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 Table of Contents  
ORIGINAL RESEARCH
Year : 2013  |  Volume : 2  |  Issue : 3  |  Page : 142-148

Appropriate anthropometric indices to identify cardiometabolic risk in South Asians


1 Sudhir Heart Centre, Berhampur, Odisha, India
2 Department of Epidemiology and Public Health, University College Cork, Cork, Ireland
3 Australian Medical Research Foundation Ltd; Fresh Start Recovery Programme, Perth, WA, Australia
4 MKCG Medical College, Berhampur, Odisha, India
5 Kalinga Institute of Medical Sciences, Bhubaneshwar, Odisha, India

Date of Web Publication23-May-2017

Correspondence Address:
D S Prasad
Consultant Cardiologist, Sudhir Heart Centre, Main Road, Dharmanagar, Berhampur-760002, Odisha
India
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DOI: 10.4103/2224-3151.206760

PMID: 28615589

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  Abstract 


Background: South Asians show an elevated cardiometabolic risk compared to Caucasians. They are clinically metabolically obese but are considered normal weight based on current international cut-off levels of several anthropometric indices. This study has two main objectives: (i) to predict the most sensitive anthropometric measures for commonly studied cardiometabolic risk factors, and (ii) to determine optimal cut-off levels of each of the anthropometric indices in relation to these cardiometabolic risk factors in South Asians.
Methods: The study was conducted on a random sample of 1178 adults of 20–80 years of age from an urban population of eastern India. Obesity, as evaluated by standard anthropometric indices of BMI (body mass index), WC (waist circumference), WHpR (waist-to-hip ratio) and WHtR (waist-to-height ratio), was individually correlated with cardiometabolic risk factors. Receiver operating characteristic (ROC) curve analyses were performed which includes: (i) the area under the receiver operating characteristic curve (AUROC) analysis to assess the predictive validity of each cardiometabolic risk factor; and (ii) Youden index to determine optimal cut-off levels of each of the anthropometric indices.
Results: Overall, AUROC values for WHtR were the highest, but showed variations within the sexes for each of the cardiometabolic risk factors studied. Further, WHpR cut-offs were higher for men (0.93–0.95) than women (0.85–0.88). WC cut-offs were 84.5–89.5 cm in men and 77.5–82.0 cm in women. For both sexes the optimal WHtR cut-off value was 0.51–0.55. The optimal BMI cut-offs were 23.4–24.2 kg/m2 in men and 23.6–25.3 kg/m2 in women.
Conclusion: WHtR may be a better anthropometric marker of cardiometabolic risks in South Asian adults than BMI, WC or WHpR.

Keywords: obesity, abdominal obesity, BMI, waist circumference, waist-to-height ratio, South Asians, Asian Indians.


How to cite this article:
Prasad D S, Kabir Z, Suganthy J P, Dash A K, Das B C. Appropriate anthropometric indices to identify cardiometabolic risk in South Asians. WHO South-East Asia J Public Health 2013;2:142-8

How to cite this URL:
Prasad D S, Kabir Z, Suganthy J P, Dash A K, Das B C. Appropriate anthropometric indices to identify cardiometabolic risk in South Asians. WHO South-East Asia J Public Health [serial online] 2013 [cited 2019 Aug 23];2:142-8. Available from: http://www.who-seajph.org/text.asp?2013/2/3/142/206760




  Introduction Top


The prevalence of overweight and obesity has been rapidly increasing in countries of the South Asian region, with adverse consequences on health.[1],[2],[3] But there is a dearth of accurate nationwide data from these countries.[4] Various anthropometric indices such as body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHpR), and waist-to-height ratio (WHtR) are often used to evaluate adiposity levels.[5] Both general and abdominal obesity have been associated with several cardiometabolic abnormalities. However, studies show potential ethnic differences in such associations.[6],[7]

Most studies examining obesity with cardiovascular risk have been based on data from high-income countries. For example, several epidemiological studies of South Asian populations have shown that they have higher levels of body fat content for a given BMI and WC than western populations, thus resulting in an increased cardiometabolic risk at a lower BMI level.[8], However, the prevalence of overweight and obesity is based on World Health Organization (WHO) criteria for Caucasian populations, based on high sensitivity and specificity profiles against cut-off values of BMI (25 kg/m2 for overweight and 30 kg/m2 for obesity) and WHpR (0.95 for men and 0.80 for women) for both categories in adult European populations. Moreover, these BMI limits, against which the waist circumference sensitivity and specificity information were calculated, have been shown to underestimate the true burden of obesity in South Asian populations for whom lower cut-offs are proposed (BMI ≥ 23 kg/m2 for overweight and ≥ 25 kg/m2 for obesity).[8],[9]

It is well known that obesity is associated with diabetes mellitus, hypertension, dyslipidemia, and cardiovascular disease (CVD).[10] Epidemiological surveys use BMI as an indicator of generalized obesity and WC, WHpR or WHtR as a measure of central, or abdominal obesity. Abdominal obesity, which suggests unwarranted deposition of intra-abdominal fat, is a significant predictor of cardiometabolic risk. WC has been proposed as an easy measure for both intra-abdominal fat mass and total fat. However, a limitation is that WC measurement does not take into account differences in body height, and hence the WHtR value is preferred as a predictor of cardiometabolic risk factors.[11],[12],[13]

Controversy remains regarding the best anthropometric indices for CVD risk. There is ongoing debate regarding which adiposity measure best represents health risks associated with excess body weight both at individual and community level.[14] Even though BMI is the most commonly used index, it does not correspond to fatness uniformly in all populations, and inter-ethnic extrapolations are not possible.[15]

This study had two main objectives: (i) to predict the most sensitive anthropometric measure for commonly studied cardiometabolic risk factors; and (ii) to determine the optimal cut-off levels of each anthropometric index in relation to these cardiometabolic risk factors in South Asians.


  Methods Top


Study design and sampling method

This epidemiological, population-based study was conducted in Berhampur city of Odisha state in eastern India. Based on an estimated prevalence p of hypertension of 25% with an allowable error (L) of 10%, the sample size was calculated as 1200 by use of the formula n=4pq/L2, where q = 100 – p. Details of the survey methodology are published elsewhere,[16] The sample was obtained by means of a multi-stage random sampling technique among residents of Berhampur Municipal Corporation spread across 37 electoral wards, of which 30 were chosen at random to spot the sampling unit: a household. Each ward of the city comprises 12–14 streets and each street contains two rows of households. Two rows of households were randomly selected and the sampling unit household was selected by simple random sampling to enrol approximately 40 subjects who were ≥ 20 years of age from each ward. Of the 1200 subjects invited to participate in the study, 1178 subjects ≥ 20 years of age were enrolled.

The study proposal was in line with the Indian Council of Medical Research guidelines on bioethics,[17] and was approved by the institutional review board of the Kalinga Institute of Medical Sciences, Bhubaneswar, Odisha. Written informed consent was obtained from all subjects.

Definitions of cardiovascular risk factors used

Diabetes mellitus and intermediate hyperglycaemia: based on WHO/IDF Consultation. Geneva: World Health Organization; 2006.[18],[19]

Diabetes: based on physician diagnosis and antidiabetic treatment or those who had fasting plasma glucose level of ≥ ≥ 126 mg/dl (≥ 7.0 mmol/L) or 2-h plasma glucose ≥ 200 mg/dL (11.1 mmol/L).[18],[19]

Obesity and overweight: based on the revised criteria specific for Asian, Pacific populations.[8] Value of BMI ≥ 23 kg/m2 used to define overweight and ≥ 25 kg/m2 to define obese.[20]

Hypertension: based on physician diagnosis and antihypertensive treatment or systolic blood pressure > 140 or diastolic blood pressure ≥ 90 mmHg.[21],[22]

Dyslipidemia: based on the Third Report of the National Cholesterol Education Program (NCEP).[23] Hypercholesterolemia: serum cholesterol > 200 mg/dL; hypertriglyceridemia: serum triglycerides > 150 mg/dL; low high-dens1ty lipoprotein cholesterol (HDLC): males ≤ 40 mg/ dL, females ≤ 50 mg/dL.

Metabolic syndrome: We followed a recent unified definition of various international medical bodies.[24],[25]

The presence of any three of the following five conditions was essential: waist circumference ≥ 90 cm for males and ≥ 80 cm for females; hypertriglyceridemia ≥ 150 mg/dL; low HDL (males < 40 mg/dL, females < 50 mg/dL); elevated blood pressure (systolic blood pressure ≥ 130 mmHg and/or diastolic blood pressure ≥ 85 mmHg or drug treatment for hypertension) and elevated blood sugar (fasting blood sugar ≥ 100 mg/dL or drug treatment for diabetes mellitus).

Clinical profile and anthropometric measurements: All relevant individual-level data pertaining to blood pressure and anthropometric parameters (height, weight, and waist and hip circumferences) available to the study were abstracted.[16] Details of these clinical and anthropometric measurements were published earlier.[16]

Biochemical measurements: Fasting blood glucose levels and lipid profiles, total cholesterol (TC) triglycerides (TG), and HDLC were estimated or measured according to previously described methods.[16] Low-density lipoprotein cholesterol (LDLC) levels were calculated using the Friedewald formula.[26]

Statistical analysis: Data were analysed using SPSS for Windows (SPSS, Inc, Chicago IL, USA). Continuous variables were expressed as mean, and categorical variables were expressed as frequencies and proportions. Comparisons between groups were performed using the Student’s t-test for continuous variables and the chi-square test for categorical data, P < 0.05 was considered to be statistically significant. Pearson’s correlation coefficient estimations were computed to quantify correlations between anthropometric indices and cardiometabolic risk factors.

Further receiver operating characteristic (ROC) curve analyses were performed to compare predictive validity and to determine optimal cut-off levels for each of the anthropometric indices against the cardiometabolic risk factors. The predictive validity was assessed using the area under the ROC curve (AUROC), while the optimal cut-off levels were determined using the Youden Index.[27] AUROC were plotted using measures of sensitivity (true-positive rate) on the y-axis against l-specificity (true-negative rate) on the x-axis. In general the discriminatory power of a diagnostic test is established by AUROC over the whole range of testing values. AUROC is a measure of the diagnostic power of a test. A perfect test will have an AUROC of 1.0, and an AUROC equal to 0.5 means the test is a poor discriminator. The Youden index intuitively corresponds to the point on the curve farthest from chance.[27]


  Results Top


Basic characteristics of the study subjects

The characteristics of the study population and the prevalence of CVD risk factors, stratified by gender, are shown in [Table 1]. The mean age of the study population was 47.6 years in men and 44.2 years in women The mean BMI was 24.1 kg/m2 in men and 25.0 kg/m2 in women. The mean WC, WHpR and WHtR in men were 86.4, 0.93 and 0.53 cm respectively. For women, the values were 81.2, 0.87 and 0.54 cm, respectively.
Table 1: Demographic and anthropometric/biochemical profile

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Correlations between anthropometric indices and cardiovascular risk factors

Pearson’s correlation coefficients, as measured among the anthropometric indices and CVD risk factors, are shown in [Table 2]. In general, WHtR has relatively higher correlation coefficients with cardiovascular risk factors analysed for both men and women, except for LDLC compared with other anthropometric indices.
Table 2: Pearson’s correlation coefficients of anthropometric indices and cardiovascular risk factors

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Overall AUROC curve results

The AUROC curves of various anthropometric indices and CVD risk factors are shown in [Table 3]’ Overall, WHtR had highest AUROC for all the cardiovascular risk factors studied, although there were variations between men and women.
Table 3: AUROC curves for various anthropometric indices and cardiometabolic risk factors in men, women and overalla

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Cut-off points to predict CVD risk factors

[Table 4] summarizes the cut-off points for the various anthropometric indices to predict CVD risk factors using AUROC analysis. The ROC curves in Figures 1–6 show the AUROC from which the values in [Table 4] were derived. The optimal BMI cut-off values for men were 23.4–24.2 kg/m2, while the range for women was wider at 23.6–25.3 kg/m2. WC cut-off values were higher for men (84.5–89.5 cm) than women (77.5–82.0 cm). WHpR cut-offs were also higher for men (0.93–0.95) than women (0.85–0.88). For both sexes the optimal WHtR cut-off value was 0.51–0.55, i.e. WHtR > 0.5 correlated well with cardiometabolic risk parameters.
Table 4: Cut-off points for anthropometric indices predictive of cvd risk factors

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  Discussion Top


Main findings

Based on the two primary study objectives, the following are the main study findings.

First, WHtR may be considered the most sensitive anthropometric index for the several cardiometabolic risk factors under investigation in an urban population in eastern India. However, variations within sexes – e.g. WC for triglycerides in men and for hypertension in women – were observed. Such analyses were based on the AUROC outputs ([Table 3]). Second, this study identified optimal cut-off levels for each anthropometric index against individual cardiometabolic risk factors. In general in both sexes, lower cut-off levels were determined to be optimal for BMI and WC, while relatively higher cut-off levels were found to be optimal for WHtR and WHR ([Table 4]). Such computations were based on Youden Index derivations of ROC analyses. In addition, several ROC graphs clearly illustrate the representations of each index against individual cardiometabolic risk factors.

The study findings suggest that the proposed cut-offs of various organizations to define overweight and obesity are not appropriate for Indians, who are at risk of developing obesity-related cardiometabolic morbidities at lower levels of BMI and WC. [Table 5] compares the optimal cut-off points derived from this study with current international recommendations.[23],[24],[28],[29],[30] Our findings also suggest that, unlike standard BMI cut-off levels for Identifying obesity, AUROC values indicated that WHtR was consistently a better indicator for the majority of cardiometabolic risk factors except for high TC where BMI is a better predictor overall and in women, and WC is a better predictor in men. These observations are consistent with recent studies[11],[12],[13] as well as studies from the mid 1990s.[31],[32]
Table 5: Obesity cut-off points: comparison between published guideline and results of this study

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Strengths of the study

Strengths of this study include a broad baseline of available adiposity parameters and metabolic disease data from a large population-based cohort. Other strengths are representative sampling methodology, the use of standardized data collection protocols, in-depth assessment of multiple cardiometabolic risk factors, and a high response rate to the survey (98.1%). Furthermore, this is the first study from eastern India comparing all four adiposity measures – BMI, WC, WHpR and WHtR – with cardiometabolic risk factors.

Limitations of the study

One limitation of our study is that it relates cardiovascular risk to BMI, WC, WHpR and WHtR in a cross-sectional setting using established cardiometabolic risk factors as a proxy estimate rather than clinical end-points or mortality data. Thus, the cross-sectional design does not allow cause–effect relationships to be made.

Given that individuals were approached at home, it is possible that the sample was biased towards home-based or more sedentary individuals rather than a more active professional workforce. Finally, we had no data on body fat mass, and further studies may be needed to assess body fat mass and its relationship with surrogate anthropometric indices. Prospective studies are also required that relate anthropometric indices to clinical CVD mortality and all-cause mortality in South Asians.


  Conclusions Top


It is evident from the present study findings that, despite overlap in 95% confidence intervals, WHtR is a relatively superior indicator of obesity-related cardiometabolic risk in adult Indians in terms of AUROC values compared with other relevant anthropometric indices studied. Our study indicates that the cut-off values for BMI, WC, or WHpR to define obesity could be much lower in South Asia than in western countries. Based on these conclusions and those of similar findings elsewhere,[13] we suggest that WHtR is a better screening tool to identify obesity-associated cardiometabolic risks among adult Indians, and probably across other ethnic communities. Nevertheless, further studies are required for such a population-based screening tool prior to its universal adoption in both clinical and community health settings.


  Acknowledgements Top


Dr K Revathi Devi, Medical Officer, Sudhir Heart Centre, Berhampur, Odisha, India; Professor BK Sahu, Berhampur University, Berhampur, Odisha, India; Mrs Mohini Sahu, Child Development Project Officer, Berhampur, Odisha, India; Dr US Panigrahi, former Professor of Psychiatry, Dr Ram Manohar Lohiya Hospital, New Delhi, India.

Source of Support: Nil

Conflict of Interest: None declared.

Contributorship: DSP, ZK, AKD, BCD conceived the idea and planned the study. DSP, ZK, JPS did the review of literature. JPS, DSP, ZK and BCD, performed data extraction, statistical analysis and tabulation. DSP and ZK prepared the manuscript. DSP, ZK, AKD and BCD provided technical suggestions; DSP and AKD supervised the study. All the authors read and approved the final draft manuscript.



 
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    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]


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