In Chronobiology international
Cardiovascular physiology and pathophysiology display pronounced circadian rhythms. The study is designed to examine whether the time of day of physical activity is associated with cardiovascular mortality. We analyzed 94,489 UK Biobank adults with objectively measured physical activity, including 53,328 morning-type participants and 30,962 evening-type participants based on self-reported chronotypes. The time of day of peak physical activity was categorized using a machine learning algorithm: early morning (n = 18,477), late morning (n = 25,700), midday (reference) (n = 27,803), and night (n = 22,509). Hazard ratios of cardiovascular mortality were examined using the Cox proportional hazards model. During a median follow-up of 6.9 years (interquartile range, 6.3-7.4 years), we identified 629 cardiovascular deaths. The hazard of cardiovascular mortality was elevated in the early morning group (hazard ratio = 1.56, 95% Confidence Interval [1.23-1.98]) and night group (1.49, [1.18-1.88]) but not in the late morning group (1.21, [0.98-1.47]) compared to the referent midday group. In the chronotype-stratified analysis, the increased cardiovascular mortality in the morning group was only observed in the evening-type participants, while the increased cardiovascular mortality in the night group was only observed in the morning-type participants. In conclusion, optimizing the timing of peak physical activity according to cardiovascular circadian rhythms and individual chronotypes could be a potential therapeutic target that brings additional health benefits.
Ma Tongyu, Jennings Lydia, Sirard John R, Xie Yao Jie, Lee Chong-Do
2023-Jan-24
Cohort study, accelerometry, cardiovascular physiology, chronotype, circadian rhythm, machine learning, timing of physical activity
