# Gut microbiota signatures across BMI categories in adults and combined diagnostic value with clinical indicators for early obesity risk stratification

## TL;DR

This study identifies gut microbiota differences linked to BMI categories and shows that combining these with clinical data improves early obesity detection.

## Contribution

The study introduces a diagnostic model integrating gut microbiota and clinical indicators for improved obesity risk stratification.

## Key findings

- Gut microbiota diversity and composition differ significantly across normal weight, overweight, and obesity groups.
- Combining microbial and clinical data improves classification accuracy for obesity risk with AUCs up to 0.995.
- Specific bacterial genera like Escherichia-Shigella and Faecalibacterium correlate with BMI and metabolic markers.

## Abstract

The global prevalence of obesity continues to rise, with gut microbiota implicated as a key environmental factor in its development. However, microbial heterogeneity across BMI categories and its potential as an early diagnostic marker remain poorly understood. Here, we systematically compared the gut microbiota of adults with normal weight, overweight, and obesity, and evaluated the diagnostic utility of integrated microbial and clinical indicators.

In this study, a cohort of 214 participants was recruited and categorized into three groups based on BMI: normal weight (Nor, n = 85), overweight (Ow, n = 91), and obesity (Ob, n = 38). The gut microbiota was analyzed using 16S rRNA sequencing, which facilitated the assessment of α- and β-diversity, the identification of differentially abundant bacterial genera, and the evaluation of Spearman correlations with clinical indicators. Additionally, a diagnostic model was developed utilizing random forest, Least Absolute Shrinkage and Selection Operator (LASSO) regression and receiver operating characteristic (ROC) curve analysis.

Clinical data showed no significant age differences among groups (p > 0.05), but the Ob group had markedly higher weight, BMI, total cholesterol, triglycerides, and uric acid levels (p < 0.001). Microbial analysis revealed reduced alpha diversity (Shannon index) in the Ob group and distinct microbial community structures among groups (PCoA, p < 0.001). LEfSe analysis showed enrichment of Escherichia-Shigella, Lactobacillus, and Parabacteroides in the Ob group, while Faecalibacterium, Enterobacter, and Agathobacter were predominant in the Nor group. The Ow group exhibited intermediate or specific enrichment, particularly in Ruminococcus gnavus group, Morganella, and Clostridium innocuum group. Correlation analysis indicated that bacteria abundant in the Nor group negatively correlated with BMI, TG, LDL-C, and positively with HDL-C, whereas those enriched in the Ow and Ob groups showed opposite trends. Four genera differentiated Ob from Ow individuals (AUC = 0.684), eight distinguished Ob from Nor (AUC = 0.787), and five separated Ow from Nor (AUC = 0.721). Integrating microbial and clinical data notably improved classification, with AUCs increasing to 0.908, 0.969, and 0.995, respectively.

Intestinal microecological profiles vary significantly across BMI categories, and a model combining microbiota with metabolic markers shows strong potential for early obesity detection.