Contributions of sleep to preclinical and clinical Alzheimer's disease

确定与偶发痴呆症有关的睡眠的具体方面是朝着发展的第一步 睡眠干预以降低痴呆症风险。详细的通宵睡眠研究，称为多导睡眠图 (PSG)，提供睡眠的黄金标准评估。由于获得PSG是繁重的，因此对PSG的研究倾向于 招募有限数量的参与者，因此具有有限的统计能力来检测小但 睡眠和痴呆症之间潜在的重要联系。我们建议精选5个大型数据库的数据 以人群为基础的队列(社区动脉粥样硬化风险，心血管健康研究，Framingham 心脏研究、男性骨质疏松性骨折和骨质疏松性骨折研究)的方法论 一致的睡眠研究和神经认知结果。通过在元分析中结合研究水平的数据，我们 提出以下目标：目标1是检查睡眠中与较高事故风险相关的方面 阿尔茨海默病(AD)痴呆(N=2776,499例)。我们将捕获134个睡眠指标， 测量睡眠神经生理学的各个方面。然后我们将识别集群并计算第一个主体 组件作为曝光器。我们将进一步评估特定集群之间的关联 睡眠指标和结果使用最小绝对收缩和选择算子(套索)回归1.A.我们 将检查睡眠神经生理学的每一个方面，关于发生AD痴呆的风险，在 解释了已知的混杂因素。1.B.我们将利用我们的统计能力来探索不同年龄的差异 年龄、性别和遗传风险(例如，载脂蛋白ε4阳性)。目标2是检查睡眠的各个方面(定义 目的1)与痴呆症内表型横断面相关。由于睡眠不佳可能是可以改变的， 重要的是要知道睡眠不佳是否与痴呆症的临床前表型有关-在这种情况下 痴呆症的风险可能仍然是可塑性的。MRI上的脑萎缩和认知能力的细微缺陷是先兆 痴呆症的诊断时间最长可达十年。我们将把每个睡眠标记与一般的和特定领域的认知联系起来 表现(N=6723)以及脑体积(总脑和海马体)和脑损伤(白质 MRI(N=1157)表现为无症状性脑梗塞。目标3是检查睡眠神经生理学的变化 超过6年可预测痴呆事件(N=1558,275个事件)、认知(N=3065)或脑容量 (n=763)。利用重复的PSG~6年，我们将检查睡眠神经生理学的变化是否与 与阿尔茨海默病、脑容量或认知功能有关。我们对社区参与者的大量分析 来自美国各地的研究人员将就睡眠和老年痴呆症之间的联系提供迄今最有力的证据 痴呆症风险。此外，利用我们的大集合样本量来检查子组的差异(例如，按年龄 几十年、性别和载脂蛋白E)和睡眠神经生理学的全面调查，包括创新 睡眠测量(例如，纺锤体密度)可以通过确定 风险最高的亚群，改善痴呆症风险分层的新生物标记物，以及新的生物途径。

Identifying the specific aspects of sleep that relate to incident dementia is the first step towards the development of sleep interventions to reduce dementia risk. Detailed overnight sleep studies, known as polysomnography (PSG), provide the gold-standard assessment of sleep. As obtaining PSG is burdensome, studies with PSG tend to enroll a limited number of participants and consequently have limited statistical power to detect small but potentially important associations between sleep and dementia. We propose to curate data from 5 large population-based cohorts (Atherosclerosis Risk in Communities, Cardiovascular Health Study, Framingham Heart Study, Osteoporotic Fractures in Men, and the Study of Osteoporotic Fractures) with methodologically consistent sleep studies and neurocognitive outcomes. By combining study-level data in meta-analysis, we propose the following aims: Aim 1 is to examine the aspects of sleep that relate to a higher risk of incident Alzheimer's disease (AD) dementia (N=2776, 499 incident cases). We will capture 134 sleep metrics, measuring all aspects of sleep neurophysiology. We will then identify clusters and calculate the first principal component from each cluster as the exposers. We will further assess the association between cluster specific sleep metrics and outcomes using least absolute shrinkage and selection operator (LASSO) regression 1.a. We will examine each aspect of sleep neurophysiology with respect to the risk of incident AD dementia, after accounting for known confounders. 1.b. We will leverage our statistical power to explore differences by age decades, sex, and genetic risk (e.g., APOE ε4 positivity). Aim 2 is to examine the aspects of sleep (defined in Aim 1) that relate cross-sectionally to dementia endophenotypes. As poor sleep is potentially modifiable, it is important to know whether poor sleep is related to preclinical phenotypes of dementia—a time when dementia risk may still be malleable. Brain atrophy on MRI and subtle deficits in cognitive ability precede dementia diagnosis by up to a decade. We will relate each sleep marker to general and domain-specific cognitive performance (N=6723) as well as brain volume (total brain and hippocampal) and brain injury (white matter disease, silent infarcts) on MRI (N=1157). Aim 3 is to examine whether changes in sleep neurophysiology over ~6 years predict incident dementia (N=1558, 275 events), cognition (N=3065), or brain volume (N=763). Leveraging repeated PSGs ~6 years apart, we will examine if changes in sleep neurophysiology relate to incident AD dementia, brain volume, or cognitive function. Our large analysis of community-based participants from across the U.S. will provide the most robust evidence yet on the associations between sleep and AD dementia risk. Moreover, leveraging our large pooled sample size to examine subgroup differences (e.g., by age decades, sex and APOE) and the comprehensive investigation of sleep neurophysiology, including innovative sleep measures (e.g., spindle density), may inform therapeutic strategies for dementia prevention by identifying subgroups most at risk, new biomarkers to improve dementia risk stratification, and novel biological pathways.