eNose breath prints as a surrogate biomarker for classifying patients with asthma by atopy.,Journal of Allergy and Clinical Immunology

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eNose breath prints as a surrogate biomarker for classifying patients with asthma by atopy.
Journal of Allergy and Clinical Immunology ( IF 11.4 ) Pub Date : 2020-06-10 , DOI: 10.1016/j.jaci.2020.05.038
Mahmoud I Abdel-Aziz ₁ , Paul Brinkman ₂ , Susanne J H Vijverberg ₃ , Anne H Neerincx ₂ , Rianne de Vries ₄ , Yennece W F Dagelet ₂ , John H Riley ₅ , Simone Hashimoto ₆ , Paolo Montuschi ₇ , Kian Fan Chung ₈ , Ratko Djukanovic ₉ , Louise J Fleming ₈ , Clare S Murray ₁₀ , Urs Frey ₁₁ , Andrew Bush ₈ , Florian Singer ₁₂ , Gunilla Hedlin ₁₃ , Graham Roberts ₉ , Sven-Erik Dahlén ₁₄ , Ian M Adcock ₈ , Stephen J Fowler ₁₀ , Karen Knipping ₁₅ , Peter J Sterk ₂ , Aletta D Kraneveld ₁₆ , Anke H Maitland-van der Zee ₁₇ , ,

Affiliation

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.

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.

中文翻译：

eNose呼吸指纹可作为通过特应性对哮喘患者进行分类的替代生物标记。

背景

电子鼻（eNoses）是新兴的即时医疗点工具，可有助于慢性呼吸系统疾病（如哮喘）的亚表型分析。

目的

我们旨在研究eNoses是否可以对小儿和成年哮喘患者的特应性进行分类。

方法

来自4个独立队列的患有哮喘和/或喘息的参与者包括在内；BreathCloud参与者（n = 429），预测呼吸系统疾病结果的无偏见生物标志物（n = 96），预测呼吸系统疾病的无偏见生物标志物（n = 100）和小儿哮喘的药物遗传学：儿童哮喘的药物遗传学：抗抑郁药物-炎症影响2名参与者（n = 30）。特应性被定义为对常见过敏原阳性的皮肤点刺试验结果（≥3mm）和/或阳性的特异性IgE水平（≥0.35kU / L）。通过使用集成的eNose平台或SpiroNose测量呼出的呼吸曲线。根据使用的技术，将数据分为2个训练集和2个验证集。监督数据分析涉及使用3种不同的机器学习算法来对特应性哮喘和非特应性哮喘患者进行分类，并报告接收器工作特征曲线下的面积作为模型性能的度量。此外，通过使用贝叶斯网络执行无监督方法来揭示eNose挥发性有机化合物概况与哮喘特征之间的数据驱动关系。

结果

这项研究纳入了655名参与者（n = 601名成人和学龄期哮喘儿童和54名学龄前儿童喘息[其中68.2％为特应性]）的呼吸特征。机器学习模型利用易失性有机化合物的分布来区分特应性和非特应性参与者，其在训练和验证集中的接收器工作特性曲线下的面积分别至少为0.84和0.72。这种无监督的方法显示，对特应性进行分类的呼吸特征不会与其他患者特征混淆。