Metabolomic characterization of COVID-19 survivors in Jilin province

Abstract

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

• COVID-19
• SARS-CoV-2
• UPLC-MS/MS
• Metabolomics
• Biomarkers