Clinical implications and Proteomics of Bronchiectasis Progression in Smokers

Project Summary/Abstract Individuals with coexisting COPD and bronchiectasis have worse lung function, longer hospital stays, and an increased risk of death. Bronchiectasis, a pathologic airway enlargement, is increasingly recognized in the US, with 522,000 adults treated annually for bronchiectasis. Bronchiectasis is also a relevant abnormality in chronic obstructive pulmonary disease (COPD), affecting up to 72% of these individuals. While the development of advanced imaging methods has facilitated our understanding of COPD progression, a critical factor hampering our ability to examine bronchiectasis progression fully is the lack of an objective imaging tool applicable in large studies. In this proposal, we will use objective, automated, artificial intelligence-based computed tomography (CT) measures of bronchiectasis. Our overarching hypotheses are 1) our artificial intelligence-based CT measures are effective in detecting bronchiectasis changes in smoking populations and determining its clinical consequences; 2) our approach of defining proteomic biomarkers will help identify subjects at risk of structural progression, and ultimately, inform clinical care. We will quantify the extent of enlarged airways, a measure of bronchiectasis, on baseline and follow-up chest CT scans from smoking individuals participating in two well characterized cohorts, the COPDGene and Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE). In Aim 1, we will determine the association between pulmonary vascular changes and longitudinal measures of radiographic bronchiectasis, gaining insight into pathogenesis. In Aim 2a, we will determine changes in artificial intelligence-based CT measures of bronchiectasis and their association with clinical measures of disease and lung-function trajectories; in Aim 2b, we will also determine clinical factors and imaging features associated with the development and worsening of bronchiectasis on CT. In Aim 3, we will determine blood-based proteomic biomarkers to identify bronchiectasis and its progression on CT. This study will validate the effectiveness of our new AI-based imaging tool for determining bronchiectasis progression; and proteomic biomarkers to identify subjects at risk of progression, which will inform the development of new intervention strategies.