eNose breath prints as a surrogate biomarker for classifying patients with asthma by atopy.

Mahmoud I Abdel-Aziz, Paul Brinkman, Susanne J H Vijverberg, Anne H Neerincx, Rianne de Vries, Yennece W F Dagelet, John H Riley, Simone Hashimoto, Paolo Montuschi, Kian Fan Chung, Ratko Djukanovic, Louise J Fleming, Clare S Murray, Urs Frey, Andrew Bush, Florian Singer, Gunilla Hedlin, Graham Roberts, Sven-Erik Dahlén, Ian M Adcock, Stephen J Fowler, Karen Knipping, Peter J Sterk, Aletta D Kraneveld, Anke H Maitland-van der Zee

Journal: The Journal of allergy and clinical immunology 2021;146(5):1045-1055

PMID: 32531371

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Abstract

BACKGROUND

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.

Address: Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Department of Clinical Pharmacy, Faculty of Pharmacy, Assiut University, Assiut, Egypt.; Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.; Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands.; Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Breathomix BV, Reeuwijk, The Netherlands.; Respiratory Therapeutic Unit, GlaxoSmithKline, Stockley Park, United Kingdom.; Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Department of Paediatric Respiratory Medicine, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.; Department of Pharmacology, Faculty of Medicine, Catholic University of the Sacred Heart, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome.; National Heart and Lung Institute, Imperial College London, and Royal Brompton and Harefield NHS Trust, London, United Kingdom.; NIHR Southampton Respiratory Biomedical Research Unit, Clinical and Experimental Sciences and Human Development and Health, University of Southampton, Southampton, United Kingdom.; Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, and Manchester Academic Health Science Centre and NIHR Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom.; University Children's Hospital Basel, University of Basel, Basel, Switzerland.; University Children's Hospital Bern, Bern, Switzerland.; Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden; Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden.; Centre for Allergy Research, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.; Danone Nutricia Research, Utrecht, The Netherlands; Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands.; Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands; Institute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.; Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands; Department of Paediatric Respiratory Medicine, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands. Electronic address: [email protected].

DOI: 10.1016/j.jaci.2020.05.038

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MeSH Terms: Adolescent, Adult, Asthma, Biomarkers, Child, Child, Preschool, Computer Simulation, Electronic Nose, Exhalation, Humans, Hypersensitivity, Immediate, Infant, Machine Learning, Middle Aged, Phenotype