Abdel-Aziz, Mahmoud IMahmoud IAbdel-AzizBrinkman, PaulPaulBrinkmanVijverberg, Susanne J HSusanne J HVijverbergNeerincx, Anne HAnne HNeerincxde Vries, RianneRiannede VriesDagelet, Yennece W FYennece W FDageletRiley, John HJohn HRileyHashimoto, SimoneSimoneHashimotoChung, Kian FanKian FanChungDjukanovic, RatkoRatkoDjukanovicFleming, Louise JLouise JFlemingMurray, Clare SClare SMurrayFrey, UrsUrsFreyBush, AndrewAndrewBushSinger, FlorianFlorianSingerHedlin, GunillaGunillaHedlinRoberts, GrahamGrahamRobertsDahlén, Sven-ErikSven-ErikDahlénAdcock, Ian MIan MAdcockFowler, Stephen JStephen JFowlerKnipping, KarenKarenKnippingSterk, Peter JPeter JSterkKraneveld, Aletta DAletta DKraneveldMaitland-van der Zee, Anke HAnke HMaitland-van der Zee2024-10-052024-10-052020-11https://boris-portal.unibe.ch/handle/20.500.12422/55536BACKGROUND Electronic noses (eNoses) are emerging point-of-care tools that may help in the subphenotyping of chronic respiratory diseases such as asthma. OBJECTIVE We aimed to investigate whether eNoses can classify atopy in pediatric and adult patients with asthma. METHODS Participants with asthma and/or wheezing from 4 independent cohorts were included; BreathCloud participants (n = 429), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes adults (n = 96), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes pediatric participants (n = 100), and Pharmacogenetics of Asthma Medication in Children: Medication with Anti-Inflammatory Effects 2 participants (n = 30). Atopy was defined as a positive skin prick test result (≥3 mm) and/or a positive specific IgE level (≥0.35 kU/L) for common allergens. Exhaled breath profiles were measured by using either an integrated eNose platform or the SpiroNose. Data were divided into 2 training and 2 validation sets according to the technology used. Supervised data analysis involved the use of 3 different machine learning algorithms to classify patients with atopic versus nonatopic asthma with reporting of areas under the receiver operating characteristic curves as a measure of model performance. In addition, an unsupervised approach was performed by using a bayesian network to reveal data-driven relationships between eNose volatile organic compound profiles and asthma characteristics. RESULTS Breath profiles of 655 participants (n = 601 adults and school-aged children with asthma and 54 preschool children with wheezing [68.2% of whom were atopic]) were included in this study. Machine learning models utilizing volatile organic compound profiles discriminated between atopic and nonatopic participants with areas under the receiver operating characteristic curves of at least 0.84 and 0.72 in the training and validation sets, respectively. The unsupervised approach revealed that breath profiles classifying atopy are not confounded by other patient characteristics. CONCLUSION eNoses accurately detect atopy in individuals with asthma and wheezing in cohorts with different age groups and could be used in asthma phenotyping.enVOCs asthma atopy discrimination eNose machine learning600 - Technology::610 - Medicine & healtharticle10.7892/boris.1477623253137110.1016/j.jaci.2020.05.038