Development of a Shapley Additive Explanations (SHAP)-Interpretable Model for Cardiovascular Risk Stratification in Type 2 Diabetes Mellitus: Implications of MDA-Modified Protein Adducts and Autoantibodies

Abstract

Coronary artery disease (CAD) is the leading cause of mortality in type 2 diabetes mellitus (T2DM). This study explores the two-hit oxidative–immune hypothesis by evaluating malondialdehyde (MDA)-modified protein (MDA-protein) adducts and their corresponding autoantibodies for precise and fair CAD severity stratification and classification. Novel MDA-peptide epitopes (apolipoprotein B-100 (ApoB-100), fibronectin (FINC), and complement C4A/C4B (C4A/C4B)) were identified via proteomics, and plasma levels of MDA, adducts, and autoantibodies were measured in 165 Taiwanese T2DM patients. Multivariate logistic regression estimated odds ratios (ORs) per 1-SD increase. Machine learning with Shapley additive explanations (SHAP)-interpretable analysis and nested cross validation distinguished obstructive from nonobstructive CAD. Fairness was assessed across age and sex subgroups. MDA (OR 3.148) and MDA-protein adducts (OR 2.090) were independent markers associated with advanced CAD severity, while IgG anti-ApoB-1001662–1683 MDA (OR 0.481) was independently associated with a lower risk. In differentiating obstructive CAD in T2DM patients, random forest achieved a pooled AUC of 0.958, a Brier score of 0.089, and a net benefit of 0.433 at the 20% threshold, outperforming conventional markers. SHAP analysis highlighted IgG anti-C4A/B167–187 as the dominant positive contributor within the machine learning framework, suggesting a possible proinflammatory signature, underscoring nonlinear push–pull dynamics between oxidative–immune signatures. As an exploratory finding, fairness analysis demonstrated consistent performance across sexes and a notable benefit in older patients, while revealing age-related imbalance in calibration and decision utility. Integrating MDA-related immune signatures into interpretable, fairness-aware categorical boosting and logistic regression models provides a robust, noninvasive framework for CAD severity stratification in T2DM, clarifying immunometabolic interactions and supporting equitable clinical decision-making.