Sleep Patterns and Traditional Cardiovascular Health Metrics: Joint Impact on Major Adverse Cardiovascular Events in a Prospective Cohort Study
Si You, Haifeng Zhang, Shao‐Ling Zhang, Qing‐Yuan Gao, Yangwei Cai, Zegui Huang, Yu‐Biao Wu, Yangxin Chen, Jingfeng Wang, Jing‐Wei Gao, Pin‐Ming Liu
Abstract
Background This study examines the association between traditional cardiovascular health (CVH) metrics and major adverse cardiovascular events (MACE) incidence in individuals with diverse sleep patterns. Methods and Results We analyzed data from 208 621 participants initially free of cardiovascular disease (CVD) in the UK Biobank study. Sleep patterns were assessed using scores for chronotype, duration, insomnia, snoring, and daytime dozing. Traditional CVH scores were derived from the Life's Simple 7 metrics. Cox proportional hazards multivariate regression assessed associations between distinct combinations of CVH and sleep scores and MACE, including nonfatal myocardial infarction, nonfatal stroke, and CVD mortality. Over a mean follow‐up of 12.73 years, 9253 participants experienced incident MACE. Individuals with both a healthy sleep pattern and ideal CVH levels had the lowest MACE risk compared with those with a poor sleep pattern and poor CVH levels (hazard ratio, 0.306 [95% CI, 0.257–0.365]; P <0.001). Elevated CVH scores were associated with a reduced risk of MACE across different sleep patterns. Similar trends were observed for individual MACE components, heart failure, and all‐cause mortality. These findings remained robust in sensitivity analyses and across various subgroups. Conclusions In individuals without known CVD, maintaining a favorable sleep pattern and achieving optimal CVH levels, as measured by traditional metrics, were associated with the lowest MACE risk. Enhanced CVH significantly reduced CVD risk, even in individuals with a poor sleep pattern. These results emphasize the importance of considering multiple dimensions of sleep health alongside CVH to mitigate CVD risk. Registration URL: https://www.ukbiobank.ac.uk ; Unique identifier: 91090.