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Measurement of exhaled volatile organic compounds as a biomarker for personalised medicine: assessment of short-term repeatability in severe asthma

Measurement of exhaled volatile organic compounds as a biomarker for personalised medicine: assessment of short-term repeatability in severe asthma
Measurement of exhaled volatile organic compounds as a biomarker for personalised medicine: assessment of short-term repeatability in severe asthma
The measurement of exhaled volatile organic compounds (VOCs) in exhaled breath (breathomics) represents an exciting biomarker matrix for airways disease, with early research indicating a sensitivity to airway inflammation. One of the key aspects to analytical validity for any clinical biomarker is an understanding of the short-term repeatability of measures. We collected exhaled breath samples on 5 consecutive days in 14 subjects with severe asthma who had undergone extensive clinical characterisation. Principal component analysis on VOC abundance across all breath samples revealed no variance due to the day of sampling. Samples from the same patients clustered together and there was some separation according to T2 inflammatory markers. The intra-subject and between-subject variability of each VOC was calculated across the 70 samples and identified 30.35% of VOCs to be erratic: variable between subjects but also variable in the same subject. Exclusion of these erratic VOCs from machine learning approaches revealed no apparent loss of structure to the underlying data or loss of relationship with salient clinical characteristics. Moreover, cluster evaluation by the silhouette coefficient indicates more distinct clustering. We are able to describe the short-term repeatability of breath samples in a severe asthma population and corroborate its sensitivity to airway inflammation. We also describe a novel variance-based feature selection tool that, when applied to larger clinical studies, could improve machine learning model predictions.
VOC, asthma, breathomics, repeatability, respiratory, severe asthma, volatile organic compounds
2075-4426
Azim, Adnan
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Rezwan, Faisal I.
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Barber, Clair
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Harvey, Matthew
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Kurukulaaratchy, Ramesh J.
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Holloway, John W.
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Howarth, Peter H.
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Azim, Adnan
87c31e0e-c9bf-4258-9ae9-889e2382e7ba
Rezwan, Faisal I.
203f8f38-1f5d-485b-ab11-c546b4276338
Barber, Clair
ff31b460-34c3-466c-90e4-f70b3e954c82
Harvey, Matthew
d34fdc10-eecc-45be-aa7b-e2cd70318617
Kurukulaaratchy, Ramesh J.
9c7b8105-2892-49f2-8775-54d4961e3e74
Holloway, John W.
4bbd77e6-c095-445d-a36b-a50a72f6fe1a
Howarth, Peter H.
ff19c8c4-86b0-4a88-8f76-b3d87f142a21

Azim, Adnan, Rezwan, Faisal I., Barber, Clair, Harvey, Matthew, Kurukulaaratchy, Ramesh J., Holloway, John W. and Howarth, Peter H. (2022) Measurement of exhaled volatile organic compounds as a biomarker for personalised medicine: assessment of short-term repeatability in severe asthma. Journal of Personalized Medicine, 12 (10), [1635]. (doi:10.3390/jpm12101635).

Record type: Article

Abstract

The measurement of exhaled volatile organic compounds (VOCs) in exhaled breath (breathomics) represents an exciting biomarker matrix for airways disease, with early research indicating a sensitivity to airway inflammation. One of the key aspects to analytical validity for any clinical biomarker is an understanding of the short-term repeatability of measures. We collected exhaled breath samples on 5 consecutive days in 14 subjects with severe asthma who had undergone extensive clinical characterisation. Principal component analysis on VOC abundance across all breath samples revealed no variance due to the day of sampling. Samples from the same patients clustered together and there was some separation according to T2 inflammatory markers. The intra-subject and between-subject variability of each VOC was calculated across the 70 samples and identified 30.35% of VOCs to be erratic: variable between subjects but also variable in the same subject. Exclusion of these erratic VOCs from machine learning approaches revealed no apparent loss of structure to the underlying data or loss of relationship with salient clinical characteristics. Moreover, cluster evaluation by the silhouette coefficient indicates more distinct clustering. We are able to describe the short-term repeatability of breath samples in a severe asthma population and corroborate its sensitivity to airway inflammation. We also describe a novel variance-based feature selection tool that, when applied to larger clinical studies, could improve machine learning model predictions.

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Accepted/In Press date: 26 September 2022
Published date: 2 October 2022
Additional Information: Funding Information: The WATCH study uses the NIHR Southampton BRC and Clinical Research Facility at UHSFT that is funded by the NIHR. The WATCH study itself is not externally funded. Funding assistance for database support for the WATCH study was initially obtained from a nonpromotional grant from Novartis (GBP 35,000). Funding assistance for patient costs (e.g, parking) was initially provided by a charitable grant (GBP 3500) from the Asthma, Allergy & Inflammation Research (AAIR) Charity. Funding for the Breathomics Analysis was supported by a research grant from GSK. Funding Information: The authors wish to thank the patients who are participating in this study. They also wish to acknowledge the support of the Southampton NIHR Clinical Research Facility and BRC. The Clinical Research Facility and BRC are funded by Southampton NIHR and are a partnership between the University of Southampton and University Hospital Southampton NHS Foundation Trust. The authors also acknowledge funding support from Owlstone Medical and the AAIR Charity. Publisher Copyright: © 2022 by the authors.
Keywords: VOC, asthma, breathomics, repeatability, respiratory, severe asthma, volatile organic compounds

Identifiers

Local EPrints ID: 472170
URI: http://eprints.soton.ac.uk/id/eprint/472170
ISSN: 2075-4426
PURE UUID: d7c0d93a-f895-4875-bb16-e7a664aad4ad
ORCID for Faisal I. Rezwan: ORCID iD orcid.org/0000-0001-9921-222X
ORCID for Clair Barber: ORCID iD orcid.org/0000-0001-5335-5129
ORCID for Ramesh J. Kurukulaaratchy: ORCID iD orcid.org/0000-0002-1588-2400
ORCID for John W. Holloway: ORCID iD orcid.org/0000-0001-9998-0464

Catalogue record

Date deposited: 28 Nov 2022 18:05
Last modified: 29 Nov 2022 02:49

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Contributors

Author: Adnan Azim
Author: Faisal I. Rezwan ORCID iD
Author: Clair Barber ORCID iD
Author: Matthew Harvey

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