Zhang Tao, Du Xiaojiao, Gu Yue, Dong Yingying, Zhang Wei, Yuan Zhirong, Huang Xingmei, Zou Cao, Zhou Yafeng, Liu Zhiwei, Tao Hui, Yang Ling, Wu Gang, Hogenesch John B, Zhou Chengji J, Zhou Fei, Xu Ying
Cambridge-Su Genomic Resource Center, Jiangsu Key Laboratory of Neuropsychiatric Diseases Research, Suzhou Medical College of Soochow University, Suzhou, China.
Division of Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Front Physiol. 2022 Mar 8;13:835198. doi: 10.3389/fphys.2022.835198. eCollection 2022.
Circadian factors likely influence the occurrence, development, therapy, and prognosis of cardiovascular diseases (CVDs). To determine the association between the heart rate (HR) diurnal parameters and CVD risks, we designed an analytical strategy to detect diurnal rhythms of HR using longitudinal data collected by clinically used Holter monitors and wearable devices. By combining in-house developed algorithms with existing analytical tools, we obtained trough phase and nocturnal variation in HR for different purposes. The analytical strategy is robust and also sensitive enough to identify variations in HR rhythms influenced by multiple effectors such as jet lag, geological location and altitude, and age from total 211 volunteers. A total of 10,094 sets of 24-h Holter ECG data were analyzed by stepwise partial correlation to determine the critical points of HR trough phase and nocturnal variation. The following HR diurnal patterns correlate with high CVD risk: arrhythmic pattern, anti-phase pattern, rhythmic patterns with trough phase less than 0 (extremely advanced diurnal pattern) or more than 5 (extremely delayed diurnal pattern), and nocturnal variation less than 2.75 (extremely low) or more than 26 (extremely high). In addition, HR trough phases from wearable devices were nearly identical to those from 24-h Holter monitoring from 12 volunteers by linear correlation and Bland-Altman analysis. Our analytical system provides useful information to identify functional diurnal patterns and parameters by monitoring personalized, HR-based diurnal changes. These findings have important implications for understanding how a regular heart diurnal pattern benefits cardiac function and raising the possibility of non-pharmacological intervention against circadian related CVDs. With the rapid expansion of wearable devices, public cardiovascular health can be promoted if the analytical strategy is widely applied.
昼夜节律因素可能影响心血管疾病(CVD)的发生、发展、治疗及预后。为确定心率(HR)昼夜参数与CVD风险之间的关联,我们设计了一种分析策略,利用临床使用的动态心电图监测仪和可穿戴设备收集的纵向数据来检测HR的昼夜节律。通过将内部开发的算法与现有分析工具相结合,我们针对不同目的获得了HR的谷值相位和夜间变化情况。该分析策略稳健且灵敏,足以识别受时差、地理位置和海拔以及年龄等多种因素影响的HR节律变化,研究对象为总共211名志愿者。通过逐步偏相关分析了总共10,094组24小时动态心电图数据,以确定HR谷值相位和夜间变化的关键点。以下HR昼夜模式与高CVD风险相关:无节律模式、反相模式、谷值相位小于0(极度提前的昼夜模式)或大于5(极度延迟的昼夜模式)的节律模式,以及夜间变化小于2.75(极低)或大于26(极高)的模式。此外,通过线性相关和布兰德-奥特曼分析,12名志愿者可穿戴设备的HR谷值相位与24小时动态心电图监测的结果几乎相同。我们的分析系统通过监测基于HR的个性化昼夜变化,为识别功能性昼夜模式和参数提供了有用信息。这些发现对于理解正常的心脏昼夜模式如何有益于心脏功能以及增加针对与昼夜节律相关的CVD进行非药物干预的可能性具有重要意义。随着可穿戴设备的迅速普及,如果广泛应用该分析策略,公众心血管健康有望得到改善。