Mengjiang Liu,1 Zhaodan Yan,1 Yi Zhang,1 Shengli Zhang2 

1Department of Endocrinology, Hubei NO.3 People’s Hospital of Jianghan University, Wuhan, 430032, People’s Republic of China; 2Department of Cardiology, Hubei NO.3 People’s Hospital of Jianghan University, Wuhan, 430032, People’s Republic of China

Correspondence: Shengli Zhang, Department of Cardiology, Hubei NO.3 People’s Hospital of Jianghan University, Wuhan, 430032, People’s Republic of China, Email 13971408346@163.com

Aim: This study aimed to explore the relationship between the triglyceride-glucose index (TyG) and all-cause mortality among patients with diabetes and chronic kidney disease (CKD).
Methods: This was a retrospective cohort study that included 512 patients with diabetes and CKD. The TyG index was considered the exposure factor, and patients were divided into three groups based on the tertiles of the TyG index. The association between the TyG index and all-cause mortality was evaluated using multivariate Cox regression analysis, subgroup analysis, sensitivity analysis, restricted cubic spline (RCS) plot, and receiver operating characteristic (ROC) curves.
Results: Significant differences in clinical and metabolic parameters were observed across TyG tertiles, and all-cause mortality was markedly higher in the T3 group (P < 0.001). Multivariate Cox regression analysis showed that in the fully adjusted model (Model 3), the TyG index remained an independent risk factor, both as a continuous variable (HR = 1.582, 95% CI: 1.089– 2.298, P = 0.016) and as a categorical variable (T3 vs T1, HR = 3.300, 95% CI: 1.820– 5.984, P < 0.001). Subgroup analysis further confirmed consistent associations across various populations, including different age, sex, and comorbidity strata. Sensitivity analysis excluding patients with estimated glomerular filtration rate < 15 mL/min/1.73m2 showed robust associations in both continuous and categorical forms (P < 0.05). RCS analysis revealed a significant nonlinear relationship between Log10-transformed TyG index and all-cause mortality (P-nonlinear < 0.001). ROC curve analysis demonstrated that the TyG index alone had better predictive ability for all-cause mortality (AUC = 0.690) than age, hemoglobin A1c, or total cholesterol. The baseline model had an AUC of 0.809, which increased significantly to 0.878 (95% CI: 0.846– 0.911) when the TyG index was added.
Conclusion: The TyG index was independently and nonlinearly associated with all-cause mortality in patients with diabetes and CKD. These findings suggest that the TyG index may serve as a useful, non-invasive biomarker for risk stratification and mortality prediction in this high-risk population, with potential clinical implications for improving long-term management and prognosis.

Keywords: diabetes, chronic kidney disease, triglyceride-glucose index, all-cause mortality, receiver operating characteristic