Novel physiologically-driven phenotypes for the prognosis of cardiovascular outcomes in sleep apnea: Toward precision medicine in sleep health

项目总结/摘要 阻塞性睡眠呼吸暂停是一种严重的疾病，与主要合并症的风险增加有关，包括高血压， 糖尿病，心血管疾病（CVD）和死亡率的风险增加。呼吸暂停/呼吸不足的重复循环 在睡眠期间导致氧饱和度和心脏自主波动的频繁下降， 事件这些急性和重复的生理变化被认为在不良反应的发展中起着关键作用。 心脏代谢后果。 基于社区的观察性队列研究显示，OSA与CVD结局密切相关。但 由于OSA人群的显著异质性，识别/预测高风险个体具有挑战性。 一个可能的解释是呼吸暂停低通气指数（AHI），OSA诊断和管理的主要指标， 以捕获睡眠期间OSA相关低氧血症和自主反应的异质性。更重要的是这些 生理变化被认为因年龄、性别和种族而异。因此，鉴定表型标记， 需要更好地量化OSA特异性低氧血症和自主神经反应， 心脏代谢结局。 在此，我们提出研究两种新的生理表型，可以很容易地从脉搏血氧饱和度信号中提取， 睡眠呼吸暂停特异性呼吸不足负担（“SASHB”）和对呼吸暂停/呼吸不足的心率响应（“SAHR”）。我们将 使用定义明确的大型社区前瞻性队列研究，包括睡眠心脏健康研究（SHHS）， 多种族动脉粥样硬化研究（梅萨），拉美裔社区健康研究（HCHS/SOL），结果 老年男性睡眠障碍研究（MrOS）、杰克逊心脏研究（JHS）和威斯康星州睡眠队列研究（WSC）， 追求以下目标。在目标1中，我们计划使用个体参与者荟萃分析来研究SASHB/SASHHR 确定不同种族/背景的成年人中高血压、糖尿病和心血管疾病的发病率。过去的研究 表明，由AHI量化的OSA与结果之间的关联强度因年龄、性别和种族而异。是 不清楚这一观察结果是由于潜在的生物学差异还是AHI无法捕获异质性 在OSA。在目标1（a）中，我们计划测试SASHB/SAHR与糖尿病、高血压、 和CVD因年龄、性别和种族而异。最后，在目标2中，我们计划测试SASHB/SAHHR在预测 已确定的10年CVD风险（估计动脉粥样硬化性心血管疾病的合并队列方程）， 与AHI和其他传统CVD危险因素的比较。 总的来说，我们的建议有望证明SASHB/SAHR与不良心脏代谢相关性更强。 改善社区的结果，并改善CVD风险预测。此外，考虑到这些表型可以很容易地 从家庭睡眠测试设备中提取，这种个性化的医学方法将可能改变OSA的范式 社区的评估和管理。

PROJECT SUMMARY/ABSTRACT Obstructive sleep apnea is a serious condition associated with increased risk for major comorbidities, including hypertension, diabetes, and an increased risk of cardiovascular diseases (CVD) and mortality. The repetitive cycles of apnea/hypopnea during sleep lead to frequent drops in oxygen saturation and cardiac autonomic surges following obstructive respiratory events. These acute and repeated physiological changes are thought to play a key role in the development of adverse cardiometabolic consequences. Observational community-based cohort studies have shown strong associations of OSA with CVD outcomes. However, it has been challenging to identify/predict high risk individuals potentially due to substantial heterogeneity in OSA population. A potential explanation is the inability of apnea hypopnea index (AHI), the main metric for OSA diagnosis and management, to capture the heterogeneity in the OSA-related hypoxemia and autonomic responses during sleep. More importantly, these physiological changes are thought to vary by age, sex, and race. Therefore, the identification of phenotypic markers that better quantify OSA-specific hypoxemia and autonomic responses are needed to provide improved prediction of incident cardiometabolic outcomes. Herein, we propose to study two novel physiological phenotypes that can be readily extracted from pulse oximetry signal, the Sleep Apnea Specific Hypoxic Burden (“SASHB”) and the heart rate response to apneas/hypopneas (“∆HR”). We will use well-defined large community based prospective cohort studies, including the Sleep Heart Health study (SHHS), Multiethnic Study of Atherosclerosis (MESA), Hispanic community health study of Latinos (HCHS/SOL), the Outcomes of Sleep Disorders in Older Men (MrOS), and the Jackson Heart Study (JHS), and the Wisconsin Sleep Cohort (WSC) to pursue the following Aims. In Aim 1, we plan to use individual participant meta-analysis to examine how SASHB/∆HR determine the incident hypertension, diabetes, and CVD in adults with diverse ethnicity/background. Past studies have shown that the strength of associations between OSA, as quantified by AHI, and outcomes vary by age, sex, and race. It is unclear if this observation was due to underlying biological differences or the inability of AHI to capture the heterogeneity in OSA. In Aim 1(a), we plan to test the extent to which the associations of SASHB/∆HR and incident diabetes, hypertension, and CVD vary by age, sex, and race. Finally, in Aim 2, we plan to test the added value of SASHB/∆HR in predicting an established 10-year CVD risk (Pooled Cohort Equations to estimate the atherosclerotic cardiovascular disease) in comparison with AHI and other conventional CVD risk factors. Overall, our proposal is expected to demonstrate that SASHB/∆HR are more strongly linked with adverse cardiometabolic outcomes across the community and improve CVD risk prediction. Furthermore, given that these phenotypes can be easily extracted from home sleep testing devices, this personalized medicine approach will potentially alter the paradigm of OSA assessment and management in the community.