Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome with multiple clinical and inflammatory phenotypes. The coexistence of bronchiectasis, known as bronchiectasis–COPD overlap (BCO), identifies a subgroup with increased morbidity and mortality. Non-invasive breath analysis using electronic noses (e-noses) has shown promise in identifying disease-specific volatile organic compound (VOC) patterns (“breathprints”). Our aim was to evaluate the ability of an e-nose to differentiate between COPD and BCO patients, and to assess its utility in detecting inflammatory endotypes (neutrophilic vs. eosinophilic). In a monocentric, prospective, real-life study, 98 patients were enrolled over nine months. Forty-two patients had radiologically confirmed BCO, while fifty-six had COPD without bronchiectasis. Exhaled breath samples were analyzed using the Cyranose 320 e-nose. Principal component analysis (PCA) and discriminant analysis were used to identify group-specific breathprints and inflammatory profiles. PCA revealed significant breathprint differences between BCO and COPD (p = 0.021). Discriminant analysis yielded an overall accuracy of 69.6% (AUC 0.768, p = 0.037). The highest classification performance (76.8%) was achieved when distinguishing eosinophilic COPD from neutrophilic BCO. These findings suggest distinct inflammatory profiles that may be captured non-invasively. E-nose technology holds potential for the non-invasive endotyping of COPD, especially in identifying neutrophilic BCO as a unique inflammatory entity. Breathomics may support early, personalized treatment strategies.

Background/Objective: The Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommends using the COPD Assessment Test (CAT) and the modified Medical Research Council (mMRC) scale for symptom assessment. However, discordant results between these tools are common and may affect clinical phenotyping and treatment decisions. This study aims to identify clinical, functional, and radiological characteristics associated with discordant CAT and mMRC scores in COPD and assess the stability of this classification over time. Methods: We retrospectively analyzed 222 COPD patients classified into GOLD A, B, E, or a newly defined discordant group (GOLD_D), characterized by CAT ≥ 10 with mMRC < 2 or CAT < 10 with mMRC ≥ 2. Clinical, functional, laboratory, and imaging data were collected. Logistic regression identified predictors of GOLD classification. GOLD group changes were assessed at follow-up. Results: At baseline, 12.8% of patients belonged to the GOLD_D group. Compared to GOLD A and B, these patients had lower occupational exposure and higher rates of chest HRCT findings such as consolidations and centrilobular nodules. Regression models confirmed these features and identified FEV1 and FVC as independent predictors. GOLD classification showed notable variability during follow-up. Conclusions: Patients with discordant CAT-mMRC scores display distinct clinical and radiologic traits not captured by standard GOLD categories. These results underscore the limitations of relying solely on symptom scores and support a more comprehensive, trait-based approach to COPD assessment and management.

Introduction: The inflammatory pattern in COPD can vary, and in some patients, eosinophils may cause inflammation, that promotes airway obstruction. GOLD guidelines recommend adding inhaled corticosteroids (ICS) to long-acting bronchodilators only in patients with at least one moderate exacerbation per year and peripheral blood eosinophils ≥100 cells/uL. However, the ICS response could be uneven in these patients, suggesting the presence of other features that could impact the response to treatment. Aims and Objectives: To identify different clusters of patients with eosinophilic COPD. Methods: We enrolled 173 patients with a diagnosis of COPD and peripheral blood eosinophils ≥100 cells/uL. We performed a cluster analysis, considering several anamnestic, clinical, functional and radiological features. Results: The analysis showed the presence of two clusters. Patients belonging to the first one were younger (p= 0.02), had a less frequent familiarity for COPD (p = 0.01) and for work exposure to toxic agents (p = 0.04), had fewer comorbidities (p = 0.01) and fewer COPD-related symptoms (p < 0.0001). The second cluster of patients, on the other hand, showed more severe symptoms and exacerbations despite a lower blood eosinophilic count (p <0.0001). Conclusions: Patients with a higher blood eosinophilic count tend to show a milder COPD phenotype, while those with a lower eosinophilic trait are more prone to develop exacerbations and COPD-related symptoms. Finding additional biomarkers is necessary to pinpoint more specific treatable traits and to develop more individualized therapies

Background and Objectives: COPD is a heterogeneous disease in which conventional clinical classifications may not fully capture the complexity of patient profiles. This exploratory study aimed to examine whether multidimensional COPD phenotypes could be identified using unsupervised cluster analysis integrating clinical, functional, radiological and laboratory features. Materials and Methods: We enrolled 161 patients with confirmed COPD evaluated between January 2020 and January 2024. Demographic, clinical, functional, radiological and laboratory findings were collected. Mixed-type data were analyzed using Gower distance and Partitioning Around Medoids (PAM) clustering. The optimal solution was selected by average silhouette width; stability was assessed by 1000 bootstrap resamples and sensitivity analyses. Results: The two-cluster solution had the highest silhouette width (0.184), although separation was modest. Cluster 1 (n = 78) was characterized by greater symptom and exacerbation burden, worse lung function, greater static hyperinflation, shorter 6-min walking distance and more frequent emphysema than cluster 2 (n = 83). Bootstrap resampling indicated internal stability, although concordance with the primary partition varied across sensitivity analyses. After correction for multiple post hoc comparisons, only LAMA/LABA/ICS use differed between clusters, whereas demographic characteristics, comorbidity burden and blood eosinophil levels were comparable. Conclusions: These exploratory findings suggest multidimensional assessment may complement conventional classifications, but external and longitudinal validation is needed before clinical implementation.