Endotype-driven prediction of acute exacerbations in chronic obstructive pulmonary disease (EndAECOPD): protocol for a prospective cohort study.

Abstract

Current strategies for the prevention of acute exacerbations in chronic obstructive pulmonary disease (COPD) are primarily based on clinical measurements but fail to target the pathophysiological mechanisms, namely endotypes, of the disease. Studies identifying endotypes underlying exacerbation susceptibility and discovering specific biomarkers may lead to the development of targeted therapeutics but are lacking. This study aims to assess a broad spectrum of biomarkers at multiple biological levels (genetics, airway inflammation and respiratory microbiome) for their ability in predicting acute exacerbations of COPD, thus enables high-resolution disease endotyping and may lead to precision treatment of the disease. In this prospective cohort study, participants with stable COPD (n=600) will be recruited and assessed for demographics, symptom scores, spirometry, medication use and comorbidities at baseline. Blood will be obtained for genotyping variants in a panel of nine genes. Induced sputum will be collected for the profile of microbiota using 16S rRNA gene sequencing, quantification of bacterial load, inflammatory mediators assay and sputum cytometry. Participants will be followed up for their exacerbations till 12 months and reassessed for the clinical measurements as baseline. The primary outcomes are total number of exacerbations, severe exacerbations, moderate exacerbations and time to first exacerbation. The secondary outcomes are changes in lung function and symptom scores. The effect of biomarkers representing genetic variants, airway inflammation and respiratory microbiome on predicting the frequent exacerbator phenotype and exacerbation frequency will be analysed with multivariable modelling, and time to first exacerbation with a Cox regression model. The study has been approved by the Clinical Trial and Biomedical Ethics Committee of West China Hospital of Sichuan University (No. 2018-298). The results of the study will be published on peer-reviewed journals. ChiCTR1800019063.