: Osteoporosis and Sarcopenia in COPD and CT-based Phenotypes

Abstract Overview. This study will characterize clinically relevant novel phenotypes related to osteoporosis and sarcopenia in chronic obstructive pulmonary disease (COPD) by leveraging established datasets from nationwide lung studies, artificial intelligence, and image and data analytic methods. This project will build normative models of distinct phenotypic aging and deliver chest CT-based automated measures of breathing-related diaphragm deformation between two lung volumes (ΔLV) and other static features of thoracic bone and muscle. Data of healthy never-smoking participants of the Multi- Ethnic Study of Atherosclerosis Lung (MESA Lung) and Genetic Epidemiology of COPD study (COPDGene) will be used to develop normative models of phenotypic aging, while COPDGene data will be analyzed to uncover distinct pathways of thoracic musculoskeletal aging and characterize relevant phenotypes in COPD. Methods. Osteoporosis phenotypes will be characterized using spinal bone density, strength, and fractures, while sarcopenia in mass and function phenotypes will be defined using pectoral muscle mass and ΔLV metrics of lung diaphragmatic surface deformation, respectively. Static bone and muscle metrics will be computed from an inspiratory chest CT scan, while the ΔLV metrics of lung diaphragmatic surface deformation will be derived over inspiratory and expiratory scans. Aim 1 involves development and validation of CT-based automated methods to drive static metrics of spine, individual vertebrae, and pectoral muscles and ΔLV metrics of lung diaphragmatic surface deformation between inspiration and expiration. Aim 2 to characterize different phenotypic groups in COPD by (i) developing normative aging models for different phenotypic metric groups, (ii) establishing a method to compute the phenotype-specific age of each participant, and (iii) determining participant’s phenotype(s) based on their chronological and phenotypic ages. Aim 3 to characterize the association of body-size, spirometric and radiographic markers, and clinical outcome metrics with different phenotypic groups and to explain the prevalence and overlaps of these groups with COPD severity. Novelty. (i) Establishment of clinically relevant osteoporosis and sarcopenia phenotypes in COPD. (ii) Development of normative models for different phenotypic aging. (iii) Development of methods to determine phenotype-specific age of individual participants. (iv) CT-based automation of ΔLV metrics of breathing-related lung diaphragmatic surface deformation. Strengths. Established longitudinal data repositories, multi-disciplinary expertise of the research team, the PI’s experience with artificial intelligence and quantitative analysis in lung and musculoskeletal imaging, and strong preliminary data. Deliverables and Significance. Characterization of new phenotypes will facilitate the understanding of mechanistic associations of osteoporosis and sarcopenia with different risk factors and comorbidities in COPD and their impacts on disease progression and clinical outcomes. Normative models of phenotypic aging will offer references to distinguish different pathways of musculoskeletal aging and study their impacts on various pulmonary and cardiac diseases. Automation of CT-based measures of bone, muscle, and lung diaphragmatic surface deformation will enable translation of the study outcomes to large research and clinical collections of chest CT scans.