Mij
Senior Member (Voting Rights)
Abstract Body
Background
Post-exertional malaise (PEM) is a disabling condition characterized by worsened symptoms after mental or physical exertion, commonly seen in Long-COVID (LC). While its underlying pathophysiology is unclear, potential mechanisms include oxidative stress, and metabolic derangements. We conducted a multiomic analysis to identify changes in energy metabolism, immune function and cellular stress responses in patients with LC affected by PEM (LC-PEM).
Methods
We included 25 LC-PEM and 25 COVID-19 survivors without persistent symptoms (Recovered) matched by sex, age and COVID-19 severity. We detected the levels in plasma of 6 short-chain fatty acids (SCFA) by GC-MS/MS, 95 metabolites by GC-qTOF and 795 proteins in the proteomic analysis by nanoLC-MS/MS. Statistical analyses included t-tests, partial-least square discriminant analysis (PLS-DA), Random Forest, Joint-Pathway analyses (KEGG database) and ROC Curve.
Results
Acetic acid was the only SCFA with lower levels in the LC-PEM group (p=0.001). Metabolomics revealed 29 differentially expressed metabolites (23 increased, 6 decreased in LC-PEM, all p<0.04), while proteomics showed 51 differentially expressed proteins between groups (39 increased and 11 decreased, p<0.01 in all cases). In a multiomic analysis, the PLS-DA of the identified 81 significant compounds demonstrated clear separation between groups (Fig1A). The enrichment pathway analysis identified 25 affected pathways, including complement and coagulation cascades, glucagon signalling pathway, TCA cycle, pyruvate metabolism, fatty acid biosynthesis, different amino acid metabolisms, glycolysis/gluconeogenesis, HIF-1 signalling pathway, butanoate metabolism and glyoxylate and dicarboxylate metabolism. Random Forest highlighted glyceraldehyde-3-phosphate dehydrogenase, Protein S100-A9, Fumaric acid and Galectin-3-binding protein as most associated with LC-PEM. The ROC curve analysis of the combination of 4 compounds yielded an AUC of 0.978 (95% CI: 0.942–1.013, p<0.001, Fig1B), indicating an excellent discriminatory ability between the LC-PEM and recovered groups.
Conclusions
Coagulation and inflammatory problems linked to metabolic changes, such as upregulated glycolysis and fatty acid synthesis, are associated with LC-PEM. These findings highlight the need for a multifaceted approach addressing both metabolic and immunological factors. Future research should validate these biomarkers in larger cohorts and explore targeted treatments to address these imbalances.
LINK
Background
Post-exertional malaise (PEM) is a disabling condition characterized by worsened symptoms after mental or physical exertion, commonly seen in Long-COVID (LC). While its underlying pathophysiology is unclear, potential mechanisms include oxidative stress, and metabolic derangements. We conducted a multiomic analysis to identify changes in energy metabolism, immune function and cellular stress responses in patients with LC affected by PEM (LC-PEM).
Methods
We included 25 LC-PEM and 25 COVID-19 survivors without persistent symptoms (Recovered) matched by sex, age and COVID-19 severity. We detected the levels in plasma of 6 short-chain fatty acids (SCFA) by GC-MS/MS, 95 metabolites by GC-qTOF and 795 proteins in the proteomic analysis by nanoLC-MS/MS. Statistical analyses included t-tests, partial-least square discriminant analysis (PLS-DA), Random Forest, Joint-Pathway analyses (KEGG database) and ROC Curve.
Results
Acetic acid was the only SCFA with lower levels in the LC-PEM group (p=0.001). Metabolomics revealed 29 differentially expressed metabolites (23 increased, 6 decreased in LC-PEM, all p<0.04), while proteomics showed 51 differentially expressed proteins between groups (39 increased and 11 decreased, p<0.01 in all cases). In a multiomic analysis, the PLS-DA of the identified 81 significant compounds demonstrated clear separation between groups (Fig1A). The enrichment pathway analysis identified 25 affected pathways, including complement and coagulation cascades, glucagon signalling pathway, TCA cycle, pyruvate metabolism, fatty acid biosynthesis, different amino acid metabolisms, glycolysis/gluconeogenesis, HIF-1 signalling pathway, butanoate metabolism and glyoxylate and dicarboxylate metabolism. Random Forest highlighted glyceraldehyde-3-phosphate dehydrogenase, Protein S100-A9, Fumaric acid and Galectin-3-binding protein as most associated with LC-PEM. The ROC curve analysis of the combination of 4 compounds yielded an AUC of 0.978 (95% CI: 0.942–1.013, p<0.001, Fig1B), indicating an excellent discriminatory ability between the LC-PEM and recovered groups.
Conclusions
Coagulation and inflammatory problems linked to metabolic changes, such as upregulated glycolysis and fatty acid synthesis, are associated with LC-PEM. These findings highlight the need for a multifaceted approach addressing both metabolic and immunological factors. Future research should validate these biomarkers in larger cohorts and explore targeted treatments to address these imbalances.
LINK
