Sleep disturbance and hypertension mediated by systemic inflammatory response index in NHANES

Abstract

Both hypertension and sleep disturbance have been closely linked to inflammation. However, the specific role of inflammation in the relationship between sleep disturbance and hypertension remains unclear. We investigated the pairwise associations among a novel inflammatory marker (systemic immune inflammation index ), sleep disturbance, and hypertension in a powerful, racially diverse sample (n = 23,414) from the 2005–2020 National Health and Nutrition Examination Survey (NHANES). We found that participants with high blood pressure and/or sleep disorders had higher levels of Systemic Immune-Inflammatory Index (SII) than those without hypertension or sleep disorders.SII slightly but significantly mediated the relationship between sleep disturbance and hypertension in males but not in females, after adjusting for all covariates (proportion mediate [PM], 1.36%; total effect [TE], 0.0931; direct effect [ADE], 0.0917; verage causal mediation effects [ACME] = 0.0013 [95% CI = 0.0004–0.0024], p = 0.002, in males. Our research showed that SII, sleep disturbance, and hypertension are related. Increased SII levels slightly mediated the association between sleep disturbance and hypertension.

Introduction

Sleep health is becoming a growing concern in the field of public health. With an increasing pace of life and social pressure, an increasing number of people are afflicted with sleep disorders. Many people suffer from sleep problems such as sleep deprivation, poor sleep quality, and sleep disorders, which seriously affect their health¹.Over the years, several studies have shown that poor sleep patterns are associated with an increased risk of hypertension^{2, 3–4}.Studies have consistently found that promoting adequate sleep in individuals with hypertension, as part of the other recommended hypertension management guidelines, may positively impact their BP control⁵.

Hypertension, one of the most common risk factors for cardiovascular disease, is currently defined as a systolic blood pressure130mmHg and/or diastolic blood pressure80mmHg, whether for untreated patients or current intake of BP-lowering medications⁶. Hypertension, affecting over 1.2 billion people worldwide, has become a critical and costly public health issue⁷.It is a major risk factor for heart disease and stroke, and remains the leading predictor of mortality, disability-adjusted life years, and life loss years globally^5,8.Therefore, it is crucial to study the risk factors and effective predictors of hypertension in order to alleviate the burden on public health.

Sleep disorders are associated with most diseases and are thought to disrupt physiological processes that regulate the immune system, resulting in abnormally increased inflammatory responses that drive disease progression^2,9.Previous studies have shown that sleep-related disorders have a stronger correlation with SII than PLR and NLR¹⁰.Notably, the important role of immune inflammatory reactions in the development of hypertension has been confirmed in a large number of studies^{11, 12, 13, 14–15}. Particularly,

SII may be a superior systemic inflammation warning marker for hypertension¹⁶.

However, the role of the SII in sleep disorders and hypertension comorbidities remains poorly understood. This study aimed to investigate the effects of SII on sleep disorders and hypertension in a large multi-ethnic adult cohort. The SII combines the combined manifestations of three types of inflammatory cells: platelets, neutrophils, and lymphocytes. The calculation formula is platelets × neutrophils ÷ lymphocytes, which can reflect the systemic immune response and inflammatory level while having inexpensive, stable and effective characteristics¹⁷.Previous studies have shown that sex hormones affect inflammation, hypertension, and sleep^{18, 19–20}.Given that sex differences exist in the relationship between inflammation, hypertension, and sleep disturbance, we investigated these associations and described the mediating role of the SII in the relationship between sleep disturbance and hypertension in both males and females.

The purpose of this study was to examine whether (1) there is a correlation between sleep disorders, SII, and hypertension, and.

(2) whether SII mediates the relationship between sleep disturbance and hypertension. We assumed that SII, sleep disturbance, and hypertension are pairwise correlated and hypothesized that SII could partially mediate the effects of sleep disturbance on hypertension.

Results

Participant characteristics

In total, 23,414 participants were enrolled in this study (Fig. 1). Table 1 presents the participants’ characteristics. We divided participants (n = 23,414) into four groups: “hypertension with sleep disturbance” group, “hypertension without sleep disturbance” group, “sleep disturbance without hypertension” group, and “without hypertension or sleep disturbance.” The gender distribution was relatively equal.Of the included participants, 22.95%(n = 5,374) were non-Hispanic white, followed by non-Hispanic black (n = 2,978, 12.72%), Mexican Americans (n = 1,869, 7.98%), other races (n = 11,726, 50.08%), and Hispanics (n = 1,467, 6.27%). Data shows that the median logSII level was 6.134 × 109/L. Furthermore, the data showed that 25.49% of participants had sleep disturbance and 35.92% had hypertension. The baseline characteristics of included individuals are listed in Table 1.

Fig. 1 [Images not available. See PDF.]

Flowchart of the systematic selection process.

Table 1. Characteristics of the study participants (n = 23,414). Abbreviations: BMI, body mass index.

Characteristic	Total participants	Hypertension with sleep disturbance	Hypertension without sleep disturbance	Sleep disturbance without hypertension	Without hypertension or sleep disturbance	P-value
	N = 23,414	N = 2,958	N = 5,452	N = 3,010	N = 11,994
SII, median (IQR)	6.134(0.424–10.254)	6.187(2.621–8.748)	6.163(0.424–10.254)	6.157(3.138–8.588)	6.104(2.184–8.55)	< 0.001
Gender, n (%)
Male	11,520 (49.20)	1,235 (41.75)	2,800 (51.36)	1,233 (40.96)	6,252 (52.13)	< 0.001
Female	11,894 (50.80)	1,723 (58.25)	2,652 (48.64)	1,777 (59.04)	5,742 (47.87)
Education, n (%)						< 0.001
Less than high scool	5,797 (24.76)	781 (26.40)	1,576 (28.91)	588 (19.53)	2,852 (23.78)
High school graduate or GED	5,334 (22.78)	747 (25.25)	1,315 (24.12)	646 (21.46)	2,626 (21.89)
Some college or above	12,283 (52.46)	1,430 (48.34)	2,561 (46.97)	1,776 (59.00)	6,516 (54.33)
Marital status, n (%)						< 0.001
Never married	4,315 (18.43)	327 (11.05)	525 (9.63)	617 (20.50)	2,846 (23.73)
Married/Living with partner	13,930 (59.49)	1,576 (53.28)	3,328 (61.04)	1,714 (56.94)	7,312 (60.96)
Widowed/Divorced/Separated	5,169 (22.08)	1,055 (35.67)	1,599 (29.33)	679 (22.56)	1,836 (15.31)
Race/ethnicity, n (%)						< 0.001
Non-Hispanic White	5,374 (22.95)	839 (28.36)	1,204 (22.08)	916 (30.43)	2,415 (20.14)
Non-Hispanic Black	2,978 (12.72)	461 (15.58)	908 (16.65)	291 (9.67)	1,318 (10.99)
Mexican American	1,869 (7.98)	179 (6.05)	386 (7.08)	194 (6.45)	1,110 (9.25)
Other Hispanic	1,467 (6.27)	169 (5.71)	334 (6.13)	187 (6.21)	777 (6.48)
Other race/multiple races	11,726 (50.08)	1,310 (44.29)	2,620 (48.06)	1,422 (47.24)	6,374 (53.14)
BMI, kg/m2, mean (SD)						< 0.001
18.5 < = BMI < 24	4,916 (21.00)	336 (11.36)	702 (12.88)	738 (24.52)	3,140 (26.18)
BMI < 18.5	351 (1.50)	15 (0.51)	48 (0.88)	57 (1.89)	231 (1.93)
BMI > = 24	18,147 (77.50)	2,607 (88.13)	4,702 (86.24)	2,215 (73.59)	8,623 (71.89)
Comorbid condition, n (%)						< 0.001
No	12,849 (54.88)	626 (21.16)	1,865 (34.21)	1,529 (50.80)	8,829 (73.61)
Yes	10,565 (45.12)	2,332 (78.84)	3,587 (65.79)	1,481 (49.20)	3,165 (26.39)
Alcohol Drinking, n (%)						< 0.001
No	6,647 (28.39)	882 (29.82)	1,815 (33.29)	677 (22.49)	3,273 (27.29)
Yes	16,767 (71.61)	2,076 (70.18)	3,637 (66.71)	2,333 (77.51)	8,721 (72.71)
Smoking history, n (%)						< 0.001
No	12,944 (55.28)	1,354 (45.77)	2,864 (52.53)	1,432 (47.57)	7,294 (60.81)
Yes	10,470 (44.72)	1,604 (54.23)	2,588 (47.47)	1,578 (52.43)	4,700 (39.19)

Association between trouble sleeping and hypertension

Compared to those who did not have trouble sleeping, those who had trouble sleeping were positively associated with the risk of hypertension [crude odds ratio (OR) with 95% confidence interval (CI) of 2.1490(2.0238–2.2821)]. After adjusting for gender, BMI, race, marital status, education, alcohol drinking, smoking history, comorbid conditions, and BMI, those who had trouble sleeping had a higher risk of hypertension [OR = 1.5616(95% CI: (1.4611–1.6689)] than those without trouble sleeping. Sex moderated the relationship between sleep disturbance and hypertension, and the positive correlation between sleep disturbance and hypertension was stronger in males than in females. The positive correlation between trouble sleeping and hypertension is shown in (Table 2).

Table 2. Association between trouble sleeping and hypertension. Multivariate logistic regression analysis of trouble sleeping and hypertension (OR, odds ratio; CI, confidence interval; crude model, unadjusted model; adjusted model, adjustment for sex, BMI, race, marital status, educational level, alcohol consumption, smoking history, and comorbid conditions). Sex was adjusted in the analyses of the total population, but not in subgroups of male or female.

	Number of participants	OR (95% CI)	P-Value
Crude Model
Total	23,414	2.1490 (2.0238–2.2821)	< 0.001
Male	11,520	2.2005 (2.0094-2.4100)	< 0.001
Female	11,894	2.0988 (1.9361–2.2752)	< 0.001
Adjusted Model
Total	23,414	1.5593 (1.4572–1.6686)	< 0.001
Male	11,520	1.6064 (1.4520–1.7772)	< 0.001
Female	11,894	1.5225 (1.3891–1.6687)	< 0.001

Association between SII and trouble sleeping

Compared to those who did not have trouble sleeping, those who had trouble sleeping were positively associated with the inflammatory marker in males (SII: =0.0811, 95% CI = 0.0566–0.1055, p < 0.001) but not in females (SII: =0.0182, 95% CI=−0.0028–0.0391, p = 0.0901). After adjusting for potential confounders, sleep disturbance was associated with the novel inflammatory marker in males (SII: =0.0380, 95% CI = 0.0133–0.0627, p = 0.0025) but not in females (SII: =0.0083, 95% CI=−0.0132- 0.0298, p = 0.4498). Therefore, regardless of whether confounding factors are regulated, male sleep disorders are positively correlated with inflammatory markers shown in (Table 3).

Table 3. Association between SII and trouble sleeping. Multi-factor linear regression of SII and trouble sleeping. Adjusted analyses adjusted for gender, race/ethnicity, education level, marital status, BMI, smoking status, alcohol status and comorbid conditions. Sex was adjusted in the analyses of the total population, but not in subgroups of male or female.

	Number of participants	(95% CI)	P-Value
Crude Model
Total	23,414	0.0533 (0.0373–0.0692)	< 0.001
Male	11,520	0.0811(0.0566–0.1055)	< 0.001
Female	11,894	0.0182 (−0.0028–0.0391)	0.0901
Adjusted Model
Total	23,414	0.0198 (0.0035–0.0360)	0.0171
Male	11,520	0.0380 (0.0133–0.0627)	0.0025
Female	11,894	0.0083 (−0.0132–0.0298)	0.4498

Association between SII and hypertension

The results in Table 4 indicate that hypertension was associated with SII in males (SII: OR = 1.2699, 95% CI = 1.1827–1.3640, p < 0.001 in the crude model); SII: OR = 1.1992, 95% CI = 1.1090–1.2970, p < 0.001 in the adjusted model) but not in females (SII: OR = 1.0162, 95% CI = 0.9465–1.0909, p = 0.6582 in the crude model); SII: OR = 1.0575, 95% CI = 0.9771–1.1446, p < = 0.1662 in. the adjusted model).

Table 4. Association between SII and hypertension. Multivariate logistic regression of SII and hypertension (OR, odds ratio; CI, confidence interval; crude model, unadjusted model; adjusted model, adjustment for sex, BMI, race, marital status, educational level, alcohol consumption, smoking history, and comorbid conditions). Sex was adjusted in the analyses of the total population, but not in subgroups of male or female.

	Number of participants	OR(95% CI)	P-Value
Crude Model
Total	23,414	1.1371 (1.0816–1.1955)	< 0.001
Male	11,520	1.2699 (1.1827–1.3640)	< 0.001
Female	11,894	1.0162 (0.9465–1.0909)	0.6582
Adjusted Model
Total	23,414	1.1178 (1.0577–1.1814)	< 0.001
Male	11,988	1.1992 (1.1090–1.2970)	< 0.001
Female	12,380	1.0575 (0.9771–1.1446)	0.1662

Mediating effect of SII on sleep troubles and hypertension

As shown in Tables 5 and 6, the SII significantly mediated the association between sleep disturbance and hypertension (proportion mediated [PM], 0.79%; total effect [TE], 0.1830; direct effect [ADE], 0.1813; average causal mediation effects [ACME] = 0.0015 [95% CI = 0.0008–0.0023], p < 0.001.When the participants were divided into subgroups based on sex, SII slightly but significantly mediated the relationship between sleep disturbance and hypertension in males but not in females, after adjusting for all covariates ([PM], 1.36%; [TE], 0.0931; [ADE], 0.0917; [ACME] = 0.0013 [95% CI = 0.0004–0.0024], p = 0.002, in males; [PM], 0.06%; [TE], 0.0796; [ADE], 0.0796; [ACME] = 0.0001 [95% CI=−0.0002-0.0004], p = 0.582 in females). These.

Table 5. Inflammatory marker mediating the association between sleep disturbance and hypertension. PM indicates proportion mediated effects, for example, proportion of the total effect due to the mediator, TE, total effect (TotalEffect): statistically significant, indicating that the independent variable has a significant total effect on the dependent variable. Crude model, unadjusted model; adjusted model, adjustment for sex, BMI, race, marital status, educational level, alcohol consumption, smoking history, and comorbid conditions). Sex was adjusted in the analyses of the total population, but not in subgroups of male or female.

SII	PM				TE
	Estimate	95% CI lower	95% CI upper	P-value	Estimate	95% CI lower	95% CI upper	P-value
Unadjusted Analyses
Total	0.0079	0.0045	0.0127	< 0.0001	0.1830	0.1684	0.1971	< 0.0001
Male	0.0213	0.0131	0.0330	< 0.0001	0.1909	0.1686	0.2123	< 0.0001
Female	0.0002	−0.0013	0.0027	0.71	0.1762	0.1567	0.1951	< 0.0001
Adjusted Analyses
Total	0.0045	0.0006	0.0106	0.022	0.0858	0.0721	0.0994	< 0.0001
Male	0.0136	0.0043	0.0274	0.002	0.0931	0.0715	0.1152	< 0.0001
Female	0.0006	−0.0023	0.0055	0.582	0.0796	0.0610	0.0987	< 0.0001

Table 6. Inflammatory marker mediating the association between sleep disturbance and hypertension. ACME indicates average causal mediation effects (e.g., indirect effects), ADE, and average direct effects. Analyses were adjusted for age, race/ethnicity, education level, marital status, BMI, smoking status, alcohol status, and comorbid conditions. Sex was adjusted in the analyses of the total population, but not in subgroups of male or female.

SII	ACME				ADE
	Estimate	95% CI lower	95% CI upper	P-value	Estimate	95% CI lower	95% CI upper	P-value
Unadjusted Analyses
Total	0.0015	0.0008	0.0023	< 0.0001	0.1813	0.1667	0.1952	< 0.0001
Male	0.0041	0.0025	0.0062	< 0.0001	0.1860	0.1637	0.2071	< 0.0001
Female	0.0001	−0.0002	0.0005	0.71	0.1761	0.1566	0.1949	< 0.0001
Adjusted Analyses
Total	0.0004	0.0000	0.0009	0.022	0.0854	0.0716	0.0989	< 0.0001
Male	0.0013	0.0004	0.0024	0.002	0.0917	0.0715	0.1152	< 0.0001
Female	0.0001	−0.0002	0.0004	0.582	0.0796	0.0610	0.09	< 0.0001

results indicate that SII significantly mediates the association between sleep disturbance and hypertension in males. In males, there were significant but very small indirect effects, while the direct and total effects were statistically significant. In short, when the participants were divided into subgroups based on sex, the SII slightly but significantly mediated the relationship between sleep disturbance and hypertension in males but not in females.

Discussion

In the present study, the objective was to investigate the association between sleep disturbance and hypertension in adults in the United States and to determine whether this association is partly mediated by the SII.

Our present study observed a significant positive association between trouble sleeping and hypertension in the whole study population. The association between trouble sleeping and hypertension has been proposed and explored previously. A large population-based study observed significant associations between hypertension and poor sleep quality. Rahim et al. found an association between a history of shift work (poor sleep quality) and rates of hypertension in 7,420 Ontario workers aged 35–69years in a 12-year longitudinal cohort study²¹.Moreover, Guo et al. found that long sleep duration was positively associated with hypertension in a systematic review and meta-analysis²². These are attributed to changes in the quality or quantity of sleep that may lead to the loss of blood pressure drop patterns and an increase in sympathetic activity at night, followed by a continuous increase in sympathetic tension, leading to hypertension²³.

The results showed that sleep disturbance was associated with higher SII level. Previous studies have also indicated that sleep-related disorders are more strongly associated with SII than with PLR and NLR²⁴. The following mechanisms may explain the relationship between sleep disturbance and inflammation. First, sleep disturbance results in repeated or persistent activation of the hypothalamic–pituitary–adrenal axis and induces glucocorticoid resistance in immune cells^25,26. Second, Insomnia disorder increases sympathetic outflow and circulating norepinephrine and epinephrine levels, which are also associated with increased levels of SII²⁷. Taken together, the results of these studies show that disturbed sleep may cause damage to host health by overactivating the inflammatory response. Additionally, our results showed that the association between sleep disturbance and the SII was stronger in females. Consistent with our research, previous studies have suggested that the SII and other inflammation-related indices are inversely correlated with male testosterone and estradiol levels. In contrast, an elevated inflammation-related index has been associated with a reduction in serum testosterone and an increase in estradiol concentrations in females²⁰.As testosterone mainly exhibits anti-inflammatory properties, estrogen exhibits both anti- and pro-inflammatory properties²⁸.

Indeed, we found positive associations between the SII and the prevalence of hypertension in this cross-sectional study. In other words, hypertension is caused by many factors, such as genetic susceptibility, sympathetic nervous system, hormonal changes, vascular abnormalities and so on²⁹. The mechanism may be an increase in the number of platelets, activation of platelets, thrombosis, increased leukocyte recruitment, and endothelial cell injury, which can increase systemic vascular resistance, leading to the development of hypertension^30,31.In addition, extracellular trapping of neutrophils can induce a variety of thrombotic effects³².It is worth noting that autoimmune-inflammatory infiltration increases oxidative stress in the renal and arterial walls, leading to elevated blood pressure through a variety of common mechanisms³³. Clearly, because the SII integrates information on neutrophil, platelet, and lymphocyte counts, we believe that there is an independent correlation between the SII and the prevalence of hypertension.

Our findings suggest that SII modestly mediate the association between sleep disturbance and hypertension among adults. Inflammation is hypothesized to play an important role in modulating the comorbidity of sleep disturbance and hypertension; patients with hypertension and sleep disturbance have elevated levels of inflammatory markers³⁴. Consistent with our study, a small mediating effect of SII was found in the relationship between sleep disturbance and hypertension. In addition, our study found that the mediating effect was significant in males but not in females, after adjusting for covariates. The difference in results may be due to the different populations included, and hormone levels and ruminative thinking in older women may contribute to their susceptibility to inflammation-related sleep disturbance and hypertension.

The advantages of this study are as follows. Firstly, we used eight cycles, high-quality, representative data from the NHANES on Americans, which was large and multistage. Second, we used the SII level as an indicator of inflammation, which is economical and easy for clinical application. Third, we included a large number of covariates to control for confounding factors, and the definition of the outcome of sleep-related disorders was consistent with previous studies.

However, our study had some limitations. First, the cross-sectional study design did not provide the ability to make causal inferences, and prospective studies are needed to further explore these results. Moreover, our analysis was based on the NHANES dataset collected from the American population, which may have caused regional bias. Finally, the sleep-related disorders used in this study were based on self-reported data from participants rather than objective measures like polysomnography or actigraphy. Further research is needed to determine if objective measures of sleep are also linked to SII.

Conclusions

The results of this study showed that the SII, sleep disturbance, and hypertension were correlated pairwise. The SII marginally mediated the association between sleep disturbance and hypertension. Our findings highlight the importance of sex in the study of inflammation, sleep, and depression, and may help explain the mechanism behind the comorbidity of sleep disturbance and hypertension. Further longitudinal studies are required to investigate whether patients with sleep disturbance are susceptible to inflammation-driven hypertension, as this may ultimately guide individual treatment strategies for hypertension.

.

Methods

Study population

We analyzed the data from the NHANES, a representative cross-sectional survey of all non-institutionalized civilian populations in the United States. All data and guidance on analytical approaches are publicly and freely available from the US Centers for Dis-.

ease Control and Prevention’s National Center for Health Statis- tics and can be accessed at https://www.cdc.gov/nchs/nhanes/index.htm.

The National Center for Health Statistics Research Ethics Review Board authorized the NHANES study protocols in compliance with the revised Declaration of Helsinki. The NHANES data were released over 2-year cycles. To obtain a large sample for analysis, we combined eight cycles of continuous NHANES data from 2005 to 2020. The National Center for Health Statistics Ethics Review Committee granted ethics approval. All participants provided written informed consent before participating in the study. More information about the NHANES could be obtained at:http://www.cdc.gov/nhanes.

Of the 85,750 participants extracted from the NHANES database, we excluded those with missing information on the hyper- tension questionnaire (n = 30,855), SII data (n = 5,361), trouble sleeping (n = 28), aged 20 years (44,285), and other covariates (n = 20,871). Finally, a total of 23,414 participants were included in this study. The flow chart of the systematic selection process is shown in Fig. 1.

Definition and assessment of hypertension and trouble sleeping in NHANES

The protocols used for blood pressure measurements followed the procedures established by the American Heart Association. After measuring blood pressure thrice under quiescent conditions, the average values of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were calculated. The 2017 American Heart Association/American College of Cardiology (AHA/ACC) guideline recommended that individuals with an SBP 130 mmHg and/or DBP 80 mmHg should be defined as having hypertension³⁵.Meanwhile, participants who answered “yes” to the question: “Ever told you had high blood pressure?” are also classified as having hypertension.

Trouble sleeping was measured according to the following question “Have you ever told a doctor or other health professional that you have trouble sleeping?” (yes/no). If the participant’s answer to the question is yes, he or she is considered to have trouble sleeping.

Laboratory measures

Lymphocyte, neutrophil, and platelet counts were obtained by performing a complete blood count on blood specimens with a Beckman Coulter automated blood analyzer in an MEC, and were expressed as×103cells/µL. According to previous studies^{36, 37, 38–39}, the SII was defined as follows: SII = Platelet count × neutrophil count/lymphocyte count.

Covariates

Gender, age and race were self-reported demographic information. Body mass index (BMI) was calculated by dividing the weight of the participant by the square of height (kg/m2). Participants were divided into four categories based on marital status (married, partner/widowed/divorced, or separated/never married). Education level was assessed by the question “What is the highest grade or level of school you have completed or the highest degree you have received?” [Less than high school/high school graduate or General Educational Development (GED)/some college or above]. Smoking history and alcohol drinking were assessed by the questions “Have you smoked at least 100 cigarettes in your entire life?” and “In any one year, have you had at least 12 drinks of any type of alcoholic beverage?” (yes/no). Doctors diagnosed diseases, including diabetes and stroke, by asking participants “Have you ever been told by a doctor or health professional that you have-?”. Participants were defined as having comorbid conditions if they reported at least one of the following medical conditions: diabetes, kidney failure, kidney stones, heart failure, stroke, hepatopathy, rheumat-oid arthritis, or cancer.

Statistical analysis

DecisionLinnc1.0 software was employed for data analysis(https://www.statsape.com/). DecisionLinnc1.0 is a platform that integrates multiple programming language environments and enables data processing, data analysis, and machine learning through a visual interface. P < 0.05 was considered as statistically significant. In descriptive statistics, continuous variables are expressed as means and standard deviations or medians and interquartile ranges, and categorical variables as proportions and percentages of the total. The 2test was used to compare the classified variables among the 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 distribution variables between groups.

Multivariate logistic regression was used to analyze the correlation between sleep disorder and hypertension. The correlation between SII and hypertension was analyzed in the same way. Multivariate linear regression analysis was used to evaluate.

the correlation between sleep disturbance and the SII. In all analyses, the levels of SII were converted to natural logarithms. To explore the regulatory role of gender, we calculated the correlation intensity between hypertension, sleep disorders and SII in male and female subgroups. Finally, an intermediary analysis was conducted to examine whether the SII mediates the relationship between sleep disorders and hypertension. The magnitude of the indirect pathway effect, the proportion of the intermediary effect, and the p-value of the intermediary effect are all shown in the results.

Acknowledgements

We would like to express our sincere gratitude to all the members of the research team and all the participants who took part inthis study.

Author contributions

Z X C and F Z were responsible for study design, statistical software operation, data analysis, and manuscript writing. Z X C, F Z, R H, F Y and H X were involved in project management and data collection. GX and CH were involved in data collection and verification. T L and Z P reviewed and revised the manuscript. ZC supervised the research project and guided the methodological and conceptual components.

Funding

This work was supported by the National Natural Science Foundation of China(No.81960085), the National Natural Science Foundation of China(No.32460222), Guizhou Medical University Doctoral Research Initiation Fund (gyfybsky-2022-44),the Science and Technology Fund of Guizhou Provincial Health Department (No.qiankehejichu-ZK[2023]yiban372).

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Consent for publication

Not applicable.

Ethical approval

Ethics approval for the survey protocol was granted by the Ethics Review Board of the National Center for Health Statistics. Written informed consent was secured from each participant before their involvement in the study.

Abbreviations

CDC

Centers for Disease Control and Prevention

SII

Systemic immune inflammation index

NHANES

National Health and Nutrition Examination Survey

BMI

Body mass index

OR

Odds ratio

CI

Confidence interva

PM

Proportion mediated effects

TE

Total effect (TotalEffect)

ACME

Average causal mediation effects

ADE

Average direct effects

PLR

Platelet-to-lymphocyte ratio

NLR

Neutrophil-to-lymphocyte ratio

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