Waist-to-height ratio, an optimal predictor for obesity and metabolic syndrome in Chinese adults
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Anthropometric indices to obesity were evaluated as predictors of metabolic syndrome risk factors. Our purpose was to explore an optimal or more reliable anthropometric indicator and optimal cut-off points for obesity on metabolic syndrome in Chinese adults.
Participants and methods
The survey was conducted involving 2947 participants, aged 20 or above with cross-sectional study of population. The predictive validity and optimal cut-off values were analyzed by receiver operating characteristic (ROC) curves, area under curve (AUC) and the largest Youden’s index (sensitivity + specificity −1) by gender group, respectively. Kappa value showed diagnostic consistency.
(1) According to the criteria of CDS 2004, IDF 2005 and AHA/NHLBI 2005, the prevalence of metabolic syndrome was 10.32%, 9.64% and 16.12% respectively, which indicated that the prevalence was higher in men than in women and increased with age (P < 0.05). (2) The BMI, WC, WHR and WHtR in metabolic syndrome patients were greater than those in healthy volunteers and the indices in men were higher than those in women. (3) With adjusted age and gender, the partial correlation coefficient for BMI-WC, BMI-WHR and BMI-WHtR was 0.7991, 0.5278 and 0.8196, respectively (P < 0.05). (4) The area under curves (AUCs) of receiver operating characteristic (ROC) curves for WHtR was larger (P < 0.05) than that for WC and WHR. The cut-point of WHtR was approximately 0.5 in both genders with a satisfactory balance between sensitivity and specificity, where the Kappa (κ) value for WHtR-BMI was higher than that for WHtR-WHR, and WHtR-WC.
The results indicated that WHtR might be an optimal anthropometric predictor of metabolic syndrome risk factors and the cut-point of WHtR was approximately 0.50 in both genders of Chinese adults.
Key wordsWaist-to-height ratio obesity metabolic syndrome receiver operating characteristics
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- 16.Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 2001, 285: 2486–2497.Google Scholar
- 17.Shao JH, Wang K, Sheng X, Li DY, Li X, Fang XF, He QF, Lu WW, Ren XM, Zhang MR, Jin YL: Study on prevalence of metabolic syndrome among city dweller in Xuzhou city. Chin J Public Health 2008, 24: 1118–1119.Google Scholar
- 18.Chinese Medical Association Diabetes Branch:Suggestion about Metabolic Syndrome. Chin J Diabetes 2004, 12: 156–158.Google Scholar
- 20.Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, Smith SC, Jr., et al: Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 2005, 112: 2735–2752.CrossRefPubMedGoogle Scholar
- 21.Central obesity and risk of cardiovascular disease in the Asia Pacific Region. Asia Pac J Clin Nutr 2006, 15: 287–292.Google Scholar
- 29.Alberti KG, 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.CrossRefPubMedGoogle Scholar