Combined analysis of the triglyceride-glucose index and melanin-concentrating hormone in metabolic dysfunction-associated fatty liver disease: a machine learning-based study.

Objective: Metabolic dysfunction-associated fatty liver disease (MAFLD), a highly prevalent global liver disorder, requires simple and accessible screening approaches. As current diagnostic methods, such as the Controlled Attenuation Parameter (CAP), are limited in their applicability in obese patients and are primarily designed for fibrosis assessment. This study aim to investigate the associations of the serum melanin-concentrating hormone (MCH) and triglyceride-glucose (TyG) indices with MAFLD and to explore the risk factors and disease probability of MAFLD by developing machine learning models. Methods: In this cross-sectional study of 212 MAFLD patients and 107 healthy controls, and feature selection were identified through the least absolute shrinkage and selection operator (LASSO) regression analysis and Variance Inflation Factor (VIF). Three predictive models-Logistic Regression, Random Forest, Support Vector Machinemodel (SVM)-were constructed using the training set and evaluated in an independent test set. Construction of nomogram using independent risk factors screened by machine learning. Multivariate logistic regression analysis was used to explore further assess independent risk factors. Mediation analysis was conducted to explore potential pathways. Results: Logistic regression model was found to outperform other classifier models in testing data [area under the curve (AUC) of 92.6, 95% CI: 0.865-0.987] and achieve the lowest Brier Score as well. Decision curve analysis suggested potential clinical utility. Logistic regression analysis indicated that MCH (OR, 2.193; 95% CI, 1.242-3.873; Conclusion: Serum MCH and the TyG index were independently associated with MAFLD. A machine learning-based screening model was developed and internally validated, showing promising performance for identifying individuals at higher risk. However, external validation in larger multicenter prospective cohorts is warranted before broader clinical application.