Online breath analysis with SESI/HRMS for metabolic signatures in children with allergic asthma

• Published in: Frontiers in molecular biosciences Vol. 10; p. 1154536
• Main Authors: Weber, Ronja, Streckenbach, Bettina, Welti, Lara, Inci, Demet, Kohler, Malcolm, Perkins, Nathan, Zenobi, Renato, Micic, Srdjan, Moeller, Alexander
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 31.03.2023
• Subjects: allergic asthma; breath analysis; children; metabolites; Molecular Biosciences; SESI/HRMS; volatile organic compounds (VOCs); allergic asthma; breath analysis; volatile organic compounds (VOCs); children; metabolites; SESI/HRMS
• ISSN: 2296-889X; 2296-889X
• DOI: 10.3389/fmolb.2023.1154536

There is a need to improve the diagnosis and management of pediatric asthma. Breath analysis aims to address this by non-invasively assessing altered metabolism and disease-associated processes. Our goal was to identify exhaled metabolic signatures that distinguish children with allergic asthma from healthy controls using secondary electrospray ionization high-resolution mass spectrometry (SESI/HRMS) in a cross-sectional observational study. Breath analysis was performed with SESI/HRMS. Significant differentially expressed mass-to-charge features in breath were extracted using the empirical Bayes moderated t-statistics test. Corresponding molecules were putatively annotated by tandem mass spectrometry database matching and pathway analysis. 48 allergic asthmatics and 56 healthy controls were included in the study. Among 375 significant mass-to-charge features, 134 were putatively identified. Many of these could be grouped to metabolites of common pathways or chemical families. We found several pathways that are well-represented by the significant metabolites, for example, lysine degradation elevated and two arginine pathways downregulated in the asthmatic group. Assessing the ability of breath profiles to classify samples as asthmatic or healthy with supervised machine learning in a 10 times repeated 10-fold cross-validation revealed an area under the receiver operating characteristic curve of 0.83. For the first time, a large number of breath-derived metabolites that discriminate children with allergic asthma from healthy controls were identified by online breath analysis. Many are linked to well-described metabolic pathways and chemical families involved in pathophysiological processes of asthma. Furthermore, a subset of these volatile organic compounds showed high potential for clinical diagnostic applications.