Association between triglyceride glucose index-related indices and kidney stones in adults based on NHANES 2007–2020 - Liu, Ming; Yang, Ping; Gou, Yunpeng (Frontiers in Endocrinology, 2025)
BackgroundThe triglyceride-glucose (TyG) index and related indices, including the triglyceride-glucose body mass index (TyG-BMI), triglyceride-glucose waist circumference (TyG-WC), and triglyceride-glucose waist-to-height ratio (TyG-WHtR), are increasingly recognized as valuable markers of insulin resistance (IR). This study aimed to assess the associations between these TyG-related indices and kidney stones.MethodsThis cross-sectional study analyzed data from 10,824 participants obtained from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2020. Weighted logistic regression models were employed to evaluate the associations between TyG-related indices and kidney stones, with adjustments for potential confounding factors. Subgroup analyses and smooth curve fittings were performed to further examine these associations, while receiver operating characteristic (ROC) curves were used to compare the predictive performance of each index.ResultsAll TyG-related indices demonstrated significant positive associations with kidney stones when analyzed as continuous variables. The odds ratios (OR) with 95% confidence intervals (CI) were 1.0040 (1.0028, 1.0052) for TyG-BMI, 1.0015 (1.0011, 1.0020) for TyG-WC, and 1.3305 (1.2277, 1.4419) for TyG-WHtR. Similar trends were observed in subgroup and smooth curve analyses. When stratified into tertiles, higher tertiles of each TyG-related index were associated with increased odds of kidney stones. TyG-WC demonstrated the strongest predictive capability for kidney stones (AUC = 0.6158), followed closely by TyG-WHtR (AUC = 0.6156) and TyG-BMI (AUC = 0.5949), with TyG showing the lowest AUC (0.5815).ConclusionThis study identified significant positive associations between TyG-related indices and kidney stone formation. Among these indices, TyG-WHtR exhibited the highest predictive power for identifying kidney stone risk.