Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet
Open accessAbstract
Multiple research groups have shown that diet impacts the gut microbiome; however, variability in experimental design and quantitative assessment have made it challenging to assess the degree to which similar diets have reproducible effects across studies. Through an unbiased subject-level meta-analysis framework, we re-analyzed 27 dietary studies including 1,101 samples from rodents and humans. We demonstrate that a high-fat diet (HFD) reproducibly changes gut microbial community structure. Finer taxonomic analysis revealed that the most reproducible signals of a HFD are Lactococcus species, which we experimentally demonstrate to be common dietary contaminants. Additionally, a machine-learning approach defined a signature that predicts the dietary intake of mice and demonstrated that phylogenetic and gene-centric transformations of this model can be translated to humans. Together, these results demonstrate the utility of microbiome meta-analyses in identifying robust and reproducible features for mechanistic studies in preclinical models.
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Variation in microbial profiling techniques may act as a confounding variable resulting in divergent findings due entirely to laboratory techniques rather than treatment.
“Indeed, variation in profiling techniques (e.g., sequencing strategy, platform, variable regions, sequencing depth, etc.) may act as a confounding variable resulting in divergent findings due entirely to laboratory techniques rather than treatment [51].”
From The athletic gut microbiota (2020) · cites this paper as [51]
High-fat diets in animal studies reproducibly change gut microbial community structure, decreasing overall microbiota diversity and beneficial bacteria and increasing the Firmicutes:Bacteroidetes ratio and abundance of Enterobacteriales, Clostridium cluster XVIa, Mollicutes, and B. wadsworthia.
“Similar to the Western diet, high-fat diets in animal studies reproducibly change gut microbial community structure, decreasing the overall microbiota diversity and beneficial bacteria (e.g., A. muciniphila, Bifidobacterium, Lactobacillus, and Lactococcus) and increasing the Firmicutes:Bacteroidetes ratio and the abundance of Enterobacteriales, Clostridium cluster XVIa, Mollicutes, and B. wadswort”
From Diet and the Microbiota–Gut–Brain Axis: Sowing the Seeds of Good Mental Health (2021) · cites this paper as [91]
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- Authors
- Bisanz, J. E., Upadhyay, V., Turnbaugh, J. A., Ly, K., & Turnbaugh, P. J.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Turnbaugh, Peter J.
- Venue
- Cell Host & Microbe
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 26
- Issue
- 2
- Pages
- 265-272.e4
- ISSN
- 1931-3128 (ISSN-L)
- PMC
- PMC6708278
- OpenAlex
- W2959992540
- Cited by
- 333
- References
- 60
- Citations by year
- 2026: 25 · 2025: 53 · 2024: 59 · 2023: 55 · 2022: 51 · 2021: 49 · 2020: 39 · 2019: 1
- FWCI
- 3.08
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Host & Microbe (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Kinship Foundation
- National Institutes of Health
- Damon Runyon Cancer Research Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
- Health Sciences › Medicine › Gastroenterology › Gastrointestinal motility and disorders
- Keywords
- gut microbiota, high-fat diet, reproducibility, Lactococcus, food contaminants, machine learning, microbial community structure, preclinical models
- MeSH terms
- Gastrointestinal Microbiome, Machine Learning, Animals, Bacteria, Diet, Humans, Phylogeny, Databases, Factual, Lactococcus, Mice, Diet, High-Fat
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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