Association of the triglyceride-to-high-density lipoprotein cholesterol ratio with kidney stone status: cross-sectional and machine-learning analyses.

This study investigated the association between triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-C) and kidney stone status and evaluated its contribution to multivariable classification models. A total of 1,174 patients from the First Affiliated Hospital of Anhui Medical University were included, with kidney stone status confirmed by urological imaging. Multivariable analyses were used to assess the association between TG/HDL-C and kidney stone status. Supplementary population-based analyses were conducted using weighted data from the National Health and Nutrition Examination Survey (NHANES). Seven machine-learning models were developed and internally evaluated within the clinical cohort, and SHapley Additive Explanations (SHAP) were used to interpret model outputs. In the clinical cohort, a higher TG/HDL-C ratio was associated with greater odds of kidney stone status after multivariable adjustment (OR, 2.16; 95% CI, 1.73-2.70). The association was generally consistent across subgroups defined by age, sex, body mass index, hypertension, and diabetes, with no significant interactions observed. A directionally similar association was identified in the weighted NHANES analysis (OR, 1.04; 95% CI, 1.01-1.07). Among the evaluated machine-learning models, the random forest model achieved the highest area under the receiver operating characteristic curve in the internal validation set (AUROC, 0.818; 95% CI, 0.775-0.859). SHAP analysis identified the TG/HDL-C ratio as an important contributor to model classification. These findings indicate that the TG/HDL-C ratio is an accessible metabolic marker associated with kidney stone status. However, the observational design precludes causal inference, and prospective studies with external model validation are warranted.