https://imcjms.com Ibrahim Medical College Journal of Medical Science https://imcjms.com/registration/journal_full_text/129 Sun, 06 Nov 2016 10:31:15 +0000 In spite of acknowledged importance, no unified definition exists for central obesity. Several anthropometric indexes such as waist circumference, waist-hip ratio, waist-to-height ratio, conicity index etc, are being used. Cindex has been shown to correlate well with various cardiovascular risk factors associated with visceral fat accumulation in some population. Data were collected through interviewing and measuring 22,995 adult males and females of an urban (Mirpur, Dhaka City) and rural area (Kaliganj sub-district) in 2002 and 2003. Overall the mean (SD) conicity index was 1.20 (0.10) and 40.8% of this sample had a high Cindex. Females, increasing age, urban residents, Christians, the better educated, married and farmers were more likely to have higher Cindex than their counterparts. There is a scarcity of data about the conicity index of Bangladeshis and this cross-sectional study is the first large-scale attempt. So it can be used as a baseline data for further research in this field. Address for Correspondence: Dr. Meerjady Sabrina Flora, Associate Professor, Department of Epidemiology, National Institute of Preventive and Social Medicine (NIPSOM), Mohakhali, Dhaka. e-mail: flora@citechco.net   Anthropometry is the single most universally applicable, inexpensive, and non-invasive method available to assess the size, proportion, and composition of the human body.1 It is being increasingly recognised that central obesity, rather than general, is likely to coexist with type 2 diabetes and lead to complications including cardiovascular diseases. If abdominal obesity is more predictive of multiple risk factors, it is necessary to determine a suitable and widely accepted parameter for this kind of obesity as Body Mass Index is for general obesity. But in spite of its acknowledged importance, no unified definition exists for central obesity; several anthropometric indexes such as waist circumference (WC), waist-hip ratio (WHR), waist-to-height ratio (WHtR), conicity index (Cindex) etc, are being used.2 There is no universally agreed way of measuring adiposity, nor is it known which measure is the best predictor of cardiovascular disease. BMI, WC, WHR, WHtR, Cindex all are found to associate with cardiovascular risk factors.3 Valdez et al. (1993) proposed that ‘the conicity index (Cindex) seems to be a viable approach to assess abdominal adiposity and its concomitant health risks in large-scale studies.4 Cindex has been shown to correlate well with various cardiovascular risk factors associated with visceral fat accumulation in some population.4,5 Cindex showed the highest correlation with total cholesterol, and low density lipoproteins (LDL) in a study by Yasmin & Mascie-Taylor (2003).3 There was evidence that the central obesity indices, especially Cindex and WHR, are better at discriminating High Coronary Risk (HCR) than of general obesity (BMI). The largest area under the Receiver Operating Characteristics (ROC) curve was found between Cindex and HCR, in males, which was significantly different from other obesity indices. In women, the largest area found under the ROC curve was equally between Cindex, WHR and HCR indices.6 No study, so far, has been conducted to assess the centralobesityofBangladeshipopulationusingconicity index. This study is the first attempt to do so. Materials and Methods Subjects were measured wearing minimal attire. All the equipments were checked regularly to minimise random errors. Height was measured to the nearest 0.1 cm with a specially constructed wooden height stand to which a plastic measuring tape was attached. The subject stood without shoes or head gear (cap, ribbon etc) in an upright posture with their head in the Frankfurt plane. Subjects were asked to keep their heels close together with their hands hanging freely by their side, palms facing inwards. The horizontal blade of the stadiometer was gently placed on the crown of the head to take the measurement. Weight was measured using a bathroom scale accurate to 0.5 kg with the subject wearing minimal attire. The scale was placed on a hard flat surface and the subject was requested to step onto it in bare feet without holding onto anything. The weighing scale was set to zero before every measurement. A flexible plastic tape was used to measure waist circumference, accurate up to the nearest 0.1cm. Waist circumference was measured at the level mid way between the lowest rib margin and the superior iliac crest on the mid-axillary line in a horizontal plane. The subjects stood erect with abdomen relaxed, the arms at the side and feet together and breathing normally. Cindex = Waist Circumference (m)/ [0.109 XÖ {Body weight (kg)/ Height (m)}] The analyses were carried out primarily using the Statistical Package for Social Sciences (SPSS) version 14.0. Statistical tests used to determine the association between exposure and outcome variables included c2 test and Student t-test. A result was considered significant at a p value level <0.05 but given the large sample sizes a more stringent cut-off of p<0.01, or less, was usually used. In addition because a number of statistical tests were conducted, the Bonferroni correction (a/K, where a is the p value & K is the number of tests used) was used. Effects of exposure variables were also assessed after adjusting for other variables by multivariate analyses. Result     Sequential multiple regression analyses were also undertaken to determine the effect of each socio-demographic variable after correcting for all the other socio-demographic variables. The full model was significant (F = 133.3; p<0.001) but only explained 10.8% of the variance in Cindex. After adjustment for the other socio-demographic variables it was found that females, increasing age, urban residents, Christians, the better educated, married and farmers were more likely to have a higher Cindex than their counterparts. Sequential binary logistic regression models were used to test the effect of individual socio-demographic variables, after adjusting for the other variables. Table-3 shows that the likelihood of high Cindex increased with age and better education. Gender was strongly associated with Cindex; females were 7.5 times more likely to have high Cindex than males. High Cindex was more often found in urban residents, married, farmers and business persons. When all the socio-demographic variables were entered into the model they significantly predicted Cindex (c2=4974.2; p<0.001; Nagelkerke R2 = .264) and overall 69.9% and 76.5% of normal Cindex, and 60.4% of high Cindex, were correctly predicted. The forward binary logistic regression revealed sex and age group as the best predictors of Cindex categories. When the analyses were repeated for each sex separately, age was the best predictor of Cindex categories in both sexes, followed by occupation in males and locality in females. Discussion There is a dearth of adult anthropometric data in Bangladesh other than weight and BMI and most nutrition research has focused on under-nutrition, particularly among women and children. To meet the scarcity of data in regard to Cindex of Bangladeshi population, this study was an attempt to measure the level of Cindex and magnitude of central obesity as classified by the Cindex. The study also observed the variation in Cindex statistically with differences in the socio-demographic status of the Bangladeshi population. This could work as a baseline data for further studies. Given the large sample size of this study, particular care was taken when interpreting ‘significant’ results and a more stringent cut-off of p<0.01, or less, was usually used. In addition because a number of statistical tests were conducted, the Bonferroni correction (a/K, where K is the number of tests used) was used to reduce Type I errors. The combination of more stringent p value and correction for the number of test undertaken, lowered the cut-off p value for significance to <0.0014 and most of the p-values were <0.001. The magnitude of the difference for statistically significant results was also considered. For example, with a quantitative (continuous) variable a small difference in means might be significant because the standard errors will be small given these sample sizes. However, for a qualitative variable, much larger differences would be required in a chi-square test because the denominator is the expected value, which would be large. Even so, the primary aim of inferential statistics is to generalize from a sample to a population and so the large sample size used here will more closely approximate to the adult Bangladesh population and the 95% confidence intervals will be small. However, this was a cross-sectional study and is the simplest form of epidemiological study and so the associations discussed later do not indicate causality.14 A survey on medical students of United Kingdom showed that females of South Asian descent had a significantly higher conicity index than females of European descent irrespective of how the groups were compared. This difference in conicity was not significant in the male group as a whole, or when ethnic pairs were matched for body weight or body mass index. Male students of South Asian origin in the top tertile for body weight or body mass index had a significantly greater conicity index than European males in these top tertiles. However, the trend towards higher conicity (i.e. abdominal obesity) in young Asians may help explain the higher incidence of diabetes and cardiovascular disease seen in elderly Asians living in the United Kingdom.7   The authors are indebted to the Department for International Development (DfID), United Kingdom, Board of Graduate Studies, the University of Cambridge, The British Federation of Women Graduates Charitable Foundation, The Charles Wallace Bangladesh Trust, and Churchill College, the University of Cambridge for their support. References 2.   Mamtani MR & Kulkarni HR. Predictive Performance of Anthropometric Indexes of Central Obesity for the Risk of Type 2 Diabetes. Arch Med Res 2005; 36: 581-589. 4.   Valdez R, Seidell JC, Ahn YI & Weiss KM. A New Index of Abdominal Adiposity as an Indicator of Risk for Cardiovascular Disease. A Cross-population Study. Int J Obes 1993; 17: 77-82. 6.   Pitanga FJG & Lessa I. Sensitivity and Specificity of the Conicity Index as a Coronary Risk Predictor among Adults in Salvador, Brazil. Rev Bras Epidemiol 2004; 7: 259-269. 8.   Lohman TG, Roche AF & Martorell R, eds Anthropometric Standardization Reference Manual. Champaign, Illinois: Human Kinetic Books: 1988. 10.World Health Organization. Obesity: Preventing and Managing the Global Epidemic: Report of WHO Consultation. WHO Technical Report Series No. 894. Geneva: WHO 2000. 12.Stevens J & Plankey MW. The Conicity Index (letter). Int J Obes 1993; 17: 727. 14.Cole TJ. Sampling, Study Size, and Power. In: Margetts BM & Nelson M eds. Design Concepts in Nutritional Epidemiology. 2nd ed. UK: Oxford University Press 2004. p.64-86. 2026 Ibrahim Medical College. All rights reserved.