Background: It is not clear which is the best anthropometric indicator to predict metabolic syndrome (MetS) in adolescents. Our objective was to identify the predictive power, with respective cut-off points, of anthropometric indicators associated with the quantity and distribution of body fat for the presence of MetS and to determine the strength of the association between the proposed cut-off points and MetS in adolescents.
Methods: The sample consisted of 1035 adolescents (565 girls and 470 boys) aged between 12 and 20 years. Four anthropometric indicators were considered: waist circumference (WC), body mass index (BMI), waist-height ratio (WHtR), and conicity index (C-Index). MetS was defined according to the criteria of the International Diabetes Federation. Predictive performance was described through analysis of Receiver Operating Characteristic (ROC) curves with a 95% confidence interval. The most accurate cut-off points were identified through sensitivity, specificity and Area Under the Curve (AUC) values.
Results: The four anthropometric indicators presented significant AUCs close to 0.70. At younger ages (12-15 years) the girls presented a statistically greater capacity to discriminate MetS; however, at more advanced ages (16-20 years) both sexes presented similar AUCs. Among the anthropometric indicators investigated, regardless of sex and age, the WHtR showed the highest discriminant value for MetS, while the C-Index demonstrated a significantly lower capacity to predict MetS. The AUCs equivalent to WC and BMI did not differ statistically. The proposed cut-off points for WHtR (12-15 years = 0.46, 16-20 years = 0.48) presented the highest values of sensitivity and specificity, between 60% and 70%, respectively.
Conclusion: Considering that the best AUC was found for WHtR, we suggest the use of this anthropometric indicator, with the cut-off points presented herein, for the prediction of MetS in adolescents with characteristics similar to the study sample.
Keywords: Accuracy; Adiposity; Anthropometry; Diagnosis; Obesity; Prediction.