# Multi-omic definition of metabolic obesity through adipose tissue–microbiome interactions

## TL;DR

This study introduces a new obesity metric based on multi-omics data that better captures metabolic dysfunction than BMI.

## Contribution

The novel metBMI metric integrates metabolomics and microbiome data to improve obesity risk stratification.

## Key findings

- metBMI explained 52% of BMI variance and better reflected adiposity than other omics models.
- Higher metBMI was linked to increased odds of diabetes, fatty liver disease, and insulin resistance.
- A 66-metabolite panel showed strong explanatory power and microbiome covariation.

## Abstract

Obesity’s metabolic heterogeneity is not fully captured by body mass index (BMI). Here we show that deep multi-omics phenotyping of 1,408 individuals defines a metabolome-informed obesity metric (metBMI) that captures adipose tissue-related dysfunction across organ systems. In an external cohort (n = 466), metBMI explained 52% of BMI variance and more accurately reflected adiposity than other omics models. Individuals with higher-than-expected metBMI had 2–5-fold higher odds of fatty liver disease, diabetes, severe visceral fat accumulation and attenuation, insulin resistance, hyperinsulinemia and inflammation and, in bariatric surgery (n = 75), achieved 30% less weight loss. This obesogenic signature aligned with reduced microbiome richness, altered ecology and functional potential. A 66-metabolite panel retained 38.6% explanatory power, with 90% covarying with the microbiome. Mediation analysis revealed a bidirectional, metabolite-centered host–microbiome axis, mediated by lipids, amino acids and diet-derived metabolites. These findings define an adipose-linked, microbiome-connected metabolic signature that outperforms BMI in stratifying cardiometabolic risk and guiding precision interventions.

Metabolomics, metagenomics, proteins and genetics were combined with clinical data to define a metabolic signature of obesity.