Cited by 1,638Gut microbiota and healthOral microbiology and periodontitis researchNutritional Studies and Diet

The DOI matches its Crossref record; 64 of the 65 references it cites were found in a registry or an index; 3 of the 85 sources its claims cite were checked for support.

The gut microbiome in atherosclerotic cardiovascular disease

Open access

Abstract

The gut microbiota has been linked to cardiovascular diseases. However, the composition and functional capacity of the gut microbiome in relation to cardiovascular diseases have not been systematically examined. Here, we perform a metagenome-wide association study on stools from 218 individuals with atherosclerotic cardiovascular disease (ACVD) and 187 healthy controls. The ACVD gut microbiome deviates from the healthy status by increased abundance of Enterobacteriaceae and Streptococcus spp. and, functionally, in the potential for metabolism or transport of several molecules important for cardiovascular health. Although drug treatment represents a confounding factor, ACVD status, and not current drug use, is the major distinguishing feature in this cohort. We identify common themes by comparison with gut microbiome data associated with other cardiometabolic diseases (obesity and type 2 diabetes), with liver cirrhosis, and rheumatoid arthritis. Our data represent a comprehensive resource for further investigations on the role of the gut microbiome in promoting or preventing ACVD as well as other related diseases.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

In a metagenome-wide association study of 405 Chinese fecal samples, atherosclerotic cardiovascular disease (ACVD) was associated with altered gut microbiome composition and function, including increased Enterobacteriaceae and Streptococcus and reduced butyrate-producing bacteria. A microbiome-based classifier distinguished ACVD from controls with an AUC of 0.86, and cross-disease comparisons linked cardiometabolic diseases and cirrhosis to a less fermentative, more inflammatory gut environment.

Key findings

  • ACVD samples differed from controls in multivariate analyses (PERMANOVA P < 10⁻⁶ with or without medication adjustment), with increased Enterobacteriaceae, Streptococcus spp., Ruminococcus gnavus, and Eggerthella lenta, and depletion of butyrate producers such as Roseburia intestinalis and Faecalibacterium cf. prausnitzii.
  • Of 2,982 metagenomic linkage groups (MLGs) with >100 genes, 536 differed significantly between ACVD and controls (Wilcoxon rank-sum, BH q < 0.05), with 320 more abundant in ACVD; these represented on average 56.5% of relative abundance across all MLGs.
  • A random forest classifier using 47 MLGs achieved an AUC of 0.86 for ACVD, whereas TMA lyases alone gave an AUC of 0.63; ACVD-enriched bacteria encoded choline-TMA lyase (CutC) and/or the promiscuous TMA lyase YeaW/X.
  • Functionally, ACVD microbiomes showed higher potential for simple sugar and amino acid transport, O-antigen LPS synthesis, glycerolipid metabolism, fatty acid degradation, and TMA production, but lower potential for most vitamin biosynthesis, tetrahydrofolate synthesis, glycan degradation, and butyrate/propionate synthesis.

Methods

  • Metagenomic shotgun sequencing was performed on 405 fecal samples from 218 ACVD patients (≥50% stenosis in one or more vessels) and 187 healthy controls; after filtering, 2.2 Tb of high-quality reads (average 55.2 million reads/sample) were mapped to a 9.9-million-gene reference catalog, with average mapping of 80.0 ± 3.5%.
  • Genes were clustered into 2,982 MLGs (>100 genes) based on co-abundance; differential abundance used Wilcoxon rank-sum tests with BH q < 0.05, and analyses included PCA, dbRDA, PERMANOVA, random forest cross-validation, group LASSO, MaAsLin, KEGG reporter scores, and PanPhlAn.
  • Cross-disease comparisons mapped published Chinese metagenomic data to the same catalog and pipeline: liver cirrhosis (120 cases/111 controls), obesity (72/79), type 2 diabetes (171/174), and rheumatoid arthritis (95/74), totaling 1,250 samples across diseases.

Limitations

  • The paper states that drug treatment may affect gut microbiota composition and constitutes a confounding factor, although ACVD status, not current drug use, was the major distinguishing feature; effects of different drugs remain to be explored in intervention trials in larger cohorts.
  • The paper states that validation of the identified markers would require independent and much larger cohorts.
  • Evident from the text: this is a cross-sectional case–control study, so it cannot establish causality; additionally, only Chinese cohorts were used because of substantial population differences in the gut microbiome, limiting generalizability.

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Integrity signals

Registry

Crossref · 10.1038/s41467-017-00900-1

Cites

64 of 64 references verified0 citation errors · 0 not checkable

Statistics

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Figures

Fig. 1 Major genera in the ACVD gut microbiome. a PCA of genus-level ACVD gut microbiomes. Control samples, n = 187 (cyan); ACVD samples, n = 218 (red). For the ACVD samples (Supplementary Data 1), 205 were stable angina (circles), 8 were unstable angina (triangles), and 5 were acute myocardial infarction (AMI) (squares). Genera with the largest weights on each principal component are shown. b Relative abundances of the top 20 most abundant genera. The genus names were colored according to significant differences between the ACVD and control samples, i.e., red or cyan, q-value <0.01; light red or green, q-value <0.05; black, q-value ≥0.05, Wilcoxon rank-sum test, controlled for multiple testing. Boxes represent the median and interquartile ranges (IQRs) between the first and third quartiles; whiskers represent the lowest or highest values within 1.5 times IQR from the first or third quartiles. Circles represent all data points. c Differentially changed genera in ACVD and controls according to dbRDA based on Bray–Curtis distance. Genera with the largest weights on each principal coordinate are shown. CAP constrained analysis of principal coordinates
Fig. 1 Major genera in the ACVD gut microbiome. a PCA of genus-level ACVD gut microbiomes. Control samples, n = 187 (cyan); ACVD samples, n = 218 (red). For the ACVD samples (Supplementary Data 1), 205 were stable angina (circles), 8 were unstable angina (triangles), and 5 were acute myocardial infarction (AMI) (squares). Genera with the largest weights on each principal component are shown. b Relative abundances of the top 20 most abundant genera. The genus names were colored according to significant differences between the ACVD and control samples, i.e., red or cyan, q-value <0.01; light red or green, q-value <0.05; black, q-value ≥0.05, Wilcoxon rank-sum test, controlled for multiple testing. Boxes represent the median and interquartile ranges (IQRs) between the first and third quartiles; whiskers represent the lowest or highest values within 1.5 times IQR from the first or third quartiles. Circles represent all data points. c Differentially changed genera in ACVD and controls according to dbRDA based on Bray–Curtis distance. Genera with the largest weights on each principal coordinate are shown. CAP constrained analysis of principal coordinatesCC BY · PubMed Central
Fig. 2 Co-abundance network of MLGs differentially enriched in individuals with and without ACVD. Left, network in healthy controls (n = 187); right, network in individuals with ACVD (n = 218), arranged in the same order. MLGs (>100 genes) whose relative abundances were significantly different between the groups are shown (q-value <0.05, FDR-controlled Wilcoxon rank-sum test). Red circles, ACVD-enriched; cyan circles, control-enriched. The size of each circle indicates the number of genes in an MLG (100–3723). MLGs not annotated to a known species are shown with their identification number only. Please see Supplementary Data 3 for more information on taxonomic annotations. Green edges, positive correlations; red edges, negative correlations. The width of the edges decreases with the absolute value of the Spearman’s cc: thick edges, |cc| > 0.7; medium, 0.5 < |cc| < 0.7; thin, 0.3 < |cc| < 0.5
Fig. 2 Co-abundance network of MLGs differentially enriched in individuals with and without ACVD. Left, network in healthy controls (n = 187); right, network in individuals with ACVD (n = 218), arranged in the same order. MLGs (>100 genes) whose relative abundances were significantly different between the groups are shown (q-value <0.05, FDR-controlled Wilcoxon rank-sum test). Red circles, ACVD-enriched; cyan circles, control-enriched. The size of each circle indicates the number of genes in an MLG (100–3723). MLGs not annotated to a known species are shown with their identification number only. Please see Supplementary Data 3 for more information on taxonomic annotations. Green edges, positive correlations; red edges, negative correlations. The width of the edges decreases with the absolute value of the Spearman’s cc: thick edges, |cc| > 0.7; medium, 0.5 < |cc| < 0.7; thin, 0.3 < |cc| < 0.5CC BY · PubMed Central
Fig. 3 Gut microbiome-based identification of ACVD. Receiver operating curve (ROC, red) according to cross-validated random forest models on MLGs (fivefold RFCV performed five times) from stool samples of 218 ACVD and 187 healthy individuals. The 47 MLGs selected are shown in Supplementary Fig. 5 and Supplementary Data 3. The 95% confidence intervals (CIs) of the AUCs are shown in parentheses. The blue line indicates ROC based on TMA lyases (CutC/D, YeaW/X), with its corresponding ACVD probability shown in Supplementary Data 8 and Supplementary Table 3. The best cutoff points were marked on the ROCs
Fig. 3 Gut microbiome-based identification of ACVD. Receiver operating curve (ROC, red) according to cross-validated random forest models on MLGs (fivefold RFCV performed five times) from stool samples of 218 ACVD and 187 healthy individuals. The 47 MLGs selected are shown in Supplementary Fig. 5 and Supplementary Data 3. The 95% confidence intervals (CIs) of the AUCs are shown in parentheses. The blue line indicates ROC based on TMA lyases (CutC/D, YeaW/X), with its corresponding ACVD probability shown in Supplementary Data 8 and Supplementary Table 3. The best cutoff points were marked on the ROCsCC BY · PubMed Central
Fig. 4 Associations between ACVD-enriched or depleted MLGs and clinical indices. Differentially enriched MLGs (q-value <0.05, FDR-controlled Wilcoxon rank-sum test, Fig. 2, Supplementary Data 3) were analyzed for associations with clinical indices (Supplementary Data 1). ACVD-enriched MLGs are represented by red circles, and control-enriched MLGs are represented by cyan circles. The size of each circle indicates the number of genes in an MLG (100–3723, detailed in Supplementary Data 3). Associations were defined as permutational Wilcoxon rank-sum test P-value for Spearman correlation <0.05, |Spearman’s cc| ≥ 0.2 and selected by an MLG-based RFCV model for the clinical index. The thicker lines had a stronger association of |Spearman’s cc| > 0.25. Green lines indicate positive association, and red lines—negative associations. Dotted grey lines indicate correlations between MLGs in the control samples, as shown in Fig. 2. Clinical indices: age, ALB (albumin), ALT, APOA, APOB, AST, BMI (body mass index), BUN (blood urea nitrogen), CHOL (cholesterol), CKMB, CREA (creatinine), DBIL (direct bilirubin), diastolic BP (diastolic blood pressure), HBDH, HDLC (high-density lipoprotein cholesterol), hip (hip circumference), LDLC (LDL cholesterol), Lpa (lysophosphatidic acid), systolic BP (systolic blood pressure), TBIL, TP (total protein), TRIG (triglyceride), URIC (uric acid), and waist-to-hip ratio
Fig. 4 Associations between ACVD-enriched or depleted MLGs and clinical indices. Differentially enriched MLGs (q-value <0.05, FDR-controlled Wilcoxon rank-sum test, Fig. 2, Supplementary Data 3) were analyzed for associations with clinical indices (Supplementary Data 1). ACVD-enriched MLGs are represented by red circles, and control-enriched MLGs are represented by cyan circles. The size of each circle indicates the number of genes in an MLG (100–3723, detailed in Supplementary Data 3). Associations were defined as permutational Wilcoxon rank-sum test P-value for Spearman correlation <0.05, |Spearman’s cc| ≥ 0.2 and selected by an MLG-based RFCV model for the clinical index. The thicker lines had a stronger association of |Spearman’s cc| > 0.25. Green lines indicate positive association, and red lines—negative associations. Dotted grey lines indicate correlations between MLGs in the control samples, as shown in Fig. 2. Clinical indices: age, ALB (albumin), ALT, APOA, APOB, AST, BMI (body mass index), BUN (blood urea nitrogen), CHOL (cholesterol), CKMB, CREA (creatinine), DBIL (direct bilirubin), diastolic BP (diastolic blood pressure), HBDH, HDLC (high-density lipoprotein cholesterol), hip (hip circumference), LDLC (LDL cholesterol), Lpa (lysophosphatidic acid), systolic BP (systolic blood pressure), TBIL, TP (total protein), TRIG (triglyceride), URIC (uric acid), and waist-to-hip ratioCC BY · PubMed Central
Fig. 5 Performance of random forest classifiers for ACVD samples stratified by medication. a ROC for two-way classification models of ACVD patients with and without the drug and healthy controls. Fivefold cross-validation repeated five times. Numbers of individuals treated with each drug are given in Supplementary Data 2. b AUC and Youden’s index for the ROC plots in a
Fig. 5 Performance of random forest classifiers for ACVD samples stratified by medication. a ROC for two-way classification models of ACVD patients with and without the drug and healthy controls. Fivefold cross-validation repeated five times. Numbers of individuals treated with each drug are given in Supplementary Data 2. b AUC and Youden’s index for the ROC plots in aCC BY · PubMed Central
Fig. 6 Alterations in gut microbial functional modules in ACVD and other diseases. a PTS transport systems. b Amino acid transporters. c Vitamin metabolism. d LPS biosynthesis. Red, case-enriched; cyan, control-enriched, compared within each disease cohort (ACVD, total n = 405 including 218 cases; cirrhosis, total n = 231 including 120 cases; obesity, total n = 151 including 72 cases; T2D, total n = 345 including 171 cases; RA, total n = 169 including 95 cases). Dashed lines indicate a reporter score of 1.96, corresponding to 95% confidence in a normal distribution
Fig. 6 Alterations in gut microbial functional modules in ACVD and other diseases. a PTS transport systems. b Amino acid transporters. c Vitamin metabolism. d LPS biosynthesis. Red, case-enriched; cyan, control-enriched, compared within each disease cohort (ACVD, total n = 405 including 218 cases; cirrhosis, total n = 231 including 120 cases; obesity, total n = 151 including 72 cases; T2D, total n = 345 including 171 cases; RA, total n = 169 including 95 cases). Dashed lines indicate a reporter score of 1.96, corresponding to 95% confidence in a normal distributionCC BY · PubMed Central
Fig. 7 Differential enrichment of membrane transport pathways, lipid metabolism pathways, and modules for host glycan degradation. X-axis represents reporter score. Red, case-enriched; cyan, control-enriched, compared within each disease cohort (ACVD, total n = 405 including 218 cases; Cirrhosis, total n = 231 including 120 cases; Obesity, total n = 151 including 72 cases; T2D, total n = 345 including 171 cases; RA, total n = 169 including 95 cases). Dashed lines indicate a reporter score of 1.96, corresponding to 95% confidence in a normal distribution
Fig. 7 Differential enrichment of membrane transport pathways, lipid metabolism pathways, and modules for host glycan degradation. X-axis represents reporter score. Red, case-enriched; cyan, control-enriched, compared within each disease cohort (ACVD, total n = 405 including 218 cases; Cirrhosis, total n = 231 including 120 cases; Obesity, total n = 151 including 72 cases; T2D, total n = 345 including 171 cases; RA, total n = 169 including 95 cases). Dashed lines indicate a reporter score of 1.96, corresponding to 95% confidence in a normal distributionCC BY · PubMed Central
Fig. 8 Abundance differences in specific gut microbial KOs in ACVD and other diseases. a Enzymes for production of SCFAs. b Enzymes for TMA production. CutD is an activator for CutC. The cohort names were colored according to the direction of enrichment, i.e. green and red for control- and case-enriched, respectively within each disease cohort (ACVD, total n = 405 including 218 cases; Cirrhosis, total n = 231 including 120 cases; Obesity, total n = 151 including 72 cases; T2D, total n = 345 including 171 cases; RA, total n = 169 including 95 cases). Wilcoxon rank-sum test, P-value < 0.01; light green and light red, Wilcoxon rank-sum test 0.01 ≤ P-value < 0.05; black, P-value ≥ 0.05. Boxes represent the median and interquartile ranges (IQRs) between the first and third quartiles; whiskers represent the lowest or highest values within 1.5 times IQR from the first or third quartiles. Circles represent all data points
Fig. 8 Abundance differences in specific gut microbial KOs in ACVD and other diseases. a Enzymes for production of SCFAs. b Enzymes for TMA production. CutD is an activator for CutC. The cohort names were colored according to the direction of enrichment, i.e. green and red for control- and case-enriched, respectively within each disease cohort (ACVD, total n = 405 including 218 cases; Cirrhosis, total n = 231 including 120 cases; Obesity, total n = 151 including 72 cases; T2D, total n = 345 including 171 cases; RA, total n = 169 including 95 cases). Wilcoxon rank-sum test, P-value < 0.01; light green and light red, Wilcoxon rank-sum test 0.01 ≤ P-value < 0.05; black, P-value ≥ 0.05. Boxes represent the median and interquartile ranges (IQRs) between the first and third quartiles; whiskers represent the lowest or highest values within 1.5 times IQR from the first or third quartiles. Circles represent all data pointsCC BY · PubMed Central

Verification

Claims the paper makes

59 Not checked yet · 18 Own result · 9 Own interpretation · 4 Own method · 2 No basis given · 1 Partly supported · accuracy not yet measured

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Show all 55 claims

  • It may state two separate claims as one. 28
  • Probably not a substantial claim. 13
38 of 38
  • The authors state this as an assumption.

    Stool samples are representative of the gut microbiome.

    “we sequenced stool samples, representative of the gut microbiome, from 218 individuals with atherosclerotic cardiovascular disease (ACVD) and 187 healthy controls”
  • The paper relies on this without support.

    The functional potential inferred from metagenomic gene content reflects actual metabolic activity of the gut microbiome.

    “functionally, in the potential for metabolism or transport of several molecules important for cardiovascular health”
  • The paper relies on this without support.

    Only RA and ACVD have been epidemiologically associated with periodontitis.

    “Although the gut microbiome of liver cirrhosis, colorectal cancer, RA, and ACVD patients have all showed an increase in the abundance of oral bacteria3, the set of oral bacterial species were, however, observed to differ, and only RA and ACVD have been epidemiologically associated with periodontitis.”
  • The paper relies on this without support.

    Taurine is important for cardiometabolic health.

    “The ACVD-microbiome depleted taurine transport system was also important for this classifier (Supplementary Figs. 2c and 9b, Supplementary Data 6), consistent with the importance of taurine for cardiometabolic health.”
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Metadata

Authors
Jie, Z., Xia, H., Zhong, S.-L., Feng, Q., Li, S., Liang, S., Zhong, H., Liu, Z., Gao, Y., Zhao, H., Zhang, D., Su, Z., Fang, Z., Lan, Z., Li, J., Xiao, L., Li, J., Li, R., Li, X., Li, F., Ren, H., Huang, Y., Peng, Y., Li, G., Wen, B., Dong, B., Chen, J.-Y., Geng, Q.-S., Zhang, Z.-W., Yang, H., Wang, J., Wang, J., Zhang, X., Madsen, L., Brix, S., Ning, G., Xu, X., Liu, X., Hou, Y., Jia, H., He, K., & Kristiansen, K.
Affiliations
  • BGI Group (China) (CN)
  • Chinese PLA General Hospital (CN)
  • Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
  • Technical University of Denmark (DK)
  • Shanghai Jiao Tong University (CN)
Corresponding author
  • Jia, Huijue
  • He, Kunlun
  • Kristiansen, Karsten
Venue
Nature Communications
Published
2017-10-10
Volume
8
Issue
1
Pages
845-845
ISSN
2041-1723 (ISSN-L)
OpenAlex
W2761865284
Cited by
1,638
References
65
Citations by year
2026: 134 · 2025: 197 · 2024: 252 · 2023: 202 · 2022: 232 · 2021: 235 · 2020: 193 · 2019: 125 · 2018: 57 · 2017: 4
FWCI
42.76
Type
journal article
Language
en
Open access
Locations
  • Nature Communications (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
  • Digital Commons@Becker (Washington University School of Medicine) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, cc-by)
Funders
  • National Natural Science Foundation of China (81373486, 81673514)
  • Government of Jiangxi Province
  • Fundo para o Desenvolvimento das Ciências e da Tecnologia
  • National Key Research and Development Program of China
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
Keywords
gut microbiota, atherosclerotic cardiovascular disease, Enterobacteriaceae, Streptococcus, metagenome-wide association study, stool microbiome, cardiovascular health, cardiometabolic disease, obesity, type 2 diabetes, liver cirrhosis, rheumatoid arthritis, microbial metabolism
MeSH terms
Gastrointestinal Microbiome, Fermentation, Humans, Inflammation, Liver Cirrhosis, Case-Control Studies, Atherosclerosis, Metagenome, Genome-Wide Association Study, Metagenomics

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