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. 2024 Sep 19;25(1):343.
doi: 10.1186/s12931-024-02974-0.

Metabolomic characterization of COVID-19 survivors in Jilin province

Panyang Xu  1 , Lei Zeng  2   3 , Chunyu Wang  4 , Jiatong Chai  5 , Junguo Yin  6 , Jiancheng Xu  7
Affiliations

Metabolomic characterization of COVID-19 survivors in Jilin province

Panyang Xu et al. Respir Res. .

Abstract

Background: The COVID-19 pandemic has escalated into a severe global public health crisis, with persistent sequelae observed in some patients post-discharge. However, metabolomic characterization of the reconvalescent remains unclear.

Methods: In this study, serum and urine samples from COVID-19 survivors (n = 16) and healthy subjects (n = 16) underwent testing via the non-targeted metabolomics approach using UPLC-MS/MS. Univariate and multivariate statistical analyses were conducted to delineate the separation between the two sample groups and identify differentially expressed metabolites. By integrating random forest and cluster analysis, potential biomarkers were screened, and the differential metabolites were subsequently subjected to KEGG pathway enrichment analysis.

Results: Significant differences were observed in the serum and urine metabolic profiles between the two groups. In serum samples, 1187 metabolites were detected, with 874 identified as significant (457 up-regulated, 417 down-regulated); in urine samples, 960 metabolites were detected, with 39 deemed significant (12 up-regulated, 27 down-regulated). Eight potential biomarkers were identified, with KEGG analysis revealing significant enrichment in several metabolic pathways, including arginine biosynthesis.

Conclusions: This study offers an overview of the metabolic profiles in serum and urine of COVID-19 survivors, providing a reference for post-discharge monitoring and the prognosis of COVID-19 patients.

Keywords: Biomarkers; COVID-19; Metabolomics; SARS-CoV-2; UPLC-MS/MS.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Proportion of identified metabolites in each chemical class. a Serum b Urine
Fig. 2
Fig. 2
Plot of the PCA scores a Serum c Urine. Plot of the OPLS-DA scores b Serum d Urine
Fig. 3
Fig. 3
Fold-change plot showing of metabolism data between Case and Control a Serum b Urine; OPLS-DA VIP score charts. c Serum d Urine
Fig. 4
Fig. 4
Random forest model a Serum b Urine. Clustering heatmap of significant metabolism c
Fig. 5
Fig. 5
Enriched KEGG iterms a Serum b Urine
See this image and copyright information in PMC

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