Urinary Peptidomic Profiling In Post-Acute Sequelae of SARS-CoV-2 Infection: A Case-Control Study.

Observed Findings

• 195 urine peptides showed significantly different abundance in PASC patients compared to controls, predominantly collagen alpha-chain fragments.
• The PASC195 classifier achieved 94.9% accuracy (AUC 0.949) in the training set and 96.2% accuracy (AUC 0.962) in independent validation.
• Peptide profile patterns suggest ongoing collagen turnover dysregulation, persistent inflammation, and endothelial damage in PASC patients.
• The PASC195 signature effectively discriminated PASC from both healthy controls and ME/CFS patients (p < 0.0001).
• In silico analysis proposed exercise, GLP-1 receptor agonists, and mineralocorticoid receptor antagonists as potentially therapeutic interventions.

Inferred Conclusions

• Urinary peptidomics provides a novel, non-invasive molecular signature capable of distinguishing PASC from healthy controls and non-COVID ME/CFS with high diagnostic accuracy.
• The peptide signature reflects dysregulated collagen metabolism, persistent inflammation, and endothelial dysfunction as key pathophysiological mechanisms in PASC.
• Biomarker-guided interventions targeting identified molecular pathways may offer new therapeutic approaches for PASC management.
• Urinary peptide profiling complements symptom-based diagnostic criteria and may reduce diagnostic uncertainty in post-acute COVID syndromes.

Remaining Questions