Exhaled breath-based clusters in children with post-COVID condition

Le résumé fourni par la source

Background: Pediatric post-COVID-19 condition (PPCC) presents as a heterogeneous disease with a broad spectrum of symptoms. This study aimed to identify phenotypes of PPCC through unbiased cluster analysis of exhaled metabolites, with the goal of identifying biomarkers to stratify patients. Methods: Exhaled breath samples were collected from children with physician-diagnosed PPCC. An unsupervised clustering approach was applied to the exhaled breath metabolites, and clinical metadata were compared between the resulting clusters. Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) was applied to find most discriminative metabolites between clusters. Results: A total of 54 children were included and categorized into two clusters. Compared to Cluster 1 (n=38), Cluster 2 (n=16) consisted predominantly of older girls (69%) with a median age of 16 years and exhibited more severe PPCC-related outcomes, including higher PROMIS fatigue scores. Six volatile organic compounds (VOCs) were identified as biomarkers that effectively differentiated the two clusters. These VOCs, previously reported in the literature, highlight metabolic and inflammatory disruptions and demonstrated perfect classification performance (area under the receiver operating characteristic curve=1) Conclusion: This study found two distinct phenotypes of PPCC, and identified six discriminating VOCs, underscoring the potential of VOCs as non-invasive biomarkers for disease stratification in PPCC. While it could be a building block towards a better understanding of the metabolic disruptions underlying long COVID, further research with larger patient cohorts is necessary to elucidate the mechanisms driving these differences.

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