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| Item Type: | Preprint |
|---|---|
| Title: | Machine learning identifies microbiome and clinical predictors of sustained weight loss following prolonged fasting |
| Creators Name: | Kaufhold, G.N., Bartolomaeus, T.U.P., Kräker, K., Schütte, T., Kamboj, S., Löber, U., Rahn, G., McParland, V., Braun, L., Markó, L., Mammadli, M., Krannich, A., Bahr, L.S., Gutmann, F., Paul, F., Ducarmon, Q.R., Zeller, G., Mesnage, R., Wilck, N., Zernecke, A., Oefner, P.J., Gronwald, W., Müller, D.N., Forslund-Startceva, S.K., Bähring, S., Bartolomaeus, H. and Siebert, N. |
| Abstract: | Prolonged fasting may benefit metabolic health, but data in healthy individuals remain limited. We conducted a randomized, waitlist-controlled study, in which 38 healthy participants completed a 5-day fasting intervention with a 12-week follow-up (LEANER study, ClinicalTrials.gov: NCT04452916). Fasting acutely reduced body mass index (BMI), primarily due to fat mass loss. These changes partially persisted at follow-up. Fasting altered the gut microbiome composition and induced metabolite shifts in plasma and feces. Long-term and post-fasting changes to gut microbiome alpha diversity after fasting correlated with baseline microbiome diversity. Long-term BMI response at follow-up could be predicted using baseline microbiome and clinical data, highlighting an unclassified Faecalibacterium sp., Oscillibacter sp. 50_27, LDL cholesterol, and systolic blood pressure as predictors. The model was successfully applied to three independent cohorts: first, patients with metabolic syndrome undergoing a 5-day fasting intervention followed by a dietary intervention; second, patients with multiple sclerosis undergoing two periods of prolonged fasting with intermittent fasting in between and afterwards; and third, healthy volunteers undergoing between 6 and 12 days of prolonged fasting. Our results show that prolonged fasting is a safe and effective metabolic intervention in healthy adults and demonstrate that baseline characteristics can predict individual metabolic responses to fasting across both healthy and diverse patient groups. |
| Keywords: | Fasting, Gut Microbiome, Weight Loss, Machine Learning, Personalized Nutrition, Precision Medicine, Multi-Omics, Prevention, Metabolic Health |
| Source: | medRxiv |
| Publisher: | Cold Spring Harbor Laboratory Press |
| Article Number: | 2025.06.26.25330331 |
| Date: | 30 March 2026 |
| Official Publication: | https://doi.org/10.1101/2025.06.26.25330331 |
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