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

用于预测老年人不良健康结果的新型计算机断层扫描 (CT) 成像生物标志物

• 批准号: 10303313
• 负责人: Peggy Mannen Cawthon
• 金额: $ 21.37万
• 依托单位: CALIFORNIA PACIFIC MED CTR RES INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10303313
• 关键词: Abdomen; Adipose tissue; Adult; Aging; Archives; Area; Biological Markers; Blood; Cell Aging; Chronic; Clinical; Clinical Trials Design; Communities; Data; Data Analyses; Diabetes Mellitus; Elderly; Endothelium; Energy Metabolism; Fatty acid glycerol esters; Fracture; Health; Heart; Heterogeneity; Hip Fractures; Hip region structure; Hong Kong; Hospitalization; Image; Image Analysis; Inflammation; Insulin Resistance; Intervention; Longitudinal Studies; Machine Learning; Measures; Medicare; Metabolic Pathway; Muscle; Musculoskeletal; Obesity; Osteoporosis; Outcome; Participant; Patient-Focused Outcomes; Patients; Pelvis; Phenotype; Physical Performance; Prospective cohort study; Radiation Dose Unit; Risk; Risk Factors; Scanning; Skeletal Muscle; Techniques; Technology; Testing; Texture; Thigh structure; Time; Tissues; United States; Work; X-Ray Computed Tomography; abdominal CT; base; blood-based biomarker; clinical risk; cohort; density; epidemiology study; fall risk; falls; healthy aging; image processing; imaging Segmentation; imaging biomarker; improved; machine learning algorithm; men; mortality; novel; older men; outcome prediction; predictive marker; radiomics; skeletal; tool; walking speed

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.

基于血液的生物标志物已被广泛用于研究导致衰老的各种代谢途径， 包括能量代谢、慢性炎症、细胞衰老和内皮功能。像血一样- 衍生的生物标志物，基于成像的生物标志物可以被评估为老化结果的潜在预测因子。 对于非神经老化的研究，来自计算机断层扫描（CT）的生物标志物提供了很大的希望。 扫描仪技术和图像处理的最新进展意味着大多数CT检查可以 在不到一分钟的时间内完成，降低了参与者的负担。此外，辐射剂量也有所降低 并且改善了扫描器内和扫描器间的可变性。与此同时，机器学习工具允许 自动化图像处理和分割，提高图像分析效率，减少偏差。 由于这些原因，CT越来越多地用于研究骨骼肌和脂肪组织。 在CT上，肌肉量通常通过横截面积（CSA）来测量。传统上，肌肉质量 通过骨骼肌密度（SMD）和肌间脂肪组织（IMAT）横截面积定量。在 除了作为肌肉质量的量度之外，IMAT还可以被认为是脂肪量的量度。我们 最近开发并验证了一种自动化机器学习工具，以确定传统的CT测量值， 肌肉和脂肪组织的数量和质量。为了更好地表征组织质量，我们还应用了 对CT图像上的肌肉组织进行“放射组学”纹理分析。纹理分析是指量化 成像体素相互关系并提供组织异质性的测量。据我们所知，这 这项技术从未应用于社区流行病学研究的CT图像。我们提出 将这些基于CT的肌肉和脂肪组织评估与重要的老年结局联系起来， 对髋部和其他骨折以及福尔斯、体能和力量的影响。我们将完成这些 对MrOS中存档的CT图像进行分析（一项老年男性健康老龄化的前瞻性队列研究， 特别关注骨质疏松症）和健康ABC（非残疾黑人和白色人的前瞻性队列研究 老年人）。腹部CT图像是在美国MrOS男性的基线检查时收集的 （2000-2年N~3700）、香港MrOS男性（2001-3年N~400）和Health ABC（1997-8年N~3000）。健康 ABC还收集了大腿中部的CT图像。在健康ABC中， 五年后在一个子集中重复（2000-3年N~600）。我们将增加三个目标：1）测试假设， 腹部和大腿中部的肌肉和脂肪组织异质性特征更大， 增加髋部和其他骨折的风险，2）检验肌肉和脂肪组织异质性更大的假设 腹部和大腿中部的特征与较低的力量和较差的身体表现有关 （步行速度和椅子站立）;它们随时间的下降;和福尔斯的风险，以及3）表征 6年来大腿中部的肌肉和脂肪组织异质性特征。