Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/28987
Title: Microbiome and metabolome features of the cardiometabolic disease spectrum
Authors: Fromentin, S
Forslund, SK
Chechi, K
Aron-Wisnewsky, J
Chakaroun, R
Nielsen, T
Tremaroli, V
Ji, B
Prifti, E
Myridakis, A
Chilloux, J
Andrikopoulos, P
Fan, Y
Olanipekun, MT
Alves, R
Adiouch, S
Bar, N
Talmor-Barkan, Y
Belda, E
Caesar, R
Coelho, LP
Falony, G
Fellahi, S
Galan, P
Galleron, N
Helft, G
Hoyles, L
Isnard, R
Le Chatelier, E
Julienne, H
Olsson, L
Pedersen, HK
Pons, N
Quinquis, B
Rouault, C
Roume, H
Salem, JE
Schmidt, TSB
Vieira-Silva, S
Li, P
Zimmermann-Kogadeeva, M
Lewinter, C
Søndertoft, NB
Hansen, TH
Gauguier, D
Gøtze, JP
Køber, L
Kornowski, R
Vestergaard, H
Hansen, T
Zucker, JD
Hercberg, S
Letunic, I
Bäckhed, F
Oppert, JM
Nielsen, J
Raes, J
Bork, P
Stumvoll, M
Segal, E
Clément, K
Dumas, M-E
Ehrlich, SD
Pedersen, O
Keywords: cardiovascular diseases;metabolic syndrome
Issue Date: 17-Feb-2022
Publisher: Springer Nature
Citation: Fromentin, S. et al. (2022) 'Microbiome and metabolome features of the cardiometabolic disease spectrum', Nature Medicine,, 28 (2), pp. 303 - 314. doi: 10.1038/s41591-022-01688-4.
Abstract: Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages—acute coronary syndrome, chronic IHD and IHD with heart failure—and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.
Description: Data availability: Supplementary information on data availability is linked to the online version of the paper at www.nature.com/nature. Raw shotgun sequencing data that support the findings of this study have been deposited in the European Nucleotide Archive with accession codes PRJEB37249, PRJEB38742, PRJEB41311 and PRJEB46098, with public access. Metabolome data have been uploaded to Metabolights and MassIVE with respective accession numbers—that is, serum NMR and urine NMR with accession number MTBLS3429, serum GCMS with accession number MassIVE MSV000088042 and additional isotopically quantified serum metabolites using UPLC–MS/MS with accession number MassIVE MSV000088043. Processed pseudonymized per-subject -omics and metadata are provided in Supplementary Tables 9–13, and medication profiles are given in Supplementary Table 14.
Code availability: The novel drug-aware univariate biomarker testing pipeline, described in full elsewhere8, is available as an R package (metadeconfoundR; Birkner et al., manuscript in preparation) on GitHub (https://github.com/TillBirkner/metadeconfoundR) and also at https://doi.org/10.5281/zenodo.4721078. The latest version (0.1.8) of this package was used to generate the data shown in this publication. In addition, the scripts using this package to perform the analysis presented here are available at https://doi.org/10.5281/zenodo.5516219.
Extended data are available online at: https://www.nature.com/articles/s41591-022-01688-4#Sec26 .
Supplementary information is available online at: https://www.nature.com/articles/s41591-022-01688-4#Sec27 .
URI: https://bura.brunel.ac.uk/handle/2438/28987
DOI: https://doi.org/10.1038/s41591-022-01688-4
ISSN: 1078-8956
Other Identifiers: ORCiD: Karine Clément https://orcid.org/0000-0002-2489-3355
ORCiD: Marc-Emmanuel Dumas https://orcid.org/0000-0001-9523-7024
ORCiD: S. Dusko Ehrlich https://orcid.org/0000-0002-7563-4046
ORCiD: Oluf Pedersen https://orcid.org/0000-0002-3321-3972
ORCiD: Antonis Myridakis https://orcid.org/0000-0003-1690-6651
Appears in Collections:Dept of Life Sciences Research Papers

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