Biological clustering supports both "Dutch" and "British" hypotheses of asthma and chronic obstructive pulmonary disease

Abstract

Background: Asthma and chronic obstructive pulmonary disease (COPD) are heterogeneous diseases.

Objective: We sought to determine, in terms of their sputum cellular and mediator profiles, the extent to which they represent distinct or overlapping conditions supporting either the "British" or "Dutch" hypotheses of airway disease pathogenesis.

Methods: We compared the clinical and physiological characteristics and sputum mediators between 86 subjects with severe asthma and 75 with moderate-to-severe COPD. Biological subgroups were determined using factor and cluster analyses on 18 sputum cytokines. The subgroups were validated on independent severe asthma (n = 166) and COPD (n = 58) cohorts. Two techniques were used to assign the validation subjects to subgroups: linear discriminant analysis, or the best identified discriminator (single cytokine) in combination with subject disease status (asthma or COPD).

Results: Discriminant analysis distinguished severe asthma from COPD completely using a combination of clinical and biological variables. Factor and cluster analyses of the sputum cytokine profiles revealed 3 biological clusters: cluster 1: asthma predominant, eosinophilic, high TH2 cytokines; cluster 2: asthma and COPD overlap, neutrophilic; cluster 3: COPD predominant, mixed eosinophilic and neutrophilic. Validation subjects were classified into 3 subgroups using discriminant analysis, or disease status with a binary assessment of sputum IL-1β expression. Sputum cellular and cytokine profiles of the validation subgroups were similar to the subgroups from the test study.

Conclusions: Sputum cytokine profiling can determine distinct and overlapping groups of subjects with asthma and COPD, supporting both the British and Dutch hypotheses. These findings may contribute to improved patient classification to enable stratified medicine.

Keywords: Asthma and COPD overlap; cytokines; factor and cluster analyses.

Fig E1

Sputum cytokines, which discriminated across asthma and COPD. ROC AUC with 95% CI illustrating biomarkers that predict asthma (A) or COPD (B).

Fig E2

Cytokines across bacterial colonization and low-bacterial colonization. ROC AUC with 95% CI illustrating biomarkers that predict bacterial colonization.

Fig E3

Absolute TNF-α concentrations on a log scale (base 10) across the 3 identified biological clusters. A, Asthma; C, COPD. P is the P value for mean comparison between cluster 1 or cluster 3 versus cluster 2 (overlap).

Fig 1

Discriminant function of demographic, clinical, lung function, and sputum cytokines characteristics across asthma and COPD. Hollow triangles indicate asthma and hollow circles indicate COPD.

Fig 2

The 3 identified biological clusters presented using subjects' discriminant scores. Hollow triangles indicate eosinophilic asthma dominant (95% asthma, n = 58); bold triangle and bold circle, neutrophilic asthma and COPD (overlap) dominant (59.6% asthma, n = 47); hollow circle, COPD dominant (95% COPD, n = 41); bold triangle, overlapped asthma; bold circle, overlapped COPD.

Fig 3

Absolute IL-1β concentrations on a log scale (base 10) across the 3 identified biological clusters. A, Asthma; C, COPD. P is the P value for mean comparison between cluster 1 or cluster 3 versus cluster 2 (overlap).

Fig 4

Cytokine profiles in the test and the validation groups using linear discriminant analysis or IL-1β cutoff and disease for cluster 1 (A), cluster 2 (B), and cluster 3 (C). Circles indicate test study, triangles indicate validation using linear discriminant analysis, and rectangles indicate validation using IL-1β cutoff at 130 pg/mL and disease status (asthma or COPD). The y-axes depict the mean z value (standardized) of each cytokine in each test and validation subgroup.