MASS: Muscle and disease in postmenopausal women

点击翻译按钮获取中文摘要

ABSTRACT Sarcopenia (low muscle mass) and diabetes are two highly prevalent and disabling chronic health conditions affecting older women. There is emerging evidence that diabetes and impaired insulin-glucose homeostasis may accelerate age-related loss of muscle mass. It is critically important to examine modifiable risk factors (e.g., muscle mass, dysglycemia) that contribute to risk of functional impairment, falls and fractures (the 3Fs) in older adults. Our study will address a key knowledge gap: the extent to which insulin-glucose homeostasis is associated with loss of muscle mass over time in aging women and whether this varies by race/ethnicity. Moreover, this study will examine the feedback loop between impaired insulin-glucose homeostasis and loss of skeletal muscle mass. Until recently, the ability to fully characterize change in muscle mass in large studies of community dwelling older adults has been limited by challenges associated with obtaining measures of muscle mass. This is a critical barrier to progress. This project will use a novel at-home remote sample collection protocol (D3Cr method) and finger-prick blood collection kit to examine longitudinal change in muscle mass and insulin-glucose homeostasis in 3177 postmenopausal women from the Women’s Health Initiative (WHI). The WHI is a large, ongoing prospective cohort study of postmenopausal women with 30 years of follow-up. Study participants will be drawn from a WHI sub-cohort, the Long Life Study 2 (LLS2), a diverse cohort of older women (56% from racial/ethnic minority groups; mean age 85 years). The study aims are to: 1) provide insight into longitudinal patterns of change in D3Cr-muscle mass in postmenopausal women as they age, and their risk for functional decline increases, by race/ethnicity, diabetes status, and according to level of glycemic control, 2) examine the cyclical relationship between impaired insulin-glucose homeostasis and change in D3Cr-muscle mass using advanced statistical approaches, and 3) use machine learning methods to develop multivariate risk stratification algorithms, including D3Cr-muscle mass, indices of impaired insulin-glucose homeostasis, and objective measures of functional capacity (grip strength, gait speed), to identify postmenopausal women at highest risk of the 3 Fs. This project addresses an urgent need identified by the NIA for research on geroscience, including research that determines the rate of aging processes and mechanisms underlying age-related change. In the future, these results will be used to develop interventions designed to mitigate harmful effects of sarcopenia and diabetes and promote healthy aging in postmenopausal women, including women from racial/ethnic minority groups that are typically underrepresented in health research.