Gut Microbiota and Type 2 Diabetes: Genetic Associations, Biological Mechanisms, Drug Repurposing, and Diagnostic Modeling

Gut microbiota is a potential therapeutic target for type 2 diabetes (T2D), but its role remains unclear. Investigating causal associations between them could further our understanding of their biological and clinical significance. A two-sample Mendelian randomization (MR) analysis was conducted to assess the causal relationship between gut microbiota and T2D. Key genes and mechanisms were identified through the integration of Genome-Wide Association Studies (GWAS) and cis-expression quantitative trait loci (cis-eQTL) data. Network pharmacology was applied to identify potential drugs and targets. Additionally, gut microbiota community analysis and machine learning models were used to construct a diagnostic model for T2D. MR analysis identified 17 gut microbiota taxa associated with T2D, with three showing significant associations: <i>Actinomyces</i> (odds ratio [OR] = 1.106; 95% confidence interval [CI]: 1.06-1.15; <i>p</i> < 0.01; adjusted <i>p</i>-value [<i>p</i>_adj] = 0.0003), <i>Ruminococcaceae</i> (UCG010 group) (OR = 0.897; 95% CI: 0.85-0.95; <i>p</i> < 0.01; <i>p</i>_adj = 0.018), and <i>Deltaproteobacteria</i> (OR = 1.072; 95% CI: 1.03-1.12; <i>p</i> < 0.01; <i>p</i>_adj = 0.029). Ten key genes, such as <i>EXOC4</i> and <i>IGF1R</i>, were linked to T2D risk. Network pharmacology identified <i>INSR</i> and <i>ESR1</i> as target driver genes, with drugs like Dienestrol showing promise. Gut microbiota analysis revealed reduced α-diversity in T2D patients (<i>p</i> < 0.05), and β-diversity showed microbial community differences (R² = 0.012, <i>p</i> = 0.001). Furthermore, molecular docking confirmed the binding affinity of potential therapeutic agents to their targets. Finally, we developed a class-weight optimized Extreme Gradient Boosting (XGBoost) diagnostic model, which achieved an area under the curve (AUC) of 0.84 with balanced sensitivity (95.1%) and specificity (83.8%). Integrating machine learning predictions with MR causal inference highlighted <i>Bacteroides</i> as a key biomarker. Our findings elucidate the gut microbiota-T2D causal axis, identify therapeutic targets, and provide a robust tool for precision diagnosis.