Association between sleep duration and depression in adults with hypertension: a cross-sectional study

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This study provided a theoretical basis for early screening of depression in patients with hypertension by analyzing the relationship between sleep time and depression in patients with hypertension. Methods The data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES) from 2004 to 2015. Depressive symptoms were tested using the Patient Health Questionnaire (PHQ-9), and depression was diagnosed when PHQ-9 total score >10. The association between sleep duration and depression in hypertension was estimated using weighted multivariate logistic regression, and its nonlinearity was examined by restricted cubic spline (RCS) regression. Multivariate logistic regression analysis was used to identify independent risk factors for depression in hypertensive patients, a risk prediction nomogram model was established, and the receiver operating characteristic (ROC) curve was drawn to evaluate its ability to discriminate depression in hypertensive patients. Results A total of 8,696 hypertension participants were included in this study. Multivariate logistic regression showed that an increment of sleep duration was strongly associated with depression in hypertension (OR = 0.772, 95%CI = (0.705, 0.883). The RCS curve showed L-shaped relationships between sleep duration and depressive risk in hypertension. The nomogram model showed a favorable discriminatory power with an area under the curve (AUC) of 73.4%. Sleep duration at 7.22 hour might be most beneficial for the mental health of patients with hypertension. Conclusion Our study demonstrated the relationship between sleep duration and depression in patients with hypertension, Effective interventions for sleep disorders in patient with hypertension are needed for preventing depression. Biological sciences/Neuroscience Health sciences/Neurology sleep duration depression hypertension a cross-sectional study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Hypertension was a globally recognized risk factor for cardiovascular and cerebrovascular diseases[1]. It increased the risk of cinditions such as myocardial infarction, heart failure, and stroke[2; 3]. Hypertension could also cause damage to target organs including the heart, brain, and kidneys, leading to significant disability[4], high morbidity, and mortality. With the continuous development of the social economy and the acceleration of modern life, the prevalence of hypertension had increased annually[2]. Since the 21st century, the prevalence of hypertension in China had exceeded 30%[5], with most cases not well-controlled, making it a significant public health problem. Depression was an affective disorder and currently the most common mental health issue worldwide[6]. It was primarily characterized by significant and persistent low mood, loss of interest, inattention, and sleep disorders. Severe depression could lead to a higher suicide rate, placing a heavy burden on patients, families, and the healthcare system. According to the World Health Organization (WHO), depression would become the second most disabling disease in the world by 2030[7]. However, the treatment outcomes for depression were not satisfactory, with issues such as side effects and recurring symptoms associated with pharmacotherapy. Hypertension and depression often co-occur and were currently considered combined risk factors for cardiovascular and cerebrovascular diseases[8]. A meta-analysis suggested that approximately one-third of patients with hypertension experience depressive symptoms[9]. The incidence of hypertension combined with sleep disorders, depression, and other physical and mental diseases was increasing. Treatment of hypertension had been reported to affect depression and vice versa[10]. Many studies had shown that both short and long sleep duration were associated with increased risk of morbidity and all-cause mortality[11; 12]. A recent systematic review of patients with cardiovascular disease reported that either too short or too long a night's sleep duration increases the risk of depression[13]. Depression was also associated with poor sleep quality, including insomnia and abnormal sleep duration. Some studies indicated that sleep disorders were a common symptom of depression[14]. It had been reported that up to 83% of patients with depression had altered sleep patterns, including early awakening, difficulty falling asleep (i.e., prolonged sleep latency), abnormal sleep duration (short or long), and insomnia symptoms[15]. In the past, sleep disturbances were considered accompanying symptoms of depression and were thought to resolve with depression treatment, leading to their rare consideration. Recently, there had been considerable evidence that sleep disorders preceded depression, although there might be some correlation between these factors exists. So far, there had been few studies on the impact of sleep duration on depression in people with hypertension. Therefore, the purpose of this study was to explore the relationship between sleep duration and depression in patients with hypertension, thereby providing a theoretical basis for early screening and intervention for hypertension and depression. 2. Materials and Methods 2.1 Study sample and design The NHANES incorporates interviews and physical examinations and comprises five key components: demographic data, laboratory results, dietary information, physical examination findings, and questionnaire responses. In this study, out of 21760 subjects diagnosed with hypertension surveyed from 2004 to 2015, 8696 participants were included in the final analysis after applying exclusion criteria(Figure 1). The exclusion criteria involved missing sleep duration data (n= 9785), missing PHQ-9 data (n = 1549), and other missing covariate data (n =1730). 2.2 Variables The key exposure variable in this study was sleep duration. While the outcome variable was depression. Covariates were divided into demographic data, medical conditions, and examination results. Demographic data included age, sex, race, marital status, and education. Medical conditions encompassed hypertension (defined as a history of physician-diagnosed hypertension, a measured average systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or current use of antihypertensive medication), and diabetes mellitus. Diabetes mellitus was classified as follows: Diabetes mellitus(DM): A history of physician-diagnosed diabetes Hemoglobin A1c level ≥ 6.5% Fasting glucose≥ 7.0mmol/L Random blood glucose≥ 11.1mmol/L Two-hour oral glucose tolerance test (OGTT) blood glucose≥ 11.1mmol/L Current use of diabetes medication or insulin; impaired fasting glucose (IFG): Fasting glucose5.6–7.0mmol/L impaired glucose tolerance (IGT): Two-hour OGTT blood glucose7.8–11.0 mmol/L Hemoglobin A1c level 5.7–6.5% Other medical conditions included obesity (characterized by standard body mass index (BMI), under 25 kg/m 2 , 25–30 kg/m 2 , ≥ 30 kg/m 2 ), CVD, chronic kidney disease (CKD), and hyperlipidemia. Sleep duration was determined by asking participants "How much sleep do you typically receive on a workday?". Based on their responses, patients were divided into four groups according to sleep duration: short sleep duration (9 hours). Depressive symptoms were assessed using the Patient Health Questionnaire (PHQ-9)[16], which contains 9 questions addressing the frequency of depressive symptoms over the past two weeks. The total score ranges from 0 to 27, with 0 to 9 indicating no depression, 10 to 14 indicating moderate depression, 15 to 19 indicating moderately severe depression, and 20 to 27 indicating severe depression[16]. Based on previous studies, we used a cutoff value of 10 points to identify clinically relevant depression.[17; 18]. 3. Statistical analysis R Software was used for statistical analysis. Given the complex sampling design and sampling weights, this study used the Mobile Examination Center (MEC) weights were used for all analyses. Baseline characteristics were presented as means and standard errors (SE) for continuous variables and proportions for categorical variables. Sleep duration was categorized into quartiles. Data were analyzed using t-tests for continuous variables and chi-square tests for categorical variables to compare the sleep duration quartile groups. A logistic regression model was employed to estimate the odds ratios(OR) and 95% confidence intervals (CI) for the association between sleep duration and depression in patients with hypertension. The criteria for selecting adjusted variables were determined through a consideration of biological factors and a review of existing literature on the topic. Our statistical inferences were based on the three models: Model 1 had no adjusted variables. Model 2 incorporated age, sex, marital status, education, and race. Model 3 included all variables from Model 2, in addition to BMI, smoking status,diabetes mellitus (DM), chronic kidney disease (CKD), cardiovascular disease (CVD), cancer, hyperlipidemia, and blood examination results (creatinine (CR), ALT, BUN, CKD-EPI Scr, CR urine). This study stratified participants based on age (individuals over 60, individuals under 60), sex(male, female), race(white, black, others), education (<high school, high school, college or above), marital status (married, divorced, widowed, others), BMI (< 25, 25–30, ≥30 kg/m 2 ), smoking status (never, former, current), history of hypertension (yes, no), history of diabetes (DM, IFG, IGT, no), history of CKD (yes, no), history of CVD (yes, no), and history of hyperlipidemia (yes, no). The restricted cubic splines (RCS) regression model was employed to flexibly model the association between sleep duration and depression. With the inflection point of sleep duration serving as the reference value. Multivariate logistic regression analysis (Model 3) was performed to identify independent risk factors for depression in patients with hypertension. A nomogram, based on the logistic regression model(Model 3), was developed to derive probability estimates for the presence of depression in hypertensive patients, The discriminatory power of the nomogram model in identifying depression risk in hypertensive subjects was evaluated using the receiver operating characteristic (ROC) curve. Statistical analyses were performed using R Studio 4.2.0. The level of statistical significance was set at P < 0.05. 4. Results 4.1 Baseline characteristics Among 65,101,139 participants from the NHANES database, 8696 participants diagonosed with hypertension and without exclusion criteria was included in this study. Sleep duration was divided into four categories: short sleep duration (9 hours).Table 1 presents the baseline characteristics according to the sleep duration and depression status. The analysis showed that participants with depression were typically younger, female, with DM, stroke, CHD, smoking habits, alcohol use, and increased urine creatinine and BMI. They also had lower BUN, shorter sleep duration, and lower education levels (Table 1, all P <0.05). However, Hyperlipidemia, CKD, alanine aminotransferase (ALT), and creatinine(CR) levels showed no significant differences across the sleep duration categories. 4.2 Association between sleep duration and depression in Hypertension The sleep duration was analyzed both as a continuous variable and as a categorized variable (four groups).When sleep duration was analyzed as a continuous variable, it was positively correlated with the risk of depression in patients with hypertension. In model 1 was OR = 0.72, 95%CI: 0.66, 0.79, P < 0.0001). After adjusting for age and gender in model 2, sleep duration reminded positively correlated with the risk of depression in patients with hypertension (OR = 0.73, 95%C: 0.67, 0.79, P < 0.0001). Further adjustment for age, sex, BMI, education, ethnicity, DM, blood urea nitrogen (BUN), hyperlipidemia, smoking history, stroke, ALT, chronic kidney disease estimated by CKD-EPI Scr, and alcohol use in model 3 still showed a positive correlation.(OR = 0.77, 95%CI: 0.71, 0.88, P < 0.0001). When analyzed as a categorized variable, participants with short sleep duration had an increased risk of depression compared to those with normal sleep duration. In Model 1(unadjusted), the odds ratio(OR) was 3.52(95%CI: 2.84, 4.36, P < 0.0001); In Model 2 adjused for age and sex, the OR was 3.44(95%CI: 2.80, 4.23, P = 0.001); In Model 3,which was adjusted for age, sex, BMI, education, ethnicity, DM, BUN, hyperlipidemia, smoking history, stroke, Alt, CKD-EPI Scr, and alcohol use, the OR was 2.87(95%CI: 2.31, 3.55), P= 0.006）.The association between sleep duration and depression in hypertension was detailed in Table 2. 4.3 Nonlinear association between sleep duration and depression in Hypertension Using the retricted cubic alpine(RCS) model, it was found that the relationship between sleep duration and depression presents an L-shaped curve in patients with Hypertension ( P = 0.000 for nonlinearity).When sleep duration was less than 7.22 hours, it was positively correlated with the risk of depression in hypertensive patients, with an OR value of 0.60(95%CI: 0.55, 0.66; P< 0.0001)(Figure 2).For sleep durations greater than 7.22 hours, the correlation was no longer statistically significant (Table 3). 4.4 Subgroup analysis of the relationship between sleep duration and depression in hypertensive patients from different populations We also conducted subgroup analyses stratified by sex (female, male), age (<60, ≥60years), poverty-income ratio (PIR<1.5, ≥1.5), BMI (<25, 25-30, ≥30) to further explore the association between sleep duration and depression in hypertension patients across different populations. The results showed no differences in the association between sleep duration and depression in hypertension patients across the subgroups of sex, BMI, PIR, and age, indicating that the conclusion of the present study was not affected by these confounders (Table 4). 4.5 Logistic regression and the nomogram model Multivariate logistic regression analysis (Model 2) was applied to identify the key factors most closely related to depression in hypertension patients. A total of 11 variables (age, sex, sleep duration, stroke, BMI, education, DM, BUN, smoking status, serum creatinine (SCR), alcohol use) were included to establish the crude nomogram model (Figure 3). The receiver operating characteristic (ROC) curve of the test set was used to evaluate the discriminative ability of the model. The ROC curve results demonstrated that the nomogram model established in this study had good discriminative power, and an area under the curve (AUC) was 73.2% (Figure 4), indicating good stability and prediction accuracy. The decision curve analysis (DCA) for the nomogram was conducted to measure the risks and benefits. According to the results of multiple regression analysis, predictive factors such as age, gender, sleep duration, stroke, BMI, education level, diabetes, blood urea nitrogen, smoking, serum creatinine (SCR), and alcohol use were identified, Based on these factors, a nomogram model for predicting the risk of depression in hypertensive individuals was constructed (Figure 5).. 5. Discussion The prevalence of sleep disorders in hypertensive patients varied widely, ranging from 14.9% to 85.7% globally[19]. A meta-analysis in China suggests that more than 50% of Chinese hypertensive patients had poor sleep quality[20]. According to another survey report, among 850 hypertensive patients, the proportion of insomnia reached 41%[21]. In addition, studies had shown that compared with non-hypertensive people, hypertensive patients had a 40%-60% higher incidence of insomnia[22]. In this study, the proportion of short sleep duration (9 h) was 33.34%, and the proportion of abnormal sleep duration was 40.19%, consistent with previous research results. The current mechanism was mainly that hypertensive patients were prone to anxiety, tension, depression, and other emotions due to their illness[23-25]. These emotions could lead to autonomic nervous system disorders, which in turn lead to sleep disorders. In this study, the prevalence of depression among hypertensive patients was 12.81%. Studies had shown that the incidence of depression among hypertensive patients could reach 20%-30%[26]. A Chinese study showed that the prevalence of depression among hypertensive patients was about 15.8%[27], slightly higher than the results of this study. The analysis might be due to differences in the gender, age, region, and socio-economic income of the subjects included in this study. In our recent study, we observed a negative correlation between sleep duration and depression risk in hypertensive patients when sleep time was less than 7.22 hours. Remarkably, this was the first study to report that excessively short sleep durations among hypertensive individuals significantly elevate the risk of depression. Echoing our findings, Yujie Li's team identified short nocturnal sleep duration as an independent risk factor for the onset and persistence of depression among elderly Chinese individuals[28]. Furthermore, a longitudinal study revealed a higher prevalence of depressive symptoms among those sleeping less than 5 hours compared to those with optimal sleep durations (7-8 hours)[29]. Strikingly, another study found that individuals sleeping less than 6 hours per night had a four-fold increased risk of depression compared to those sleeping 7-8 hours[30]. These findings resonate strongly with our observations in the hypertensive population. The potential mechanisms underlying this association were multifaceted. Firstly, inadequate sleep was known to augment daytime physical and psychological fatigue, thereby impacting daily life and work negatively, which might contribute to the development of depression[31]. Secondly, sleep deprivation could activate the sympathetic nervous system and β-adrenergic signaling, leading to increased secretion of inflammatory markers like IL-6 and CRP[32]. This, in turn, boosts gene expression related with inflammatory[33]. Moreover, insufficient sleep could overstimulate the hypothalamic-pituitary-adrenal axis, elevating cortisol levels[34]. Elevated cortisol could trigger a cascade of physiological and psychological effects, ultimately leading to depressive symptoms[35]. Additionally, shortened sleep durations were associated with increased 24-hour average blood pressure and heart rate, alongside elevated psychosocial stress, which might contribute to persistent hypertension[36]. Factors liked elevated cortisol, renal function impairment, and endothelial dysfunction might also contribute to hypertension[37]. Several studies had highlighted that chronic sleep deprivation could disrupt circadian rhythms and autonomic nervous system balance, leading to elevated blood pressure[38-41]. Accompanying these physiological changes, patients might also experience emotional shifts such as depression and anxiety[42]. Furthermore, inadequate sleep could enhance oxidative stress, accelerate atherosclerosis, trigger metabolic disorders, and ultimately increase the risk of cardiovascular diseases[43]. Furthermore, our study revealed that longer sleep durations were not necessarily beneficial for hypertensive patients. Beyond a certain threshold (7.22 hours), the risk of depression among hypertensive individuals begins to escalate. This finding aligns with a meta-analysis conducted by Long Zhai's team, which demonstrated that both short and long sleep durations were significantly associated with an increased risk of depression in adults[44]. Similarly, Lu Dong's team observed a U-shaped association between sleep duration and depression incidence in a cross-sectional study of Americans[45]. Although characteristics such as age, comorbid chronic diseases, and monitored sleep durations differed across these studies, the consensus indicates that both insufficient and excessive sleep could elevate depression risk. The current understanding suggested that extended sleep durations might disrupt the body's circadian rhythm and endocrine system, leading to hormonal imbalances that affect neurotransmitter functions, thereby increasing depression vulnerability[46]. Additionally, prolonged sleep might reflect an inadequate coping mechanism for stress and negative emotions[47]. Some individuals might attempt to avoid real-life problems and pressured by increasing sleep, but this approach often backfires, exacerbating emotional distress[48]. Our study observed similar conclusions in hypertensive patients, potentially due to physical discomforts like high intracranial pressure, dizziness, and headaches, which might prompt longer sleeping as a coping mechanism. However, this extended sleep duration could alter hormonal levels and neurotransmitter functions, ultimately heightening the risk of depression. The intimate relationship between sleep disorders and depression was currently recognized as bidirectional; sleep disorders could lead to depression, while depression could trigger or exacerbate sleep issues such as difficulty falling asleep, early awakening, and poor sleep quality[49; 50]. These sleep disturbances might further intensify depressive symptoms[51], forming a vicious cycle. Sleep disorders were not only a common symptom of depression but could also be its first presenting sign[52]. Prolonged lack of good sleep could cause physical and psychological fatigue, making individuals more susceptible to depressive moods[53]. Studies had revealed a statistically significant bidirectional correlation between insomnia and hypertension[54]. Adequate sleep was a crucial process for the body's recovery and repair, and sleep deprivation may interfere with normal blood pressure regulation [55]. Blood pressure typically decreases during sleep, but sleep deprivation could reduce this drop or lead to elevated nighttime blood pressure[56]. Conversely, elevated blood pressure increases sympathetic nervous system activity[57], keeping it in a persistent state of excitement and causing sleep disorders like insomnia and dreaminess[58]. Increased intracranial pressure affects the brain's normal blood circulation in a prolonged hypertensive state[59]. Persistent cerebral ischemia and hypoxia could trigger symptoms like dizziness and headaches, leading to sleep disturbances[60]. Additionally, hypertension-related damage to cardiac target organs and the stimulating effects of antihypertensive medications could contribute to sleep disorders[10]. Hypertension itself might bring psychological stress, such as concerns about disease progression and treatment costs, which could cause or exacerbate depressive symptoms[61]. Common symptoms of hypertension, such as headaches, fatigue, and restlessness, may overlap with depressive symptoms, increasing the risk of depression[62; 63]. Depression could disrupt normal sleep regulation mechanisms, such as affecting melatonin secretion and circadian rhythm adjustment, leading to sleep disorders[57]. Hypertension, depression, and sleep disorders were all linked to certain neurotransmitter systems like serotonin and dopamine, which played crucial roles in regulating mood, sleep, and blood pressure[64]. Thus, their interaction could lead to mutual influences among these three conditions. Furthermore, hypertension, depression, and sleep disorders were all associated with psychological stress[65; 66]. Prolonged psychological stress could elevate blood pressure, worsen depressive symptoms, and cause sleep disturbances, forming a vicious cycle[67]. Therefore, emphasizing emotional assessment and conducting sleep disorder screenings in the hypertensive population could aid in better management and reduce comorbidities like depression. Our study has several limitations. First of all, This study was based on the NHANES database, indicating that the data were retrospective, and some variables were based on self-report(sleep duration, hypertension ), which increased the selection bias. In this study, a nomogram model was constructed from cross-sectional data, which may limit its predictive accuracy. Therefore, the findings should be interpreted cautiously. 6. Conclusion This study explored the relationship between sleep duration and hypertension combined with depression and found that sleep duration of less than 7.22 hours was associated with an increased risk of depressive symptoms in hypertensive patients. We further established a model based on the correlation between sleep duration and depression risk in hypertensive patients, which has certain guiding significance for the sleep duration of hypertensive patients. Declarations Author contributions Tian Lv, and Jie Li were responsible for the study design; Tian Lv, Han Zhaowas responsible for data extraction; Tian Lv, and Xiaoling Wang performed statistical analysis and interpretation; Lehui Chen was responsible for manuscript drafting; Lehui Chen, and Qiongbin Zhu were responsible for the revision. The final manuscript was read and approved by all authors. Ethical statement Not required. Disclosure statement Financial disclosure: None. 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A. & Bareno-Silva, J. Depression and Sleep Disorders Related to Hypertension: A Cross-Sectional Study in Medellin, Colombia. Rev Colomb Psiquiatr (Engl Ed). 49, 109–115 (2020). Hamam, M. S. et al. Anxiety, Depression, and Pain: Considerations in the Treatment of Patients with Uncontrolled Hypertension. CURR HYPERTENS REP. 22, 106 (2020). Tables Table 1 Baseline characteristics of study participants Variable no-Depression N = 7776 Depression N = 1142 P value Age 57.116(0.304) 53.406(0.664) < 0.0001 Sex < 0.0001 Female 3700(48.334) 589(63.977) Male 4076(51.666) 331(36.023) Education College 3760(57.648) 345(43.365) < 0.0001 High school 3139(36.392) 436(47.881) Below high school 877(5.960) 142(8.754) Poverty income ratio 3.090(0.038) 2.053(0.101) < 0.0001 Alcohol use, < 0.0001 Former 1862(19.535) 274(29.727) Heavy 1121(15.877) 187(20.246) Mild 2659(37.922) 229(25.854) Moderate 965(14.718) 110(14.254) Never 1169(11.948) 120( 9.918) Smoking history < 0.0001 Never 3925(50.453) 357(36.458) Former 2516(32.540) 246(27.145) Current 1335(17.006) 317(36.397) Ethnicity < 0.0001 White 3856(73.998) 421(66.795) Black 1932(12.069) 239(16.422) Other 1988(13.932) 260(16.783) BMI, kg/m 2 < 0.001 =30 3638(47.199) 548(58.737) BMI, kg/m 2 30.698(0.122) 32.711(0.365) < 0.0001 Serum Creatinine 0.963(0.006) 0.975(0.036) 0.738 BUN 14.740(0.097) 13.386(0.242) < 0.0001 Urine Creatinine 113.377(1.146) 124.822(3.760) 0.006 Alt 26.686(0.251) 28.725(1.602) 0.205 Stroke < 0.0001 No 7259(94.680) 811(89.353) Yes 517( 5.320) 109(10.647) CKD/Chronic kidney disease 0.949 No 5414(74.629) 634(74.763) Yes 2362(25.335) 286(25.237) Diabetes 0.002 No 4598(63.930) 488(59.358) IGT 446(5.792) 45(5.718) IFG 436(6.179) 35(3.759) DM 2296(24.099) 352(31.165) CVD < 0.0001 No 6328(84.201) 646(74.748) Yes 1448(15.799) 274(25.252) Hyperlipidemia 0.442 No 1376(16.697) 140(15.269) Yes 6400(83.303) 780(84.731) Scr < 0.001 90 3097(42.479) 449(48.852) CKD-EPI Scr 83.942(0.403) 86.142(0.976) 0.014 Sleep hours 6.915(0.019) 6.217(0.085) < 0.0001 Sleep duration(hours) < 0.0001 6–8 3828(56.328) 360(45.356) 9 650(8.564) 72(8.344) Table 2 The association between sleep duration and depression in Hypertension model 1 Model 2 Model 3 95%CI P 95%CI P 95%CI P How much sleep do you get in hours 0.72(0.66,0.78) < 0.0001 0.72(0.67,0.79) < 0.0001 0.77(0.71,0.83) < 0.0001 6–8 ref ref ref < 6 3.52(2.84,4.36) < 0.0001 3.44(2.80,4.23) < 0.0001 2.87(2.31,3.35) 9 1.21(0.91,1.62) 0.191 1.27(0.94,1.72) 0.110 1.15(0.85,1.55) 0.365 Table 3 The association between sleep duration and depression in Hypertension time OR 95%CI P < 7.22 0.601 0.60(0.55,0.66) 7.22 1.232 1.23(0.98,1.53) 0.070 Table 4 Subgroup analysis of the association between sleep duration and hypertension depression among different populations character 95% CI p p for interaction Age 0.683 < 60 0.766(0.694,0.845) < 0.0001 ≥ 60 0.781(0.712,0.856) < 0.0001 Sex 0.269 Female 0.728(0.662,0.799) < 0.0001 Male 0.778(0.683,0.885) < 0.001 BMI 0.091 < 25 0.647(0.511,0.818) < 0.001 25–30 0.776(0.671,0.896) < 0.001 ≥ 30 0.792(0.729,0.861) < 0.0001 PIR 0.175 ≥ 1.5 0.725(0.644,0.816) < 0.0001 < 1.5 0.803(0.727,0.887) < 0.0001 Additional Declarations No competing interests reported. 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Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tian","middleName":"","lastName":"Lv","suffix":""},{"id":329783985,"identity":"d31d1558-24f9-4f0f-9756-7f8f8ada6fd7","order_by":2,"name":"Xiaoling Wang","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital, Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoling","middleName":"","lastName":"Wang","suffix":""},{"id":329783986,"identity":"e0dbed87-218d-4925-a0b1-6c8fb059473a","order_by":3,"name":"Han Zhao","email":"","orcid":"","institution":"Zhuji Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Zhao","suffix":""},{"id":329783987,"identity":"8a77b234-8825-4bb9-b51c-88d75ef25331","order_by":4,"name":"Qiongbin Zhu","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital, Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Qiongbin","middleName":"","lastName":"Zhu","suffix":""},{"id":329783988,"identity":"7afd57c9-a55a-406c-92ea-475acb0d641f","order_by":5,"name":"Jie Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIie3RsQqDMBCA4QsHmWzTUd/iRHASX6SLIrgVOjo4BAp27NrHEPoClYBdBF/BR7B7oY1TR28sNP+Q6T5yIQAu1w+2BdQ0U5IqxG5iEQlCh9eqLIKzLIhN0BuMaEcv3vGI6vRx0yBGBmKAOtmvEz/XFDRyGxsoJ+jLg14nQlPYeGjJg4Q2DGIXo7zxxe0k7MkiYBe7DyRaRMkk9i2hrrLCNxIp47xFKdNHL3qn6jI+p7lO1sly0fcHM8b4Ek7MQZfL5frXPqx2Os0TppBzAAAAAElFTkSuQmCC","orcid":"","institution":"Lishui People's Hospital, The Sixth Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-06-25 14:41:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4637255/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4637255/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60851496,"identity":"fb7128e4-42fe-412d-af59-cf6b84795089","added_by":"auto","created_at":"2024-07-22 21:01:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":169044,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study population\u003c/p\u003e","description":"","filename":"Fig1Flowchartofthestudypopulation.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4637255/v1/55e37f477972a3cb545a82ac.jpg"},{"id":60852350,"identity":"8b32cfdc-e353-4481-bc2d-73316726afc0","added_by":"auto","created_at":"2024-07-22 21:09:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78593,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between sleep duration and depression severity in patients with hypertension\u003c/p\u003e","description":"","filename":"Fig2Correlationanalysisbetweensleepdurationanddepressionseverity.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4637255/v1/236049d21d8201ae9967172c.jpg"},{"id":60851499,"identity":"3ae3737a-26ef-4288-9ad8-10f1a32188cf","added_by":"auto","created_at":"2024-07-22 21:01:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100921,"visible":true,"origin":"","legend":"\u003cp\u003eNonlinear association between sleep duration and depression in Hypertension\u003c/p\u003e","description":"","filename":"Fig3NonlinearassociationbetweensleepdurationanddepressioninHypertension.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4637255/v1/9c20d582908b9c8c045e045c.jpg"},{"id":60851494,"identity":"b6a360f6-e61f-4d2c-9eae-0c4436a6cc16","added_by":"auto","created_at":"2024-07-22 21:01:40","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74467,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curve of the nomogram\u003c/p\u003e","description":"","filename":"Fig4Receiveroperatingcharacteristiccurveofthenomogram.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4637255/v1/2699515a79cea2fa94d2d48c.jpg"},{"id":60851497,"identity":"58d653dd-2f21-44b5-abf8-810802e410b5","added_by":"auto","created_at":"2024-07-22 21:01:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":75379,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve assessment for the nomogram\u003c/p\u003e","description":"","filename":"Fig5Decisioncurveassessmentforthenomogram.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4637255/v1/6eeee9271a4c61a2680e0c66.jpg"},{"id":73230220,"identity":"bc85ecad-a2fe-43e6-9ef2-2259d7abf0bc","added_by":"auto","created_at":"2025-01-08 03:31:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1061515,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4637255/v1/76e88180-1018-4d1e-a54b-f02946894e14.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between sleep duration and depression in adults with hypertension: a cross-sectional study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHypertension was a globally recognized risk factor for cardiovascular and cerebrovascular diseases[1]. It increased the risk of cinditions such as myocardial infarction, heart failure, and stroke[2; 3]. Hypertension could also cause damage to target organs including the heart, brain, and kidneys, leading to significant disability[4], high morbidity, and mortality. With the continuous development of the social economy and the acceleration of modern life, the prevalence of hypertension had increased annually[2]. Since the 21st century, the prevalence of hypertension in China had exceeded 30%[5], with most cases not well-controlled, making it a significant public health problem.\u003c/p\u003e\n\u003cp\u003eDepression was an affective disorder and currently the most common mental health issue worldwide[6]. It was primarily characterized by significant and persistent low mood, loss of interest, inattention, and sleep disorders. Severe depression could lead to a higher suicide rate, placing a heavy burden on patients, families, and the healthcare system. According to the World Health Organization (WHO), depression would become the second most disabling disease in the world by 2030[7]. However, the treatment outcomes for depression were not satisfactory, with issues such as side effects and recurring symptoms associated with pharmacotherapy.\u003c/p\u003e\n\u003cp\u003eHypertension and depression often co-occur and were currently considered combined risk factors for cardiovascular and cerebrovascular diseases[8]. A meta-analysis suggested that approximately one-third of patients with hypertension experience depressive symptoms[9]. The incidence of hypertension combined with sleep disorders, depression, and other physical and mental diseases was increasing. Treatment of hypertension had been reported to affect depression and vice versa[10]. Many studies had shown that both short and long sleep duration were associated with increased risk of morbidity and all-cause mortality[11; 12]. A recent systematic review of patients with cardiovascular disease reported that either too short or too long a night's sleep duration increases the risk of depression[13]. Depression was also associated with poor sleep quality, including insomnia and abnormal sleep duration. Some studies indicated that sleep disorders were a common symptom of depression[14]. It had been reported that up to 83% of patients with depression had altered sleep patterns, including early awakening, difficulty falling asleep (i.e., prolonged sleep latency), abnormal sleep duration (short or long), and insomnia symptoms[15]. In the past, sleep disturbances were considered accompanying symptoms of depression and were thought to resolve with depression treatment, leading to their rare consideration. Recently, there had been considerable evidence that sleep disorders preceded depression, although there might be some correlation between these factors exists. So far, there had been few studies on the impact of sleep duration on depression in people with hypertension. Therefore, the purpose of this study was to explore the relationship between sleep duration and depression in patients with hypertension, thereby providing a theoretical basis for early screening and intervention for hypertension and depression.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e2.1 Study sample and design\u003c/p\u003e\n\u003cp\u003eThe NHANES incorporates interviews and physical examinations and comprises five key components: demographic data, laboratory results, dietary information, physical examination findings, and questionnaire responses. In this study, out of 21760 subjects diagnosed with hypertension surveyed from 2004 to 2015, 8696 participants were included in the final analysis after applying exclusion criteria(Figure 1). The exclusion criteria involved missing sleep duration data (n= 9785), missing PHQ-9 data (n = 1549), and other missing covariate data (n =1730).\u003c/p\u003e\n\u003cp\u003e2.2 Variables\u003c/p\u003e\n\u003cp\u003eThe key exposure variable in this study was sleep duration. While the outcome variable was depression. Covariates were divided into demographic data, medical conditions, and examination results. Demographic data included age, sex, race, marital status, and education. Medical conditions encompassed hypertension (defined as a history of physician-diagnosed hypertension, a measured average systolic blood pressure \u0026ge; 140 mmHg, diastolic blood pressure \u0026ge; 90 mmHg, or current use of antihypertensive medication), and diabetes mellitus.\u003c/p\u003e\n\u003cp\u003eDiabetes mellitus was classified as follows:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDiabetes mellitus(DM):\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eA history of physician-diagnosed diabetes\u003c/p\u003e\n\u003cp\u003eHemoglobin A1c level \u0026ge; 6.5%\u003c/p\u003e\n\u003cp\u003eFasting glucose\u0026ge; 7.0mmol/L\u003c/p\u003e\n\u003cp\u003eRandom blood glucose\u0026ge; 11.1mmol/L\u003c/p\u003e\n\u003cp\u003eTwo-hour oral glucose tolerance test (OGTT) blood glucose\u0026ge; 11.1mmol/L\u003c/p\u003e\n\u003cp\u003eCurrent use of diabetes medication or insulin;\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eimpaired fasting glucose (IFG):\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFasting glucose5.6\u0026ndash;7.0mmol/L\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eimpaired glucose tolerance (IGT):\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTwo-hour OGTT blood glucose7.8\u0026ndash;11.0 mmol/L\u003c/p\u003e\n\u003cp\u003eHemoglobin A1c level 5.7\u0026ndash;6.5%\u003c/p\u003e\n\u003cp\u003eOther medical conditions included obesity (characterized by standard body mass index (BMI), under 25 kg/m\u003csup\u003e2\u003c/sup\u003e, 25\u0026ndash;30 kg/m\u003csup\u003e2\u003c/sup\u003e, \u0026ge; 30 kg/m\u003csup\u003e2\u003c/sup\u003e), CVD, chronic kidney disease (CKD), and hyperlipidemia.\u003c/p\u003e\n\u003cp\u003eSleep duration was determined by asking participants \"How much sleep do you typically receive on a workday?\". Based on their responses, patients were divided into four groups according to sleep duration: short sleep duration (\u0026lt;6 hours), normal sleep duration (6-8hours), mild excessive sleep duration (8-9 hours), and excessive sleep duration (\u0026gt;9 hours).\u003c/p\u003e\n\u003cp\u003eDepressive symptoms were assessed using the Patient Health Questionnaire (PHQ-9)[16], which contains 9 questions addressing the frequency of depressive symptoms over the past two weeks. The total score ranges from 0 to 27, with 0 to 9 indicating no depression, 10 to 14 indicating moderate depression, 15 to 19 indicating moderately severe depression, and 20 to 27 indicating severe depression[16]. Based on previous studies, we used a cutoff value of 10 points to identify clinically relevant depression.[17; 18].\u003c/p\u003e"},{"header":"3. Statistical analysis","content":"\u003cp\u003eR Software was used for statistical analysis. Given the complex sampling design and sampling weights, this study used the Mobile Examination Center (MEC) weights were used for all analyses. Baseline characteristics were presented as means and standard errors (SE) for continuous variables and proportions for categorical variables. Sleep duration was categorized into quartiles. Data were analyzed using t-tests for continuous variables and chi-square tests for categorical variables to compare the sleep duration quartile groups. A logistic regression model was employed to estimate the odds ratios(OR) and 95% confidence intervals (CI) for the association between sleep duration and depression in patients with hypertension.\u003c/p\u003e\n\u003cp\u003eThe criteria for selecting adjusted variables were determined through a consideration of biological factors and a review of existing literature on the topic.\u003c/p\u003e\n\u003cp\u003eOur statistical inferences were based on the three models: Model 1 had no adjusted variables. Model 2 incorporated age, sex, marital status, education, and race. Model 3 included all variables from Model 2, in addition to BMI, smoking status,diabetes mellitus (DM), chronic kidney disease (CKD), cardiovascular disease (CVD), cancer, hyperlipidemia, and blood examination results (creatinine (CR), ALT, BUN, CKD-EPI Scr, CR urine).\u003c/p\u003e\n\u003cp\u003eThis\u0026nbsp;study\u0026nbsp;stratified\u0026nbsp;participants\u0026nbsp;based\u0026nbsp;on\u0026nbsp;age\u0026nbsp;(individuals\u0026nbsp;over\u0026nbsp;60,\u0026nbsp;individuals\u0026nbsp;under\u0026nbsp;60),\u0026nbsp;sex(male, female), race(white, black, others), education (\u0026lt;high school, high school, college or above), marital status (married, divorced, widowed, others), BMI (\u0026lt; 25, 25\u0026ndash;30, \u0026ge;30 kg/m\u003csup\u003e2\u003c/sup\u003e), smoking status (never, former, current), history of hypertension (yes, no), history of diabetes (DM, IFG, IGT, no), history of CKD (yes, no), history of CVD (yes, no), and history of hyperlipidemia (yes, no).\u003c/p\u003e\n\u003cp\u003eThe restricted cubic splines (RCS) regression model was employed to flexibly model the association between sleep duration and depression. With the inflection point of sleep duration serving as the reference value. Multivariate logistic regression analysis (Model 3) was performed to identify independent risk factors for depression in patients with hypertension. A nomogram, based on the logistic regression model(Model 3), was developed to derive probability estimates for the presence of depression in hypertensive patients, The discriminatory power of the nomogram model in identifying depression risk in hypertensive subjects was evaluated using the receiver operating characteristic (ROC) curve.\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using R Studio 4.2.0. The level of statistical significance was set at P \u0026lt; 0.05.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003e4.1 Baseline characteristics\u003c/p\u003e\n\u003cp\u003eAmong 65,101,139 participants from the NHANES database, 8696 participants diagonosed with hypertension and without exclusion criteria was included in this study. Sleep duration was divided into four categories: short sleep duration (\u0026lt;6 hours), normal sleep duration (6-8 hours), mild excessive sleep time (8-9 hours), and excessive sleep time (\u0026gt;9 hours).Table 1 presents the baseline characteristics according to the sleep duration and depression status. The analysis showed that participants with depression were typically younger, female, with DM, stroke, CHD, smoking habits, alcohol use, and increased urine creatinine and BMI. They also had lower BUN, shorter sleep duration, and lower education levels (Table 1, all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). However, Hyperlipidemia, CKD, alanine aminotransferase (ALT), and creatinine(CR) levels showed no significant differences across the sleep duration categories.\u003c/p\u003e\n\u003cp\u003e4.2 Association\u0026nbsp;between sleep duration and depression in Hypertension\u003c/p\u003e\n\u003cp\u003eThe sleep duration was analyzed both as a continuous variable and as a categorized variable (four groups).When sleep duration was analyzed as a continuous variable, it was positively correlated with the risk of depression in patients with hypertension. In model 1 was OR = 0.72, 95%CI: 0.66, 0.79, P \u0026lt; 0.0001). After adjusting for age and gender in model 2, sleep duration reminded positively correlated with the risk of depression in patients with hypertension (OR = 0.73, 95%C: 0.67, 0.79, P \u0026lt; 0.0001). Further adjustment for age, sex, BMI, education, ethnicity, DM, blood urea nitrogen (BUN), hyperlipidemia, smoking history, stroke, ALT, chronic kidney disease estimated by CKD-EPI Scr, and alcohol use in model 3 still showed a positive correlation.(OR = 0.77, 95%CI: 0.71, 0.88, P \u0026lt; 0.0001).\u003c/p\u003e\n\u003cp\u003eWhen analyzed as a categorized variable, participants with short sleep duration had an increased risk of depression compared to those with normal sleep duration. In Model 1(unadjusted), the odds ratio(OR) was 3.52(95%CI: 2.84, 4.36, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.0001); In Model 2 adjused for age and sex, the OR was 3.44(95%CI: 2.80, 4.23, P = 0.001); In Model 3,which was adjusted for age, sex, BMI, education, ethnicity, DM, BUN, hyperlipidemia, smoking history, stroke, Alt, CKD-EPI Scr, and alcohol use, the OR was 2.87(95%CI: 2.31, 3.55), P= 0.006）.The association between sleep duration and depression in hypertension was detailed in Table 2.\u003c/p\u003e\n\u003cp\u003e4.3 Nonlinear association between sleep duration and depression in Hypertension\u003c/p\u003e\n\u003cp\u003eUsing the retricted cubic alpine(RCS) model, it was found that the relationship between sleep duration and depression presents an L-shaped curve in patients with Hypertension (\u003cem\u003eP\u003c/em\u003e = 0.000 for nonlinearity).When sleep duration was less than 7.22 hours, it was positively correlated with the risk of depression in hypertensive patients, with an OR value of 0.60(95%CI: 0.55, 0.66; P\u0026lt; 0.0001)(Figure 2).For sleep durations greater than 7.22 hours, the correlation was no longer statistically significant (Table 3).\u003c/p\u003e\n\u003cp\u003e4.4 Subgroup analysis of the relationship between sleep duration and depression in hypertensive patients from different populations\u003c/p\u003e\n\u003cp\u003eWe also conducted subgroup analyses stratified by sex (female, male), age (\u0026lt;60, \u0026ge;60years), poverty-income ratio (PIR\u0026lt;1.5, \u0026ge;1.5), BMI (\u0026lt;25, 25-30, \u0026ge;30) to further explore the association between sleep duration and depression in hypertension patients across different populations. The results showed no differences in the association between sleep duration and depression in hypertension patients across the subgroups of sex, BMI, PIR, and age, indicating that the conclusion of the present study was not affected by these confounders (Table 4).\u003c/p\u003e\n\u003cp\u003e4.5 Logistic regression and the nomogram model\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression analysis (Model 2) was applied to identify the key factors most closely related to depression in hypertension patients. A total of 11 variables (age, sex, sleep duration, stroke, BMI, education, DM, BUN, smoking status, serum creatinine (SCR), alcohol use) were included to establish the crude nomogram model (Figure\u0026ensp;3). The receiver operating characteristic (ROC) curve of the test set was used to evaluate the discriminative ability of the model. The ROC curve results demonstrated that the nomogram model established in this study had good discriminative power, and an area under the curve (AUC) was 73.2% (Figure 4), indicating good stability and prediction accuracy.\u003c/p\u003e\n\u003cp\u003eThe decision curve analysis (DCA) for the nomogram was conducted to measure the risks and benefits. According to the results of multiple regression analysis, predictive factors such as age, gender, sleep duration, stroke, BMI, education level, diabetes, blood urea nitrogen, smoking, serum creatinine (SCR), and alcohol use were identified, Based on these factors, a nomogram model for predicting the risk of depression in hypertensive individuals was constructed (Figure 5)..\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe prevalence of sleep disorders in hypertensive patients varied widely, ranging from 14.9% to 85.7% globally[19]. A meta-analysis in China suggests that more than 50% of Chinese hypertensive patients had poor sleep quality[20]. According to another survey report, among 850 hypertensive patients, the proportion of insomnia reached 41%[21]. In addition, studies had shown that compared with non-hypertensive people, hypertensive patients had a 40%-60% higher incidence of insomnia[22]. In this study, the proportion of short sleep duration (\u0026lt;6 h) was 6.85%, the proportion of long sleep duration(\u0026gt;9 h) was 33.34%, and the proportion of abnormal sleep duration was 40.19%, consistent with previous research results. The current mechanism was mainly that hypertensive patients were prone to anxiety, tension, depression, and other emotions due to their illness[23-25]. These emotions could lead to autonomic nervous system disorders, which in turn lead to sleep disorders. In this study, the prevalence of depression among hypertensive patients was 12.81%. Studies had shown that the incidence of depression among hypertensive patients could reach 20%-30%[26]. A Chinese study showed that the prevalence of depression among hypertensive patients was about 15.8%[27], slightly higher than the results of this study. The analysis might be due to differences in the gender, age, region, and socio-economic income of the subjects included in this study.\u003c/p\u003e\n\u003cp\u003eIn our recent study, we observed a negative correlation between sleep duration and depression risk in hypertensive patients when sleep time was less than 7.22 hours. Remarkably, this was the first study to report that excessively short sleep durations among hypertensive individuals significantly elevate the risk of depression. Echoing our findings, Yujie Li\u0026apos;s team identified short nocturnal sleep duration as an independent risk factor for the onset and persistence of depression among elderly Chinese individuals[28]. Furthermore, a longitudinal study revealed a higher prevalence of depressive symptoms among those sleeping less than 5 hours compared to those with optimal sleep durations (7-8 hours)[29]. Strikingly, another study found that individuals sleeping less than 6 hours per night had a four-fold increased risk of depression compared to those sleeping 7-8 hours[30]. These findings resonate strongly with our observations in the hypertensive population. The potential mechanisms underlying this association were multifaceted. Firstly, inadequate sleep was known to augment daytime physical and psychological fatigue, thereby impacting daily life and work negatively, which might contribute to the development of depression[31]. Secondly, sleep deprivation could activate the sympathetic nervous system and \u0026beta;-adrenergic signaling, leading to increased secretion of inflammatory markers like IL-6 and CRP[32]. This, in turn, boosts gene expression related with inflammatory[33]. Moreover, insufficient sleep could overstimulate the hypothalamic-pituitary-adrenal axis, elevating cortisol levels[34]. Elevated cortisol could trigger a cascade of physiological and psychological effects, ultimately leading to depressive symptoms[35]. Additionally, shortened sleep durations were associated with increased 24-hour average blood pressure and heart rate, alongside elevated psychosocial stress, which might contribute to persistent hypertension[36]. Factors liked elevated cortisol, renal function impairment, and endothelial dysfunction might also contribute to hypertension[37]. Several studies had highlighted that chronic sleep deprivation could disrupt circadian rhythms and autonomic nervous system balance, leading to elevated blood pressure[38-41]. Accompanying these physiological changes, patients might also experience emotional shifts such as depression and anxiety[42]. Furthermore, inadequate sleep could enhance oxidative stress, accelerate atherosclerosis, trigger metabolic disorders, and ultimately increase the risk of cardiovascular diseases[43].\u003c/p\u003e\n\u003cp\u003eFurthermore, our study revealed that longer sleep durations were not necessarily beneficial for hypertensive patients. Beyond a certain threshold (7.22 hours), the risk of depression among hypertensive individuals begins to escalate. This finding aligns with a meta-analysis conducted by Long Zhai\u0026apos;s team, which demonstrated that both short and long sleep durations were significantly associated with an increased risk of depression in adults[44]. Similarly, Lu Dong\u0026apos;s team observed a U-shaped association between sleep duration and depression incidence in a cross-sectional study of Americans[45]. Although characteristics such as age, comorbid chronic diseases, and monitored sleep durations differed across these studies, the consensus indicates that both insufficient and excessive sleep could elevate depression risk. The current understanding suggested that extended sleep durations might disrupt the body\u0026apos;s circadian rhythm and endocrine system, leading to hormonal imbalances that affect neurotransmitter functions, thereby increasing depression vulnerability[46]. Additionally, prolonged sleep might reflect an inadequate coping mechanism for stress and negative emotions[47]. Some individuals might attempt to avoid real-life problems and pressured by increasing sleep, but this approach often backfires, exacerbating emotional distress[48]. Our study observed similar conclusions in hypertensive patients, potentially due to physical discomforts like high intracranial pressure, dizziness, and headaches, which might prompt longer sleeping as a coping mechanism. However, this extended sleep duration could alter hormonal levels and neurotransmitter functions, ultimately heightening the risk of depression.\u003c/p\u003e\n\u003cp\u003eThe intimate relationship between sleep disorders and depression was currently recognized as bidirectional; sleep disorders could lead to depression, while depression could trigger or exacerbate sleep issues such as difficulty falling asleep, early awakening, and poor sleep quality[49; 50]. These sleep disturbances might further intensify depressive symptoms[51], forming a vicious cycle. Sleep disorders were not only a common symptom of depression but could also be its first presenting sign[52]. Prolonged lack of good sleep could cause physical and psychological fatigue, making individuals more susceptible to depressive moods[53]. Studies had revealed a statistically significant bidirectional correlation between insomnia and hypertension[54]. Adequate sleep was a crucial process for the body\u0026apos;s recovery and repair, and sleep deprivation may interfere with normal blood pressure regulation [55]. Blood pressure typically decreases during sleep, but sleep deprivation could reduce this drop or lead to elevated nighttime blood pressure[56]. Conversely, elevated blood pressure increases sympathetic nervous system activity[57], keeping it in a persistent state of excitement and causing sleep disorders like insomnia and dreaminess[58]. Increased intracranial pressure affects the brain\u0026apos;s normal blood circulation in a prolonged hypertensive state[59]. Persistent cerebral ischemia and hypoxia could trigger symptoms like dizziness and headaches, leading to sleep disturbances[60]. Additionally, hypertension-related damage to cardiac target organs and the stimulating effects of antihypertensive medications could contribute to sleep disorders[10]. Hypertension itself might bring psychological stress, such as concerns about disease progression and treatment costs, which could cause or exacerbate depressive symptoms[61]. Common symptoms of hypertension, such as headaches, fatigue, and restlessness, may overlap with depressive symptoms, increasing the risk of depression[62; 63]. Depression could disrupt normal sleep regulation mechanisms, such as affecting melatonin secretion and circadian rhythm adjustment, leading to sleep disorders[57]. Hypertension, depression, and sleep disorders were all linked to certain neurotransmitter systems like serotonin and dopamine, which played crucial roles in regulating mood, sleep, and blood pressure[64]. Thus, their interaction could lead to mutual influences among these three conditions. Furthermore, hypertension, depression, and sleep disorders were all associated with psychological stress[65; 66]. Prolonged psychological stress could elevate blood pressure, worsen depressive symptoms, and cause sleep disturbances, forming a vicious cycle[67]. Therefore, emphasizing emotional assessment and conducting sleep disorder screenings in the hypertensive population could aid in better management and reduce comorbidities like depression.\u003c/p\u003e\n\u003cp\u003eOur study has several limitations. First of all, This study was based on the NHANES database, indicating that the data were retrospective, and some variables were based on self-report(sleep duration, hypertension ), which increased the selection bias. In this study, a nomogram model was constructed from cross-sectional data, which may limit its predictive accuracy. Therefore, the findings should be interpreted cautiously.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study explored the relationship between sleep duration and hypertension combined with depression and found that sleep duration of less than 7.22 hours was associated with an increased risk of depressive symptoms in hypertensive patients. We further established a model based on the correlation between sleep duration and depression risk in hypertensive patients, which has certain guiding significance for the sleep duration of hypertensive patients.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eTian Lv, and Jie Li were responsible for the study design;\u003c/p\u003e\n\u003cp\u003eTian Lv, Han Zhaowas responsible for data extraction;\u003c/p\u003e\n\u003cp\u003eTian Lv, and Xiaoling Wang performed statistical analysis and interpretation;\u003c/p\u003e\n\u003cp\u003eLehui Chen was responsible for manuscript drafting;\u003c/p\u003e\n\u003cp\u003eLehui Chen, and Qiongbin Zhu were responsible for the revision.\u003c/p\u003e\n\u003cp\u003eThe final manuscript was read and approved by all authors.\u003c/p\u003e\n\u003cp\u003eEthical statement\u003c/p\u003e\n\u003cp\u003eNot required.\u003c/p\u003e\n\u003cp\u003eDisclosure statement\u003c/p\u003e\n\u003cp\u003eFinancial disclosure:\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003eNon-financial disclosure:\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e# Equal contribution\u003c/p\u003e\n\u003cp\u003e* Corresponding author\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are publicly available in NHANES (https://www.cdc.gov/nchs/nhanes), FinnGen Biobank (https://www.finngen.fi/en), IEU Open GWAS (https://gwas.mrcieu.ac.uk/), and GWAS Catalog (https://www.ebi.ac.uk/gwas/downloads/summary-statistics).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJames, J. 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HYPERTENS RES. 46, 1020\u0026ndash;1030 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKinlein, S. A. \u0026amp; Karatsoreos, I. N. The Hypothalamic-Pituitary-Adrenal Axis as a Substrate for Stress Resilience: Interactions with the Circadian Clock. FRONT NEUROENDOCRIN. 56, 100819 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuiz, N. et al. Inflammatory Process and Immune System in Major Depressive Disorder. INT J NEUROPSYCHOPH. 25, 46\u0026ndash;53 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson, K. A. et al. The Association of Insomnia Disorder Characterised by Objective Short Sleep Duration with Hypertension, Diabetes and Body Mass Index: A Systematic Review and Meta-Analysis. SLEEP MED REV. 59, 101456 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSher, L. D. et al. Chronic Stress and Endothelial Dysfunction: Mechanisms, Experimental Challenges, and the Way Ahead. 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NAT REV IMMUNOL. 19, 702\u0026ndash;715 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKay, D. B., Tanner, J. J. \u0026amp; Bowers, D. Sleep Disturbances and Depression Severity in Patients with Parkinson's Disease. BRAIN BEHAV. 8, e967 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen, S. et al. Associations of Poor Sleep Quality, Chronic Pain and Depressive Symptoms with Frailty in Older Patients: Is there a Sex Difference? BMC GERIATR. 22, 862 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Z., Li, Y., Chen, L., Chen, P. \u0026amp; Hu, Y. Prevalence of Depression in Patients with Hypertension: A Systematic Review and Meta-Analysis. MEDICINE. 94, e1317 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong, L., Xie, Y. \u0026amp; Zou, X. Association Between Sleep Duration and Depression in Us Adults: A Cross-Sectional Study. 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Sympathetic Nervous System and Hypertension: New Evidences. AUTON NEUROSCI-BASIC. 238, 102954 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirooka, Y. Sympathetic Activation in Hypertension: Importance of the Central Nervous System. AM J HYPERTENS. 33, 914\u0026ndash;926 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly, D. M. \u0026amp; Rothwell, P. M. Blood Pressure and the Brain: The Neurology of Hypertension. PRACT NEUROL. 20, 100\u0026ndash;108 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernald, F. et al. Depression and Hypertension Awareness, Treatment, and Control in a Multiethnic Population in the Netherlands: Helius Study. INTERN EMERG MED. 16, 1895\u0026ndash;1903 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah, R. M. et al. Impacts of Anxiety and Depression On Clinical Hypertension in Low-Income Us Adults. HIGH BLOOD PRESS CAR. 30, 337\u0026ndash;342 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGan, Q. et al. Unraveling the Link Between Hypertension and Depression in Older Adults: A Meta-Analysis. FRONT PUBLIC HEALTH. 11, 1302341 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBussotti, M. \u0026amp; Sommaruga, M. Anxiety and Depression in Patients with Pulmonary Hypertension: Impact and Management Challenges. VASC HEALTH RISK MAN. 14, 349\u0026ndash;360 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, J. et al. Hypertension, Sleep Quality, Depression, and Cognitive Function in Elderly: A Cross-Sectional Study. FRONT AGING NEUROSCI. 15, 1051298 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai, Y., Chen, M., Zhai, W. \u0026amp; Wang, C. Interaction Between Trouble Sleeping and Depression On Hypertension in the Nhanes 2005\u0026ndash;2018. BMC PUBLIC HEALTH. 22, 481 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlorez-Garcia, V., Rojas-Bernal, L. A. \u0026amp; Bareno-Silva, J. Depression and Sleep Disorders Related to Hypertension: A Cross-Sectional Study in Medellin, Colombia. Rev Colomb Psiquiatr (Engl Ed). 49, 109\u0026ndash;115 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamam, M. S. et al. Anxiety, Depression, and Pain: Considerations in the Treatment of Patients with Uncontrolled Hypertension. 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align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2659(37.922)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e229(25.854)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eModerate\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e965(14.718)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e110(14.254)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNever\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1169(11.948)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e120( 9.918)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSmoking history\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNever\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3925(50.453)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e357(36.458)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFormer\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2516(32.540)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e246(27.145)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCurrent\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1335(17.006)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e317(36.397)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eEthnicity\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWhite\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3856(73.998)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e421(66.795)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBlack\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1932(12.069)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e239(16.422)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eOther\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1988(13.932)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e260(16.783)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;25\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1531(19.141)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e148(15.891)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e25\u0026ndash;30\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2607(33.660)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e226(25.472)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026gt;=30\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3638(47.199)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e548(58.737)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e30.698(0.122)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e32.711(0.365)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSerum Creatinine\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.963(0.006)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.975(0.036)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.738\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBUN\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e14.740(0.097)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e13.386(0.242)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eUrine Creatinine\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e113.377(1.146)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e124.822(3.760)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.006\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAlt\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e26.686(0.251)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e28.725(1.602)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.205\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eStroke\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e7259(94.680)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e811(89.353)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e517( 5.320)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e109(10.647)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCKD/Chronic kidney disease\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.949\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e5414(74.629)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e634(74.763)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2362(25.335)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e286(25.237)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDiabetes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.002\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4598(63.930)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e488(59.358)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIGT\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e446(5.792)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e45(5.718)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIFG\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e436(6.179)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e35(3.759)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDM\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2296(24.099)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e352(31.165)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCVD\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e6328(84.201)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e646(74.748)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1448(15.799)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e274(25.252)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHyperlipidemia\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.442\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1376(16.697)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e140(15.269)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e6400(83.303)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e780(84.731)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eScr\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;30\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e149(1.346)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e25(1.859)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e30\u0026ndash;60\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1220(13.461)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e112(10.265)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e60\u0026ndash;90\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3310(42.715)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e334(39.015)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026gt;\u0026thinsp;90\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3097(42.479)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e449(48.852)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCKD-EPI Scr\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e83.942(0.403)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e86.142(0.976)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.014\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSleep hours\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.915(0.019)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.217(0.085)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSleep duration(hours)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u0026ndash;8\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3828(56.328)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e360(45.356)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e429( 5.148)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e182(22.694)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e8\u0026ndash;9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2084(29.960)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e167(23.606)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026gt;9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e650(8.564)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e72(8.344)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eThe association between sleep duration and depression in Hypertension\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003emodel 1\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eModel 2\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eModel 3\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e95%CI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e95%CI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e95%CI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHow much sleep do you get in hours\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.72(0.66,0.78)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.72(0.67,0.79)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.77(0.71,0.83)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u0026ndash;8\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eref\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eref\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eref\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.52(2.84,4.36)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.44(2.80,4.23)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.87(2.31,3.35)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e8\u0026ndash;9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.98(0.76,1.268)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.870\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.99(0.76,1.30)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.954\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.98(0.75,1.28)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.901\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026gt;\u0026thinsp;9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.21(0.91,1.62)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.191\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.27(0.94,1.72)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.110\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.15(0.85,1.55)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.365\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eThe association between sleep duration and depression in Hypertension\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e 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\u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sleep duration, depression, hypertension, a cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-4637255/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4637255/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eThere is an increasing awareness of sleep duration and depression in hypertension. This study provided a theoretical basis for early screening of depression in patients with hypertension by analyzing the relationship between sleep time and depression in patients with hypertension.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eThe data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES) from 2004 to 2015. Depressive symptoms were tested using the Patient Health Questionnaire (PHQ-9), and depression was diagnosed when PHQ-9 total score \u0026gt;10. The association between sleep duration and depression in hypertension was estimated using weighted multivariate logistic regression, and its nonlinearity was examined by restricted cubic spline (RCS) regression. Multivariate logistic regression analysis was used to identify independent risk factors for depression in hypertensive patients, a risk prediction nomogram model was established, and the receiver operating characteristic (ROC) curve was drawn to evaluate its ability to discriminate depression in hypertensive patients.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eA total of 8,696 hypertension participants were included in this study. Multivariate logistic regression showed that an increment of sleep duration was strongly associated with depression in hypertension (OR = 0.772, 95%CI = (0.705, 0.883). The RCS curve showed L-shaped relationships between sleep duration and depressive risk in hypertension. The nomogram model showed a favorable discriminatory power with an area under the curve (AUC) of 73.4%. Sleep duration at 7.22 hour might be most beneficial for the mental health of patients with hypertension.\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003eOur study demonstrated the relationship between sleep duration and depression in patients with hypertension, Effective interventions for sleep disorders in patient with hypertension are needed for preventing depression.\u003c/p\u003e","manuscriptTitle":"Association between sleep duration and depression in adults with hypertension: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-22 21:01:35","doi":"10.21203/rs.3.rs-4637255/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f07f6fe6-b908-46e3-8db5-84fe728c74c0","owner":[],"postedDate":"July 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34908685,"name":"Biological sciences/Neuroscience"},{"id":34908686,"name":"Health sciences/Neurology"}],"tags":[],"updatedAt":"2025-01-08T03:23:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-22 21:01:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4637255","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4637255","identity":"rs-4637255","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}