Breath metabolomic profiling by nuclear magnetic resonance spectroscopy in asthma

Authors

  • B. Ibrahim,

    1. The University of Manchester, Manchester Academic Health Science Centre, NIHR South Manchester Respiratory and Allergy Clinical Research Facility, University Hospital of South Manchester, Manchester, UK
    2. School of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, Malaysia
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  • P. Marsden,

    1. The University of Manchester, Manchester Academic Health Science Centre, NIHR South Manchester Respiratory and Allergy Clinical Research Facility, University Hospital of South Manchester, Manchester, UK
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  • J. A. Smith,

    1. The University of Manchester, Manchester Academic Health Science Centre, NIHR South Manchester Respiratory and Allergy Clinical Research Facility, University Hospital of South Manchester, Manchester, UK
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  • A. Custovic,

    1. The University of Manchester, Manchester Academic Health Science Centre, NIHR South Manchester Respiratory and Allergy Clinical Research Facility, University Hospital of South Manchester, Manchester, UK
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  • M. Nilsson,

    1. School of Chemistry, University of Manchester, Manchester, UK
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  • S. J. Fowler

    Corresponding author
    1. Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK
    • The University of Manchester, Manchester Academic Health Science Centre, NIHR South Manchester Respiratory and Allergy Clinical Research Facility, University Hospital of South Manchester, Manchester, UK
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  • Edited by: Michael Wechsler

Correspondence

Stephen J. Fowler, Education and Research Centre, University Hospital of South Manchester, Southmoor Road, Manchester M23 9LT, UK.

Tel.: +44 161 291 5864 or +44 1772 523237

Fax: +44 161 291 5730

E-mail: stephen.fowler@manchester.ac.uk

Abstract

Background

Metabolomic profiling of exhaled breath condensate offers opportunities for the development of noninvasive diagnostics in asthma. We aimed to determine and validate discriminatory metabolomic profiles in adult asthma and to explore profiles in clinically relevant disease phenotypes.

Methods

Nuclear magnetic resonance spectroscopy was used to analyse breath condensate samples from 82 subjects with asthma and 35 healthy volunteers. Multivariate modelling was performed on a ‘training set’ (70% of the total sample) in order to produce a discriminatory model classifying asthmatics from healthy controls, and the model tested in the remaining subjects. Secondary analyses were performed to determine the models for the identification of asthmatic subgroups based on sputum eosinophilia, neutrophilia, asthma control and inhaled corticosteroid use.

Results

A classification model consisting of five discriminating spectral regions was derived using data from the training set with an area under the receiver operating curve (AUROC) of 0.84. In the test set (the remaining 30% of subjects), the AUROC was 0.91, thus providing external validation for the model. The success of the technique for classifying asthma phenotypes was variable, with AUROC for: sputum eosinophilia (3% cut-off) 0.69; neutrophilia (65% cut-off) 0.88; asthma control (cut-off Asthma Control Questionnaire score of 1) 0.63; and inhaled corticosteroid use 0.89.

Conclusion

Nuclear magnetic resonance spectroscopy of breath condensate successfully differentiates asthmatics from healthy subjects. With identification of the discriminatory compounds, this technique has the potential to provide novel diagnostics and identify novel pathophysiological mechanisms, biomarkers and therapeutic targets.

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