Uncovering cystic fibrosis patient profiles and exposome associations through unsupervised multidimensional phenotyping

Introduction Cystic fibrosis (CF) is a genetic disorder that affects the respiratory and digestive systems. CF patients exhibit considerable variation in their symptoms and disease progression, suggesting complex genotype–phenotype relationships that may involve environmental factors. This study aimed to use unsupervised clustering analyses to identify distinct profiles and trajectories of CF patients, while also assessing their associations with various environmental factors. Methods Data from the French CF Registry, which covers 90% of CF patients in France and provides comprehensive health information for monitoring and research purposes, were utilized. By employing dimensionality reduction and clustering techniques, such as self-organizing maps (SOMs), reverse graph embedding (DDRTree algorithm, ClinTrajAn), and trajectory analyses (latent class analysis) based on longitudinal lung function tests, patients were grouped based on their clinical characteristics. Results Preliminary findings revealed the existence of different subgroups among CF children and adult patients, characterized by significant differences in overall health status, decline in lung function, comorbidities, incidence of infections, and exposure to environmental factors like passive smoking. Additionally, the study investigates the connections between CF profiles and air pollution at the geographic level of French departments. Conclusion Applying clustering techniques to large medical datasets reveals valuable insights into the impact of the environment on the physiological and pathological processes of CF. By uncovering distinct patient profiles, this approach can optimize treatment strategies and improve patient outcomes.