Classifying asthma control using salivary and fecal bacterial microbiome in children with moderate‐to‐severe asthma. Issue 2 (22nd February 2023)

• Record Type: Journal Article
• Authors: Blankestijn, Jelle M.; Lopez‐Rincon, Alejandro; Neerincx, Anne H.; Vijverberg, Susanne J. H.; Hashimoto, Simone; Gorenjak, Mario; Sardón Prado, Olaia; Corcuera‐Elosegui, Paula; Korta‐Murua, Javier; Pino‐Yanes, Maria; Potočnik, Uroš; Bang, Corinna; Franke, Andre; Wolff, Christine; Brandstetter, Susanne; Toncheva, Antoaneta A.; Kheiroddin, Parastoo; Harner, Susanne; Kabesch, Michael; Kraneveld, Aletta D.; Abdel‐Aziz, Mahmoud I.; Maitland‐van der Zee, Anke H.
• Is Part Of: Pediatric allergy and immunology. Volume 34:Issue 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-02-22
• Subjects: asthma: disease management -- asthma: treatment; Allergy in children -- Periodicals; Immunologic diseases in children -- Periodicals; 617
• DOI: 10.1111/pai.13919
• Languages: English
• ISSNs: 0905-6157

Abstract

Abstract: Background: Uncontrolled asthma can lead to severe exacerbations and reduced quality of life. Research has shown that the microbiome may be linked with asthma characteristics; however, its association with asthma control has not been explored. We aimed to investigate whether the gastrointestinal microbiome can be used to discriminate between uncontrolled and controlled asthma in children. Methods: 143 and 103 feces samples were obtained from 143 children with moderate‐to‐severe asthma aged 6 to 17 years from the SysPharmPediA study. Patients were classified as controlled or uncontrolled asthmatics, and their microbiome at species level was compared using global (alpha/beta) diversity, conventional differential abundance analysis (DAA, analysis of compositions of microbiomes with bias correction), and machine learning [Recursive Ensemble Feature Selection (REFS)]. Results: Global diversity and DAA did not find significant differences between controlled and uncontrolled pediatric asthmatics. REFS detected a set of taxa, including Haemophilus and Veillonella, differentiating uncontrolled and controlled asthma with an average classification accuracy of 81% (saliva) and 86% (feces). These taxa showed enrichment in taxa previously associated with inflammatory diseases for both sampling compartments, and with COPD for the saliva samples. Conclusion: Controlled and uncontrolled children with asthma can be differentiated based on their gastrointestinal microbiome using machine learning, specifically REFS. Our results show an association between asthma control and the gastrointestinal microbiome. This suggests that the gastrointestinal microbiome may be a potential biomarker for treatment responsiveness and thereby help to improve asthma control in children.