Hayoung Choi1,2, Soorack Ryu3, Holly R Keir1, Yan Hui Giam1, Alison J Dicker1, Lidia Perea1, Hollian Richardson1, Jeffrey T J Huang4, Erin Cant1, Francesco Blasi5,6, Jennifer Pollock1, Michal Shteinberg7, Simon Finch1, Stefano Aliberti8,9, Oriol Sibila10, Amelia Shoemark1, James D Chalmers1
1Division of Molecular and Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, United Kingdom
2Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Hallym University Kangnam Sacred Heart Hospital, Seoul, Republic of Korea
3Biostatistical Consulting and Research Lab, Medical Research Collaborating Center, Hanyang University, Seoul, Republic of Korea
4Division of Systems Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, United Kingdom
5Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
6Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
7Pulmonology Institute and CF Center, Carmel Medical Center and the Technion-Israel Institute of Technology, Haifa, Israel
8Department of Biomedical Sciences, Humanitas University, Milan, Italy
9Respiratory Unit, IRCCS Humanitas Research Hospital, Milan, Italy
10Respiratory Department, Hospital Clínic, IDIBAPS, CIBERES, Universitat de Barcelona, Barcelona, Spain
Correspondence to: James D. Chalmers, MBChB, PhD
Rationale: Although inflammation and infection are key disease drivers in bronchiectasis, few studies have integrated host inflammatory and microbiome data to guide precision medicine.
Objectives: To identify clusters among bronchiectasis patients based on inflammatory markers and assess the association between inflammatory endotypes, microbiome characteristics, and exacerbation risk.
Methods: Stable bronchiectasis patients were enrolled at three European centers and cluster analysis was used to stratify the patients according to the levels of 33 sputum and serum inflammatory markers. Clusters were compared in terms of microbiome composition (16S rRNA sequencing) and exacerbation risk over 12 months follow-up.
Measurements and main results: 199 patients were enrolled (109 [54.8%] female, median age 69 years). Four clusters of patients were defined according to their inflammatory profiles: cluster 1 (milder neutrophilic inflammation), cluster 2 (mixed-neutrophilic and type 2), cluster 3 (most severe neutrophilic), and cluster 4 (mixed-epithelial and type 2). Lower microbiome diversity was associated with more severe inflammatory clusters (P<0.001), and beta-diversity analysis demonstrated distinct microbiome profiles associated with each inflammatory cluster (P=0.001). Proteobacteria and Pseudomonas at phylum and genus levels, respectively, were more enriched in clusters 2 and 3 than in clusters 1 and 4. Furthermore, patients in clusters 2 (rate ratio [RR] 1.49, 95% CI 1.16-1.92) and 3 (RR 1.61, 95% CI 1.12-2.32) were at higher risk of exacerbation over 12 months follow-up compared to cluster 1 even after adjustment for prior exacerbation history.
Conclusion: Bronchiectasis inflammatory endotypes are associated with distinct microbiome profiles and future exacerbation risk.