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

## TL;DR

This study explores how gut bacteria relate to type 2 diabetes, identifying key microbes, genes, and drugs, and building a machine learning model for diagnosis.

## Contribution

The study integrates genetic, microbiome, and machine learning approaches to uncover causal links and potential therapies for type 2 diabetes.

## Key findings

- Three gut microbiota taxa (Actinomyces, Ruminococcaceae UCG010, Deltaproteobacteria) show significant causal associations with T2D.
- A machine learning diagnostic model achieved an AUC of 0.84 with high sensitivity and specificity for T2D detection.
- Network pharmacology identified INSR and ESR1 as key drug targets, with Dienestrol showing therapeutic potential.

## Abstract

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: Actinomyces (odds ratio [OR] = 1.106; 95% confidence interval [CI]: 1.06–1.15; p < 0.01; adjusted p-value [padj] = 0.0003), Ruminococcaceae (UCG010 group) (OR = 0.897; 95% CI: 0.85–0.95; p < 0.01; padj = 0.018), and Deltaproteobacteria (OR = 1.072; 95% CI: 1.03–1.12; p < 0.01; padj = 0.029). Ten key genes, such as EXOC4 and IGF1R, were linked to T2D risk. Network pharmacology identified INSR and ESR1 as target driver genes, with drugs like Dienestrol showing promise. Gut microbiota analysis revealed reduced α-diversity in T2D patients (p < 0.05), and β-diversity showed microbial community differences (R2 = 0.012, p = 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 Bacteroides as a key biomarker. Our findings elucidate the gut microbiota-T2D causal axis, identify therapeutic targets, and provide a robust tool for precision diagnosis.