Comparative performance of traditional and novel adiposity indices for predicting insulin resistance and metabolic syndrome in Chinese women with polycystic ovary syndrome

Objective This study aimed to comprehensively evaluate and compare the predictive performance of seven adiposity indices—both traditional (waist-to-hip ratio, WHR; waist-to-height ratio, WHtR) and novel (visceral adiposity index, VAI; Chinese visceral adiposity index, CVAI; lipid accumulation product, LAP; triglyceride-glucose index, TyG; cardiometabolic index, CMI)—for identifying insulin resistance (IR) and metabolic syndrome (MetS) in a large cohort of Chinese women with polycystic ovary syndrome (PCOS). Methods This secondary analysis enrolled 944 Chinese women with PCOS from the PCOSAct trial. IR was defined as homeostasis model assessment of insulin resistance (HOMA-IR) ≥ 2.69, and MetS was diagnosed in accordance with Chinese female-specific criteria. Receiver operating characteristic (ROC) curve analysis was applied to assess the discriminatory ability of each adiposity index, and correlations between these indices and metabolic, reproductive, hepatic as well as renal parameters were analyzed. Results The prevalence of IR and MetS in this cohort was 50 and 15.68%, respectively. All adiposity indices were significantly correlated with key metabolic parameters (all p < 0.001). For predicting IR, LAP (AUC = 0.815) and CVAI (AUC = 0.813) showed the highest diagnostic accuracy. For predicting MetS, CMI (AUC = 0.905), LAP (AUC = 0.898) and VAI (AUC = 0.893) presented the optimal predictive efficacy. Additionally, these adiposity indices were significantly correlated with sex hormone-binding globulin, free androgen index, liver enzymes and bilirubin levels. Conclusion Novel adiposity indices, especially CVAI, LAP and CMI, are effective and non-invasive tools for the early identification of IR and MetS in Chinese women with PCOS. The integration of these indices into clinical practice can improve risk stratification and guide timely interventions for metabolic comorbidities in this high-risk population. Trial registration ClinicalTrials.gov , identifier: NCT01573858; Chictr.org.cn , identifier: ChiCTR-TRC-12002081.