Circadian Role in Diurnal Pattern of Cardiovascular Risk

大量流行病学研究表明，心源性猝死、肺栓塞、 心肌梗塞和中风具有每天 24 小时的模式，在 9:00 至 11:00 之间出现宽峰 上午。这种日常模式背后的机制尚不清楚。作为阐明的重要的第一步 机制，我们提出了一种将昼夜心血管生理学与分析相结合的方法 使用我们开发并证明的新颖统计方法的独特现有数据集 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）同时记录在表面健康的情况下 个体在两个互补的昼夜节律协议中，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。协议是：（i） 10 天的强制不同步方案（其中受试者的睡眠-觉醒周期调整为 28 小时，以便 他们的行为发生在所有昼夜节律阶段）和 (ii) 38 小时恒定的常规方案（其中 受试者保持清醒且半卧位）。核心体温将用作昼夜节律阶段 标记。从这些数据中，我们将使用我们的方法提取动态结构的补充统计指数 新颖且灵敏的分析工具：（i）累积变化幅度分析； (ii)去趋势波动 分析； (ii)震级分析； (iii) 多重分形分析。对这些统计数据的分析 与昼夜节律的阶段或单独的行为模式的关系，可以揭示心脏 与心血管脆弱性的日常模式相关的动态，并使我们能够推断出 可能构成心脏动力学基础的变量之间的机械联系。进一步，通过比较指数 在相同的实验室条件下从年轻人和老年人身上获得数据，我们的目标是发现特定的 与年龄相关的心脏动力学变化可能是与年龄相关的心脏病风险增加的基础。我们的 未来的目标（超出本应用范围）是确定健康个体的结果是否相关 已知有心血管风险的患者。我们相信，此类研究最终可能有助于合理化 通过改变活动水平或定时来治疗心血管疾病 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.