Gut microbiome and gut-derived metabolites to identify the transition from metabolically healthy to unhealthy obesity: a mini review.
- 1School of Medicine, St. George's University, St George's, Grenada.
- 2Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States.
Abstract
Obesity is a metabolically heterogeneous condition with distinct metabolic phenotypes, ranging from metabolically healthy obesity (MHO) to metabolically unhealthy obesity (MUO). Although individuals with MHO initially lack cardiometabolic dysfunction, many transition to MUO over time, exponentially increasing their risk for type 2 diabetes and cardiovascular disease. In this mini-review, we synthesize recent evidence from multi-omic cohort studies evaluating gut microbiome composition and gut-derived metabolites as functional biomarkers of obesity heterogeneity and drivers of metabolic decline. While microbial diversity yields inconsistent signatures across cohorts, MHO is generally characterized by higher relative abundance of Ruminococcaceae, Oscillospira, Akkermansia, and Bifidobacterium and enrichment of other taxa predicted to be short-chain fatty acid-producers. Conversely, circulating gut-derived metabolomic signatures including altered secondary bile acid pools, elevated branched-chain amino acids, and trimethylamine N-oxide more reliably discriminate MUO from MHO than traditional measures, and correlate strongly with visceral adiposity, insulin resistance, and systemic inflammation. Furthermore, new systems biology approaches, such as integrating high-dimensional metagenomics and metabolomics via machine learning models, offer a potentially superior early indicators tool in precision medicine for identifying obesity phenotypes compared with traditional metrics (e.g., body mass index, fasting glucose). Overall, current evidence remains predominantly cross-sectional and based on secondary analyses of large cohort studies, underscoring the need for prospective longitudinal studies to validate these multi-omic biomarkers for precise phenotype identification and clinical risk assessment.
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BibTeX
@article{manrique2026microbiome,
title = {{Gut microbiome and gut-derived metabolites to identify the transition from metabolically healthy to unhealthy obesity: a mini review.}},
author = {Eugenia I Manrique and Lawrence Sciglitano and Carmen P Ortega-Santos},
journal = {Frontiers in nutrition},
year = {2026},
month = oct,
doi = {10.3389/fnut.2026.1989174},
url = {https://europepmc.org/article/MED/42953211},
note = {CC BY},
}
RIS
TY - JOUR
TI - Gut microbiome and gut-derived metabolites to identify the transition from metabolically healthy to unhealthy obesity: a mini review.
AU - Eugenia I Manrique
AU - Lawrence Sciglitano
AU - Carmen P Ortega-Santos
PY - 2026
DA - 2026/10/14
JO - Frontiers in nutrition
DO - 10.3389/fnut.2026.1989174
UR - https://europepmc.org/article/MED/42953211
AB - Obesity is a metabolically heterogeneous condition with distinct metabolic phenotypes, ranging from metabolically healthy obesity (MHO) to metabolically unhealthy obesity (MUO). Although individuals with MHO initially lack cardiometabolic dysfunction, many transition to MUO over time, exponentially increasing their risk for type 2 diabetes and cardiovascular disease. In this mini-review, we synthesize recent evidence from multi-omic cohort studies evaluating gut microbiome composition and gut-derived metabolites as functional biomarkers of obesity heterogeneity and drivers of metabolic decline. While microbial diversity yields inconsistent signatures across cohorts, MHO is generally characterized by higher relative abundance of Ruminococcaceae, Oscillospira, Akkermansia, and Bifidobacterium and enrichment of other taxa predicted to be short-chain fatty acid-producers. Conversely, circulating gut-derived metabolomic signatures including altered secondary bile acid pools, elevated branched-chain amino acids, and trimethylamine N-oxide more reliably discriminate MUO from MHO than traditional measures, and correlate strongly with visceral adiposity, insulin resistance, and systemic inflammation. Furthermore, new systems biology approaches, such as integrating high-dimensional metagenomics and metabolomics via machine learning models, offer a potentially superior early indicators tool in precision medicine for identifying obesity phenotypes compared with traditional metrics (e.g., body mass index, fasting glucose). Overall, current evidence remains predominantly cross-sectional and based on secondary analyses of large cohort studies, underscoring the need for prospective longitudinal studies to validate these multi-omic biomarkers for precise phenotype identification and clinical risk assessment.
SN - 2296-861X
ER -