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Cambridge University Press, Public Health Nutrition, 1(14), p. 77-92, 2010

DOI: 10.1017/s1368980010002648

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Receiver-operating characteristics of adiposity for metabolic syndrome: the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

AbstractObjectiveTo assess the predictive values of various adiposity indices for metabolic syndrome (MetS) among adults using baseline data from the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) cohort.DesignIn a cross-sectional study, BMI, waist circumference (WC), body composition by dual-energy X-ray absorptiometry (DEXA) and metabolic risk factors such as TAG, HDL cholesterol, blood pressure, fasting glucose and insulin, uric acid and C-reactive protein were measured. Receiver-operating characteristic (ROC) curves and logistic regression analyses were conducted.SettingBaltimore, Maryland.SubjectsWhite and African-American US adults (n 1981), aged 30–64 years.ResultsIn predicting risk of MetS using obesity-independent National Cholesterol Education Program Adult Treatment Panel III criteria, percentage total body fat mass (TtFM) assessed using DEXA measuring overall adiposity had no added value over WC. This was true among both men (area under the ROC curve (AUC) = 0·680 v. 0·733 for TtFM and WC, respectively; P < 0·05) and women (AUC = 0·581 v. 0·686). Percentage rib fat mass (RbFM) was superior to TtFM only in women for MetS (AUC = 0·701 and 0·581 for RbFM and TtFM, respectively; P < 0·05), particularly among African-American women. Elevated percentage leg fat mass (LgFM) was protective against MetS among African-American men. Among white men, BMI was inferior to WC in predicting MetS. Optimal WC cut-off points varied across ethnic–sex groups and differed from those recommended by the National Institutes of Health/North American Association for the Study of Obesity.ConclusionsThe study provides evidence that WC is among the most powerful tools to predict MetS, and that optimal cut-off points for various indices including WC may differ by sex and race.