A 5-day prolonged fasting intervention in healthy adults produced robust short-term metabolic changes with partial persistence, consistent gut microbiome remodeling, and a machine learning model combining baseline microbiome and clinical variables predicted 12-week BMI response and generalized to three independent cohorts.
Key Findings
Results
Fasting reduced body mass index acutely, predominantly driven by loss of fat mass, and these improvements partially persisted at 12 weeks.
The study enrolled 38 healthy adults in a randomized, waitlist-controlled trial (LEANER study) with 12-week follow-up.
The fasting intervention lasted 5 days.
Body composition outcomes were tracked, with fat mass identified as the primary driver of BMI reduction.
Partial but not complete persistence of BMI improvements was observed at the 12-week follow-up timepoint.
Results
Fasting induced marked shifts in gut microbiome composition and in both plasma and fecal metabolites.
Changes were assessed using regression-based models and paired non-parametric tests.
Permutation-based multivariate testing was performed on microbiome and metabolome data.
Both plasma and fecal metabolite profiles were assessed alongside gut microbiome composition.
The shifts in microbiome and metabolites were described as 'marked,' indicating substantial remodeling during the fasting period.
Results
Post-fasting and longer-term changes in microbial diversity were associated with baseline microbiome diversity.
Baseline microbiome diversity was identified as a predictor of subsequent microbial diversity changes.
This association held for both the immediate post-fasting period and the longer-term 12-week follow-up.
The finding suggests that pre-intervention microbiome state influences the trajectory of microbiome remodeling during and after fasting.
Results
A machine learning model combining baseline microbiome and clinical variables predicted body mass index response at 12 weeks.
The model was built using data-driven machine learning with cross-validation.
Prominent predictors included an unclassified Faecalibacterium species, Oscillibacter sp. 50_27, low-density lipoprotein cholesterol, and systolic blood pressure.
Both microbial and clinical variables contributed to predictive performance.
The model was developed in the 38-participant LEANER cohort.
Results
The predictive model generalized to three independent external cohorts undergoing prolonged fasting protocols.
The three cohorts included individuals with metabolic syndrome, patients with multiple sclerosis exposed to repeated fasting, and healthy volunteers fasting for 6–12 days.
External validation demonstrated generalizability across different health conditions and fasting durations.
Fasting durations in the external cohorts ranged from 6 to 12 days, compared to 5 days in the primary study.
The model's performance across diverse populations supports its potential clinical utility for stratifying fasting responses.
Conclusions
Baseline microbiome and clinical characteristics can help stratify expected longer-term weight-loss responses to fasting.
The authors conclude this supports 'the development of individualized fasting-based interventions.'
Stratification was based on pre-intervention data, making it applicable before an intervention is initiated.
Specific microbial taxa (Faecalibacterium sp. and Oscillibacter sp. 50_27) and clinical biomarkers (LDL cholesterol and systolic blood pressure) were identified as actionable predictors.
What This Means
This research suggests that a 5-day fasting intervention in healthy adults leads to meaningful short-term reductions in body weight and fat mass, along with significant changes in the gut microbiome and metabolites in both blood and stool. While these benefits did not fully persist over the 12-week follow-up period, some improvements remained, indicating that even a brief fasting period can have lasting metabolic effects. Importantly, the degree to which a person's gut microbiome changed during and after fasting was related to how diverse their microbiome was before they started fasting.
Using machine learning, the researchers developed a model that could predict how much weight a person would lose and keep off 12 weeks after fasting, based on their gut bacteria composition and standard clinical measurements taken before the fast. Key predictors included two specific gut bacterial species (an unclassified Faecalibacterium and Oscillibacter sp. 50_27), LDL (bad) cholesterol levels, and systolic blood pressure. Notably, this prediction model worked not just in the original study group but also in three other independent groups of people who underwent different fasting protocols, including people with metabolic syndrome, multiple sclerosis patients, and healthy volunteers fasting for longer periods.
This research suggests that it may be possible to identify, before someone starts a fasting program, whether they are likely to achieve sustained weight loss based on simple clinical measurements and microbiome profiling. This could eventually help tailor fasting-based interventions to individuals most likely to benefit, and may point toward a role for gut bacteria in determining long-term outcomes of dietary interventions.
Check Your Own Numbers
Upload your bloodwork. We'll cross-reference your results against this study and 4,700 others.