Triglyceride–Glucose (TyG) Index as a Predictor of Early Metabolic Alterations in a Tertiary Care Laboratory Population

Background: The Triglyceride–Glucose (TyG) index, derived from fasting triglyceride and glucose levels, has emerged as a practical surrogate marker of insulin resistance. Growing evidence links TyG index with metabolic syndrome, type 2 diabetes mellitus, and cardiovascular disease; however, data from routine tertiary laboratory populations remain limited.

Objective: To evaluate the association between TyG index and early metabolic alterations in an adult tertiary care laboratory population.

Materials and Methods: A cross-sectional analytical study was conducted among 300 adults undergoing routine fasting investigations at a tertiary care center. TyG index was calculated as ln [(Triglycerides (mg/dL) × Fasting Blood Glucose (mg/dL)) / 2]. Associations between TyG index and metabolic parameters including body mass index (BMI), lipid profile, and glycemic indices were analyzed using Pearson correlation, one-way ANOVA, multiple linear regression, and receiver operating characteristic (ROC) curve analysis.

Results: The mean TyG index was 8.71 ± 0.62. TyG index showed moderate positive correlations with fasting blood glucose (r = 0.401), BMI (r = 0.382), total cholesterol (r = 0.356), and LDL cholesterol (r = 0.341), and a significant inverse correlation with HDL cholesterol (r = –0.298) (all p < 0.001). Individuals in the highest TyG tertile demonstrated significantly greater prevalence of moderate and high metabolic risk categories (p < 0.001). On multivariate regression analysis, BMI, LDL, and HDL emerged as independent predictors of TyG index (R² = 0.42). ROC curve analysis revealed good discriminatory performance of TyG index for identifying ≥2 metabolic abnormalities (AUC = 0.79).

Conclusion: The TyG index is significantly associated with adverse metabolic profiles and demonstrates good predictive capability for early metabolic risk. Given its simplicity and cost-effectiveness, it may serve as a valuable adjunct tool for routine metabolic risk assessment in clinical practice.