Blankestijn, Jelle M. and Lopez-Rincon, Alejandro and Neerincx, Anne H. and Vijverberg, Susanne J. H. and Hashimoto, Simone and Gorenjak, Mario and Prado, Olaia Sardon and Corcuera-Elosegui, Paula and Korta-Murua, Javier and Pino-Yanes, Maria and Potocnik, Uros and Bang, Corinna and Franke, Andre and Wolff, Christine and Brandstetter, Susanne and Toncheva, Antoaneta A. and Kheiroddin, Parastoo and Harner, Susanne and Kabesch, Michael and Kraneveld, Aletta D. and Abdel-Aziz, Mahmoud and Maitland-van der Zee, Anke H. (2023) Classifying asthma control using salivary and fecal bacterial microbiome in children with moderate-to-severe asthma. PEDIATRIC ALLERGY AND IMMUNOLOGY, 34 (2): e13919. ISSN 0905-6157, 1399-3038
Full text not available from this repository. (Request a copy)Abstract
BackgroundUncontrolled 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. Methods143 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)]. ResultsGlobal 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. ConclusionControlled 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | INFLAMMATION; asthma; disease management; treatment |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Kinder- und Jugendmedizin |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 16 Mar 2024 13:50 |
| Last Modified: | 16 Mar 2024 13:50 |
| URI: | https://pred.uni-regensburg.de/id/eprint/60206 |
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