RESUMEN
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, ßrecurrence = 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.