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Brinkman P, Wagener AH, Hekking PP, Bansal AT, Maitland-van der Zee AH, Wang Y, Weda H, Knobel HH, Vink TJ, Rattray NJ, D'Amico A, Pennazza G, Santonico M, Lefaudeux D, De Meulder B, Auffray C, Bakke PS, Caruso M, Chanez P, Chung KF, Corfield J, Dahlén SE, Djukanovic R, Geiser T, Horvath I, Krug N, Musial J, Sun K, Riley JH, Shaw DE, Sandström T, Sousa AR, Montuschi P, Fowler SJ, Sterk PJ. Identification and prospective stability of electronic nose (eNose)-derived inflammatory phenotypes in patients with severe asthma. J Allergy Clin Immunol 2018; 143:1811-1820.e7. [PMID: 30529449 DOI: 10.1016/j.jaci.2018.10.058] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Revised: 10/04/2018] [Accepted: 10/22/2018] [Indexed: 01/24/2023]
Abstract
BACKGROUND Severe asthma is a heterogeneous condition, as shown by independent cluster analyses based on demographic, clinical, and inflammatory characteristics. A next step is to identify molecularly driven phenotypes using "omics" technologies. Molecular fingerprints of exhaled breath are associated with inflammation and can qualify as noninvasive assessment of severe asthma phenotypes. OBJECTIVES We aimed (1) to identify severe asthma phenotypes using exhaled metabolomic fingerprints obtained from a composite of electronic noses (eNoses) and (2) to assess the stability of eNose-derived phenotypes in relation to within-patient clinical and inflammatory changes. METHODS In this longitudinal multicenter study exhaled breath samples were taken from an unselected subset of adults with severe asthma from the U-BIOPRED cohort. Exhaled metabolites were analyzed centrally by using an assembly of eNoses. Unsupervised Ward clustering enhanced by similarity profile analysis together with K-means clustering was performed. For internal validation, partitioning around medoids and topological data analysis were applied. Samples at 12 to 18 months of prospective follow-up were used to assess longitudinal within-patient stability. RESULTS Data were available for 78 subjects (age, 55 years [interquartile range, 45-64 years]; 41% male). Three eNose-driven clusters (n = 26/33/19) were revealed, showing differences in circulating eosinophil (P = .045) and neutrophil (P = .017) percentages and ratios of patients using oral corticosteroids (P = .035). Longitudinal within-patient cluster stability was associated with changes in sputum eosinophil percentages (P = .045). CONCLUSIONS We have identified and followed up exhaled molecular phenotypes of severe asthma, which were associated with changing inflammatory profile and oral steroid use. This suggests that breath analysis can contribute to the management of severe asthma.
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Affiliation(s)
- Paul Brinkman
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
| | - Ariane H Wagener
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Pieter-Paul Hekking
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Aruna T Bansal
- Acclarogen, St John's Innovation Centre, Cambridge, United Kingdom
| | | | | | - Hans Weda
- Philips Research, Eindhoven, The Netherlands
| | | | | | - Nicholas J Rattray
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, Conn
| | - Arnaldo D'Amico
- Department of Electronic Engineering, University of Rome "Tor Vergata," Rome, Italy
| | - Giorgio Pennazza
- Center for Integrated Research-CIR, Unit for Electronics for Sensor Systems, Campus Bio-Medico U, Rome, Italy
| | - Marco Santonico
- Center for Integrated Research-CIR, Unit for Electronics for Sensor Systems, Campus Bio-Medico U, Rome, Italy
| | - Diane Lefaudeux
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Lyon, France
| | - Bertrand De Meulder
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Lyon, France
| | - Charles Auffray
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Lyon, France
| | - Per S Bakke
- Institute of Medicine, University of Bergen, Bergen, Norway
| | - Massimo Caruso
- Department of Clinical and Experimental Medicine Hospital University, University of Catania, Catania, Italy
| | - Pascal Chanez
- Département des Maladies Respiratoires APHM,U1067 INSERM, Aix Marseille Université Marseille, Marseille, Italy
| | - Kian F Chung
- National Heart and Lung Institute, Imperial College, London, UK Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom
| | - Julie Corfield
- AstraZeneca R&D, Mölndal, Sweden; Areteva R&D, Nottingham, United Kingdom
| | - Sven-Erik Dahlén
- Centre for Allergy Research, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Ratko Djukanovic
- NIHR Southampton Respiratory Biomedical Research Unit, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Thomas Geiser
- the Department of Pulmonary Medicine, University Hospital Bern, Bern, Switzerland
| | - Ildiko Horvath
- Department of Pulmonology, Semmelweis University, Budapest, Hungary
| | - Nobert Krug
- Fraunhofer Institute for Toxicology and Experimental Medicine Hannover, Hannover, Germany
| | - Jacek Musial
- Department of Medicine, Jagiellonian University Medical College, Krakow, Poland
| | - Kai Sun
- Data Science Institute, South Kensington Campus, Imperial College Londont, London, United Kingdom
| | - John H Riley
- Respiratory Therapeutic Unit, GlaxoSmithKline, Stockley Park, United Kingdom
| | - Dominic E Shaw
- Respiratory Research Unit, University of Nottingham, Nottingham, United Kingdom
| | - Thomas Sandström
- Department of Public Health and Clinical Medicine, Department of Medicine, Respiratory Medicine Unit, Umeå University, Umeå, Sweden
| | - Ana R Sousa
- Respiratory Therapeutic Unit, GlaxoSmithKline, Stockley Park, United Kingdom
| | - Paolo Montuschi
- Department of Pharmacology, Faculty of Medicine, Catholic University of the Sacred Heart, Rome, Italy
| | - Stephen J Fowler
- Respiratory Research Group, Faculty of Medical and Human Sciences, University of Manchester, Manchester Academic Healthy Science Centre, and NIHR Translational Research Faculty in Respiratory Medicine, University Hospital of South Manchester, Manchester, United Kingdom; Lancashire Teaching Hospitals NHS Foundation Trust, Preston, United Kingdom
| | - Peter J Sterk
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
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Abstract
Scientific investigations in medicine and beyond increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.
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Affiliation(s)
- Tom Lorimer
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Jenny Held
- Eawag, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Ruedi Stoop
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
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