Circadian Role in Diurnal Pattern of Cardiovascular Risk

大量流行病学研究表明，心脏性猝死、肺栓塞、 心肌梗塞和中风有一个24小时的每日模式，在9：00到11：00之间有一个宽的高峰 AM.这种日常模式背后的机制尚不清楚。作为阐明 机制，我们提出了一种方法，结合昼夜心血管生理学与分析， 一个独特的现有数据集，使用新的统计方法，我们已经开发和证明， 心脏动力学和心脏风险的敏感指标。我们的主要机械目标是区分 对心脏动力学的（i）内在昼夜节律和（ii） 活动水平。我们将分析四个生理相关变量（心率，血液 压力，温度和活动水平），同时记录在表面上健康的 个体通过两个互补的昼夜节律协议，其中受试者的行为（包括 活动水平和睡眠-觉醒周期）受到控制并且环境是恒定的。这些方案是：（i） 10天强迫睡眠方案（其中受试者的睡眠-觉醒周期被调整为28小时， 他们的行为发生在所有昼夜节律阶段）和（ii）38小时恒定常规方案（其中 受试者保持清醒和半卧位）。核心体温将用作昼夜节律阶段 标记。从这些数据中，我们将提取动力学结构的补充统计指标， 新颖和灵敏的分析工具：（i）累积变化幅度分析;（ii）去趋势波动 分析;（ii）幅度标度分析;（iii）多重分形分析。对这些统计数据的分析， 与昼夜节律的相位的关系，或单独的行为模式，可以揭示心脏 与心血管脆弱性的日常模式相关的动力学，并将使我们能够推断出 可能构成心脏动力学基础的变量之间的机械联系。此外，通过比较指数 在相同的实验室条件下从年轻人和老年人中获得，我们的目标是揭示特定的 年龄相关的心脏动力学变化可能是年龄相关的心脏风险增加的基础。我们 未来的目标（超出本申请）将是确定健康个体的结果是否与 有心血管风险的患者。我们认为，这些研究最终可能有助于合理化 在改变活动水平或定时方面用于心血管疾病的时间生物学疗法 药物干预

Numerous epidemiological studies demonstrate that sudden cardiac death, pulmonary embolism, myocardial infarction, and stroke have a 24-hour daily pattern with a broad peak between 9:00to11:00 AM. The mechanisms underlying this daily pattern are unknown. As an important first step to elucidate mechanisms, we propose an approach that combines circadian cardiovascular physiology with analysis of a unique existing data set using novel statistical approaches that we have developed and shown to be sensitive indicators of cardiac dynamics and cardiac risk. Our primary mechanistic aim is to distinguish the separate effects upon cardiac dynamics of (i) the intrinsic circadian rhythm and of (ii)circadian variations in activity level. We will analyze existing data sets of four physiologically related variables (heart rate, blood pressure, temperature and activity level) that were simultaneously recorded in ostensibly healthy individuals throughout two complementary circadian protocols in which subjects' behaviors (including activity level and sleep-wake cycle) are controlled and the environment is constant. The protocols were: (i) a 10 day Forced Desynchrony protocol (wherein subjects' sleep-wake cycles are adjusted to 28 hours so that their behaviors occur across all circadian phases) and (ii) a 38 hour constant routine protocol (wherein subjects remain awake and semi-recumbent). Core body temperature will be used as a circadian phase marker. From these data we will extract complementary statistical indices of dynamical structure with our novel and sensitive analysis tools: (i) Cumulative Variation Amplitude Analysis; (ii)Detrended Fluctuation Analysis; (ii)Magnitude Scaling Analysis; and (iii) Multifractal Analysis. Analyses of these statistics in relation to the phase of the circadian rhythm, or separately the behavioral pattern, may reveal cardiac dynamics related to the daily pattern of cardiovascular vulnerability, and will enable us to deduce mechanistic links among variables that could underlie the cardiac dynamics. Further, by comparing indices obtained from young and older individuals under identical laboratory conditions, we aim to uncover specific age-related changes in cardiac dynamics that may underlie the age-related increase in cardiac risk. Our future aim (beyond this application) would be to determine whether the results in healthy individuals relate to patients with known cardiovascular risk. We believe that such studies may ultimately help rationalize chronobiological therapies for cardiovascular diseases in terms of modification of activity levels or timed pharmacological intervention.