Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/28987
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dc.contributor.authorFromentin, S-
dc.contributor.authorForslund, SK-
dc.contributor.authorChechi, K-
dc.contributor.authorAron-Wisnewsky, J-
dc.contributor.authorChakaroun, R-
dc.contributor.authorNielsen, T-
dc.contributor.authorTremaroli, V-
dc.contributor.authorJi, B-
dc.contributor.authorPrifti, E-
dc.contributor.authorMyridakis, A-
dc.contributor.authorChilloux, J-
dc.contributor.authorAndrikopoulos, P-
dc.contributor.authorFan, Y-
dc.contributor.authorOlanipekun, MT-
dc.contributor.authorAlves, R-
dc.contributor.authorAdiouch, S-
dc.contributor.authorBar, N-
dc.contributor.authorTalmor-Barkan, Y-
dc.contributor.authorBelda, E-
dc.contributor.authorCaesar, R-
dc.contributor.authorCoelho, LP-
dc.contributor.authorFalony, G-
dc.contributor.authorFellahi, S-
dc.contributor.authorGalan, P-
dc.contributor.authorGalleron, N-
dc.contributor.authorHelft, G-
dc.contributor.authorHoyles, L-
dc.contributor.authorIsnard, R-
dc.contributor.authorLe Chatelier, E-
dc.contributor.authorJulienne, H-
dc.contributor.authorOlsson, L-
dc.contributor.authorPedersen, HK-
dc.contributor.authorPons, N-
dc.contributor.authorQuinquis, B-
dc.contributor.authorRouault, C-
dc.contributor.authorRoume, H-
dc.contributor.authorSalem, JE-
dc.contributor.authorSchmidt, TSB-
dc.contributor.authorVieira-Silva, S-
dc.contributor.authorLi, P-
dc.contributor.authorZimmermann-Kogadeeva, M-
dc.contributor.authorLewinter, C-
dc.contributor.authorSøndertoft, NB-
dc.contributor.authorHansen, TH-
dc.contributor.authorGauguier, D-
dc.contributor.authorGøtze, JP-
dc.contributor.authorKøber, L-
dc.contributor.authorKornowski, R-
dc.contributor.authorVestergaard, H-
dc.contributor.authorHansen, T-
dc.contributor.authorZucker, JD-
dc.contributor.authorHercberg, S-
dc.contributor.authorLetunic, I-
dc.contributor.authorBäckhed, F-
dc.contributor.authorOppert, JM-
dc.contributor.authorNielsen, J-
dc.contributor.authorRaes, J-
dc.contributor.authorBork, P-
dc.contributor.authorStumvoll, M-
dc.contributor.authorSegal, E-
dc.contributor.authorClément, K-
dc.contributor.authorDumas, M-E-
dc.contributor.authorEhrlich, SD-
dc.contributor.authorPedersen, O-
dc.date.accessioned2024-05-13T12:13:33Z-
dc.date.available2024-05-13T12:13:33Z-
dc.date.issued2022-02-17-
dc.identifierORCiD: Karine Clément https://orcid.org/0000-0002-2489-3355-
dc.identifierORCiD: Marc-Emmanuel Dumas https://orcid.org/0000-0001-9523-7024-
dc.identifierORCiD: S. Dusko Ehrlich https://orcid.org/0000-0002-7563-4046-
dc.identifierORCiD: Oluf Pedersen https://orcid.org/0000-0002-3321-3972-
dc.identifierORCiD: Antonis Myridakis https://orcid.org/0000-0003-1690-6651-
dc.identifier.citationFromentin, 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.en_US
dc.identifier.issn1078-8956-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/28987-
dc.descriptionData 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.en_US
dc.descriptionCode 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.-
dc.descriptionExtended data are available online at: https://www.nature.com/articles/s41591-022-01688-4#Sec26 .-
dc.descriptionSupplementary information is available online at: https://www.nature.com/articles/s41591-022-01688-4#Sec27 .-
dc.description.abstractPrevious 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.en_US
dc.description.sponsorshipEuropean Union’s Seventh Framework Program for research, technological development and demonstration under grant agreement HEALTH-F4-2012-305312. Parts of the studies were also supported by Metagenopolis grant ANR-11-DPBS-0001, the Leducq Foundation, the Novo Nordisk Foundation and the NIHR Imperial Biomedical Research Centre and by grants from the French National Research Agency (ANR-10-LABX-46 (European Genomics Institute for Diabetes)), the National Center for Precision Diabetic Medicine, which is jointly supported by the French National Agency for Research (ANR-18-IBHU-0001), the European Union (FEDER), the Hauts-de-France Regional Council (Agreement 20001891/NP0025517), the European Metropolis of Lille (agreement 2019_ESR_11) and site ULNE (R-002-20-TALENT-DUMAS), which is also jointly funded by ANR (ANR-16-IDEX-0004-ULNE), the Hauts-de-France Regional Council (20002845) and the European Metropolis of Lille. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent research institution at the University of Copenhagen, partially funded by an unrestricted donation from the Novo Nordisk Foundation. K. Chechi is supported by the Medical Research Council Skills Development Fellowship (grant no. MR/S020039/1) and the Wellcome Trust-funded Institutional Strategic Support Fellowship (grant no. 204834/Z/16/Z). L.H. was in receipt of a Medical Research Council Intermediate Research Fellowship in Data Science (grant no. MR/L01632X/1, UK Med-Bio). S.K.F. was supported by the German Centre for Cardiovascular Research, the German Research Council (projects SFB1365, SFB1470 and KFO339) and the German Ministry of Education and Research.en_US
dc.format.extent303 - 314-
dc.format.mediumPrint-Electronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dc.rightsCopyright © The Author(s) 2022. Rights and permissions: Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectcardiovascular diseasesen_US
dc.subjectmetabolic syndromeen_US
dc.titleMicrobiome and metabolome features of the cardiometabolic disease spectrumen_US
dc.typeArticleen_US
dc.date.dateAccepted2022-01-07-
dc.identifier.doihttps://doi.org/10.1038/s41591-022-01688-4-
dc.relation.isPartOfNature Medicine-
pubs.issue2-
pubs.publication-statusPublished-
pubs.volume28-
dc.identifier.eissn1546-170X-
dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/legalcode.en-
dc.rights.holderThe Author(s)-
Appears in Collections:Dept of Life Sciences Research Papers

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