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Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease

Nature Medicine volume 28, pages 295–302 (2022)Cite this article

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Abstract

Complex diseases, such as coronary artery disease (CAD), are often multifactorial, caused by multiple underlying pathological mechanisms. Here, to study the multifactorial nature of CAD, we performed comprehensive clinical and multi-omic profiling, including serum metabolomics and gut microbiome data, for 199 patients with acute coronary syndrome (ACS) recruited from two major Israeli hospitals, and validated these results in a geographically distinct cohort. ACS patients had distinct serum metabolome and gut microbial signatures as compared with control individuals, and were depleted in a previously unknown bacterial species of the Clostridiaceae family. This bacterial species was associated with levels of multiple circulating metabolites in control individuals, several of which have previously been linked to an increased risk of CAD. Metabolic deviations in ACS patients were found to be person specific with respect to their potential genetic or environmental origin, and to correlate with clinical parameters and cardiovascular outcomes. Moreover, metabolic aberrations in ACS patients linked to microbiome and diet were also observed to a lesser extent in control individuals with metabolic impairment, suggesting the involvement of these aberrations in earlier dysmetabolic phases preceding clinically overt CAD. Finally, a metabolomics-based model of body mass index (BMI) trained on the non-ACS cohort predicted higher-than-actual BMI when applied to ACS patients, and the excess BMI predictions independently correlated with both diabetes mellitus (DM) and CAD severity, as defined by the number of vessels involved. These results highlight the utility of the serum metabolome in understanding the basis of risk-factor heterogeneity in CAD.

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Fig. 1: Microbiome and serum metabolomics signatures of ACS.
Fig. 2: Metabolic deviations explained by potential determinants and correlate with clinical parameters.
Fig. 3: Microbiome and diet-related metabolic deviations are present in control participants with metabolic impairment.
Fig. 4: A metabolomics-based model of BMI predicts higher BMI in ACS patients and correlates with disease severity.

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Data availability

The raw metagenomic sequencing data per sample of the controls are available from the European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena): PRJEB11532. The raw metabolomics data and phenotypes per sample of the controls are available from the European Genome-phenome Archive (EGA; https://ega-archive.org/): EGAS00001004512. The raw metabolomics data and full clinical phenotypes for the cohort of individuals with ACS are available from the EGA: EGAS00001005342. Additional data regarding SGB 4712, including the genome sequence, gene annotation and closest references are available at https://github.com/noambar/ACStudy/tree/master/SGB_4712.

Code availability

Analysis source code is available at https://github.com/noambar/ACStudy.

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Acknowledgements

We thank past and present members of the Segal group and the Cardiology Department at Rabin Medical Center for useful discussions. Y.T.-B. received a research grant from the Tel Aviv University Faculty Funds, and from the Gassner Fund for Medical Research. N.B. received a PhD scholarship for Data Science from the Israeli Council for Higher Education (CHE) via the Weizmann Data Science Research Center and is supported by a research grant from the Estate of Tully and Michele Plesser. E.S. is supported by the Crown Human Genome Center, by D. L. Schwarz, J. N. Halpern and L. Steinberg, and by grants funded by the European Research Council and the Israel Science Foundation. M.-E.D. is supported by the NIHR Imperial Biomedical Research Centre, and by grants from the French National Research Agency (ANR-10-LABX-46 [European Genomics Institute for Diabetes]), from the National Center for Precision Diabetic Medici–e – PreciDIAB, which is jointly supported by the French National Agency for Research (ANR-18-IBHU-0001), by the European Union (FEDER), by the Hauts-de-France Regional Council (Agreement 20001891/NP0025517), by the European Metropolis of Lille (MEL, Agreement 2019_ESR_11) and Isite ULNE (R-002-20-TALENT-DUMAS), also jointly funded by ANR (ANR-16-IDEX-0004-ULNE), the Hauts-de-France Regional Council (20002845) and by the European Metropolis of Lille (MEL). K.C. is supported by Medical Research Council (MRC) Skills Development Fellowship (grant number MR/S020039/1) and Wellcome Trust funded Institutional Strategic Support Fellowship (grant number 204834/Z/16/Z).

Author information

Author notes

  1. These authors contributed equally: Yeela Talmor-Barkan, Noam Bar.

  2. These authors jointly supervised this work: Ran Kornowski, Eran Segal.

Authors and Affiliations

  1. Department of Cardiology, Rabin Medical Center, Petah Tikva, Israel

    Yeela Talmor-Barkan, Aviv A. Shaul, Alon Shechter, Chava Chezar-Azerrad, Ziad Arow, Yoav Hammer & Ran Kornowski

  2. Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel

    Yeela Talmor-Barkan, Aviv A. Shaul, Alon Shechter, Chava Chezar-Azerrad, Ziad Arow, Yoav Hammer & Ran Kornowski

  3. Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel

    Yeela Talmor-Barkan, Noam Bar, Nir Shahaf, Anastasia Godneva, Yuval Bussi, Maya Lotan-Pompan, Adina Weinberger & Eran Segal

  4. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    Yeela Talmor-Barkan, Noam Bar, Nir Shahaf, Anastasia Godneva, Yuval Bussi, Maya Lotan-Pompan, Adina Weinberger & Eran Segal

  5. Genomic and Environmental Medicine, National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, UK

    Kanta Chechi & Marc-Emmanuel Dumas

  6. Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK

    Kanta Chechi & Marc-Emmanuel Dumas

  7. School of Public Health, Faculty of Medicine, Imperial College London, Medical School Building, St Mary’s Hospital, London, UK

    Kanta Chechi

  8. Experimental and Clinical Research Center, a cooperation of Charité-Universitätsmedizin and the Max-Delbrück Center, Berlin, Germany

    Sofia K. Forslund

  9. Max Delbrück Center for Molecular Medicine (MDC), Berlin, Germany

    Sofia K. Forslund

  10. MCharité University Hospital, Berlin, Germany

    Sofia K. Forslund

  11. DZHK (German Centre for Cardiovascular Research), Berlin, Germany

    Sofia K. Forslund

  12. University College London, Department of Clinical and Movement Neurosciences, London, UK

    Sebastien Fromentin & S. Dusko Ehrlich

  13. European Genomics Institute for Diabetes, UMR1283/8199 INSERM, CNRS, Institut Pasteur de Lille, Lille University Hospital, University of Lille, Lille, France

    Marc-Emmanuel Dumas

  14. Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark

    Oluf Pedersen

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  1. Yeela Talmor-Barkan
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Contributions

Y.T.-B. and N.B. conceived the project, designed and conducted all analyses, interpreted the results, wrote the manuscript and are listed in random order. N.R. performed metabolomics analyses and interpreted the results. A.G. conducted microbiome analysis. Y.B. conducted microbiome analysis and provided additional information regarding SGB 4712. A.A.S., A.S., C.C.-A., Z.A. and Y.H. coordinated and designed data collection. M.L.-P. and A.W. developed protocols, performed microbiome sequencing, and processed serum samples. A.W. designed the project and oversaw sample collection and processing. K.C., S.K.F., S.F., M.-E.D., S.D.E. and O.P. performed the replication analysis on the MetaCardis cohort. R.K. and E.S. conceived and directed the project and analyses, designed the analyses, interpreted the results and wrote the manuscript.

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Correspondence to Eran Segal.

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Nature Medicine thanks Matej Oresic, Manuel Mayr, Ellen Blaak and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Michael Basson was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Cohort selection and data acquisition pipeline.

This study includes a total of 199 participants with ACS and 970 non-ACS individuals. Each cell shows the number of individuals who were profiled for the corresponding omic platform indicated on the left. Colored bars connecting cells represent the number of overlapping individuals. For example, there were 169 non-ACS individuals that were profiled both for serum metabolomics using the Metabolon platform and for microbiome composition. The 156 samples of individuals with ACS that were profiled using the Metabolon platform are the first to be enrolled in this study. The 473 samples of non-ACS individuals that were profiled using the Metabolon platform, were profiled as part of our previous study (Bar et al. 2020). All samples of individuals with ACS (n = 191) and of non-ACS individuals (n = 961) for which we had available serum obtained during their recruitment, were profiled using the Nightingale platform. While microbiome data were available for all individuals with and without ACS, we only considered samples for which the collection, DNA extraction and sequencing procedures were identical (n = 199 for ACS; n = 340 for non-ACS). Differential abundance analysis was performed based on subcohorts resulting from 1:1 matching for age, sex, BMI, DM, and smoking status. ACS, Acute Corony Syndrome; BMI Body Mass Index; DM, Diabetes Mellitius.

Extended Data Fig. 2 Breakdown of ACS serum metabolomics pattern by the origin of metabolites and biological pathway.

(a) Box plots (y axis: center, median; box, IQR; whiskers, 1.5×IQR) showing the explained variance of metabolites by different feature groups (x-axis) separated to metabolites enriched in ACS (N = 175; orange) and enriched in matched non-ACS controls (N = 358; blue). (b) explained variance of metabolites (y axis: center, median; box, IQR; whiskers, 1.5×IQR) by their super pathways (x axis) separated to metabolites enriched in ACS (orange) and enriched in matched non-ACS controls (blue). The number of metabolites per group is shown below each box. Trad., Traditional; C&V, cofactors and vitamins.

Extended Data Fig. 3 Depletion of ACS-related bacteria SGB 4712 replicates in an independent validation cohort.

(a) Box plots showing the relative abundance of the unknown bacterial species SGB 4712 (y-axis: center, median; box, IQR; whiskers, 1.5×IQR; log scaled) in our ACS and matched controls (x-axis; n = 80 each). The P-value shown is computed using the two-sided Mann–Whitney U-test. (b) Relative abundance of the unknown bacterial species SGB 4712 (y-axis: center, median; box, IQR; whiskers, 1.5×IQR; log scaled) in four groups from the MetaCardis validation cohort (x-axis; HC, healthy controls, n = 275; MMC, metabolically matched controls, n = 218; UMCC, untreated metabolically compromised controls, n = 211; IHD, ischaemic heart disease, n = 319). The P-value shown is computed using the two-sided Mann–Whitney U-test. r.a., relative abundance.

Extended Data Fig. 4 Clinical data correlates with metabolic deviations.

(a) The mean weighted R2 of genetics for ACS-enriched metabolites (y-axis) versus chronological age (x-axis). Dots are colored by sex. Spearman correlation is computed over all samples (Spearman ⍴ = 0.18; p = 0.032). (b) The mean weighted R2 of traditional risk factors for ACS-depleted metabolites (y axis) versus chronological age (x axis; Spearman ⍴ = 0.33; p = 7.7 × 10−5). (c) The mean weighted R2 of genetics for ACS-enriched metabolites (y axis: center, median; box, IQR; whiskers, 1.5×IQR) in ACS patients who had a combined CVD outcome (defined as either: acute myocardial infarction, acute stroke, unplanned PCI, or cardiovascular-related death; x axis) versus not (two-sided Mann–Whitney U-test, p = 0.002).

Extended Data Fig. 5 Replication of higher predicted BMI in ACS individuals based on NMR metabolomics.

Figure panels refer to results of serum metabolomics-based prediction model of BMI trained in a non-ACS control cohort (n = 763) and evaluated on held-out test sets consisting of both controls (n = 179) and individuals with ACS (n = 179; Methods). (a) Measured (x axis) versus predicted (y-axis) BMI for both controls (blue) and ACS (orange) individuals. Line and shaded coloring represent the fitting of a linear model and the 95% confidence interval. (b) Difference between predicted and measured BMI (y axis: center, median; box, IQR; whiskers, 1.5×IQR) of individuals, binned into three BMI groups (<25, 25-30, >30; x-axis). The P-values shown are computed using the two-sided Mann–Whitney U-test. BMI, body mass index.

Extended Data Fig. 6 Replication of higher predicted BMI in IHD individuals in the MetaCardis study.

Figure panels refer to results of serum metabolomics-based prediction model of BMI trained in a cohort of individuals without IHD and evaluated on held-out test sets consisting of both 319 IHD and 319 non-IHD individuals (Methods). (a) Measured (x axis) versus predicted (y axis) BMI for healthy controls (HC; blue), metabolically matched controls (MMC; blue), and untreated metabolically compromised controls (UMCC; blue), and individuals with ischaemic heart disease (IHD; orange). Line and shaded coloring represent the fitting of a linear model and the 95% confidence interval. (b) Difference between predicted and measured BMI (y axis: center, median; box, IQR; whiskers, 1.5×IQR) of individuals, binned into three BMI groups (<25, 25-30, >30; x axis). (c) Same as in (b) only for individuals with IHD, and each bin is separated into normoglycemic versus T2DM patients. Higher predicted BMI is associated with an increased incidence of T2DM (OR = 1.13, 95% CI = 1.05-1.22, p = 0.002; a logistic regression model adjusted for BMI and age; Methods). The p values shown are computed using the two-sided Mann–Whitney U-test. BMI, body mass index; T2DM, type 2 diabetes mellitus; OR, odds ratio; CI, confidence interval.

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Talmor-Barkan, Y., Bar, N., Shaul, A.A. et al. Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease. Nat Med 28, 295–302 (2022). https://doi.org/10.1038/s41591-022-01686-6

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