Abstract
BACKGROUND: The triglyceride-glucose (TyG) index, proven a reliable and simple surrogate of insulin resistance, has shown potential associations with cardiovascular outcomes and renal diseases. This research delved into the utility of the TyG index in predicting the risk of acute kidney injury (AKI) in patients with coronary artery disease (CAD), an area not extensively covered in existing literature. METHODS: A cohort of patients with CAD was recruited from the Medical Information Mart for Intensive Care-IV database, and categorized into quartiles based on their TyG index. The primary outcome was AKI incidence, and the secondary outcome was renal replacement therapy (RRT). Scatterplot histograms, cox proportional hazards models, Kaplan-Meier survival curves, and restricted cubic splines were employed to investigate the association between the TyG index and the risk of AKI in patients with CAD. RESULTS: A total of 1,501 patients were enrolled in this study, predominantly male (61.56%), with a median age of 69.80 years. The AKI incidence was 67.22% among all patients, with the AKI stages increased with higher TyG levels (P for trend <0.001). The Kaplan-Meier survival analyses demonstrated statistically significant differences in AKI incidence and RRT application throughout the entire cohort, stratified by the TyG index quartiles (p < 0.001). Additionally, the restricted cubic spline analysis revealed a non-linear association between the TyG index and the risk of AKI (P for non-linear =0.637). Both multivariate Cox proportional hazards analyses (HR 1.62; 95% CI 1.15-2.27; p = 0.005) and multivariate logistic regression analyses (OR 2.16; 95% CI 1.18-3.94; p = 0.012) showed that the elevated TyG index was significantly related to AKI incidence. The association between TyG index and the risk of AKI is more significant in patients without diabetes (HR 1.27; 95% CI 1.14-1.42; p < 0.001), compared to patients with diabetes (P for interaction =0.013). CONCLUSIONS: In summary, the TyG index emerged as a reliable predictor for the occurrence of AKI in CAD patients during ICU stay. Furthermore, it is also anticipated to serve as a valuable indicator for non-diabetic patients in predicting the incidence of AKI.