Cardiovascular implications of sleep characteristics using real-world objective sleep data

项目摘要 心血管疾病(CVD)是美国人死亡的主要原因。睡眠障碍被认为是 心血管疾病的强烈危险因素。睡眠障碍，如阻塞性睡眠呼吸暂停(OSA)、失眠、睡眠异常 持续时间和睡眠质量差都与心血管疾病相关的发病率和死亡率有关。主修 现有关于睡眠和心血管疾病的研究的局限性是缺乏客观的睡眠测量和 了解睡眠的多维性及其与心血管疾病的复杂相互作用。此外, 新出现的证据还表明，睡眠障碍和临床前简历之间存在潜在的关系 病情，常发生在脑血管病的临床表现之前。血压参数包括 例如，收缩压变异性(SBPV)和平均收缩压(SBP) 对未来心血管事件有预后价值的临床前心血管疾病。然而，目前还没有研究探索是否 OSA以外的睡眠障碍(如睡眠质量受损和睡眠时间异常)是危险因素 归因于临床前的心血管疾病。这是一个关键的调查领域，因为了解了这个复杂的 睡眠与临床前和临床简历状况之间的关系将使医疗保健提供者能够 实施有针对性的干预措施，减少心血管疾病。 这项拟议的研究将检验PSG衍生的客观睡眠测量是否在临床上获得 环境可以预测CVD和临床前CVD。鉴于高血压是主要的 重要的心血管风险，研究最充分的心血管风险因素与睡眠、SBPV、平均SBP和 其他血压指标将是临床前心血管疾病的重点。我们的主要目标是研究 多维睡眠特征之间的关系(在持续时间、效率、质量和 睡眠呼吸障碍)和临床CV情况，在根据个人、临床和其他因素进行调整后 混血儿。我们将使用从7000多名完成诊断的人收集的睡眠数据 弗吉尼亚大学健康系统2010-2018年的睡眠研究。机器学习(ML)模型将是 用于分析多维PSG度量。这项拟议的研究代表了世界上最大的现实世界 客观睡眠测量的数据集，这将使我们能够同时检查整个光谱 睡眠并加深我们对睡眠对简历结果影响的理解。

Project Summary Cardiovascular disease (CVD) is the leading cause of mortality in the U.S. Impaired sleep is recognized as a strong risk factor of CVD. Sleep disorders such as obstructive sleep apnea (OSA), insomnia, abnormal sleep duration, and poor sleep quality have each been associated with CVD-related morbidity and mortality. Major limitations of existing studies on sleep and CVD is the lack of objective sleep measurement and the lack of understanding of the multi-dimensional nature of sleep and its complex interactions with CVD. In addition, emerging evidence also suggests potential relationships between sleep disorders and preclinical CV conditions, which often occur before the clinical manifestation of CVD. Blood pressure parameters including systolic blood pressure variability (SBPV) and mean systolic blood pressure (SBP) are examples of preclinical CVD that have prognostic value for future CV events. However, no research has yet explored if sleep disorders beyond OSA (such as impaired sleep quality and abnormal sleep duration) are risk factors attributable to preclinical CV conditions. This is a critical area of inquiry since understanding the complex relationships between sleep and preclinical and clinical CV conditions will allow healthcare providers to implement targeted interventions to reduce CVD. The proposed study will examine whether PSG-derived objective measures of sleep obtained in the clinical setting would be predictive of CVD and preclinical CVD. Given that hypertension is one of the major important CV risks and has been most well studied CV risk factor in relation to sleep, SBPV, mean SBP, and other blood pressure metrics will be the focus of the preclinical CVD. Our main aim is to examine the relationships between multidimensional sleep characteristics (in terms of duration, efficiency, quality, and disordered sleep breathing) and clinical CV conditions, after adjusting for personal, clinical, and other confounders. We will use sleep data collected from more than 7,000 individuals who completed a diagnostic sleep study at the University of Virginia Health System in 2010 - 2018. Machine learning (ML) models will be used to analyze the multidimensional PSG measures. This proposed study represents the largest real-world dataset on objective sleep measures, which will allow us to simultaneously examine the entire spectrum of sleep and advance our understanding about the impact of sleep on CV outcomes.