Asthma and chronic obstructive pulmonary disease (COPD) are complex and overlapping diseases that include inflammatory phenotypes. Novel anti-eosinophilic/anti-neutrophilic strategies demand rapid inflammatory phenotyping, which might be accessible from exhaled breath. Our objective was to capture clinical/inflammatory phenotypes in patients with chronic airway disease using an electronic nose (eNose) in a training and validation set. This was a multicentre cross-sectional study in which exhaled breath from asthma and COPD patients (n=435; training n=321 and validation n=114) was analysed using eNose technology. Data analysis involved signal processing and statistics based on principal component analysis followed by unsupervised cluster analysis and supervised linear regression. Clustering based on eNose resulted in five significant combined asthma and COPD clusters that differed regarding ethnicity (p=0.01), systemic eosinophilia (p=0.02) and neutrophilia (p=0.03), body mass index (p=0.04), exhaled nitric oxide fraction (p<0.01), atopy (p<0.01) and exacerbation rate (p<0.01). Significant regression models were found for the prediction of eosinophilic (R2=0.581) and neutrophilic (R2=0.409) blood counts based on eNose. Similar clusters and regression results were obtained in the validation set. Phenotyping a combined sample of asthma and COPD patients using eNose provides validated clusters that are not determined by diagnosis, but rather by clinical/inflammatory characteristics. eNose identified systemic neutrophilia and/or eosinophilia in a dose-dependent manner. Breathomics may qualify for rapid clinical/inflammatory phenotyping of chronic airway disease at the point of care http://ow.ly/E16p30gE1Cl

中文翻译：

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无论诊断标签如何，呼吸组学对慢性气道疾病的临床和炎症表型分析

哮喘和慢性阻塞性肺疾病 (COPD) 是复杂且重叠的疾病，包括炎症表型。新型抗嗜酸性粒细胞/抗中性粒细胞策略需要快速的炎症表型分析，这可能可以从呼气中获得。我们的目标是在训练和验证集中使用电子鼻 (eNose) 捕获慢性气道疾病患者的临床/炎症表型。这是一项多中心横断面研究，其中使用 eNose 技术分析了哮喘和 COPD 患者（n=435；培训 n=321 和验证 n=114）的呼出气。数据分析涉及基于主成分分析的信号处理和统计，然后是无监督聚类分析和监督线性回归。基于 eNose 的聚类导致五个显着的哮喘和 COPD 组合聚类，它们在种族 (p=0.01)、全身性嗜酸性粒细胞增多 (p=0.02) 和中性粒细胞增多 (p=0.03)、体重指数 (p=0.04)、呼出的一氧化氮方面存在差异分数 (p<0.01)、特应性 (p<0.01) 和恶化率 (p<0.01)。发现了基于 eNose 预测嗜酸性粒细胞 (R2=0.581) 和中性粒细胞 (R2=0.409) 血细胞计数的显着回归模型。在验证集中获得了类似的聚类和回归结果。使用 eNose 对哮喘和 COPD 患者的组合样本进行表型分型提供了经过验证的集群，这些集群不是由诊断决定的，而是由临床/炎症特征决定的。eNose 以剂量依赖性方式识别全身性中性粒细胞增多症和/或嗜酸性粒细胞增多症。