Gender-Specific Predictive Utility of Twelve Anthropometric Indices for Metabolic Syndrome in Chinese Lahu Adults with Dyslipidemia

Objective: To evaluate the gender-specific predictive performance of 12 anthropometric indices for metabolic syndrome (MetS) among Lahu ethnic adults with dyslipidemia. Methods: This cross-sectional study employed stratified cluster sampling in two Lahu communities in southwest China. MetS was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATPIII) criteria. Twelve indices were assessed: body mass index (BMI), waist circumference (WC), lipid accumulation product (LAP), visceral adiposity index (VAI), a body shape index (ABSI), body adiposity index (BAI), body roundness index (BRI), relative fat mass (RFM), uric acid-to-HDL-C ratio (UHR), triglyceride-glucose index (TyG), cardiometabolic index (CMI), and Chinese visceral adiposity index (CVAI). Receiver operating characteristic (ROC) curve analysis evaluated predictive performance, and binary logistic regression assessed associations with MetS and its components. Results: This study of 1257 Lahu adults with dyslipidemia (48.5% male) revealed a 23.4% MetS prevalence, significantly higher in females (29.2% vs 17.2%, p<0.001). The TyG index emerged as the strongest MetS predictor in both sexes (AUC: males 0.828, females 0.784), followed by CMI, VAI, LAP and CVAI with moderate predictive ability (AUC range: 0.678-0.779). Other indices showed limited predictive value, while ABSI was not significant. For MetS components, BRI showed strong associations with elevated BP in both genders (males: 1.666[1.228-2.261]; females: 1.804[1.388-2.344]) and with elevated TG (1.644[1.248-2.166]) in males. Female‑specific associations included VAI with reduced HDL‑C, and BMI/CVAI with elevated FPG. Conclusion: Among dyslipidemic Lahu adults, all indices except ABSI demonstrated predictive efficacy for MetS, with the TyG index outperforming all others. These simple, low-cost indices could be applied for early MetS screening or risk stratification in this population, particularly in rural or resource-limited settings. Notably, gender-specific differences should be considered in clinical applications.