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健康和满足需求的能力至关重要。 生命大多数心血管（CV）死亡发生在从睡眠中醒来的早期。例如，突然 心脏骤停（SCA）是一种非常普遍和毁灭性的疾病，在美国和欧洲， 美国所有与疾病相关的死亡原因的总和。除颤器可以预防SCA，但目前的临床策略 无论是在识别风险人群方面，还是重要的是，在监测和控制CV方面， 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-大脑相互作用 在睡眠中。据我们所知，我们引人注目的新初步数据和创新战略是前所未有的。 在对动物和人类进行的强有力的初步研究中，我们已经确定了进化的独特特征， 心电图（ECG）和脑电图（EEG）波形内的致死性CV疾病，否则无法 通过当前的临床方法和常规统计学检测。我们强大的新工具揭示了这些“隐藏” 睡眠期间的签名（即，随着有意识活动的减少和心脏的自主控制变得突出）。 我们已经确定了心脏、大脑、自发性内源性觉醒和严重CV疾病之间的缺失环节 独立于（在多变量分析中）睡眠呼吸障碍（例如，呼吸暂停）和确定的风险因素。我们 新的和非常有前途的发现可能解释了与睡眠相关的CV死亡的高发生率， 潜在的广泛应用，从动物模型，以改善目前认为个人的重新分类， 在当代临床实践中，风险被分为“低”、“中”或“高”风险。这一点很重要，因为无症状个体- 无晚期CV疾病的患者占SCA患者的大多数。它们也是被电流“错过”的 风险分层方法和最具挑战性的识别。此外，我们对脑电图和 ECG分析将为晚期CV疾病患者（例如，心脏 失败）。这种强大的，廉价的，个性化的策略，用于确定谁将和谁不需要救生治疗 还将避免不必要的程序和并发症，因此将提供巨大的社会经济效益。 NIH主任新创新者奖的这一范式转变应用将临床心脏电- 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，以实现对严重CV疾病的早期诊断和治疗。 我们独特的方法获得新的机制洞察心血管疾病的病理和风险在睡眠中很可能产生 多学科合作研究的新途径。因为我们的新模式可以无缝地融入 到现有的技术在医院和诊所现成的，我们希望我们的研究结果迅速改变概念， 在多个领域的临床实践。重要的是，识别和解码“隐藏”的脑电信号和心电图信号的能力 致死性亚临床CV疾病的早期发作，包括SCA，这是工业化研究中的主要死亡原因 在世界范围内，它对人类健康具有非凡的影响，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.