Purpose: Understanding the intricate relationships between sleep quality and cardiovascular outcomes can potentially offer new avenues in risk stratification for cardiovascular diseases (CVD). This study aimed to evaluate the significance of biological age predicted through the analysis of sleep stages and nocturnal heart rhythms as a marker for cardiovascular risk. Methods: We leveraged an unsupervised learning approach to generate time-series clusters utilizing whole-night sleep data from N=900 patients, focusing on identifying shifts and consistencies in nocturnal heart rhythms that may indicate variations in cardiac health. Following this, a deep learning model was applied to the time-series clusters to estimate the biological age of the individuals, thereby delineating potential relationships between predicted age, biological age, sleep patterns, and heart rhythms. Results: In a distinct test set of 736 individuals, the predicted age based on this experiment showcased a higher association with mortality (Hazard Ratio (HR) 2.27, p<0.05) and CVD risk (HR 3.56, p<0.001). Conversely, the age estimated through only nocturnal heart rhythms demonstrated a HR of 2.29 (p<0.05) for all-cause mortality and 3.13 (p<0.01) for CVD risk. Conclusion: Our findings underscore the high prognostic potential of sleep and electrocardiography data in predicting cardiovascular risks. The method of utilizing predicted biological age derived from sleep stages and nocturnal heart rhythms stands as a significant metric in risk stratification for CVD. Further research in this area might foster novel strategies for early interventions based on sleep quality and cardiac health markers, potentially saving numerous lives through early detection and intervention.

The authors have declared no competing interest.

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The SHHS dataset used in this study has been archived by the National Sleep Research Resource with appropriate de-identification. Permissions and access for these datasets were obtained via the online portal: www.sleepdata.org

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