Integrative Multi-Omics and Machine Learning Reveal Shared Biomarkers in Type 2 Diabetes and Atherosclerosis

Area of Science:

• Genomics and Bioinformatics
• Immunology
• Metabolic Diseases

Background:

• Atherosclerosis (AS) is a major complication of type 2 diabetes mellitus (T2DM).
• Shared molecular mechanisms between T2DM and AS remain incompletely understood.
• Identifying these links is crucial for developing targeted therapies.

Purpose of the Study:

• To identify overlapping molecular signatures between T2DM and AS.
• To explore the functional pathways and biological roles of shared genes.
• To evaluate potential diagnostic biomarkers and therapeutic targets for T2DM-related AS.

Main Methods:

• Analysis of Gene Expression Omnibus (GEO) datasets for AS and T2DM.
• Identification of differentially expressed and co-expressed genes.
• Functional enrichment (GO/KEGG) and protein-protein interaction (PPI) network analysis.
• Machine learning for hub gene prioritization and diagnostic potential evaluation.
• Immune cell infiltration analysis (CIBERSORT) and single-cell RNA sequencing.

Main Results:

• Identified 72 shared feature genes between T2DM and AS.
• Functional enrichment revealed significant inflammatory and metabolism-related pathways.
• Three hub genes (IL1B, MMP9, P2RY13) showed robust predictive performance.
• Immune analyses indicated inflammatory amplification and macrophage polarization imbalance.
• IL1B, MMP9, and P2RY13 are linked to inflammation, extracellular matrix remodeling, and cholesterol transport.

Conclusions:

• T2DM and AS share common immune and inflammatory pathways centered on macrophage dysregulation.
• IL1B, MMP9, and P2RY13 are potential biomarkers and therapeutic targets for T2DM-related AS.
• These genes may influence disease progression by modulating macrophage states, supporting translational applications.