JOURNAL ARTICLE

BMI percentiles for the identification of abdominal obesity and metabolic risk in children and adolescents: evidence in support of the CDC 95th percentile

D M Harrington, A E Staiano, S T Broyles, A K Gupta, P T Katzmarzyk
European Journal of Clinical Nutrition 2013, 67 (2): 218-22
23232587

OBJECTIVES: Body mass index (BMI) percentiles have been routinely and historically used to identify elevated adiposity. The aim of this study was to investigate the optimal Centers for Disease Control and Prevention (CDC) BMI percentile that predicts elevated visceral adipose tissue (VAT), fat mass and cardiometabolic risk in a biracial sample of children and adolescents.

PARTICIPANTS AND METHODS: This cross-sectional analysis included 369 white and African-American children (5-18 years). BMI was calculated using height and weight and converted to BMI percentiles based on CDC growth charts. Receiver operating characteristic curve analysis identified the optimal (balance of sensitivity and specificity) BMI percentile to predict the upper quartile of age-adjusted VAT (measured by magnetic resonance imaging), age-adjusted fat mass (measured by dual-energy X-ray absorptiometry) and elevated cardiometabolic risk (≥ 2 of high glucose, triglycerides and blood pressure, and low high-density lipoprotein cholesterol) for each race-by-sex group.

RESULTS: The optimal CDC BMI percentile to predict those in the top quartile of age-adjusted VAT, age-adjusted fat mass and elevated cardiometabolic risk were the 96th, the 96th and the 94th percentiles, respectively, for the sample as a whole. Sensitivity and specificity was satisfactory (>0.70) for VAT and fat mass. Compared to VAT and fat mass, there was a lower overall accuracy of the optimal percentile in identifying those with elevated cardiometabolic risk.

CONCLUSIONS: The present findings support the utility of the 95th CDC BMI percentile as a useful threshold for the prediction of elevated levels of VAT, fat mass and cardiometabolic risk in children and adolescents.

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