The value of prospective metabolomic susceptibility endotypes: broad applicability for infectious diseases.

Humans Metabolome Prospective Studies Influenza, Human / epidemiology COVID-19 Metabolomics Communicable Diseases / etiology
COVID-19 severity Electronic medical records Endotypes Mass general brigham biobank Metabolomics Similarity network fusion

Journal

EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039

Informations de publication

Date de publication:
Oct 2023
Historique:
received: 28 03 2023
revised: 22 08 2023
accepted: 23 08 2023
medline: 23 10 2023
pubmed: 22 9 2023
entrez: 21 9 2023
Statut: ppublish

Résumé

As new infectious diseases (ID) emerge and others continue to mutate, there remains an imminent threat, especially for vulnerable individuals. Yet no generalizable framework exists to identify the at-risk group prior to infection. Metabolomics has the advantage of capturing the existing physiologic state, unobserved via current clinical measures. Furthermore, metabolomics profiling during acute disease can be influenced by confounding factors such as indications, medical treatments, and lifestyles. We employed metabolomic profiling to cluster infection-free individuals and assessed their relationship with COVID severity and influenza incidence/recurrence. We identified a metabolomic susceptibility endotype that was strongly associated with both severe COVID (OR These metabolites may be identified prior to infection to enable protective measures for these individuals. The Longitudinal EMR and Omics COVID-19 Cohort (LEOCC) and metabolomic profiling were supported by the National Heart, Lung, and Blood Institute and the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health.

Sections du résumé

BACKGROUND BACKGROUND
As new infectious diseases (ID) emerge and others continue to mutate, there remains an imminent threat, especially for vulnerable individuals. Yet no generalizable framework exists to identify the at-risk group prior to infection. Metabolomics has the advantage of capturing the existing physiologic state, unobserved via current clinical measures. Furthermore, metabolomics profiling during acute disease can be influenced by confounding factors such as indications, medical treatments, and lifestyles.
METHODS METHODS
We employed metabolomic profiling to cluster infection-free individuals and assessed their relationship with COVID severity and influenza incidence/recurrence.
FINDINGS RESULTS
We identified a metabolomic susceptibility endotype that was strongly associated with both severe COVID (OR
INTERPRETATIONS CONCLUSIONS
These metabolites may be identified prior to infection to enable protective measures for these individuals.
FUNDING BACKGROUND
The Longitudinal EMR and Omics COVID-19 Cohort (LEOCC) and metabolomic profiling were supported by the National Heart, Lung, and Blood Institute and the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health.

Identifiants

DOI: 10.1016/j.ebiom.2023.104791 PMID: 37734204 PMC: PMC10518609
pubmed: 37734204
pii: S2352-3964(23)00357-2
doi: 10.1016/j.ebiom.2023.104791
pmc: PMC10518609
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

104791

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests JL-S is a scientific advisor to Precion, Inc and a consultant to Tru Diagnostic, Inc. J.D.A is supported by 5U19AI118608-05, HHSN272201800047C, 75N93019C00044, U19Al168643, and U01AI167892. All other authors declare no potential, perceived, or real conflict of interest regarding the content of this manuscript.

Auteurs

Yulu Chen (Y)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Kevin Mendez (K)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Sofina Begum (S)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Emily Dean (E)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Haley Chatelaine (H)

Division of Preclinical Innovation, National Center for Advancing Translational Science, National Institutes of Health, Rockville, MD, USA.

John Braisted (J)

Division of Preclinical Innovation, National Center for Advancing Translational Science, National Institutes of Health, Rockville, MD, USA.

Vrushali D Fangal (VD)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Margaret Cote (M)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Mengna Huang (M)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Su H Chu (SH)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Meryl Stav (M)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Qingwen Chen (Q)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Nicole Prince (N)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Rachel Kelly (R)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Kenneth B Christopher (KB)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Division of Renal Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Joann Diray-Arce (J)

Precision Vaccines Program, Division of Infectious Diseases, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.

Ewy A Mathé (EA)

Division of Preclinical Innovation, National Center for Advancing Translational Science, National Institutes of Health, Rockville, MD, USA. Electronic address: ewy.mathe@nih.gov.

Jessica Lasky-Su (J)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. Electronic address: rejas@channing.harvard.edu.

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › The value of prospective metabolomic...
questionsmedicales.fr › Métabolisme › Métabolome › The value of prospective metabolomic...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études de cohortes › Études prospectives › The value of prospective metabolomic...
questionsmedicales.fr › Maladies de l'appareil respiratoire › Infections de l'appareil respiratoire › Grippe humaine › The value of prospective metabolomic...
questionsmedicales.fr › Maladies de l'appareil respiratoire › Infections de l'appareil respiratoire › Pneumopathie infectieuse › Pneumopathie virale › COVID-19 › The value of prospective metabolomic...
questionsmedicales.fr › Disciplines des sciences naturelles › Chimie › Biochimie › Métabolomique › The value of prospective metabolomic...
questionsmedicales.fr › États, signes et symptômes pathologiques › Processus pathologiques › Caractéristiques de la maladie › Maladies transmissibles › The value of prospective metabolomic...