Dissecting heterogeneity of excessive daytime sleepiness and impact on cardiovascular diseases

摘要 白天过度嗜睡(EDS)影响到10%-20%的人口，并与 心血管疾病(CVD)和死亡率。新出现的数据表明，瞄准EDS可能会 为改善CVD提供了一种新的干预手段。然而，研究结果受到自我报告的限制。 数据和异构性。有必要剖析和了解EDS的潜在驱动因素 亚型，并确定是否存在与心血管疾病相关的亚型，并潜在地 可修改的。我们最近的工作确定了EDS的两种亚型-睡眠倾向(SP；特征 通过客观测量长睡眠时间、高效率和更少的碎片化)和睡眠 碎片化(SF；睡眠时间短，效率低)。它们中的每一个都是在 与心血管不良结局和不同遗传背景相关的人群。 我们假设SP是一种新的睡眠表型，它反映了一种需要停留的属性 将EDS分成SP和SF亚型将有助于识别遗传和 EDS的生理机制，并提高对多效性或因果关系的理解 与心血管疾病风险的关系。为了检验这些假设，我们将利用宏观和微观 睡眠结构测量、基因组学和基于人群的队列中的其他组学数据。 我们将解决以下具体目标：1)确定人口统计、行为、临床和 SP和SF的神经生理因素(用肌动描记和脑电图评估)， 并根据需要细化EDS亚型的分类；2)识别遗传变异和分子 与EDS亚型相关的通路，并为风险生成稳健的多基因风险分数 分层；3)系统评价EDS之间的因果关系或非因果关系 4)确定各EDS亚型对遗传的修饰作用 用基因-环境交互作用分析研究心血管疾病的易感性。这项工作将推动我们的 了解EDS的异质性，揭示EDS的生物学机制和通路 ，并提供指导临床和公共卫生干预的信息以及 为未来的实验室研究提供方向。

ABSTRACT Excessive daytime sleepiness (EDS) affects 10-20% of the population and is associated with cardiovascular diseases (CVD) and mortality. Emerging data suggest that targeting EDS may provide a novel intervention for improving CVD. However, findings are limited by self-reported data and heterogeneity. There is a need to dissect and understand the underlying drivers of EDS subtypes, and to determine whether there are subtypes causally related to CVD and potentially modifiable. Our recent work identified two subtypes of EDS – sleep propensity (SP; characterized by objectively measured long sleep duration, high efficiency, and less fragmentation) and sleep fragmentation (SF; short sleep duration and low efficiency). Each of them is common in the population, associated with adverse cardiovascular outcomes and different genetic backgrounds. We hypothesize that SP is a novel sleep phenotype that reflects a property of the need of staying asleep; dissecting EDS into SP and SF subtypes will facilitate identification of genetic and physiological mechanisms for EDS, and improve understanding of pleiotropic or causal associations with CVD risk. In order to test these hypotheses, we will leverage macro- and micro- sleep architecture measurements, genomics, and other -omics data in population-based cohorts. We will address the following specific aims: 1) To identify demographic, behavioral, clinical and neurophysiological factors (assessed by actigraphy and electroencephalography) for SP and SF, and refine classification of EDS subtypes if needed; 2) To identify genetic variants and molecular pathways associated with EDS subtypes and generate robust polygenetic risk score for risk stratification; 3) To systematically evaluate the causal or non-causal associations between EDS subtypes and CVD traits; 4) To identify the modification effect of each EDS subtype on genetic susceptibility of CVD using gene-environment interaction analyses. This work will advance our understanding of the heterogeneity of EDS, reveal biological mechanisms and pathways linking to CVD, and provide information that will guide clinical and public health interventions as well as provide directions for future laboratory research.