Association between triglyceride-glucose index and its composite obesity indexes and cardio-renal disease: analysis of the NHANES 2013-2018 cycle.

BACKGROUND

The association between triglyceride-glucose (TYG) and its composite obesity indexes and cardio-renal disease in the American population remains insufficiently researched.

METHODS

This study examined a cohort of 11,491 American adults aged 20 years and older from the 2003-2018 National Health and Nutrition Examination Survey (NHANES). To explore the associations between TYG, TyG-Waist-to-Height Ratio (TyG-WHtR), TyG-Body Mass Index (TyG-BMI), TyG-Waist Circumference (TyG-WC), and chronic kidney disease (CKD), cardiovascular disease (CVD), and cardiorenal syndrome (CRS), we utilized weighted multivariate logistic regression, restricted cubic spline (RCS), Receiver operating characteristic (ROC), and subgroup analyses.

RESULTS

Adjusted for confounding factors, there are positive associations between the likelihood of CKD, CVD, and CRS, as well as TYG and its composite obesity indexes. The TYG index was correlated most strongly with CKD (OR 1.42, 95% CI 1.11, 1.82; P = 0.007), while TyG-WHtR had the strongest correlations with CVD (OR 1.63, 95% CI 1.19, 2.22; P = 0.003) and CRS (OR 1.44, 95% CI 1.00, 2.08; P = 0.055). A nonlinear connection was found by RCS analysis between TYG and its composite obesity indexes with CKD (P for overall < 0.001, P for nonlinear < 0.05), while the association with CVD and CRS was predominantly linear (P for overall < 0.001, P for nonlinear > 0.05). Based on ROC curves, TyG-WHtR and TyG-WC emerged as more reliable diagnostic tools than TYG for cardiac and renal diseases. According to subgroup analyses, TyG and its composite obesity measurements were more strongly associated with CKD in younger individuals (≤ 50), males, and those with diabetes mellitus (P for interaction 0.05).

CONCLUSIONS

The TyG-WHtR and TyG-WC are associated with an increased risk of cardiac and renal disease, indicating enhanced diagnostic accuracy. These metrics provide an effective tool for identifying early cardiorenal disease and improving risk stratification.