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BACKGROUND: The aim of the present study of the general population was to identify the best predictor of metabolic risk among the body index variables evaluated with dual-energy X-ray absorptiometry (DEXA) or anthropometric indices including the waist to height ratio (WHtR). PATIENTS AND METHODS: Data from the Korean National Health and Nutrition Examination Survey 2008-2011 were used for the analyses. As a result, 15,965 participants were included in this study. The body mass (BM) index was calculated as the body weight divided by the height squared. The WHtR was calculated as the waist circumference divided by height. Body composition indices such as lean mass (LM), fat mass (FM), trunk fat mass (TFM), and bone mineral content (BMC) were determined by using DEXA. Skeletal muscle mass (SM) was defined as the sum of the lean soft masses of both extremities. The LM, FM, BMC, TFM, and SM indices were calculated by dividing the total LM, total FM, total BMC, TFM, or SM by the height squared. RESULTS: The WHtR had the highest area under the curve (AUC) and was the best predictor of metabolic syndrome for both sexes. In addition, the WHtR had the highest AUCs for components of metabolic syndrome (male: AUC 0.823, 95 % confidence interval [CI] 0.814-0.832; female: AUC 0.870, 95 % CI 0.863-0.877). There was a small statistically significant difference in AUC between WHtR and the other indices. Multivariate logistic regression showed that male participants in the second, third, and fourth quartiles had a 4.0 (95 % CI, 3.1-5.2), 9.6 (95 % CI, 7.5-12.3), and 36.1 (95 % CI, 28.0-46.4) times increased risk of metabolic syndrome compared with patients in the first quartile and female participants in the second, third, and fourth quartiles had a 4.3 (95 % CI, 3.1-6.0), 18.0 (95 % CI, 13.3-24.5), and 58.5 (95 % CI, 42.9-79.9) times increased risk of metabolic syndrome compared with patients in the first quartile. CONCLUSION: Among the BM, FM, LM, SM, TFM, and WHtR indices, WHtR is most useful to predict the presence of metabolic syndrome and insulin resistance in the Korean population.
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Kang et al. (2015) studied this question.
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