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

基本信息

批准号：
10446331
负责人：
Hailey Rose Banack
金额：
$ 72.84万
依托单位：
STATE UNIVERSITY OF NEW YORK AT BUFFALO
依托单位国家：
美国
项目类别：
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/10446331
关键词：
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 Woman Women&apos 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 malignant muscle neoplasm melanoma mortality muscle form normal aging novel novel strategies older women population based preclinical study prediction algorithm prevent reduced muscle mass risk prediction skeletal muscle wasting survivorship

项目摘要

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.

抽象的有新的证据表明癌症及其治疗可能会加速正常衰老过程中，功能能力下降的幅度和速度加快。衰老过程加速了与年龄相关的常见不良事件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的关系该项目将直接解决三个关键问题。癌症生存研究中的方法论挑战：1）获得准确的测量结果大型社区居住老年人群体的骨骼肌质量，2) 阐明年龄与癌症对肌肉质量、身体素质之间关系的影响功能（步态速度、平衡、力量）和功能衰退，以及 3）大样本量需要使用大数据（机器学习）了解低肌肉质量的预测因素 D3-肌酸稀释法（D3Cr）将用于直接测量。远程肌肉质量，使用先前已在临床和临床中验证过的协议本研究将测量 6614 名参与者（3044 名）的 D3Cr 肌肉质量。妇女健康倡议 (WHI) 中的癌症幸存者和 3570 名无癌症对照者对绝经后妇女进行的前瞻性队列研究 (n=161,808)，随访时间超过 25 年。参与者将使用发生率从 WHI 内的两个子队列中抽取癌症幸存者将从现有的 NCI 资助的方法中抽取。幸存者队列、癌症后的生命和长寿 (LILAC) 队列以及无癌对照将取自 WHI 长寿命研究 2。该应用程序的总体目标是检查癌症幸存者低肌肉质量的前因和后果，使用该研究克服了癌症研究中常见的主要偏见来源的创新方法。目标是：1) 创建年龄标准化的肌肉质量百分位数曲线和 z 分数表征癌症幸存者和非癌症对照者中 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，3) 使用机器学习方法生成多变量风险预测该项目解决了检测低肌肉质量的迫切需求。 NCI 针对老年和长期癌症幸存者的研究这项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）和高龄老人（> 85 岁）年龄组癌症幸存者的健康生存。