Evidence mapPaperPMID 35235560Full record

ArticlePLoS biology2022

Systematically assessing microbiome-disease associations identifies drivers of inconsistency in metagenomic research.

Braden T Tierney, Yingxuan Tan, Zhen Yang, Bing Shui, Michaela J Walker, Benjamin M Kent, Aleksandar D Kostic, Chirag J Patel

Open access · goldAbstract read
In one paragraph

Article in PLoS biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 3 pooled it
4.2field-weighted citation impact, top 5% of its field
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

22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 46 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Braden T TierneyDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.
Yingxuan TanDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.ORCID 0000-0001-6674-3219
Zhen YangSection on Pathophysiology and Molecular Pharmacology, Joslin Diabetes Center, Boston, Massachusetts, United States of America.ORCID 0000-0001-6057-6958
Bing ShuiDepartment of Cancer Biology, Dana Farber Cancer Institute, Boston, Massachusetts, United States of America.ORCID 0000-0002-5956-130X
Michaela J WalkerUPSIDE Foods, Berkeley, California, United States of America.ORCID 0000-0003-1652-3378
Benjamin M KentUS Marine Corps, Camp Pendleton, California, United States of America.
Aleksandar D KosticSection on Pathophysiology and Molecular Pharmacology, Joslin Diabetes Center, Boston, Massachusetts, United States of America.
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.ORCID 0000-0002-8756-8525
Joslin Diabetes Center · USHarvard University · USDana-Farber Cancer Institute · USUnited States Marine Corps · US

Funding

Data science tools to identify robust exposure-phenotype associations for precision medicineR01ES032470 · HARVARD MEDICAL SCHOOL · 2025 to 2025
$620k
NIAID NIH HHS R01 AI127250NIEHS NIH HHS R01 ES032470
6 · The paper itself

Abstract

Evaluating the relationship between the human gut microbiome and disease requires computing reliable statistical associations. Here, using millions of different association modeling strategies, we evaluated the consistency-or robustness-of microbiome-based disease indicators for 6 prevalent and well-studied phenotypes (across 15 public cohorts and 2,343 individuals). We were able to discriminate between analytically robust versus nonrobust results. In many cases, different models yielded contradictory associations for the same taxon-disease pairing, some showing positive correlations and others negative. When querying a subset of 581 microbe-disease associations that have been previously reported in the literature, 1 out of 3 taxa demonstrated substantial inconsistency in association sign. Notably, >90% of published findings for type 1 diabetes (T1D) and type 2 diabetes (T2D) were particularly nonrobust in this regard. We additionally quantified how potential confounders-sequencing depth, glucose levels, cholesterol, and body mass index, for example-influenced associations, analyzing how these variables affect the ostensible correlation between Faecalibacterium prausnitzii abundance and a healthy gut. Overall, we propose our approach as a method to maximize confidence when prioritizing findings that emerge from microbiome association studies.

Indexed as

AlgorithmsBacteriaBiomedical ResearchCardiovascular DiseasesColorectal NeoplasmsDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2FecesGastrointestinal MicrobiomeHumansInflammatory Bowel DiseasesLiver CirrhosisMetagenomeMetagenomicsModels, TheoreticalRNA, Ribosomal, 16SRNA, Ribosomal, 16S

Identifiers

PMID35235560
PMCPMC8890741
OpenAlexW4214929190

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.