The aim of this study was to integrate multiple metabolic indicators from diverse systems and clinical research data to develop a predictive model for metabolic associated fatty liver disease (MAFLD) complicated by coronary heart disease (CHD).

Patients diagnosed with MAFLD between January 2020 and January 2025 were recruited from Xinjiang Production and Construction Corps Hospital. Those with malignant tumors or cognitive impairments were excluded. The patients were categorized into two groups according to the presence or absence of concurrent coronary heart disease. Univariate analysis was performed on commonly used clinical indicators to screen out those with significant inter-group differences. These selected indicators were then subjected to multivariate Logistic regression analysis to identify risk factors.

Univariate analysis revealed significant abnormalities in the CHD group regarding age (mean difference: -12.35 years, P=5.4e-37), liver function markers (ALT increased by 11.72 U/L, GGT increased by 23.58 U/L), lipid profiles (TG increased by 0.56 mmol/L, LDL-C increased by 0.43 mmol/L), and urinary protein levels (increased by 49.69 mg/dl) (P＜0.05). A binary predictive model was developed using logistic regression, incorporating key predictors including age, urinary protein, AST, and GGT. The model demonstrated superior performance with an area under the curve of 0.798 in the 80% training set and 0.85 in cross-validation, exhibiting a sensitivity of 0.89 sensitivity, specificity of 0.9, and f1-score of 0.9, significantly outperforming traditional single-indicator assessment methods.

The MAFLD-CHD risk prediction model established in this study exhibits high clinical applicability, providing a quantitative tool for individualized screening and stratified management. Routine combined assessment of urinary protein, liver enzymes, and age is recommended for MAFLD patients to facilitate early identification of high-risk populations and initiate lifestyle interventions or pharmacological treatments, thereby reducing cardiovascular event incidence.