Defining adult asthma endotypes by clinical features and patterns of volatile organic compounds in exhaled air.

BACKGROUND: Several classifications of adult asthma patients using cluster analyses based on clinical and demographic information has resulted in clinical phenotypic clusters that do not address molecular mechanisms. Volatile organic compounds (VOC) in exhaled air are released during inflammation in response to oxidative stress as a result of activated leukocytes. VOC profiles in exhaled air could distinguish between asthma patients and healthy subjects. In this study, we aimed to classify new asthma endotypes by combining inflammatory mechanisms investigated by VOC profiles in exhaled air and clinical information of asthma patients. METHODS: Breath samples were analyzed for VOC profiles by gas chromatography-mass spectrometry from asthma patients (n = 195) and healthy controls (n = 40). A total of 945 determined compounds were subjected to discriminant analysis to find those that could discriminate healthy from asthmatic subjects. 2-step cluster analysis based on clinical information and VOCs in exhaled air were used to form asthma endotypes. RESULTS: We identified 16 VOCs, which could distinguish between healthy and asthma subjects with a sensitivity of 100% and a specificity of 91.1%. Cluster analysis based on VOCs in exhaled air and the clinical parameters FEV1, FEV1 change after 3 weeks of hospitalization, allergic sensitization, Junipers symptoms score and asthma medications resulted in the formation of 7 different asthma endotype clusters. We identified asthma clusters with different VOC profiles but similar clinical characteristics and endotypes with similar VOC profiles, but distinct clinical characteristics. CONCLUSION: This study demonstrates that both, clinical presentation of asthma and inflammatory mechanisms in the airways should be considered for classification of asthma subtypes.

Differential serum protein markers and the clinical severity of asthma.

BACKGROUND: Asthma is a heterogeneous disease characterized by different clinical phenotypes and the involvement of multiple inflammatory pathways. During airway inflammation, many cytokines and chemokines are released and some are detectable in the sera. OBJECTIVE: Serum chemokines and cytokines, involved in airway inflammation in asthma patients, were investigated. METHODS: A total of 191 asthma patients were classified by hierarchical cluster analysis, including the following parameters: forced expiratory volume in 1 second (FEV1), eosinophil cationic protein (ECP) serum levels, blood eosinophils, Junipers asthma symptom score, and the change in FEV1, ECP serum levels, and blood eosinophils after 3 weeks of asthma therapy. Serum proteins were measured by multiplex analysis. Receiver operating characteristic (ROC) curves were used to evaluate the validity of serum proteins for discriminating between asthma clusters. RESULTS: Classification of asthma patients identified one cluster with high ECP serum levels, increased blood eosinophils, low FEV1 values, and good FEV1 improvement in response to asthma therapy (n=60) and one cluster with low ECP serum levels, low numbers of blood eosinophils, higher FEV1 values, and no FEV1 improvement in response to asthma therapy (n=131). Serum interleukin (IL)-8, eotaxin, vascular endothelial growth factor (VEGF), cutaneous T-cell-attracting chemokine (CTACK), growth-related oncogene (GRO)-α, and hepatocyte growth factor (HGF) were significantly different between the two clusters of asthma patients. ROC analysis for serum proteins calculated a sensitivity of 55.9% and specificity of 75.8% for discriminating between them. CONCLUSION: Serum cytokine and chemokine levels might be predictors for the severity of asthmatic inflammation, asthma control, and response to therapy, and therefore might be useful for treatment optimization.