Introduction Long COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC), manifests as persistent and often debilitating symptoms enduring well beyond the initial COVID-19 infection. This disease is especially worrying in children since it can seriously alter their development. Presently, a specific diagnostic test or definitive biomarker set for confirming long COVID is lacking, relying instead on the protracted presence of symptoms post-acute infection. Methods We measured the levels of 13 biomarkers in 105 saliva samples (49 from children with long COVID and 56 controls), and the Pearson correlation coefficient was used to analyse the correlations between the levels of the different salivary biomarkers. Multivariate logistic regression analyses were performed to determine which of the 13 analysed salivary biomarkers were useful to discriminate between children with long COVID and controls, as well as between children with mild and severe long COVID symptoms. Results Pediatric long COVID exhibited increased oxidant biomarkers and decreased antioxidant, immune response, and stress-related biomarkers. Correlation analyses unveiled distinct patterns between biomarkers in long COVID and controls. Notably, a multivariate logistic regression pinpointed TOS, ADA2, total proteins, and AOPP as pivotal variables, culminating in a remarkably accurate predictive model distinguishing long COVID from controls. Furthermore, total proteins and ADA1 were instrumental in discerning between mild and severe long COVID symptoms. Discussion This research sheds light on the potential clinical utility of salivary biomarkers in diagnosing and categorizing the severity of pediatric long COVID. It also lays the groundwork for future investigations aimed at unravelling the prognostic value of these biomarkers in predicting the trajectory of long COVID in affected individuals.
【저자키워드】 SARS-CoV-2, bioinformatics, predictive models, salivary biomarkers, pediatric long COVID,