Ms. LILAC: Muscle Mass in the Life and Longevity After Cancer (LILAC) Study

LILAC 女士：癌症后生命和长寿 (LILAC) 研究中的肌肉质量

• 批准号: 10589078
• 负责人: Hailey Rose Banack
• 金额: $ 66.96万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-09 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10589078
• 关键词: Acceleration; Activities of Daily Living; Address; Age; Aging; Big Data; Blood; Breast; Cancer Patient; Cancer Survivor; Cancer Survivorship; Clinical Research; Colorectal; Communities; Creatine; Data; Development; Diagnosis; Dual-Energy X-Ray Absorptiometry; Elderly; Epidemiology; Equilibrium; Female; Funding; Future; Gait speed; Gynecologic; Health; Home; Incidence; Intervention; Knowledge; Life; Longevity; Lung; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Measures; Metabolic; Methodology; Methods; Morbidity - disease rate; Muscle; Muscular Atrophy; National Cancer Institute; Outcome; Participant; Pathway interactions; Physical Function; Physical Performance; Physiological; Population; Postmenopause; Process; Prospective, cohort study; Protocols documentation; Public Health; Quality of life; Questionnaires; Recommendation; Research; Research Design; Sample Size; Sampling; Skeletal Muscle; Source; Standardization; Surgical complication; Toxic effect; Woman; Women' s Health; X-Ray Computed Tomography; age effect; age group; age related; age-related muscle loss; anticancer research; cancer invasiveness; cancer therapy; cohort; comparison group; density; disability; epidemiology study; follow-up; functional decline; functional outcomes; healthspan; improved; innovation; lean body mass; machine learning method; melanoma; mortality; muscle form; normal aging; novel; novel strategies; older women; population based; preclinical study; prediction algorithm; prevent; reduced muscle mass; risk prediction model; skeletal muscle wasting; survivorship

ABSTRACT There is emerging evidence that cancer and its treatments may accelerate the normal aging process, increasing the magnitude and rate of decline in functional capacity. This accelerated aging process is hypothesized to hasten the occurrence of common adverse age-related outcomes in cancer survivors, including loss of muscle mass and decrease in physical function. However, there is no data describing age-related loss of muscle mass and its relation to physical function in the long-term in cancer survivors. This project will directly address three key methodological challenges in research on cancer survivorship: 1) obtaining accurate measures of skeletal muscle mass in large population-based cohorts of community dwelling older adults, 2) disentangling the effect of age versus cancer on the relationship between muscle mass, physical function (gait speed, balance, strength), and functional decline, and 3) the large sample size required to understand predictors of low muscle mass using big data (machine learning) approaches. The D3-creatine dilution method (D3Cr) will be used to obtain a direct measure of muscle mass remotely, using a protocol that has been previously validated in clinical and epidemiologic research. This study will measure D3Cr muscle mass in 6614 participants (3044 cancer survivors and 3570 cancer-free controls) in the Women’s Health Initiative (WHI), a large prospective cohort study (n=161,808) of postmenopausal women with over 25 years of follow-up. Participants will be drawn from two sub-cohorts embedded within the WHI using an incidence density sampling approach. Cancer survivors will be drawn from an existing NCI-funded survivorship cohort, the Life and Longevity After Cancer (LILAC) cohort, and cancer-free controls will be drawn from the WHI Long Life Study 2. The overall objective of this application is to examine the antecedents and consequences of low muscle mass in cancer survivors, using innovative methods to overcome major sources of bias common in cancer research. The study aims are to: 1) create age-standardized muscle mass percentile curves and z-scores to characterize the distribution of D3- muscle mass in cancer survivors and non-cancer controls, 2) compare muscle mass, physical function, and functional decline in cancer survivors and non- cancer controls, and 3) use machine learning approaches to generate multivariate risk-prediction algorithms to detect low muscle mass. This project addresses an urgent need identified by the NCI for research in older and long-term cancer survivors. The results of this study will be used to develop interventions to mitigate the harmful effects of low muscle mass in older adults and promote healthy survivorship in cancer survivors in the old (>65) and oldest-old (>85) age groups.

摘要 有新的证据表明，癌症及其治疗可能会加速正常衰老 过程，增加功能能力下降的幅度和速度。这种加速 假设衰老过程加速了常见的与年龄相关的不良反应的发生， 癌症幸存者的结果，包括肌肉质量损失和身体功能下降。 然而，没有数据描述与年龄相关的肌肉质量损失及其与身体状况的关系。 在癌症幸存者中长期发挥作用。该项目将直接解决三个关键问题 癌症生存率研究中的方法学挑战：1）获得准确的测量值 的骨骼肌质量在社区居住的老年人的大群体为基础的队列，2） 解开年龄与癌症对肌肉质量、身体素质、 功能（步态速度、平衡、力量）和功能下降，以及3）大样本量 需要使用大数据（机器学习）来了解低肌肉质量的预测因素 接近。将使用D3-肌酸稀释法（D3 Cr）直接测量 肌肉质量远程，使用先前已在临床和 流行病学研究。本研究将测量6614名参与者（3044 癌症幸存者和3570无癌症对照）在妇女健康倡议（WHI），一个大的 前瞻性队列研究（n= 161，808），对绝经后妇女进行了超过25年的随访。 将使用发生率从WHI中嵌入的两个子队列中抽取受试者 密度抽样法癌症幸存者将从现有的国家癌症研究所资助的 生存率队列、癌症后生命和寿命（LILAC）队列和无癌症对照 将从WHI长寿命研究2中抽取。本申请的总体目标是 检查癌症幸存者低肌肉质量的前因和后果，使用 创新的方法来克服癌症研究中常见的主要偏见来源。研究 目的是：1）创建年龄标准化肌肉质量百分位曲线和z分数， 表征癌症幸存者和非癌症对照中D3肌肉质量的分布，2） 比较癌症幸存者和非癌症幸存者的肌肉质量、身体功能和功能下降。 癌症控制，以及3）使用机器学习方法来生成多变量风险预测 算法来检测低肌肉质量。该项目解决了联合国难民事务高级专员办事处确定的一项迫切需要， NCI用于老年和长期癌症幸存者的研究。本研究的结果将用于 制定干预措施，以减轻老年人肌肉质量低的有害影响， 促进老年（>65岁）和高龄（>85岁）年龄组癌症幸存者的健康存活率。