Post-acute sequelae of severe acute respiratory syndrome coronavirus 2-infection (PASC) is challenging to diagnose and treat, and its molecular pathophysiology remains unclear. Urinary peptidomics can provide valuable information on urine peptides that may enable improved and specified PASC diagnosis. Using standardized capillary electrophoresis-MS, we examined the urinary peptidomes of 50 patients with PASC 10 months after COVID-19 and 50 controls, including healthy individuals (n = 42) and patients with non-COVID-19-associated myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) (n = 8). Based on peptide abundance differences between cases and controls, we developed a diagnostic model using a support vector machine. The abundance of 195 urine peptides among PASC patients significantly differed from that in controls, with a predominant abundance of collagen alpha chains. This molecular signature (PASC195) effectively distinguished PASC cases from controls in the training set (AUC of 0.949 [95% CI 0.900-0.998; p < 0.0001]) and independent validation set (AUC of 0.962 [95% CI 0.897-1.00]; p < 0.0001]). In silico assessment suggested exercise, GLP-1RAs and mineralocorticoid receptor antagonists (MRAs) as potentially efficacious interventions. We present a novel and non-invasive diagnostic model for PASC. Reflecting its molecular pathophysiology, PASC195 has the potential to advance diagnostics and inform therapeutic interventions. STATEMENT OF SIGNIFICANCE OF THE STUDY: Despite the recent emergence of omics-derived candidates for post-acute sequelae of SARS-CoV-2 infection (PASC), the pending validation of proposed markers and lack of consensus result in the continuous reliance on symptom-based criteria, being subject to diagnostic uncertainties and potential recall bias. Building upon prior findings of renal involvement in acute COVID-19 pathophysiology and PASC-associated alterations, we hypothesized that the use of urinary peptides for PASC-specific biomarker discovery, unlike conventional specimens that have been utilized thus far, may offer complementary information on putative disease mechanisms. In the present study, 195 significantly expressed peptides were used to form a classifier termed PASC195, which effectively discriminated PASC from non-PASC (p < 0.0001), including healthy individuals and non-COVID-19-associated myalgic encephalomyelitis/chronic fatigue syndrome, in both the derivation (n = 60) and an independent validation set (n = 40). The peptidome profile associated with PASC was consistent with a shift in collagen turnover, with most PASC195 peptides derived from alpha chains. Ongoing inflammatory responses, hemostatic imbalances, and endothelial damage were indicated by cross-sectional variations in endogenous peptide excretion.
