Background Breath volatile organic compounds (VOCs) may be useful for asthma diagnosis and phenotyping, identifying patients who could benefit from personalised therapeutic strategies. The authors aimed to identify specific patterns of breath VOCs in patients with asthma and in clinically relevant disease phenotypes. Methods Breath samples were analysed by gas chromatographyemass spectrometry. The Asthma Control Questionnaire was completed, together with lung function and induced sputum cell counts. Breath data were reduced to principal components, and these principal components were used in multiple logistic regression to identify discriminatory models for diagnosis, sputum inflammatory cell profile and asthma control. Results The authors recruited 35 patients with asthma and 23 matched controls. A model derived from 15 VOCs classified patients with asthma with an accuracy of 86%, and positive and negative predictive values of 0.85 and 0.89, respectively. Models also classified patients with asthma based on the following phenotypes: sputum (obtained in 18 patients with asthma) eosinophilia $2% area under the receiver operating characteristics (AUROC) curve 0.98, neutrophilia $40% AUROC 0.90 and uncontrolled asthma (Asthma Control Questionnaire $1) AUROC 0.96. Conclusions Detection of characteristic breath VOC profiles could classify patients with asthma versus controls, and clinically relevant disease phenotypes based on sputum inflammatory profile and asthma control. Prospective validation of these models may lead to clinical application of non-invasive breath profiling in asthma.

The rapid, accurate and non-invasive diagnosis of respiratory disease represents a challenge to clinicians, and the development of new treatments can be confounded by insufficient knowledge of lung disease phenotypes. Exhaled breath contains a complex mixture of volatile organic compounds (VOCs), some of which could potentially represent biomarkers for lung diseases. We have developed an adaptive sampling methodology for collecting concentrated samples of exhaled air from participants with impaired respiratory function, against which we employed two-stage thermal desorption gas chromatography-differential mobility spectrometry (GC-DMS) analysis, and showed that it was possible to discriminate between participants with and without chronic obstructive pulmonary disease (COPD). A 2.5 dm(3) volume of end tidal breath was collected onto adsorbent traps (Tenax TA/Carbotrap), from participants with severe COPD and healthy volunteers. Samples were thermally desorbed and analysed by GC-DMS, and the chromatograms analysed by univariate and multivariate analyses. Kruskal-Wallis ANOVA indicated several discriminatory (p < 0.01) signals, with good classification performance (receiver operator characteristic area up to 0.82). Partial least squares discriminant analysis using the full DMS chromatograms also gave excellent discrimination between groups (alpha = 19% and beta = 12.4%).