This study integrates multi-omics analyses to elucidate the molecular mechanisms connecting diabetes subtypes with coronary artery disease (CAD). Weighted gene co-expression network analysis (WGCNA) of Gene Expression Omnibus (GEO) datasets revealed distinct gene modules for type 1 diabetes (tan and turquoise) and type 2 diabetes (yellow-green and dark slate blue). Integration of three independent machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forest, identified Potassium inwardly rectifying channel subfamily J member 2 (KCNJ2) and HSPG2 as key genes associated with both type 1 diabetes mellitus (T1DM)-CAD and type 2 diabetes mellitus (T2DM)-CAD. Furthermore, Mendelian randomization (MR) analysis and immune cell infiltration studies revealed that M1 macrophage markers (IL-1β and IL-6) were positively associated with CAD, whereas M2 macrophage markers Arginase-1 (ARG1) and IL-10 showed negative associations. These results suggest that KCNJ2 and HSPG2 act as central mediators of diabetes-associated CAD through macrophage phenotype switching, providing potential therapeutic targets for metabolic–cardiovascular disease intervention. GRAPHICAL ABSTRACT Exploration of the gene regulatory network of T1DM and T2DM and CAD based on WGCNA and MR analysis.
Huang et al. (Mon,) studied this question.