The value of prospective metabolomic susceptibility endotypes: broad applicability for infectious diseasesResearch in context
Yulu Chen,
Kevin Mendez,
Sofina Begum,
Emily Dean,
Haley Chatelaine,
John Braisted,
Vrushali D. Fangal,
Margaret Cote,
Mengna Huang,
Su H. Chu,
Meryl Stav,
Qingwen Chen,
Nicole Prince,
Rachel Kelly,
Kenneth B. Christopher,
Joann Diray-Arce,
Ewy A. Mathé,
Jessica Lasky-Su
Affiliations
Yulu Chen
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
Kevin Mendez
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
Sofina Begum
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
Emily Dean
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
Haley Chatelaine
Division of Preclinical Innovation, National Center for Advancing Translational Science, National Institutes of Health, Rockville, MD, USA
John Braisted
Division of Preclinical Innovation, National Center for Advancing Translational Science, National Institutes of Health, Rockville, MD, USA
Vrushali D. Fangal
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
Margaret Cote
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
Mengna Huang
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
Su H. Chu
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
Meryl Stav
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
Qingwen Chen
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
Nicole Prince
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
Rachel Kelly
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
Kenneth B. Christopher
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
Precision Vaccines Program, Division of Infectious Diseases, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
Ewy A. Mathé
Division of Preclinical Innovation, National Center for Advancing Translational Science, National Institutes of Health, Rockville, MD, USA; Corresponding authors.
Jessica Lasky-Su
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Corresponding author.
Summary: 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: We employed metabolomic profiling to cluster infection-free individuals and assessed their relationship with COVID severity and influenza incidence/recurrence. Findings: We identified a metabolomic susceptibility endotype that was strongly associated with both severe COVID (ORICUadmission = 6.7, p-value = 1.2 × 10−08, ORmortality = 4.7, p-value = 1.6 × 10−04) and influenza (ORincidence = 2.9; p-values = 2.2 × 10−4, βrecurrence = 1.03; p-value = 5.1 × 10−3). We observed similar severity associations when recapitulating this susceptibility endotype using metabolomics from individuals during and after acute COVID infection. We demonstrate the value of using metabolomic endotyping to identify a metabolically susceptible group for two–and potentially more–IDs that are driven by increases in specific amino acids, including microbial-related metabolites such as tryptophan, bile acids, histidine, polyamine, phenylalanine, and tyrosine metabolism, as well as carbohydrates involved in glycolysis. Interpretations: These metabolites may be identified prior to infection to enable protective measures for these individuals. Funding: 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.
