Circadian Role in Diurnal Pattern of Cardiovascular Risk

描述（由申请人提供）： 许多流行病学研究表明，心脏性猝死、肺栓塞、心肌梗死和中风具有24小时的每日模式，在上午9：00至11：00之间具有宽峰。 这种日常模式背后的机制尚不清楚。 作为阐明机制的重要的第一步，我们提出了一种创新，将昼夜心血管生理学与使用我们开发的新型统计方法分析独特的现有数据集相结合，并证明这些方法是心脏动力学和心脏风险的敏感指标。 我们的主要机制目的是区分（i）内在昼夜节律和（ii）与睡眠-觉醒周期和活动水平相关的日常行为模式对心脏动力学的单独影响。我们将分析四个生理相关变量（心率，血压，体温和活动水平）的现有数据集，这些数据集在两个互补的昼夜节律协议中同时记录在表面健康的个体中，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，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，使得他们的行为发生在所有昼夜节律相位上）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相位标记。 从这些数据中，我们将提取动力学结构和同步的补充统计指标与我们的新的和敏感的分析工具：（i）去趋势波动分析;（ii）幅度和符号分析;（iii）小波变换;（iv）希尔伯特变换;（v）分形和多重分形分析;（vi）相位同步分析。 这些统计数据的分析与昼夜节律的阶段，或单独的行为模式可以揭示心脏动力学相关的日常模式的心血管脆弱性。 此外，同步模式之间的生理相关的变量的分析将使我们能够推断出机械变量之间的联系，可能是心脏动力学。 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已知心血管风险的患者有关。

DESCRIPTION (provided by applicant): 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 9:00 to 11:00 AM. The mechanisms underlying this daily pattern are unknown. As an important first step to elucidate mechanisms, we propose an innovation 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 (ii) the daily pattern in behavior related to the sleep-wake cycle and 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 10day 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 and synchronization with our novel and sensitive analysis tools: (i) Detrended Fluctuation Analysis; (ii) Magnitude and Sign Analysis; (iii) Wavelet Transform; (iv) Hilbert Transform; (v) Fractal and Multifractal Analysis; (vi) Phase Synchronization 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. Furthermore, analysis of synchronization patterns among the physiologically related variables will enable us to deduce mechanistic links among variables that could underlie the cardiac dynamics. Our future aim (beyond this application) would be to determine whether the results in healthy individuals relate to patients with known cardiovascular risk.