Summary of Study ST002829

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, 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.


This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

Perform statistical analysis  |  Show all samples  |  Show named metabolites  |  Download named metabolite data  
Download mwTab file (text)   |  Download mwTab file(JSON)   |  Download data files
Study IDST002829
Study TitleNucleotide, phospholipid, and kynurenine metabolites are robustly associated with COVID-19 severity and time of plasma sample collection in a prospective cohort study
Study SummaryIntroduction: 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.
National Institutes of Health
DepartmentDivision of Preclinical Innovation - National Center for Advancing Translational Sciences
LaboratoryInformatics Core - Division of Preclinical Innovation
Last NameChatelaine
First NameHaley
Address9800 Medical Center Drive
Submit Date2023-08-24
Num Groups4
Total Subjects609
Num Males232
Num Females377
Analysis Type DetailOther
Release Date2023-09-19
Release Version1
Haley Chatelaine Haley Chatelaine application/zip

Select appropriate tab below to view additional metadata details:


Collection ID:CO002931
Collection Summary:The Mass General Brigham (MGB) Biobank contains ~100,000 banked plasma, serum, and DNA samples from >100,000 consented patients. Electronic Medical Record (EMR) data and lifestyle, environment, and family history surveys can also be linked to the banked samples. The Longitudinal EMR and Omics COVID-19 Cohort (LEOCC) consists of a subset of individuals with prospective plasma samples from the MGB Biobank. Patients with a positive COVID-19 diagnosis (defined as a COVID-19 positive infection control flag, COVID-19 presumed infection control flag, or SARS-CoV-2 RNA positive test result) and available plasma samples prior to COVID-19 (up to October 27, 2020) were included. No additional exclusion criteria were applied. Clinical data relevant to COVID-19 infection, including clinical measures, disease diagnoses, and COVID-19 severity were also extracted from EMR data for use in statistical models. This study was approved by the Brigham and Women’s Institutional Review Board (IRB: 2014P001109). A total of 940 plasma samples from 661 individuals were collected from consented patients and were stored at –80 C. These samples are categorized by the time point of collection relative to a positive COVID-19 diagnosis, including 474 pre-COVID-19 samples (date of collection < date of diagnosis), 282 during COVID-19 samples (collected within 28 days of diagnosis), and 182 post-COVID-19 samples (collected more than 28 days after COVID-19 diagnosis). For patients with multiple during and/or post-COVID-19 samples, only the sample collected at the date closest to diagnosis was retained for during-COVID-19, and only the sample collected at the date furthest from diagnosis was retained for post-COVID-19. Patients without BMI data were also excluded from the sample sets, yielding a total of n = 441 pre-COVID-19, n = 86 during COVID-19, and n = 82 post-COVID-19 samples used for analysis.
Sample Type:Blood (plasma)