Evidence map›Paper›PMID 36357685›Full record

ArticleNature metabolism2022

Genome-microbiome interplay provides insight into the determinants of the human blood metabolome.

Christian Diener, Chengzhen L Dai, Tomasz Wilmanski, Priyanka Baloni, Brett Smith, Noa Rappaport, Leroy Hood, Andrew T Magis, Sean M Gibbons

Open access · greenAbstract read
In one paragraph

Article in Nature metabolism, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
67citing papers in PubMed, 2 pooled it
7.5field-weighted citation impact, top 2% 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

67 citing papers in PubMed, 2 syntheses or guidelines pooled it, 94 citations in OpenAlex.

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7 more citing papers are in PubMed but not listed here.

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

9 authors at 2 institutions in 1 country.

Christian Diener *Institute for Systems Biology, Seattle, WA, USA.
Chengzhen L Dai *Institute for Systems Biology, Seattle, WA, USA.
Tomasz WilmanskiInstitute for Systems Biology, Seattle, WA, USA.
Priyanka BaloniInstitute for Systems Biology, Seattle, WA, USA.
Brett SmithInstitute for Systems Biology, Seattle, WA, USA.
Noa RappaportInstitute for Systems Biology, Seattle, WA, USA.ORCID http://orcid.org/0000-0001-8643-2055
Leroy HoodInstitute for Systems Biology, Seattle, WA, USA.
Andrew T MagisInstitute for Systems Biology, Seattle, WA, USA. andrew.magis@isbscience.org.ORCID http://orcid.org/0000-0003-0576-677X
Sean M GibbonsInstitute for Systems Biology, Seattle, WA, USA. sgibbons@isbscience.org.ORCID http://orcid.org/0000-0002-8724-7916
Institute for Systems Biology · USUniversity of Washington · US

Funding

Systems BiologyU19AG023122 · NIA · TRANSLATIONAL GENOMICS RESEARCH INST · PI Thomas Girke, NICHOLAS Joseph SCHORK · 2004 to 2026
$102.6M
CyberGut: towards personalized human-microbiome metabolic modeling for precision health and nutritionR01DK133468 · NIDDK · INSTITUTE FOR SYSTEMS BIOLOGY · PI Sean Michael Gibbons · 2022 to 2026
$3.6M
Integrative Omics to enhance therapeutics development for healthy agingUH3AG064706 · NIA · TRANSLATIONAL GENOMICS RESEARCH INST · PI MILLER, RICHARD A, SCHORK, NICHOLAS JOSEPH · 2021 to 2024
$3.3M
NIA NIH HHS U19 AG023122NIA NIH HHS UH3 AG064706NIDDK NIH HHS R01 DK133468
6 · The paper itself

Abstract

Variation in the blood metabolome is intimately related to human health. However, few details are known about the interplay between genetics and the microbiome in explaining this variation on a metabolite-by-metabolite level. Here, we perform analyses of variance for each of 930 blood metabolites robustly detected across a cohort of 1,569 individuals with paired genomic and microbiome data while controlling for a number of relevant covariates. We find that 595 (64%) of these blood metabolites are significantly associated with either host genetics or the gut microbiome, with 69% of these associations driven solely by the microbiome, 15% driven solely by genetics and 16% under hybrid genome-microbiome control. Additionally, interaction effects, where a metabolite-microbe association is specific to a particular genetic background, are quite common, albeit with modest effect sizes. This knowledge will help to guide targeted interventions designed to alter the composition of the human blood metabolome.

Indexed as

MetabolomicsMicrobiotaFecesHumansMetabolomeRNA, Ribosomal, 16SRNA, Ribosomal, 16S

Identifiers

PMID36357685
PMCPMC9691620
OpenAlexW4308772947

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.