Work

Development of a nomogram model for predicting coronary heart disease in patients with metabolic-associated fatty liver disease

Year: 2025

Type: article

Abstract: Objective: To investigate the risk factors associated with coronary heart disease (CHD) in patients with metabolic-associated fatty liver disease (MAFLD) and develop a nomogram prediction model. Methods: = 220) groups. LASSO and multivariable logistic regression analyses were performed to identify the risk factors of concomitant coronary heart disease in patients with MAFLD. A nomogram was constructed and validated internally to predict CHD risk in the patients. We evaluated the nomogram's predictive performance using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) in the training and validation groups. Results: Of the 394 MAFLD cases, 313 had CHD-related complications. Of the 277 patients in the training set, 220 had CHD, and of the 117 patients in the validation set, 93 had CHD. LASSO regression analysis revealed that the following variables were associated with the risk of CHD: sex, lipoprotein(a) (Lp[a]), low-density lipoprotein cholesterol, white blood cell count (WBC), glycated triglyceride-glucose index (TyG), and atherosclerosis index (AIP). Multivariate logistic regression analysis revealed that sex, Lp(a), WBC, TyG, and AIP were independent risk factors for CHD in MAFLD cases. A nomogram was constructed and an ROC curve was plotted, based on which the optimal cutoff value was determined as 0.698. The area under the curve of the nomogram in the training and validation cohorts was 0.860 (95% CI = 0.807-0.913) and 0.843 (95% CI = 0.757-0.929), respectively. Calibration curves for CHD risk probability showed good agreement between the nomogram's predicted probabilities and the observed event rates. DCA demonstrated the net clinical benefit of the constructed nomogram. Conclusion: Sex, Lp(a), WBC, TyG, and AIP emerged as independent risk factors for CHD in patients with MAFLD and the nomogram prediction model constructed using these factors could effectively predict CHD occurrence.

Source: Frontiers in Cardiovascular Medicine

DOI: 10.3389/fcvm.2025.1652321