A Cohort for Inflammatory Respiratory Diseases: From Phenotyping to Personalised Medicine

The cohort for inflammatory respiratory diseases: from phenotyping to personalised medicine (The PALMIRE project) is a monocentric study conducted at the University Hospital of Reims, France. Study Population : Adult patients (\>18 year-old) followed at the University Hospital of Reims and diagnosed with asthma, COPD, bronchiectasis, CF, PCD, and IPF will be considered for inclusion. Patients will sign an informed consent for inclusion. Exclusion criteria include "subjects protected by the law" as required by the French authorities. Control patients with no respiratory diseases after clinical and pulmonary function tests assessment will also be included. The expected number of patients included is 470 (Asthma, n=100; COPD, n=150; bronchiectasis, n=50; CF, n=60; PCD, n=30; ILD, n=30; controls, n=50). Inclusion will be conducted for 60 months from July 2025 to July 2030. Study Procedures: For all asthma, COPD, bronchiectasis, CF, PCD, and IPF patients included, data will be registered at inclusion, and at follow-up visits for 10 years. Patients will be followed-up as usual care with no specific therapeutic intervention. For control patients, data will be registered at inclusion with no follow-up. The recorded data will include demographics, history of respiratory disease and comorbidities, respiratory symptoms, results of lung function tests and CT-scan, microbiological and pathological features of respiratory sampling when performed. Data Analysis: Data will be registered in a centralized anonymized database. The characteristics of the patients will be described as mean and standard deviation for quantitative data and as number and percentages for qualitative data. Comparisons and associations between groups and variables will be analyzed by Student, Wilcoxon, Chi2, Fischer exact, and Spearman tests as applicable. A p\<0.05 will be considered as significant. Multivariate and longitudinal statistical models will be used to identify clusters of patients with shared endotypes. Machine learning approaches will be employed to integrate multi-omic data and generate predictive models for disease trajectories and treatment responses. Significance: This study should help better understand the pathogenesis and heterogeneity of chronic respiratory diseases by integrating the analysis of phenotypic and endotypic characteristics of patients.