Benefits and risks of sleep medication in individuals with hypertension and sleep disturbance: Evidence from a large population-based study

Benefits and risks of sleep medication in individuals with hypertension and sleep disturbance: Evidence from a large population-based study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Benefits and risks of sleep medication in individuals with hypertension and sleep disturbance: Evidence from a large population-based study Zerui You, Duan Liu, Tuzhi Wang, Heng Wang, Jiyang Pan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6505576/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Oct, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 12 You are reading this latest preprint version Abstract Background: The benefits and risks of sleep medications among patients with hypertension and sleep disturbance remain unclear. This study aims to investigate the potential benefits and risks of sleep medications in this population. Methods: This was a prospective cohort study among US adults, using hypertension and medication data from the National Health and Nutrition Examination Survey (NHANES). Linear regression assessed the efficacy of sleep medications in controlling blood pressure. Cox regression explored associations between sleep medications and mortality. Results: This study included 4836 participants taking antihypertensive medication with sleep disturbance. Compared with non-users, benzodiazepine users had an adjusted estimated systolic blood pressure (SBP) difference of -2.20 mmHg (95% CI, -3.68 to -0.73; P = 0.003), while Z-drug users had a more pronounced difference of -3.34 mmHg (95% CI, -5.86 to -0.82; P = 0.009), with diazepam, clonazepam, and zolpidem demonstrating significant antihypertensive effects. The median follow-up time was 82.3 months, and 809 all-cause deaths occurred. Sleep medications (hazard ratio [HR]: 1.17; 95% CI, 1.00 to 1.37; P = 0.055) and benzodiazepine users (HR: 1.07; 95% CI, 0.89 to 1.29; P = 0.455) was not associated with an increased risk of all-cause mortality, while Z-drug users were linked to a higher risk (HR: 1.43; 95% CI, 1.08 to 1.91; P = 0.013) compared to non-users. No significant association was found with cardiovascular mortality. Conclusions: Sleep medications may assist in regulating blood pressure and were not significantly associated with an elevated mortality risk among hypertensive participants with sleep disturbance. Hypertension Sleep disturbance Sleep medication Mortality NHANES Figures Figure 1 Figure 2 Introduction Hypertension is projected to affect about one-quarter of the global adult population by 2025, potentially reaching nearly 1.5 billion people 1 . As a major risk factor for cardiovascular disease, effective management of hypertension is crucial for reducing morbidity and mortality. Sleep plays a critical role in cardiovascular health, and any deterioration in sleep quality or quantity, such as insomnia, obstructive sleep apnea, or sleep deprivation, could contribute to blood pressure instability, potentially exacerbating both the development and management of hypertension 2 . For example, poor sleep can lead to fluctuations or spikes in blood pressure 3 . Research has shown a strong association between impaired sleep and an elevated risk of cardiovascular disease (CVD) mortality in hypertensive workers 4 . Both meta-analytic and population-based studies reinforce these findings, highlighting the significant impact of sleep disturbance on cardiovascular outcomes 5 , 6 . Moreover, relying solely on antihypertensive medication may not be sufficient to maintain optimal blood pressure levels in individuals with poor sleep, suggesting a need for integrative management strategies that consider both antihypertensive and sleep-related interventions in this population 7 . Addressing sleep disorders or habits is an effective strategy for mitigating the risk of developing or managing hypertension. Li et al. investigated 402 individuals with insomnia and hypertension and found that combining antihypertensive medications with estazolam led to greater improvements in sleep quality and reductions in blood pressure when compared with a placebo 8 . This finding highlighted the importance of integrating sleep-focused therapies alongside traditional antihypertensive treatments for satisfactory blood pressure management. However, despite this promising result, there is still a significant gap in the research regarding the broader use of sleep medications for hypertensive individuals experiencing sleep disturbance. The benefits and potential risks associated with the long-term use of such medications, particularly in relation to cardiovascular health, remain underexplored. A more comprehensive understanding of how sleep medications could complement antihypertensive therapies would be valuable for developing more effective treatment strategies for this population. To the best of our knowledge, this is the first study based on the US National Health and Nutrition Examination Survey (NHANES) data to assess the benefits and risks of sleep medications in individuals with hypertension and sleep disturbance. Insights into the effect of sleep medication will facilitate the internists and psychiatrists in the clinical choice of treatment for hypertension. In this study, we aimed to identify: (i) the effect of sleep medications on blood pressure regulation in hypertensive individuals with sleep disturbance; (ii) whether sleep medications confer any survival risk, particularly in terms of reducing all-cause mortality and cardiovascular mortality. Our research focused extensively on exploring the similarities and differences between benzodiazepines and Z-drugs, which are among the most commonly prescribed sleep medications. By comparing their effects on hypertensive patients with sleep disturbance, we aimed to provide valuable insights into which medication class offers the most favorable risk-benefit profile. We hypothesized that sleep medications would not only stabilize the blood pressure but also reduce the risk of all-cause mortality and cardiovascular mortality. Methods Study population This cross-sectional study was designed using data from the 2005–2018 NHANES, which was carried out by the Centers for Disease Control and Prevention (CDC) to evaluate the health and nutritional status of the population in the United States. The NHANES study protocols were authorized by the National Center for Health Statistics Research Ethics Review Board in accordance with the updated Declaration of Helsinki. All participants gave written informed consent. Additional details about the NHANES initiative can be found on the CDC website (Centers for Disease Control and Prevention (CDC), 2022). Eligibility criteria for participants taking antihypertensive medication included: (1) aged 18 years or older; (2) participants diagnosed with hypertension and experiencing sleep disturbance; (3) underwent the blood pressure test and had survival data; (4) had a clear record of drug use; (5) with no missing data on potential covariates. Finally, our study involved 4836 participants, as illustrated in Fig. 1 . Hypertension and blood pressure According to the 2017 American College of Cardiology/American Heart Association (ACC/AHA) guidelines, hypertension was classified based on either elevated blood pressure (BP) measurements (systolic BP [SBP] ≥ 130 mm Hg or diastolic BP [DBP] ≥ 80 mm Hg) or the self-reported ongoing use of antihypertensive therapies, regardless of recorded BP values 9 . BP was assessed by trained examiners following a standardized protocol. Following a 5-minute resting interval and determination of the maximum inflation level, three successive blood pressure readings were recorded from participants. The average systolic and diastolic blood pressures were determined through three successive blood pressure readings 10 . Mean arterial pressure (MAP) was calculated as one-third pulse pressure plus diastolic pressure. The values of systolic or diastolic blood pressure that are abnormal are excluded. Assessment of sleep disturbance Sleep disturbance was defined based on responses to the sleep questionnaire from the NHANES survey or by identifying the use of sleep-related medications within the NHANES medication dataset. In the survey, participants were asked: “Have you ever reported sleep difficulties to a healthcare provider or medical professional?” Responses included “Yes,” “No,” “Refused,” and “Do not know.” Participants who answered “Yes” were categorized as having sleep disturbances, whereas responses of “Refused” and “Do not know” were excluded from analysis and treated as missing data. Medication for sleep disturbance In the NHANES, the Prescription Medication Questionnaire was interviewer-administered in the participant household using the Computer-Assisted Personal Interviewing (CAPI) system. Participants were queried about their use of prescribed medications within the past month. Those who responded affirmatively were requested to present the medication packaging to the interviewer or verbally disclose the name of the medication. The interviewer observed around 75% of the prescribed medications. This study focused on the prescribed use of sleep medications 11 . We incorporated sleep medications that assess pharmaceutical agents authorized for treating insomnia by either the British National Formulary, the Food and Drug Administration, the European Medicines Agency, the Pharmaceuticals and Medical Devices Agency, and the Therapeutic Goods Administration 11 . Based on the 27 drugs evaluated in the study 11 , a total of 17 sleep medications were found to be recorded in the NHANES data (Fig. 2 A and Table S1 -2). The participants were found to have been prescribed at least one of the following types of sleep medication: benzodiazepines, benzodiazepine-like agents (Z-drugs, including zopiclone, eszopiclone, zaleplon, and zolpidem), melatoninergic drugs, orexin receptor antagonists, and so on. Among these, six drugs administered as monotherapy were evaluated, with each drug used by at least 60 individuals and an average treatment duration exceeding 65 months. We included two main categories of medications, benzodiazepines and Z-drugs, and assessed their benefits and long-term risks for hypertensive participants with sleep disturbance. Other types of sleep medications were not analyzed due to their limited numbers. Mortality outcomes of the study population The National Death Index (NDI) database provided the mortality data used in this study. The NDI database was last updated on December 31, 2019, and this date served as the endpoint for calculating follow-up duration. For each participant, the follow-up period extended from the date of enrollment to either this endpoint or the date of death, whichever occurred first. Cardiovascular-related deaths were identified through the application of the International Classification of Diseases, Tenth Revision (ICD-10) codes, specifically I00-I09, I11, I13, I20-I51, and I60-I69. Covariates We included clinically significant covariates in our study, such as age at interview, gender, body mass index (BMI) group, income level, race/ethnicity, education level, marital status, smoking status (never/former/current), alcohol consumption (yes/no), history of CVD (yes/no), diabetes mellitus (yes/no), and depressive symptoms (yes/no). The BMI group is categorized according to the World Health Organization classification: underweight (BMI < 18.5), normal weight (BMI 18.5 to < 25), overweight (BMI 25 to < 30), and obesity (BMI ≥ 30). The participant's income level was assessed using the ratio of household income to the poverty threshold (PIR). Educational attainment was stratified into five groups: less than 9th grade, 9th to 11th grade, high school diploma recipient, college or Associate of Arts (AA) degree holder, and those with a college degree or higher. Marital status was classified as single (widowed, divorced, or separated) or non-single (married or living with a partner), determined by their living situation. Smoking status was classified into three categories: never smoked, formerly smoked, and currently smoked. Smoking status was assessed as never smoked (smoked < 100 cigarettes), former smoker (not currently smoking but smoked ≥ 100 cigarettes), or current smoker (≥ 100 cigarettes and currently smoking every day or on some days). Alcohol use was categorized into two categories: never drinking and ever or current drinking. The Patient Health Questionnaire (PHQ) depression scale categorized participants into two groups: no depressive symptoms (0–9 points) and depressive symptoms (≥ 10 points). Diabetes mellitus and CVD history were determined based on self-reported diagnoses provided by medical professionals. Statistical Analysis The descriptive statistic was performed on baseline features of the participants diagnosed with hypertension and sleep disturbance, comparing those using sleep medication to those not using it. Continuous variables were summarized as mean and standard deviation (SD) or median with interquartile range (IQR), whereas categorical variables were reported as frequency and proportion. Normality testing was performed by the Shapiro-Wilks test. We compared the characteristics by using independent Student t-test or Chi-squared test accordingly. Using the generalized linear regression model, we screened for the influence of sleep medication on blood pressure across different subgroups. We employed Cox proportional hazard models to analyze the relationship between sleep medicine and the risk of all-cause death and cardiovascular disease mortality. The risk of sleep medication on all-cause mortality was determined with additive adjustments of covariates in different models. Crude model was a univariate model. Model I was adjusted for all demographic covariates, and Model II was adjusted for all demographic covariates and the history of heart disease and the presence of diabetes to explore stability. Statistical analysis was conducted using the “survey” package in R software (version 4.0.4). Weights were applied to account for the complex survey design of NHANES, which includes oversampling, survey nonresponse, and poststratification adjustments to align with total U.S. population counts. To enhance the robustness of the findings, the weighted analysis was conducted as part of the sensitivity analysis, adhering to the NHANES analytical guidelines 12 . The sampling weight was calculated using the following formula: fasting subsample 14-year MEC weight = fasting subsample 2-year MEC weight/7. All statistical analyses and modelling were performed using R software (version 4.4.1), with a significance level set at p < 0.05. Results Population characteristics Table 1 presented the demographic features of the 4836 individuals from NHANES 2005–2018. The participants had an average age of 58.5 years, slept an average of 6.8 hours per day, and 44.9% of them were male. Among them, 961 (19.9%) were currently using sleep medication. Of the documented sleep medication users, 679 (70.7%) used only benzodiazepines, 205 (21.3%) used only Z-drugs, 58 (6.0%) used both types, and only 19 (2.0%) used other types of sleep medications. Participants with sleep medication were more likely to be female, white, and smokers and report a higher prevalence of depressive symptoms and CVD history compared to non-users. Table 1 Characteristics of the study population with various groups. Variables Total ( N = 4836) Without sleep medication ( n = 3875) With sleep medication ( n = 961) Statistics p Age (years) 60.0 (49.0–70.0) 59.0 (48.0–69.0) 62.0 (51.0–73.0) <0.001 Gender, n (%) <0.001 Male 2171 (44.9%) 1806 (46.6%) 365 (38.0%) Female 2665 (55.1%) 2069 (53.4%) 596 (62.0%) BMI group, n (%) <0.001 Underweight (< 18.5) 35 (0.72%) 25 (0.65%) 10 (1.04%) Normal (18.5 to < 25) 805 (16.6%) 605 (15.6%) 200 (20.8%) Overweight (25 to < 30) 1391 (28.8%) 1109 (28.6%) 282 (29.3%) Obese (30 or greater) 2605 (53.9%) 2136 (55.1%) 469 (48.8%) PIR 2.0 (1.1–3.9) 2.0 (1.1–4.0) 1.8 (1.0–3.5) 0.001 Race, n (%) <0.001 Non-Hispanic White 2527 (52.3%) 1913 (49.4%) 614 (63.9%) Non-Hispanic Black 1141 (23.6%) 1010 (26.1%) 131 (13.6%) Mexican American 464 (9.6%) 391 (10.1%) 73 (7.6%) Other Hispanic 359 (7.4%) 276 (7.1%) 83 (8.6%) Other Race/multiracial 345 (7.1%) 285 (7.4%) 60 (6.2%) Education, n (%) 0.038 Less Than 9th Grade 452 (9.3%) 345 (8.9%) 107 (11.1%) 9-11th Grade 659 (13.6%) 516 (13.3%) 143 (14.9%) High School Grad/GED 1220 (25.2%) 979 (25.3%) 241 (25.1%) Some College or AA degree 1605 (33.2%) 1288 (33.2%) 317 (33.0%) College Graduate or above 900 (18.6%) 747 (19.3%) 153 (15.9%) Marry status, n (%) 0.090 Marry/Living with partner 2634 (54.5%) 2134 (55.1%) 500 (52.0%) Widowed/Divorced/Separated/Never married 2202 (45.5%) 1741 (44.9%) 461 (48.0%) Drinking status, n (%) 0.157 Yes 3638 (75.2%) 2932 (75.7%) 706 (73.5%) No 1198 (24.8%) 943 (24.3%) 255 (26.5%) Smoking status, n (%) <0.001 Never smoker 2202 (45.5%) 1822 (47.0%) 380 (39.5%) Former smoker 1573 (32.5%) 1240 (32.0%) 333 (34.7%) Current smoker 1061 (21.9%) 813 (21.0%) 248 (25.8%) Diabetes mellitus, n (%) <0.001 Yes 1207 (25.0%) 1012 (26.1%) 195 (20.3%) No 3629 (75.0%) 2863 (73.9%) 766 (79.7%) CVD history, n (%) < 0.001 Yes 1022 (21.1%) 779 (20.1%) 243 (25.3%) No 3814 (78.9%) 3096 (79.9%) 718 (74.7%) Depressive symptom, n (%) < 0.001 Yes 951 (19.7%) 668 (17.2%) 283 (29.4%) No 3885 (80.3%) 3207 (82.8%) 678 (70.6%) Continuous variables are expressed as Median with Interquartile Range (IQR). The categorical variables were presented as numbers and percentages. Abbreviations: BMI, body mass index; PIR, family poverty income ratio; CVD, cardiovascular disease. Bold p-values denote statistical significance at the p < .05 level. Beneficial effects of sleep medication on blood pressure Of the 17 sleep medications selected for the main analysis, the most commonly used were alprazolam (31.4% of total users, 302 participants), zolpidem (24.7%, 237 participants), clonazepam (15.4%, 148 participants), and others (Fig. 2 A and Table S2). Two major drug classes will be included as subgroups: benzodiazepine-only and Z-drug-only populations. Benzodiazepine users included individuals who used any of the 10 kinds of benzodiazepines, such as alprazolam and lorazepam, while Z-drug users primarily included those who used eszopiclone, zaleplon, or zolpidem. Additionally, except for smoking status, education, and income level, no significant differences were observed between benzodiazepine and Z-drug users (Table S3). Taking sleeping medication produced an improvement in blood pressure management. After accounting for demographic covariates, sleep medication users had an adjusted mean systolic BP difference of -2.77 mmHg (95% CI, -4.05 to -1.49; P < 0.001, Fig. 2 B and Table 2 ) compared to non-users. Specifically, benzodiazepine users had a SBP difference of -2.20 mmHg (95% CI, -3.68 to -0.73; P = 0.003), while Z-drug users had a more pronounced SBP difference of -3.34 mmHg (95% CI, -5.86 to -0.82; P = 0.009). In comparing specific medications (Fig. 2 C and Table S4), diazepam users had an SBP difference of -5.93 mmHg (95% CI, -10.34 to -1.51; P = 0.008). Clonazepam showed − 4.15 mmHg (95% CI, -7.01 to -1.20; P = 0.006), and zolpidem showed − 4.62 mmHg (95% CI, -6.99 to -2.261; P < 0.001). Furthermore, sleep medication users showed an adjusted mean difference in diastolic BP of -1.39 mmHg (95% CI, -2.20 to -0.57; P = 0.001, Table 2 ), whereas the adjusted difference in mean arterial pressure (MAP) is -1.85 mmHg (95% CI, -2.66 to -1.03; P < 0.001). Table 2 Estimation of blood pressure difference in participants with hypertension and short sleep duration. Variables With sleep medication a ( n = 961) Benzodiazepine users a ( n = 679) Z-drugs users a ( n = 205) Beta (95% CI) P -value Beta (95% CI) P -value Beta (95% CI) P -value SBP -2.77 (-4.05 ~ -1.49) < 0.001 -2.20 (-3.68 ~ -0.73) 0.003 -3.34 (-5.86 ~ -0.82) 0.009 DBP -1.39 (-2.20 ~ -0.57) 0.001 -1.28 (-2.21 ~ -0.34) 0.007 -1.10 (-2.69 ~ 0.49) 0.176 MAP -1.85 (-2.66 ~ -1.03) < 0.001 -1.59 (-2.52 ~ -0.65) 0.001 -1.85 (-3.45 ~ -0.24) 0.024 a Compared to participants who didn't use sleeping medication. Unstandardized coefficients are presented with 95%CI in parentheses. All estimated models were adjusted for age, sex, race, BMI group, poverty income ratio, education, smoking status (never, former, current), drinking status (never, former, current), marital status, CVD history (yes/no), diabetes status (yes/no), and depressive symptom (yes/no). Abbreviation: SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure. Relationships of sleep medication with mortality The average follow-up period was 82.3 months, during which there were 809 deaths from all causes and 133 deaths from cardiovascular disease. Crude models linked sleep medications (hazard ratio [HR]: 1.48; 95% CI, 1.27 to 1.73; P < 0.001), benzodiazepines (HR: 1.46; 95% CI, 1.22 to 1.75; P < 0.001) and Z-drugs (HR: 1.62; 95% CI, 1.22 to 2.14; P = 0.001) with higher all-cause mortality but not cardiovascular mortality (Table 3 ). However, all of the associations became statistically insignificant after multivariable adjustments except that Z-drug use was associated with a higher risk of all-cause mortality (adjusted HR: 1.43; 95% CI, 1.08 to 1.91; P = 0.013) versus non-users. In the monotherapy analysis, HRs for lorazepam users (HR, 1.45; 95% CI, 1.04 to 2.01; P = 0.028), diazepam users (HR, 1.80; 95% CI, 1.07 to 3.03; P = 0.027), and zolpidem users (HR, 1.47; 95% CI, 1.11 to 1.93; P = 0.007) were associated with a higher risk of all-cause mortality, while no such association was observed with other medications, such as alprazolam, temazepam, and clonazepam (Table S5). There was no significant association of sleep medications with cardiovascular mortality. Table 3 Multivariate Cox regression analysis examining the impact of sleep medication across different treatment groups on all-cause mortality. Group Number (Death/Total) Crude Model Model 1 Model 2 HR (95% CI) P -value HR (95% CI) P -value HR (95% CI) P -value All-cause mortality Without sleep medication 809/3875 1[Ref.] - 1[Ref.] - 1[Ref.] - With any sleep medication 218/961 1.48 (1.27–1.73) < 0.001 1.17 (0.99–1.37) 0.061 1.17 (1.00–1.37) 0.055 Benzodiazepines 149/679 1.46 (1.22–1.75) < 0.001 1.08 (0.90–1.30) 0.403 1.07 (0.89–1.29) 0.455 Z-drugs 53/205 1.62 (1.22–2.14) 0.001 1.41 (1.06–1.88) 0.018 1.43 (1.08–1.91) 0.013 Cardiovascular mortality Without sleep medication 107/3875 1[Ref.] - 1[Ref.] - 1[Ref.] - With any sleep medication 26/961 0.97 (0.63–1.49) 0.903 0.76 (0.49–1.18) 0.217 0.76 (0.49–1.18) 0.217 Benzodiazepines 18/679 0.98 (0.59–1.61) 0.925 0.70 (0.42–1.17) 0.172 0.68 (0.41–1.13) 0.138 Z-drugs 5/205 0.84 (0.34–2.06) 0.703 0.70 (0.28–1.74) 0.446 0.73 (0.29–1.81) 0.495 Abbreviation: HR, Hazard Ratio; CI, confidence interval. Cox proportional hazard models were performed for all-cause mortality. Competing risk analyses of cause-specific mortality were performed. The crude model did not adjust for any covariates. Model 1 was adjusted for age, sex, race, BMI group, poverty income ratio, education, smoking status, drinking status, marital status, and depressive symptoms (yes/no). Model 2 was adjusted for CVD history and diabetes status in addition to model 1. Bold p-values denote statistical significance at the p < .05 level. Sensitivity analysis To assess the robustness and stability of the findings, sensitivity analyses were performed using weighted analysis on the NHANES dataset. Our observations revealed trends consistent with those found in the primary analysis (Table S6-9). Furthermore, users of alprazolam exhibited a lower adjusted mean difference in SBP (alprazolam: -2.69 [95% CI: -5.22 to -0.17] mmHg, P = 0.037). In the monotherapy analysis, the use of diazepam and zolpidem remained associated with a higher risk of all-cause mortality, whereas lorazepam use was no longer associated with an increased risk. Discussion The objective of this study was to investigate the potential benefits and risks of sleep medications in hypertensive individuals experiencing sleep disturbance, using a national dataset. Our results showed that sleep medications, including both benzodiazepines and Z-drugs, were beneficial in controlling blood pressure in hypertensive participants with sleep disturbance, consistent with results from previous literature 8 . Among the sleep medications we evaluated, diazepam, clonazepam, and zolpidem were particularly notable for their antihypertensive effects, with diazepam showing the most significant impact in reducing blood pressure. As a whole, no evidence for an increased risk of all-cause or cardiovascular mortality after the use of sleep medications was found in any of these analyses. However, analysis across two main types revealed that only Z-drugs were associated with an elevated risk of all-cause mortality, suggesting the need for caution with long-term use and further confirmation of this relationship. In conclusion, for hypertensive patients with suboptimal blood pressure control, a comprehensive evaluation and management of sleep disturbance are essential. The antihypertensive effect of sleep medications may be attributed to their ability to improve sleep architecture and reduce nighttime arousals, which are known to contribute to blood pressure variability 13 . Furthermore, the reduction in SBP among sleep medication users could be partly explained by the alleviation of stress and anxiety, which are common comorbidities in individuals with sleep disturbances and hypertension 8 . Additionally, benzodiazepines have been shown to reduce blood pressure, as evidenced by ambulatory blood pressure monitoring, which is consistent with our findings 14 . However, it is important to note that while sleep medications may help regulate blood pressure, they should not be considered a substitute for antihypertensive therapies. Instead, they should be viewed as part of a comprehensive management strategy. The integrated management of hypertension emphasizes the importance of a multifaceted approach that combines targeted pharmacological interventions, lifestyle modifications, and emerging therapies to control blood pressure effectively 15 . The identification of blood pressure-lowering effects of sleep medications, coupled with the effective management of sleep disorders such as obstructive sleep apnea, could broaden the range of treatment options for hypertension 8 , 16 , 17 . These advancements may also lead to a meaningful adjustment in clinical strategies for treating hypertensive patients who experience inadequately controlled blood pressure alongside sleep disturbances. Dual-action therapies that target both sleep quality and blood pressure control may offer advantages over antihypertensive drugs alone. This study strengthens the argument for a holistic approach to health, where managing sleep could have far-reaching benefits beyond just improving rest. We initially observed that any sleep medication use was associated with a higher risk of all-cause mortality in hypertensive individuals with sleep disturbance. However, this association became statistically insignificant after adjusting for demographic variables, lifestyle factors, and CVD history. This result implies that sleep medication use itself does not independently contribute to mortality risk. The association between sleep medication use and increased mortality risk may be confounded by unhealthy lifestyle factors, as users in our data exhibited lower income levels and poorer health profiles—having depressive symptoms, a history of CVD, older, and smoking—compared to non-users. For instance, Wang et al. identified a potential synergistic interaction between depressive symptoms and sleep disorders, which may increase the occurrence of CVD 18 . Lu et al. focused on the association of lifestyle factors and antihypertensive medication use with mortality risk among adults with hypertension. They concluded that improvements in lifestyle after hypertension diagnosis were associated with a reduced risk of all-cause and cardiovascular mortality 19 . As noted by Hausken et al., lifestyle and socioeconomic factors, which may contribute to the remaining excess mortality, should also be considered 20 . Our results along with those of others 14 , 21 suggest that long-term use of sleep medications, at least benzodiazepines, does not increase mortality. There are, of course, a few studies 22 , 23 with contradicting results, indicating that sleep medications are associated with higher mortality, potentially due to differences in clinical populations. Taken together, the totality of the current evidence in our study does not support mortality risks associated with the use of sleep medications in hypertensive individuals with sleep disturbance. Future research could focus on long-term follow-up of this population to provide more robust data on the effect of sleep medications. The unexpected finding in the analysis across two main types of sleep medication is the association between Z-drugs use and increased all-cause mortality after adjusting for covariates, a pattern not observed with benzodiazepines use. This association is in contrast to previous research 24 , 25 , which often highlighted that the mortality risks associated with Z-drugs are lower than those associated with benzodiazepines. In common perceptions, compared to benzodiazepines, Z-drugs offer better efficacy in reducing nocturnal awakenings, enhancing restfulness upon waking, and improving daytime performance, with fewer side effects 26 , 27 . However, there is also evidence suggesting that Z-drugs may have more adverse effects than benzodiazepines. A meta-analysis showed that Z-drugs were significantly associated with an increased risk of hip fracture, with the risk being higher than that associated with benzodiazepines 28 . Similar findings have been reported in a larger cohort study 29 . The use of Z-drugs was associated with an increased risk for all-cause mortality and composite CVD outcomes in post-menopausal women with sleep disturbances. These findings suggest that while Z-drugs are often considered safer than benzodiazepines, they may involve unforeseen risks, highlighting the need for caution and further investigation into their long-term impacts. The key strength of our study is that the present study is a prospective cohort study utilizing the NHANES database. This authoritative and representative resource augments the generalizability of our findings. We leveraged the database to investigate the unverified clinical benefits and mortality risks of sleep medications in hypertensive individuals with sleep disturbance. Nevertheless, the study had several limitations. Despite accounting for several confounders, some unmeasured or complex confounders remain. Moreover, the diagnoses of hypertension, diabetes, CVD history, and sleep disturbance were based on self-reported questionnaires, potentially introducing recall bias. Finally, participants frequently used both antihypertensive and sleep medications, which complicated the interactions and mechanisms involved. Consequently, these findings should be viewed as pilot analyses and interpreted with caution. Conclusion In summary, our results suggested a potential benefit of sleep medications in blood pressure control among hypertensive participants with sleep disturbance. When all sleep medication prescriptions are considered as a whole, no statistically significant association with all-cause mortality was observed; however, an increase in mortality associated with Z-drugs was observed. More empirical studies could be conducted to validate the association between sleep medications and the prevalence of hypertension with sleep disorders in the future. Abbreviations NHANES National Health and Nutrition Examination Survey ACC/AHA American College of Cardiology/American Heart Association BP Blood Pressure SBP Systolic Blood Pressure DBP Diastolic Blood Pressure MAP Mean Arterial Pressure Z-drugs Benzodiazepine-like agents NDI National Death Index ICD-10 International Classification of Diseases, Tenth Revision BMI Body Mass Index PIR Poverty Income Ratio PHQ Patient Health Questionnaire CVD Cardiovascular Disease HR Hazard Ratio CI Confidence Interval SD Standard Deviation IQR Interquartile Range CDC Centers for Disease Control and Prevention Declarations Acknowledgments Not applicable. A uthorship contribution s YZ designed the study, performed data extraction and statistical analysis, and wrote the original draft of the manuscript. LD, WT and WH contributed to the study design and methodology. PJ supervised the project, acquired funding, and provided critical revisions to the manuscript. All authors reviewed, edited, and approved the final version of the manuscript. Funding This work was funded by National Key R&D Program of China (Grant No.: 2022YFC2503902), and Guangzhou Key Laboratory for Germ-free animals and Microbiota Application (Grant No.: 202201020381). Data a vailability All relevant data are described within the paper. The NHANES data underpinning the findings of this research are accessible to the public at https://www.cdc.gov/nchs/nhanes/index.htm. Competing interests The authors declare no competing interests. Clinical trial number Not applicable. Ethics approval and consent to participate The requirement of ethical approval for this was waived by the Institutional Review Board of First Affiliated Hospital of Jinan University, because the data was accessed from NHANES (a publicly available database). The need for written informed consent was waived by the Institutional Review Board of First Affiliated Hospital of Jinan University due to retrospective nature of the study. The study protocol was approved by the NCHS Research Ethics Review Board (ERB) and all methods were performed in accordance with the relevant guidelines and regulations. Consent for publication Not applicable. References Sorlie, P.D., Allison, M.A., Avilés-Santa, M.L., Cai, J., Daviglus, M.L., Howard, A.G., Kaplan, R., Lavange, L.M., Raij, L., Schneiderman, N., et al. (2014). Prevalence of hypertension, awareness, treatment, and control in the Hispanic Community Health Study/Study of Latinos. Am. J. Hypertens. 27 , 793–800. https://doi.org/10.1093/ajh/hpu003. Pepin, J.-L., Borel, A.-L., Tamisier, R., Baguet, J.-P., Levy, P., and Dauvilliers, Y. (2014). Hypertension and sleep: overview of a tight relationship. Sleep Med. Rev. 18 , 509–519. https://doi.org/10.1016/j.smrv.2014.03.003. Garcia, C.E.V., Drager, L.F., Krieger, E.M., Negrão, C.E., Bortolotto, L.A., Lorenzi-Filho, G., and Ueno, L.M. (2013). Arousals are frequent and associated with exacerbated blood pressure response in patients with primary hypertension. Am. J. Hypertens. 26 , 617–623. https://doi.org/10.1093/ajh/hps065. Li, J., Atasoy, S., Fang, X., Angerer, P., and Ladwig, K.-H. (2021). Combined effect of work stress and impaired sleep on coronary and cardiovascular mortality in hypertensive workers: The MONICA/KORA cohort study. Eur. J. Prev. Cardiol. 28 , 220–226. https://doi.org/10.1177/2047487319839183. Cappuccio, F.P., D’Elia, L., Strazzullo, P., and Miller, M.A. (2010). Sleep duration and all-cause mortality: a systematic review and meta-analysis of prospective studies. Sleep 33 , 585–592. https://doi.org/10.1093/sleep/33.5.585. Meng, L., Zheng, Y., and Hui, R. (2013). The relationship of sleep duration and insomnia to risk of hypertension incidence: a meta-analysis of prospective cohort studies. Hypertens. Res. Off. J. Jpn. Soc. Hypertens. 36 , 985–995. https://doi.org/10.1038/hr.2013.70. Knutson, K.L., Van Cauter, E., Rathouz, P.J., Yan, L.L., Hulley, S.B., Liu, K., and Lauderdale, D.S. (2009). Association between sleep and blood pressure in midlife: the CARDIA sleep study. Arch. Intern. Med. 169 , 1055–1061. https://doi.org/10.1001/archinternmed.2009.119. Li, Y., Yang, Y., Li, Q., Yang, X., Wang, Y., Ku, W.L., and Li, H. (2017). The impact of the improvement of insomnia on blood pressure in hypertensive patients. J. Sleep Res. 26 , 105–114. https://doi.org/10.1111/jsr.12411. Richardson, L.C., Vaughan, A.S., Wright, J.S., and Coronado, F. (2024). Examining the Hypertension Control Cascade in Adults With Uncontrolled Hypertension in the US. JAMA Netw. Open 7 , e2431997. https://doi.org/10.1001/jamanetworkopen.2024.31997. Peters, S.A.E., Muntner, P., and Woodward, M. (2019). Sex Differences in the Prevalence of, and Trends in, Cardiovascular Risk Factors, Treatment, and Control in the United States, 2001 to 2016. Circulation 139 , 1025–1035. https://doi.org/10.1161/CIRCULATIONAHA.118.035550. De Crescenzo, F., D’Alò, G.L., Ostinelli, E.G., Ciabattini, M., Di Franco, V., Watanabe, N., Kurtulmus, A., Tomlinson, A., Mitrova, Z., Foti, F., et al. (2022). Comparative effects of pharmacological interventions for the acute and long-term management of insomnia disorder in adults: a systematic review and network meta-analysis. Lancet Lond. Engl. 400 , 170–184. https://doi.org/10.1016/S0140-6736(22)00878-9. Johnson, C.L., Paulose-Ram, R., Ogden, C.L., Carroll, M.D., Kruszon-Moran, D., Dohrmann, S.M., and Curtin, L.R. (2013). National health and nutrition examination survey: analytic guidelines, 1999-2010. Vital Health Stat. 2., 1–24. Knutson, K.L., Van Cauter, E., Rathouz, P.J., Yan, L.L., Hulley, S.B., Liu, K., and Lauderdale, D.S. (2009). Association between sleep and blood pressure in midlife: the CARDIA sleep study. Arch. Intern. Med. 169 , 1055–1061. https://doi.org/10.1001/archinternmed.2009.119. Mendelson, N., Gontmacher, B., Vodonos, A., Novack, V., Abu-AjAj, M., Wolak, A., Shalev, H., and Wolak, T. (2018). Benzodiazepine Consumption Is Associated With Lower Blood Pressure in Ambulatory Blood Pressure Monitoring (ABPM): Retrospective Analysis of 4938 ABPMs. Am. J. Hypertens. 31 , 431–437. https://doi.org/10.1093/ajh/hpx188. Carey, R.M., Moran, A.E., and Whelton, P.K. (2022). Treatment of Hypertension: A Review. JAMA 328 , 1849–1861. https://doi.org/10.1001/jama.2022.19590. Redline, S., Azarbarzin, A., and Peker, Y. (2023). Obstructive sleep apnoea heterogeneity and cardiovascular disease. Nat. Rev. Cardiol. 20 , 560–573. https://doi.org/10.1038/s41569-023-00846-6. Yeghiazarians, Y., Jneid, H., Tietjens, J.R., Redline, S., Brown, D.L., El-Sherif, N., Mehra, R., Bozkurt, B., Ndumele, C.E., and Somers, V.K. (2021). Obstructive Sleep Apnea and Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation 144 , e56–e67. https://doi.org/10.1161/CIR.0000000000000988. Wang, C., and Hu, J. (2021). Influence of the Interaction Between Depressive Symptoms and Sleep Disorders on Cardiovascular Diseases Occurrence. Int. J. Gen. Med. 14 , 10327–10335. https://doi.org/10.2147/IJGM.S334894. Lu, Q., Zhang, Y., Geng, T., Yang, K., Guo, K., Min, X., He, M., Guo, H., Zhang, X., Yang, H., et al. (2022). Association of Lifestyle Factors and Antihypertensive Medication Use With Risk of All-Cause and Cause-Specific Mortality Among Adults With Hypertension in China. JAMA Netw. Open 5 , e2146118. https://doi.org/10.1001/jamanetworkopen.2021.46118. Hausken, A.M., Skurtveit, S., and Tverdal, A. (2007). Use of anxiolytic or hypnotic drugs and total mortality in a general middle-aged population. Pharmacoepidemiol. Drug Saf. 16 , 913–918. https://doi.org/10.1002/pds.1417. Vinkers, D.J., Gussekloo, J., van der Mast, R.C., Zitman, F.G., and Westendorp, R.G.J. (2003). Benzodiazepine use and risk of mortality in individuals aged 85 years or older. JAMA 290 , 2942–2943. https://doi.org/10.1001/jama.290.22.2942. Weich, S., Pearce, H.L., Croft, P., Singh, S., Crome, I., Bashford, J., and Frisher, M. (2014). Effect of anxiolytic and hypnotic drug prescriptions on mortality hazards: retrospective cohort study. BMJ 348 , g1996. https://doi.org/10.1136/bmj.g1996. Kripke, D.F., Langer, R.D., and Kline, L.E. (2012). Hypnotics’ association with mortality or cancer: a matched cohort study. BMJ Open 2 , e000850. https://doi.org/10.1136/bmjopen-2012-000850. Palmaro, A., Dupouy, J., and Lapeyre-Mestre, M. (2015). Benzodiazepines and risk of death: Results from two large cohort studies in France and UK. Eur. Neuropsychopharmacol. J. Eur. Coll. Neuropsychopharmacol. 25 , 1566–1577. https://doi.org/10.1016/j.euroneuro.2015.07.006. Abrahamsson, T., Berge, J., Öjehagen, A., and Håkansson, A. (2017). Benzodiazepine, z-drug and pregabalin prescriptions and mortality among patients in opioid maintenance treatment-A nation-wide register-based open cohort study. Drug Alcohol Depend. 174 , 58–64. https://doi.org/10.1016/j.drugalcdep.2017.01.013. Hoffmann, F. (2013). Perceptions of German GPs on benefits and risks of benzodiazepines and Z-drugs. Swiss Med. Wkly. 143 , w13745. https://doi.org/10.4414/smw.2013.13745. Siriwardena, A.N., Qureshi, Z., Gibson, S., Collier, S., and Latham, M. (2006). GPs’ attitudes to benzodiazepine and “Z-drug” prescribing: a barrier to implementation of evidence and guidance on hypnotics. Br. J. Gen. Pract. J. R. Coll. Gen. Pract. 56 , 964–967. Donnelly, K., Bracchi, R., Hewitt, J., Routledge, P.A., and Carter, B. (2017). Benzodiazepines, Z-drugs and the risk of hip fracture: A systematic review and meta-analysis. PloS One 12 , e0174730. https://doi.org/10.1371/journal.pone.0174730. Haines, A., Shadyab, A.H., Saquib, N., Kamensky, V., Stone, K., and Wassertheil-Smoller, S. (2021). The association of hypnotics with incident cardiovascular disease and mortality in older women with sleep disturbances. Sleep Med. 83 , 304–310. https://doi.org/10.1016/j.sleep.2021.04.032. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 24 Oct, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 02 Jul, 2025 Reviews received at journal 30 Jun, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviews received at journal 06 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers agreed at journal 28 May, 2025 Reviewers invited by journal 28 May, 2025 Editor invited by journal 28 May, 2025 Editor assigned by journal 26 May, 2025 Submission checks completed at journal 26 May, 2025 First submitted to journal 22 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6505576","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463387427,"identity":"f6510f58-850c-437f-be0a-ea7f37cd6882","order_by":0,"name":"Zerui You","email":"","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Zerui","middleName":"","lastName":"You","suffix":""},{"id":463387428,"identity":"43307a0d-9750-471b-b962-3f69b2181e84","order_by":1,"name":"Duan Liu","email":"","orcid":"","institution":"Shenzhen Yantian District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Duan","middleName":"","lastName":"Liu","suffix":""},{"id":463387429,"identity":"160bb97e-d8d6-4e03-9cda-82f2b7668fac","order_by":2,"name":"Tuzhi Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Tuzhi","middleName":"","lastName":"Wang","suffix":""},{"id":463387430,"identity":"f99a9758-e275-4cbe-968a-532d917fcc73","order_by":3,"name":"Heng Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Wang","suffix":""},{"id":463387431,"identity":"4bddba20-b5bf-4236-9b10-1974c1409ace","order_by":4,"name":"Jiyang Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYBACxmYwZcPDz99AmpY0GckZB0iz7LCNQUMCkWqZ23nMHvzccZ7HgOEA44ePOUQ5jMfcsPfMbR5z5gZmyZnbiNNiJsHbdpvHsuEAGzMvsVok/7ad4zE4kECCFmnetgMkaWErN5ZtS+aRnHGwmTi/GPYf3vbwbZudPT9/88EPH4nS0sBhBrOwgQj1QCDPwP6MOJWjYBSMglEwcgEA3OIyN6L7pXUAAAAASUVORK5CYII=","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":true,"prefix":"","firstName":"Jiyang","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2025-04-22 15:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6505576/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6505576/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-025-05208-3","type":"published","date":"2025-10-24T16:16:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83836432,"identity":"721c6446-9eaa-4743-b0bb-f9419a0a46b4","added_by":"auto","created_at":"2025-06-03 13:17:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":175495,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of participants included in this study. \u003c/strong\u003eAbbreviation: NHANES, National Health and Nutrition Examination Survey; PIR, family poverty income ratio; BMI, body mass index.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6505576/v1/1d01ef9f9b248eaa54cae29a.png"},{"id":83837550,"identity":"6e5f1c4d-89f5-4f9e-a7c1-62b6259b4b68","added_by":"auto","created_at":"2025-06-03 13:25:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of drug investigation and estimated mean blood pressure differences in sleep medications. A \u003c/strong\u003eOverview of drug investigation and analysis. A total of 27 drugs were investigated in this study, of which 17 were recorded in the database and included in the main analysis. Among these, 6 drugs were selected in the monotherapy analysis according to user number (\u0026gt;30). \u003cstrong\u003eB \u003c/strong\u003eEstimated mean blood pressure differences between sleep medication groups. The three major groups consist of individuals who use any sleep medication, those who use only benzodiazepines, and those who use only Z-drugs. \u003cstrong\u003eC\u003c/strong\u003e Estimated mean blood pressure differences between users of different sleep medications. Unstandardized coefficients are presented with 95%CI in parentheses using a linear regression model. In all estimated models, the control group consisted of hypertensive participants who did not use sleep medication. The models were adjusted for age, sex, race, BMI group, poverty income ratio, education, smoking status (never, former, current), drinking status (never, former, current), marital status, CVD history (yes/no), diabetes status (yes/no), and depressive symptom (yes/no). Abbreviation: SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure. ***: P \u0026lt; 0.001; **: P \u0026lt; 0.01; *: P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"placeholderimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6505576/v1/4a957b66d433e1a7387be51e.png"},{"id":94490313,"identity":"4f7a81f1-6c0e-443a-aa5a-206c331d6eed","added_by":"auto","created_at":"2025-10-27 17:09:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1406372,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6505576/v1/2d985d3c-0488-4ab8-bdcb-f39c5ad9f4bb.pdf"},{"id":83836435,"identity":"fbb45eb5-aee6-4c82-9fa0-aec782b1affd","added_by":"auto","created_at":"2025-06-03 13:17:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":61427,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6505576/v1/7dad89ee0ad82cceba140ba6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Benefits and risks of sleep medication in individuals with hypertension and sleep disturbance: Evidence from a large population-based study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension is projected to affect about one-quarter of the global adult population by 2025, potentially reaching nearly 1.5\u0026nbsp;billion people\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. As a major risk factor for cardiovascular disease, effective management of hypertension is crucial for reducing morbidity and mortality. Sleep plays a critical role in cardiovascular health, and any deterioration in sleep quality or quantity, such as insomnia, obstructive sleep apnea, or sleep deprivation, could contribute to blood pressure instability, potentially exacerbating both the development and management of hypertension\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. For example, poor sleep can lead to fluctuations or spikes in blood pressure\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Research has shown a strong association between impaired sleep and an elevated risk of cardiovascular disease (CVD) mortality in hypertensive workers\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Both meta-analytic and population-based studies reinforce these findings, highlighting the significant impact of sleep disturbance on cardiovascular outcomes\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Moreover, relying solely on antihypertensive medication may not be sufficient to maintain optimal blood pressure levels in individuals with poor sleep, suggesting a need for integrative management strategies that consider both antihypertensive and sleep-related interventions in this population\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAddressing sleep disorders or habits is an effective strategy for mitigating the risk of developing or managing hypertension. Li et al. investigated 402 individuals with insomnia and hypertension and found that combining antihypertensive medications with estazolam led to greater improvements in sleep quality and reductions in blood pressure when compared with a placebo\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This finding highlighted the importance of integrating sleep-focused therapies alongside traditional antihypertensive treatments for satisfactory blood pressure management. However, despite this promising result, there is still a significant gap in the research regarding the broader use of sleep medications for hypertensive individuals experiencing sleep disturbance. The benefits and potential risks associated with the long-term use of such medications, particularly in relation to cardiovascular health, remain underexplored. A more comprehensive understanding of how sleep medications could complement antihypertensive therapies would be valuable for developing more effective treatment strategies for this population.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study based on the US National Health and Nutrition Examination Survey (NHANES) data to assess the benefits and risks of sleep medications in individuals with hypertension and sleep disturbance. Insights into the effect of sleep medication will facilitate the internists and psychiatrists in the clinical choice of treatment for hypertension. In this study, we aimed to identify: (i) the effect of sleep medications on blood pressure regulation in hypertensive individuals with sleep disturbance; (ii) whether sleep medications confer any survival risk, particularly in terms of reducing all-cause mortality and cardiovascular mortality. Our research focused extensively on exploring the similarities and differences between benzodiazepines and Z-drugs, which are among the most commonly prescribed sleep medications. By comparing their effects on hypertensive patients with sleep disturbance, we aimed to provide valuable insights into which medication class offers the most favorable risk-benefit profile. We hypothesized that sleep medications would not only stabilize the blood pressure but also reduce the risk of all-cause mortality and cardiovascular mortality.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was designed using data from the 2005\u0026ndash;2018 NHANES, which was carried out by the Centers for Disease Control and Prevention (CDC) to evaluate the health and nutritional status of the population in the United States. The NHANES study protocols were authorized by the National Center for Health Statistics Research Ethics Review Board in accordance with the updated Declaration of Helsinki. All participants gave written informed consent. Additional details about the NHANES initiative can be found on the CDC website (Centers for Disease Control and Prevention (CDC), 2022).\u003c/p\u003e \u003cp\u003eEligibility criteria for participants taking antihypertensive medication included: (1) aged 18 years or older; (2) participants diagnosed with hypertension and experiencing sleep disturbance; (3) underwent the blood pressure test and had survival data; (4) had a clear record of drug use; (5) with no missing data on potential covariates. Finally, our study involved 4836 participants, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHypertension and blood pressure\u003c/h3\u003e\n\u003cp\u003eAccording to the 2017 American College of Cardiology/American Heart Association (ACC/AHA) guidelines, hypertension was classified based on either elevated blood pressure (BP) measurements (systolic BP [SBP]\u0026thinsp;\u0026ge;\u0026thinsp;130 mm Hg or diastolic BP [DBP]\u0026thinsp;\u0026ge;\u0026thinsp;80 mm Hg) or the self-reported ongoing use of antihypertensive therapies, regardless of recorded BP values\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. BP was assessed by trained examiners following a standardized protocol. Following a 5-minute resting interval and determination of the maximum inflation level, three successive blood pressure readings were recorded from participants. The average systolic and diastolic blood pressures were determined through three successive blood pressure readings\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Mean arterial pressure (MAP) was calculated as one-third pulse pressure plus diastolic pressure. The values of systolic or diastolic blood pressure that are abnormal are excluded.\u003c/p\u003e\n\u003ch3\u003eAssessment of sleep disturbance\u003c/h3\u003e\n\u003cp\u003eSleep disturbance was defined based on responses to the sleep questionnaire from the NHANES survey or by identifying the use of sleep-related medications within the NHANES medication dataset. In the survey, participants were asked: \u0026ldquo;Have you ever reported sleep difficulties to a healthcare provider or medical professional?\u0026rdquo; Responses included \u0026ldquo;Yes,\u0026rdquo; \u0026ldquo;No,\u0026rdquo; \u0026ldquo;Refused,\u0026rdquo; and \u0026ldquo;Do not know.\u0026rdquo; Participants who answered \u0026ldquo;Yes\u0026rdquo; were categorized as having sleep disturbances, whereas responses of \u0026ldquo;Refused\u0026rdquo; and \u0026ldquo;Do not know\u0026rdquo; were excluded from analysis and treated as missing data.\u003c/p\u003e\n\u003ch3\u003eMedication for sleep disturbance\u003c/h3\u003e\n\u003cp\u003eIn the NHANES, the Prescription Medication Questionnaire was interviewer-administered in the participant household using the Computer-Assisted Personal Interviewing (CAPI) system. Participants were queried about their use of prescribed medications within the past month. Those who responded affirmatively were requested to present the medication packaging to the interviewer or verbally disclose the name of the medication. The interviewer observed around 75% of the prescribed medications.\u003c/p\u003e \u003cp\u003eThis study focused on the prescribed use of sleep medications\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. We incorporated sleep medications that assess pharmaceutical agents authorized for treating insomnia by either the British National Formulary, the Food and Drug Administration, the European Medicines Agency, the Pharmaceuticals and Medical Devices Agency, and the Therapeutic Goods Administration\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Based on the 27 drugs evaluated in the study\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, a total of 17 sleep medications were found to be recorded in the NHANES data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-2). The participants were found to have been prescribed at least one of the following types of sleep medication: benzodiazepines, benzodiazepine-like agents (Z-drugs, including zopiclone, eszopiclone, zaleplon, and zolpidem), melatoninergic drugs, orexin receptor antagonists, and so on. Among these, six drugs administered as monotherapy were evaluated, with each drug used by at least 60 individuals and an average treatment duration exceeding 65 months. We included two main categories of medications, benzodiazepines and Z-drugs, and assessed their benefits and long-term risks for hypertensive participants with sleep disturbance. Other types of sleep medications were not analyzed due to their limited numbers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMortality outcomes of the study population\u003c/h3\u003e\n\u003cp\u003eThe National Death Index (NDI) database provided the mortality data used in this study. The NDI database was last updated on December 31, 2019, and this date served as the endpoint for calculating follow-up duration. For each participant, the follow-up period extended from the date of enrollment to either this endpoint or the date of death, whichever occurred first. Cardiovascular-related deaths were identified through the application of the International Classification of Diseases, Tenth Revision (ICD-10) codes, specifically I00-I09, I11, I13, I20-I51, and I60-I69.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eWe included clinically significant covariates in our study, such as age at interview, gender, body mass index (BMI) group, income level, race/ethnicity, education level, marital status, smoking status (never/former/current), alcohol consumption (yes/no), history of CVD (yes/no), diabetes mellitus (yes/no), and depressive symptoms (yes/no). The BMI group is categorized according to the World Health Organization classification: underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5), normal weight (BMI 18.5 to \u0026lt;\u0026thinsp;25), overweight (BMI 25 to \u0026lt;\u0026thinsp;30), and obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30). The participant's income level was assessed using the ratio of household income to the poverty threshold (PIR). Educational attainment was stratified into five groups: less than 9th grade, 9th to 11th grade, high school diploma recipient, college or Associate of Arts (AA) degree holder, and those with a college degree or higher. Marital status was classified as single (widowed, divorced, or separated) or non-single (married or living with a partner), determined by their living situation. Smoking status was classified into three categories: never smoked, formerly smoked, and currently smoked. Smoking status was assessed as never smoked (smoked\u0026thinsp;\u0026lt;\u0026thinsp;100 cigarettes), former smoker (not currently smoking but smoked\u0026thinsp;\u0026ge;\u0026thinsp;100 cigarettes), or current smoker (\u0026ge;\u0026thinsp;100 cigarettes and currently smoking every day or on some days). Alcohol use was categorized into two categories: never drinking and ever or current drinking. The Patient Health Questionnaire (PHQ) depression scale categorized participants into two groups: no depressive symptoms (0\u0026ndash;9 points) and depressive symptoms (\u0026ge;\u0026thinsp;10 points). Diabetes mellitus and CVD history were determined based on self-reported diagnoses provided by medical professionals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe descriptive statistic was performed on baseline features of the participants diagnosed with hypertension and sleep disturbance, comparing those using sleep medication to those not using it. Continuous variables were summarized as mean and standard deviation (SD) or median with interquartile range (IQR), whereas categorical variables were reported as frequency and proportion. Normality testing was performed by the Shapiro-Wilks test. We compared the characteristics by using independent Student t-test or Chi-squared test accordingly. Using the generalized linear regression model, we screened for the influence of sleep medication on blood pressure across different subgroups. We employed Cox proportional hazard models to analyze the relationship between sleep medicine and the risk of all-cause death and cardiovascular disease mortality. The risk of sleep medication on all-cause mortality was determined with additive adjustments of covariates in different models. Crude model was a univariate model. Model I was adjusted for all demographic covariates, and Model II was adjusted for all demographic covariates and the history of heart disease and the presence of diabetes to explore stability. Statistical analysis was conducted using the \u0026ldquo;survey\u0026rdquo; package in R software (version 4.0.4).\u003c/p\u003e \u003cp\u003eWeights were applied to account for the complex survey design of NHANES, which includes oversampling, survey nonresponse, and poststratification adjustments to align with total U.S. population counts. To enhance the robustness of the findings, the weighted analysis was conducted as part of the sensitivity analysis, adhering to the NHANES analytical guidelines\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The sampling weight was calculated using the following formula: fasting subsample 14-year MEC weight\u0026thinsp;=\u0026thinsp;fasting subsample 2-year MEC weight/7. All statistical analyses and modelling were performed using R software (version 4.4.1), with a significance level set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePopulation characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presented the demographic features of the 4836 individuals from NHANES 2005\u0026ndash;2018. The participants had an average age of 58.5 years, slept an average of 6.8 hours per day, and 44.9% of them were male. Among them, 961 (19.9%) were currently using sleep medication. Of the documented sleep medication users, 679 (70.7%) used only benzodiazepines, 205 (21.3%) used only Z-drugs, 58 (6.0%) used both types, and only 19 (2.0%) used other types of sleep medications. Participants with sleep medication were more likely to be female, white, and smokers and report a higher prevalence of depressive symptoms and CVD history compared to non-users.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study population with various groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4836)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout sleep medication\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3875)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith sleep medication\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;961)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.0 (49.0\u0026ndash;70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.0 (48.0\u0026ndash;69.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.0 (51.0\u0026ndash;73.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2171 (44.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1806 (46.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e365 (38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2665 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2069 (53.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e596 (62.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI group, n (%)\u003c/p\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 (0.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (0.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (1.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (18.5 to \u0026lt;\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e805 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e605 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e200 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (25 to \u0026lt;\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1391 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1109 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e282 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (30 or greater)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2605 (53.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2136 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e469 (48.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0 (1.1\u0026ndash;3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0 (1.1\u0026ndash;4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8 (1.0\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2527 (52.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1913 (49.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e614 (63.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1141 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1010 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e131 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e464 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e391 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73 (7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e359 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e276 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race/multiracial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e345 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e285 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, n (%)\u003c/p\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess Than 9th Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e452 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e345 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e659 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e516 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e143 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School Grad/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1220 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e979 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e241 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome College or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1605 (33.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1288 (33.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e317 (33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege Graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e900 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e747 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarry status, n (%)\u003c/p\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarry/Living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2634 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2134 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500 (52.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/Separated/Never married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2202 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1741 (44.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e461 (48.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status, n (%)\u003c/p\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3638 (75.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2932 (75.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e706 (73.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1198 (24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e943 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e255 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\u003c/p\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2202 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1822 (47.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e380 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1573 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1240 (32.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e333 (34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1061 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e813 (21.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e248 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1207 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1012 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195 (20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3629 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2863 (73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e766 (79.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD history, n (%)\u003c/p\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1022 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e779 (20.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e243 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3814 (78.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3096 (79.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e718 (74.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepressive symptom, n (%)\u003c/p\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e951 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e668 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e283 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3885 (80.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3207 (82.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e678 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eContinuous variables are expressed as Median with Interquartile Range (IQR). The categorical variables were presented as numbers and percentages. Abbreviations: BMI, body mass index; PIR, family poverty income ratio; CVD, cardiovascular disease. Bold p-values denote statistical significance at the p\u0026thinsp;\u0026lt;\u0026thinsp;.05 level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBeneficial effects of sleep medication on blood pressure\u003c/h2\u003e \u003cp\u003eOf the 17 sleep medications selected for the main analysis, the most commonly used were alprazolam (31.4% of total users, 302 participants), zolpidem (24.7%, 237 participants), clonazepam (15.4%, 148 participants), and others (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Table S2). Two major drug classes will be included as subgroups: benzodiazepine-only and Z-drug-only populations. Benzodiazepine users included individuals who used any of the 10 kinds of benzodiazepines, such as alprazolam and lorazepam, while Z-drug users primarily included those who used eszopiclone, zaleplon, or zolpidem. Additionally, except for smoking status, education, and income level, no significant differences were observed between benzodiazepine and Z-drug users (Table S3).\u003c/p\u003e \u003cp\u003eTaking sleeping medication produced an improvement in blood pressure management. After accounting for demographic covariates, sleep medication users had an adjusted mean systolic BP difference of -2.77 mmHg (95% CI, -4.05 to -1.49; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) compared to non-users. Specifically, benzodiazepine users had a SBP difference of -2.20 mmHg (95% CI, -3.68 to -0.73; P\u0026thinsp;=\u0026thinsp;0.003), while Z-drug users had a more pronounced SBP difference of -3.34 mmHg (95% CI, -5.86 to -0.82; P\u0026thinsp;=\u0026thinsp;0.009). In comparing specific medications (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and Table S4), diazepam users had an SBP difference of -5.93 mmHg (95% CI, -10.34 to -1.51; P\u0026thinsp;=\u0026thinsp;0.008). Clonazepam showed \u0026minus;\u0026thinsp;4.15 mmHg (95% CI, -7.01 to -1.20; P\u0026thinsp;=\u0026thinsp;0.006), and zolpidem showed \u0026minus;\u0026thinsp;4.62 mmHg (95% CI, -6.99 to -2.261; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, sleep medication users showed an adjusted mean difference in diastolic BP of -1.39 mmHg (95% CI, -2.20 to -0.57; P\u0026thinsp;=\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), whereas the adjusted difference in mean arterial pressure (MAP) is -1.85 mmHg (95% CI, -2.66 to -1.03; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\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 \u003cp\u003eEstimation of blood pressure difference in participants with hypertension and short sleep duration.\u003c/p\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=\"char\" char=\"\u0026minus;\" 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=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWith sleep medication\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;961)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eBenzodiazepine users\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;679)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eZ-drugs users\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;205)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBeta (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e-2.77 (-4.05 ~ -1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-2.20 (-3.68 ~ -0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-3.34 (-5.86 ~ -0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDBP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e-1.39 (-2.20 ~ -0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-1.28 (-2.21 ~ -0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-1.10 (-2.69\u0026thinsp;~\u0026thinsp;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e-1.85 (-2.66 ~ -1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-1.59 (-2.52 ~ -0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-1.85 (-3.45 ~ -0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e Compared to participants who didn't use sleeping medication.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eUnstandardized coefficients are presented with 95%CI in parentheses. All estimated models were adjusted for age, sex, race, BMI group, poverty income ratio, education, smoking status (never, former, current), drinking status (never, former, current), marital status, CVD history (yes/no), diabetes status (yes/no), and depressive symptom (yes/no).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviation: SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRelationships of sleep medication with mortality\u003c/h2\u003e \u003cp\u003eThe average follow-up period was 82.3 months, during which there were 809 deaths from all causes and 133 deaths from cardiovascular disease. Crude models linked sleep medications (hazard ratio [HR]: 1.48; 95% CI, 1.27 to 1.73; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), benzodiazepines (HR: 1.46; 95% CI, 1.22 to 1.75; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Z-drugs (HR: 1.62; 95% CI, 1.22 to 2.14; P\u0026thinsp;=\u0026thinsp;0.001) with higher all-cause mortality but not cardiovascular mortality (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, all of the associations became statistically insignificant after multivariable adjustments except that Z-drug use was associated with a higher risk of all-cause mortality (adjusted HR: 1.43; 95% CI, 1.08 to 1.91; P\u0026thinsp;=\u0026thinsp;0.013) versus non-users. In the monotherapy analysis, HRs for lorazepam users (HR, 1.45; 95% CI, 1.04 to 2.01; P\u0026thinsp;=\u0026thinsp;0.028), diazepam users (HR, 1.80; 95% CI, 1.07 to 3.03; P\u0026thinsp;=\u0026thinsp;0.027), and zolpidem users (HR, 1.47; 95% CI, 1.11 to 1.93; P\u0026thinsp;=\u0026thinsp;0.007) were associated with a higher risk of all-cause mortality, while no such association was observed with other medications, such as alprazolam, temazepam, and clonazepam (Table S5). There was no significant association of sleep medications with cardiovascular mortality.\u003c/p\u003e \u003cp\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 \u003cp\u003eMultivariate Cox regression analysis examining the impact of sleep medication across different treatment groups on all-cause mortality.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003cp\u003e(Death/Total)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCrude Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout sleep medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e809/3875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1[Ref.]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1[Ref.]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1[Ref.]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith any sleep medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218/961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48 (1.27\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17 (0.99\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17 (1.00\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzodiazepines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149/679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46 (1.22\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08 (0.90\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.07 (0.89\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53/205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62 (1.22\u0026ndash;2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41 (1.06\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.43 (1.08\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout sleep medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107/3875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1[Ref.]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1[Ref.]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1[Ref.]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith any sleep medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26/961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.63\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76 (0.49\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76 (0.49\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzodiazepines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18/679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.59\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70 (0.42\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.68 (0.41\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5/205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.34\u0026ndash;2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70 (0.28\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73 (0.29\u0026ndash;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviation: HR, Hazard Ratio; CI, confidence interval. Cox proportional hazard models were performed for all-cause mortality. Competing risk analyses of cause-specific mortality were performed. The crude model did not adjust for any covariates. Model 1 was adjusted for age, sex, race, BMI group, poverty income ratio, education, smoking status, drinking status, marital status, and depressive symptoms (yes/no). Model 2 was adjusted for CVD history and diabetes status in addition to model 1. Bold p-values denote statistical significance at the p\u0026thinsp;\u0026lt;\u0026thinsp;.05 level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eTo assess the robustness and stability of the findings, sensitivity analyses were performed using weighted analysis on the NHANES dataset. Our observations revealed trends consistent with those found in the primary analysis (Table S6-9). Furthermore, users of alprazolam exhibited a lower adjusted mean difference in SBP (alprazolam: -2.69 [95% CI: -5.22 to -0.17] mmHg, P\u0026thinsp;=\u0026thinsp;0.037). In the monotherapy analysis, the use of diazepam and zolpidem remained associated with a higher risk of all-cause mortality, whereas lorazepam use was no longer associated with an increased risk.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe objective of this study was to investigate the potential benefits and risks of sleep medications in hypertensive individuals experiencing sleep disturbance, using a national dataset. Our results showed that sleep medications, including both benzodiazepines and Z-drugs, were beneficial in controlling blood pressure in hypertensive participants with sleep disturbance, consistent with results from previous literature\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Among the sleep medications we evaluated, diazepam, clonazepam, and zolpidem were particularly notable for their antihypertensive effects, with diazepam showing the most significant impact in reducing blood pressure. As a whole, no evidence for an increased risk of all-cause or cardiovascular mortality after the use of sleep medications was found in any of these analyses. However, analysis across two main types revealed that only Z-drugs were associated with an elevated risk of all-cause mortality, suggesting the need for caution with long-term use and further confirmation of this relationship. In conclusion, for hypertensive patients with suboptimal blood pressure control, a comprehensive evaluation and management of sleep disturbance are essential.\u003c/p\u003e \u003cp\u003eThe antihypertensive effect of sleep medications may be attributed to their ability to improve sleep architecture and reduce nighttime arousals, which are known to contribute to blood pressure variability\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Furthermore, the reduction in SBP among sleep medication users could be partly explained by the alleviation of stress and anxiety, which are common comorbidities in individuals with sleep disturbances and hypertension\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Additionally, benzodiazepines have been shown to reduce blood pressure, as evidenced by ambulatory blood pressure monitoring, which is consistent with our findings\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, it is important to note that while sleep medications may help regulate blood pressure, they should not be considered a substitute for antihypertensive therapies. Instead, they should be viewed as part of a comprehensive management strategy. The integrated management of hypertension emphasizes the importance of a multifaceted approach that combines targeted pharmacological interventions, lifestyle modifications, and emerging therapies to control blood pressure effectively\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The identification of blood pressure-lowering effects of sleep medications, coupled with the effective management of sleep disorders such as obstructive sleep apnea, could broaden the range of treatment options for hypertension\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. These advancements may also lead to a meaningful adjustment in clinical strategies for treating hypertensive patients who experience inadequately controlled blood pressure alongside sleep disturbances. Dual-action therapies that target both sleep quality and blood pressure control may offer advantages over antihypertensive drugs alone. This study strengthens the argument for a holistic approach to health, where managing sleep could have far-reaching benefits beyond just improving rest.\u003c/p\u003e \u003cp\u003eWe initially observed that any sleep medication use was associated with a higher risk of all-cause mortality in hypertensive individuals with sleep disturbance. However, this association became statistically insignificant after adjusting for demographic variables, lifestyle factors, and CVD history. This result implies that sleep medication use itself does not independently contribute to mortality risk. The association between sleep medication use and increased mortality risk may be confounded by unhealthy lifestyle factors, as users in our data exhibited lower income levels and poorer health profiles\u0026mdash;having depressive symptoms, a history of CVD, older, and smoking\u0026mdash;compared to non-users. For instance, Wang et al. identified a potential synergistic interaction between depressive symptoms and sleep disorders, which may increase the occurrence of CVD\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Lu et al. focused on the association of lifestyle factors and antihypertensive medication use with mortality risk among adults with hypertension. They concluded that improvements in lifestyle after hypertension diagnosis were associated with a reduced risk of all-cause and cardiovascular mortality\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. As noted by Hausken et al., lifestyle and socioeconomic factors, which may contribute to the remaining excess mortality, should also be considered\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Our results along with those of others\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e suggest that long-term use of sleep medications, at least benzodiazepines, does not increase mortality. There are, of course, a few studies\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e with contradicting results, indicating that sleep medications are associated with higher mortality, potentially due to differences in clinical populations. Taken together, the totality of the current evidence in our study does not support mortality risks associated with the use of sleep medications in hypertensive individuals with sleep disturbance. Future research could focus on long-term follow-up of this population to provide more robust data on the effect of sleep medications.\u003c/p\u003e \u003cp\u003eThe unexpected finding in the analysis across two main types of sleep medication is the association between Z-drugs use and increased all-cause mortality after adjusting for covariates, a pattern not observed with benzodiazepines use. This association is in contrast to previous research\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, which often highlighted that the mortality risks associated with Z-drugs are lower than those associated with benzodiazepines. In common perceptions, compared to benzodiazepines, Z-drugs offer better efficacy in reducing nocturnal awakenings, enhancing restfulness upon waking, and improving daytime performance, with fewer side effects\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, there is also evidence suggesting that Z-drugs may have more adverse effects than benzodiazepines. A meta-analysis showed that Z-drugs were significantly associated with an increased risk of hip fracture, with the risk being higher than that associated with benzodiazepines\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Similar findings have been reported in a larger cohort study\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The use of Z-drugs was associated with an increased risk for all-cause mortality and composite CVD outcomes in post-menopausal women with sleep disturbances. These findings suggest that while Z-drugs are often considered safer than benzodiazepines, they may involve unforeseen risks, highlighting the need for caution and further investigation into their long-term impacts.\u003c/p\u003e \u003cp\u003eThe key strength of our study is that the present study is a prospective cohort study utilizing the NHANES database. This authoritative and representative resource augments the generalizability of our findings. We leveraged the database to investigate the unverified clinical benefits and mortality risks of sleep medications in hypertensive individuals with sleep disturbance. Nevertheless, the study had several limitations. Despite accounting for several confounders, some unmeasured or complex confounders remain. Moreover, the diagnoses of hypertension, diabetes, CVD history, and sleep disturbance were based on self-reported questionnaires, potentially introducing recall bias. Finally, participants frequently used both antihypertensive and sleep medications, which complicated the interactions and mechanisms involved. Consequently, these findings should be viewed as pilot analyses and interpreted with caution.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our results suggested a potential benefit of sleep medications in blood pressure control among hypertensive participants with sleep disturbance. When all sleep medication prescriptions are considered as a whole, no statistically significant association with all-cause mortality was observed; however, an increase in mortality associated with Z-drugs was observed. More empirical studies could be conducted to validate the association between sleep medications and the prevalence of hypertension with sleep disorders in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eNHANES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACC/AHA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmerican College of Cardiology/American Heart Association\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood Pressure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSBP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSystolic Blood Pressure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDBP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiastolic Blood Pressure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean Arterial Pressure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZ-drugs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBenzodiazepine-like agents\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNDI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Death Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eICD-10\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInternational Classification of Diseases, Tenth Revision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBody Mass Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePIR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePoverty Income Ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePHQ\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient Health Questionnaire\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCVD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCardiovascular Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHazard Ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConfidence Interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStandard Deviation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIQR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInterquartile Range\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCDC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCenters for Disease Control and Prevention\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003euthorship contribution\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYZ designed the study, performed data extraction and statistical analysis, and wrote the original draft of the manuscript. LD, WT and WH contributed to the study design and methodology. PJ supervised the project, acquired funding, and provided critical revisions to the manuscript. All authors reviewed, edited, and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by National Key R\u0026amp;D Program of China (Grant No.: 2022YFC2503902), and Guangzhou Key Laboratory for Germ-free animals and Microbiota Application (Grant No.: 202201020381).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003evailability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data are described within the paper. The NHANES data underpinning the findings of this research are accessible to the public at https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe requirement of ethical approval for this was waived by the Institutional Review Board of First Affiliated Hospital of Jinan University, because the data was accessed from NHANES (a publicly available database). The need for written informed consent was waived by the Institutional Review Board of First Affiliated Hospital of Jinan University due to retrospective nature of the study. The study protocol was approved by the NCHS Research Ethics Review Board (ERB) and all methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSorlie, P.D., Allison, M.A., Avil\u0026eacute;s-Santa, M.L., Cai, J., Daviglus, M.L., Howard, A.G., Kaplan, R., Lavange, L.M., Raij, L., Schneiderman, N., et al. (2014). Prevalence of hypertension, awareness, treatment, and control in the Hispanic Community Health Study/Study of Latinos. Am. J. Hypertens. \u003cem\u003e27\u003c/em\u003e, 793\u0026ndash;800. https://doi.org/10.1093/ajh/hpu003.\u003c/li\u003e\n\u003cli\u003ePepin, J.-L., Borel, A.-L., Tamisier, R., Baguet, J.-P., Levy, P., and Dauvilliers, Y. (2014). Hypertension and sleep: overview of a tight relationship. Sleep Med. Rev. \u003cem\u003e18\u003c/em\u003e, 509\u0026ndash;519. https://doi.org/10.1016/j.smrv.2014.03.003.\u003c/li\u003e\n\u003cli\u003eGarcia, C.E.V., Drager, L.F., Krieger, E.M., Negr\u0026atilde;o, C.E., Bortolotto, L.A., Lorenzi-Filho, G., and Ueno, L.M. (2013). Arousals are frequent and associated with exacerbated blood pressure response in patients with primary hypertension. Am. J. Hypertens. \u003cem\u003e26\u003c/em\u003e, 617\u0026ndash;623. https://doi.org/10.1093/ajh/hps065.\u003c/li\u003e\n\u003cli\u003eLi, J., Atasoy, S., Fang, X., Angerer, P., and Ladwig, K.-H. (2021). Combined effect of work stress and impaired sleep on coronary and cardiovascular mortality in hypertensive workers: The MONICA/KORA cohort study. Eur. J. Prev. Cardiol. \u003cem\u003e28\u003c/em\u003e, 220\u0026ndash;226. https://doi.org/10.1177/2047487319839183.\u003c/li\u003e\n\u003cli\u003eCappuccio, F.P., D\u0026rsquo;Elia, L., Strazzullo, P., and Miller, M.A. (2010). Sleep duration and all-cause mortality: a systematic review and meta-analysis of prospective studies. Sleep \u003cem\u003e33\u003c/em\u003e, 585\u0026ndash;592. https://doi.org/10.1093/sleep/33.5.585.\u003c/li\u003e\n\u003cli\u003eMeng, L., Zheng, Y., and Hui, R. (2013). The relationship of sleep duration and insomnia to risk of hypertension incidence: a meta-analysis of prospective cohort studies. Hypertens. Res. Off. J. Jpn. Soc. Hypertens. \u003cem\u003e36\u003c/em\u003e, 985\u0026ndash;995. https://doi.org/10.1038/hr.2013.70.\u003c/li\u003e\n\u003cli\u003eKnutson, K.L., Van Cauter, E., Rathouz, P.J., Yan, L.L., Hulley, S.B., Liu, K., and Lauderdale, D.S. (2009). Association between sleep and blood pressure in midlife: the CARDIA sleep study. Arch. Intern. Med. \u003cem\u003e169\u003c/em\u003e, 1055\u0026ndash;1061. https://doi.org/10.1001/archinternmed.2009.119.\u003c/li\u003e\n\u003cli\u003eLi, Y., Yang, Y., Li, Q., Yang, X., Wang, Y., Ku, W.L., and Li, H. (2017). The impact of the improvement of insomnia on blood pressure in hypertensive patients. J. Sleep Res. \u003cem\u003e26\u003c/em\u003e, 105\u0026ndash;114. https://doi.org/10.1111/jsr.12411.\u003c/li\u003e\n\u003cli\u003eRichardson, L.C., Vaughan, A.S., Wright, J.S., and Coronado, F. (2024). Examining the Hypertension Control Cascade in Adults With Uncontrolled Hypertension in the US. JAMA Netw. Open \u003cem\u003e7\u003c/em\u003e, e2431997. https://doi.org/10.1001/jamanetworkopen.2024.31997.\u003c/li\u003e\n\u003cli\u003ePeters, S.A.E., Muntner, P., and Woodward, M. (2019). Sex Differences in the Prevalence of, and Trends in, Cardiovascular Risk Factors, Treatment, and Control in the United States, 2001 to 2016. Circulation \u003cem\u003e139\u003c/em\u003e, 1025\u0026ndash;1035. https://doi.org/10.1161/CIRCULATIONAHA.118.035550.\u003c/li\u003e\n\u003cli\u003eDe Crescenzo, F., D\u0026rsquo;Al\u0026ograve;, G.L., Ostinelli, E.G., Ciabattini, M., Di Franco, V., Watanabe, N., Kurtulmus, A., Tomlinson, A., Mitrova, Z., Foti, F., et al. (2022). Comparative effects of pharmacological interventions for the acute and long-term management of insomnia disorder in adults: a systematic review and network meta-analysis. Lancet Lond. Engl. \u003cem\u003e400\u003c/em\u003e, 170\u0026ndash;184. https://doi.org/10.1016/S0140-6736(22)00878-9.\u003c/li\u003e\n\u003cli\u003eJohnson, C.L., Paulose-Ram, R., Ogden, C.L., Carroll, M.D., Kruszon-Moran, D., Dohrmann, S.M., and Curtin, L.R. (2013). National health and nutrition examination survey: analytic guidelines, 1999-2010. Vital Health Stat. 2., 1\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eKnutson, K.L., Van Cauter, E., Rathouz, P.J., Yan, L.L., Hulley, S.B., Liu, K., and Lauderdale, D.S. (2009). Association between sleep and blood pressure in midlife: the CARDIA sleep study. Arch. Intern. Med. \u003cem\u003e169\u003c/em\u003e, 1055\u0026ndash;1061. https://doi.org/10.1001/archinternmed.2009.119.\u003c/li\u003e\n\u003cli\u003eMendelson, N., Gontmacher, B., Vodonos, A., Novack, V., Abu-AjAj, M., Wolak, A., Shalev, H., and Wolak, T. (2018). Benzodiazepine Consumption Is Associated With Lower Blood Pressure in Ambulatory Blood Pressure Monitoring (ABPM): Retrospective Analysis of 4938 ABPMs. Am. J. Hypertens. \u003cem\u003e31\u003c/em\u003e, 431\u0026ndash;437. https://doi.org/10.1093/ajh/hpx188.\u003c/li\u003e\n\u003cli\u003eCarey, R.M., Moran, A.E., and Whelton, P.K. (2022). Treatment of Hypertension: A Review. JAMA \u003cem\u003e328\u003c/em\u003e, 1849\u0026ndash;1861. https://doi.org/10.1001/jama.2022.19590.\u003c/li\u003e\n\u003cli\u003eRedline, S., Azarbarzin, A., and Peker, Y. (2023). Obstructive sleep apnoea heterogeneity and cardiovascular disease. Nat. Rev. Cardiol. \u003cem\u003e20\u003c/em\u003e, 560\u0026ndash;573. https://doi.org/10.1038/s41569-023-00846-6.\u003c/li\u003e\n\u003cli\u003eYeghiazarians, Y., Jneid, H., Tietjens, J.R., Redline, S., Brown, D.L., El-Sherif, N., Mehra, R., Bozkurt, B., Ndumele, C.E., and Somers, V.K. (2021). Obstructive Sleep Apnea and Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation \u003cem\u003e144\u003c/em\u003e, e56\u0026ndash;e67. https://doi.org/10.1161/CIR.0000000000000988.\u003c/li\u003e\n\u003cli\u003eWang, C., and Hu, J. (2021). Influence of the Interaction Between Depressive Symptoms and Sleep Disorders on Cardiovascular Diseases Occurrence. Int. J. Gen. Med. \u003cem\u003e14\u003c/em\u003e, 10327\u0026ndash;10335. https://doi.org/10.2147/IJGM.S334894.\u003c/li\u003e\n\u003cli\u003eLu, Q., Zhang, Y., Geng, T., Yang, K., Guo, K., Min, X., He, M., Guo, H., Zhang, X., Yang, H., et al. (2022). Association of Lifestyle Factors and Antihypertensive Medication Use With Risk of All-Cause and Cause-Specific Mortality Among Adults With Hypertension in China. JAMA Netw. Open \u003cem\u003e5\u003c/em\u003e, e2146118. https://doi.org/10.1001/jamanetworkopen.2021.46118.\u003c/li\u003e\n\u003cli\u003eHausken, A.M., Skurtveit, S., and Tverdal, A. (2007). Use of anxiolytic or hypnotic drugs and total mortality in a general middle-aged population. Pharmacoepidemiol. Drug Saf. \u003cem\u003e16\u003c/em\u003e, 913\u0026ndash;918. https://doi.org/10.1002/pds.1417.\u003c/li\u003e\n\u003cli\u003eVinkers, D.J., Gussekloo, J., van der Mast, R.C., Zitman, F.G., and Westendorp, R.G.J. (2003). Benzodiazepine use and risk of mortality in individuals aged 85 years or older. JAMA \u003cem\u003e290\u003c/em\u003e, 2942\u0026ndash;2943. https://doi.org/10.1001/jama.290.22.2942.\u003c/li\u003e\n\u003cli\u003eWeich, S., Pearce, H.L., Croft, P., Singh, S., Crome, I., Bashford, J., and Frisher, M. (2014). Effect of anxiolytic and hypnotic drug prescriptions on mortality hazards: retrospective cohort study. BMJ \u003cem\u003e348\u003c/em\u003e, g1996. https://doi.org/10.1136/bmj.g1996.\u003c/li\u003e\n\u003cli\u003eKripke, D.F., Langer, R.D., and Kline, L.E. (2012). Hypnotics\u0026rsquo; association with mortality or cancer: a matched cohort study. BMJ Open \u003cem\u003e2\u003c/em\u003e, e000850. https://doi.org/10.1136/bmjopen-2012-000850.\u003c/li\u003e\n\u003cli\u003ePalmaro, A., Dupouy, J., and Lapeyre-Mestre, M. (2015). Benzodiazepines and risk of death: Results from two large cohort studies in France and UK. Eur. Neuropsychopharmacol. J. Eur. Coll. Neuropsychopharmacol. \u003cem\u003e25\u003c/em\u003e, 1566\u0026ndash;1577. https://doi.org/10.1016/j.euroneuro.2015.07.006.\u003c/li\u003e\n\u003cli\u003eAbrahamsson, T., Berge, J., \u0026Ouml;jehagen, A., and H\u0026aring;kansson, A. (2017). Benzodiazepine, z-drug and pregabalin prescriptions and mortality among patients in opioid maintenance treatment-A nation-wide register-based open cohort study. Drug Alcohol Depend. \u003cem\u003e174\u003c/em\u003e, 58\u0026ndash;64. https://doi.org/10.1016/j.drugalcdep.2017.01.013.\u003c/li\u003e\n\u003cli\u003eHoffmann, F. (2013). Perceptions of German GPs on benefits and risks of benzodiazepines and Z-drugs. Swiss Med. Wkly. \u003cem\u003e143\u003c/em\u003e, w13745. https://doi.org/10.4414/smw.2013.13745.\u003c/li\u003e\n\u003cli\u003eSiriwardena, A.N., Qureshi, Z., Gibson, S., Collier, S., and Latham, M. (2006). GPs\u0026rsquo; attitudes to benzodiazepine and \u0026ldquo;Z-drug\u0026rdquo; prescribing: a barrier to implementation of evidence and guidance on hypnotics. Br. J. Gen. Pract. J. R. Coll. Gen. Pract. \u003cem\u003e56\u003c/em\u003e, 964\u0026ndash;967.\u003c/li\u003e\n\u003cli\u003eDonnelly, K., Bracchi, R., Hewitt, J., Routledge, P.A., and Carter, B. (2017). Benzodiazepines, Z-drugs and the risk of hip fracture: A systematic review and meta-analysis. PloS One \u003cem\u003e12\u003c/em\u003e, e0174730. https://doi.org/10.1371/journal.pone.0174730.\u003c/li\u003e\n\u003cli\u003eHaines, A., Shadyab, A.H., Saquib, N., Kamensky, V., Stone, K., and Wassertheil-Smoller, S. (2021). The association of hypnotics with incident cardiovascular disease and mortality in older women with sleep disturbances. Sleep Med. \u003cem\u003e83\u003c/em\u003e, 304\u0026ndash;310. https://doi.org/10.1016/j.sleep.2021.04.032.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hypertension, Sleep disturbance, Sleep medication, Mortality, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-6505576/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6505576/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe benefits and risks of sleep medications among patients with hypertension and sleep disturbance remain unclear. This study aims to investigate the potential benefits and risks of sleep medications in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This was a prospective cohort study among US adults, using hypertension and medication data from the National Health and Nutrition Examination Survey (NHANES). Linear regression assessed the efficacy of sleep medications in controlling blood pressure. Cox regression explored associations between sleep medications and mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e This study included 4836 participants taking antihypertensive medication with sleep disturbance. Compared with non-users, benzodiazepine users had an adjusted estimated systolic blood pressure (SBP) difference of -2.20 mmHg (95% CI, -3.68 to -0.73; P = 0.003), while Z-drug users had a more pronounced difference of -3.34 mmHg (95% CI, -5.86 to -0.82; P = 0.009), with diazepam, clonazepam, and zolpidem demonstrating significant antihypertensive effects. The median follow-up time was 82.3 months, and 809 all-cause deaths occurred. Sleep medications \u0026nbsp;(hazard ratio [HR]: 1.17; 95% CI, 1.00 to 1.37; P = 0.055) and benzodiazepine users \u0026nbsp;(HR: 1.07; 95% CI, 0.89 to 1.29; P = 0.455) was not associated with an increased risk of all-cause mortality, while Z-drug users were linked to a higher risk (HR: 1.43; 95% CI, 1.08 to 1.91; P = 0.013) compared to non-users. No significant association was found with cardiovascular mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Sleep medications may assist in regulating blood pressure and were not significantly associated with an elevated mortality risk among hypertensive participants with sleep disturbance.\u003c/p\u003e","manuscriptTitle":"Benefits and risks of sleep medication in individuals with hypertension and sleep disturbance: Evidence from a large population-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 13:17:04","doi":"10.21203/rs.3.rs-6505576/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-02T07:28:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-30T23:05:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332029427764537024583156326372910109961","date":"2025-06-20T02:09:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-07T02:31:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20978766703589141923068298361772091115","date":"2025-06-07T01:28:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241480046025866325088915858991227191121","date":"2025-06-05T04:01:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23931278883208873898637738309089379081","date":"2025-05-28T16:23:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-28T13:52:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-28T12:52:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-26T11:45:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-26T11:43:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-04-22T14:52:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"28b18a23-5ef9-4cf2-b599-00b2dfd06691","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:25:21+00:00","versionOfRecord":{"articleIdentity":"rs-6505576","link":"https://doi.org/10.1186/s12872-025-05208-3","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2025-10-24 16:16:25","publishedOnDateReadable":"October 24th, 2025"},"versionCreatedAt":"2025-06-03 13:17:04","video":"","vorDoi":"10.1186/s12872-025-05208-3","vorDoiUrl":"https://doi.org/10.1186/s12872-025-05208-3","workflowStages":[]},"version":"v1","identity":"rs-6505576","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6505576","identity":"rs-6505576","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}