Determining the best method for evaluating obesity and the risk for non-communicable diseases in women of childbearing age by measuring the body mass index, waist circumference, waist-to-hip ratio, waist-to-height ratio, A Body Shape Index, and hip index.
Non-communicable diseases (NCDs) are linked to excessive adiposity and anthropometric indices can be used to identify those at risk. The aim of this study was to evaluate the precision of anthropometric indices in identifying obesity and risk factors for NCDs and to investigate the emergence of obesity-related NCDs in young women in Sri Lanka.
We recruited 282 women 18 to 35 y of age from suburban and rural areas in Sri Lanka. We measured the women's height, weight, body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), A Body Shape Index(ABSI), hip circumference (HC), hip index (HI), anthropometric risk index (ARI), fasting serum glucose, fasting serum insulin, homeostatic model assessment for insulin resistance, cholesterol, high-density lipoprotein, low-density lipoprotein, triacylglycerols, and ovulatory gonadal hormones (progesterone, testosterone). Comparisons were made between women with normal BMI and those who were overweight or obese using anthropometric and biochemical characteristics.
The prevalence of obesity was highest in WC and in receiver operating characteristic analysis, BMI, WC, and WHtR showed higher sensitivity and lower 1-specificity as indicators of obesity. BMI had an area under the curve (AUC) of 1.000 with 100% sensitivity and 0% 1-specificity. WC had an AUC of 0.941 with 80% sensitivity and 13.4% 1-specificity. Additionally, WHtR showed a 0.974 AUC, 92.1% sensitivity, and 4.9% 1-specificity. The correlations between body size and shapes were assessed among the study participants using Pearson's correlation. More than other measures, WC and WHtR showed a significant correlation with BMI with P < 0.05 (r = 0.888 and 0.737, respectively). Although ABSI and BMI showed only a weak correlation (P = 0.006, r = 0.162), WHR and BMI showed a moderate correlation (P = 0.001, r = 0.477). Although HI demonstrated a negative association with BMI (P = 0.618, r = -0.030), HC exhibited a strong association (P = 0.001, r = 0.749). A significant association with higher odds ratios was found for obesity-related NCD risk factors such as hypertension, homeostatic model assessment for insulin resistance, hypercholesterolemia, altered ovulatory hormones with these (BMI, WC, WHR, WHtR, ABSI, HI) obesity-assessing criteria (P < 0.05). A significant correlation between WC and hypertriacylglycerolmia (P = 0.001, r = 0.781, odds ratio, >16) was identified. A positive correlation was observed between all MS components and ARI, indicating that ARI may serve as a potential indicator of cardiometabolic risk.
BMI, WC, WHtR, and HC are intercorrelated anthropometric measurements that can be used either alone or in combination to define obesity and detect the risk for NCDs, including diabetes mellitus, cardiovascular disease, and infertility. On the other hand, BMI, ABSI, and HI are designed to be mutually independent indices and have the advantage of combining the separate risks to generate an overall ARI. Furthermore, ARI appears to be a highly effective predictor of cardiovascular disease.
Hewage N
，Wijesekara U
，Perera R
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Association between the abdominal obesity anthropometric indicators and metabolic disorders in a Chinese population.
Obesity has become a major health problem in contemporary society and it is closely related to many chronic diseases, so it is an important issue for measuring adiposity accurately and predicting its future. Prevention and treatment of overweight and obesity has become one of the key prevention and treatment of metabolic disorders.
In this study, we compared the ability of the four anthropometric indicators (body mass index, waist circumstance, waist-height ratio, waist-to-hip ratio) to identify metabolic disorders (hypertension, hyperlipidaemia, hyperglycemia and hyperuricemia) by receiver operating characteristic (ROC) curve analyses and to provide evidence for clinical practice.
In this large scale cross-sectional study, 13,275 Han adults (including 7595 males and 5680 females) received physical examination between January, 2009 and January, 2010 in Xuanwu Hospital of Capital Medical University were investigated by the means of questionnaire, Meanwhile, the physical examination and serological results were recorded. A package known as Statistical Package for Social Scientist (SPSS) was employed to analyse the responses while t-test, one-way analysis of variance (ANOVA), ROC analysis and chi-square statistical methods were used to test the hypotheses.
WC, WHtR, WHR and BMI were all significantly (P < 0.001) correlated with all metabolic risk factors regardless of gender. And the area under the curve (AUC) of WHtR was significantly greater than that of WC, BMI or WHR in the prediction of hypertension, hyperlipidaemia, hyperglycemia and hyperuricemia.
Our data show that WHtR was the best predictor of various metabolic disorders. The diagnostic value in descending order was WHtR > WHR > WC > BMI. Therefore we recommend WHtR in assessment of obese patients, in order to better assess the risks of their metabolic diseases.
Dong J
，Ni YQ
，Chu X
，Liu YQ
，Liu GX
，Zhao J
，Yang YB
，Yan YX
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