Baseline gut microbiome and metabolome profiles predict weight loss after a structured lifestyle intervention.

Authors

Benjamin Seethaler, Maryam Basrai, Nathalie M Delzenne, Jens Walter, Nguyen K Nguyen, Stephan C Bischoff

Published in

Microbiome. Volume 14. Issue 1. Aug 28, 2026. Epub Aug 28, 2026.

Abstract

Obesity remains a global health challenge, and responses to lifestyle-based weight-loss interventions are heterogeneous. Here, we evaluate a one-year structured lifestyle program in 50 adults with obesity (mean BMI 42 ± 7.0 kg/m²), integrating clinical, microbiome, and metabolomic profiling, to identify predictors of weight-loss success and metabolic improvement (ClinicalTrials.gov: NCT01344525). The intervention included a 3-month very low-calorie formula diet (approximately 850 kcal/day), a 3-month transition phase from a formula diet to a balanced diet (approximately 1000 kcal/day), and a 6-month maintenance period in which the participants followed a balanced diet (gradually increasing to a maximum of 2000 kcal/day).
Following the intervention, the participants exhibited marked reductions in body weight, body fat percentage, C-reactive protein, and glycated hemoglobin. Longitudinal analyses revealed that shifts in the gut microbiota composition were associated with changes in clinical and anthropometric data, as well as gut barrier function. An increased abundance of Lachnospiraceae was associated with improved gut barrier function; the relationship was mediated by fecal butyrate and propionate. Multivariate analyses revealed that serum baseline levels of diacylphosphatidylcholine C40:1 predicted postintervention BMI, indicating that this metabolite could serve as a biomarker of weight loss success. A random forest model incorporating baseline microbial and clinical features was used to predict weight loss and clinical improvements with high accuracy.
Our findings elucidate the interplay between the gut microbiota and host metabolism during weight loss and highlight the potential utility of baseline profiling to achieve a high success rate in personalized obesity treatment. Video Abstract.

PMID:
42668355
Bibliographic data and abstract were imported from PubMed on 30 Aug 2026.

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