Baseline gut microbiome and metabolome profiles, particularly serum diacylphosphatidylcholine C40:1 and a random forest model incorporating microbial and clinical features, predict weight loss success following a one-year structured lifestyle intervention in adults with obesity.
Key Findings
Results
A one-year structured lifestyle intervention produced marked reductions in body weight, body fat percentage, C-reactive protein, and glycated hemoglobin in adults with obesity.
Study included 50 adults with obesity with a mean BMI of 42 ± 7.0 kg/m²
The intervention consisted of three phases: a 3-month very low-calorie formula diet (~850 kcal/day), a 3-month transition phase (~1000 kcal/day), and a 6-month maintenance period (gradually increasing to maximum 2000 kcal/day)
Outcomes measured included body weight, body fat percentage, C-reactive protein (CRP), and glycated hemoglobin (HbA1c)
Trial registered at ClinicalTrials.gov: NCT01344525
Results
Longitudinal shifts in gut microbiota composition were associated with changes in clinical and anthropometric data as well as gut barrier function.
Multivariate longitudinal analyses were used to identify associations between microbiome changes and clinical outcomes
Associations were found between microbial shifts and both anthropometric measures and gut barrier function markers
These analyses integrated clinical, microbiome, and metabolomic profiling across the one-year intervention
Results
Increased abundance of Lachnospiraceae was associated with improved gut barrier function, with the relationship mediated by fecal butyrate and propionate.
Lachnospiraceae, a family of short-chain fatty acid-producing bacteria, showed increased abundance during the intervention
Fecal butyrate and propionate were identified as mediators of the relationship between Lachnospiraceae and gut barrier function
This finding suggests a mechanistic link between specific microbial taxa, their metabolic products, and intestinal barrier integrity
Results
Baseline serum levels of diacylphosphatidylcholine C40:1 predicted post-intervention BMI, indicating its potential as a biomarker of weight loss success.
Diacylphosphatidylcholine C40:1 was identified through multivariate analyses of baseline metabolomic profiles
This metabolite was specifically associated with post-intervention BMI rather than baseline BMI, suggesting predictive rather than descriptive utility
The finding highlights the potential utility of baseline metabolomic profiling for identifying individuals likely to succeed in lifestyle-based weight loss programs
Results
A random forest model incorporating baseline microbial and clinical features predicted weight loss and clinical improvements with high accuracy.
The model integrated both gut microbiome composition and clinical variables measured at baseline
The random forest approach was used to predict both weight loss outcomes and clinical improvements over the one-year intervention
This machine learning approach demonstrated that pre-intervention profiling can stratify likely responders to structured lifestyle interventions
What This Means
This research suggests that the composition of bacteria living in the gut, along with certain molecules in the blood, can predict how well a person with obesity will respond to a structured weight-loss program before the program even begins. In a year-long study of 50 people with obesity, participants followed a carefully designed diet that started very low in calories and gradually increased to a balanced diet. By the end, participants showed meaningful improvements in body weight, body fat, inflammation (measured by CRP), and blood sugar control (measured by HbA1c).
The study found that certain gut bacteria — specifically a family called Lachnospiraceae — increased during the program and were linked to improvements in gut health. This relationship appeared to work through two molecules called butyrate and propionate, which these bacteria produce and which help maintain the intestinal lining. Additionally, a specific blood molecule called diacylphosphatidylcholine C40:1, measured before the intervention began, was able to predict how much weight a person would lose, suggesting it could serve as an early indicator of who will benefit most from this type of program.
This research suggests that personalized obesity treatment could be improved by analyzing a person's gut microbiome and blood metabolites before starting a weight-loss program. Using a type of machine learning called a random forest model, the researchers were able to accurately predict weight loss outcomes using only baseline measurements. This opens the door to tailoring lifestyle interventions based on an individual's biological profile, potentially improving success rates in obesity management.
Seethaler B, Basrai M, Delzenne N, Walter J, Nguyen N, Bischoff S. (2026). Baseline gut microbiome and metabolome profiles predict weight loss after a structured lifestyle intervention.. Microbiome. https://doi.org/10.1186/s40168-026-02501-x