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：
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
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/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&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 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

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.

抽象的有新兴的证据表明，癌症ATS tereatments可能会加速正常衰老过程，增加功能能力的幅度和下降速度假设衰老过程以加快常见不良年龄相关的发生癌症幸存者的结果，包括肌肉丧失和身体机能的减少。但是，无数据描述说明与年龄相关的肌肉质量损失，并且与物理有关长期在癌症幸存者中发挥作用。癌症生存研究中的方法论挑战：1）获得准确的措施大型社区居住老年人的骨骼肌质量，2）消除年龄与癌症对肌肉质量之间关系的影响功能（步态速度，平衡，强度）和功能下降，3）大样本量使用大数据（机器学习）了解低肌肉质量的预测指标方法。使用以前在临床和流行病学研究将测量6614名参与者的D3CR肌肉女性健康计划（WHI）中的癌症幸存者和3570个无癌对照）预期队列研究（N = 161,808）的绝经后妇女超过25个随访。参与者将使用Ang Angidigentives中的两个子孔嵌入嵌入在嵌入在Wiwi中的子孔。密度采样方法。生存队列，癌症后的寿命和寿命（淡紫色）队列和无癌症对照将从长期研究2中得出。该应用的总体目的是使用癌症幸存者中低肌肉肌肉的先例和奉献者使用克服癌症研究中常见的主要偏见的创新方法目的是：1）创建标准化的肌肉质量百分位曲线和z得分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 - 癌症控制和3）使用机器学习方法来产生多元风险预测检测低肌肉质量的算法。该项目解决了您的紧急需求 NCI用于较旧的和长期的癌症幸存者。制定干预措施，以减轻老年人低肌肉质量的Harmoful影响在旧（> 65）和最大的（> 85）年龄段的癌症幸存者中促进健康的生存。