The relative and combined ability of triglyceride-glucose index and stress hyperglycemia ratio to predict major adverse cardio-cerebral events in patients with multivessel coronary artery disease.

BACKGROUND

Cardiovascular disease continues to be the leading cause of global mortality and disability, particularly posing elevated risks in patients diagnosed with multivessel disease (MVD). Efficient risk stratification in MVD patients is crucial for improving prognosis, prompting investigation into novel biomarkers such as the triglyceride-glucose index (TyG index) and the stress hyperglycemia ratio (SHR).

METHODS

This study enrolled a cohort comprising 679 patients diagnosed with MVD who underwent coronary angiography at Tianjin Chest Hospital. Patients were stratified into four groups based on their TyG index levels, categorized as TyG index-L and TyG index-H, and SHR levels, categorized as SHR-L and SHR-H. The primary endpoint was the occurrence of major adverse cardio-cerebral events (MACCEs). This Study conducted univariate and multivariable Cox regression analyses to assess the association between TyG index and SHR levels, both as continuous and categorical variables, in relation to MACCEs. Kaplan-Meier survival curves were employed to evaluate the correlation among patient groups.

RESULTS

During a mean follow-up of 61 months, 153 cases of MACCEs were recorded. The TyG index and SHR served as independent predictors of long-term prognosis in patients with MVD, whether considered as continuous or categorical variables. Multivariable analysis revealed that patients with TyG index-H + SHR-H group exhibited the highest incidence of MACCEs (HR: 2.227; 95% CI 1.295-3.831; P = 0.004). The area under the curve (AUC) for predicting MACCEs was 0.655 for TyG index, 0.647 for SHR, and 0.674 when combined.

CONCLUSION

This study underscores the potential of the TyG index and SHR as independent and combined predictive markers for MACCEs in patients with MVD. Their integrated assessment enhances risk stratification, providing valuable insights for personalized treatment strategies aimed at optimizing patient prognosis.