A new study has defined a metabolome-informed body mass index (metBMI) that more accurately reflects adiposity and cardiometabolic risk than the traditional BMI calculation.² The findings, published in Nature Medicine, suggest this multi-omic approach could help identify high-risk individuals whose metabolic dysfunction is not captured by conventional measures.¹
Methodology
Researchers developed the metBMI using data from 1,408 participants in the Impaired Glucose Tolerance and Microbiota Study (IGT-microbiota). They integrated deep phenotyping data, including metabolomics, proteomics, metagenomics, and computed tomography (CT)-based adipose tissue quantification, using a machine learning model. The model was then validated in an external cohort of 466 individuals from the Swedish Cardiopulmonary Bioimage Study (SCAPIS) and tested for its ability to predict intervention outcomes in a cohort of 75 bariatric surgery patients.
Results
The circulating metabolome was found to be the most informative single data layer for predicting obesity and adiposity. In the external validation cohort, the metBMI model explained 52% of the variance in measured BMI.
Individuals with a metBMI higher than expected for their actual BMI had a significantly greater cardiometabolic burden. They demonstrated 2–5-fold higher odds of metabolic dysfunction-associated steatotic liver disease (MASLD), diabetes, severe visceral fat accumulation, insulin resistance, and inflammation. Furthermore, in the surgical cohort, patients with a high baseline metBMI signature achieved 30% less weight loss at 12 months post-surgery.
The obesogenic signature was strongly associated with the gut microbiome, characterised by reduced microbial richness and altered functional potential. A refined panel of 66 metabolites, of which 90% covaried with the microbiome, retained 38.6% of the model's explanatory power.
In Practice/Interpretation
This research highlights the limitations of BMI as a standalone metric for cardiometabolic risk assessment. The metBMI provides a more biologically grounded measure of adiposity-related dysfunction that is closely linked to the gut microbiome. The study defines "an adipose-linked, microbiome-connected metabolic signature that outperforms BMI in stratifying cardiometabolic risk and guiding precision interventions."¹ By capturing signals from adipose tissue dysfunction and host-microbiome interactions, this approach may enable earlier and more targeted interventions for individuals across the entire BMI spectrum, including those not classified as having obesity by traditional criteria.
This study was supported by multiple foundations, including the Knut and Alice Wallenberg Foundation, the Swedish Heart and Lung Foundation, and the Novo Nordisk Foundation.
References
1. Chakaroun RM, Pradhan M, Björnson E, et al. Multi-omic definition of metabolic obesity through adipose tissue–microbiome interactions. Nat Med (2026). https://doi.org/10.1038/s41591-025-04009-7
2. Busetto L, Dicker D, Azran C, et al. A new framework for the diagnosis, staging and management of obesity in adults. Nat Med. 2024;30:2395–2399. https://doi.org/10.1038/s41591-024-03095-3
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