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

增强睡眠电生理学测量和建模，以更好地了解睡眠差异

• 批准号: 10020195
• 负责人: Shaun M Purcell
• 金额: $ 21.68万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-17 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10020195
• 关键词: African American; Age; Attenuated; Circadian Rhythms; Cognitive; Communities; Complement; Complex; Coupling; Data; Data Science; Development; Disease; Electrophysiology (science); Ethnic Origin; Ethnic group; Family; Frequencies; Gender; Health; Individual; Individual Differences; Inherited; Intervention; Life Cycle Stages; Life Style; Link; Machine Learning; Measurement; Measures; Mediating; Mediator of activation protein; Mental Health; Methods; Modeling; Nature; Outcome; Patient Self-Report; Pharmaceutical Preparations; Phenotype; Physiological; Polysomnography; Positioning Attribute; Process; Public Health; Race; Records; Reporting; Research; Research Personnel; Resources; Role; Sampling; Signal Transduction; Sleep; Sleep Architecture; Sleep disturbances; Slow-Wave Sleep; Social Environment; Socioeconomic Status; Techniques; Testing; United States; Ursidae Family; Variant; age effect; age related; allostatic load; analytical method; attenuation; base; behavioral pharmacology; cardiovascular disorder risk; causal model; cohort; computational neuroscience; contextual factors; demographics; density; ethnic difference; ethnic health disparity; ethnic minority population; gender difference; genetic makeup; health disparity; healthy aging; improved; indexing; lifestyle factors; novel; physical conditioning; physical state; population health; primary outcome; psychosocial; racial and ethnic; racial and ethnic disparities; racial difference; racial minority; sex; signal processing; sleep behavior; sleep pattern; sleep physiology; sleep quality; sleep quantity; sleep spindle; social; social disparities; socioeconomics; trait; African American; Age; Attenuated; Circadian Rhythms; Cognitive; Communities; Complement; Complex; Coupling; Data; Data Science; Development; Disease; Electrophysiology (science); Ethnic Origin; Ethnic group; Family; Frequencies; Gender; Health; Individual; Individual Differences; Inherited; Intervention; Life Cycle Stages; Life Style; Link; Machine Learning; Measurement; Measures; Mediating; Mediator of activation protein; Mental Health; Methods; Modeling; Nature; Outcome; Patient Self-Report; Pharmaceutical Preparations; Phenotype; Physiological; Polysomnography; Positioning Attribute; Process; Public Health; Race; Records; Reporting; Research; Research Personnel; Resources; Role; Sampling; Signal Transduction; Sleep; Sleep Architecture; Sleep disturbances; Slow-Wave Sleep; Social Environment; Socioeconomic Status; Techniques; Testing; United States; Ursidae Family; Variant; age effect; age related; allostatic load; analytical method; attenuation; base; behavioral pharmacology; cardiovascular disorder risk; causal model; cohort; computational neuroscience; contextual factors; demographics; density; ethnic difference; ethnic health disparity; ethnic minority population; gender difference; genetic makeup; health disparity; healthy aging; improved; indexing; lifestyle factors; novel; physical conditioning; physical state; population health; primary outcome; psychosocial; racial and ethnic; racial and ethnic disparities; racial difference; racial minority; sex; signal processing; sleep behavior; sleep pattern; sleep physiology; sleep quality; sleep quantity; sleep spindle; social; social disparities; socioeconomics; trait

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.

项目摘要 与种族和种族相关的健康差异是联合国的主要公共卫生问题 国家。多种证据表明，人们的质量和睡眠数量的差异可能 解释了此问题的一部分。在这里，我们希望使用完整的多聚会学研究睡眠模式 大量多样的队列中的数据，以更好地了解导致某些个人和群体的原因 它们的典型睡眠与众不同，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/种族相关的健康分配的调解人，我们建议 首先将“睡眠”术语解开为定量特征的概况（AIM 1）。第二，我们将测试 种族/民族与睡眠特征之间关联的人口统计学主持人，这可以指向 可能的社会经济和社会/环境环境调解人（AIM 2）。最后，我们会的 通过测试与健康结果相关的测试来证明这些发现的相关性 - 主要 心血管疾病的风险和同层负荷的措施 - 估计睡眠的程度 种族/种族健康差异的解释（AIM 3）。 我们将采用来自数据科学，机器学习，机器学习的目标驱动分析方法 计算神经科学和因果建模，以表征 睡觉。对构成“不良睡眠”的构成的更细微的理解很重要，因为不同 睡眠的各个方面可能与人口统计学和生物医学因素有质量不同的联系， 包括年龄，性别，生活方式因素，继承的基因构，昼夜节律，药物效应 以及当前的身心健康状态。通过拆卸这个相互关联变量网络， 我们将更好地理解并改善众多因素的影响 可以在睡眠中引起种族/种族差异，因此也可以在健康中引起种族/种族差异。