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: 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
• 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' 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

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