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- Title
Alterations of Urinary Microbial Metabolites and Immune Indexes Linked With COVID-19 Infection and Prognosis.
- Authors
Jing, Yixian; Wang, Jing; Zhang, Haiyan; Yang, Kun; Li, Jungang; Zhao, Ting; Liu, Jiaxiu; Wu, Jing; Chen, Yaokai
- Abstract
Coronavirus disease 2019 (COVID-19) has evolved into an established global pandemic. Metabolomic studies in COVID-19 patients is worth exploring for further available screening methods. In our study, we recruited a study cohort of 350 subjects comprising 248 COVID-19 patients (161 non-severe cases, 60 asymptomatic cases, and 27 severe cases) and 102 healthy controls (HCs), and herein present data with respect to their demographic features, urinary metabolome, immunological indices, and follow-up health status. We found that COVID-19 resulted in alterations of 39 urinary, mainly microbial, metabolites. Using random forest analysis, a simplified marker panel including three microbial metabolites (oxoglutaric acid, indoxyl, and phenylacetamide) was constructed (AUC=0.963, 95% CI, 0.930-0.983), which exhibited higher diagnostic performance than immune feature-based panels between COVID-19 and HC groups (P <0.0001). Meanwhile, we observed that urine metabolic markers enabled discriminating asymptomatic patients (ASY) from HCs (AUC = 0.981, 95% CI, 0.946-0.996), and predicting the incidence of high-risk sequalae in COVID-19 individuals (AUC=0.931, 95% CI, 0.877-0.966). Co-expression network analysis showed that 13 urinary microbial metabolites (e.g., oxoglutaric acid) were significantly correlated with alterations of CD4+, CD3+, and CD8+ T-cells, as well as IFN-γ, IL-2 and IL-4 levels, suggesting close interactions between microbial metabolites and host immune dysregulation in COVID-19. Taken together, our findings indicate that urinary metabolites may have promising potential for screening of COVID-19 in different application scenarios, and provide a new entry point to understand the microbial metabolites and related immune dysfunction in COVID-19.
- Subjects
MICROBIAL metabolites; COVID-19; PROGNOSIS; ASYMPTOMATIC patients; RANDOM forest algorithms
- Publication
Frontiers in Immunology, 2022, Vol 13, p1
- ISSN
1664-3224
- Publication type
Article
- DOI
10.3389/fimmu.2022.841739