Summary of project PR001771
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001771. The data can be accessed directly via it's Project DOI: 10.21228/M8SM6H This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
| Project ID: | PR001771 |
| Project DOI: | doi: 10.21228/M8SM6H |
| Project Title: | Nucleotide, phospholipid, and kynurenine metabolites are robustly associated with COVID-19 severity and time of plasma sample collection in a prospective cohort study |
| Project Summary: | 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. |
| Institute: | National Institutes of Health |
| Department: | Division of Preclinical Innovation - National Center for Advancing Translational Sciences |
| Laboratory: | Informatics Core |
| Last Name: | Chatelaine |
| First Name: | Haley |
| Address: | 9800 Medical Center Drive |
| Email: | haley.chatelaine@nih.gov |
| Phone: | 952-738-2061 |
Summary of all studies in project PR001771
| Study ID | Study Title | Species | Institute | Analysis(* : Contains Untargted data) | Release Date | Version | Samples | Download(* : Contains raw data) |
|---|---|---|---|---|---|---|---|---|
| ST002829 | Nucleotide, phospholipid, and kynurenine metabolites are robustly associated with COVID-19 severity and time of plasma sample collection in a prospective cohort study | Homo sapiens | National Institutes of Health | MS | 2023-09-19 | 1 | 609 | Uploaded data (25K) |
