Microbiome and metabolome features of the cardiometabolic disease spectrum
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Microbiome and metabolome features of the cardiometabolic disease spectrum. / Fromentin, Sebastien; Forslund, Sofia K.; Chechi, Kanta; Aron-Wisnewsky, Judith; Chakaroun, Rima; Nielsen, Trine; Tremaroli, Valentina; Ji, Boyang; Prifti, Edi; Myridakis, Antonis; Chilloux, Julien; Andrikopoulos, Petros; Fan, Yong; Olanipekun, Michael T.; Alves, Renato; Adiouch, Solia; Bar, Noam; Talmor-Barkan, Yeela; Belda, Eugeni; Caesar, Robert; Coelho, Luis Pedro; Falony, Gwen; Fellahi, Soraya; Galan, Pilar; Galleron, Nathalie; Helft, Gerard; Hoyles, Lesley; Isnard, Richard; Le Chatelier, Emmanuelle; Julienne, Hanna; Olsson, Lisa; Pedersen, Helle Krogh; Pons, Nicolas; Quinquis, Benoit; Rouault, Christine; Roume, Hugo; Salem, Joe Elie; Schmidt, Thomas S.B.; Vieira-Silva, Sara; Li, Peishun; Zimmermann-Kogadeeva, Maria; Lewinter, Christian; Søndertoft, Nadja B.; Hansen, Tue H.; Gauguier, Dominique; Gøtze, Jens Peter; Køber, Lars; Kornowski, Ran; Vestergaard, Henrik; Hansen, Torben; Zucker, Jean Daniel; Hercberg, Serge; Letunic, Ivica; Bäckhed, Fredrik; Oppert, Jean Michel; Nielsen, Jens; Raes, Jeroen; Bork, Peer; Stumvoll, Michael; Segal, Eran; Clément, Karine; Dumas, Marc Emmanuel; Ehrlich, S. Dusko; Pedersen, Oluf.
In: Nature Medicine, Vol. 28, No. 2, 2022, p. 303-314.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Microbiome and metabolome features of the cardiometabolic disease spectrum
AU - Fromentin, Sebastien
AU - Forslund, Sofia K.
AU - Chechi, Kanta
AU - Aron-Wisnewsky, Judith
AU - Chakaroun, Rima
AU - Nielsen, Trine
AU - Tremaroli, Valentina
AU - Ji, Boyang
AU - Prifti, Edi
AU - Myridakis, Antonis
AU - Chilloux, Julien
AU - Andrikopoulos, Petros
AU - Fan, Yong
AU - Olanipekun, Michael T.
AU - Alves, Renato
AU - Adiouch, Solia
AU - Bar, Noam
AU - Talmor-Barkan, Yeela
AU - Belda, Eugeni
AU - Caesar, Robert
AU - Coelho, Luis Pedro
AU - Falony, Gwen
AU - Fellahi, Soraya
AU - Galan, Pilar
AU - Galleron, Nathalie
AU - Helft, Gerard
AU - Hoyles, Lesley
AU - Isnard, Richard
AU - Le Chatelier, Emmanuelle
AU - Julienne, Hanna
AU - Olsson, Lisa
AU - Pedersen, Helle Krogh
AU - Pons, Nicolas
AU - Quinquis, Benoit
AU - Rouault, Christine
AU - Roume, Hugo
AU - Salem, Joe Elie
AU - Schmidt, Thomas S.B.
AU - Vieira-Silva, Sara
AU - Li, Peishun
AU - Zimmermann-Kogadeeva, Maria
AU - Lewinter, Christian
AU - Søndertoft, Nadja B.
AU - Hansen, Tue H.
AU - Gauguier, Dominique
AU - Gøtze, Jens Peter
AU - Køber, Lars
AU - Kornowski, Ran
AU - Vestergaard, Henrik
AU - Hansen, Torben
AU - Zucker, Jean Daniel
AU - Hercberg, Serge
AU - Letunic, Ivica
AU - Bäckhed, Fredrik
AU - Oppert, Jean Michel
AU - Nielsen, Jens
AU - Raes, Jeroen
AU - Bork, Peer
AU - Stumvoll, Michael
AU - Segal, Eran
AU - Clément, Karine
AU - Dumas, Marc Emmanuel
AU - Ehrlich, S. Dusko
AU - Pedersen, Oluf
N1 - Publisher Copyright: © 2022, The Author(s).
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
U2 - 10.1038/s41591-022-01688-4
DO - 10.1038/s41591-022-01688-4
M3 - Journal article
C2 - 35177860
AN - SCOPUS:85124750537
VL - 28
SP - 303
EP - 314
JO - Nature Medicine
JF - Nature Medicine
SN - 1078-8956
IS - 2
ER -
ID: 299198940