# Exploring the gut microbiome in type 2 diabetes across different insulin resistance levels: a machine learning approach

## TL;DR

This study explores how gut bacteria are linked to insulin resistance in type 2 diabetes and identifies specific microbes that could help improve metabolic health.

## Contribution

The study introduces a machine learning approach to identify gut microbiome features associated with insulin resistance in type 2 diabetes.

## Key findings

- XGBoost models using gut microbiome profiles effectively distinguished individuals with higher insulin resistance in T2DM.
- Bacteroides and Faecalibacterium were key contributors to model performance and correlated with clinical measures of insulin resistance.
- Several gut microbes were associated with IR indices, suggesting potential targets for metabolic interventions.

## Abstract

Insulin resistance (IR) is central to type 2 diabetes mellitus (T2DM). Composite indices including the atherogenic index of plasma (AIP), metabolic score for insulin resistance (METS-IR), triglyceride–glucose index (TyG), and TyG-BMI, are widely used to quantify IR severity. The gut microbiome (GM) has been implicated in metabolic dysregulation, but its associations with IR remain incompletely defined.

We collected blood test results and stool samples from participants with T2DM and healthy controls. Stool samples underwent 16S rRNA gene sequencing. We trained XGBoost models to distinguish individuals with higher IR from healthy controls based on GM profiles and performed correlation analyses between GM features, clinical measures, and IR indices.

Triglycerides (TG), fasting blood glucose (FBG), and high-density lipoprotein cholesterol (HDL-C) differed significantly between the T2DM and control groups. IR indices (AIP, METS-IR, TyG, and TyG-BMI) were markedly higher in the T2DM group. XGBoost models based on GM profiles showed high discriminatory performance for identifying T2DM individuals with higher IR, with Bacteroides and Faecalibacterium contributing most to model performance. Correlation analyses further indicated that Lachnospiraceae_UCG-010, Bacteroides, Faecalibacterium, Lachnospira, Parasutterella, and Escherichia–Shigella were associated with clinical measures and IR indices.

Specific GM features are associated with IR-related clinical measures and composite indices in T2DM, supporting their potential as intervention targets to improve insulin resistance and restore carbohydrate and lipid metabolism.