Journal of clinical lipidology · 2026
The integrated triglyceride-glucose index and body shape indices for improvements in myocardial infarction risk prediction: Findings from the Tehran Lipid and Glucose Study.
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Ramezankhani Azra, Barzin Maryam, Azizi Fereidoun, Hadaegh Farzad
Аннотация
BACKGROUND: The triglyceride-glucose (TyG) index, a surrogate marker of insulin resistance (IR), may improve myocardial infarction (MI) risk prediction when combined with anthropometric indices (ANI). However, comparative evidence on their performance remains limited.
OBJECTIVE: This study aimed to identify which TyG-ANI were most strongly associated with incident MI in a Middle Eastern population.
METHODS: This cohort study included 5044 adults aged ≥40 years from the Tehran Lipid and Glucose Study, followed for up to 20 years. Eleven TyG-ANI were evaluated. Random survival forest (RSF) identified predictive indices, and associations with incident MI were examined using Cox proportional hazards models. Incremental predictive performance was assessed using the C-index, net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike, and Bayesian information criteria (AIC, BIC).
RESULTS: During a median follow-up of 19.8 years, 215 participants developed MI. The RSF identified 6 TyG-ANI as predictive indices. In fully adjusted continuous models, TyG-relative fat mass showed the strongest association with incident MI (hazard ratio per SD: 1.36; 95% CI: 1.04-1.80). Adding TyG-ANI to the base model, which included conventional cardiovascular risk factors, resulted in modest but significant improvements in MI risk prediction. TyG-waist-to-hip ratio (WHR) yielded the greatest improvement in discrimination (ΔC-index = 0.006), followed by TyG-a body shape index (ABSI), TyG-conicity index, and TyG-weight-adjusted waist index (ΔC-index ≈ 0.005). TyG-ABSI demonstrated the greatest improvement in NRI (0.144; P = 0.022) and IDI (0.003; P = 0.032). TyG-WHR and TyG-ABSI also improved model fit, as indicated by lower AIC and BIC than the base model.
CONCLUSION: TyG-ABSI and TyG-WHR were independently associated with incident MI and provided modest improvements in risk prediction beyond traditional cardiovascular risk factors.
OBJECTIVE: This study aimed to identify which TyG-ANI were most strongly associated with incident MI in a Middle Eastern population.
METHODS: This cohort study included 5044 adults aged ≥40 years from the Tehran Lipid and Glucose Study, followed for up to 20 years. Eleven TyG-ANI were evaluated. Random survival forest (RSF) identified predictive indices, and associations with incident MI were examined using Cox proportional hazards models. Incremental predictive performance was assessed using the C-index, net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike, and Bayesian information criteria (AIC, BIC).
RESULTS: During a median follow-up of 19.8 years, 215 participants developed MI. The RSF identified 6 TyG-ANI as predictive indices. In fully adjusted continuous models, TyG-relative fat mass showed the strongest association with incident MI (hazard ratio per SD: 1.36; 95% CI: 1.04-1.80). Adding TyG-ANI to the base model, which included conventional cardiovascular risk factors, resulted in modest but significant improvements in MI risk prediction. TyG-waist-to-hip ratio (WHR) yielded the greatest improvement in discrimination (ΔC-index = 0.006), followed by TyG-a body shape index (ABSI), TyG-conicity index, and TyG-weight-adjusted waist index (ΔC-index ≈ 0.005). TyG-ABSI demonstrated the greatest improvement in NRI (0.144; P = 0.022) and IDI (0.003; P = 0.032). TyG-WHR and TyG-ABSI also improved model fit, as indicated by lower AIC and BIC than the base model.
CONCLUSION: TyG-ABSI and TyG-WHR were independently associated with incident MI and provided modest improvements in risk prediction beyond traditional cardiovascular risk factors.