Background: Because ACO (Asthma-COPD-Overlap) does not fill out asthma or COPD (Chronic Obstructive Pulmonary Disease) criteria, such patients are poorly evaluated. The aim of this study was to screen asthma and COPD for an alternative diagnosis of ACO, then to determine subgroups of patients, using cluster analysis.
Material and methods: Using GINA-GOLD stepwise approach, asthmatics and COPD were screened for ACO. Clusterization was then performed employing Multiple Correspondent Analysis (MCA) model, encompassing 9 variables (age, symptoms onset, sex, BMI (Body Mass Index), smoking, FEV-1, dyspnea, exacerbation, comorbidity). Finally, clusters were compared to determine phenotypes.
Results: MCA analysis was performed on 172 ACO subjects. To better distinguish clusters, the analysis was then focused on 55 subjects, having at least one cosine squared >0.3. Six clusters were identified, allowing the description of 4 phenotypes. Phenotype A represented overweighed heavy smokers, with an early onset and a severe disease (27% of ACO patients). Phenotype B gathered similar patients, with a late onset (29%). Patients from Phenotypes C-D were slighter smokers, presenting a moderate disease, with early and late onset respectively (respectively 13% and 31%).
Conclusions: By providing evidences for clusters within ACO, our study confirms its heterogeneity, allowing the identification of 4 phenotypes. Further prospective studies are mandatory to confirm these data, to determine both specific management requirements and prognostic value.
Keywords: ACO; Asthma; COPD; Cluster; Heterogeneity; MCA; Phenotype.