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Updated: May 9, 2025

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
Published on: February 16, 2022
Serum metabolomic characteristics of COVID-19 patients co-infection with echovirus
Chunhua Wang1,2, Tingyu Yu2, Ying Xia2
1Department of Clinical Laboratory, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, China.
Abstract:
Currently, the Omicron variant of the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to circulate globally. In our multiplex respiratory pathogen detection, we identified numerous instances of co-infection with Echovirus (ECHO) among Coronavirus Disease 2019 (COVID-19) patients, which exacerbated the clinical symptoms of these patients. Such co-infections are likely to impact the subsequent medical treatment. To date, there are no reports on the pathogenic mechanisms related to COVID-19 co-infected with ECHO. Therefore, this study employed the TM Widely-Targeted metabolomics approach to analyze the serum metabolomes of COVID-19 patients with single SARS-CoV-2 infection (COVID-19), COVID-19 patients co-infected with ECHO (COVID-19 + ECHO), and healthy individuals (Control) recruited from routine physical examinations during the same period. Concurrent clinical laboratory tests were performed on the patients to reveal the differences in metabolomic characteristics between the COVID-19 patients and the COVID-19 + ECHO patients, as well as to explore potential metabolic pathways that may exacerbate disease progression. Our findings indicate that both clinical examination indicators and the pathways enriched by differential metabolites confirm that patients with dual infection exhibit higher inflammatory and immune responses compared to those with single COVID-19 infections. This difference is likely reflected through abnormalities in the glycerophospholipid metabolic pathway, with the metabolite Sn-Glycero-3-Phosphocholine playing a crucial role in this process. Finally, we established a diagnostic model based on logistic regression using five differential metabolites, which accurately differentiates between the dual infection population and the single COVID-19 infection population (AUC = 0.828).
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