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EvA (Emphysema versus Airway disease) is a multicentre project to study mechanisms and identify biomarkers of emphysema and airway disease in chronic obstructive pulmonary disease (COPD). The objective of this study was to delineate objectively imaging-based emphysema-dominant and airway disease-dominant phenotypes using quantitative computed tomography (QCT) indices, standardised with a novel phantom-based approach.441 subjects with COPD (Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages 1–3) were assessed in terms of clinical and physiological measurements, laboratory testing and standardised QCT indices of emphysema and airway wall geometry.QCT indices were influenced by scanner non-conformity, but standardisation significantly reduced variability (p<0.001) and led to more robust phenotypes. Four imaging-derived phenotypes were identified, reflecting “emphysema-dominant”, “airway disease-dominant”, “mixed” disease and “mild” disease. The emphysema-dominant group had significantly higher lung volumes, lower gas transfer coefficient, lower oxygen (PO2) and carbon dioxide (PCO2) tensions, higher haemoglobin and higher blood leukocyte numbers than the airway disease-dominant group.The utility of QCT for phenotyping in the setting of an international multicentre study is improved by standardisation. QCT indices of emphysema and airway disease can delineate within a population of patients with COPD, phenotypic groups that have typical clinical features known to be associated with emphysema-dominant and airway-dominant disease.

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The EvA study is a European Union-funded project under the Seventh Framework Programme (FP7), which aims at defining new markers for chronic obstructive pulmonary disease (COPD) and its subtypes. The acronym is derived from emphysema versus airway disease, indicating that the project targets these two main phenotypes of the disease. The EvA study is based on the concept that emphysema and airway disease are governed by different pathophysiological processes, are driven by different genes and have differential gene expression in the lung. To define these genes, patients and non-COPD controls are recruited for clinical examination, lung function analysis and computed tomography (CT) of the lung. CT scans are used to define the phenotypes based on lung density and airway wall thickness. This is followed by bronchoscopy in order to obtain samples from the airways and the alveoli. These tissue samples, along with blood samples, are then subjected to genome-wide expression and association analysis and markers linked to the phenotypes are identified. The population of the EvA study is different from other COPD study populations, since patients with current oral glucocorticoids, antibiotics and exacerbations or current smokers are excluded, such that the signals detected in the molecular analysis are due to the distinct inflammatory process of emphysema and airway disease in COPD.

Background Whether the clinical or pathophysiologic significance of the “treatable trait” high blood eosinophil count in COPD is the same as for asthma remains controversial. We sought to determine the relationship between the blood eosinophil count, clinical characteristics and gene expression from bronchial brushings in COPD and asthma. Methods Subjects were recruited into a COPD (emphysema versus airway disease [EvA]) or asthma cohort (Unbiased BIOmarkers in PREDiction of respiratory disease outcomes, U‐BIOPRED). We determined gene expression using RNAseq in EvA (n = 283) and Affymetrix microarrays in U‐BIOPRED (n = 85). We ran linear regression analysis of the bronchial brushings transcriptional signal versus blood eosinophil counts as well as differential expression using a blood eosinophil > 200 cells/μL as a cut‐off. The false discovery rate was controlled at 1% (with continuous values) and 5% (with dichotomized values). Results There were no differences in age, gender, lung function, exercise capacity and quantitative computed tomography between eosinophilic versus noneosinophilic COPD cases. Total serum IgE was increased in eosinophilic asthma and COPD. In EvA, there were 12 genes with a statistically significant positive association with the linear blood eosinophil count, whereas in U‐BIOPRED, 1197 genes showed significant associations (266 positive and 931 negative). The transcriptome showed little overlap between genes and pathways associated with blood eosinophil counts in asthma versus COPD. Only CST1 was common to eosinophilic asthma and COPD and was replicated in independent cohorts. Conclusion Despite shared “treatable traits” between asthma and COPD, the molecular mechanisms underlying these clinical entities are predominately different.