Predicting insulin resistance in children: anthropometric and metabolic indicators.

OBJECTIVE To predict insulin resistance in children based on anthropometric and metabolic indicators by analyzing the sensitivity and specificity of different cutoff points. METHODS A cross-sectional study was carried out of 109 children aged 7 to 11 years, 55 of whom were obese, 23 overweight and 31 well-nourished, classified by body mass index (BMI) for age. Measurements were taken to determine BMI, waist and hips circumferences, waist circumference/hip circumference ratio, conicity index and body fat percentage (dual emission X-ray absorptiometry). Fasting blood samples were taken to measure triglyceridemia, glycemia and insulinemia. Insulin resistance was evaluated by the glycemic homeostasis method, taking the 90th percentile as the cutoff point. Receiver operating characteristic curves were analyzed to a 95% confidence interval in order to identify predictors of glycemic homeostasis, and sensitivity and specificity were then calculated. RESULTS After analysis of the area under the receiver operating characteristic curve (confidence interval), indicators that demonstrated the power to predict insulin resistance were, in the following order: insulinemia = 0.99 (0.99-1.00), 18.7 microU mL(-1); body fat percentage = 0.88 (0.81-0.95), 41.3%; BMI = 0.90 (0.83-0.97), 23.69 kg m(2-(1)); waist circumference= 0.88 (0.79-0.96), 78.0 cm; glycemia = 0.71 (0.54-0.88), 88.0 mg dL(-1); triglyceridemia = 0.78 (0.66-0.90), 116.0 mg dL(-1) and conicity index = 0.69 (0.50-0.87), 1.23 for the whole sample; and were: insulinemia = 0.99 (0.98-1.00), 19.54 microU mL(-1); body fat percentage = 0.76 (0.64-0.89), 42.2%; BMI = 0.78 (0.64-0.92), 24.53 kg m(2-(1)); waist circumference = 0.77 (0.61-0.92), 79.0 cm and triglyceridemia = 0.72 (0.56-0.87), 127.0 mg dL(-1), for the obese subgroup. CONCLUSIONS Anthropometric and metabolic indicators appear to offer good predictive power for insulin resistance in children between 7 and 11 years old, employing the cutoff points with the best balance between sensitivity and specificity of the predictive technique.