PROJECT SUMMARY Chronic lung disease (CLD) has emerged globally as an important but incompletely understood comorbidity among youth living with HIV (YLWH) in the contemporary antiretroviral therapy era. Among YLWH, CLD encompasses multiple subtypes, including obliterative bronchiolitis, bronchiectasis and asthma. This heterogeneity adds complexity to characterizing CLD subtypes using clinical data such as symptoms and spirometry that can overlap. In the ongoing, longitudinal BREATHE II Study (K23 HL129888), which obtained 161 chest CTs among YLWH and in other chest CT imaging studies in YLWH, chest CT abnormalities are common and often concomitant. Studies among YLWH have not fully incorporated this degree of heterogeneity in examining or identifying CLD subtypes, limiting understanding of the types of CLD among YLWH. Chest quantitative CT methods (quantCT) for CLD assessment are rapidly gaining importance as clinical and research tools. Compared to visual assessment of chest CT and clinical characteristics, quantCT can improve subtype characterization, prognostication and outcome prediction in CLDs. The BREATHE II Study, combined with earlier pilot work, provides a unique opportunity to apply quantCT to expand and refine CLD subtyping in the largest chest CT imaging cohort in YLWH to date. QuantCT metrics include static and functional lung density, % parenchymal involvement by texture categories (e.g., ground glass, hyperlucency, reticulation) and airway wall thickness. Overall objectives of this research are to determine clinical characteristics associated with lung abnormalities defined by quantCT metrics in YLWH and determine whether these quantCT metrics can identify subtypes of CLD that are associated with lung function growth trajectories over time. In addition to low-dose inspiratory and expiratory CT scans of the entire lung fields, available data include repeat pre- and post-bronchodilator spirometry, respiratory-focused questionnaires and physical exam data, and baseline serum inflammatory biomarkers. This project will efficiently leverage these rigorously collected data and add quantCT analyses to provide novel, innovative, objective metrics to improve CLD characterization in YLWH. The study team has collective expertise in HIV-related lung disease, epidemiology, data science and quantCT, ensuring successful completion of the following Aims: 1) Determine baseline clinical characteristics, including spirometry, markers of HIV disease severity and serum biomarkers associated with lung abnormalities defined by quantCT metrics in YLWH; 2) Identify CLD subtypes using unsupervised clustering methods, and determine whether these subtypes are associated with lung function growth trajectories. Characterization of chest CT using objective, operator-independent, quantitative measures may improve identification of CLD subtypes among YLWH. Findings can provide insights into pathophysiologic mechanisms of CLD in YLWH and can inform targeted, patien...