Edited by: Michael Wechsler
Breath metabolomic profiling by nuclear magnetic resonance spectroscopy in asthma
Article first published online: 29 JUL 2013
© 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
Volume 68, Issue 8, pages 1050–1056, August 2013
How to Cite
Breath metabolomic profiling by nuclear magnetic resonance spectroscopy in asthma. Allergy 2013; 68: 1050–1056., , , , , .
- Issue published online: 23 AUG 2013
- Article first published online: 29 JUL 2013
- Manuscript Accepted: 21 MAY 2013
- breath analysis;
- exhaled airway markers
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.
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.
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.
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.