Circadian Role in Diurnal Pattern of Cardiovascular Risk

大量的流行病学研究表明，心源性猝死、肺栓塞、 心肌梗死和中风有24小时的每日模式，在9：00到11：00之间有一个宽广的高峰 上午好。这种日常模式背后的机制尚不清楚。作为阐明以下问题的重要第一步 机制，我们提出了一种方法，结合昼夜心血管生理学和分析 一个独特的现有数据集，使用我们开发并证明是新的统计方法 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 (I)内在昼夜节律和(Ii)昼夜节律变化对心脏动力学的单独影响 活动级别。我们将分析四个生理相关变量(心率、血液)的现有数据集 压力、温度和活动水平)同时记录在表面健康的 在两个互补的昼夜节律方案中，受试者的行为(包括 活动水平和睡眠-觉醒周期)是可控制的，并且环境是恒定的。协议是：(I) 10天强制去同步方案(其中受试者的睡眠-醒来周期调整为28小时 它们的行为发生在所有昼夜节律阶段)和(Ii)38小时不变的例行公事(其中 受试者保持清醒和半卧位)。核心体温将被用作昼夜节律 记号笔。我们将从这些数据中提取动力结构的互补统计指标 新颖而灵敏的分析工具：(I)累计变化幅度分析；(Ii)消除趋势波动 分析；(Ii)幅度标度分析；和(Iii)多重分形分析。年对这些统计数据的分析 与昼夜节律阶段的关系，或单独的行为模式，可以揭示心脏 与心血管易损性的日常模式相关的动力学，将使我们能够推断 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 从年轻人和老年人在相同的实验室条件下获得的，我们的目标是揭示特定的 心脏动力学的年龄相关变化可能是与年龄相关的心脏风险增加的基础。我们的 未来的目标(超越这一应用)将是确定健康个体的结果是否与 已知有心血管风险的患者。我们相信，这样的研究最终可能有助于使 心血管疾病的时间生物学治疗：活动水平或时间的改变 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.