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

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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 (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. Sleep disorders Atherosclerotic cardiovascular disease (ASCVD) NHANES Neural network model Figures Figure 1 Figure 2 Figure 3 1 Introduction Sleep is a vital component of life, playing a crucial role in physiological functions, immune system regulation, and metabolic processes. This regulatory effect not only supports host defense during infections but also maintains inflammatory homeostasis, thereby influencing the development of various chronic diseases [ 1 ] . However, with modern lifestyle changes, the prevalence of sleep disorders has significantly increased, becoming a major public health challenge. According to data from the 2017–2020 National Health and Nutrition Examination Survey (NHANES), 29.8% of U.S. adults reported experiencing sleep disorders, and 27.2% experienced daytime sleepiness [ 2 ] . Additionally, the 2014 Behavioral Risk Factor Surveillance System (BRFSS) data indicated that over one-third of American adults slept less than seven hours per night, a behavior significantly associated with obesity, diabetes, hypertension, and other health issues [ 3 ] . Sleep disorders not only negatively impact individual quality of life and mental health but are also closely linked to multiple chronic diseases, particularly cardiovascular diseases [ 4 – 6 ] . Atherosclerotic cardiovascular disease (ASCVD), a major subtype of cardiovascular diseases, is one of the leading causes of global mortality and disability. Its pathological core lies in atherosclerosis, a process driven by lipid metabolism, chronic inflammation, and endothelial dysfunction [ 7 , 8 ] . Although traditional risk factors such as elevated low-density lipoprotein cholesterol (LDL-C), hypertension, and smoking have been extensively studied and validated, many patients continue to face residual risks even after significant control of these factors through medications [ 9 – 11 ] . Recently, sleep disorders have emerged as potential risk factors for ASCVD, garnering increasing attention in clinical and epidemiological research [ 12 ] . This phenomenon highlights the need to explore less well-defined non-traditional risk factors, among which sleep disorders and abnormal sleep duration may play critical roles. Abnormal sleep duration may affect cardiovascular health through metabolic, oxidative, and autonomic pathways. Previous studies have shown that sleep disorders may influence cardiovascular health through multiple mechanisms, including activation of the sympathetic nervous system, disruption of circadian rhythms, exacerbation of inflammatory responses, and increased oxidative stress levels [ 13 – 16 ] . Obstructive sleep apnea (OSA) and chronic insomnia have been demonstrated to significantly elevate the risk of cardiovascular diseases [ 17 , 18 ] . Furthermore, the relationship between sleep duration and cardiovascular metabolic risk exhibits a nonlinear pattern, with both short and long sleep durations associated with increased cardiovascular risk [ 19 ] . This "U-shaped" trend highlights that maintaining an optimal sleep duration of 6–8 hours may be a crucial strategy for protecting cardiovascular health. However, most existing studies are limited to specific populations or small sample sizes, lacking large-scale and representative data. Additionally, there remains considerable debate regarding whether sleep disorders serve as independent risk factors for ASCVD and their effects after adjusting for multiple covariates. To address these gaps, this study utilized multi-cycle data from the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2018. Through methods such as restricted cubic spline regression, logistic regression analysis, and neural network modeling, the study systematically examined the relationship between sleep disorders, sleep duration, and ASCVD risk. The objectives of this study were to elucidate the potential mechanisms by which sleep-related factors influence the risk of ASCVD, verify the role of sleep disorders as independent risk factors for ASCVD, and explore precise prevention strategies for ASCVD based on sleep health management. This study explores the link between sleep problems and ASCVD to guide prevention and integrate sleep management into public health. 2 Materials and Methods 2.1 Clinical Data The data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES) ( https://www.cdc.gov/nchs/nhanes/index.htm ), a cross-sectional survey conducted by the National Center for Health Statistics (NCHS) in collaboration with the Centers for Disease Control and Prevention (CDC). NHANES aims to provide nationally representative data on the civilian, non-institutionalized population of the United States. The data collection protocol was approved by the NCHS Ethics Review Board, and all survey participants provided informed consent before undergoing interviews and examinations. In this study, data from three NHANES cycles (2013–2014, 2015–2016, and 2017–2018) were combined into a dataset using publicly available response data files. The study population included all respondents from these NHANES cycles. 2.2 Data Collection The covariates used in this study were selected based on prior literature and substantive reasoning and were considered to have potential associations with sleep quality and cardiovascular disease prevalence. Information on a variety of factors was collected using standardized questionnaires, including age, gender, race, education level, marital status, poverty-to-income ratio (PIR), body mass index (BMI), sedentary time, moderate activity duration, smoking status, alcohol use, and medical comorbidities such as hypertension, hyperlipidemia, and diabetes. Missing data were supplemented using imputation methods implemented in R software. Survey data from 17,962 participants were initially collected for this study. After excluding individuals with missing outcome or exposure data, as well as those who did not provide complete information on BMI, education level, marital status, PIR, smoking, or alcohol use, a total of 4,719 participants were included in the final analysis. According to the 2013 American College of Cardiology/American Heart Association (ACC/AHA) Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults, ASCVD is defined as having at least one diagnosis of coronary heart disease, angina, myocardial infarction, or stroke, with myocardial infarction and stroke classified as strict criteria. In this study, the assessment of ASCVD was based on a series of self-reported questionnaires. Participants were identified as having ASCVD if they responded affirmatively to the NHANES multiple-choice question (MCQ): "Have you ever been told you have coronary heart disease, angina, a heart attack, or a stroke?" Sleep disorders were identified based on affirmative responses to either of the following questions: “Have you ever told a doctor or other health professional that you have trouble sleeping?” or “Has a doctor or other health professional ever told you that you have a sleep disorder?” Sleep duration was self-reported in response to the question: “How many hours of sleep do you usually get at night on weekdays or workdays?” The reported sleep duration was categorized as short ( 9 hours/night). Hypertension was defined as participants self-reporting a diagnosis of hypertension, having an average systolic blood pressure (SBP) ≥ 130 mmHg or an average diastolic blood pressure (DBP) ≥ 80 mmHg, or currently taking antihypertensive medications. Hyperlipidemia was defined based on the criteria of the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III), including total cholesterol ≥ 200 mg/dL, triglycerides ≥ 150 mg/dL, high-density lipoprotein cholesterol (HDL-C) levels < 40 mg/dL for men or < 50 mg/dL for women, or low-density lipoprotein cholesterol (LDL-C) levels ≥ 130 mg/dL. Additionally, participants who reported using lipid-lowering medications were also classified as having hyperlipidemia. 2.3 Statistical Analysis Statistical analyses were performed using SPSS 25.0 software, and the sample was divided into two groups: ASCVD and non-ASCVD. Continuous variables were presented as mean ± standard deviation (SD), while categorical variables were expressed as numbers and percentages. For normally distributed continuous data, the results were described as mean ± SD (x ± sx ± sx ± s) and evaluated using t-tests to assess group differences. For non-normally distributed data, results were described as medians with interquartile ranges (M [P25, P75]). First, Spearman correlation analysis was conducted to explore the association between ASCVD and the included variables. Then, multivariate linear regression was used to further investigate the indirect effects of other variables on ASCVD through the Atherogenic Index of Plasma (AIP). Restricted cubic spline (RCS) regression was applied to explore the nonlinear relationship between sleep duration and ASCVD. Logistic regression was employed to identify the causal influence of sleep disorders on ASCVD. Finally, a neural network model was constructed to classify and predict ASCVD and to assess the importance of associated variables. Statistical significance was determined with a threshold of P ≤ 0.05. 3 Results 3.1 Baseline Characteristics of Patients The distribution of patient characteristics grouped by the presence or absence of ASCVD revealed significant differences across multiple clinical variables in the preliminary analysis. Among individuals with ASCVD (n=359) and those without ASCVD (n=4360), there were notable disparities. Specifically, in the group with sleep disorders, 192 individuals (53.48%) had ASCVD, compared to 167 individuals (46.52%) in the group without sleep disorders. This indicates that sleep disorders are significantly associated with an increased risk of ASCVD (P<0.001). The mean age of the ASCVD group was 67 years (range: 57–75), significantly higher than the mean age of 46 years (range: 33–60) in the non-ASCVD group (P<0.001). Regarding educational level and household income, a higher proportion of individuals with ASCVD had lower income and education levels (P<0.001), suggesting that lower socioeconomic status may increase the risk of ASCVD. The median Atherogenic Index of Plasma (AIP) in the ASCVD group was 1.82 (interquartile range: 1.53–2.18), higher than the 1.69 (1.56–1.88) observed in the non-ASCVD group (P=0.049). The mean BMI of the ASCVD group was 30.58 ± 8.04, significantly higher than 29.07 ± 7.13 in the non-ASCVD group (P<0.001). In terms of chronic conditions, the prevalence of diabetes and hypertension was significantly higher in the ASCVD group compared to the non-ASCVD group (P<0.001). The prevalence of hyperlipidemia showed borderline significance (P=0.064). Additionally, the ASCVD group had a higher proportion of current or former smokers and a lower proportion of individuals who had never consumed alcohol compared to the non-ASCVD group (P9 hours/night or <7 hours/night) was coded as 1, while normal sleep duration (7–9 hours/night) was coded as 0. According to the results of Spearman correlation analysis, sleep disorders, sleep quality, diabetes, hypertension, smoking, sedentary behavior, education level, income level, gender, age, and BMI were significantly correlated with ASCVD. Specifically, the presence of sleep disorders and abnormal sleep quality were positively associated with ASCVD (r=0.150**, p<0.01; r=0.055**, p<0.01), indicating that individuals with poor sleep quality may have a higher likelihood of developing ASCVD. Additionally, ASCVD risk increased significantly with age (r=0.272**, p<0.01). Smoking, sedentary behavior, hypertension, and diabetes also showed significant positive correlations with ASCVD (p<0.01). Conversely, income level was negatively correlated with ASCVD (p<0.01), suggesting that socioeconomic status influences the prevalence of cardiovascular disease (Table 2). 3.3 Exploration of the Nonlinear Relationship Between Sleep Duration and ASCVD Using Restricted Cubic Spline (RCS) Regression In the RCS regression analysis, a significant nonlinear relationship between sleep duration and ASCVD was detected after adjusting for potential covariates (P<0.001 for the test of nonlinearity). The relationship exhibited a "U-shaped" pattern, with the lowest risk observed at approximately 6 to 8 hours of sleep per night. Sleep durations under 6 or over 8 hours were linked to progressively higher ASCVD risk (Figure 1). 3.4 Multivariate Linear Regression Analysis of the Relationship Between AIP and ASCVD The Atherogenic Index of Plasma (AIP) is an important biomarker for identifying ASCVD. By analyzing AIP, further insights into the factors influencing ASCVD were obtained. Results from the multivariate linear regression model showed a significant association between ASCVD and AIP. In addition, age (P=0.033), race (P=0.036), and hyperlipidemia (P<0.001) were also significantly associated with elevated AIP levels (Table 3). 3.5 Logistic Regression Analysis of the Association Between Sleep Disorders and ASCVD Multivariable logistic regression analysis was conducted to determine whether sleep disorders are a risk factor for ASCVD, using participants without sleep disorders as the reference group. In the unadjusted analysis, the odds ratio (OR) for ASCVD risk associated with sleep disorders was 2.04 (95% CI: 1.44–2.89; P<0.001). After adjusting for age, gender, and race (Model 1), the OR decreased to 1.77 (95% CI: 1.22–2.57; P=0.0029). Further adjustment for BMI categories, sedentary time, and physical activity duration (Model 2) slightly reduced the OR to 1.73 (95% CI: 1.19–2.52; P=0.0041). In the fully adjusted model (Model 3), which accounted for marital status, educational level, income level, smoking, and alcohol consumption, the OR for ASCVD risk associated with sleep disorders was 1.69 (95% CI: 1.15–2.49; P=0.0079). The results demonstrated high statistical significance, and although the OR values varied slightly across models, the association between sleep disorders and ASCVD remained significant after adjusting for all covariates. This indicates that sleep disorders are an independent risk factor for ASCVD. These findings underscore the importance of improving sleep quality and addressing sleep disorders as key strategies for ASCVD prevention (Table 4). 3.6 Neural Network Model for Predicting ASCVD: Scoring System 3.6.1 Model Evaluation The confusion matrix of the model (Figure 2A) showed a true positive (TP) count of 509 and a true negative (TN) count of 16, with a total sample size of 603. The overall accuracy of the model was 87%. When addressing the issue of class imbalance, the model performed well in predicting the non-diseased category, achieving a precision of 94% and a recall of 92%, indicating strong recognition ability for this majority class. However, for the diseased category, the model exhibited weaker predictive performance, with a precision of 26% and a recall of 33%, reflecting its limited ability to identify this minority class (Figure 2B). An analysis of the raw data suggests that this performance disparity may stem from class imbalance, as the non-diseased group significantly outnumbered the diseased group. 3.6.2 Model Prediction Results and Scoring Performance Based on the statistical analysis of the model’s prediction results, the study included a total of 603 samples. Among these, 542 samples were predicted as belonging to the non-diseased category, while 61 samples were predicted as belonging to the diseased category, indicating that approximately 89.8% of the samples were classified as non-diseased. The probability statistics showed an average predicted probability of 0.13562 with a standard deviation of 0.25406. The minimum predicted probability was close to zero, while the maximum reached 0.9999, demonstrating the model’s high confidence for certain samples, particularly in positive class predictions. Regarding the scoring statistics, the average score of the samples was 71.1733 points with a standard deviation of 15.2422 points, and the score range varied from 20.001917 to 80 points. While most samples received relatively high scores, the model exhibited notable variation in performance across different samples. Specifically, the score distribution showed a certain degree of central tendency but also displayed some dispersion, particularly in the high-score range (Table 5). The probability distribution bar chart in Figure 3A reveals that the predicted probabilities for most samples are concentrated at lower levels, indicating that the model tends to conservatively predict the likelihood of disease. Meanwhile, the score distribution bar chart in Figure 3B shows that the majority of sample scores are at relatively high levels, though there is noticeable variability between scores. This suggests that the model achieves a balance between consistency and flexibility when evaluating different samples (Figure3). 4 Discussion ASCVD is a leading cause of global mortality and disability, underscoring its significant public health implications [ 20 ] . While traditional risk factors such as hypertension, elevated LDL-C, and smoking have been extensively studied and validated, sleep disorders have recently emerged as novel, non-traditional risk factors that warrant attention [ 12 ] . By analyzing NHANES data, this study systematically explored the association between sleep disorders and ASCVD risk, revealing potential biological mechanisms underlying this relationship. The findings demonstrated that both sleep disorders and abnormal sleep durations significantly increase ASCVD risk, potentially impacting cardiovascular health through multiple biological pathways. In this study, restricted cubic spline regression analysis revealed a "U-shaped" nonlinear relationship between sleep duration and ASCVD risk. Specifically, individuals sleeping 6 to 8 hours per night exhibited the lowest ASCVD risk, whereas those sleeping less than 6 hours or more than 8 hours had significantly increased risks. This finding aligns with previous research, further confirming the importance of sleep duration for cardiovascular health [ 21 ] . A Mendelian randomization study further validated this "U-shaped" nonlinear relationship, showing that genetically predicted short sleep duration (≤ 6 hours) significantly increased the risk of coronary artery disease, myocardial infarction, and arterial hypertension, while long sleep duration (≥ 9 hours) was not associated with a significant causal relationship [ 22 ] . Additionally, prior studies have suggested that short sleep duration may elevate ASCVD risk through various mechanisms [ 23 ] . On the other hand, sleep duration may also be closely linked to conditions such as depression, chronic inflammation, and cardiac dysfunction [ 14 , 24 , 25 ] . Logistic regression analysis further confirmed that sleep disorders are independent risk factors for ASCVD. After comprehensive adjustments for covariates including age, gender, BMI, income level, and health behaviors, the significant association between sleep disorders and ASCVD risk persisted. Sleep disorders may impact cardiovascular health via metabolic, oxidative, and autonomic pathways, driving atherosclerosis, which contribute to the development and progression of atherosclerosis. These mechanisms are consistent with previous literature discussing the interaction between sleep disorders, oxidative stress, and inflammatory responses [ 15 , 26 , 27 ] . Moreover, the study found that the prevalence of sleep disorders was significantly higher among middle-aged and older adults, particularly those with other chronic conditions such as hypertension and diabetes [ 19 ] . This finding underscores the importance of prioritizing sleep health in public health interventions targeting middle-aged and older populations to reduce the risk of ASCVD. Through multivariate linear regression analysis, this study also highlighted a significant association between the Atherogenic Index of Plasma (AIP) and ASCVD risk, establishing AIP as an important predictive marker for ASCVD. The results showed that factors such as age, hyperlipidemia, and racial differences significantly influenced AIP levels, suggesting that clinical practice should integrate multiple factors to optimize ASCVD risk assessment. Furthermore, this study attempted to construct a neural network model to predict ASCVD risk and evaluate the importance of sleep-related factors. Although the model performed well in terms of overall accuracy, its predictive ability for the diseased category was relatively weak. This limitation may be attributed to class imbalance in the sample and feature selection within the model. Future studies could improve model performance by optimizing data preprocessing, increasing sample size, and exploring more complex model architectures, such as deep learning. Additionally, enhancing the interpretability of the neural network model is necessary to facilitate its application in clinical practice. This study holds significant clinical and public health implications. These findings highlight the value of sleep management in ASCVD prevention and health education. The study confirms AIP and sleep disorders as ASCVD risk factors, aiding personalized interventions. Neural networks offer new potential for predicting cardiovascular risk, encouraging future use of multi-omics data. However, the study has certain limitations. First, the cross-sectional nature of the data does not allow for establishing causal relationships; longitudinal studies are needed to further validate these findings. Second, the sample was derived from a U.S. population, potentially introducing racial and regional biases; future research should verify the generalizability of the results in different populations. Finally, the performance of the neural network model was limited by sample size and feature selection; future studies could enhance model performance by integrating multimodal data. 5 Conclusion In conclusion, this study utilized multi-cycle NHANES data to reveal a significant association between sleep disorders and ASCVD risk, further confirming the "U-shaped" nonlinear relationship of sleep duration and the potential role of sleep disorders as independent risk factors for ASCVD. The findings underscore the importance of incorporating sleep health management into ASCVD prevention strategies and provide a new perspective for the individualized prevention and management of cardiovascular diseases. Abbreviations National Health and Nutrition Examination Survey (NHANES), Behavioral Risk Factor Surveillance System (BRFSS), Atherosclerotic cardiovascular disease (ASCVD), low-density lipoprotein cholesterol (LDL-C),Obstructive sleep apnea (OSA), he National Center for Health Statistics (NCHS), the Centers for Disease Control and Prevention (CDC), poverty-to-income ratio (PIR), body mass index (BMI), multiple-choice questions (MCQ), the Patient Health Questionnaire (PHQ-9) Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Data Availability Statement The research data was sourced from the NHANES database, with the data website being https://www.cdc.gov/nchs/nhanes/index.html. Competing interests The authors declare that they have no competing interests. Funding The research was funded by National Natural Science Foundation of China (Grant no. 82400543). Authors' contributions Ling-bing Meng make the acquisition, analysis, Kongyong Cui interpretate of data; Yanjun Song and Bowen Li create of new software used in the work; Yongbao Zhang and Kefei Dou have drafted the work or substantively revised it. Acknowledgements Not applicable References Besedovsky L, Lange T, Haack M. The Sleep-Immune Crosstalk in Health and Disease. Physiol Rev. 2019. 99(3): 1325-1380. Di H, Guo Y, Daghlas I, et al. Evaluation of Sleep Habits and Disturbances Among US Adults, 2017-2020. JAMA Netw Open. 2022. 5(11): e2240788. Liu Y, Wheaton AG, Chapman DP, Cunningham TJ, Lu H, Croft JB. Prevalence of Healthy Sleep Duration among Adults--United States, 2014. MMWR Morb Mortal Wkly Rep. 2016. 65(6): 137-41. Reimer MA, Flemons WW. Quality of life in sleep disorders. Sleep Med Rev. 2003. 7(4): 335-49. Lyons OD. Sleep disorders in chronic kidney disease. Nat Rev Nephrol. 2024. 20(10): 690-700. Covassin N, Somers VK. Sleep, melatonin, and cardiovascular disease. 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Tables Table 1: Baseline Characteristics Variable ASCVD P-value No(n=4360) Yes（n=359） Age 46(33~60) 67(57~75) <0.001 Gender Male 2039(46.77%) 191(53.20%) 0.019 Female 2321(53.23%) 168(46.80%) Race Mexican American 567(13.00%) 35(9.75%) 0.203 Non-Hispanic White 377(8.65%) 29(8.08%) Non-Hispanic Black 1883(43.19%) 197(54.87%) Non-Hispanic Asian 872(20.00%) 70(19.50%) Other Hispanic 661(15.16%) 28(7.80%) Education level < high school 299(6.86%) 43(11.98%) <0.001 nine-eleven year 552(12.66%) 63(17.55%) high school 964(22.11%) 95(26.46%) college or above 2545(58.37%) 158(44.01%) Marial status live alone 2606(59.77%) 204(56.82%) 0.274 not live alone 1754(40.23%) 155(43.18%) Income status low income 1475(33.83%) 156(43.45%) <0.001 middle income 1486(34.08%) 132(36.77%) high income 1399(32.09%) 71(19.78%) AIP 1.69(1.56,1.88) 1.82(1.53,2.18) 0.049 BMI 29.07±7.13 30.58±8.04 <0.001 Sitting time(min) 441.05±520.14 447.28±202.19 0.822 Physical activity time(min) 63.04±58.65 66.02±98.21 0.619 Sleep duration (hours) 9 123(2.82%) 21(5.85%) Sleep disorder No 3154(72.34%) 167(46.52%) <0.001 Yes 1206(27.66%) 192(53.48%) Smoke Never 2535(58.14%) 149(41.50%) <0.001 Former 939(21.54%) 122(33.98%) Current 886(20.32%) 88(24.51%) Drink Never Drank 1537(35.25%) 184(51.25%) <0.001 Occasionally Drank 1519(34.84%) 114(31.75%) Regularly Drank 955(21.90%) 38(10.58%) Heavy Drinker 349(8.00%) 23(6.41%) Hypertension No 3800(87.16%) 160(44.57%) <0.001 Yes 560(12.84%) 199(55.43%) Hyperlipidemia No 2700(61.93%) 240(66.85%) 0.064 Yes 1660(38.07%) 119(33.15%) Diabetes No 3877(88.92%) 134(37.33%) <0.001 Yes 483(11.08%) 225(62.67%) BMI, body mass index; PIR, poverty income ratio; AIP, Atherogenic Index of Plasma; ASCVD, atherosclerotic cardiovascular disease. Table 2: Spearman correlation analysis of ASCVD Variable Correlation Coefficient P-value Age 0.272** <0.01 Gender -0.034* 0.019 Race -0.0024 0.106 BMI 0.051** <0.01 Marital status 0.016 0.274 Education level 0.082** <0.01 Income level -0.072** <0.01 Sitting time 0.035* 0.017 physical active time -0.043 0.058 Smoke 0.077** <0.01 Drink -0.065 0.233 Hypertension 0.234** <0.01 Hyperlipidemia 0.027 0.064 Diabetes 0.206** <0.01 Sleep disorder 0.150** <0.01 Sleep quality status 0.055** <0.01 *When the confidence level (double test) is 0.05, the correlation is significant. **When the confidence level (double test) is 0.01, the correlation is significant. Table3: Multiple linear regression model for AIP B S. E t P-value VIF Age 0.004 0.002 2.145 0.033 1.181 Gender 0.050 0.047 1.050 0.295 1.217 Race 0.040 0.019 2.109 0.036 1.183 Marital status 0.065 0.047 1.388 0.166 1.207 Education level -0.003 0.025 -0.141 0.888 1.344 Income level -0.018 0.048 -0.374 0.709 1.255 Sitting time 0.000 0.000 -0.391 0.696 1.103 Physical active time 0.000 0.000 -1.006 0.315 1.078 Smoke 0.020 0.031 0.652 0.515 1.298 Drink 0.022 0.025 0.887 0.376 1.154 Hypertension 0.067 0.062 1.090 0.277 1.088 Hyperlipidemia 0.271*** 0.045 6.083 <0.001 1.082 ASCVD 0.134** 0.065 2.051 0.041 1.138 *When the confidence level (double test) is 0.05, the correlation is significant. **When the confidence level (double test) is 0.01, the correlation is significant. Table 4: Logistic regression analysis of inertia between sleep disorder and ASCVD B S. E OR (95%CI) P-value Un adjusted 0.714 0.177 2.04(1.44,2.89) <0.001 Model 1 0.570 0.191 1.77(1.22,2.57) 0.0029 Model 2 0.550 0.192 1.73(1.19,2.52) 0.0041 Model 3 0.524 0.197 1.69(1.15,2.49) 0.0079 Model 1 was adjusted for Age, Gender, Race. Model 2 was adjusted for Age, Gender, Race, BMI group, Sitting time, Physical active time. Model 3 was adjusted for Age, Gender, Race, BMI group, Marital status, Education level, Income level, Sitting time, Physical active time, Smoke, Drink. Table 5: Statistical Results of Prediction Probabilities/Scores Predicted-Disease Category（61） Predicted-Non-Disease Category（542） count mean std min 25% 50% 75% max Probability Statistics 603 0.13562 0.25406 0.000009 0.0000145 0.0059 0.12707 0.9999 Score Statistics 603 71.1733 15.2422 20.001917 69.834397 79.5273 79.9988 80 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Mar, 2026 Read the published version in Journal of Cardiothoracic Surgery → Version 1 posted Editorial decision: Revision requested 24 Dec, 2025 Reviews received at journal 31 Aug, 2025 Reviews received at journal 24 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers invited by journal 14 Aug, 2025 Editor assigned by journal 10 Jul, 2025 Submission checks completed at journal 10 Jul, 2025 First submitted to journal 09 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7088296","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503897808,"identity":"1667ad5d-36c2-4bd5-9168-5806718fb71a","order_by":0,"name":"Ling-bing Meng","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ling-bing","middleName":"","lastName":"Meng","suffix":""},{"id":503897809,"identity":"bb58ee5a-9341-42cf-9139-200340ce3076","order_by":1,"name":"Kongyong Cui","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Kongyong","middleName":"","lastName":"Cui","suffix":""},{"id":503897810,"identity":"8bd5ea21-456d-4fa5-9b10-af9b5a33eaff","order_by":2,"name":"Yanjun Song","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yanjun","middleName":"","lastName":"Song","suffix":""},{"id":503897811,"identity":"fff20a89-67c3-4131-82b4-a004bad34def","order_by":3,"name":"Bowen Li","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Bowen","middleName":"","lastName":"Li","suffix":""},{"id":503897812,"identity":"0e371529-b191-4916-a442-d990218cb035","order_by":4,"name":"Yongbao Zhang","email":"","orcid":"","institution":"Fuwai Hospital, National Centre for Cardiovascular Diseases","correspondingAuthor":false,"prefix":"","firstName":"Yongbao","middleName":"","lastName":"Zhang","suffix":""},{"id":503897813,"identity":"f8aa14de-1650-4bf0-a3c9-536141903d6c","order_by":5,"name":"Kefei Dou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDCCAwwGIIKfgZn5wIEPFSRokWxgZks8OOMMSVoYeIwP87YQoYPv9uGNj3nO2Ejws/N8OMDbwCDPL3YAvxbJc2nFxjw30iQkm3k3HJDcwWA4c3YCfi0GZ3jMpHk+HK4zOAzUYniGIcHgNnFa/kvYH+Z5cCCxjWgtNw5IGDDzMBw4SIwWyTNsxYZzziRLSBxmMzjYcEaCsF/4zjBvfPDmmJ0Ef//hx5//VNjI80sT0IIOJEhTPgpGwSgYBaMAOwAAwEJIgBbf1pkAAAAASUVORK5CYII=","orcid":"","institution":"Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Kefei","middleName":"","lastName":"Dou","suffix":""}],"badges":[],"createdAt":"2025-07-10 02:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7088296/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7088296/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13019-026-03911-6","type":"published","date":"2026-03-02T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89655493,"identity":"ed8da2c5-bbdf-4e51-80be-e4c1ae03e4f1","added_by":"auto","created_at":"2025-08-22 10:23:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32671,"visible":true,"origin":"","legend":"\u003cp\u003eDetermination of the association between Sleep time(hour) and ASCVD by restricted cubic spline (RCS) regression analysis.\u003c/p\u003e\n\u003cp\u003eThe chart demonstrates a significant nonlinear relationship between sleep time（hour） and the odds ratio (OR) for atherosclerotic cardiovascular disease. As the sleep time increases, the OR shows \"U\" shape association, indicating that this relationship is statistically significant (p \u0026lt; 0.001). The shaded area represents the 95% confidence interval for the OR estimate, illustrating the uncertainty around the OR estimate.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7088296/v1/7bdad87e253cc24c3ab980ce.png"},{"id":89655483,"identity":"e3800030-76c3-4539-9de2-aa2a8322370a","added_by":"auto","created_at":"2025-08-22 10:23:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37708,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of Model Confusion Matrix and Classification Report.\u003c/p\u003e\n\u003cp\u003e(A) The confusion matrix is depicted with the horizontal axis representing the model's predictions and the vertical axis showing the actual sample conditions. The color bar indicates the logarithmic values of each element, ranging from light green for values around 100 to dark green for values around 500.(B) This panel presents the performance evaluation results of the classification model, comparing the model's performance in predicting non-deceased versus deceased patients. It provides insights into how well the model distinguishes between these two groups.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7088296/v1/83c76661ecd64b09bc00c5ec.png"},{"id":89655488,"identity":"47a38596-6756-4150-b309-b679bc41b858","added_by":"auto","created_at":"2025-08-22 10:23:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28221,"visible":true,"origin":"","legend":"\u003cp\u003eBar Charts of Probability and Score Distributions.\u003c/p\u003e\n\u003cp\u003e(A) This chart displays the distribution of samples across different probability intervals. The horizontal axis represents the predicted probability, while the vertical axis shows the corresponding number of samples. This figure reveals how the model's predictions are distributed across various probability ranges.(B) This chart illustrates the distribution of samples across different score intervals. The horizontal axis represents the model score, and the vertical axis shows the corresponding number of samples. This figure provides characteristics of the model score distribution, aiding in the analysis of the model's predictive performance.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7088296/v1/16acc55fc4d501dc2a8edd15.png"},{"id":104250846,"identity":"32d8798e-03a3-425c-9a88-740f1aa9589a","added_by":"auto","created_at":"2026-03-09 16:10:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1021741,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7088296/v1/6b93d55e-eab2-4b8d-b626-d83724c58b32.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association Between Sleep Disorders and the Risk of Atherosclerotic Cardiovascular Disease: Regression Analysis and Neural Network Prediction Based on NHANES Data","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eSleep is a vital component of life, playing a crucial role in physiological functions, immune system regulation, and metabolic processes. This regulatory effect not only supports host defense during infections but also maintains inflammatory homeostasis, thereby influencing the development of various chronic diseases\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. However, with modern lifestyle changes, the prevalence of sleep disorders has significantly increased, becoming a major public health challenge. According to data from the 2017\u0026ndash;2020 National Health and Nutrition Examination Survey (NHANES), 29.8% of U.S. adults reported experiencing sleep disorders, and 27.2% experienced daytime sleepiness\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Additionally, the 2014 Behavioral Risk Factor Surveillance System (BRFSS) data indicated that over one-third of American adults slept less than seven hours per night, a behavior significantly associated with obesity, diabetes, hypertension, and other health issues\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Sleep disorders not only negatively impact individual quality of life and mental health but are also closely linked to multiple chronic diseases, particularly cardiovascular diseases\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAtherosclerotic cardiovascular disease (ASCVD), a major subtype of cardiovascular diseases, is one of the leading causes of global mortality and disability. Its pathological core lies in atherosclerosis, a process driven by lipid metabolism, chronic inflammation, and endothelial dysfunction\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Although traditional risk factors such as elevated low-density lipoprotein cholesterol (LDL-C), hypertension, and smoking have been extensively studied and validated, many patients continue to face residual risks even after significant control of these factors through medications\u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Recently, sleep disorders have emerged as potential risk factors for ASCVD, garnering increasing attention in clinical and epidemiological research\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. This phenomenon highlights the need to explore less well-defined non-traditional risk factors, among which sleep disorders and abnormal sleep duration may play critical roles.\u003c/p\u003e\u003cp\u003eAbnormal sleep duration may affect cardiovascular health through metabolic, oxidative, and autonomic pathways. Previous studies have shown that sleep disorders may influence cardiovascular health through multiple mechanisms, including activation of the sympathetic nervous system, disruption of circadian rhythms, exacerbation of inflammatory responses, and increased oxidative stress levels\u003csup\u003e[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Obstructive sleep apnea (OSA) and chronic insomnia have been demonstrated to significantly elevate the risk of cardiovascular diseases\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the relationship between sleep duration and cardiovascular metabolic risk exhibits a nonlinear pattern, with both short and long sleep durations associated with increased cardiovascular risk\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. This \"U-shaped\" trend highlights that maintaining an optimal sleep duration of 6\u0026ndash;8 hours may be a crucial strategy for protecting cardiovascular health.\u003c/p\u003e\u003cp\u003eHowever, most existing studies are limited to specific populations or small sample sizes, lacking large-scale and representative data. Additionally, there remains considerable debate regarding whether sleep disorders serve as independent risk factors for ASCVD and their effects after adjusting for multiple covariates. To address these gaps, this study utilized multi-cycle data from the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2018. Through methods such as restricted cubic spline regression, logistic regression analysis, and neural network modeling, the study systematically examined the relationship between sleep disorders, sleep duration, and ASCVD risk.\u003c/p\u003e\u003cp\u003eThe objectives of this study were to elucidate the potential mechanisms by which sleep-related factors influence the risk of ASCVD, verify the role of sleep disorders as independent risk factors for ASCVD, and explore precise prevention strategies for ASCVD based on sleep health management. This study explores the link between sleep problems and ASCVD to guide prevention and integrate sleep management into public health.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Clinical Data\u003c/h2\u003e\u003cp\u003eThe data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a cross-sectional survey conducted by the National Center for Health Statistics (NCHS) in collaboration with the Centers for Disease Control and Prevention (CDC). NHANES aims to provide nationally representative data on the civilian, non-institutionalized population of the United States.\u003c/p\u003e\u003cp\u003eThe data collection protocol was approved by the NCHS Ethics Review Board, and all survey participants provided informed consent before undergoing interviews and examinations. In this study, data from three NHANES cycles (2013\u0026ndash;2014, 2015\u0026ndash;2016, and 2017\u0026ndash;2018) were combined into a dataset using publicly available response data files. The study population included all respondents from these NHANES cycles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Collection\u003c/h2\u003e\u003cp\u003eThe covariates used in this study were selected based on prior literature and substantive reasoning and were considered to have potential associations with sleep quality and cardiovascular disease prevalence. Information on a variety of factors was collected using standardized questionnaires, including age, gender, race, education level, marital status, poverty-to-income ratio (PIR), body mass index (BMI), sedentary time, moderate activity duration, smoking status, alcohol use, and medical comorbidities such as hypertension, hyperlipidemia, and diabetes. Missing data were supplemented using imputation methods implemented in R software.\u003c/p\u003e\u003cp\u003eSurvey data from 17,962 participants were initially collected for this study. After excluding individuals with missing outcome or exposure data, as well as those who did not provide complete information on BMI, education level, marital status, PIR, smoking, or alcohol use, a total of 4,719 participants were included in the final analysis.\u003c/p\u003e\u003cp\u003e According to the 2013 American College of Cardiology/American Heart Association (ACC/AHA) Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults, ASCVD is defined as having at least one diagnosis of coronary heart disease, angina, myocardial infarction, or stroke, with myocardial infarction and stroke classified as strict criteria. In this study, the assessment of ASCVD was based on a series of self-reported questionnaires. Participants were identified as having ASCVD if they responded affirmatively to the NHANES multiple-choice question (MCQ): \"Have you ever been told you have coronary heart disease, angina, a heart attack, or a stroke?\"\u003c/p\u003e\u003cp\u003eSleep disorders were identified based on affirmative responses to either of the following questions: \u0026ldquo;Have you ever told a doctor or other health professional that you have trouble sleeping?\u0026rdquo; or \u0026ldquo;Has a doctor or other health professional ever told you that you have a sleep disorder?\u0026rdquo; Sleep duration was self-reported in response to the question: \u0026ldquo;How many hours of sleep do you usually get at night on weekdays or workdays?\u0026rdquo; The reported sleep duration was categorized as short (\u0026lt;\u0026thinsp;7 hours/night), normal (7\u0026ndash;9 hours/night), and long (\u0026gt;\u0026thinsp;9 hours/night).\u003c/p\u003e\u003cp\u003eHypertension was defined as participants self-reporting a diagnosis of hypertension, having an average systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg or an average diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;80 mmHg, or currently taking antihypertensive medications. Hyperlipidemia was defined based on the criteria of the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III), including total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL, triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL, high-density lipoprotein cholesterol (HDL-C) levels\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL for men or \u0026lt;\u0026thinsp;50 mg/dL for women, or low-density lipoprotein cholesterol (LDL-C) levels\u0026thinsp;\u0026ge;\u0026thinsp;130 mg/dL. Additionally, participants who reported using lipid-lowering medications were also classified as having hyperlipidemia.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using SPSS 25.0 software, and the sample was divided into two groups: ASCVD and non-ASCVD. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while categorical variables were expressed as numbers and percentages. For normally distributed continuous data, the results were described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (x\u0026thinsp;\u0026plusmn;\u0026thinsp;sx\u0026thinsp;\u0026plusmn;\u0026thinsp;sx\u0026thinsp;\u0026plusmn;\u0026thinsp;s) and evaluated using t-tests to assess group differences. For non-normally distributed data, results were described as medians with interquartile ranges (M [P25, P75]).\u003c/p\u003e\u003cp\u003eFirst, Spearman correlation analysis was conducted to explore the association between ASCVD and the included variables. Then, multivariate linear regression was used to further investigate the indirect effects of other variables on ASCVD through the Atherogenic Index of Plasma (AIP). Restricted cubic spline (RCS) regression was applied to explore the nonlinear relationship between sleep duration and ASCVD. Logistic regression was employed to identify the causal influence of sleep disorders on ASCVD. Finally, a neural network model was constructed to classify and predict ASCVD and to assess the importance of associated variables. Statistical significance was determined with a threshold of P\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline Characteristics of Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distribution of patient characteristics grouped by the presence or absence of ASCVD revealed significant differences across multiple clinical variables in the preliminary analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong individuals with ASCVD (n=359) and those without ASCVD (n=4360), there were notable disparities. Specifically, in the group with sleep disorders, 192 individuals (53.48%) had ASCVD, compared to 167 individuals (46.52%) in the group without sleep disorders. This indicates that sleep disorders are significantly associated with an increased risk of ASCVD (P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eThe mean age of the ASCVD group was 67 years (range: 57\u0026ndash;75), significantly higher than the mean age of 46 years (range: 33\u0026ndash;60) in the non-ASCVD group (P\u0026lt;0.001). Regarding educational level and household income, a higher proportion of individuals with ASCVD had lower income and education levels (P\u0026lt;0.001), suggesting that lower socioeconomic status may increase the risk of ASCVD.\u003c/p\u003e\n\u003cp\u003eThe median Atherogenic Index of Plasma (AIP) in the ASCVD group was 1.82 (interquartile range: 1.53\u0026ndash;2.18), higher than the 1.69 (1.56\u0026ndash;1.88) observed in the non-ASCVD group (P=0.049). The mean BMI of the ASCVD group was 30.58 \u0026plusmn; 8.04, significantly higher than 29.07 \u0026plusmn; 7.13 in the non-ASCVD group (P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eIn terms of chronic conditions, the prevalence of diabetes and hypertension was significantly higher in the ASCVD group compared to the non-ASCVD group (P\u0026lt;0.001). The prevalence of hyperlipidemia showed borderline significance (P=0.064).\u003c/p\u003e\n\u003cp\u003eAdditionally, the ASCVD group had a higher proportion of current or former smokers and a lower proportion of individuals who had never consumed alcohol compared to the non-ASCVD group (P\u0026lt;0.001) (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Correlation Analysis of ASCVD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSleep duration was converted into a binary sleep quality variable: abnormal sleep duration (\u0026gt;9 hours/night or \u0026lt;7 hours/night) was coded as 1, while normal sleep duration (7\u0026ndash;9 hours/night) was coded as 0. According to the results of Spearman correlation analysis, sleep disorders, sleep quality, diabetes, hypertension, smoking, sedentary behavior, education level, income level, gender, age, and BMI were significantly correlated with ASCVD.\u003c/p\u003e\n\u003cp\u003eSpecifically, the presence of sleep disorders and abnormal sleep quality were positively associated with ASCVD (r=0.150**, p\u0026lt;0.01; r=0.055**, p\u0026lt;0.01), indicating that individuals with poor sleep quality may have a higher likelihood of developing ASCVD. Additionally, ASCVD risk increased significantly with age (r=0.272**, p\u0026lt;0.01). Smoking, sedentary behavior, hypertension, and diabetes also showed significant positive correlations with ASCVD (p\u0026lt;0.01). Conversely, income level was negatively correlated with ASCVD (p\u0026lt;0.01), suggesting that socioeconomic status influences the prevalence of cardiovascular disease (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Exploration of the Nonlinear Relationship Between Sleep Duration and ASCVD Using Restricted Cubic Spline (RCS) Regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the RCS regression analysis, a significant nonlinear relationship between sleep duration and ASCVD was detected after adjusting for potential covariates (P\u0026lt;0.001 for the test of nonlinearity). The relationship exhibited a \u0026quot;U-shaped\u0026quot; pattern, with the lowest risk observed at approximately 6 to 8 hours of sleep per night. Sleep durations under 6 or over 8 hours were linked to progressively higher ASCVD risk (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Multivariate Linear Regression Analysis of the Relationship Between AIP and ASCVD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Atherogenic Index of Plasma (AIP) is an important biomarker for identifying ASCVD. By analyzing AIP, further insights into the factors influencing ASCVD were obtained. Results from the multivariate linear regression model showed a significant association between ASCVD and AIP. In addition, age (P=0.033), race (P=0.036), and hyperlipidemia (P\u0026lt;0.001) were also significantly associated with elevated AIP levels (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Logistic Regression Analysis of the Association Between Sleep Disorders and ASCVD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression analysis was conducted to determine whether sleep disorders are a risk factor for ASCVD, using participants without sleep disorders as the reference group. In the unadjusted analysis, the odds ratio (OR) for ASCVD risk associated with sleep disorders was 2.04 (95% CI: 1.44\u0026ndash;2.89; P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eAfter adjusting for age, gender, and race (Model 1), the OR decreased to 1.77 (95% CI: 1.22\u0026ndash;2.57; P=0.0029). Further adjustment for BMI categories, sedentary time, and physical activity duration (Model 2) slightly reduced the OR to 1.73 (95% CI: 1.19\u0026ndash;2.52; P=0.0041). In the fully adjusted model (Model 3), which accounted for marital status, educational level, income level, smoking, and alcohol consumption, the OR for ASCVD risk associated with sleep disorders was 1.69 (95% CI: 1.15\u0026ndash;2.49; P=0.0079).\u003c/p\u003e\n\u003cp\u003eThe results demonstrated high statistical significance, and although the OR values varied slightly across models, the association between sleep disorders and ASCVD remained significant after adjusting for all covariates. This indicates that sleep disorders are an independent risk factor for ASCVD. These findings underscore the importance of improving sleep quality and addressing sleep disorders as key strategies for ASCVD prevention (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Neural Network Model for Predicting ASCVD: Scoring System\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.1 Model Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe confusion matrix of the model (Figure 2A) showed a true positive (TP) count of 509 and a true negative (TN) count of 16, with a total sample size of 603. The overall accuracy of the model was 87%. When addressing the issue of class imbalance, the model performed well in predicting the non-diseased category, achieving a precision of 94% and a recall of 92%, indicating strong recognition ability for this majority class.\u003c/p\u003e\n\u003cp\u003eHowever, for the diseased category, the model exhibited weaker predictive performance, with a precision of 26% and a recall of 33%, reflecting its limited ability to identify this minority class (Figure 2B). An analysis of the raw data suggests that this performance disparity may stem from class imbalance, as the non-diseased group significantly outnumbered the diseased group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.2 Model Prediction Results and Scoring Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the statistical analysis of the model\u0026rsquo;s prediction results, the study included a total of 603 samples. Among these, 542 samples were predicted as belonging to the non-diseased category, while 61 samples were predicted as belonging to the diseased category, indicating that approximately 89.8% of the samples were classified as non-diseased. The probability statistics showed an average predicted probability of 0.13562 with a standard deviation of 0.25406. The minimum predicted probability was close to zero, while the maximum reached 0.9999, demonstrating the model\u0026rsquo;s high confidence for certain samples, particularly in positive class predictions.\u003c/p\u003e\n\u003cp\u003eRegarding the scoring statistics, the average score of the samples was 71.1733 points with a standard deviation of 15.2422 points, and the score range varied from 20.001917 to 80 points. While most samples received relatively high scores, the model exhibited notable variation in performance across different samples. Specifically, the score distribution showed a certain degree of central tendency but also displayed some dispersion, particularly in the high-score range (Table 5).\u003c/p\u003e\n\u003cp\u003eThe probability distribution bar chart in Figure 3A reveals that the predicted probabilities for most samples are concentrated at lower levels, indicating that the model tends to conservatively predict the likelihood of disease. Meanwhile, the score distribution bar chart in Figure 3B shows that the majority of sample scores are at relatively high levels, though there is noticeable variability between scores. This suggests that the model achieves a balance between consistency and flexibility when evaluating different samples (Figure3).\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eASCVD is a leading cause of global mortality and disability, underscoring its significant public health implications\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. While traditional risk factors such as hypertension, elevated LDL-C, and smoking have been extensively studied and validated, sleep disorders have recently emerged as novel, non-traditional risk factors that warrant attention\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. By analyzing NHANES data, this study systematically explored the association between sleep disorders and ASCVD risk, revealing potential biological mechanisms underlying this relationship. The findings demonstrated that both sleep disorders and abnormal sleep durations significantly increase ASCVD risk, potentially impacting cardiovascular health through multiple biological pathways.\u003c/p\u003e\u003cp\u003eIn this study, restricted cubic spline regression analysis revealed a \"U-shaped\" nonlinear relationship between sleep duration and ASCVD risk. Specifically, individuals sleeping 6 to 8 hours per night exhibited the lowest ASCVD risk, whereas those sleeping less than 6 hours or more than 8 hours had significantly increased risks. This finding aligns with previous research, further confirming the importance of sleep duration for cardiovascular health\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA Mendelian randomization study further validated this \"U-shaped\" nonlinear relationship, showing that genetically predicted short sleep duration (\u0026le;\u0026thinsp;6 hours) significantly increased the risk of coronary artery disease, myocardial infarction, and arterial hypertension, while long sleep duration (\u0026ge;\u0026thinsp;9 hours) was not associated with a significant causal relationship\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Additionally, prior studies have suggested that short sleep duration may elevate ASCVD risk through various mechanisms\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. On the other hand, sleep duration may also be closely linked to conditions such as depression, chronic inflammation, and cardiac dysfunction\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLogistic regression analysis further confirmed that sleep disorders are independent risk factors for ASCVD. After comprehensive adjustments for covariates including age, gender, BMI, income level, and health behaviors, the significant association between sleep disorders and ASCVD risk persisted. Sleep disorders may impact cardiovascular health via metabolic, oxidative, and autonomic pathways, driving atherosclerosis, which contribute to the development and progression of atherosclerosis. These mechanisms are consistent with previous literature discussing the interaction between sleep disorders, oxidative stress, and inflammatory responses\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMoreover, the study found that the prevalence of sleep disorders was significantly higher among middle-aged and older adults, particularly those with other chronic conditions such as hypertension and diabetes\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. This finding underscores the importance of prioritizing sleep health in public health interventions targeting middle-aged and older populations to reduce the risk of ASCVD.\u003c/p\u003e\u003cp\u003eThrough multivariate linear regression analysis, this study also highlighted a significant association between the Atherogenic Index of Plasma (AIP) and ASCVD risk, establishing AIP as an important predictive marker for ASCVD. The results showed that factors such as age, hyperlipidemia, and racial differences significantly influenced AIP levels, suggesting that clinical practice should integrate multiple factors to optimize ASCVD risk assessment.\u003c/p\u003e\u003cp\u003eFurthermore, this study attempted to construct a neural network model to predict ASCVD risk and evaluate the importance of sleep-related factors. Although the model performed well in terms of overall accuracy, its predictive ability for the diseased category was relatively weak. This limitation may be attributed to class imbalance in the sample and feature selection within the model. Future studies could improve model performance by optimizing data preprocessing, increasing sample size, and exploring more complex model architectures, such as deep learning. Additionally, enhancing the interpretability of the neural network model is necessary to facilitate its application in clinical practice.\u003c/p\u003e\u003cp\u003eThis study holds significant clinical and public health implications. These findings highlight the value of sleep management in ASCVD prevention and health education. The study confirms AIP and sleep disorders as ASCVD risk factors, aiding personalized interventions. Neural networks offer new potential for predicting cardiovascular risk, encouraging future use of multi-omics data.\u003c/p\u003e\u003cp\u003eHowever, the study has certain limitations. First, the cross-sectional nature of the data does not allow for establishing causal relationships; longitudinal studies are needed to further validate these findings. Second, the sample was derived from a U.S. population, potentially introducing racial and regional biases; future research should verify the generalizability of the results in different populations. Finally, the performance of the neural network model was limited by sample size and feature selection; future studies could enhance model performance by integrating multimodal data.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, this study utilized multi-cycle NHANES data to reveal a significant association between sleep disorders and ASCVD risk, further confirming the \"U-shaped\" nonlinear relationship of sleep duration and the potential role of sleep disorders as independent risk factors for ASCVD. The findings underscore the importance of incorporating sleep health management into ASCVD prevention strategies and provide a new perspective for the individualized prevention and management of cardiovascular diseases.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNational Health and Nutrition Examination Survey (NHANES), Behavioral Risk Factor Surveillance System (BRFSS), Atherosclerotic cardiovascular disease (ASCVD), low-density lipoprotein cholesterol (LDL-C),Obstructive sleep apnea (OSA), he National Center for Health Statistics (NCHS), the Centers for Disease Control and Prevention (CDC), poverty-to-income ratio (PIR), body mass index (BMI), multiple-choice questions (MCQ), the Patient Health Questionnaire (PHQ-9)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research data was sourced from the NHANES database, with the data website being https://www.cdc.gov/nchs/nhanes/index.html.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was funded by National Natural Science Foundation of China (Grant no. 82400543).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLing-bing Meng make the acquisition, analysis,\u0026nbsp;\u003cstrong\u003eKongyong Cui\u003c/strong\u003e interpretate of data;\u0026nbsp;\u003cstrong\u003eYanjun Song\u003c/strong\u003e and\u0026nbsp;\u003cstrong\u003eBowen Li\u003c/strong\u003e create of new software used in the work; Yongbao Zhang and Kefei Dou have drafted the work or substantively revised it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBesedovsky L, Lange T, Haack M. The Sleep-Immune Crosstalk in Health and Disease. Physiol Rev. 2019. 99(3): 1325-1380.\u003c/li\u003e\n\u003cli\u003eDi H, Guo Y, Daghlas I, et al. Evaluation of Sleep Habits and Disturbances Among US Adults, 2017-2020. JAMA Netw Open. 2022. 5(11): e2240788.\u003c/li\u003e\n\u003cli\u003eLiu Y, Wheaton AG, Chapman DP, Cunningham TJ, Lu H, Croft JB. Prevalence of Healthy Sleep Duration among Adults--United States, 2014. MMWR Morb Mortal Wkly Rep. 2016. 65(6): 137-41.\u003c/li\u003e\n\u003cli\u003eReimer MA, Flemons WW. Quality of life in sleep disorders. Sleep Med Rev. 2003. 7(4): 335-49.\u003c/li\u003e\n\u003cli\u003eLyons OD. Sleep disorders in chronic kidney disease. Nat Rev Nephrol. 2024. 20(10): 690-700.\u003c/li\u003e\n\u003cli\u003eCovassin N, Somers VK. Sleep, melatonin, and cardiovascular disease. Lancet Neurol. 2023. 22(11): 979-981.\u003c/li\u003e\n\u003cli\u003eSandesara PB, Virani SS, Fazio S, Shapiro MD. The Forgotten Lipids: Triglycerides, Remnant Cholesterol, and Atherosclerotic Cardiovascular Disease Risk. ENDOCRINE REVIEWS. 2019. 40(2): 537-557.\u003c/li\u003e\n\u003cli\u003eLawler PR, Bhatt DL, Godoy LC, et al. Targeting cardiovascular inflammation: next steps in clinical translation. Eur Heart J. 2021. 42(1): 113-131.\u003c/li\u003e\n\u003cli\u003eCatapano AL, Graham I, De Backer G, et al. 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias. Eur Heart J. 2016. 37(39): 2999-3058.\u003c/li\u003e\n\u003cli\u003eStone NJ, Robinson JG, Lichtenstein AH, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014. 63(25 Pt B): 2889-934.\u003c/li\u003e\n\u003cli\u003eFerence BA, Braunwald E, Catapano AL. The LDL cumulative exposure hypothesis: evidence and practical applications. 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Nat Rev Cardiol. 2023. 20(8): 560-573.\u003c/li\u003e\n\u003cli\u003eThurston RC, Chang Y, Kline CE, et al. Trajectories of Sleep Over Midlife and Incident Cardiovascular Disease Events in the Study of Women\u0026apos;s Health Across the Nation. Circulation. 2024. 149(7): 545-555.\u003c/li\u003e\n\u003cli\u003eSt-Onge MP, Grandner MA, Brown D, et al. Sleep Duration and Quality: Impact on Lifestyle Behaviors and Cardiometabolic Health: A Scientific Statement From the American Heart Association. Circulation. 2016. 134(18): e367-e386.\u003c/li\u003e\n\u003cli\u003eNayor M, Brown KJ, Vasan RS. The Molecular Basis of Predicting Atherosclerotic Cardiovascular Disease Risk. Circulation ResearchCirculation ResearchCirculation Research. 2021. 128(2): 287-303.\u003c/li\u003e\n\u003cli\u003eLinz D, Kadhim K, Kalman JM, McEvoy RD, Sanders P. Sleep and cardiovascular risk: how much is too much of a good thing. 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Metabolism. 2018. 84: 67-75.\u003c/li\u003e\n\u003cli\u003eMorris G, Stubbs B, K\u0026ouml;hler CA, et al. The putative role of oxidative stress and inflammation in the pathophysiology of sleep dysfunction across neuropsychiatric disorders: Focus on chronic fatigue syndrome, bipolar disorder and multiple sclerosis. Sleep Med Rev. 2018. 41: 255-265.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Baseline Characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 211px;\"\u003e\n \u003cp\u003eASCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eNo(n=4360)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eYes（n=359）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e46(33~60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e67(57~75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2039(46.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e191(53.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2321(53.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e168(46.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e567(13.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e35(9.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e377(8.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e29(8.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1883(43.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e197(54.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eNon-Hispanic Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e872(20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e70(19.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e661(15.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e28(7.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026lt; high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e299(6.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e43(11.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003enine-eleven year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e552(12.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e63(17.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003ehigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e964(22.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e95(26.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003ecollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2545(58.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e158(44.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eMarial status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003elive alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2606(59.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e204(56.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003enot live alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1754(40.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e155(43.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eIncome status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003elow income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1475(33.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e156(43.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003emiddle income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1486(34.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e132(36.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003ehigh income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1399(32.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e71(19.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 290px;\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.69(1.56,1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.82(1.53,2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 290px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e29.07\u0026plusmn;7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e30.58\u0026plusmn;8.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 290px;\"\u003e\n \u003cp\u003eSitting time(min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e441.05\u0026plusmn;520.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e447.28\u0026plusmn;202.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 290px;\"\u003e\n \u003cp\u003ePhysical activity time(min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e63.04\u0026plusmn;58.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e66.02\u0026plusmn;98.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSleep duration (hours)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026lt;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1686(38.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e165(45.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e7-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2551(58.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e173(48.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026gt;9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e123(2.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e21(5.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSleep disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3154(72.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e167(46.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1206(27.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e192(53.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSmoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2535(58.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e149(41.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e939(21.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e122(33.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e886(20.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e88(24.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eDrink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eNever Drank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1537(35.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e184(51.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eOccasionally Drank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1519(34.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e114(31.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eRegularly Drank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e955(21.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e38(10.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eHeavy Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e349(8.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e23(6.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3800(87.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e160(44.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e560(12.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e199(55.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2700(61.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e240(66.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1660(38.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e119(33.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3877(88.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e134(37.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e483(11.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e225(62.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003eBMI, body mass index; PIR, poverty income ratio; AIP, Atherogenic Index of Plasma; ASCVD, atherosclerotic cardiovascular disease.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2: Spearman correlation analysis of ASCVD\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.272**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e-0.034*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e-0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.051**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.082**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eIncome level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e-0.072**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eSitting time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.035*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003ephysical active time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eSmoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.077**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eDrink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e-0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.234**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.206**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eSleep disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.150**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eSleep quality status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.055**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e*When the confidence level (double test) is 0.05, the correlation is significant.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e**When the confidence level (double test) is 0.01, the correlation is significant.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable3: Multiple linear regression model for AIP\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eS. E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eIncome level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSitting time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003ePhysical active time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSmoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eDrink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.271***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e6.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eASCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.134**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e*When the confidence level (double test) is 0.05, the correlation is significant.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e**When the confidence level (double test) is 0.01, the correlation is significant.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv align=\"center\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 27.8563%;\"\u003e\n \u003cp\u003eTable 4: Logistic regression analysis of inertia between sleep disorder and ASCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.1048%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8594%;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8594%;\"\u003e\n \u003cp\u003eS. E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0032%;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0324%;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.1048%;\"\u003e\n \u003cp\u003eUn adjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8594%;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8594%;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0032%;\"\u003e\n \u003cp\u003e2.04(1.44,2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0324%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.1048%;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8594%;\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8594%;\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0032%;\"\u003e\n \u003cp\u003e1.77(1.22,2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0324%;\"\u003e\n \u003cp\u003e0.0029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.1048%;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8594%;\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8594%;\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0032%;\"\u003e\n \u003cp\u003e1.73(1.19,2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0324%;\"\u003e\n \u003cp\u003e0.0041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.1048%;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8594%;\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8594%;\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0032%;\"\u003e\n \u003cp\u003e1.69(1.15,2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0324%;\"\u003e\n \u003cp\u003e0.0079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 35.8257%;\"\u003e\n \u003cp\u003eModel 1 was adjusted for Age, Gender, Race.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 35.8257%;\"\u003e\n \u003cp\u003eModel 2 was adjusted for Age, Gender, Race, BMI group, Sitting time, Physical active time.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 35.8257%;\"\u003e\n \u003cp\u003eModel 3 was adjusted for Age, Gender, Race, BMI group, Marital status, Education level, Income level, Sitting time, Physical active time, Smoke, Drink.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 5: Statistical Results of Prediction Probabilities/Scores\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"590\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePredicted-Disease Category（61）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003ePredicted-Non-Disease Category（542）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003ecount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003estd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003emin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003emax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eProbability Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.13562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.25406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0000145\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.12707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eScore Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e71.1733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15.2422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e20.001917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e69.834397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e79.5273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e79.9988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sleep disorders, Atherosclerotic cardiovascular disease (ASCVD), NHANES, Neural network model","lastPublishedDoi":"10.21203/rs.3.rs-7088296/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7088296/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe 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 (\u0026lt;6 hours) and long sleep (\u0026gt;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eSleep disorders and abnormal sleep durations significantly increase the risk of ASCVD. Sleep disorders can serve as independent risk factors for ASCVD.\u003c/p\u003e","manuscriptTitle":"The Association Between Sleep Disorders and the Risk of Atherosclerotic Cardiovascular Disease: Regression Analysis and Neural Network Prediction Based on NHANES Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 10:23:17","doi":"10.21203/rs.3.rs-7088296/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-24T22:15:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T01:38:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T13:12:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58267273814711300807299791293872924792","date":"2025-08-21T16:17:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22278993757703987905055854952812335699","date":"2025-08-15T03:09:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-14T09:48:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-10T12:20:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-10T12:19:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cardiothoracic Surgery","date":"2025-07-10T02:45:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1ee1c2f7-83de-4086-ba4c-8fc53a2f5fed","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:06:09+00:00","versionOfRecord":{"articleIdentity":"rs-7088296","link":"https://doi.org/10.1186/s13019-026-03911-6","journal":{"identity":"journal-of-cardiothoracic-surgery","isVorOnly":false,"title":"Journal of Cardiothoracic Surgery"},"publishedOn":"2026-03-02 15:57:43","publishedOnDateReadable":"March 2nd, 2026"},"versionCreatedAt":"2025-08-22 10:23:17","video":"","vorDoi":"10.1186/s13019-026-03911-6","vorDoiUrl":"https://doi.org/10.1186/s13019-026-03911-6","workflowStages":[]},"version":"v1","identity":"rs-7088296","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7088296","identity":"rs-7088296","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}