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Sleep Health Profiles Predicting Impaired Cognition and Depressive Symptoms in Older Adults: Extending Novel Statistical Methods in Multi-Cohort Applications

睡眠健康状况预测老年人认知受损和抑郁症状：在多队列应用中扩展新颖的统计方法

基本信息

批准号：
10209375
负责人：
MEREDITH JOANNE LOTZ WALLACE
金额：
$ 196.81万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2024-03-31
项目状态：
已结题

项目摘要

PROJECT SUMMARY / ABSTRACT Depression has an enormous impact: it is the leading cause of disability worldwide and affects more than 264 million people of all ages. Moreover, major depression at any age doubles the risk of Alzheimer’s Disease and related dementias, which affect over 50 million people worldwide — a number projected to triple by 2050. Interventions for depression in older adults have limited efficacy to date, and directly treating cognitive impairment and/or Alzheimer’s Disease is not feasible or effective. Thus, identifying modifiable risk factors that favorably influence both depression and cognition before dementia onset is an urgent public health need. Sleep is one such risk factor. However, sleep is not a uni-dimensional construct represented by merely its duration or the presence/absence of a sleep disorder. Rather sleep is multidimensional: it is comprised of multiple domains (e.g., Regularity, Satisfaction, Sleepiness, Timing, Efficiency, Duration) and measured on multiple levels (e.g. self-report or behavioral [via actigraphy]). In our initial R01, we leveraged our biostatistical and sleep expertise to develop and hone methods for examining multidimensional sleep health as a predictor of mortality in a high- dimensional machine learning (ML) context that flexibly accounts for the complex interactions that exist among sleep and non-sleep risk factors. We now seek to build on the success of the initial funding period by using our novel methods to examine multidimensional sleep health as a predictor of changes in cognition and depressive symptoms. To enhance generalizability and power, we are developing a Pooled Sample of N~3,400 adults aged ≥65 without cognitive impairment from the Osteoporotic Fractures in Men Study, Study of Osteoporotic Fractures, Memory and Aging Project (MAP) and Minority Aging Research Study (MARS). With these methods and data, we will examine multidimensional sleep health for predicting changes in global cognition and incident dementia (Aim 1) and depressive symptoms (Aim 2) in a high-dimensional machine learning context. We will also examine depression as a pathway through which multidimensional sleep health predicts impaired cognition (Aim 3). Our Secondary Aims are to: (a) apply parallel methods in two additional cohorts (the Rotterdam Study and Multi- Ethnic Study of Atherosclerosis) to replicate and extend our findings to cohorts with different demographic profiles and clinical Alzheimer’s Disease and related dementias diagnoses; (b) examine effects by sex and race; and (c) identify the sleep health characteristics driving overall effects. Identifying multidimensional sleep health profiles that reliably predict changes in global cognition, incident dementia, and changes in depressive symptoms in a realistic, high dimensional context will directly inform the design of novel targeted interventions and prospective studies focused on preventing Alzheimer’s Disease and related dementias. Moreover, we will amplify the impact of our work by demonstrating new methods for studying health and depositing harmonized data on the National Sleep Research Resource to facilitate future multi-cohort secondary analyses.

项目摘要/摘要抑郁症有巨大的影响：它是全球残疾的主要原因，影响着超过264人上百万各个年龄段的人。此外，任何年龄段的重度抑郁症都会使患阿尔茨海默病的风险增加一倍，影响全球5000多万人的相关痴呆症--预计到2050年，这一数字将增加两倍。到目前为止，对老年人抑郁症的干预效果有限，直接治疗认知能力损害和/或阿尔茨海默氏症是不可行或有效的。因此，确定可修改的风险因素在痴呆症发作前对抑郁和认知产生积极影响是一项紧迫的公共卫生需求。沉睡就是这样的风险因素之一。然而，睡眠并不是仅仅由睡眠的持续时间或时间来表示的一维结构睡眠障碍的存在/不存在。相反，睡眠是多维的：它由多个领域组成 (例如，规律性、满足感、困倦、时间、效率、持续时间)，并在多个水平上测量(例如自我报告或行为[通过活动记录])。在我们最初的R01中，我们利用了我们的生物统计学和睡眠专业知识开发和磨练检查多维睡眠健康作为高死亡率预测因子的方法。维度机器学习(ML)上下文，灵活地解决存在于睡眠和非睡眠风险因素。我们现在寻求在最初资助期的成功基础上，利用我们的检查多维睡眠健康作为认知改变和抑郁的预测因子的新方法症状。为了增强普适性和能力，我们正在开发一个N~3,400岁成年人的混合样本 ≥65男性骨质疏松性骨折无认知损害研究，骨质疏松性骨折研究，记忆与老龄化项目(MAP)和少数民族老龄化研究(MARS)。有了这些方法和数据，我们将检查多维睡眠健康以预测全球认知和痴呆症事件的变化 (目标1)和抑郁症状(目标2)在高维机器学习环境中。我们还将检查抑郁作为多维睡眠健康预测认知受损的途径(目标3)。我们的次要目标是：(A)在另外两个群体中应用平行方法(鹿特丹研究和多学科研究)。动脉粥样硬化的种族研究)将我们的发现复制并扩展到不同人口统计学的队列阿尔茨海默病和相关痴呆症的概况和临床诊断；(B)按性别和种族检查影响；以及(C)确定影响整体效果的睡眠健康特征。识别多维睡眠健康可靠地预测整体认知、偶发痴呆症和抑郁症状变化的概况在现实、高维度的背景下，将直接为设计新的有针对性的干预措施和前瞻性研究的重点是预防阿尔茨海默病和相关的痴呆症。此外，我们还将扩大我们的工作通过展示研究健康的新方法和将协调一致的数据存放在国家睡眠研究资源，以促进未来多队列二次分析。