The Correlation Between Triglyceride-Glucose-Body Mass Index, and the Risk of Silent Myocardial Infarction: Construction of a Predictive Model.

Abstract

Background: The incidence of silent myocardial infarction (SMI) is increasing. Meanwhile, due to the atypical clinical symptoms and signs associated with SMI, the prognosis for patients is often poor.

Methods: This prediction model used the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analyses to screen variables. Predictive accuracy was assessed using the area under the receiver operating characteristic (ROC) curve (AUC). The clinical decision curve analysis (DCA), alongside the calibration curve and clinical impact curve (CIC) analyses, were used to assess model validity.

Results: This study included 174 patients, 64 (36.8%) of whom experienced SMI; logistic regression analysis identified six variables: gender, age, high-density lipoprotein cholesterol (HDL-C), apolipoprotein B/apolipoprotein A1 (ApoB/A1), uric acid (UA), and triglyceride glucose-body mass index (TyG-BMI). The results identified the TyG-BMI as a predictor of SMI (odds ratios (OR) = 1.02, 95% CI: 1.01-1.03; p = 0.003). The ROC curve of the model demonstrated an AUC of 0.772 (95% CI: 0.699-0.844), which increased to 0.774 (95% CI: 0.707-0.841) following a bootstrap analysis with 1000 repetitions. The calibration curve of the model was in high agreement with the ideal curve. The DCA demonstrated that the prediction probability threshold of the model ranged from 12% to 83%, where the patient achieved a significant net clinical benefit. The CIC showed that the model effectively identified high-risk SMI patients when the threshold probability exceeded 0.7.

Conclusions: The TyG-BMI is an independent predictor of SMI. A prediction model based on the TyG-BMI showed good predictive ability for SMI.