Diagnostic accuracy of various anthropometric indexes for assessing insulin resistance in the Peruvian population: An analytical cross-sectional study.

Background: Insulin resistance (IR) is a key driver of type 2 diabetes mellitus and cardiovascular diseases. Traditional methods for IR diagnosis are costly, highlighting the need for accessible alternatives like anthropometric indices. However, their accuracy in developing countries remains underexplored. This study aimed to assess the diagnostic accuracy of anthropometric indices for assessing IR and to analyze the association of key anthropometric variables with IR in a Peruvian population. Design and methods: A cross-sectional analysis was conducted on 908 participants from the PERU MIGRANT cohort. IR was defined using the Homeostatic Model Assessment for Insulin Resistance. Anthropometric indices, including waist circumference (WC), skinfold measurements, and fat mass index (FMI), were evaluated. Diagnostic accuracy was assessed using ROC curves. Subsequently, adjusted logistic regression models were used to assess the association between key anthropometric indices and IR. Results: FMI exhibited the highest diagnostic accuracy (AUC: 0.80; 95% CI: 0.75-0.84 in women, 0.81; 95% CI: 0.74-0.87 in men), with high sensitivity (>80%) and specificity (>70%) for both sexes. Sex-specific FMI cut-offs were 11.70 kg/m Conclusion: FMI and upper limb skinfold are reliable anthropometric measures for assessing IR in the Peruvian population. These findings support their application in public health interventions aimed at early IR detection, especially in resource-limited settings.