Q.25) 2. Ex-smoker (smoked regularly for at least 6 months before but have stopped

smoking completely) (Go to Q.24) 3. Occasional smoker(smoke less than 7 cigarettes/week) (Go to Q.25) 4. Regular smoker (smoke at least 7 cigarettes/week) (Go to Q.23)

H- CIGARETTE SMOKING

77

_____________________________________________________________________ For Regular Smoker Only 23. a) On average, how many sticks of cigarettes do you smoke a day? b) At what age did you start smoking regularly? Go to Q.25 _____________________________________________________________________ For Ex-Smoker only 24. a) How long ago did you stop smoking cigarettes? 1. Less than 6 months 2. 6 months to 1 year 3. More than 1 year b) Hong long did you smoke before you stopped smoking? c) What were the two MAIN reasons that made you stop smoking? (Please rank them accordingly)

1. Advised to stop smoking by doctor 2. Learnt about ill effects of smoking from mass-media/health fair/nurse 3. Cigarettes have become too expensive 4. Fewer places to smoke in, due to banning of smoking in public areas 5. Family/peer pressure 6. Do not get a kick out of smoking anymore 7. Other reasons (specify)_ _ _ _ _ _ _ _ _ _

25. Do you consume alcohol? 1. Yes 2. No (Go to Q.29) _____________________________________________________________________ For those who drink only 26. How often do you have alcoholic drinks? 1. Daily/most days (4-7 days a week) 2. 1 to 3 days a week 3. 1 to 3 days a month 4. Less than once a month (eg. on special occasions such as Christmas, New Year, social gatherings etc.) _____________________________________________________________________ 27. a) What is your MAIN alcoholic drink? 1. Beer 2. Stout 3. Wines

I- ALCOHOL INTAKE

78

4. Spirits (gin, whisky, rum,brandy, vodka) 5. No specific preference b) How many cans/bottles/glasses of this do you usually drink on any one occasion? _____________________________________________________________________ 28. During the past month, have you ever had 5 or more drinks on any one occasion? (If “YES”, Probe : How many occasions?) (A drink is 1 can/small bottle of beer, 1 glass of wine or 1 measure (peg) of spirits)

1. No 2. Yes, one occasion 3. Yes, two occasions 4. Yes, three occasions or more

DIABETES 29. a) Has anyone in your family ever had diabetes?

1. Yes 2. No 3. Don’t know

b) If “YES”: Specify relationship (up to a maximum of 3 members):

1. Father 2. Mother 3. Brother 4. Sister 5. Grandfather 6. Grandmother

If respondent is not a known Diabetic (i.e. “No” to Q.8a), Go to Q.31 _____________________________________________________________________ For Diabetics only (i.e. if “YES” to Q.8a) 30. a) How many years have you had diabetes? b) Does your doctor (Western-trained) currently prescribe any tablets/insulin injections for your diabetes? 1. Yes 2. No (Go to Q.30d) c) If “YES”: are you taking the tablets/insulin injections prescribed by your doctor (Western-trained)? 1. Yes 2. No (Go to Q.30d) If you are on tablets/insulin injections, are you following the amounts and frequency

J- MEDICAL AND FAMILY HISTORY

79

as prescribed by your doctor (Western-trained)? 1. Yes 2. No, reduce what has been prescribed 3. No, increase what has been prescribed

d) Do you try to control your diabetes by taking any particular foods/traditional medicine?

1. Yes 2. No (Go to Q.30f) e) If “YES”: What do you take? (May indicate more than one answer) 1. Certain fruits or vegetables (specify)_ _ _ _ _ _ 2. Herbal/traditional medicine f) (i) What other measures or lifestyle changes do you practice to control your

diabetes? (ii) What measures or lifestyle changes, if any, did your doctor advise you for

your diabetes? Measure (Read Out) Practice? Advice? (1.Yes 2.No) (1.Yes 2.No) 1. Lose weight/maintain ideal weight

2. Reduce food intake

3. Reduce intake of simple carbohydrates (eg. sugar, white rice, white bread, etc) 4. Increase intake of complex carbohydrates/high fiber food (eg. wholemeal bread, brown rice, vegetables) 5. Reduce fat intake

6. Cutting down/stop smoking

7. Reduce alcohol intake

8. Exercise

9. Special care of feet/toes

10. Others (specify): _ _ _ _ _ _ _

g) To your knowledge, is your diabetes now under control? 1. Yes 2. No (Go to Q.31) 3. Don’t know (Go to Q.31) h) If “YES”: (i) How do you know that your diabetes is under control? Was it based on- (May indicate more than one answer)

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1. Blood test by doctor/nurse 2. Urine test by doctor/nurse 3. Own blood test 4. Own urine test 5. Feel well (Go to Q.31)

(ii) if a test was done, how long ago was the most recent test done?

1. Less than 1 month

2. 1 to 3 months

3. 4 to 6 months

4. More than 6 months

HYPERTENSION

31. Have you ever been told by a doctor (Western-trained) that you have high blood pressure?

1. Yes

2. No

_______________________________________________________________________________

32.a) Has anyone in your family ever had high blood pressure?

1. Yes

2. No

3. Don’t know

b) If “YES”: Specify relationship (up to a maximum of 3 members):

1. Father

2. Mother (i)

3. Brother

4. Sister (ii)

5. Grandfather

6. Grandmother (iii)

If “No” to Q.31, Go to Q.34

____________________________________________________________________________

For Hypertensives only (i.e. if “YES” to Q.31)

33.a) How many years have you had high blood pressure?

b) Does your doctor (Western-trained) currently prescribe any tablets for your high blood

pressure?

1. Yes

2. No (Go to Q.33d)

c) If “YES”: Are you taking the tablets prescribed by your doctor (Western-trained)?

1. Yes

2. No (Go to Q.33d)

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If you are on tablets, are you following the amounts and frequency as prescribed by

your doctor (Western-trained)?

1. Yes

2. No, reduce what has been prescribed

3. No, increase what has been prescribed

d) Do you try to lower or control your blood pressure by taking any particular

foods/traditional medicine?

1. Yes

2. No (Go to Q33f)

e) If “YES”: What do you take? (May indicate more than one answer)

1. Certain fruits or vegetables (specify):_ _ _ _ _ _ _

2. Herbal/traditional medicine

33. f) (i) What other measures or lifestyle changes do you practice to control your blood

pressure?

(ii) What measures or lifestyle changes, if any, did your doctor advise you for your high

blood pressure?

Measure (Read Out) Practice ? Advice?

(1.Yes 2.No) (1. Yes 2. No)

1. Lose weight

2. Reduce salt intake

3. Reduce fat intake

4. Reduce food intake

5. Exercise

6. Cutting down/stop smoking

7. Reduce alcohol intak

8. Reduce/cope with stress

9. Others (specify): _ _ _ _ _ _ _ _

g) To your knowledge, is your blood pressure now under control?

1. Yes

2. No (Go to Q.34)

3.Don’t know (Go to Q.34)

h) If “ YES”:

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(i) How do you know that your blood pressure is under control?

Was it based on-(may indicate more than one answer)

1. Information from doctor/nurse

2. Own recording

3. Feel Well ( Go to Q.34)

(ii) If your blood pressure was checked, how long ago was the most recent check done?

1. Less than 1 month

2. 1 to 3 months

3. 4 to 6 months

4. More than 6 months

_______________________________________________________________________________

CARDIOVASCULAR DISEASE

34. Have you ever been told by a doctor (Western-trained) that you have ischaemic heart

disease/coronary heart disease/angina (manifested by chest pain on exertion) or had a heart

attack?

1. Yes

2. No

35. Have you ever had a stroke before ( manifested by numbness or weakness/paralysis on one

side of the body affecting one side of the face, one arm and one leg lasting either less or more

than 24 hours)?

1. Yes

2. No

If Male, Skip Q.36 & Q.37

COMMON PREVENTABLE CANCERS AMONG WOMEN

PAP Smears

( A PAP Smear is a simple test involving the scraping of cells from the mouth of the womb to

detect early cancer)

36. a) Have you ever had a PAP Smear test?

1. Yes

2. No (Go to Q.36c)

3. Don’t know (Go to Q.36c)

b) If “ YES”:

(i) Hong long ago have you had your last smear done?

1. Less than 1 year

2. 1 to 3 years

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3. More than 3 years

(ii) Where was your last smear done?

1. MCH clinic

2. Government specialist clinic

3. Private medical clinic/hospital, done by a gynaecologist

4. Private medical clinic, done by a general practitioner

5. Singapore Cancer Society

6. Others (specify):_ _ _ _ _ _ _ _

c) Do you think it is important to have a PAP Smear test ?

1. Not important

2. Important

3. Don’t know

_______________________________________________________________________________

Breast Examination

37.a) Have you ever had your breasts examined by a doctor or nurse?

1. Yes

2. No (Go to Q.37c)

b) If “YES”: How long ago was it that you had such an examination?

1. Less than 1 year

2. 1 to 3 years

3. More than 3 years

c) Have you been taught Breast Self-Examination by a doctor or nurse?

1. Yes

2. No

d) How often do you perform breast examination on yourself?

1. At least once a month

2. Once in every few months

3. Less often

4. Never

e) Do you think it is important to have a breast examination (whether by doctor/nurse/self)?

1. Not important

2. Important

3. Don’t know

Interviewer No. Date of Interview :_ _ _ _ _ _ _

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_____________________________________________________________________________

_____________________________________________________________________________

_____________________________________________________________________________

Survey Form Checked By:

Staff No. Date :_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

K-CHECKLIST (please () after completion of each procedure

Fasting Blood Specimen Height/Weight

Glucose Load: Waist/Hip Girth

Given Blood Pressure

. Exempte ECG ( For males aged more than 35 years only)

2 Hr Blood Specimen

38. Glucose Load (Time given) ________________________________________________________________

39. 2 Hr Blood Specimen (Time taken)

L- COMMENTS BY STAFF (if any)