Immunoglobulin E as a Biomarker for the Overlap of Atopic Asthma and Chronic Obstructive Pulmonary Disease

Asthma-COPD overlap (ACO) is a common clinical syndrome, yet there is no single objective definition. We hypothesized that immunoglobulin E (IgE) measurements could be used to refine the definition of ACO. In baseline plasma samples from 2870 participants in the COPD Genetic Epidemiology (COPDGene®) study, we measured total IgE levels and specific IgE levels to 6 common allergens. Compared to usual chronic obstructive pulmonary disease (COPD), participants with ACO (based on self-report of asthma) had higher total IgE levels (median 67.0 versus 42.2 IU/ml) and more frequently had at least one positive specific IgE (43.5% versus 24.5%). We previously used a strict definition of ACO in participants with COPD, based on self-report of a doctor's diagnosis of asthma before age 40. This strict ACO definition was refined by the presence of atopy, determined by total IgE > 100 IU/ml or at least one positive specific IgE, as was the broader definition of ACO based on self-reported asthma history. Participants with all 3 ACO definitions were younger (mean age 60.0-61.3 years), were more commonly African American (36.8%-44.2%), had a higher exacerbation frequency (1.0-1.2 in the past year), and had more airway wall thickening on quantitative analysis of chest computed tomography (CT) scans. Among participants with ACO, 37%-46% did not have atopy; these individuals had more emphysema on chest CT scan. Based on associations with exacerbations and CT airway disease, IgE did not clearly improve the clinical definition of ACO. However, IgE measurements could be used to subdivide individuals with atopic and non-atopic ACO, who might have different biologic mechanisms and potential treatments.

Subtyping Chronic Obstructive Pulmonary Disease Using Peripheral Blood Proteomics

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disorder. COPD patients may have different clinical features, imaging characteristics and natural history. Multiple studies have investigated heterogeneity using statistical methods such as unsupervised clustering to define different subgroups of COPD based largely on clinical phenotypes. Some studies have performed clustering using genetic data or limited numbers of blood biomarkers. Our primary goal was to use proteomic data to find subtypes of COPD within clinically similar individuals. In the Treatment of Emphysema with a gamma-Selective Retinoid Agonist (TESRA) study, multiplex biomarker panels were run in serum samples collected prior to randomization. After implementing an algorithm to minimize missing values, the dataset included 396 COPD individuals and 87 biomarkers. Using hierarchical clustering, we identified 3 COPD subgroups, containing 267 (67.4%), 104 (26.3%), and 25 (6.3%) individuals, respectively. The third cluster had less emphysema on quantitative analysis of chest computed tomography scans (p=0.03) and worse disease-related quality of life based on the St. George's Respiratory Questionnaire (total score cluster 1: 45.6, cluster 2: 45.4, cluster 3: 56.6; p=0.01), despite similar levels of lung function impairment (forced expiratory volume in 1 second (49.2%, 49.2%, 48.2 % predicted, respectively). Enrichment analysis showed the biomarkers distinguishing cluster 3 mapped to platelet alpha granule and cell chemotaxis pathways. Thus, we identified a subgroup which has less emphysema but may have greater inflammation, which could be potentially targeted with anti-inflammatory therapies.