Cross-sectional biomarker comparisons with a longitudinal perspective in asthma monitoring: the eNose premise

Background: Cross-sectional and longitudinal studies capture different aspects of physiological signals. Objective: To investigate whether single snapshots of exhaled breathprints from eNose can discriminate asthmatics from healthy controls and capture differences in breath profiles triggered by a viral challenge. Methods: The study followed-up 12 asthmatic and 12 healthy controls thrice weekly for 2 months before and 1 month after a rhinovirus challenge. The eNose ability to classify asthmatics from controls at each visit and between pre and post-viral states, was estimated by area under receiver operating characteristic curves (AUCs-ROC) for eNose sensors. The influence of the viral challenge on longitudinal variations of eNose signals was estimated by a mixed effects model (MEM). Results: The eNose was able to provide 100% discrimination between asthmatics and controls at each study visit, irrespective of the viral-challenge (Fig A). However, more changes in VOCs were found in asthmatics than in controls as indicated from the MEM. Discrimination of pre and post-viral phases was highest at visits 1, 3 and 7 in asthmatics vs only visit 7 in controls (Fig B & C). Conclusion: Cross-sectional study designs may uncover biomarkers distinguishing disease from health, but longitudinal designs are essential to detect influences by external triggers. In both cases, eNose may serve as a non-invasive asthma monitoring tool.