Salivary biomarkers as pioneering indicators for diagnosis and severity stratification of pediatric long COVID

Front Cell Infect Microbiol. 2024 May 31:14:1396263. doi: 10.3389/fcimb.2024.1396263. eCollection 2024.

Abstract

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.

Keywords: SARS-CoV-2; bioinformatics; pediatric long COVID; predictive models; salivary biomarkers.

MeSH terms

  • Adolescent
  • Biomarkers* / analysis
  • COVID-19* / diagnosis
  • Child
  • Child, Preschool
  • Female
  • Humans
  • Male
  • SARS-CoV-2* / isolation & purification
  • Saliva* / chemistry
  • Saliva* / virology
  • Severity of Illness Index*

Substances

  • Biomarkers

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the 00006/COVI/20 grant to VM and MLC funded by Fundación Séneca-Murcia, the Saavedra Fajardo contract 21118/SF/19 to SC funded by Fundación Séneca-Murcia, the Juan de la Cierva-Incorporación contract IJC2019-039619-I to SDT funded by MCIN/AEI/10.13039/501100011033, a Margarita Salas grant to LF-M funded by the University of Murcia within the mark of “Ayudas en el marco del Programa para la Recualificación del Sistema Universitario Español” through the European Union Next Generation funds, and the Ramón y Cajal contract RYC2021-034764-I to CPR funded by Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación (AEI), Spain, and the European Next Generation Funds. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.