4.6 Article

Metabolomic Profiling of Plasma Reveals Differential Disease Severity Markers in COVID-19 Patients

期刊

FRONTIERS IN MICROBIOLOGY
卷 13, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fmicb.2022.844283

关键词

metabolomics; mass spectrometry; COVID-19; SARS-CoV-2; metabolites; prognosis

资金

  1. Fundacao de Amparo a Pesquisa do Estado do Amazonas -AM through the EMERGESAUDE -AM project funds [FAPEAM] [005/2020 -PCTIEMERGESAUDE-AM]
  2. Fundacao de Amparo a Pesquisa do Estado do Amazonas (FAPEAM) [(s) 002/2008, 007/2018, 005/2019, 006/2020]
  3. VS PRO-ESTADO
  4. POSGRAD 2021 calls
  5. a Support Programfor the Consolidation of State Education

向作者/读者索取更多资源

This study used metabolomics to identify potential metabolic signatures for discriminating severe COVID-19 from non-severe cases. The results showed that metabolites from porphyrin and purine pathways were significantly elevated in the severe disease group, while elevated levels of cholesteryl ester in non-severe patients were consistent with differences in blood cholesterol components. Pathway analysis revealed the impact of COVID-19 on glycerophospholipid and porphyrin metabolism, particularly in the glycerophospholipid and linoleic acid metabolism pathways.
The severity, disabilities, and lethality caused by the coronavirus 2019 (COVID-19) disease have dumbfounded the entire world on an unprecedented scale. The multifactorial aspect of the infection has generated interest in understanding the clinical history of COVID-19, particularly the classification of severity and early prediction on prognosis. Metabolomics is a powerful tool for identifying metabolite signatures when profiling parasitic, metabolic, and microbial diseases. This study undertook a metabolomic approach to identify potential metabolic signatures to discriminate severe COVID-19 from non-severe COVID-19. The secondary aim was to determine whether the clinical and laboratory data from the severe and non-severe COVID-19 patients were compatible with the metabolomic findings. Metabolomic analysis of samples revealed that 43 metabolites from 9 classes indicated COVID-19 severity: 29 metabolites for non-severe and 14 metabolites for severe disease. The metabolites from porphyrin and purine pathways were significantly elevated in the severe disease group, suggesting that they could be potential prognostic biomarkers. Elevated levels of the cholesteryl ester CE (18:3) in non-severe patients matched the significantly different blood cholesterol components (total cholesterol and HDL, both p < 0.001) that were detected. Pathway analysis identified 8 metabolomic pathways associated with the 43 discriminating metabolites. Metabolomic pathway analysis revealed that COVID-19 affected glycerophospholipid and porphyrin metabolism but significantly affected the glycerophospholipid and linoleic acid metabolism pathways (p = 0.025 and p = 0.035, respectively). Our results indicate that these metabolomics-based markers could have prognostic and diagnostic potential when managing and understanding the evolution of COVID-19.

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