Eavesdropping on heart-brain conversations during sleep for early detection and prevention of fatal cardiovascular disease

摘要 心脏、大脑和睡眠之间的密切联系对我们的幸福感和满足需求的能力至关重要 生活的一部分。大多数心血管(CV)死亡发生在从睡眠中醒来的较早时间。例如，突然 心脏骤停(SCA)是一种极其普遍和毁灭性的疾病，每年夺走更多的生命(&gt；350,000/年)。 美国比所有与疾病相关的死因的总和还要多。除颤器可以预防SCA，但目前的临床策略 无论是在识别有风险的人方面，还是在监控和控制简历方面，都严重不足 风险。为了解决这一重大差距，我们提出了一种全新的方法来研究心脏和大脑的相互作用。 在睡觉的时候。据我们所知，我们令人信服的新初步数据和创新战略是前所未有的。 在对动物和人类的强有力的初步研究中，我们已经确定了进化的独特特征，潜在地 致命性心血管疾病，在其他情况下不能出现的心电图和脑电波形 通过目前的临床方法和常规统计方法进行检测。我们强大的新工具揭示了这些“隐藏的” 睡眠中的信号(即意识活动减少，心脏自主控制变得突出)。 我们已经确定了心脏、大脑、自发的内在觉醒和严重的心血管疾病之间缺失的联系 (在多变量分析中)独立于睡眠障碍呼吸(例如，呼吸暂停)和已确定的风险因素。我们的 新的和非常有希望的发现可能解释了与睡眠相关的心血管死亡的高发生率 潜在的广泛应用，从动物模型到改进的重新分类的个人目前认为- 在当代临床实践中，风险分为“低”、“中”或“高”。这一点很重要，因为无症状个体- 大多数SCA患者没有晚期心血管疾病。他们也是被Current“怀念”的人 风险分层方法和最具挑战性的识别。此外，我们对脑电和脑电的基本新方法 心电分析将为晚期心血管疾病(如心脏)患者增加新的、有临床价值的预后洞察力 失败)。这是一种强大、廉价、个性化的策略，用于确定谁将需要和将不需要救命治疗 也将避免不必要的程序和并发症，因此，将提供可观的社会经济效益。 NIH董事新创新者奖的这一范式转变申请纳入了临床心脏电信号- 生理学、重症监护和睡眠医学与工程学、数学、人工智能、统计动力学 系统，以及分子、细胞和临床研究，使危重心血管疾病的早期诊断和治疗成为可能。 我们对睡眠期间心血管病变和风险的独特机械洞察力可能会催生 多学科协作研究的新途径。因为我们的新范式可以无缝地结合在一起 应用到医院和诊所随时可用的现有技术中，我们预计我们的发现将迅速转变为 多领域的临时性临床实践。重要的是，识别和解码隐藏的脑电和心电信号的能力- 早期发病的致命性亚临床心血管疾病的类型，包括SCA，工业化人群的主要死亡原因 它对人类健康具有非同寻常的影响，并有可能在世界范围内广泛应用。

ABSTRACT The intimate link between the heart, brain and sleep is central to our well-being and ability to meet the demands of life. A majority of cardiovascular (CV) deaths occur in the early waking hours from sleep. For example, sudden cardiac arrest (SCA), an extremely prevalent and devastating condition, claims more lives (>350,000/year) in the USA than all disease-related causes of death combined. A defibrillator can prevent SCA, but current clinical strategies are grossly inadequate, both in terms of identifying people at risk and importantly, monitoring and controlling the CV risk. To address this major gap, we are proposing an entirely novel approach for studying heart-brain interactions during sleep. To our knowledge, our compelling new preliminary data and innovative strategy are unprecedented. In robust preliminary studies of animals and humans, we have identified unique signatures of evolving, potentially fatal, CV disease within electrocardiogram (ECG) and electroencephalogram (EEG) waveforms that otherwise cannot be detected by current clinical methods and conventional statistics. Our powerful new tools reveal these “hidden” signatures during sleep (i.e., as conscious activity decreases and autonomic control of the heart becomes prominent). This missing link we have identified between the heart, brain, spontaneous intrinsic arousals, and critical CV disease is independent (in multivariate analyses) of sleep disordered breathing (e.g., apnea) and established risk factors. Our novel and highly promising findings may account for the high incidence of CV deaths associated with sleep and have potential for broad application, ranging from animal models to improved reclassification of individuals currently consid- ered “low”, “moderate” or “high” risk in contemporary clinical practice. This is important because asymptomatic individ- uals without advanced CV disease comprise the majority of SCA victims. They also are the ones “missed” by current risk stratification methods and the most challenging to identify. Further, our fundamental new approach to EEG and ECG analysis will add new, clinically valuable, prognostic insight for patients with advanced CV disease (e.g., heart failure). This robust, inexpensive, personalized strategy for identifying who will and will not need lifesaving therapy will also avoid unnecessary procedures and complications, and thus, will provide substantial socioeconomic benefits. This paradigm-shifting application for a NIH Director’s New Innovator Award incorporates clinical cardiac electro- physiology, critical care and sleep medicine with engineering, mathematics, artificial intelligence, statistical dynamical systems, and molecular, cellular and clinical research to enable early diagnosis and therapy of critical CV disease. Our unique approach for gaining novel mechanistic insight into CV pathology and risk during sleep is likely to spawn new avenues in collaborative multidisciplinary research. Because our new paradigm can be seamlessly incorporated into existing technology readily available in hospitals and clinics, we expect our findings to rapidly transform contem- porary clinical practice in multiple fields. Importantly, the ability to identify and decode “hidden” EEG and ECG signa- tures of early onset of fatal, subclinical CV illness, including SCA, the leading cause of death in the industrialized world, has extraordinary implications for human health and the potential for broad worldwide application.