Novel Computed Tomography (CT) Imaging Biomarkers in Older Adults for Predicting Adverse Geriatric Health Outcomes

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Blood-based biomarkers have been widely used in studying various metabolic pathways contributing to aging, including energy metabolism, chronic inflammation, cellular senescence, and endothelial function. Like blood- derived biomarkers, imaging-based biomarkers can be evaluated as potential predictors of aging outcomes. For study of non-neurologic aging, biomarkers derived from computed tomography (CT) offer great promise. Recent advances in scanner technology and image processing mean that most CT examinations can be obtained in less than one minute, lowering participant burden. In addition, radiation doses have been lowered and the intra- and inter-scanner variability has improved. In parallel, machine learning tools allow for automated image processing and segmentation, increasing efficiency of image analysis, and reducing bias. For these reasons, CT is increasingly being used to study skeletal muscle and adipose tissue. On CT, muscle quantity is typically measured by cross-sectional area (CSA). Muscle quality is traditionally quantified by skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT) cross-sectional area. In addition to being a measure of muscle quality, IMAT may be considered as a measure of fat quantity. We recently developed and validated an automated machine learning tool to determine traditional CT measures of muscle and adipose tissue quantity and quality. To better characterize tissue quality, we have also applied "radiomic" texture analysis to muscle tissue on CT images. Texture analysis refers to the quantification of image voxel inter-relationships and provides a measure of tissue heterogeneity. To our knowledge, this technique has never been applied to CT images from community-based epidemiological studies. We propose to relate these CT-based assessments of muscle and adipose tissues to important geriatric outcomes, focusing on hip and other fractures as well as falls, physical performance, and strength. We will complete these analyses on archived CT images in MrOS (a prospective cohort study of healthy aging in older men, with a particular focus on osteoporosis) and Health ABC (a prospective cohort study of non-disabled Black and White older adults). Abdominal CT images were collected at the baseline exam for MrOS men in the United States (N~3700 in 2000-2), MrOS men in Hong Kong (N~400 in 2001-3), and Health ABC (N~3000 in 1997-8). Health ABC also collected CT images at the mid-thigh. In Health ABC, mid-thigh and abdominal CT images were repeated in a subset five years later (N~600 in 2000-3). We will add three aims: 1) test the hypothesis that that greater muscle and fat tissue heterogeneity features at the abdomen and mid-thigh are associated with increased risk of hip and other fractures, 2) test the hypothesis greater muscle and fat tissue heterogeneity features at the abdomen and mid-thigh are associated with lower strength and poor physical performance (walking speed and chair stands); their decline over time; and risk of falls, and 3) characterize changes in muscle and fat tissue heterogeneity features at the mid-thigh over 6 years.