Prediction of insulin resistance with anthropometric measures: lessons from a large adolescent population

Abstract

ObjectiveThe aim of this study was to describe the minimum number of anthropometric measures that will optimally predict insulin resistance (IR) and to characterize the utility of these measures among obese and nonobese adolescents.Research design and methodsSix anthropometric measures (selected from three categories: central adiposity, weight, and body composition) were measured from 1298 adolescents attending two New York City public high schools. Body composition was determined by bioelectric impedance analysis (BIA). The homeostatic model assessment of IR (HOMA-IR), based on fasting glucose and insulin concentrations, was used to estimate IR. Stepwise linear regression analyses were performed to predict HOMA-IR based on the six selected measures, while controlling for age.ResultsThe stepwise regression retained both waist circumference (WC) and percentage of body fat (BF%). Notably, BMI was not retained. WC was a stronger predictor of HOMA-IR than BMI was. A regression model using solely WC performed best among the obese II group, while a model using solely BF% performed best among the lean group. Receiver operator characteristic curves showed the WC and BF% model to be more sensitive in detecting IR than BMI, but with less specificity.ConclusionWC combined with BF% was the best predictor of HOMA-IR. This finding can be attributed partly to the ability of BF% to model HOMA-IR among leaner participants and to the ability of WC to model HOMA-IR among participants who are more obese. BMI was comparatively weak in predicting IR, suggesting that assessments that are more comprehensive and include body composition analysis could increase detection of IR during adolescence, especially among those who are lean, yet insulin-resistant.