Sleep Health Profiles and Mortality Risk in Older Adults: A Multi-Cohort Application of Novel Statistical Methods

项目摘要/摘要

PROJECT SUMMARY / ABSTRACT Individual sleep disorders and characteristics are known to be associated with adverse health outcomes. However, few studies consider sleep in a multivariate context. Therefore, we propose a paradigmatic shift in sleep research towards sleep health. Conducting research through a sleep health lens can change health practice by clarifying which sleep characteristics are most important to treat, enhancing screening efforts, and facilitating the generalization of findings to all individuals, not just those with specific sleep disorders or complaints. Before achieving these long-term goals, however, the crucial first steps in the study of sleep health are to determine which sleep profiles predict health outcomes (Aim 1), and develop predictive algorithms that can identify individuals at risk of adverse health outcomes based on their sleep and other risk factors (Aim 2). These aims can only be achieved with an awareness of the statistical challenges related to the inherent multidimensionality of sleep health (e.g. multiple domains of sleep, multiple characteristics to represent each domain, multiple data sources, etc.). This multidimensionality generates substantial methodological barriers regarding variable and model selection and threatens researchers' abilities to develop transparent and reproducible models. In this R01, investigators will apply rigorous, sophisticated statistical methods to a large aggregated sample of adults aged >60 with self-report and polysomnographic characterization and over 10 years of longitudinal follow-up from three parent studies: Osteoporotic Fractures in Men Sleep Study (MrOS), Study of Osteoporotic Fractures (SOF), and the Sleep Heart Health Study (SHHS). In Aim 1, investigators will determine which multidimensional sleep profiles predict time to mortality in older adults using Cox-proportional hazards models, tree-structured survival analysis, and clustering. In Aim 2, investigators will develop powerful machine learning algorithms that incorporate sleep and other risk factors to identify which older adults have the greatest risk for early mortality. Within each aim, the impact of sex and race will be rigorously investigated, and models will be internally validated in independent samples to ensure reproducibility. The Secondary Aim is to investigate generalizability to studies with different demographic profiles and sleep protocols (Wisconsin Sleep Cohort Study, Honolulu Asia Aging Study of Sleep America) and different data types (sleep diary, actigraphy). All models will be developed in the context of other important known risk factors, including but not limited to age, gender, race, sleep apnea, cardiovascular disease, smoking, and BMI. Findings from Aim 1 will jumpstart subsequent mechanistic research focused on sleep-related causal factors of mortality, leading to identification of targeted treatments for sleep problems that could reduce risk of mortality and morbidity. Findings from Aim 2 will provide important preliminary evidence for enhanced screening tools that can more accurately identify which individuals are at risk for adverse health outcomes.