PR001771 (Project)

Description:Introduction: A deep understanding of the molecular underpinnings of disease severity and progression in large human studies is necessary to develop metabolism-related preventive strategies of severe disease outcomes, particularly in viral pandemics like that of COVID-19. The use of samples collected before disease diagnosis, however, is limited and thus metabolites and metabolic pathways that predispose to severe disease are not well understood. Further, current studies are limited in sample size, number of metabolites evaluated, and/or do not adjust for comorbidities. Methods: We generated comprehensive plasma metabolomic profiles in more than 600 patients from the Longitudinal EMR and Omics COVID-19 Cohort (LEOCC). Samples were collected before (n = 441), during (n = 86), and after (n = 82) COVID-19 diagnosis. Regression models were used to determine (1) metabolites associated with predisposition to and/or persistent effects of COVID-19 severity within each time of sample collection, using logistic regression and (2) metabolites associated with time of sample collection, using linear regression, to better understand transient or lingering metabolic alterations over the disease course. All models were controlled for demographic (age, sex, race, ethnicity), risk (smoking status, BMI), and comorbidities (Charlson Index). Metabolites with an FDR-adjusted p-value < 0.05 were considered significant. Results: Of the 1,546 metabolites measured, 506 were associated with disease severity or time of sample collection. Among these, sphingolipids and phospholipids were negatively associated with severity and exhibited lingering elevations after disease, while modified nucleotides were positively associated with severity and had lingering decreases after disease. Cytidine and uridine metabolites, which were positively and negatively associated with COVID-19 severity, respectively, were transiently elevated in active disease, reflecting particular importance of pyrimidine metabolism in active COVID-19. Conclusions: We identified novel metabolites reflecting predisposition to severe disease and changes to global metabolism from before to during and after COVID-19 diagnosis. This is the first large metabolomics study using COVID-19 plasma samples before, during, and/or after disease. This study lays the groundwork for identifying putative clinical biomarkers and identifying preventative strategies for severe disease outcomes.