Accepted Manuscript Title: The discriminative ability of waist circumference, body mass index and waist-to-hip ratio in identifying metabolic syndrome: Variations by age, sex and race Author: Kee C. Cheong Sumarni M. Ghazali Lim K. Hock Soobitha Subenthiran Teh C. Huey Lim K. Kuay Feisul I. Mustapha Ahmad F. Yusoff Amal N. Mustafa PII: DOI: Reference:

S1871-4021(15)00019-3 http://dx.doi.org/doi:10.1016/j.dsx.2015.02.006 DSX 454

To appear in:

Diabetes & Metabolic Syndrome: Clinical Research & Reviews

Please cite this article as: Cheong KC, Ghazali SM, Hock LK, Subenthiran S, Huey TC, Kuay LK, Mustapha FI, Yusoff AF, Mustafa AN, The discriminative ability of waist circumference, body mass index and waist-to-hip ratio in identifying metabolic syndrome: Variations by age, sex and race, Diabetes and Metabolic Syndrome: Clinical Research and Reviews (2015), http://dx.doi.org/10.1016/j.dsx.2015.02.006 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

The discriminative ability of waist circumference, body mass index and waist-to-

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hip ratio in identifying metabolic syndrome: Variations by age, sex and race

Kee C Cheonga, MSc, Sumarni M Ghazalia, BSc, Lim K Hockb, MSc, Soobitha

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Subenthirana, MSc, Teh C Hueyb, MSc, Lim K Kuayb, Msc, Feisul I Mustaphac, MPH,

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Ahmad F Yusoffa, MPH, Amal N Mustafaa, MPH

Institute for Medical Research, Ministry of Health, Kuala Lumpur, Malaysia

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Institute for Public Health, Ministry of Health, Kuala Lumpur, Malaysia

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Non-Communicable Disease Section, Disease Control Division, Ministry of Health,

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Putrajaya, Malaysia

Kee Chee Cheong

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Corresponding author:

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Epidemiology and Biostatistics Unit, Institute for Medical Research, Jalan Pahang, 50588 Kuala Lumpur, Malaysia

Phone: +6(03) 2616 2666 Fax: +6 (03) 2279 8244

Email: [email protected]

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Abstract

Objectives: Many studies have suggested that there is variation in the capabilities of

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BMI, WC and WHR in predicting cardiometabolic risk and that it might be confounded

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by gender, ethnicity and age group. The objective of this study is to examine the

discriminative abilities of body mass index (BMI), waist circumference (WC) and

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waist-hip ratio (WHR) to predict two or more non-adipose components of the metabolic syndrome (high blood pressure, hypertriglyceridemia, low high density lipoprotein-

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cholesterol and high fasting plasma glucose) among the adult Malaysian population by gender, age group and ethnicity.

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Methods: Data from 2572 respondents (1044 men and 1528 women) aged 25-64 years who participated in the Non Communicable Disease Surveillance 2005/2006, a

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population-based cross sectional study, were analysed. Participants’ socio-demographic

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details, anthropometric indices (BMI, WC and WHR), blood pressure, fasting lipid

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profile and fasting glucose level were assessed. Receiver operating characteristics curves analysis was used to evaluate the ability of each anthropometric index to discriminate MetS cases from non-MetS cases based on the area under the curve. Results: Overall, WC had better discriminative ability than WHR for women but did not perform significantly better than BMI in both sexes, whereas BMI was better than WHR in women only. Waist circumference was a better discriminator of MetS compared to WHR in Malay men and women. Waist circumference and BMI performed better than WHR in Chinese women, men aged 25-34 years and women aged 35-44 years.

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Conclusions: The discriminative ability of BMI and WC are better than WHR for predicting two or more non-adipose components of MetS. Therefore, either BMI or WC measurements are recommended in screening for metabolic syndrome in routine clinical

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practice in the effort to combat cardiovascular disease and type II diabetes mellitus.

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Keywords: adult; metabolic syndrome; body mass index; waist circumference; waist

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hip ratio

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1. Introduction

Previous studies have shown that general obesity is positively associated with increased

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cardiovascular diseases (CVD) and diabetes [1], while abdominal obesity increases the

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risk of all cause, cardiovascular and cancer mortality [2, 3]. Anthropometric indices (body mass index, waist circumference, waist-hip ratio) are the most widely used

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methods to measure general and abdominal obesity in large epidemiological studies, body mass index (BMI) for general adiposity while waist circumference (WC) and waist-hip ratio (WHR) are proxy measures of abdominal adiposity. There is high correlation between both BMI and WC and total body adipose tissue mass. But, WC is reportedly better than BMI in estimating intra-abdominal fat tissue and provides a measure of body fat distribution [4]. Many studies have suggested that there is variation in the capabilities of BMI, WC and WHR in predicting cardiometabolic risk and that it might be differ by gender [5], ethnicity [6] and age group [7]. Therefore, our study objective is to evaluate the discriminative abilities of BMI, WC and WHR in predicting two or more non-adipose

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components of the metabolic syndrome (high blood pressure, hypertriglyceridemia, low high density lipoprotein-cholesterol and high fasting plasma glucose) in the adult

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Malaysian population by gender, age group and ethnicity.

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2. Methods

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Study design & sampling

This study was approved by the National Institutes of Health, Ministry of Health,

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Malaysia (NMRR-11-319-9194). We used data from the MyNCDS-1 study which was a cross-sectional, population-based baseline survey on non-communicable diseases and its

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risk factors conducted in 2005-2006 (Malaysia NCD Surveillance 2006). The MyNCDS-1 study was approved by the Medical Research Ethics Committee of the

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Ministry of Health, Malaysia. Participants were recruited from all thirteen states and one

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federal territory (Kuala Lumpur) in Malaysia through a complex multistage cluster sampling using the year 2000 National Household Sampling Frame with the assistance

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of the Department of Statistics, Malaysia. A sample size of 3040 subjects was calculated based on the lowest NCD risk factor prevalence, i.e prevalence of obesity of 5% [8], precision of 1.2%, design effect of 2 and non-response rate of 20%. Stratifying variables were state/federal territory and setting (urban/rural), with enumeration blocks (EBs), living quarters (LQs) and households as the primary, secondary, and elementary sampling units respectively. The numbers of EBs and LQs selected per state were based on the desired sample size and proportionate to the 2005 Malaysian adult (age 25-64 years) population size for each state. In all, a total of 398 EBs and 1683 LQs were selected. All household members in all households in the selected LQs who met the eligibility criteria were included in the sample. The inclusion criteria were Malaysian

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citizen and aged between 25 to 64 years. The exclusion criteria were pregnant women, mentally ill, very ill, and institutionalised individuals [9].

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Anthropometric and blood pressure measurements

Height was measured without footwear to the nearest 0.1 centimetre using a

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stadiometer. Weight was measured to the nearest 0.1 kilogram using a balance beam

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scale or SECA beam scale with minimal clothing and no shoes. Body mass index was calculated as weight in kilograms divided by the square of height in meters. Waist

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circumference (WC) was measured directly over skin or light clothing to the nearest 0.1 cm at the smallest circumference below the rib cage and above the umbilicus while

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standing with abdominal muscles relaxed. Hip was measured directly over the skin to the nearest 0.1cm at largest circumference of the buttocks-hip area while the person is

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standing. Waist-hip ratio was calculated as waist circumference in centimeters divided

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by hip circumference in centimeters. Resting blood pressure (BP) was measured by the

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auscultatory method. BP was measured two times, or three times if the first two readings differed by more than 10 mmHg, at no less than 30 second between measurements, and averaged.

Biochemical measurements

Five ml of venous blood samples after overnight fasting were collected for the measurement of total cholesterol, HDL-cholesterol, triglycerides and glucose. All blood samples were kept in dry ice prior to laboratory analysis. Concentrations of HDL cholesterol, triglyceride and fasting blood glucose were measured using enzymatic

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assay kits (Automated HDL Cholesterol Flex, Triglyceride Flex and Glucose Flex). Total cholesterol was determined using an enzymatic colorimetric method.

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Sociodemographic factors

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Sociodemographic factors captured were residential area (urban/rural), gender,

ethnicity, age, marital status, highest education attained, occupational status and

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monthly household income. The detailed definitions used in the classification of these

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variables are published elsewhere [9].

Definition of Metabolic Syndrome

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Metabolic syndrome (MetS) is defined by using the ‘harmonised’ criteria proposed by the IDF Task Force on Epidemiology and Prevention, National Heart, Lung and Blood

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Institute, American Heart Association, World Heart Federation and the International

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Association for the Study of Obesity [10]. MetS is said to be present with the presence

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of any three of the following in an individual: (1) Abdominal obesity (WC ≥ 90cm in men, ≥ 80cm in women); (2) Systolic blood pressure ≥ 130mmHg, or diastolic blood pressure ≥ 85mmHg or known hypertension; (3) Fasting plasma glucose ≥ 5.6mmol/L or previously diagnosed type 2 diabetes; (4) Triglycerides ≥ 1.7mmol/L; (5) HDLCholesterol < 1.0mmol/L in men and < 1.3mmol/L in women. Abdominal obesity was omitted from the ROC analyses to avoid self-correlation [5].

Statistical analysis All statistical analyses were performed using SPSS software version 18 (IBM SPSS, Chicago). Analysis of the complex sample was performed with post-stratification

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weights for locality, gender and age group applied. Socio-demographic characteristics were described in percentages with 95% confidence intervals (95% CI) by gender. The anthropometric measurements, blood pressure and biochemistry test were expressed in

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means and 95% CI. Receiver operating characteristics (ROC) curves analysis was used to evaluate the ability of each anthropometric index to discriminate MetS cases from

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non-MetS cases based on the area under the curve (AUC). The higher the value of AUC

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indicates higher discriminative ability in predicting MetS. A significant difference in the discriminative powers of two anthropometric indices is suggested when there is no

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overlap between the 95% confidence intervals of the AUC of the two indices or if the AUC value of one anthropometric index does not fall within the 95% confidence

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interval of the other. Ethnicity, age group and gender-specific ROC analyses were applied to evaluate the discriminative ability of the three indices in different ethnic, age

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statistically significant.

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and gender subpopulations. For all analyses, p values of less than 0.05 were considered

3. Results

The overall response rate achieved was 84.6% (2572/3040). The sample consisted of 1044 (40.6%) men and 1528 (59.4%) women. The median age was 44 years (interquartile range=17). The demographic characteristics, anthropometric and biochemistry results are presented in Table 1. Overall, waist circumference had better discriminative ability than WHR but its AUC value was not significantly higher than BMI for both sexes, whereas BMI was better than WHR in women only. Waist circumference was a better discriminator compared to WHR in Malay men and women. Waist circumference and BMI had better discriminative ability than WHR in Chinese

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women only. WHR was unable to discriminate MetS in Indian men and women (Table 2). Analysis of AUC for these three anthropometric indices by age group and gender showed waist circumference and BMI had significantly better discriminative ability than

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WHR in men aged 25-34 years and women aged 35-44 years. However WC was unable

to predict MetS in women aged 55-64, and WHR in women in the 25-34 and 55-64

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years age groups (Table 3).

4. Discussion

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Our results provide additional evidence for the utility of WC, BMI and WHR in predicting risk of metabolic syndrome in this study population. But, the discriminative

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ability of these anthropometric indices varied by age, gender and ethnicity. Overall, our results indicated that there is no significant difference in the discriminative abilities of

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WC and BMI measurements in identifying metabolic syndrome. Satoh et al. [11] also

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using ROC curve analysis and AUC values, reported that WC and BMI did not differ in

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middle–aged Japanese subjects indicating that BMI and WC were equally useful to detect the presence of multiple cardiovascular risk factors. Similarly, Sung et al.[12] studied the relationship between obesity and CVD risk factors in 19584 Korean adults and showed that WC and BMI were closely related in both men and women. Moreover, the strength of association between a given CVD risk factor and the two indices were not significantly different. Hence, they concluded that WC and BMI perform equally well in identifying CVD risk factors. Our findings are somewhat inconsistent with other studies. Using ROC curve analysis and area under the curve, the third National Health and Nutrition Examination Survey reported WC was superior to BMI for the identification of cardiovascular disease risk factors among the US population [13]. Lee

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et al. [14] conducted a meta-analysis involving 10 studies (9 cross-sectional and 1 prospective) with over 88,000 individuals from diverse populations. The studies used ROC curve analysis and AUC to determine which anthropometric index is a better

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discriminator of hypertension, type 2 diabetes and dyslipidemia and it was found that generally, anthropometric indices that assess central adiposity are better discriminators

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than BMI.

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WHR is less useful as a discriminator for predicting metabolic syndrome compared to WC and BMI in our study. Our results are in agreement with Wang and

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colleagues study among 75,788 Chinese adults in North China [5]. Wang et al. reported that the discriminative ability as indicated by AUC of WHR was significantly lower

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than BMI and WC. Another study which also involved Chinese subjects had demonstrated that WHR had lower discriminative value as compared to BMI and WC in

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predicting multiple risk factors [15]. In contrast, WHR has been shown to be a better

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discriminator than other anthropometric measurements for predicting type 2 diabetes

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among the adults from the Nutrition and Health Survey in Taiwan (1993-1996) [7]. However, in a meta-analysis conducted by Qiao and Nyamdorj [16] indicated that there is no difference of the anthropometric indices (WC, BMI and WHR) in relation to risk of type 2 diabetes in Chinese, Japanese, Indian, Mongolian and Filipino men. Another meta-analysis of 15 prospective studies which involved 25,8114 participants, mainly from western populations showed that both WC and WHR were significantly associated with risk of incident cardiovascular disease, but there was no significant difference between WC and WHR [17]. Our results show that the ability of WC and BMI to distinguish metabolic syndrome did not significantly differ in men and women. Strong correlations between

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both BMI and WC with non-abdominal, abdominal subcutaneous and visceral fat, independent of age and gender, has been shown [18]. Similarly, Satoh et al.[10] reported the discriminative ability of BMI and WC in predicting the presence of MetS in

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both Japanese men and women did not differ. Furthermore, our results demonstrated that WC predicted MetS better than WHR in both men and women while BMI was

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better than WHR in women only. Hence, this further supports the use of WC instead of

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WHR or BMI as a criterion for diagnosing MetS in clinical practice [10].

Our study indicates that the discriminative ability of anthropometric measures in

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relation to MetS may vary across different ethnic groups. We found that among Malays, WC was a better discriminator of MetS compared to WHR in both men and women. But

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among the Chinese, WC and BMI were better than WHR in women only. Previous studies demonstrated that there are ethnic differences in visceral adipose tissue at a

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given BMI or WC. Generally, African-American adults have lower levels of visceral fat

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than white American adults [6, 19]. The interaction of race and gender, and age and

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gender probably explains the variation in the discriminative ability of these anthropometric measures in identifying MetS in our study. In our study, age might have been a confounder in the associations between the

anthropometric measures and MetS. WC and BMI performed better than WHR in young subjects, but not in the older subjects. Ageing is associated with changes in the total and regional distribution of body fat [20]. These age-related changes in body fat distribution might not be reflected in simple anthropometric measures such body weight and waist circumference. Older individuals will likely have greater amounts of visceral fat compared to younger individuals despite having the same body weight or waist circumference. This probably explains why the discriminative ability of BMI and WC

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were no different from WHR in older subjects. On the contrary, Cheng and colleagues [7] reported no difference between WC, BMI and WHR in predicting Type 2 diabetes or hypertension across all age groups in Taiwanese adults. The differences observed

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between our study and Cheng and colleagues’ study may be due in part to the use of different age categories and health outcomes.

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There were few limitations in the study. We used cross-sectional data to identify

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the best discriminators of metabolic syndrome. Future studies using longitudinal data may provide better insight on the value of these anthropometric indices in predicting

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risk of metabolic syndrome in the Malaysian population. The prevalence of each metabolic syndrome component may have an effect on the discriminatory capabilities of

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these anthropometric measurements. Also, differences in the discriminatory capabilities of each anthropometric measurement may not be clinically relevant, therefore the

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magnitude of associations between these anthropometric measurement and metabolic

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syndrome need to be examined. However, despite these limitations, the study sample

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was nationally representative which makes the findings generalisable to the noninstitutionalised population of Malaysia.

5. Conclusion

Our findings indicated that in the Malaysian population the discriminatory capabilities of WC, BMI and WHR in predicting non-adipose components of MetS vary by age, gender and ethnicity. Generally, WC and BMI have better discriminatory capabilities than WHR. Therefore, we propose the use of WC and BMI in routine clinical practice to identify those at risk of MetS.

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Conflict of Interest The authors declare that there is no conflict of interest.

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Acknowledgements

The authors express their gratitude to the Director-General of Health, Malaysia for

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granting permission to publish this paper; and the Non Communicable Disease Section,

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Disease Control Division, Ministry of Health, Putrajaya for providing data from the

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Malaysia NCD Surveillance-1.

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REFERENCES

[1] Wilson PWF, D’Agostino RB, Sullivan L, et al. Overweight and obesity as

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determinants of cardiovascular risk. Arch Intern Med 2002; 162:1867-1872.

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[2] Balkau B, Deanfield JE, Despres JP, et al. International Day for the Evaluation of

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Abdominal Obesity (IDEA): A study of waist circumference, cardiovascular disease and diabetes mellitus in 168000 primary care patients in 63 countries. Circulation 2007; 116: 1942-1951.

[3] Zhang C, Rexrode KM, Dam RM, et al. Abdominal obesity and the risk of allcause, cardiovascular and cancer mortality: Sixteen years of follow-up in US women. Circulation 2008; 117:1658-1667.

[4] Klein S, Allison DB, Heymsfield SB, et al. Waist circumference and cardiometabolic risk: a consensus statement from Shaping America’s Health: Association for Weight Management and Obesity Prevention; NAASO, The

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Obesity Society; the American Society for Nutrition; and the American Diabetes Association. Am J Clin Nutr 2007; 85:1197-1202. [5] Wang F, Wu S, Song Y, et al. Waist circumference, body mass index and waist to

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hip ratio for prediction of the metabolic syndrome in Chinese. Nutr Metab Cardiovasc Dis 2009; 19: 542-547.

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[6] Camhi SM, Bray GA, Bouchard C, et al. The relationship of waist circumference

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and BMI to visceral, subcutaneous, and total body fat: sex and race differences. Obesity 2011; 19: 402-408.

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[7] Cheng CH, Ho CC, Yang CF, et al. Waist-to-hip ratio is a better anthropometric index than body mass index for predicting the risk of type 2 diabetes in Taiwanese

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population. Nutr Res 2010; 30: 585-593.

[8] Institute for Public Health: The Second National Health and Morbidity Survey

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(NHMS II) 1996. Kuala Lumpur: Ministry of Health Malaysia; 1996.

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[9] Disease Control Division (NCD): Malaysia NCD Surveillance 2006. NCD risk

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factors in Malaysia. Putrajaya: Ministry of Health Malaysia; 2006. [10] Alberti KGMM, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation task force on epidemiology and prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009; 120: 1640-1645. [11] Satoh H, Kishi R & Tsutsui H. Body mass index can similarly predict the presence of multiple cardiovascular risk factors in middle-aged Japanese subjects as waist circumference. Intern Med 2010; 49: 977-982.

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[12] Sung KC, Ryu S, Reaven GM et al. Relationship between obesity and several cardiovascular disease risk factors in apparently healthy Korean individuals: Comparison of body mass index and waist circumference. Metabolism 2007;

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56:297-303.

[13] Zhu SK, Wang ZM, Heshka S, et al. Waist circumference and obesity- associated

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risk factors among whites in the third National Health and Nutrition Examination

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Survey: Clinical action thresholds. Am J Clin Nutrition 2002; 76:743-749.

[14] Lee MYC, Huxley RR, Wildman RP, et al. Indices of abdominal obesity are better

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discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol 2008; 61: 646-653.

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[15] Liu Y, Tong G, Tong W, Lu L, et al. Can body mass index, waist circumference, waist-hip ratio and waist-height ratio predict the presence of multiple metabolic

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risk factors in Chinese subjects? BMC Public Health 2011; 11:35.

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[16] Qiao Q, Nyamdorj: Is the association of type II diabetes with waist circumference

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or waist-to-hip ratio stronger than that of body mass index? Eur J Clin Nutr 2010; 64: 30-34.

[17] de Koning L, Merchant AT, Pogue J, et al. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur Heart J 2007; 28:850-856.

[18] Janssen I, Heymsfield SB, Allison DB, et al. Body mass index and waist circumference independently contribute to the prediction of non-abdominal, abdominal subcutaneous, and visceral fat. Am J Clin Nutr 2002; 75: 683-688. [19] Carroll JF, Chiapa AL, Rodriquez M, et al. Visceral fat, waist circumference, and BMI: Impact of race/ethnicity. Obesity 2008; 16 (3):600-607.

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[20] Kuk JL, Saunders TJ, Davidson LE, et al. Age-related changes in total and

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regional fat distribution. Ageing Res Rev 2009; 8: 339–348.

Table 1. Subjects characteristics (n= 2572)

Women (n= 1528)

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Men (n= 1044)

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Demographic characteristics, % (95% CI) Residential area Urban Rural

50.7 (47.5, 53.9) 54.0 (51.9, 56.9)

Malay Chinese Indian Other indigenous Others

52.3 (49.3, 55.3) 53.1 (48.2, 58.0) 41.7 (34.3, 49.5) 56.2 (51.3, 60.90 45.5 (32.4, 59.3)

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Ethnicity

49.3 (46.1, 52.5) 46.0 (43.1, 49.0) 47.7 (44.7,50.7) 46.9 (42.1, 51.8) 58.3 (50.5, 63.7) 43.8 (39.1, 48.7) 54.5 (40.7, 67.6)

51.5 (46.6, 56.4) 51.9 (47.9, 55.8) 52.0 (48.3, 55.8) 52.1 (47.2, 57.0)

48.5 (43.6, 53.4) 48.1 (44.2, 52.1) 48.0 (44.2, 51.7) 47.9 (43.0, 52.8)

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25-34 35-44 45-54 55-64

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Age group (years)

Anthropometry and biochemistry, Mean (95% CI)

Weight (kg) 67.63 (66.23, 69.03) 61.05 (60.15, 61.95) Height (cm) 165.22 (164.63, 165.80) 154.44 (154.01, 154.86) BMI (kg/m2) 24.74 (24.29, 25.20) 25.64 (25.25, 26.02) WC (cm) 87.00 (86.00, 87.99) 83.57 (82.66, 84.48) Hip circumference (cm) 97.99 (96.89, 99.08) 99.36 (98.65, 100.07) WHR 0.89 (0.88, 0.90) 0.84 (0.84, 0.85) Fasting blood glucose (mmol/L) 5.33 (5.13, 5.52) 5.38 (5.21, 5.56) Total cholesterol (mmol/L) 5.22 (5.04, 5.39) 5.26 (5.15, 5.38) Triglyceride (mmol/L) 1.92 (1.71, 2.14) 1.68 (1.40, 1.97) HDL- Cholesterol(mmol/L) 1.58 (1.43, 1.73) 1.73 (1.60, 1.86) LDL-Cholesterol (mmol/L) 2.89 (2.61, 3.15) 2.87 (2.58, 3.15) Systolic (mmHg) 122.3 (121.3, 123.3) 78.4 (77.7, 79.0) Diastolic (mmHg) 79.7 (78.8, 80.6) 78.4 (77.7, 79.0) BMI, body mass index; WC, waist circumference; WHR, waist-hip ratio; CI, confidence interval

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Table 2. Area under the ROC curve (AUC) for BMI, WC and WHR associated with two or more nonadipose components of metabolic syndromea by gender and ethnicity Men Women Anthropometric Ethnic group index AUC 95%CI AUC 95%CI All BMI 0.650 0.617, 0.683 0.642 0.614, 0.670d (n=2572) WC 0.673 0.641, 0.706c 0.657 0.629, 0.685c WHR 0.622 0.588, 0.656 0.592 0.563, 0.620 Malay BMI 0.688 0.645, 0.731 0.620 0.582, 0.659 c (n= 1425) WC 0.711 0.669, 0.753 0.649 0.611, 0.687c WHR 0.647 0.603, 0.692 0.589 0.550, 0.627 Chinese BMI 0.613 0.531, 0.694 0.736 0.675, 0.798d (n= 461) WC 0.632 0.551, 0.712 0.692 0.627, 0.757c WHR 0.586 0.504, 0.668 0.611 0.543, 0.680 Indian BMI 0.680 0.563, 0.798 0.616 0.527, 0.705 (n=231) WC 0.683 0.565, 0.801 0.627 0.538, 0.716 b WHR 0.588 0.459, 0.717 0.532 0.439, 0.624b BMI, body mass index; WC, waist circumference; WHR, waist-hip ratio; ROC, receiver operating curve; CI, confidence interval. a Metabolic syndrome: high blood pressure defined as ≥ 130/85 mmHg or known hypertension; high fasting plasma glucose ≥ 5.6 mmol/ L or know DM type 2; elevated triglyceride ≥ 1.7 mmol/L; reduced high density lipoprotein-cholesterol defined as 0.05). c Statistically significant difference between WC and WHR. d Statistically significant difference between BMI and WHR.

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Table 3. Area under ROC curve (AUC) for BMI, WC and WHR for predicting two or more non-adipose components of metabolic syndromea by gender and age group Women Men Age Anthropometri AUC 95%CI group AUC 95%CI c index (years) 0.632, 25-34 BMI 0.705 0.779d 0.635 0.568, 0.702 c (n= 610) WC 0.689 0.613, 0.765 0.627 0.560, 0.695 0.414, WHR 0.591 0.512, 0.670 0.482 0.550b 0.597, 35-44 BMI 0.652 0.587, 0.718 0.649 0.700d (n= 738) WC 0.682 0.619,0.745 0.652 0.600, 0.703c WHR 0.642 0.577, 0.707 0.587 0.534, 0.640 45-54 BMI 0.622 0.560, 0.684 0.652 0.601, 0.704 (n= 747 ) WC 0.639 0.578, 0.700 0.694 0.644, 0.744 WHR 0.592 0.529, 0.655 0.654 0.603, 0.705 55-64 BMI 0.621 0.548, 0.694 0.572 0.500, 0.644 0.481, (n= 477) WC 0.658 0.586, 0.729 0.552 0.624b 0.438, WHR 0.601 0.527, 0.675 0.510 0.583b BMI, body mass index; WC, waist circumference; WHR, waist-hip ratio; ROC, receiver operating curve; CI, confidence interval. a Metabolic syndrome: high blood pressure defined as ≥ 130/85 mmHg or known hypertension; high fasting plasma glucose ≥ 5.6 mmol/ L or know DM type 2; elevated triglyceride ≥ 1.7 mmol/L; reduced high density lipoprotein-cholesterol defined as 0.05). c Statistically significant difference between WC and WHR. d Statistically significant difference between BMI and WHR.

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The discriminative ability of waist circumference, body mass index and waist-to-hip ratio in identifying metabolic syndrome: Variations by age, sex and race.

Many studies have suggested that there is variation in the capabilities of BMI, WC and WHR in predicting cardiometabolic risk and that it might be con...
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