Enhanced Measurement and Modeling of Sleep Electrophysiology to Better Understand Sleep Disparities

项目摘要 与种族和民族相关的健康差距是美国的一个主要公共卫生问题 各州。多条证据表明，人们睡眠质量和睡眠时间的不同可能 这是这个问题的部分原因。在这里，我们希望使用全多导睡眠图来研究睡眠模式 数据，以更好地了解是什么原因导致一些个人和群体 他们在典型睡眠中的差异，并将这种睡眠变化与相关的健康结果联系起来。 为了研究睡眠作为与种族/民族相关的健康差异的中介因素，我们建议 首先，将“睡眠”一词分解成数量性状的轮廓(目标1)。第二，我们将测试 种族/民族和睡眠特征之间的关系的人口统计学调节因素，这可以指出 可能的社会经济和社会/环境背景调解人(目标2)。最后，我们会 通过测试与健康结果的关联来证明这些发现的相关性-主要是 心血管疾病风险和不平衡负荷的测量--以及估计睡眠程度 说明种族/族裔健康差异的原因(目标3)。 我们将应用一系列有针对性的数据驱动分析方法，从数据科学、机器学习、 计算神经科学和因果模型，以表征个体在模式上的差异 睡吧。更细致入微地理解什么是“睡眠不佳”很重要，因为不同的 睡眠的各个方面可能与人口统计和生物医学因素有本质上不同的联系， 包括年龄、性别、生活方式因素、遗传基因构成、昼夜节律、药物效应 以及目前的身心健康状况。通过解开这个相互关联的变量网络， 我们将更好地了解-并改善-众多因素的影响 可能会导致睡眠方面的种族/族裔差异，从而也会导致健康方面的差异。

Project Summary Health disparities associated with race and ethnicity are a major public health issue in the United States. Multiple lines of evidence suggest that differences in people's quality and quantity of sleep may account for part of this problem. Here we wish to study patterns of sleep, using full polysomnography data in a large and diverse cohort, to better understand what causes some individuals and groups to differ in their typical sleep, and to link this variation in sleep to relevant health outcomes. In order to study sleep as a mediator of health disparities associated with race/ethnicity, we propose to first unpack the “sleep” term into profiles of quantitative traits (Aim 1). Second, we will test for demographic moderators of the associations between race/ethnicity and sleep traits, which can point to likely socioeconomic and social/environmental contextual mediators (Aim 2). Finally, we will demonstrate the relevance of these findings by testing for association with health outcomes – primarily cardiovascular disease risk and measures of allostatic load – and estimating the extent to which sleep accounts for racial/ethnic health disparities (Aim 3). We will apply a targeted array of data-driven analytic methods, from data science, machine learning, computational neuroscience and causal modeling, to characterize individual differences in patterns of sleep. A more nuanced understanding of what constitutes “poor sleep” is important because different aspects of sleep are likely to have qualitatively different links to demographic and biomedical factors, including age, sex, lifestyle factors, inherited genetic makeup, circadian rhythms, medication effects and current state of physical and mental health. By disentangling this network of inter-related variables, we will be better positioned to understand – and ameliorate – the effects of the numerous factors that can induce racial/ethnic disparities in sleep, and consequently, in health too.