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各种代谢途径，导致衰老， 包括能量代谢，慢性炎症，细胞衰老和内皮功能。像鲜血 - 衍生的生物标志物，基于成像的生物标志物可以评估为衰老结果的潜在预测指标。 为了研究非神经衰老的研究，源自计算机断层扫描（CT）的生物标志物提供了巨大的希望。 扫描仪技术和图像处理的最新进展意味着大多数CT检查可能是 在不到一分钟的时间内获得，减轻了参与者的负担。另外，辐射剂量已降低 并且内部和扫描仪内变异性有所提高。同时，机器学习工具允许 自动图像处理和分割，图像分析的效率提高以及降低偏差。 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，肌肉数量通常通过横截面区域（CSA）测量。传统上是肌肉质量 通过骨骼肌密度（SMD）和肌间脂肪组织（IMAT）横截面区域进行量化。在 除了衡量肌肉质量的量度外，IMAT可能被认为是脂肪数量的量度。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措施 肌肉和脂肪组织数量和质量。为了更好地表征组织质量，我们还应用了 在CT图像上对肌肉组织的“放射素”纹理分析。纹理分析是指量化 图像体素相互关系，并提供了组织异质性的量度。据我们所知，这个 技术从未应用于基于社区的流行病学研究的CT图像。我们建议 将这些基于CT的肌肉和脂肪组织的评估与重要的老年结局联系起来 在臀部和其他骨折以及跌倒，身体表现和力量上。我们将完成这些 对MROS中存档的CT图像进行分析（对老年男性健康衰老的前瞻性队列研究， 特别关注骨质疏松症）和健康ABC（对非疾病黑白的前瞻性队列研究 老年人）。在美国的MROS男士的基线考试中收集了腹部CT图像 （2000-2的N〜3700），香港的MROS男子（2001-3的N〜400）和Health ABC（1997-8的N〜3000）。健康 ABC还在大楼中部收集了CT图像。在健康ABC中，大腿中部和腹部CT图像是 五年后在子集中重复（2000-3的n〜600）。我们将添加三个目标：1）检验以下假设。 腹部和大腿中期的肌肉和脂肪组织异质性特征与 增加髋关节和其他骨折的风险，2）检验假设的肌肉和脂肪组织异质性更大 腹部和大腿中期的特征与较低的强度和身体差有关 （步行速度和椅子支架）；他们随着时间的流逝而下降；和跌倒的风险，以及3）特征的变化 肌肉和脂肪组织异质性在大腿中期的6年中的特征。