Evidence map›Paper›PMID 33444181›Full record

ArticleBMJ open2020

Does geographical variation confound the relationship between host factors and the human gut microbiota: a population-based study in China.

Shan Sun, Huijun Wang, Matthew Cb Tsilimigras, Annie Green Howard, Wei Sha, Jiguo Zhang, Chang Su, Zhihong Wang, Shufa Du, Michael Sioda and 4 more

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 16% 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

34 citing papers in PubMed, 1 synthesis or guideline 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

14 authors at 3 institutions in 2 countries.

Shan SunDepartment of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, USA.ORCID 0000-0003-0349-2664
Huijun WangNational Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, Beijing, China.
Matthew Cb TsilimigrasCarolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Annie Green HowardCarolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Wei ShaDepartment of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, USA.
Jiguo ZhangNational Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, Beijing, China.
Chang SuNational Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, Beijing, China.
Zhihong WangNational Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, Beijing, China.
Shufa DuCarolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Michael SiodaDepartment of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, USA.
Farnaz FouladiDepartment of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, USA.
Anthony Fodor *Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, USA.
Penny Gordon-Larsen *Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA pglarsen@unc.edu.ORCID 0000-0001-5322-4188
Bing Zhang *National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, Beijing, China.
Chinese Center For Disease Control and Prevention · CNUniversity of North Carolina at Charlotte · USUniversity of North Carolina at Chapel Hill · US

Funding

UNC-CH CENTER FOR ENVIRONMENTAL HEALTH &SUSCEPTIBILITYP30ES010126 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Stephanie Engel · 2001 to 2026
$36.3M
MONITORING SOCIAL CHANGE: HEALTH, REPRODUCTION, AGINGR01HD030880 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ADAIR, LINDA S, POPKIN, BARRY M · 1994 to 2024
$27.0M
Research Services CoreP2CHD050924 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN B GUZZO · 2015 to 2026
$9.6M
The Genetic Epidemiology of Heart, Lung, and Blood TraitsTraining Grant (GenHLB)T32HL129982 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Christy Leigh Avery, KAREN L. MOHLKE · 2016 to 2026
$3.8M
Monitoring Social Change: Dynamics Of Aging And Cognitive FunctionR01AG065357 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ADAIR, LINDA S, POPKIN, BARRY M · 2020 to 2024
$3.6M
Transition to a Western diet and cardiometabolic risk: biomarkers derived from the microbiomeR01DK104371 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GORDON-LARSEN, PENNY · 2015 to 2018
$2.5M
Nutrition-Related NCD Prevention Training in ChinaD43TW009077 · FIC · UNIV OF NORTH CAROLINA CHAPEL HILL · PI POPKIN, BARRY M · 2011 to 2015
$1.1M
FIC NIH HHS D43 TW009077NHLBI NIH HHS T32 HL129982NIA NIH HHS R01 AG065357NICHD NIH HHS P2C HD050924NICHD NIH HHS R01 HD030880NIDDK NIH HHS R01 DK104371NIEHS NIH HHS P30 ES010126
6 · The paper itself

Abstract

objectiveThe human gut microbiota plays important roles in human health but is also known to be highly diverse between populations from different regions. Yet most studies inadequately account for this regional diversity in their analyses. This study examines the extent to which geographical variation can act as a confounding variable for studies that associate the microbiota with human phenotypic variation.

designPopulation-based study.

settingChina.

participants2164 participants from 15 province-level divisions in China. PRIMARY AND SECONDARY OUTCOME MEASURES: We analysed the impact of geographic location on associations between the human gut microbiota and 72 host factors representing a wide variety of environmental-level, household-level and individual-level factors.

resultsWhile the gut microbiota varied across a wide range of host factors including urbanisation, occupation and dietary variables, the geographic region (province/megacity) of the participants explained the largest proportion of the variance (17.9%). The estimated effect sizes for other host factors varied substantially by region with little evidence of a reproducible signal across different areas as measured by permutational multivariate analysis of variance and random forest models.

conclusionsOur results suggest that geographic variation is an essential factor that should be explicitly considered when generalising microbiota-based models to host phenotype across different populations.

Indexed as

Gastrointestinal MicrobiomeAdultChinaDietFemaleGeographyHumansMaleMiddle Agedgeographic differencesGut microbiotamachine learningmicrobiota-host associations

Identifiers

PMID33444181
PMCPMC7678355
OpenAlexW3105654180

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.