The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
Gut Microbiome Fermentation Determines the Efficacy of Exercise for Diabetes Prevention
Open accessAbstract
Exercise is an effective strategy for diabetes management but is limited by the phenomenon of exercise resistance (i.e., the lack of or the adverse response to exercise on metabolic health). Here, in 39 medication-naive men with prediabetes, we found that exercise-induced alterations in the gut microbiota correlated closely with improvements in glucose homeostasis and insulin sensitivity (clinicaltrials.gov entry NCT03240978). The microbiome of responders exhibited an enhanced capacity for biosynthesis of short-chain fatty acids and catabolism of branched-chain amino acids, whereas those of non-responders were characterized by increased production of metabolically detrimental compounds. Fecal microbial transplantation from responders, but not non-responders, mimicked the effects of exercise on alleviation of insulin resistance in obese mice. Furthermore, a machine-learning algorithm integrating baseline microbial signatures accurately predicted personalized glycemic response to exercise in an additional 30 subjects. These findings raise the possibility of maximizing the benefits of exercise by targeting the gut microbiota.
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In the citing paper
“Observational studies show exercise alters gut microbiome composition in mice14 and humans15–18.”
From Exercise-induced microbiota metabolite enhances CD8 T cell antitumor immunity promoting immunotherapy efficacy (2025) · cites this paper as [18]
In the citing paper
“responders displayed greater gene expression of functional pathways generating SCFAs and breaking down BCAAs.”
From The athletic gut microbiota (2020) · cites this paper as [106]
In the citing paper
“Those that did respond showed a remarkable decrease in fasting insulin and Homeostatic Model Assessment of Insulin Resistance index values (− 42.70% and − 49.60%, respectively).”
From The athletic gut microbiota (2020) · cites this paper as [106]
In the citing paper
“there was a clear segregation in compositional and functional changes between exercise responders and non-responders, accompanied by distinct alterations in microbial metabolites.”
From The athletic gut microbiota (2020) · cites this paper as [106]
In the citing paper
“Conversely the microbial profiles of non-responders after the exercise intervention shared more similarity with those of the sedentary controls, suggesting a maladaptation of gut microbiota in these individuals.”
From The athletic gut microbiota (2020) · cites this paper as [106]
In the citing paper
“A substantial improvement in mice gavaged with microbiota from responders was mimicked in glycemic control and insulin sensitivity, in contrast to the lack of change in mice colonized with microbiota from non-responders.”
From The athletic gut microbiota (2020) · cites this paper as [106]
In the citing paper
“Liu and colleagues were able to establish a model based on a machine-learning algorithm from baseline microbiome signatures which accurately predicted the exercise outcomes with respect to glycemic control and insulin sensitivity.”
From The athletic gut microbiota (2020) · cites this paper as [106]
In the citing paper
“Despite the overall metabolic benefits of the intervention, ~ 30% of subjects responded poorly to exercise in terms of improvement in glycemic control and insulin sensitivity.”
From The athletic gut microbiota (2020) · cites this paper as [106]
In the citing paper
“Together, the results from both the human and animal studies indicate that exercise may impose a differential impact on the composition and function of the gut microbiota across individuals.”
From The athletic gut microbiota (2020) · cites this paper as [106]
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BackgroundMethodInfluentialGut Microbiota: An Important Player in Type 2 Diabetes Mellitus
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Metadata
- Authors
- Liu, Y., Wang, Y., Ni, Y., Cheung, C. K. Y., Lam, K. S. L., Wang, Y., Xia, Z., Ye, D., Guo, J., Tse, M. A., Panagiotou, G., & Xu, A.
- Affiliations
- University of Hong Kong (HK)
- Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie e. V. - Hans-Knöll-Institut (HKI) (DE)
- Guangdong Pharmaceutical University (CN)
- Corresponding author
- Tse, Michael Andrew
- Panagiotou, Gianni
- Xu, Aimin
- ORCID
- Venue
- Cell Metabolism
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 31
- Issue
- 1
- Pages
- 77-91.e5
- ISSN
- 1550-4131 (ISSN-L)
- OpenAlex
- W2990997421
- Cited by
- 405
- References
- 77
- Citations by year
- 2026: 44 · 2025: 70 · 2024: 66 · 2023: 69 · 2022: 61 · 2021: 65 · 2020: 27
- FWCI
- 13.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Metabolism (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China
- National Basic Research Program of China
- Deutsche Forschungsgemeinschaft
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Adipokines, Inflammation, and Metabolic Diseases
- Health Sciences › Medicine › Gastroenterology › Gastrointestinal motility and disorders
- Keywords
- gut microbiota, physical activity, prediabetes, diabetes prevention, exercise resistance, insulin resistance, glucose homeostasis, short-chain fatty acids, branched-chain amino acids, fecal microbiota transplantation, machine learning, obesity, metabolic health
- MeSH terms
- Gastrointestinal Microbiome, Fecal Microbiota Transplantation, Machine Learning, High-Intensity Interval Training, Adult, Algorithms, Animals, Bacteria, Diet, Feces, Glucose, Humans, Insulin Resistance, Male, Mice, Inbred C57BL, Middle Aged, Prediabetic State, Mice, Metabolome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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