Persistent microbiome alterations modulate the rate of post-dieting weight regain
Abstract
In tackling the obesity pandemic, considerable efforts are devoted to the development of effective weight reduction strategies, yet many dieting individuals fail to maintain a long-term weight reduction, and instead undergo excessive weight regain cycles. The mechanisms driving recurrent post-dieting obesity remain largely elusive. Here we identify an intestinal microbiome signature that persists after successful dieting of obese mice and contributes to faster weight regain and metabolic aberrations upon re-exposure to obesity-promoting conditions. Faecal transfer experiments show that the accelerated weight regain phenotype can be transmitted to germ-free mice. We develop a machine-learning algorithm that enables personalized microbiome-based prediction of the extent of post-dieting weight regain. Additionally, we find that the microbiome contributes to diminished post-dieting flavonoid levels and reduced energy expenditure, and demonstrate that flavonoid-based 'post-biotic' intervention ameliorates excessive secondary weight gain. Together, our data highlight a possible microbiome contribution to accelerated post-dieting weight regain, and suggest that microbiome-targeting approaches may help to diagnose and treat this common disorder.
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Algorithms have been developed that use microbiota profiles to predict weight regain after dieting.
“development of algorithms that incorporate microbiota profiles to predict post-dieting weight gain (86)”
From The gut microbiota at the intersection of diet and human health (2018) · cites this paper as [86]
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- Authors
- Thaiss, C. A., Itav, S., Rothschild, D., Meijer, M. T., Levy, M., Moresi, C., Dohnalová, L., Braverman, S., Rozin, S., Malitsky, S., Dori-Bachash, M., Kuperman, Y., Biton, I., Gertler, A., Harmelin, A., Shapiro, H., Halpern, Z., Aharoni, A., Segal, E., & Elinav, E.
- Affiliations
- Weizmann Institute of Science (IL)
- Hebrew University of Jerusalem (IL)
- Tel Aviv University (IL)
- Corresponding author
- Segal, Eran
- Elinav, Eran
- ORCID
- Venue
- Nature
- Published
- 2016-11-24
- Volume
- 540
- Issue
- 7634
- Pages
- 544-551
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2557198675
- Cited by
- 471
- References
- 35
- Citations by year
- 2026: 23 · 2025: 31 · 2024: 49 · 2023: 48 · 2022: 47 · 2021: 49 · 2020: 62 · 2019: 58 · 2018: 50 · 2017: 53 · 2016: 1
- FWCI
- 28.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Regulation of Appetite and Obesity
- Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
- Keywords
- gut microbiota, obesity, dieting, germ-free mice, flavonoids, energy expenditure, microbiome-based prediction, machine learning
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