Background: Exhaled breath contains thousands of volatile organic compounds (VOCs) that could serve as biomarkers of lung disease. Electronic noses can distinguish VOC mixtures by pattern recognition.
Objective: We hypothesized that an electronic nose can discriminate exhaled air of patients with asthma from healthy controls, and between patients with different disease severities.
Methods: Ten young patients with mild asthma (25.1 +/- 5.9 years; FEV(1), 99.9 +/- 7.7% predicted), 10 young controls (26.8 +/- 6.4 years; FEV(1), 101.9 +/- 10.3), 10 older patients with severe asthma (49.5 +/- 12.0 years; FEV(1), 62.3 +/- 23.6), and 10 older controls (57.3 +/- 7.1 years; FEV(1), 108.3 +/- 14.7) joined a cross-sectional study with duplicate sampling of exhaled breath with an interval of 2 to 5 minutes. Subjects inspired VOC-filtered air by tidal breathing for 5 minutes, and a single expiratory vital capacity was collected into a Tedlar bag that was sampled by electronic nose (Cyranose 320) within 10 minutes. Smellprints were analyzed by linear discriminant analysis on principal component reduction. Cross-validation values (CVVs) were calculated.
Results: Smellprints of patients with mild asthma were fully separated from young controls (CVV, 100%; Mahalanobis distance [M-distance], 5.32), and patients with severe asthma could be distinguished from old controls (CVV, 90%; M-distance, 2.77). Patients with mild and severe asthma could be less well discriminated (CVV, 65%; M-distance, 1.23), whereas the 2 control groups were indistinguishable (CVV, 50%; M-distance, 1.56). The duplicate samples replicated these results.
Conclusion: An electronic nose can discriminate exhaled breath of patients with asthma from controls but is less accurate in distinguishing asthma severities.
Clinical implication: These findings warrant validation of electronic noses in diagnosing newly presented patients with asthma.