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

项目摘要/摘要 众所周知，个体睡眠障碍和特征与不利的健康后果有关。 然而，很少有研究将睡眠放在多变量的背景下考虑。因此，我们提出了一个范例转变，在 针对睡眠健康的睡眠研究。通过睡眠健康镜片进行研究可以改变健康 通过澄清哪些睡眠特征是最需要治疗的，加强筛查工作，以及 促进将研究结果推广到所有个人，而不仅仅是那些有特定睡眠障碍或 投诉。然而，在实现这些长期目标之前，睡眠健康研究的关键第一步 是确定哪些睡眠状况预测健康结果(目标1)，并开发预测算法 可以根据个人的睡眠和其他风险因素确定有可能产生不良健康后果的个人(目标2)。 只有意识到与固有的统计挑战相关的统计挑战，才能实现这些目标 睡眠健康的多维性(例如，睡眠的多个领域，代表每个领域的多个特征 域、多个数据源等)。这种多维性产生了很大的方法论障碍 关于变量和模型选择，并威胁到研究人员制定透明和 可复制的模型。在本R01中，调查人员将应用严格、复杂的统计方法对大型 具有自我报告和多导睡眠图特征的60岁和10岁以上成年人的汇总样本 三项父母研究的多年纵向随访：男性睡眠中的骨质疏松性骨折研究(MROS)， 骨质疏松性骨折研究(SOF)和睡眠心脏健康研究(SHHS)。在目标1中，调查人员将 用COX比例法确定哪些多维睡眠曲线可预测老年人的死亡时间 风险模型、树形结构生存分析和聚类。在目标2中，调查人员将开发出强大的 机器学习算法结合了睡眠和其他风险因素，以识别哪些老年人患有 过早死亡的最大风险。在每个目标中，性别和种族的影响都将得到严格的调查， 模型将在独立样本中进行内部验证，以确保重现性。次要目标是 调查对不同人口统计学特征和睡眠方案的研究的概括性(威斯康星州 睡眠队列研究，檀香山亚洲睡眠美国老龄化研究)和不同的数据类型(睡眠日记， 动作记录法)。所有模型都将在其他重要的已知风险因素的背景下开发，包括但不是 仅限于年龄、性别、种族、睡眠呼吸暂停、心血管疾病、吸烟和体重指数。AIM 1的调查结果将 随后的机械研究集中在与睡眠相关的死亡原因因素上，导致 确定针对睡眠问题的有针对性的治疗方法，以降低死亡率和发病率。 来自AIM 2的发现将为增强的筛查工具提供重要的初步证据，该工具可以 准确识别哪些人面临不利健康后果的风险。

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.