Abstract
BACKGROUND: The triglyceride-glucose (TyG) index and the metabolic score for insulin resistance (METS-IR) are insulin resistance indicators based on different metabolic parameters. However, their cumulative effect on the outcomes of patients with acute myocardial infarction (AMI) remains unclear. This study aims to investigate whether the combined assessment of the TyG index and METS-IR can improve risk stratification and prognostic prediction in AMI patients. METHODS: This retrospective cohort study included AMI patients admitted to Cangzhou People's Hospital from January to December 2018. The baseline TyG index and METS-IR were calculated for each patient. The primary endpoint was major adverse cardiovascular and cerebrovascular events (MACCEs) during a 6-year follow-up, defined as a composite of all-cause mortality, coronary revascularization, and stroke. Logistic regression models and restricted cubic splines (RCS) were used to assess the association between TyG index, METS-IR, and the risk of MACCEs. Receiver operating characteristic (ROC) curves were applied to evaluate the discriminative ability of TyG index, METS-IR, and their combined predictive model (TyG index + BMI) for MACCEs. The area under the curve (AUC) was calculated to quantify predictive performance. Additionally, the net reclassification index (NRI) and integrated discrimination improvement (IDI) were computed to assess the incremental predictive value of TyG index + METS-IR beyond traditional risk factors. Subgroup analyses were conducted, and mediation analysis was performed to explore the potential mediating role of METS-IR in the relationship between TyG index and MACCEs. RESULTS: A total of 1,899 patients were included in the study. Multivariable logistic regression analysis showed that TyG index (OR = 1.655, 95% CI: 1.305-2.100, P < 0.001) and METS-IR (OR = 1.026, 95% CI: 1.001-1.052, P = 0.048) were both independent risk factors for MACCEs. Further analysis showed that patients with both high TyG index and high METS-IR had the highest risk of MACCEs (OR = 1.908, 95% CI: 1.188-3.114, P = 0.008). ROC curve analysis demonstrated that the combined prediction of MACCEs using TyG index and METS-IR achieved an AUC of 0.625, which was significantly superior to METS-IR alone (AUC = 0.573, P DeLong = 0.003). When compared with the traditional risk prediction model (AUC = 0.696), incorporating TyG index and METS-IR significantly improved predictive performance (optimized AUC = 0.717, P DeLong = 0.038). This also resulted in notable enhancements in NRI (0.353, P < 0.001) and IDI (0.156, P < 0.001). Subgroup analysis revealed no significant interaction effects of sex, age, hypertension, or diabetes status on the association between TyG index, METS-IR, and MACCEs (P-interaction > 0.05). Mediation analysis indicated that METS-IR partially mediated the relationship between TyG index and MACCEs. CONCLUSION: TyG index and METS-IR are predictors of adverse outcomes in AMI patients.