The Association Between Sleep Disorders and the Risk of Atherosclerotic Cardiovascular Disease: Regression Analysis and Neural Network Prediction Based on NHANES Data

Abstract Background: Sleep quality and duration play a critical role in cardiovascular health. In recent years, sleep disorders have been identified as potential risk factors for atherosclerotic cardiovascular disease (ASCVD). However, their independent effects and underlying mechanisms remain unclear. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) 2013–2018 were analyzed, including sleep quality, sleep duration, and ASCVD-related information from 4,791 participants. Spearman correlation analysis, restricted cubic spline regression, logistic regression, and neural network models were used to evaluate the relationship between sleep disorders and ASCVD risk and to assess the impact of related variables on ASCVD. Results: The findings revealed that sleep disorders were significantly associated with an increased ASCVD risk (OR = 1.69, 95% CI: 1.20–2.37, P = 0.0079). A “U-shaped” nonlinear relationship was observed between sleep duration and ASCVD, with the lowest risk identified at 6–8 hours of sleep. Compared to 6–8 hours of sleep, short sleep (<6 hours) and long sleep (>8 hours) were associated with 45% and 28% higher risks of ASCVD, respectively. The neural network model demonstrated good performance in terms of overall accuracy (87%) and AUC (0.91); however, its sensitivity for the diseased category was relatively low (76%), indicating a need for improved predictive performance under class imbalance conditions. Conclusion: Sleep disorders and abnormal sleep durations significantly increase the risk of ASCVD. Sleep disorders can serve as independent risk factors for ASCVD.