Sleep Physiology Dynamics: Quantification, Characterization and Genetic Dissection

睡眠生理学动力学：量化、表征和基因剖析

• 批准号: 9902196
• 负责人: Shaun M Purcell
• 金额: $ 13.43万
• 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: 2019-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9902196
• 关键词: Accidents; Address; Affect; Behavioral; Biological Markers; Blood Pressure; Brain; Cardiac; Cognitive; Cognitive deficits; Complex; Data; Demographic Factors; Diabetes Mellitus; Diagnostic; Disease; Disease susceptibility; Dissection; Etiology; Genes; Genetic; Genetic Risk; Genotype; Health; Heart; Heart Diseases; Heart Rate; Heritability; Hypertension; Immune; Individual; Individual Differences; Injury; Lead; Link; Measures; Mental disorders; Methods; Modeling; National Heart, Lung, and Blood Institute; Neurodegenerative Disorders; Nuclear Family; Obstructive Sleep Apnea; Outcome; Participant; Pathology; Patient Self-Report; Pattern; Persons; Phenotype; Physiological; Physiology; Polysomnography; Process; Property; Research; Resources; Respiratory physiology; Risk Factors; Risk stratification; Series; Signal Transduction; Sleep; Sleep Disorders; Sleep disturbances; Statistical Data Interpretation; Stroke; Structure; System; Techniques; Testing; Time; Variant; Work; base; cardiometabolism; circadian; cohort; disorder risk; fasting glucose; genetic approach; genome wide association study; heart function; heart rate variability; novel; novel marker; novel strategies; open source; phenotypic biomarker; pleiotropism; polygenic risk score; potential biomarker; simulation; sleep health; sleep physiology; therapeutic target; tool; trait

Project Summary. Unhealthy sleep can manifest in multi-organ associations, affecting cardiometabolic, immune, behavioral, cognitive and other systems. Research has pointed to the predictive and diagnostic potential of biomarkers derived from physiological sleep signals, not just for sleep disorders but also for a broad range of other diseases. However, although sleep is a dynamic process, that changes over the night, the typical metrics considered as possible biomarkers fail to capture this directly. Based on polysomnography data collected on over 13,000 participants from NHLBI National Sleep Research Resource (NSRR), we will develop a framework for quantifying individual differences in the dynamic structure of cardiac physiology during sleep. We will study the relationships of derived phenotypes with cardiometabolic disease state as well as its genetic diathesis, to better characterize the causal links between sleep-related physiology and health. Specifically, we will apply time series clustering methods to capture individual differences in how core physiologic parameters vary across the night, focusing on electrocardiographic signals. We will then evaluate these derived traits, in terms of reliability, heritability, demographic and chronotype associations, and prediction of cardiometabolic disease with a focus on hypertension and diabetes. Secondly, we will use genetic approaches to study the causal relationships between sleep-related dynamics and cardiometabolic disease, by developing models that use polygenic risk scores from external genome-wide association studies. In addition to generating new methods and tools for large-scale analyses of physiologic sleep signal data, this work has the potential to discover novel biomarkers for cardiometabolic disease, which can inform risk stratification models and point to therapeutic targets.

项目摘要。 不健康的睡眠可表现为多器官关联，影响心脏代谢、免疫、行为、 认知和其他系统。研究指出生物标志物的预测和诊断潜力 源自生理睡眠信号，不仅适用于睡眠障碍，还适用于其他广泛的领域 疾病。然而，尽管睡眠是一个动态过程，会在夜间发生变化，但典型指标 被认为可能的生物标志物无法直接捕获这一点。 基于 NHLBI 国家睡眠研究中心收集的 13,000 多名参与者的多导睡眠图数据 资源（NSRR），我们将开发一个量化动态结构个体差异的框架 睡眠期间的心脏生理学。我们将研究衍生表型与心脏代谢的关系 疾病状态及其遗传素质，以更好地描述与睡眠相关的因果关系 生理和健康。 具体来说，我们将应用时间序列聚类方法来捕获核心的个体差异 生理参数在夜间会发生变化，重点是心电图信号。然后我们将评估 这些衍生特征在可靠性、遗传力、人口统计和时间型关联以及预测方面 心脏代谢疾病，重点关注高血压和糖尿病。其次，我们将利用基因 研究睡眠相关动态与心脏代谢疾病之间因果关系的方法 开发使用外部全基因组关联研究的多基因风险评分的模型。此外 为了生成用于大规模分析生理睡眠信号数据的新方法和工具，这项工作已经 发现心脏代谢疾病新生物标志物的潜力，可以为风险分层提供信息 模型并指出治疗靶点。