A TyG index ≥8.87 predicted CAD with 65.58% sensitivity and 65.09% specificity (AUC 0.72), and ≥8.92 predicted severe CAD with 69.19% sensitivity and 71.90% specificity (AUC 0.76).
Does the Triglyceride-Glucose (TyG) index predict the presence and severity of coronary artery disease in patients undergoing coronary angiography?
The TyG index is a simple, cost-effective biomarker that independently predicts the presence and severity of coronary artery disease in patients undergoing coronary angiography.
Absolute Event Rate: 0% vs 0%
Abstract Background Coronary artery disease (CAD) remains the leading cause of death globally. Early identification of individuals at risk is critical for targeted interventions. Insulin resistance (IR) has been identified as an emerging risk factor for CAD, with the Triglyceride-Glucose (TyG) index serving as a promising surrogate marker for IR due to its affordability and practicality. This study aimed to evaluate the association between the TyG index and CAD presence and severity, as well as its potential role in risk stratification and management of CAD. Methodology This retrospective, cross-sectional study included 563 patients who underwent coronary angiography at a tertiary hospital from January to December 2023. Patients were categorized based on presence of CAD and its severity. Logistic regression models were employed to explore the relationship between the TyG index and CAD and severe CAD. Multivariate analyses adjusted for confounding variables. The diagnostic performance of the TyG index in predicting CAD and severe CAD (sensitivity, specificity, positive predictive value and negative predictive value) were assessed using the area under the receiver operating characteristic curve (AUROC). The TyG index optimal cut values for CAD and its severity were identified. Result Among 536 patients, 80% were diagnosed with CAD, with severe disease present in 66.23% of cases. Patients with CAD were older, had higher levels of the TyG index, fasting blood sugar, very low-density lipoprotein cholesterol, and biomarkers (HS-Troponin I and NT-proBNP), and a higher prevalence of acute coronary syndrome subtypes. TyG index remained a strong independent predictor of CAD (adjusted OR 2.83, p 0.001) and severe CAD (adjusted OR 6.41, p 0.001) after adjusting for confounders. The TyG index cutoff of ≥8.87 provided a sensitivity of 65.58% and specificity of 65.09% for CAD, while a cutoff of ≥8.92 improved sensitivity and specificity for severe CAD (69.19% and 71.90%, respectively). ROC analysis indicated good diagnostic discrimination for CAD (AUC 0.72) and severe CAD (AUC 0.76). The TyG index was also significantly associated with ACS subtypes, particularly STEMI and NSTEMI. Conclusion The TyG index is a simple and readily available biomarker with a significant association with the presence and severity of CAD. It has a good diagnostic performance in predicting CAD, underscoring its potential as a cost-effective tool for CAD risk stratification, especially in resource-limited settings. Integrating the TyG index with existing scoring systems could enhance its predictive and diagnostic capabilities, enabling more personalized preventive and therapeutic strategies for CAD management. Incorporating the TyG index into clinical guidelines may facilitate the early identification and management of patients at risk for CAD, ultimately improving cardiovascular outcomes.
Dychiching et al. (Sat,) reported a other. A TyG index ≥8.87 predicted CAD with 65.58% sensitivity and 65.09% specificity (AUC 0.72), and ≥8.92 predicted severe CAD with 69.19% sensitivity and 71.90% specificity (AUC 0.76).