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

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

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.

VOCs, asthma, atopy, discrimination, eNose, machine learning
0091-6749
1045-1055
Abdel-Aziz, Mahmoud I.
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Brinkman, Paul
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Vijverberg, Susanne J.H.
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Neerincx, Anne H.
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de Vries, Rianne
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Dagelet, Yennece W.F.
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Riley, John H.
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Hashimoto, Simone
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Chung, Kian Fan
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Djukanovic, Ratko
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Fleming, Louise J.
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Murray, Clare S.
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Frey, Urs
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Bush, Andrew
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Singer, Florian
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Hedlin, Gunilla
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Roberts, Graham
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Dahlén, Sven-Erik
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Adcock, Ian M.
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Fowler, Stephen J.
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Knipping, Karen
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Sterk, Peter J.
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Kraneveld, Aletta D.
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Maitland-van der Zee, Anke H.
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U-BIOPRED Study Group and the Amsterdam UMC Breath Research Group
Abdel-Aziz, Mahmoud I.
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Brinkman, Paul
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Vijverberg, Susanne J.H.
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Neerincx, Anne H.
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de Vries, Rianne
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Dagelet, Yennece W.F.
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Riley, John H.
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Hashimoto, Simone
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Chung, Kian Fan
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Djukanovic, Ratko
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Fleming, Louise J.
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Murray, Clare S.
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Frey, Urs
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Bush, Andrew
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Singer, Florian
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Hedlin, Gunilla
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Roberts, Graham
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Dahlén, Sven-Erik
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Adcock, Ian M.
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Fowler, Stephen J.
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Knipping, Karen
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Sterk, Peter J.
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Kraneveld, Aletta D.
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Maitland-van der Zee, Anke H.
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Abdel-Aziz, Mahmoud I., Brinkman, Paul, Vijverberg, Susanne J.H., Neerincx, Anne H., de Vries, Rianne, Dagelet, Yennece W.F., Riley, John H., Hashimoto, Simone, Chung, Kian Fan, Djukanovic, Ratko, Fleming, Louise J., Murray, Clare S., Frey, Urs, Bush, Andrew, Singer, Florian, Hedlin, Gunilla, Roberts, Graham, Dahlén, Sven-Erik, Adcock, Ian M., Fowler, Stephen J., Knipping, Karen, Sterk, Peter J., Kraneveld, Aletta D. and Maitland-van der Zee, Anke H. , U-BIOPRED Study Group and the Amsterdam UMC Breath Research Group (2020) eNose breath prints as a surrogate biomarker for classifying patients with asthma by atopy. Journal of Allergy and Clinical Immunology, 146 (5), 1045-1055. (doi:10.1016/j.jaci.2020.05.038).

Record type: Article

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.

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Accepted/In Press date: 5 May 2020
e-pub ahead of print date: 10 June 2020
Published date: November 2020
Additional Information: Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.
Keywords: VOCs, asthma, atopy, discrimination, eNose, machine learning

Identifiers

Local EPrints ID: 441555
URI: http://eprints.soton.ac.uk/id/eprint/441555
ISSN: 0091-6749
PURE UUID: e868bedb-a869-4f1e-bca2-e5a9c0d5bd07
ORCID for Ratko Djukanovic: ORCID iD orcid.org/0000-0001-6039-5612
ORCID for Graham Roberts: ORCID iD orcid.org/0000-0003-2252-1248

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Date deposited: 17 Jun 2020 16:36
Last modified: 28 Apr 2022 01:54

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Contributors

Author: Mahmoud I. Abdel-Aziz
Author: Paul Brinkman
Author: Susanne J.H. Vijverberg
Author: Anne H. Neerincx
Author: Rianne de Vries
Author: Yennece W.F. Dagelet
Author: John H. Riley
Author: Simone Hashimoto
Author: Kian Fan Chung
Author: Louise J. Fleming
Author: Clare S. Murray
Author: Urs Frey
Author: Andrew Bush
Author: Florian Singer
Author: Gunilla Hedlin
Author: Graham Roberts ORCID iD
Author: Sven-Erik Dahlén
Author: Ian M. Adcock
Author: Stephen J. Fowler
Author: Karen Knipping
Author: Peter J. Sterk
Author: Aletta D. Kraneveld
Author: Anke H. Maitland-van der Zee
Corporate Author: U-BIOPRED Study Group and the Amsterdam UMC Breath Research Group

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