The ongoing pandemic of COVID-19 has entered a new phase. Despite the reduced pathogenicity of the currently prevalent Omicron variant of SARS-CoV-2, there is still a risk of severe illness and death. Currently, there are no specific biomarkers available to accurately predict the progression and outcomes of Omicron induced COVID-19. Previous studies have mainly focused on untargeted or lipid metabolism analysis of individuals infected with the original strain of SARS-CoV-2 or Omicron with mild to moderate symptoms. Therefore, we conducted a comprehensive targeted serum metabolomics analysis using wide-targeted metabolomics technology to analyze the metabolic profiles of COVID-19 patients infected with Omicron. We also correlated differentially expressed metabolites with laboratory test parameters. Finally, we developed a machine learning model that can accurately predict key biomarkers for the progression and prognosis of severe COVID-19, aiming to provide valuable evidence for improving the prognosis and reducing mortality in severe cases caused by Omicron.

Serum Metabolomic Characteristics of COVID-19 Patients Co-infection with Echovirus

The ongoing pandemic of COVID-19 has entered a new phase. Despite the reduced pathogenicity of the currently prevalent Omicron variant of SARS-CoV-2, there is still a risk of severe illness and death. Currently, there are no specific biomarkers available to accurately predict the progression and outcomes of Omicron induced COVID-19. Previous studies have mainly focused on untargeted or lipid metabolism analysis of individuals infected with the original strain of SARS-CoV-2 or Omicron with mild to moderate symptoms. Therefore, we conducted a comprehensive targeted serum metabolomics analysis using wide-targeted metabolomics technology to analyze the metabolic profiles of COVID-19 patients infected with Omicron. We also correlated differentially expressed metabolites with laboratory test parameters. Finally, we developed a machine learning model that can accurately predict key biomarkers for the progression and prognosis of severe COVID-19, aiming to provide valuable evidence for improving the prognosis and reducing mortality in severe cases caused by Omicron.

The ongoing pandemic of COVID-19 has entered a new phase. Despite the reduced pathogenicity of the currently prevalent Omicron variant of SARS-CoV-2, there is still a risk of severe illness and death. Currently, there are no specific biomarkers available to accurately predict the progression and outcomes of Omicron induced COVID-19. Previous studies have mainly focused on untargeted or lipid metabolism analysis of individuals infected with the original strain of SARS-CoV-2 or Omicron with mild to moderate symptoms. Therefore, we conducted a comprehensive targeted serum metabolomics analysis using wide-targeted metabolomics technology to analyze the metabolic profiles of COVID-19 patients infected with Omicron. We also correlated differentially expressed metabolites with laboratory test parameters. Finally, we developed a machine learning model that can accurately predict key biomarkers for the progression and prognosis of severe COVID-19, aiming to provide valuable evidence for improving the prognosis and reducing mortality in severe cases caused by Omicron.

The ongoing pandemic of COVID-19 has entered a new phase. Despite the reduced pathogenicity of the currently prevalent Omicron variant of SARS-CoV-2, there is still a risk of severe illness and death. Currently, there are no specific biomarkers available to accurately predict the progression and outcomes of Omicron induced COVID-19. Previous studies have mainly focused on untargeted or lipid metabolism analysis of individuals infected with the original strain of SARS-CoV-2 or Omicron with mild to moderate symptoms. Therefore, we conducted a comprehensive targeted serum metabolomics analysis using wide-targeted metabolomics technology to analyze the metabolic profiles of COVID-19 patients infected with Omicron. We also correlated differentially expressed metabolites with laboratory test parameters. Finally, we developed a machine learning model that can accurately predict key biomarkers for the progression and prognosis of severe COVID-19, aiming to provide valuable evidence for improving the prognosis and reducing mortality in severe cases caused by Omicron.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

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).

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).

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

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).