The association between insulin resistance and the occurrence of metabolic syndrome in Chinese patients with type 2 diabetes, and the application of Age–Sex–Ethnicity–Specific Severity scoring

Background Metabolic syndrome (MetS) is commonly diagnosed using a binary classification approach, which does not adequately capture the substantial heterogeneity in metabolic risk among patients with type 2 diabetes mellitus (T2DM). Transforming MetS into a continuous severity score allows for a more precise quantification of the overall metabolic burden. This study aimed to investigate the associations of the metabolic score for insulin resistance (METS-IR), homeostasis model assessment for insulin resistance (HOMA-IR), and serum uric acid to high density lipoprotein-cholesterol ratio (UHR) with the presence and severity of MetS among patients with T2DM. Methods This retrospective study included 726 patients with confirmed T2DM hospitalized at Longyan First Hospital, Fujian Medical University, from June 2022 to May 2025. Correlation analysis, multivariable regression analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) were performed to evaluate the relationships between insulin resistance (IR) indicators and MetS and its severity. Results After multivariable adjustment, METS-IR, HOMA-IR, and UHR were all identified as independent risk factors for MetS (P < 0.001) and were positively associated with MetS severity (METS-IR: β = 0.12, 95% CI 0.11–0.12; HOMA-IR: β = 0.14, 95% CI 0.12–0.17; UHR: β = 0.14, 95% CI 0.13–0.15; P < 0.001). Among these, METS-IR demonstrated the strongest predictive performance (AUC = 0.893). The DCA curve shows that METS-IR has the highest clinical net benefit across a wide range of risk thresholds, confirming its practical application value as a screening tool that is superior to the other two. Conclusion The assessment of METS-IR is of great importance for the early identification and intervention of metabolic syndrome and its progression.