Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Breathomix BV, Leiden, The Netherlands.
Allergy. 2021 Aug;76(8):2488-2499. doi: 10.1111/all.14811. Epub 2021 Mar 27.
Early detection/prediction of flare-ups in asthma, commonly triggered by viruses, would enable timely treatment. Previous studies on exhaled breath analysis by electronic nose (eNose) technology could discriminate between stable and unstable episodes of asthma, using single/few time-points. To investigate its monitoring properties during these episodes, we examined day-to-day fluctuations in exhaled breath profiles, before and after a rhinovirus-16 (RV16) challenge, in healthy and asthmatic adults.
In this proof-of-concept study, 12 atopic asthmatic and 12 non-atopic healthy adults were prospectively followed thrice weekly, 60 days before, and 30 days after a RV16 challenge. Exhaled breath profiles were detected using an eNose, consisting of 7 different sensors. Per sensor, individual means were calculated using pre-challenge visits. Absolute deviations (|%|) from this baseline were derived for all visits. Within-group comparisons were tested with Mann-Whitney U tests and receiver operating characteristic (ROC) analysis. Finally, Spearman's correlations between the total change in eNose deviations and fractional exhaled nitric oxide (FeNO), cold-like symptoms, and pro-inflammatory cytokines were examined.
Both groups had significantly increased eNose fluctuations post-challenge, which in asthma started 1 day post-challenge, before the onset of symptoms. Discrimination between pre- and post-challenge reached an area under the ROC curve of 0.82 (95% CI = 0.65-0.99) in healthy and 0.97 (95% CI = 0.91-1.00) in asthmatic adults. The total change in eNose deviations moderately correlated with IL-8 and TNFα (ρ ≈ .50-0.60) in asthmatics.
Electronic nose fluctuations rapidly increase after a RV16 challenge, with distinct differences between healthy and asthmatic adults, suggesting that this technology could be useful in monitoring virus-driven unstable episodes in asthma.
哮喘的发作(通常由病毒引发)若能及早发现/预测,即可及时治疗。先前应用电子鼻(eNose)技术对呼气分析的研究,曾基于单次/少数时间点,成功区分哮喘的稳定期和不稳定期。为了研究其在这些发作期间的监测特性,我们在健康成人和哮喘成人中,检测了鼻病毒 16(RV16)挑战前后,每日呼气谱的波动情况。
在这项概念验证研究中,12 名特应性哮喘患者和 12 名非特应性健康成人前瞻性地每周随访 3 次,共随访 60 天,包括 RV16 挑战前 30 天和后 30 天。应用由 7 种不同传感器组成的 eNose 检测呼气谱。对于每个传感器,使用挑战前的就诊计算个体平均值。对所有就诊计算与基线的绝对偏差(|%|)。应用曼-惠特尼 U 检验和受试者工作特征(ROC)分析进行组内比较。最后,还检验了 eNose 偏差总变化与呼出气一氧化氮(FeNO)、类似感冒的症状和促炎细胞因子之间的 Spearman 相关性。
两组在挑战后呼气谱波动均显著增加,在哮喘患者中,该波动在症状出现前 1 天的挑战后即刻开始。健康成人和哮喘成人的 ROC 曲线下面积分别为 0.82(95%CI=0.65-0.99)和 0.97(95%CI=0.91-1.00),达到了区分挑战前后的效果。哮喘患者的 eNose 偏差总变化与白细胞介素 8 和肿瘤坏死因子α中度相关(ρ≈0.50-0.60)。
RV16 挑战后,eNose 波动迅速增加,健康成人和哮喘成人之间存在明显差异,提示该技术可能有助于监测哮喘中病毒驱动的不稳定期。