@article{discovery10163253,
           month = {February},
            year = {2022},
          number = {2},
       publisher = {NATURE PORTFOLIO},
            note = {This work is licensed under a Creative Commons Attribution 4.0 International License. The images
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          volume = {28},
           title = {Microbiome and metabolome features of the cardiometabolic disease spectrum},
         journal = {Nature Medicine},
        keywords = {Science \& Technology, Life Sciences \& Biomedicine, Biochemistry \& Molecular Biology, Cell Biology, Medicine, Research \& Experimental, Research \& Experimental Medicine, CORONARY-ARTERY-DISEASE, GUT MICROBIOTA, P-CRESOL, HEART, RISK, IMPACT, SERUM, SIGNATURES, PLASMA, HEALTH},
        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.},
             url = {https://doi.org/10.1038/s41591-022-01688-4},
          author = {Fromentin, Sebastien and Forslund, Sofia K and Chechi, Kanta and Aron-Wisnewsky, Judith and Chakaroun, Rima and Nielsen, Trine and Tremaroli, Valentina and Ji, Boyang and Prifti, Edi and Myridakis, Antonis and Chilloux, Julien and Andrikopoulos, Petros and Fan, Yong and Olanipekun, Michael T and Alves, Renato and Adiouch, Solia and Bar, Noam and Talmor-Barkan, Yeela and Belda, Eugeni and Caesar, Robert and Coelho, Luis Pedro and Falony, Gwen and Fellahi, Soraya and Galan, Pilar and Galleron, Nathalie and Helft, Gerard and Hoyles, Lesley and Isnard, Richard and Le Chatelier, Emmanuelle and Julienne, Hanna and Olsson, Lisa and Pedersen, Helle Krogh and Pons, Nicolas and Quinquis, Benoit and Rouault, Christine and Roume, Hugo and Salem, Joe-Elie and Schmidt, Thomas SB and Vieira-Silva, Sara and Li, Peishun and Zimmermann-Kogadeeva, Maria and Lewinter, Christian and Sondertoft, Nadja B and Hansen, Tue H and Gauguier, Dominique and Gotze, Jens Peter and Kober, Lars and Kornowski, Ran and Vestergaard, Henrik and Hansen, Torben and Zucker, Jean-Daniel and Hercberg, Serge and Letunic, Ivica and Backhed, Fredrik and Oppert, Jean-Michel and Nielsen, Jens and Raes, Jeroen and Bork, Peer and Stumvoll, Michael and Segal, Eran and Clement, Karine and Dumas, Marc-Emmanuel and Ehrlich, S Dusko and Pedersen, Oluf}
}