Evidence map›Paper›PMID 31324413›Full record

SynthesisCell host & microbe2019

Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet.

Jordan E Bisanz, Vaibhav Upadhyay, Jessie A Turnbaugh, Kimberly Ly, Peter J Turnbaugh

Abstract readMeta-Analysis
In one paragraph

Synthesis in Cell host & microbe, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 213 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
213citing papers in PubMed, 10 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

213 citing papers in PubMed, 10 syntheses or guidelines pooled it.

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  4. Roles of the gut microbiome in weight management.Nature reviews. Microbiology · 2023
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  8. Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2022
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153 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Jordan E BisanzDepartment of Microbiology and Immunology, University of California San Francisco, San Francisco, CA 94143, USA.
Vaibhav UpadhyayDepartment of Internal Medicine, University of California San Francisco, San Francisco, CA 94143, USA.
Jessie A TurnbaughDepartment of Microbiology and Immunology, University of California San Francisco, San Francisco, CA 94143, USA.
Kimberly LyDepartment of Microbiology and Immunology, University of California San Francisco, San Francisco, CA 94143, USA.
Peter J TurnbaughDepartment of Microbiology and Immunology, University of California San Francisco, San Francisco, CA 94143, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA. Electronic address: peter.turnbaugh@ucsf.edu.

Funding

UCSF Nutrition Obesity Research CenterP30DK098722 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHRISTIAN VAISSE · 2015 to 2026
$14.6M
Predicting and preventing drug metabolism by the human gut microbiomeR01HL122593 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Peter James Turnbaugh · 2016 to 2026
$5.7M
(PQ10) Harnessing the human gut microbiome to predict chemotherapy outcomesR21CA227232 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ATREYA, CHLOE E., TURNBAUGH, PETER JAMES · 2018 to 2019
$378k
NCI NIH HHS R21 CA227232NHLBI NIH HHS R01 HL122593NIDDK NIH HHS P30 DK098722
6 · The paper itself

Abstract

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.

Indexed as

Diet, High-FatAnimalsBacteriaDatabases, FactualDietGastrointestinal MicrobiomeHumansLactococcusMachine LearningMicePhylogenyhigh-fat dietLactococcusmachine learningmeta-analysismicrobiomemurine

Identifiers

PMID31324413
PMCPMC6708278

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.