Abstract
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.