» Articles » PMID: 38203516

Nucleotide, Phospholipid, and Kynurenine Metabolites Are Robustly Associated with COVID-19 Severity and Time of Plasma Sample Collection in a Prospective Cohort Study

Overview
Journal Int J Mol Sci
Publisher MDPI
Date 2024 Jan 11
PMID 38203516
Authors
Affiliations
Soon will be listed here.
Abstract

Understanding the molecular underpinnings of disease severity and progression in human studies is necessary to develop metabolism-related preventative strategies for severe COVID-19. Metabolites and metabolic pathways that predispose individuals to severe disease are not well understood. In this study, we generated comprehensive plasma metabolomic profiles in >550 patients from the Longitudinal EMR and Omics COVID-19 Cohort. Samples were collected before ( = 441), during ( = 86), and after ( = 82) COVID-19 diagnosis, representing 555 distinct patients, most of which had single timepoints. Regression models adjusted for demographics, risk factors, and comorbidities, were used to determine metabolites associated with predisposition to and/or persistent effects of COVID-19 severity, and metabolite changes that were transient/lingering over the disease course. Sphingolipids/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 acutely elevated, reflecting the particular importance of pyrimidine metabolism in active COVID-19. This is the first large metabolomics study using COVID-19 plasma samples before, during, and/or after disease. Our results lay the groundwork for identifying putative biomarkers and preventive strategies for severe COVID-19.

Citing Articles

Integration of metabolomics methodologies for the development of predictive models for mortality risk in elderly patients with severe COVID-19.

Cui S, Han Q, Zhang R, Zeng S, Shao Y, Li Y BMC Infect Dis. 2025; 25(1):10.

PMID: 39748307 PMC: 11697755. DOI: 10.1186/s12879-024-10402-3.


Longitudinal Metabolomics Reveals Metabolic Dysregulation Dynamics in Patients with Severe COVID-19.

Uchimido R, Kami K, Yamamoto H, Yokoe R, Tsuchiya I, Nukui Y Metabolites. 2024; 14(12).

PMID: 39728437 PMC: 11676849. DOI: 10.3390/metabo14120656.

References
1.
Lodge S, Lawler N, Gray N, Masuda R, Nitschke P, Whiley L . Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction. Int J Mol Sci. 2023; 24(14). PMC: 10380980. DOI: 10.3390/ijms241411614. View

2.
Ghini V, Vieri W, Celli T, Pecchioli V, Boccia N, Alonso-Vasquez T . COVID-19: A complex disease with a unique metabolic signature. PLoS Pathog. 2023; 19(11):e1011787. PMC: 10662774. DOI: 10.1371/journal.ppat.1011787. View

3.
Shima H, Igarashi K . N 1-methyladenosine (m1A) RNA modification: the key to ribosome control. J Biochem. 2020; 167(6):535-539. DOI: 10.1093/jb/mvaa026. View

4.
Sindelar M, Stancliffe E, Schwaiger-Haber M, Anbukumar D, Adkins-Travis K, Goss C . Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity. Cell Rep Med. 2021; 2(8):100369. PMC: 8292035. DOI: 10.1016/j.xcrm.2021.100369. View

5.
Lex A, Gehlenborg N, Strobelt H, Vuillemot R, Pfister H . UpSet: Visualization of Intersecting Sets. IEEE Trans Vis Comput Graph. 2015; 20(12):1983-92. PMC: 4720993. DOI: 10.1109/TVCG.2014.2346248. View