Evidence map›Paper›PMID 41843497›Full record

ArticlePLoS medicine2026

Circulating gut microbial metabolites and risk of coronary heart disease: A prospective multi-stage metabolomics study.

Yulu Zheng, Jae Jeong Yang, Deepak K Gupta, David M Herrington, Bing Yu, Ngoc Quynh H Nguyen, Rui Pinto, Ioanna Tzoulaki, Hui Cai, Qiuyin Cai and 4 more

Abstract read
In one paragraph

Article in PLoS medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Yulu ZhengDivision of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
Jae Jeong YangDivision of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
Deepak K GuptaDivision of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.ORCID https://orcid.org/0000-0003-2191-3485
David M HerringtonSection on Cardiology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
Bing YuDepartment of Epidemiology and Human Genetics Center, UTHealth School of Public Health, Houston, Texas, United States of America.
Ngoc Quynh H NguyenDepartment of Epidemiology and Human Genetics Center, UTHealth School of Public Health, Houston, Texas, United States of America.
Rui PintoDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-8527-4873
Ioanna TzoulakiDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-4275-9328
Hui CaiDivision of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
Qiuyin CaiDivision of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.ORCID https://orcid.org/0000-0002-9384-5648
Loren LipworthDivision of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
Xiao-Ou ShuDivision of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.ORCID https://orcid.org/0000-0002-0711-8314
Wei ZhengDivision of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
Danxia YuDivision of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.ORCID https://orcid.org/0000-0002-1710-9382

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite growing evidence linking gut microbiota and microbial metabolites to human cardiometabolic health, few studies have systematically examined associations between circulating microbial metabolites and incident coronary heart disease (CHD). METHODS AND

findingsWe conducted a multi-stage metabolomics study involving five prospective cohorts. Discovery involved untargeted plasma metabolite profiling of 896 incident cases and 896 age-/sex-/race-matched controls (~300 pairs per race: Black, White, Asian) from the Southern Community Cohort Study (SCCS; baseline: 2002-2009) and the Shanghai Women's Health Study and Shanghai Men's Health Study (SWHS/SMHS; baseline: 1996-2000 and 2002-2006). In-silico validation was conducted in the Atherosclerosis Risk in Communities Study (ARIC; N = 3,539; 663 cases; baseline: 1987-1989) and Multi-Ethnic Study of Atherosclerosis (MESA; N = 3,860; 446 cases; baseline: 2000-2002). Lastly, a quantitative assay was developed and applied to a new set of 864 cases and 864 age-/sex-/race-matched controls (~260-340 pairs per race) from the SCCS and SWHS/SMHS. Conditional logistic regression estimated odds ratios (ORs) of incident CHD per standard deviation (SD) metabolite increase in discovery and quantitative stages with a nested case-control design. Cox regression was used in ARIC and MESA with a cohort design. Similar covariates were adjusted across stages, including age, sex (if applicable), race (if applicable), education, income, smoking status, alcohol consumption, physical activity, diet quality, and body mass index (BMI). The mean (SD) time between enrollment and CHD diagnosis was 5.6 (3.8), 6.9 (4.4), 15.0 (7.4), and 8.0 (4.9) years in the SCCS, SWHS/SMHS, ARIC, and MESA, respectively. The discovery stage identified 73 circulating microbiota-related metabolites associated with incident CHD (false discovery rate <0.10). Sixty-one metabolites were available for in-silico validation, of which 24 showed a significant association (p < 0.05) in the same direction as in the discovery. The targeted assay quantified eight of the 24 metabolites, with five significantly associated with incident CHD: imidazole propionate, 3-hydroxy-2-ethylpropionate, 4-hydroxyphenylacetate, trans-4-hydroxyproline, and 3-hydroxybutyrate; OR per SD ranged from 1.18 to 1.27 after adjustment for sociodemographics, lifestyles, and BMI. The targeted assay measured eight other promising microbial metabolites, four of which were significant: trimethylamine N-oxide, phenylacetyl-L-glutamine, 4-hydroxyhippuric acid, and indolepropionate. Most associations were consistent across participant subgroups by demographics, lifestyles, metabolic disease history, family CHD history, and follow-up time, although some potential effect modifications were found by race, age, obesity status, and follow-up time. The main limitations of the study are the observational design and the inability to validate all significant metabolites due to differences in metabolomic assay coverage across the three stages.

conclusionsWe identified and validated circulating gut microbial metabolites associated with incident CHD across diverse populations. Our findings offer novel epidemiological evidence on the importance of gut microbial metabolism in CHD development and highlight specific metabolites to prioritize for mechanistic investigation, biomarker validation, and therapeutic development.

Indexed as

Coronary DiseaseMetabolomicsAgedCase-Control StudiesFemaleHumansMaleMiddle AgedProspective StudiesRisk Factors

Identifiers

PMID41843497
PMCPMC12994840

What OpenQuestion 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 OpenQuestion graph.