Associations between sleep disturbance, inflammatory markers, and high blood pressure: National Health and Nutrition Examination Survey (NHANES) 2005–2018

Associations between sleep disturbance, inflammatory markers, and high blood pressure: National Health and Nutrition Examination Survey (NHANES) 2005–2018 | 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 Article Associations between sleep disturbance, inflammatory markers, and high blood pressure: National Health and Nutrition Examination Survey (NHANES) 2005–2018 dajun lin, lisha sun, jun yuan, yunjiao yang, qian zhou, junhua pan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5016061/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Sleep disturbance leads to an active inflammatory response in the body, and the development of hypertension is also associated with inflammation; is there a definite association between the three? Methods We examined the pairwise relationships between SII (Systemic Immune-Inflammation Index), sleep disorders, and hypertension in an ethnically diverse sample (n = 22573) from the National Health and Nutrition Examination Survey (NHANES).On successfully verifying its two-by-two pair correlation, Exploring the direct intensity of sleep disorders leading to hypertension, Strength of SII as a Mediating Effect of Sleep Disorders, and Hypertension. Results The study comprised 48.69% males and 51.31% females, with an average age of 48.01(18.51) years and an average BMI of 28.88 kg/m².Hypertension prevalence was 33.75% (n = 3,710) among males and 35.09% (n = 4,064) among females. Sleep disturbance affected 22.06% (n = 2,425) of males and 29.17% (n = 3,378) of females. Participants were categorized by hypertension and sleep disturbance status. Most did not use sleep medications, but higher usage was seen in those with both conditions. Smoking and alcohol consumption rates were notably higher among individuals with hypertension and sleep disturbance. Educational attainment was slightly lower among those with hypertension. Mexican Americans showed the lowest comorbidity of these conditions compared to non-Hispanic whites and Other Hispanics. In the correlation analysis, sleep disturbance was associated with an 81% increased risk of hypertension (OR: 1.81, 95% CI: 1.69–1.95, P = 0.001). Sleep disturbance was positively correlated with an increase in the Systemic Inflammation Index (SII) (β: 16.34, 95% CI: 4.62–28.06, p = 0.006). SII was associated with hypertension (OR: 1.0002, 95% CI: 1.0001–1.0003, P = 0.001). SII mediated 0.23% (95% CI: 0.13%-0.37%, P = 0.001) of the effect between sleep disturbance and hypertension. Health sciences/Diseases/Cardiovascular diseases/Hypertension Health sciences/Risk factors Health sciences/Pathogenesis/Inflammation Sleep disturbance Inflammation Systemic Inflammation Index High blood pressure Mediating effect Figures Figure 1 Figure 2 1. Introduction More than 1.3 billion people worldwide suffer from high blood pressure[ 1 ][ 2 ], and those with sleep problems are more likely to develop high blood pressure. By summarising research from 2015 to 2020 on the association between high blood pressure and sleep in adults, a study concluded that sleep disorders increase the risk of developing high blood pressure by 45% and 80%[ 3 ]. Naima Covassin et al.[ 4 ] discovered that despite rigorous weight and dietary management, the increase in blood pressure due to shortened sleep duration remained uncorrected. By incorporating 72 studies comprising over 50,000 subjects, Michael R. Irwin[ 5 ] concluded that sleep disturbance is associated with elevated inflammatory markers. Zenglei Zhang et al.[ 6 ][ 7 ] also suggested that while the mechanism linking inflammation to the development of high blood pressure is complex, the causal relationship is indeed established. These findings collectively suggest a potential pathway: inflammation acts as an intermediary factor linking sleep disturbance to high blood pressure. Therefore, we introduce the Systemic Immune-Inflammation Index (SII)[ 8 ][ 9 ] to aid in studying the relationship among these three factors. SII is an integrated inflammatory biomarker derived from neutrophil, lymphocyte, and platelet counts. Initially developed to assess prognosis in patients with solid cancers and coronary heart disease (CHD), the SII is now acknowledged for its ability to accurately reflect the inflammatory state. Based on the NHANES database, an exploration was conducted to investigate the association between sleep disturbance, SII, and high blood pressure, aiming to contribute to the management of the comorbidities of sleep disturbance and high blood pressure. Objectives:1.To investigate the pairwise association between sleep disturbance, SII, and high blood pressure.2.To investigate whether SII mediates the progression from sleep disturbance to high blood pressure. Hypotheses:1.There is a pairwise correlation between SII, sleep disturbance, and high blood pressure.2.SII partially mediates the relationship between sleep disturbance and high blood pressure. 2. Materials & methods 2.1Study population We designed a cross-sectional study using data from the 2005–2018 NHANES survey, conducted by the Centers for Disease Control and Prevention (CDC) to assess the health and nutritional status of the United States population. The National Center for Health Statistics Research Ethics Review Board authorized the NHANES study protocols in compliance with the revised Declaration of Helsinki. All participants provided written informed consent. Further information regarding the NHANES program is available on the CDC website. 2.2Core variables (1)Information on sleep disorders was obtained from questionnaires:” Ever told by doctor have sleep disorder” and” Ever told doctor had trouble sleeping”, Answering yes to any of the questionnaires is considered to be Sleep disturbance. (2)Information on high blood pressure was obtained from questionnaires:” Ever told you had high blood pressure” In reply to yes is considered to have high blood pressure. (3)Platelets, neutrophils, and Lymphocytes are all measured in NHANES in units of 1000 cells/uL, SII = P*N/L 2.3Covariates (1)Age (2)BMI (3)Race/ethnicity (non-Hispanic white, Mexican American, nonHispanic black, other Hispanic, or other race/multiple races) (4)Educational levels categorized as less than high school, high school graduation, and college and beyond (5)Marital status is divided into two groups using married/living with a partner/divorced/separated/widower/never married (6)Smoking is defined as having smoked more than 100 cigarettes in a lifetime (7)Drinking is defined as having had more than 12 drinks in a lifetime (8)How long to fall asleep (minutes) (9) Get how much sleep (hours) (10)How often take pills to help sleep. Rarely (1 time a month); Sometimes (2–4 times a month); Often (5–15 times a month); Almost always (16–30 times a month) 3. Statistical analysis 3.1Data Cleaning 1)Summarize the records from 2005 to 2018.（n=70190） 2)Delete records that do not contain any sleep disturbance questionnaires or do not contain high blood pressure questionnaires.（n=44679） 3)Delete records where there are two or more missing values among the platelet, neutrophil, and lymphocyte counts.（n=23770） 4)Use the 3-sigma method to screen for extreme values.（n=23768） 5)Delete records that subjects <18 years old.（n=22573） 6)Impute missing values using Random Forest[10]. 3.2data processing Statistical software (R version 4.1.1; https://cran.r-project.org/) was used for statistical analysis (package interactions and mediation). A two-tailed p-value <0.05 indicated statistical significance for all analyses. In descriptive statistics, continuous variables were expressed as mean and standard deviation or median and interquartile spacing, and categorical variables were expressed as proportions and percentages of totals. X tests were used to compare categorical variables between groups. For continuous variables, one-way ANOVA was used to compare normally distributed variables, and the Kruskal-Wallis H test was used to compare skewed variables between groups. Multifactorial logistic regression analysis evaluated the relationship between sleep disorders and hypertension. Multifactorial linear regression analysis evaluated the relationship between sleep disorders and SII. To better describe the relationship between inflammatory markers and hypertension, we performed curve-fitting analyses. To explore the moderating role of gender, we calculated the strength of the association between hypertension, sleep disorders, and inflammatory markers in male and female subgroups. We performed mediation analyses to test whether SII mediated the relationship between the exposure variable (sleep disorder) and the outcome (hypertension). Thousands of bootstraps were used in our analyses. The size of the indirect pathway effect, the proportion of mediated effects, and the p-value of the mediated effect are all shown in the results. 4. results A total sample of 22,573 was included, comprising 48.69% males and 51.31% females. The mean age was 48.01 years, and the mean BMI was 28.88 kg/m². In this study, the prevalence of high blood pressure among males was 33.75% (n = 3,710), and the prevalence of sleep disturbance was 22.06% (n = 2,425). Among females, the prevalence of high blood pressure was 35.09% (n = 4,064), and the prevalence of sleep disturbance was 29.17% (n = 3,378). Subjects were divided into four groups: Without high blood pressure and sleep disturbance, high blood pressure without sleep disturbance, sleep disturbance without high blood pressure, and sleep disturbance with high blood pressure(Table 1 ). The percentage of all subjects experiencing sleep problems was 25.71% (n = 5,803). Among them, 38.86% slept less than 7 hours, making up 58.21% of respondents who slept within the recommended range of 7–9 hours, with the remaining 2.93% exceeding 9 hours of sleep. Respondents experiencing sleep disturbances took approximately eleven minutes longer to fall asleep compared to those without this disturbance, which was somewhat shorter than anticipated. Regarding the use of sleeping pills, the majority of subjects overall did not use such medications (88.52%). However, when considering the presence of both high blood pressure and sleep disturbance, subjects tended to use sleeping pills more frequently. Among respondents experiencing both sleep disturbance and high blood pressure, 14.25% required sleeping pills more than half of the time each month. This percentage decreased to 10.87% for those with sleep disturbance without high blood pressure. Among subjects without high blood pressure and sleep disturbance, 32.96% were smokers. In contrast, among those experiencing both sleep disturbance and high blood pressure, this proportion increased to 55.35%. This trend is reversed among non-smokers. The comorbid trend with alcohol consumption shows a similar pattern: among subjects without high blood pressure and sleep disturbance, 33.96% were drinkers. For those with high blood pressure without sleep disturbance, this proportion increased to 36.53%. Among subjects experiencing sleep disturbance without high blood pressure, 24.78% were drinkers, whereas for those with both sleep disturbance and high blood pressure, this figure rose to 25.69%. Among different racial groups, the comorbidity rate of sleep disturbance and high blood pressure is lowest among Mexican Americans at 7.83% (n = 271). In contrast, non-Hispanic whites have a comorbidity rate of 14.8% (n = 1465), and Other Hispanics have a rate of 15.1% (n = 702). In terms of educational attainment, 51.63% of subjects had diplomas higher than college. However, among subjects with high blood pressure, this percentage decreased to 46.49% (n = 3602). Conversely, among subjects with sleep disturbance, 52.87% had diplomas higher than college (n = 3068). The proportion of partnered subjects (57.26%) consistently exceeded that of non-partnered subjects across all groups. Significant correlations (all P values < 0.001) were observed between sleep disturbance and high blood pressure (Table 2 ), sleep disturbance and the inflammation index (Table 3 ), as well as between the inflammation index and high blood pressure (Table 4 ), as confirmed by subsequent logistic and linear regression analyses. Furthermore, mediation analysis revealed that inflammatory indicators mediated the relationship between sleep disturbance and the development of high blood pressure by 0.23% (Table 5 ). Gender-specific subgroup analyses were conducted throughout the study. Model 1 represented an unadjusted model, while Model 2 controlled for age, ethnicity, smoking status, alcohol status, BMI, marital status, and education level. Table 1 Characteristics of study participants Characteristics Total participants Without high blood pressure and sleep disturbance high blood pressure without sleep disturbance sleep disturbance without high blood pressure sleep disturbance with high blood pressure p 22573 11845 4925 2954 2849 Age (year), Mean(± SD) 48.01 ± 18.51 40.87 ± 17.02 60.15 ± 15.68 45.89 ± 16.75 58.88 ± 14.32 < 0.001 Sex, n (%) < 0.001 Male 10991 (48.69) 6062 (51.18) 2504 (50.84) 1219 (41.27) 1206 (42.33) Female 11582 (51.31) 5783 (48.82) 2421 (49.16) 1735 (58.73) 1643 (57.67) BMI Mean(± SD) 28.88 ± 6.93 27.53 ± 6.21 30.32 ± 6.77 28.51 ± 7.03 32.42 ± 8.10 < 0.001 Marital status, n (%) < 0.001 Married/living with a partner 9648 (42.74) 5049 (42.63) 1979 (40.18) 1293 (43.77) 1327 (46.58) Divorced/separated/widowed/Never married 12925 (57.26) 6796 (57.37) 2946 (59.82) 1661 (56.23) 1522 (53.42) Education level, n (%) < 0.001 <High school 5808 (25.73) 2945 (24.86) 1499 (30.44) 581 (19.67) 783 (27.48) High school 5110 (22.64) 2572 (21.71) 1167 (23.70) 648 (21.94) 723 (25.38) College 11655 (51.63) 6328 (53.42) 2259 (45.87) 1725 (58.40) 1343 (47.14) Smoking status, n (%) < 0.001 Yes 9973 (44.18) 4554 (38.45) 2318 (47.07) 1524 (51.59) 1577 (55.35) No 12600 (55.82) 7291 (61.55) 2607 (52.93) 1430 (48.41) 1272 (44.65) Alcohol status, n (%) < 0.001 Yes 7286 (32.28) 4023 (33.96) 1799 (36.53) 732 (24.78) 732 (25.69) No 15287 (67.72) 7822 (66.04) 3126 (63.47) 2222 (75.22) 2117 (74.31) Race, n (%) < 0.001 Mexican American 3459 (15.32) 2231 (18.83) 626 (12.71) 331 (11.21) 271 (9.51) Non-Hispanic Black 2307 (10.22) 1345 (11.36) 455 (9.24) 258 (8.73) 249 (8.74) Non-Hispanic White 9895 (43.84) 4625 (39.05) 2135 (3.35) 1670 (56.53) 1465 (51.42) Other Hispanic 4646 (20.58) 2190 (18.49) 1308 (26.56) 446 (15.10) 702 (24.64) Other race 2266 (10.04) 1454 (12.28) 401 (8.14) 249 (8.43) 162 (5.69) time to fall asleep(min),Mean(± SD) 23.23 ± 20.32 20.29 ± 18.29 20.40 ± 18.82 31.42 ± 22.82 31.81 ± 23.18 < 0.001 length of sleep,n, (%) < 0.001 9h 662 (2.93) 314 (2.65) 195 (3.96) 63 (2.13) 90 (3.16) dosing *frequency of sleeping pills,n (%) < 0.001 Never 19982 (88.52) 11192 (94.49) 4601 (93.42) 2174 (73.60) 2015 (70.73) Rarely 563 (2.49) 233 (1.97) 103 (2.09) 125 (4.23) 102 (3.58) Sometimes 728 (3.23) 244 (2.06) 101 (2.05) 199 (6.74) 184 (6.46) Often 400 (1.77) 79 (0.67) 44 (0.89) 135 (4.57) 142 (4.98) Almost always 900 (3.99) 97 (0.82) 76 (1.54) 321 (10.87) 406 (14.25) *SII,median (IQR) 461.842 (329.667–653.600) 445.474 (321.667-629.739) 478.154 (331.259-677.444) 473.79 (341.824-668.023) 491.429 (344.211-699) < 0.001 *dosing frequency of sleeping pills Rarely (1 time a month) Sometimes (2–4 times a month) Often (5–15 times a month) Almost always (16–30 times a month) *SII: Systemic Immune-Inflammation Index(Platelets*neutrophils/ Lymphocytes) Table 2 Associations of sleep disturbance and high blood pressure Number of participants OR(95% CI) P Model 1 Total 22573 2.32(2.18,2.47) < 0.001 Male 10991 2.40(2.19,2.63) < 0.001 Female 11582 2.26(2.08,2.46) < 0.001 Model 2 Total 22573 1.81(1.69,1.95) < 0.001 Male 10991 1.83(1.65,2.04) < 0.001 Female 11582 1.78(1.61,1.97) < 0.001 Model 1:Unadjusted Model 2:Adjusted for age, ethnicity, smoking status, alcohol status, BMI, marital status, and education level The results(Table 2 ) showed a positive correlation between sleep disorders and hypertension, both in the overall population and by gender, and this correlation remained significant after model two calibration. This result is clearly in line with common sense[ 11 ][ 12 ]. Table 3 Associations of Sleep disturbance and SII Number of participants β(95% CI) P sleep disturbance and SII Model 1 Total 22573 38.52(27.03,50.01) < 0.001 Male 10991 54.61(35.43,73.79) < 0.001 Female 11582 20.45(6.78,34.12) 0.003 Model 2 Total 22573 16.34(4.62,28.06) 0.006 Male 10991 28.62(9.04,48.21) 0.004 Female 11582 5.74(-8.23,19.71) 0.42 Model 1:Unadjusted Model 2:Adjusted for age, ethnicity, smoking status, alcohol status, BMI, marital status, and education level There was a significant association between sleep disturbance and SII, but different results emerged in the corrected model for females, and a covariate-by-covariate validation found that race and BMI had the greatest effect on the significance of the results, with a p-value back to 0.037 after validation exclusion, the mechanisms of which need to be further validated in follow-upSimilar results were found in Jiahui Yin's study[ 13 ], and the age cycle of the women, the changes in hormone levels, and the pattern of blood pressure response will be discussed later. Table 4 Associations of SII and high blood pressure Number of participants OR(95% CI) P SII and high blood pressure Model 1 Total 22573 1.0004(1.0003,1.0004) < 0.001 Male 10991 1.0005(1.0004,1.0006) < 0.001 Female 11582 1.0002(1.0001,1.0003) < 0.001 Model 2 Total 22573 1.0002(1.0001,1.0003) < 0.001 Male 10991 1.0003(1.0001,1.0004) < 0.001 Female 11582 1.0002(1.0001,1.0004) < 0.001 Model 1:Unadjusted Model 2:Adjusted for age, ethnicity, smoking status, alcohol status, BMI, marital status, and education level SII showed a significant association with high blood pressure, with an odds ratio (OR) of 1.0004 in the total population, and 1.0005 and 1.0002 in males and females, respectively. It's notable that the interquartile range of SII was 461.842 (329.667–653.6), indicating substantial variability in the computed values despite the modest OR range of 1.0002–1.0005. To further elucidate the relationship between SII and high blood pressure, curve fitting analysis was conducted for both models (Fig. 2 ). In Model 1, the odds ratio (OR) for SII was < 1 between 191.45 and 461.84 in the total population, indicating that within this range, SII serves as a protective factor against high blood pressure. Below the lower or above the upper limit of this range, the risk of high blood pressure increases. In men, this protective range was observed between 185.51 and 440.87, showing a similar trend. In contrast, in women, the range extended from 200.00 to 482.35, indicating a broader protective range compared to men. Upon adding covariates, the fitted curve showed a unidirectional trend. In men, the risk of high blood pressure began to increase at SII > 440.87, while in women, this inflection point was observed at SII > 482.35. Table 5 ACME Estimate CI_Lower95 CI_Upper95 P Unadjusted Total 0.0023 0.0013 0.0037 < 0.001 Male 0.0039 0.0020 0.0073 < 0.001 Female 0.0009 0.0001 0.0018 0.018 Adjusted Total 0.0005 0.0001 0.0010 0.006 Male 0.0010 0.0003 0.0022 0.002 Female 0.0002 -0.0004 0.0008 0.434 ADE Unadjusted Total 0.1950 0.1804 0.2102 < 0.001 Male 0.2011 0.1785 0.2230 < 0.001 Female 0.1904 0.1714 0.2115 < 0.001 Adjusted Total 0.1047 0.0914 0.1188 < 0.001 Male 0.1129 0.0920 0.1339 < 0.001 Female 0.0957 0.0774 0.1137 < 0.001 TotalEffect Unadjusted Total 0.1973 0.1825 0.2124 < 0.001 Male 0.2050 0.1826 0.2271 < 0.001 Female 0.1913 0.1721 0.2123 < 0.001 Adjusted Total 0.1052 0.0917 0.1192 < 0.001 Male 0.1139 0.0928 0.1355 < 0.001 Female 0.0959 0.0777 0.1137 < 0.001 PropMediated Unadjusted Total 0.0116 0.0067 0.0187 < 0.001 Male 0.0189 0.0098 0.0361 < 0.001 Female 0.0045 0.0006 0.0097 0.018 Adjusted Total 0.0046 0.0012 0.0092 0.006 Male 0.0086 0.0026 0.0194 0.002 Female 0.0019 -0.0037 0.0081 0.434 ACME: average causal mediation effect, ADE: average direct effect. A generalized additive model was used to the smooth mediator effect on outcome. Adjusted analyses adjusted for age, ethnicity, smoking status, alcohol status, BMI, marital status, and education level 5. Discussion This study reveals a noteworthy link between sleep disturbance and high blood pressure among adults in the US. It also identifies that this connection is partly influenced by inflammation, as indicated by mediation effects analysis. Sleep disturbance is further correlated with a higher SII index and an increased prevalence of high blood pressure. Sleep disturbance is prevalent among the population, and there has long been consensus regarding its association with the development of high blood pressure[ 14 ], it also leads to an increased risk of atherosclerosis, stroke, and cardiac arrhythmia. In Luciana Besedovsky's study[ 15 ], chronic sleep deprivation (such as short sleep duration and sleep disturbance) is linked to chronic systemic low-grade inflammation and has been associated with various diseases that have an inflammatory component. Zhe Wang's[ 16 ] analysis of intestinal flora components revealed that sleep problems not only disrupt their normal functioning but also alter their structure. Intestinal flora, crucial for immune function in the human body, undergo significant impact as a result. Tomasz J Guzik[ 17 ] presents an inflammatory paradigm for hypertension, emphasizing the crucial roles of immune cells, cytokines, and chemokines in disease initiation and progression. In a meta-analysis of 13 studies involving 152,996 participants, Zhen Ye[ 18 ] identified a correlation between elevated Systemic Immune-Inflammation Index (SII) levels and nearly all cardiovascular diseases. These studies suggest a connection between sleep disturbance, immunity, inflammation, and high blood pressure. The Systemic Immune-Inflammation Index (SII), a composite indicator of immune inflammation, is widely employed in oncology patient prognosis, monitoring inflammatory disease progression, and assessing cardiovascular disease risk. Given its role as a comprehensive indicator of immune inflammation, the SII should be a valuable reference in investigating immune-inflammation-mediated high blood pressure. In this study, sleep disturbance, SII, and high blood pressure were analyzed jointly for the first time. Sleep disturbance has been found to be significantly associated with high blood pressure. In Yingjie Cai's study[ 19 ], individuals with sleep problems had a higher risk of hypertension [OR = 1.359 (95% CI: 1.229–1.503)]. Alexandros N Vgontzas[ 20 ], when stratifying by objective sleep duration, noted a higher likelihood of hypertension in insomnia patients who slept less than 5 hours [OR 5.1 (95% CI: 2.2, 11.8)], and the second highest risk in those with 5–6 hours of sleep [OR 3.5 (95% CI: 1.6, 7.9), P < 0.01]. The odds ratios (ORs) in models 1 and 2 of this study were 2.32 and 1.81, respectively. This variation could be attributed to the inclusion of subjects reporting both subjective and objective sleep disturbances in the NHANES questionnaire-based study. This inclusion is evident from the demographic characteristics table, which shows individuals who objectively do not meet sleep time requirements but subjectively perceive no sleep disturbances. The linear regression analysis revealed that the association between sleep disturbance and SII was statistically significant in both studies. In the current study, the beta coefficient was β = 16.3 (95% CI: 4.62, 28.06), P = 0.006. Similarly, in Kaisaierjiang Kadier's study[ 21 ], the beta coefficient was β = 21.421 (95% CI: 1.484, 41.358), P = 0.038. Despite differences in covariates between the two studies, these results suggest a consistent trend. However, due to the limited number of similar studies, further verification by scholars is necessary. The relationship between SII and high blood pressure is indeed significant. From a univariate perspective, SII can act as a protective factor against high blood pressure within specific ranges (185.51-440.87 for men and 200.00-482.35 for women). However, when considering a comprehensive model that includes all three indicators within normal ranges, lower SII values correlate with better outcomes. Cao et al.[ 22 ] utilized SII in their analysis of inflammatory markers and cardiovascular disease, opting to calculate ln(SII) for their study. Although their graphical data lacked clear labels initially, upon analysis using Origin software, they identified an inflection point around ln(SII) = 6.12, corresponding to SII = 454. This finding closely aligns with the conclusion of our study, which found an inflection point at SII = 461.84, thereby reinforcing our results. Another study investigating SII and high blood pressure initially reported an unusual inflection point at ln(SII) = 5.89, SII = 361.41[ 23 ]. Upon further scrutiny of the original data and graphs using Origin software, it was determined that the correct inflection point lies closer to ln(SII) = 6.19, SII = 487.85. This adjustment brings their findings into consistency with our study. In the final mediated effects analysis, the total effect coefficient for sleep disturbance leading to high blood pressure was 19.73% in the overall population. The mediated effects coefficient for SII was 0.23%, indicating a modest but significant role of the systemic immune-inflammation index in this relationship. Interestingly, these effects were more pronounced in males than in females; Milena Kataranovski[ 24 ], In a study of cadmium-induced systemic inflammation in rats, noted that Differential increases of IL-6 and alpha2-M (higher in males than in females) in peripheral blood cell counts and types (leukocytosis and shift in the ratio of granulocytes to lymphocytes more pronounced in males vs females); In a similar study, the association between sleep disturbance and NLR (Neutrophil-to-Lymphocyte Ratio) and CRP (C-reactive protein) in the female group lost significance after adjustments. According to Jiahui Yin's analysis[ 13 ], the p-values for the association of insomnia with NLR and CRP in females were 0.221 and 0.914, respectively, in the corrected model. This contrasts with much lower p-values of 0.0035 and < 0.001, respectively, in the unadjusted model. Meanwhile, the findings in male subjects remained unaffected by the covariates and continued to show statistically significant associations. Why is insomnia in women not significantly related to SII, NLR, and CRP after correction, and why do the results of the study show that women have a larger overall SII safe space than men? Perhaps the following study can give us some hints: Harald Engler[ 25 ] also found a paradoxical phenomenon in his study, Men and women differ in inflammatory and neuroendocrine responses to endotoxin but not in the severity of sickness symptoms, he suggested that women have certain compensatory mechanisms to cope with their stronger inflammatory response, and he did find in intervention trials that women secrete more cortisol and prolactin in response to inflammation, a response not seen in men, and that these additional regulatory mechanisms have significant immune and inflammatory inhibitory effects, which makes Hypertension in women is influenced by a greater combination of factors. Georgia E. Hodes[ 26 ] also highlights gender differences in basal hormone levels, particularly sex hormones, where estrogens notably amplify activation of the HPA (Hypothalamic-Pituitary-Adrenal) axis more in women than in men. Elevated cortisol levels, influenced by this hormonal activity, are known to modulate the impact of inflammatory markers. Consequently, these findings may obscure direct causative relationships between the inciting agent and observed outcomes[ 27 ]. Moreover, the substantial fluctuation in estrogen levels before and after menopause underscores the necessity for subsequent studies to stratify samples by age or age at menopause, ensuring more nuanced interpretations of study results. This study, conducted using 22,573 samples from NHANES between 2005 and 2018, employed correlation and mediation analysis to investigate the relationship between sleep disturbance, Systemic Immune-Inflammation Index (SII), and high blood pressure in adults. The findings clearly demonstrate that immunoinflammation mediates the association between sleep disturbance and HBP. Clinically, this study holds significant implications for patients with comorbidities of SII and high blood pressure, as the involved indicators are common and the data are readily obtainable and conducive to follow-up. Mechanistically, the study provides robust statistical backing and introduces new avenues for managing high blood pressure. Future research can build upon these findings to explore or validate other physiological mechanisms underlying hypertension, thereby enhancing the precision of hypertension management strategies. 6. conclusion The study revealed a significant correlation between SII, sleep disturbance, and high blood pressure, with SII playing a slight mediating role in the link between sleep disturbance and HBP. Sleep disturbance was found to have a total effect value of 19.73% on high blood pressure, while SII's mediating effect coefficient was 0.23%. This finding enhances our understanding of the underlying mechanisms connecting sleep disturbance and high blood pressure, paving the way for comprehensive strategies in the prevention and treatment of high blood pressure. However, subgroup analysis indicated a weaker association in women, suggesting a potential compensatory inflammatory response mechanism unique to females. Further investigation into this gender difference is warranted, as it could inform tailored therapies based on gender and age demographics. Declarations Ethical statement Study protocols for NHANES (National Health and Nutrition Examination Survey) were approved by the NCHS ethics review board (Protocol #2011–17, https://www.cdc.gov/nchs/nhanes/irba98.htm). All the participants signed the informed consent before participating in the study. Declaration of Competing Interest The authors declare no conflict of interest. Funding statement The present research was supported by Exploring the clinical efficacy and related mechanisms of Sancai Lianmei Granule for early remission of type 2 diabetes Based on the combination of traditional Chinese medicine and high-throughput sequencing technology (2023zd020). Author Contribution D.L. and L.S. formulated the scientific hypothesis, designed the research approach, sought collaborators, and drafted the main body of the manuscript; J.Y. and Y.Y. enrolled participants according to the criteria and completed the processing of baseline data; Q.Z. and J.P. jointly conducted the inter-group comparisons of various variables; Q.C. conducted the initial review of the manuscript and provided revisions on the interpretation of results and the logical flow of the text; all authors agreed to the submission of the final version. Data Availability The data used in this study are available on the National Health and Nutrition Examination Survey website：https://www.cdc.gov/nchs/nhanes/index.htm. References World Health Organization. Global report on hypertension: the race against a silent killer p. 1–276 (World Health Organization, 2023). Charchar, F. J. et al. Lifestyle management of hypertension: International Society of Hypertension position paper endorsed by the World Hypertension League and European Society of Hypertension. J. Hypertens. 42 (1), 23–49. 10.1097/HJH.0000000000003563 (2024). Epub 2023 Sep 12. PMID: 37712135; PMCID: PMC10713007. Makarem, N., Alcántara, C., Williams, N., Bello, N. A. & Abdalla, M. Effect of Sleep Disturbances on Blood Pressure. Hypertension . 77 (4), 1036–1046. 10.1161/HYPERTENSIONAHA.120.14479 (2021). Epub 2021 Feb 22. PMID: 33611935; PMCID: PMC7946733. Covassin, N. et al. Effects of Experimental Sleep Restriction on Ambulatory and Sleep Blood Pressure in Healthy Young Adults: A Randomized Crossover Study. Hypertension . 78 (3), 859–870. 10.1161/HYPERTENSIONAHA.121.17622 (2021). Epub 2021 Jul 12. PMID: 34247512; PMCID: PMC8363516. Irwin, M. R., Olmstead, R., Carroll, J. E. & Sleep Disturbance Sleep Duration, and Inflammation: A Systematic Review and Meta-Analysis of Cohort Studies and Experimental Sleep Deprivation. Biol. Psychiatry . 80 (1), 40–52. 10.1016/j.biopsych.2015.05.014 (2016). Epub 2015 Jun 1. PMID: 26140821; PMCID: PMC4666828. Zhang, Z., Zhao, L., Zhou, X., Meng, X. & Zhou, X. Role of inflammation, immunity, and oxidative stress in hypertension: New insights and potential therapeutic targets. Front. Immunol. 13 , 1098725. 10.3389/fimmu.2022.1098725 (2023). PMID: 36703963; PMCID: PMC9871625. Franco, C. et al. Essential Hypertension and Oxidative Stress: Novel Future Perspectives. Int. J. Mol. Sci. 23 (22), 14489. 10.3390/ijms232214489 (2022). PMID: 36430967; PMCID: PMC9692622. Saylik, F. & Sarıkaya, R. Can Systemic Immune-Inflammation Index Detect the Presence of Exxaggerated Morning Blood Pressure Surge in Newly Diagnosed Treatment-Naive Hypertensive Patients? Clin. Exp. Hypertens. 43 (8), 772–779 (2021). Epub 2021 Aug 2. PMID: 34338559. Su, S. et al. Systemic immune-inflammation index predicted the clinical outcome in patients with type-B aortic dissection undergoing thoracic endovascular repair. Eur J Clin Invest. ;52(2):e13692. doi: (2022). 10.1111/eci.13692 . Epub 2021 Nov 3. PMID: 34695253. Stekhoven, D. J. & Bühlmann, P. MissForest–non-parametric missing value imputation for mixed-type data. Bioinformatics . 28 (1), 112–118. 10.1093/bioinformatics/btr597 (2012). Epub 2011 Oct 28. PMID: 22039212. DelRosso, L. M., Mogavero, M. P. & Ferri, R. Effect of Sleep Disorders on Blood Pressure and Hypertension in Children. Curr Hypertens Rep. ;22(11):88. doi: (2020). 10.1007/s11906-020-01100-x . PMID: 32893326. Bansil, P., Kuklina, E. V., Merritt, R. K. & Yoon, P. W. Associations between sleep disorders, sleep duration, quality of sleep, and hypertension: results from the. J. Clin. Hypertens. (Greenwich) . 13 (10), 739–743. 10.1111/j.1751-7176.2011.00500.x (2011). Epub 2011 Jul 14. PMID: 21974761; PMCID: PMC8108844National Health and Nutrition Examination Survey, 2005 to 2008. Yin, J. et al. Associations between sleep disturbance, inflammatory markers and depressive symptoms: Mediation analyses in a large NHANES community sample. Prog Neuropsychopharmacol. Biol. Psychiatry . 126 , 110786. 10.1016/j.pnpbp.2023.110786 (2023). Epub 2023 May 12. PMID: 37178815. Plante, G. E. Sleep and vascular disorders. Metabolism. ;55(10 Suppl 2):S45-9. doi: (2006). 10.1016/j.metabol.2006.07.013 . PMID: 16979427. Besedovsky, L., Lange, T. & Haack, M. The Sleep-Immune Crosstalk in Health and Disease. Physiol. Rev. 99 (3), 1325–1380. 10.1152/physrev.00010.2018 (2019). PMID: 30920354; PMCID: PMC6689741. Wang, Z. et al. The microbiota-gut-brain axis in sleep disorders. Sleep. Med. Rev. 65 , 101691 (2022). Epub 2022 Aug 31. PMID: 36099873. Guzik, T. J., Nosalski, R., Maffia, P. & Drummond, G. R. Immune and inflammatory mechanisms in hypertension. Nat Rev Cardiol. ;21(6):396–416. doi: (2024). 10.1038/s41569-023-00964-1 . Epub 2024 Jan 3. PMID: 38172242. Ye, Z. et al. Systemic immune-inflammation index as a potential biomarker of cardiovascular diseases: A systematic review and meta-analysis. Front. Cardiovasc. Med. 9 , 933913. 10.3389/fcvm.2022.933913 (2022). PMID: 36003917; PMCID: PMC9393310. Cai, Y., Chen, M., Zhai, W. & Wang, C. Interaction between trouble sleeping and depression on hypertension in the NHANES 2005–2018. BMC Public. Health . 22 (1), 481. 10.1186/s12889-022-12942-2 (2022). PMID: 35277151; PMCID: PMC8917766. Vgontzas, A. N., Liao, D., Bixler, E. O., Chrousos, G. P. & Vela-Bueno, A. Insomnia with objective short sleep duration is associated with a high risk for hypertension. Sleep . 32 (4), 491–497. 10.1093/sleep/32.4.491 (2009). PMID: 19413143; PMCID: PMC2663863. Kadier, K. et al. Analysis of the relationship between sleep-related disorder and systemic immune-inflammation index in the US population. BMC Psychiatry . 23 (1), 773. 10.1186/s12888-023-05286-7 (2023). PMID: 37872570; PMCID: PMC10594811. Cao, Y. et al. Association of systemic immune inflammatory index with all-cause and cause-specific mortality in hypertensive individuals: Results from NHANES. Front. Immunol. 14 , 1087345. 10.3389/fimmu.2023.1087345 (2023). PMID: 36817427; PMCID: PMC9932782. Chen, Y. et al. Association between systemic immunity-inflammation index and hypertension in US adults from NHANES 1999–2018. Sci. Rep. 14 (1), 5677. 10.1038/s41598-024-56387-6 (2024). PMID: 38454104; PMCID: PMC10920861. Kataranovski, M., Janković, S., Kataranovski, D., Stosić, J. & Bogojević, D. Gender differences in acute cadmium-induced systemic inflammation in rats. Biomed Environ Sci. ;22(1):1–7. doi: (2009). 10.1016/s0895-3988(09)60014-3 . PMID: 19462680. Engler, H. et al. Men and women differ in inflammatory and neuroendocrine responses to endotoxin but not in the severity of sickness symptoms. Brain Behav. Immun. 52 , 18–26 (2016). Epub 2015 Aug 17. PMID: 26291403. Hodes, G. E., Bangasser, D., Sotiropoulos, I., Kokras, N. & Dalla, C. Sex Differences in Stress Response: Classical Mechanisms and Beyond. Curr. Neuropharmacol. 22 (3), 475–494. 10.2174/1570159X22666231005090134 (2024). PMID: 37855285; PMCID: PMC10845083. Contreras-Zentella, M. L. & Hernández-Muñoz, R. Possible Gender Influence in the Mechanisms Underlying the Oxidative Stress, Inflammatory Response, and the Metabolic Alterations in Patients with Obesity and/or Type 2 Diabetes. Antioxid. (Basel) . 10 (11), 1729. 10.3390/antiox10111729 (2021). PMID: 34829598; PMCID: PMC8615031. 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07:10:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5016061/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5016061/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66947940,"identity":"2ab75356-ae71-46e5-baa1-e76bdd45c24b","added_by":"auto","created_at":"2024-10-18 09:53:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49080,"visible":true,"origin":"","legend":"\u003cp\u003esample from NHANES 2005–2018\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5016061/v1/036f352a330200f333138bce.jpg"},{"id":66947993,"identity":"4da6ac6d-8d91-4dd0-983c-88613bec8437","added_by":"auto","created_at":"2024-10-18 09:53:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":106911,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted Cubic Spline of SII and high blood pressure\u003c/p\u003e","description":"","filename":"Figure2total.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5016061/v1/9b59b44947579d79db1a9087.jpg"},{"id":97135761,"identity":"47a12baf-4d98-4d48-809b-2d81c09a4158","added_by":"auto","created_at":"2025-12-01 09:53:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1340230,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5016061/v1/6bd5db28-3632-4c34-833b-a67a04d16a16.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between sleep disturbance, inflammatory markers, and high blood pressure: National Health and Nutrition Examination Survey (NHANES) 2005–2018","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMore than 1.3\u0026nbsp;billion people worldwide suffer from high blood pressure[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and those with sleep problems are more likely to develop high blood pressure. By summarising research from 2015 to 2020 on the association between high blood pressure and sleep in adults, a study concluded that sleep disorders increase the risk of developing high blood pressure by 45% and 80%[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Naima Covassin et al.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] discovered that despite rigorous weight and dietary management, the increase in blood pressure due to shortened sleep duration remained uncorrected. By incorporating 72 studies comprising over 50,000 subjects, Michael R. Irwin[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] concluded that sleep disturbance is associated with elevated inflammatory markers. Zenglei Zhang et al.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] also suggested that while the mechanism linking inflammation to the development of high blood pressure is complex, the causal relationship is indeed established.\u003c/p\u003e \u003cp\u003eThese findings collectively suggest a potential pathway: inflammation acts as an intermediary factor linking sleep disturbance to high blood pressure. Therefore, we introduce the Systemic Immune-Inflammation Index (SII)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] to aid in studying the relationship among these three factors. SII is an integrated inflammatory biomarker derived from neutrophil, lymphocyte, and platelet counts. Initially developed to assess prognosis in patients with solid cancers and coronary heart disease (CHD), the SII is now acknowledged for its ability to accurately reflect the inflammatory state.\u003c/p\u003e \u003cp\u003eBased on the NHANES database, an exploration was conducted to investigate the association between sleep disturbance, SII, and high blood pressure, aiming to contribute to the management of the comorbidities of sleep disturbance and high blood pressure.\u003c/p\u003e \u003cp\u003eObjectives:1.To investigate the pairwise association between sleep disturbance, SII, and high blood pressure.2.To investigate whether SII mediates the progression from sleep disturbance to high blood pressure.\u003c/p\u003e \u003cp\u003eHypotheses:1.There is a pairwise correlation between SII, sleep disturbance, and high blood pressure.2.SII partially mediates the relationship between sleep disturbance and high blood pressure.\u003c/p\u003e"},{"header":"2. Materials \u0026 methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1Study population\u003c/h2\u003e \u003cp\u003eWe designed a cross-sectional study using data from the 2005\u0026ndash;2018 NHANES survey, conducted by the Centers for Disease Control and Prevention (CDC) to assess the health and nutritional status of the United States population. The National Center for Health Statistics Research Ethics Review Board authorized the NHANES study protocols in compliance with the revised Declaration of Helsinki. All participants provided written informed consent. Further information regarding the NHANES program is available on the CDC website.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2Core variables\u003c/h2\u003e \u003cp\u003e(1)Information on sleep disorders was obtained from questionnaires:\u0026rdquo; Ever told by doctor have sleep disorder\u0026rdquo; and\u0026rdquo; Ever told doctor had trouble sleeping\u0026rdquo;, Answering yes to any of the questionnaires is considered to be Sleep disturbance.\u003c/p\u003e \u003cp\u003e(2)Information on high blood pressure was obtained from questionnaires:\u0026rdquo; Ever told you had high blood pressure\u0026rdquo; In reply to yes is considered to have high blood pressure.\u003c/p\u003e \u003cp\u003e(3)Platelets, neutrophils, and Lymphocytes are all measured in NHANES in units of 1000 cells/uL, SII\u0026thinsp;=\u0026thinsp;P*N/L\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3Covariates\u003c/h2\u003e \u003cp\u003e(1)Age\u003c/p\u003e \u003cp\u003e(2)BMI\u003c/p\u003e \u003cp\u003e(3)Race/ethnicity (non-Hispanic white, Mexican American, nonHispanic black, other Hispanic, or other race/multiple races)\u003c/p\u003e \u003cp\u003e(4)Educational levels categorized as less than high school, high school graduation, and college and beyond\u003c/p\u003e \u003cp\u003e(5)Marital status is divided into two groups using married/living with a partner/divorced/separated/widower/never married\u003c/p\u003e \u003cp\u003e(6)Smoking is defined as having smoked more than 100 cigarettes in a lifetime\u003c/p\u003e \u003cp\u003e(7)Drinking is defined as having had more than 12 drinks in a lifetime\u003c/p\u003e \u003cp\u003e(8)How long to fall asleep (minutes)\u003c/p\u003e \u003cp\u003e(9) Get how much sleep (hours)\u003c/p\u003e \u003cp\u003e(10)How often take pills to help sleep. Rarely (1 time a month); Sometimes (2\u0026ndash;4 times a month); Often (5\u0026ndash;15 times a month); Almost always (16\u0026ndash;30 times a month)\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Statistical analysis","content":"\u003cp\u003e\u003cstrong\u003e3.1Data Cleaning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1)Summarize the records from 2005 to 2018.（n=70190）\u003c/p\u003e\n\u003cp\u003e2)Delete records that do not contain any sleep disturbance questionnaires or do not contain high blood pressure questionnaires.（n=44679）\u003c/p\u003e\n\u003cp\u003e3)Delete records where there are two or more missing values among the platelet, neutrophil, and lymphocyte counts.（n=23770）\u003c/p\u003e\n\u003cp\u003e4)Use the 3-sigma method to screen for extreme values.（n=23768）\u003c/p\u003e\n\u003cp\u003e5)Delete records that subjects \u0026lt;18 years old.（n=22573）\u003c/p\u003e\n\u003cp\u003e6)Impute missing values using Random Forest[10].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2data processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical software (R version 4.1.1; https://cran.r-project.org/) was used for statistical analysis (package interactions and mediation).\u003c/p\u003e\n\u003cp\u003eA two-tailed p-value \u0026lt;0.05 indicated statistical significance for all analyses. In descriptive statistics, continuous variables were expressed as mean and standard deviation or median and interquartile spacing, and categorical variables were expressed as proportions and percentages of totals. X tests were used to compare categorical variables between groups. For continuous variables, one-way ANOVA was used to compare normally distributed variables, and the Kruskal-Wallis H test was used to compare skewed variables between groups. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultifactorial logistic regression analysis evaluated the relationship between sleep disorders and hypertension. Multifactorial linear regression analysis evaluated the relationship between sleep disorders and SII. To better describe the relationship between inflammatory markers and hypertension, we performed curve-fitting analyses. To explore the moderating role of gender, we calculated the strength of the association between hypertension, sleep disorders, and inflammatory markers in male and female subgroups. We performed mediation analyses to test whether SII mediated the relationship between the exposure variable (sleep disorder) and the outcome (hypertension). Thousands of bootstraps were used in our analyses. The size of the indirect pathway effect, the proportion of mediated effects, and the p-value of the mediated effect are all shown in the results.\u003c/p\u003e"},{"header":"4. results","content":"\u003cp\u003eA total sample of 22,573 was included, comprising 48.69% males and 51.31% females. The mean age was 48.01 years, and the mean BMI was 28.88 kg/m\u0026sup2;. In this study, the prevalence of high blood pressure among males was 33.75% (n\u0026thinsp;=\u0026thinsp;3,710), and the prevalence of sleep disturbance was 22.06% (n\u0026thinsp;=\u0026thinsp;2,425). Among females, the prevalence of high blood pressure was 35.09% (n\u0026thinsp;=\u0026thinsp;4,064), and the prevalence of sleep disturbance was 29.17% (n\u0026thinsp;=\u0026thinsp;3,378).\u003c/p\u003e \u003cp\u003eSubjects were divided into four groups: Without high blood pressure and sleep disturbance, high blood pressure without sleep disturbance, sleep disturbance without high blood pressure, and sleep disturbance with high blood pressure(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The percentage of all subjects experiencing sleep problems was 25.71% (n\u0026thinsp;=\u0026thinsp;5,803). Among them, 38.86% slept less than 7 hours, making up 58.21% of respondents who slept within the recommended range of 7\u0026ndash;9 hours, with the remaining 2.93% exceeding 9 hours of sleep. Respondents experiencing sleep disturbances took approximately eleven minutes longer to fall asleep compared to those without this disturbance, which was somewhat shorter than anticipated. Regarding the use of sleeping pills, the majority of subjects overall did not use such medications (88.52%). However, when considering the presence of both high blood pressure and sleep disturbance, subjects tended to use sleeping pills more frequently. Among respondents experiencing both sleep disturbance and high blood pressure, 14.25% required sleeping pills more than half of the time each month. This percentage decreased to 10.87% for those with sleep disturbance without high blood pressure.\u003c/p\u003e \u003cp\u003eAmong subjects without high blood pressure and sleep disturbance, 32.96% were smokers. In contrast, among those experiencing both sleep disturbance and high blood pressure, this proportion increased to 55.35%. This trend is reversed among non-smokers.\u003c/p\u003e \u003cp\u003eThe comorbid trend with alcohol consumption shows a similar pattern: among subjects without high blood pressure and sleep disturbance, 33.96% were drinkers. For those with high blood pressure without sleep disturbance, this proportion increased to 36.53%. Among subjects experiencing sleep disturbance without high blood pressure, 24.78% were drinkers, whereas for those with both sleep disturbance and high blood pressure, this figure rose to 25.69%.\u003c/p\u003e \u003cp\u003eAmong different racial groups, the comorbidity rate of sleep disturbance and high blood pressure is lowest among Mexican Americans at 7.83% (n\u0026thinsp;=\u0026thinsp;271). In contrast, non-Hispanic whites have a comorbidity rate of 14.8% (n\u0026thinsp;=\u0026thinsp;1465), and Other Hispanics have a rate of 15.1% (n\u0026thinsp;=\u0026thinsp;702).\u003c/p\u003e \u003cp\u003eIn terms of educational attainment, 51.63% of subjects had diplomas higher than college. However, among subjects with high blood pressure, this percentage decreased to 46.49% (n\u0026thinsp;=\u0026thinsp;3602). Conversely, among subjects with sleep disturbance, 52.87% had diplomas higher than college (n\u0026thinsp;=\u0026thinsp;3068).\u003c/p\u003e \u003cp\u003eThe proportion of partnered subjects (57.26%) consistently exceeded that of non-partnered subjects across all groups.\u003c/p\u003e \u003cp\u003eSignificant correlations (all P values\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were observed between sleep disturbance and high blood pressure (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), sleep disturbance and the inflammation index (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), as well as between the inflammation index and high blood pressure (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), as confirmed by subsequent logistic and linear regression analyses.\u003c/p\u003e \u003cp\u003eFurthermore, mediation analysis revealed that inflammatory indicators mediated the relationship between sleep disturbance and the development of high blood pressure by 0.23% (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Gender-specific subgroup analyses were conducted throughout the study. Model 1 represented an unadjusted model, while Model 2 controlled for age, ethnicity, smoking status, alcohol status, BMI, marital status, and education level.\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 study participants\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003eparticipants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout high blood pressure and sleep disturbance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehigh blood pressure without sleep disturbance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003esleep disturbance without high blood pressure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003esleep disturbance with high blood pressure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year), Mean(\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.01\u0026thinsp;\u0026plusmn;\u0026thinsp;18.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.87\u0026thinsp;\u0026plusmn;\u0026thinsp;17.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.15\u0026thinsp;\u0026plusmn;\u0026thinsp;15.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.89\u0026thinsp;\u0026plusmn;\u0026thinsp;16.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.88\u0026thinsp;\u0026plusmn;\u0026thinsp;14.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, 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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e10991 (48.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6062 (51.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2504 (50.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1219 (41.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1206 (42.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11582 (51.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5783 (48.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2421 (49.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1735 (58.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1643 (57.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003cp\u003eMean(\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.88\u0026thinsp;\u0026plusmn;\u0026thinsp;6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.53\u0026thinsp;\u0026plusmn;\u0026thinsp;6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.32\u0026thinsp;\u0026plusmn;\u0026thinsp;6.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.51\u0026thinsp;\u0026plusmn;\u0026thinsp;7.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.42\u0026thinsp;\u0026plusmn;\u0026thinsp;8.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital 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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/living with a partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9648 (42.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5049 (42.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1979 (40.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1293 (43.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1327 (46.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/separated/widowed/Never married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12925 (57.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6796 (57.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2946 (59.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1661 (56.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1522 (53.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, 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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5808 (25.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2945 (24.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1499 (30.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e581 (19.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e783 (27.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5110 (22.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2572 (21.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1167 (23.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e648 (21.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e723 (25.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11655 (51.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6328 (53.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2259 (45.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1725 (58.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1343 (47.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e9973 (44.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4554 (38.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2318 (47.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1524 (51.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1577 (55.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12600 (55.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7291 (61.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2607 (52.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1430 (48.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1272 (44.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol 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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e7286 (32.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4023 (33.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1799 (36.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e732 (24.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e732 (25.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15287 (67.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7822 (66.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3126 (63.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2222 (75.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2117 (74.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \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=\"left\" colname=\"c2\"\u003e \u003cp\u003e3459 (15.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2231 (18.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e626 (12.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e331 (11.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e271 (9.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2307 (10.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1345 (11.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e455 (9.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e258 (8.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e249 (8.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9895 (43.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4625 (39.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2135 (3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1670 (56.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1465 (51.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4646 (20.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2190 (18.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1308 (26.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e446 (15.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e702 (24.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2266 (10.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1454 (12.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e401 (8.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e249 (8.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e162 (5.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime to fall asleep(min),Mean(\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.23\u0026thinsp;\u0026plusmn;\u0026thinsp;20.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.29\u0026thinsp;\u0026plusmn;\u0026thinsp;18.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.40\u0026thinsp;\u0026plusmn;\u0026thinsp;18.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.42\u0026thinsp;\u0026plusmn;\u0026thinsp;22.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.81\u0026thinsp;\u0026plusmn;\u0026thinsp;23.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elength of sleep,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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8772 (38.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3990 (33.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1690 (34.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1494 (50.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1598 (56.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7-9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13139 (58.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7541 (63.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3040 (61.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1397 (47.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1161 (40.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e662 (2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314 (2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195 (3.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90 (3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edosing *frequency of sleeping pills,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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19982 (88.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11192 (94.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4601 (93.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2174 (73.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2015 (70.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e563 (2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233 (1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125 (4.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102 (3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e728 (3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244 (2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e199 (6.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e184 (6.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400 (1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135 (4.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e142 (4.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlmost always\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e900 (3.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e321 (10.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e406 (14.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*SII,median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e461.842 (329.667\u0026ndash;653.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e445.474 (321.667-629.739)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e478.154 (331.259-677.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e473.79 (341.824-668.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e491.429 (344.211-699)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*dosing frequency of sleeping pills\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eRarely (1 time a month)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSometimes (2\u0026ndash;4 times a month)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOften (5\u0026ndash;15 times a month)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAlmost always (16\u0026ndash;30 times a month)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*SII: Systemic Immune-Inflammation Index(Platelets*neutrophils/ Lymphocytes)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eAssociations of sleep disturbance and high blood pressure\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=\"left\" 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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of\u003c/p\u003e \u003cp\u003eparticipants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.32(2.18,2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.40(2.19,2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.26(2.08,2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.81(1.69,1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.83(1.65,2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.78(1.61,1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1:Unadjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2:Adjusted for age, ethnicity, smoking status, alcohol status, BMI, marital status, and education level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed a positive correlation between sleep disorders and hypertension, both in the overall population and by gender, and this correlation remained significant after model two calibration. This result is clearly in line with common sense[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\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\u003eAssociations of Sleep disturbance and SII\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=\"left\" 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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of\u003c/p\u003e \u003cp\u003eparticipants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esleep disturbance and SII\u003c/p\u003e \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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.52(27.03,50.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.61(35.43,73.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.45(6.78,34.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.34(4.62,28.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.62(9.04,48.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.74(-8.23,19.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1:Unadjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2:Adjusted for age, ethnicity, smoking status, alcohol status, BMI, marital status, and education level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere was a significant association between sleep disturbance and SII, but different results emerged in the corrected model for females, and a covariate-by-covariate validation found that race and BMI had the greatest effect on the significance of the results, with a p-value back to 0.037 after validation exclusion, the mechanisms of which need to be further validated in follow-upSimilar results were found in Jiahui Yin's study[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and the age cycle of the women, the changes in hormone levels, and the pattern of blood pressure response will be discussed later.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of SII and high blood pressure\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=\"left\" 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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of\u003c/p\u003e \u003cp\u003eparticipants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSII and high blood pressure\u003c/p\u003e \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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0004(1.0003,1.0004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0005(1.0004,1.0006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0002(1.0001,1.0003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0002(1.0001,1.0003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0003(1.0001,1.0004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0002(1.0001,1.0004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1:Unadjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2:Adjusted for age, ethnicity, smoking status, alcohol status, BMI, marital status, and education level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSII showed a significant association with high blood pressure, with an odds ratio (OR) of 1.0004 in the total population, and 1.0005 and 1.0002 in males and females, respectively. It's notable that the interquartile range of SII was 461.842 (329.667\u0026ndash;653.6), indicating substantial variability in the computed values despite the modest OR range of 1.0002\u0026ndash;1.0005.\u003c/p\u003e \u003cp\u003eTo further elucidate the relationship between SII and high blood pressure, curve fitting analysis was conducted for both models (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Model 1, the odds ratio (OR) for SII was \u0026lt;\u0026thinsp;1 between 191.45 and 461.84 in the total population, indicating that within this range, SII serves as a protective factor against high blood pressure. Below the lower or above the upper limit of this range, the risk of high blood pressure increases. In men, this protective range was observed between 185.51 and 440.87, showing a similar trend. In contrast, in women, the range extended from 200.00 to 482.35, indicating a broader protective range compared to men.\u003c/p\u003e \u003cp\u003eUpon adding covariates, the fitted curve showed a unidirectional trend. In men, the risk of high blood pressure began to increase at SII\u0026thinsp;\u0026gt;\u0026thinsp;440.87, while in women, this inflection point was observed at SII\u0026thinsp;\u0026gt;\u0026thinsp;482.35.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACME\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI_Lower95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI_Upper95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnadjusted\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADE\u003c/p\u003e \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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnadjusted\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotalEffect\u003c/p\u003e \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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnadjusted\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePropMediated\u003c/p\u003e \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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnadjusted\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eACME: average causal mediation effect, ADE: average direct effect.\u003c/p\u003e \u003cp\u003eA generalized additive model was used to the smooth mediator effect on outcome.\u003c/p\u003e \u003cp\u003eAdjusted analyses adjusted for age, ethnicity, smoking status, alcohol status, BMI, marital status, and education level\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study reveals a noteworthy link between sleep disturbance and high blood pressure among adults in the US. It also identifies that this connection is partly influenced by inflammation, as indicated by mediation effects analysis. Sleep disturbance is further correlated with a higher SII index and an increased prevalence of high blood pressure.\u003c/p\u003e \u003cp\u003eSleep disturbance is prevalent among the population, and there has long been consensus regarding its association with the development of high blood pressure[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], it also leads to an increased risk of atherosclerosis, stroke, and cardiac arrhythmia. In Luciana Besedovsky's study[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], chronic sleep deprivation (such as short sleep duration and sleep disturbance) is linked to chronic systemic low-grade inflammation and has been associated with various diseases that have an inflammatory component. Zhe Wang's[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] analysis of intestinal flora components revealed that sleep problems not only disrupt their normal functioning but also alter their structure. Intestinal flora, crucial for immune function in the human body, undergo significant impact as a result. Tomasz J Guzik[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] presents an inflammatory paradigm for hypertension, emphasizing the crucial roles of immune cells, cytokines, and chemokines in disease initiation and progression. In a meta-analysis of 13 studies involving 152,996 participants, Zhen Ye[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] identified a correlation between elevated Systemic Immune-Inflammation Index (SII) levels and nearly all cardiovascular diseases. These studies suggest a connection between sleep disturbance, immunity, inflammation, and high blood pressure. The Systemic Immune-Inflammation Index (SII), a composite indicator of immune inflammation, is widely employed in oncology patient prognosis, monitoring inflammatory disease progression, and assessing cardiovascular disease risk. Given its role as a comprehensive indicator of immune inflammation, the SII should be a valuable reference in investigating immune-inflammation-mediated high blood pressure.\u003c/p\u003e \u003cp\u003eIn this study, sleep disturbance, SII, and high blood pressure were analyzed jointly for the first time. Sleep disturbance has been found to be significantly associated with high blood pressure. In Yingjie Cai's study[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], individuals with sleep problems had a higher risk of hypertension [OR\u0026thinsp;=\u0026thinsp;1.359 (95% CI: 1.229\u0026ndash;1.503)]. Alexandros N Vgontzas[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], when stratifying by objective sleep duration, noted a higher likelihood of hypertension in insomnia patients who slept less than 5 hours [OR 5.1 (95% CI: 2.2, 11.8)], and the second highest risk in those with 5\u0026ndash;6 hours of sleep [OR 3.5 (95% CI: 1.6, 7.9), P\u0026thinsp;\u0026lt;\u0026thinsp;0.01]. The odds ratios (ORs) in models 1 and 2 of this study were 2.32 and 1.81, respectively. This variation could be attributed to the inclusion of subjects reporting both subjective and objective sleep disturbances in the NHANES questionnaire-based study. This inclusion is evident from the demographic characteristics table, which shows individuals who objectively do not meet sleep time requirements but subjectively perceive no sleep disturbances.\u003c/p\u003e \u003cp\u003eThe linear regression analysis revealed that the association between sleep disturbance and SII was statistically significant in both studies. In the current study, the beta coefficient was β\u0026thinsp;=\u0026thinsp;16.3 (95% CI: 4.62, 28.06), P\u0026thinsp;=\u0026thinsp;0.006. Similarly, in Kaisaierjiang Kadier's study[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the beta coefficient was β\u0026thinsp;=\u0026thinsp;21.421 (95% CI: 1.484, 41.358), P\u0026thinsp;=\u0026thinsp;0.038. Despite differences in covariates between the two studies, these results suggest a consistent trend. However, due to the limited number of similar studies, further verification by scholars is necessary.\u003c/p\u003e \u003cp\u003eThe relationship between SII and high blood pressure is indeed significant. From a univariate perspective, SII can act as a protective factor against high blood pressure within specific ranges (185.51-440.87 for men and 200.00-482.35 for women). However, when considering a comprehensive model that includes all three indicators within normal ranges, lower SII values correlate with better outcomes. Cao et al.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] utilized SII in their analysis of inflammatory markers and cardiovascular disease, opting to calculate ln(SII) for their study. Although their graphical data lacked clear labels initially, upon analysis using Origin software, they identified an inflection point around ln(SII)\u0026thinsp;=\u0026thinsp;6.12, corresponding to SII\u0026thinsp;=\u0026thinsp;454. This finding closely aligns with the conclusion of our study, which found an inflection point at SII\u0026thinsp;=\u0026thinsp;461.84, thereby reinforcing our results. Another study investigating SII and high blood pressure initially reported an unusual inflection point at ln(SII)\u0026thinsp;=\u0026thinsp;5.89, SII\u0026thinsp;=\u0026thinsp;361.41[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Upon further scrutiny of the original data and graphs using Origin software, it was determined that the correct inflection point lies closer to ln(SII)\u0026thinsp;=\u0026thinsp;6.19, SII\u0026thinsp;=\u0026thinsp;487.85. This adjustment brings their findings into consistency with our study.\u003c/p\u003e \u003cp\u003eIn the final mediated effects analysis, the total effect coefficient for sleep disturbance leading to high blood pressure was 19.73% in the overall population. The mediated effects coefficient for SII was 0.23%, indicating a modest but significant role of the systemic immune-inflammation index in this relationship. Interestingly, these effects were more pronounced in males than in females; Milena Kataranovski[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], In a study of cadmium-induced systemic inflammation in rats, noted that Differential increases of IL-6 and alpha2-M (higher in males than in females) in peripheral blood cell counts and types (leukocytosis and shift in the ratio of granulocytes to lymphocytes more pronounced in males vs females); In a similar study, the association between sleep disturbance and NLR (Neutrophil-to-Lymphocyte Ratio) and CRP (C-reactive protein) in the female group lost significance after adjustments. According to Jiahui Yin's analysis[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], the p-values for the association of insomnia with NLR and CRP in females were 0.221 and 0.914, respectively, in the corrected model. This contrasts with much lower p-values of 0.0035 and \u0026lt;\u0026thinsp;0.001, respectively, in the unadjusted model. Meanwhile, the findings in male subjects remained unaffected by the covariates and continued to show statistically significant associations.\u003c/p\u003e \u003cp\u003eWhy is insomnia in women not significantly related to SII, NLR, and CRP after correction, and why do the results of the study show that women have a larger overall SII safe space than men? Perhaps the following study can give us some hints: Harald Engler[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] also found a paradoxical phenomenon in his study, Men and women differ in inflammatory and neuroendocrine responses to endotoxin but not in the severity of sickness symptoms, he suggested that women have certain compensatory mechanisms to cope with their stronger inflammatory response, and he did find in intervention trials that women secrete more cortisol and prolactin in response to inflammation, a response not seen in men, and that these additional regulatory mechanisms have significant immune and inflammatory inhibitory effects, which makes Hypertension in women is influenced by a greater combination of factors. Georgia E. Hodes[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] also highlights gender differences in basal hormone levels, particularly sex hormones, where estrogens notably amplify activation of the HPA (Hypothalamic-Pituitary-Adrenal) axis more in women than in men. Elevated cortisol levels, influenced by this hormonal activity, are known to modulate the impact of inflammatory markers. Consequently, these findings may obscure direct causative relationships between the inciting agent and observed outcomes[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Moreover, the substantial fluctuation in estrogen levels before and after menopause underscores the necessity for subsequent studies to stratify samples by age or age at menopause, ensuring more nuanced interpretations of study results.\u003c/p\u003e \u003cp\u003eThis study, conducted using 22,573 samples from NHANES between 2005 and 2018, employed correlation and mediation analysis to investigate the relationship between sleep disturbance, Systemic Immune-Inflammation Index (SII), and high blood pressure in adults. The findings clearly demonstrate that immunoinflammation mediates the association between sleep disturbance and HBP. Clinically, this study holds significant implications for patients with comorbidities of SII and high blood pressure, as the involved indicators are common and the data are readily obtainable and conducive to follow-up. Mechanistically, the study provides robust statistical backing and introduces new avenues for managing high blood pressure. Future research can build upon these findings to explore or validate other physiological mechanisms underlying hypertension, thereby enhancing the precision of hypertension management strategies.\u003c/p\u003e"},{"header":"6. conclusion","content":"\u003cp\u003eThe study revealed a significant correlation between SII, sleep disturbance, and high blood pressure, with SII playing a slight mediating role in the link between sleep disturbance and HBP. Sleep disturbance was found to have a total effect value of 19.73% on high blood pressure, while SII's mediating effect coefficient was 0.23%. This finding enhances our understanding of the underlying mechanisms connecting sleep disturbance and high blood pressure, paving the way for comprehensive strategies in the prevention and treatment of high blood pressure. However, subgroup analysis indicated a weaker association in women, suggesting a potential compensatory inflammatory response mechanism unique to females. Further investigation into this gender difference is warranted, as it could inform tailored therapies based on gender and age demographics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy protocols for NHANES (National Health and Nutrition Examination Survey) were approved by the NCHS ethics review board (Protocol #2011\u0026ndash;17, https://www.cdc.gov/nchs/nhanes/irba98.htm). All the participants signed the informed consent before participating in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eThe present research was supported by Exploring the clinical efficacy and related mechanisms of Sancai Lianmei Granule for early remission of type 2 diabetes Based on the combination of traditional Chinese medicine and high-throughput sequencing technology (2023zd020).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD.L. and L.S. formulated the scientific hypothesis, designed the research approach, sought collaborators, and drafted the main body of the manuscript; J.Y. and Y.Y. enrolled participants according to the criteria and completed the processing of baseline data; Q.Z. and J.P. jointly conducted the inter-group comparisons of various variables; Q.C. conducted the initial review of the manuscript and provided revisions on the interpretation of results and the logical flow of the text; all authors agreed to the submission of the final version.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used in this study are available on the National Health and Nutrition Examination Survey website：https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. \u003cem\u003eGlobal report on hypertension: the race against a silent killer\u003c/em\u003ep. 1\u0026ndash;276 (World Health Organization, 2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharchar, F. 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PMID: 34829598; PMCID: PMC8615031.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sleep disturbance, Inflammation, Systemic Inflammation Index, High blood pressure, Mediating effect","lastPublishedDoi":"10.21203/rs.3.rs-5016061/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5016061/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSleep disturbance leads to an active inflammatory response in the body, and the development of hypertension is also associated with inflammation; is there a definite association between the three?\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe examined the pairwise relationships between SII (Systemic Immune-Inflammation Index), sleep disorders, and hypertension in an ethnically diverse sample (n\u0026thinsp;=\u0026thinsp;22573) from the National Health and Nutrition Examination Survey (NHANES).On successfully verifying its two-by-two pair correlation, Exploring the direct intensity of sleep disorders leading to hypertension, Strength of SII as a Mediating Effect of Sleep Disorders, and Hypertension.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe study comprised 48.69% males and 51.31% females, with an average age of 48.01(18.51) years and an average BMI of 28.88 kg/m\u0026sup2;.Hypertension prevalence was 33.75% (n\u0026thinsp;=\u0026thinsp;3,710) among males and 35.09% (n\u0026thinsp;=\u0026thinsp;4,064) among females. Sleep disturbance affected 22.06% (n\u0026thinsp;=\u0026thinsp;2,425) of males and 29.17% (n\u0026thinsp;=\u0026thinsp;3,378) of females. Participants were categorized by hypertension and sleep disturbance status. Most did not use sleep medications, but higher usage was seen in those with both conditions. Smoking and alcohol consumption rates were notably higher among individuals with hypertension and sleep disturbance. Educational attainment was slightly lower among those with hypertension. Mexican Americans showed the lowest comorbidity of these conditions compared to non-Hispanic whites and Other Hispanics. In the correlation analysis, sleep disturbance was associated with an 81% increased risk of hypertension (OR: 1.81, 95% CI: 1.69\u0026ndash;1.95, P\u0026thinsp;=\u0026thinsp;0.001). Sleep disturbance was positively correlated with an increase in the Systemic Inflammation Index (SII) (β: 16.34, 95% CI: 4.62\u0026ndash;28.06, p\u0026thinsp;=\u0026thinsp;0.006). SII was associated with hypertension (OR: 1.0002, 95% CI: 1.0001\u0026ndash;1.0003, P\u0026thinsp;=\u0026thinsp;0.001). SII mediated 0.23% (95% CI: 0.13%-0.37%, P\u0026thinsp;=\u0026thinsp;0.001) of the effect between sleep disturbance and hypertension.\u003c/p\u003e","manuscriptTitle":"Associations between sleep disturbance, inflammatory markers, and high blood pressure: National Health and Nutrition Examination Survey (NHANES) 2005–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 09:53:13","doi":"10.21203/rs.3.rs-5016061/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"082f5fe0-1ad7-45f4-a06f-65711aac6095","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":38005050,"name":"Health sciences/Diseases/Cardiovascular diseases/Hypertension"},{"id":38005051,"name":"Health sciences/Risk factors"},{"id":38005052,"name":"Health sciences/Pathogenesis/Inflammation"}],"tags":[],"updatedAt":"2025-11-26T09:39:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 09:53:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5016061","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5016061","identity":"rs-5016061","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}