
==== Front
BMC Public Health
BMC Public Health
BMC Public Health
1471-2458
BioMed Central London

39289652
20054
10.1186/s12889-024-20054-2
Research
Association between sleep regularity and arterial stiffness among middle-age adults in Southwestern China
Shang Yuan-hao 1
Liang Da-qiang 1
Song Xiao-ling 1
Feng Xia 2
Mao Guang-yan 1
Yang Ting-ting 1
Wang Zi-yun wangzy2015@gmc.edu.cn

1
Wang Jun-hua gywangjunhua@qq.com

1
1 grid.413458.f 0000 0000 9330 9891 School of Public Health, the key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, No. 6 Ankang Road Guian New District, Guiyang, 561113 China
2 https://ror.org/042g3qa69 grid.440299.2 Department of Sleep Medicine, the Second People’s Hospital of Guizhou Province, Guiyang, China
17 9 2024
17 9 2024
2024
24 253010 7 2024
11 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Sleep regularity has been linked to a risk of arterial stiffness (AS). However, the association between sleep regularity indicators, which reflect 24-hour sleep variability, and AS has not yet been examined.

Methods

We analyzed data from 516 adults, aged 40–65 years (the median age of 51 years), from the ‘Follow-up Study of Sleep Characteristics and Chronic Diseases in the Middle-aged and Elderly Population in Guizhou Province’. Participants underwent assessments of AS (OMRON HBP-8000, baPWV ≥ 1400 cm/s) and sleep (wrist smart band (Honor band 5i) for ≥ 7 days). Logistic regression was utilized to evaluate the odds ratio (OR) and 95% confidence interval (CI) of the association between sleep regularity and AS.

Results

A total of 516 people were included in this study, of which 279 (54.07%) were in the AS group. The univariate results showed that the AS group (Median 71.18) had lower SRI compared to the No-AS group (Median 75.00) (p < 0.001). The multifactorial results showed participants with higher SRI scores were more likely to have a lower risk of AS compared to those with lower SRI scores (ORQ4 VS. Q1=0.46, 95%CI: 0.25–0.85, p = 0.013). The SRI effect was more pronounced in male (ORQ4 VS. Q1=0.28, 95%CI: 0.12–0.69, p = 0.005), snoring populations (ORQ4 VS. Q1=0.13, 95%CI: 0.04–0.48, p = 0.002), and non-retired populations (ORQ4 VS. Q1=0.45, 95%CI: 0.22–0.92, p = 0.028).

Conclusions

The present findings indicated that the effect between SRI and AS may be more sensitive than the standard deviation of sleep duration as well as the standard deviation of sleep onset.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-024-20054-2.

Keywords

Sleep regularity
Arterial stiffness
Cardiovascular disease
Circadian rhythms
http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 81960612 81960612 81960612 81960612 81960612 81960612 81960612 http://dx.doi.org/10.13039/501100018555 Science and Technology Program of Guizhou Province Qiankehe Basic-ZK[2022] General 382 Qiankehe Basic-ZK[2022] General 382 Qiankehe Basic-ZK[2022] General 382 Qiankehe Basic-ZK[2022] General 382 Qiankehe Basic-ZK[2022] General 382 Qiankehe Basic-ZK[2022] General 382 Qiankehe Basic-ZK[2022] General 382 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Cardiovascular diseases (CVDs) are the leading cause of death and burden of disease globally and one of the leading causes of death among middle-aged and older adults worldwide [1, 2]. Arterial stiffness (AS) represents one of the earliest identifiable signs of both functional and structural modifications within the vascular wall, serving as a fundamental pathological basis for the initiation of cardiovascular disorders. Moreover, it acts as an autonomous risk factor and an independent predictor for diverse cardiovascular conditions [3]. The walls of large conduit arteries thicken and lose elasticity over time, and this process results in an increase in pulse wave velocity (PWV), an important and reliable measure of arterial stiffness [4–6]. A study found that the brachial-ankle pulse wave velocity (baPWV) is significantly elevated in the population after the age of 40 [7]. And a study evaluating the baPWV cut-off values in people at high risk for cardiovascular disease found that in both men and women, the baPWV cut-off values reached 1350 cm/s at age 40 and 1430 cm/s at age 50 [8]. This also implies that individuals aged 40 and above may face a heightened risk of AS development. Therefore, early detection of factors associated with AS and identification of early lesions is crucial.

Previous studies have shown that many chronic diseases [9–12], unhealthy lifestyle behaviors [13–15], and sleep have become well-recognized factors that can lead to the development of AS. And among these influences, sleep, as one of the important factors, is also a key factor that allows for early and effective intervention. Therefore, an objective assessment of sleep can better understand and evaluate the role of sleep in the development of AS.

But sleep is a multidimensional factor in the development of cardiovascular disease. Prior research has indicated that various dimensions of sleep, including duration, quality, and fragmentation, were linked to the progression of AS [16–19]. In recent years, studies have identified an association between increased variability in sleep-wake patterns (i.e., irregular sleep) and the progression of AS [20, 21]. Irregular sleep patterns with large variability in sleep duration are important causes of circadian rhythm disturbances, which are associated with cardiometabolic risk factors. Previous studies have used standard deviation (SD) of sleep duration and sleep onset time to assessed sleep regularity and have demonstrated an association between sleep regularity and AS [21]. However, these metrics can only compare each day to an average day and cannot be applied to people who have multiple sleep periods within a 24-hour period [22].

In our study, we used the Sleep Regularity Index (SRI) [23, 24] to evaluate sleep regularity. It defines the percentage probability that an individual will be asleep or awake at any two points in time 24 h apart. The Sleep Regularity Index (SRI), used as a metric to assess sleep regularity, is designed to capture changes in sleep patterns over 24 h [23]. Unlike the standard deviation in prior studies, the SRI provides a more comprehensive measure of variability in sleep patterns across consecutive days, encompassing both nighttime wakefulness and daytime naps [22]. And no studies on the association between SRI and AS have been retrieved. Therefore, this study aimed to investigate further the association between SRI and other indicators of sleep regularity and AS, thereby supporting the refinement of individual prevention and treatment strategies for AS.

Methods

Participants

This study utilized data from the baseline survey of the ‘Follow-up Study of Sleep Characteristics and Chronic Diseases in the Middle-aged and Elderly Population in Guizhou Province’ [25, 26], conducted at the biggest Physical Examination Center in Fuquan City among eligible participants.

Inclusion and exclusion criteria

Inclusion criteria: (1) Participants aged 40 to 65 years old; (2) Regular medical checkups at Fuquan City’s First People’s Hospital; (3) Agreed to participate in the study and signed the informed consent form. Exclusion Criteria: (1) Presence of severe cardiovascular and cerebrovascular diseases, such as coronary heart disease (myocardial infarction/angina), atrial fibrillation, chronic heart failure, and stroke (cerebral infarction); (2) History of major surgeries, including brain tumor removal, coronary artery bypass grafting, and organ transplantation; (3) Clear occupational disease history, exposure, or employment in occupations involving hazardous factors such as dust, noise, or coke ovens within the past five years; (4) Systolic blood pressure ≥ 180 mmHg or diastolic blood pressure ≥ 110 mmHg; (5) Lipid testing was not performed.

From March 2022 to November 2022, 1,104 baseline questionnaires were completed, and 1,050 smart bands were distributed. Only participants with complete physical examination data and seven consecutive days of sleep monitoring data were included in this analysis. Ultimately, we included 516 survey respondents with comprehensive data in this study for analysis (Fig. 1).

Fig. 1 Sample Screening Flowchart

Methods

Methods of data collection

The survey included four parts: questionnaires, objective sleep assessment, physical examination, and laboratory tests. Questionnaires (Supplement 1) were administered via face-to-face interviews by enumerators who had undergone uniform training. These questionnaires collected general information such as age, sex, education level, income, physical activity, and medical history. Participants were instructed by the investigator to wear the smart band (Honor band 5i) on the non-dominant wrist for at least 7 consecutive days and download the mobile client (Huawei Sports Health) to connect the band, enabling the recording of daily activities and real-time uploading of sleep monitoring data to the application. Trained professionals conducted health checkups to gather participants’ information, including height, weight, waist circumference, blood pressure, blood sugar, blood lipids, and arteriosclerosis assessment.

Variable definitions and criteria

Sleep regularity Assessment

Participants were instructed to wear a smart band (Honor band 5i). The smart band conducts sleep monitoring through a three-axis acceleration sensor, combining heart rate indicators and sleep monitoring algorithms to determine whether the wearer is in a sleep state, and the validity and reliability of these products have also been verified [27]. We assessed whether participants wore the band during nighttime sleep by the heart rate and sleep waveforms recorded by the smart band. During the data compilation process, all data were collected, excluding holidays. Since some of the participants included in this study were teachers, we excluded the data during the winter and summer vacations, taking into account the specificity of their profession. The sleep structure of each participant was collected by taking screenshots for seven consecutive days. We processed image recognition using Python 3.9 and the EasyOCR package within PyCharm 2023.1 to extract parameters such as nighttime sleep duration, sleep onset time, wake-up time, and other pertinent data.

SRI was calculated using the Python open-source package Sri (https://github.com/mengelhard/sri) [28] with Python 3.9 and PyCharm 2023.1. According to the sleep structure diagram from the day of the 00:00 am, the activity data will be 24 h each period (24 h, 1440 cycles per day, a period of 1 min) sleep/wakefulness stage was assigned a value, and the sleep = “0”, wakefulness = “1”. SRI scores range from 0 to 100, with lower scores indicating irregular sleep and 100 indicating perfectly regular sleep [23]. In the present study, SRI was categorized according to quartiles (Q1, Q2, Q3, Q4), where the lowest quartile was the severely irregular sleep group and the highest quartile was the most regular sleep group.

Sleep duration was the average sleep duration per night while wearing the band. We defined the regularity of sleep onset as the standard deviation of sleep onset time per night and the regularity of sleep duration as the standard deviation of sleep duration per night. Larger values denote more significant variability or irregularity. Based on previous studies, the categories of standard deviation of sleep duration were ≤ 60, 61–90, 91–120, and > 120 min, and the categories of standard deviation of sleep onset were ≤ 30, 30–60, 60–90, and ≥ 90 min [21].

Measurement of AS

BaPWV was measured using an arterial stiffness detection device OMRON HBP-8000 (Omron Health Medical Co., Ltd., China) [29, 30]. To ensure precision during inspection, it is imperative to maintain the examination room temperature within the range of 22–25 °C. Subsequently, the subject was instructed to remove heavy clothing, leaving only lightweight attire, and to lie down with pillows removed. Limb cuffs were then affixed to the subject’s upper arms and ankles as necessary for the measurements. Instructions were given for the subject to remain motionless and refrain from speaking during the duration of the measurements. The largest baPWV on the left and right sides were taken for analysis. AS was judged to be AS when baPWV was ≥ 1400 cm/s, otherwise No-AS [31, 32].

Covariates

Relevant covariates were obtained from a face-to-face questionnaire and physical examination. Covariates in this analysis included sex, age, education, income, smoking status, alcohol consumption, exercise, hypertension, obesity, diabetes, snoring, work state, and sleep duration. The following are definitions of the relevant covariates. ①Alcohol consumption, Smoking status: Then participants were asked if they currently smoke cigarettes or drink alcohol and categorized as yes(Current) or no(Former or Never); ②Obesity: body mass index (BMI) was weight divided by the square of height in kg/m2, and BMI ≥ 28.0 kg/m2 was defined as “obesity”;③Hypertension: SBP ≥ 140 mm Hg or DBP ≥ 90 mm Hg or self-reported previous diagnosis of hypertension or taking medication for hypertension [33]; ⑤Exercise: This study was conducted by asking, “In the past six months, how many days per week have you exercised?”. Based on the definition of regular exercise in the Healthy China Initiative (2019–2030), it was categorized as never or occasional exercise (frequency of exercise < 3 days per week) and regular exercise (frequency of exercise ≥ 3 days per week); ⑥Income: obtained by asking for monthly household income and divided into < CNY 10,000 and ≥ CNY 10,000 groups; ⑦Educational level: by asking: “What is your highest level of education?” This was categorized into middle school and below, high school and junior college, college and above; ⑧Diabetes mellitus: FBG ≥ 7.0 mmol/L or self-reported past diagnosis of diabetes mellitus by a physician or on hypoglycemic medications [34]; ⑨Snoring: Participants were queried about their snoring frequency and divided into two groups: frequent snorers and non-frequent snorers. Frequent snorers were defined as those who reported snoring more than three times per week. ⑩Work state: participants were asked ‘What is your current work status’ and categorized as ‘non-retired’ and ‘retired’.

Quality control

We used Epidata 3.1 software for data entry and verification of the questionnaire. For the sleep information monitored by the band, the most recent sleep chart of the participant’s survey date was screened strictly following the screening rules. The extracted data was saved in an Excel spreadsheet for the sleep indicator and verified by two people.

Statistical analysis

For continuous variables, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\stackrel{-}{X}$$\end{document}±S was used for those that obey a normal distribution; [M (P25, P75)] for those that did not. The t-test or Mann-Whitney U test was used to compare groups. Categorical variables were described using rates or component ratios, and comparisons were assessed using the χ2 test.

Binary Logistic regression was performed to examine the association between each sleep regularity indicators (including SRI, the standard deviation of sleep duration, and the standard deviation of sleep onset time) and AS. For model 1, we adjust for basic demographic information, including age, gender, income, and educational levels. For model 2, several common lifestyle factors that may influence AS were further adjusted, including smoking, alcohol consumption, exercise and obesity. For model 3, we further adjusted for sleep duration. Given the absence of a clear cut-off point for classifying previous SRI and recognizing that quartile-based classification may overlook their association with AS and fail to account for potential nonlinear relationships, this study employed restricted cubic spline regression to determine the optimal number of nodes based on AIC. This approach explored the potential nonlinear association between each sleep regularity index and AS, subsequently analyzed them based on inflection points to derive dichotomous categories. Furthermore, subgroup analyses were conducted based on gender, snoring, and work states to assess potential differences across various populations, and we examined potential effect modifications by creating interaction terms for gender, snoring, and work status in relation to sleep regularity.

Additionally, we conducted a series of sensitivity analyses in this study. First, because AS is an early cardiovascular disease lesion and baPWV ≥ 1800 cm/s suggests the presence of vascular failure, which was a high-risk group for cardiovascular morbidity and mortality, we further excluded participants with baPWV ≥ 1800 cm/s from re-analysis, considering that under the premise of primary prevention. Second, studies have shown that hypertension and diabetes mellitus were risk factors for AS, so we excluded the above risk factor populations from this study for analysis separately. All analyses were conducted in R 4.3.3, and two-sided p-values < 0.05 were considered statistically significant.

Result

Basic characteristics of the study population

A total of 516 people were included in this study, of whom 271 (52.52%) were males and 245 (47.48%) were females. Among them, 279 (54.07%) belonged to the AS group, while 237 (45.93%) were in the No-AS group. The median age of the two groups was 48 years and 55 years, respectively.

As shown in Table 1, the AS group exhibited significantly lower SRI scores than the No-AS group of participants (p < 0.001). Moreover, statistically significant differences were observed between the two populations concerning age, gender, smoking, work states, hypertension, exercise, snoring and diabetes mellitus (p < 0.05).

Table 1 General analysis of characteristics

Variables	Total (n = 516)	No-AS (n = 237)	AS (n = 279)	t/H/\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{{\upchi\:}}^{2}$$\end{document}	P	
Age, years	51.00 (47.00, 57.00)	48.00 (45.00, 52.00)	55.00 (49.50, 58.50)	-9.88c	< 0.001	
Sleep duration(h)	6.78 ± 0.83	6.80 ± 0.81	6.76 ± 0.84	0.55a	0.580	
SD of sleep duration(min)	62.59 (46.70, 80.10)	59.67 (45.32, 77.73)	64.78 (48.38, 82.03)	-1.72c	0.086	
SD of sleep onset(min)	58.82 (39.84, 81.44)	58.20 (38.55, 80.15)	59.03 (41.66, 82.22)	-0.74c	0.457	
SRI	72.66 (67.48, 78.58)	75.00 (69.68, 80.39)	71.18 (65.05, 76.62)	-4.89c	< 0.001	
Smoke, n (%)				8.39b	0.004	
 No	373 (72.29)	186 (78.48)	187 (67.03)			
 Yes	143 (27.71)	51 (21.52)	92 (32.97)			
Drink, n (%)				2.92b	0.088	
 No	193 (37.40)	98 (41.35)	95 (34.05)			
 Yes	323 (62.60)	139 (58.65)	184 (65.95)			
Sex, n (%)				30.99b	< 0.001	
 Male	271 (52.52)	93 (39.24)	178 (63.80)			
 Female	245 (47.48)	144 (60.76)	101 (36.20)			
Educational levels, n (%)				0.90b	0.638	
 Middle school and below	62 (12.02)	25 (10.55)	37 (13.26)			
 High school and junior college	70 (13.57)	33 (13.92)	37 (13.26)			
 College and above	384 (74.42)	179 (75.53)	205 (73.48)			
Work state, n (%)				22.23b	< 0.001	
 Non-working or retired	400 (77.52)	206 (86.92)	194 (69.53)			
 Working	116 (22.48)	31 (13.08)	85 (30.47)			
Income, n (%)				1.07b	0.302	
 ≥CNY 10,000	290 (56.20)	139 (58.65)	151 (54.12)			
 < CNY 10,000	226 (43.80)	98 (41.35)	128 (45.88)			
Exercise regularly, n (%)				6.21b	0.013	
 No	220 (42.64)	115 (48.52)	105 (37.63)			
 Yes	296 (57.36)	122 (51.48)	174 (62.37)			
Obesity, n (%)				3.74 b	0.053	
 No	450 (87.21)	214 (90.30)	236 (84.59)			
 Yes	66 (12.79)	23 (9.70)	43 (15.41)			
Frequent Snoring, n (%)				14.07b	< 0.001	
 No	358 (69.38)	184 (77.64)	174 (62.37)			
 Yes	158 (30.62)	53 (22.36)	105 (37.63)			
Hypertension, n (%)				125.36b	< 0.001	
 No	342 (66.28)	217 (91.56)	125 (44.80)			
 Yes	174 (33.72)	20 (8.44)	154 (55.20)			
Diabetes, n (%)				19.51b	< 0.001	
 No	460 (89.15)	231 (97.06)	229 (82.37)			
 Yes	56 (10.85)	7 (2.94)	49 (17.63)			
Notes: a:t test, b: χ2 test, c: Mann-Whitney U test

AS indicates arterial stiffness, SD indicates standard deviation, SRI indicates sleep regularity index

Analysis of the association between sleep regularity and AS

Participants with high SRI scores were likely to have a lower risk of developing AS compared with those with lower SRI scores, after adjusting for basic demographic characteristics, lifestyle characteristics, and obesity (ORQ4 VS. Q1=0.50, 95%CI: 0.27–0.90, p = 0.022). This association persisted after additional adjustment for sleep duration that may be mediators of association (ORQ4 VS. Q1=0.46, 95%CI: 0.25–0.85, p = 0.013). There was no significant association found between sleep duration regularity, sleep onset time regularity and AS (p > 0.050) (Fig. 2-a).

Fig. 2 The association between sleep regularity and AS. a: Forest plot of the association of sleep regularity indicators (SRI, Sleep duration regularity, Sleep onset regularity) with AS in four classifications; Model 1: Adjusted for age, sex, income, and educational levels; Model 2: Adjusted for age, sex, income, educational levels, smoke, drink, exercise, work states and obesity; Model 3: Adjusted for age, sex, income, educational levels, smoke, drink, exercise, work states, obesity and sleep duration. b: The RCS plot of sleep regularity indicators (SRI, Sleep duration regularity, Sleep onset regularity) and AS. c: Forest plot of the association of sleep regularity indicators (SRI, Sleep duration regularity, Sleep onset regularity) with AS in the dichotomized results. (Adjusted for age, sex, income, educational levels, smoking, drinking, exercise, work states, obesity and sleep duration.)

Restricted Cubic Spline (RCS) analysis indicated no evidence of a nonlinear relationship between SRI, the standard deviation of sleep duration, the standard deviation of sleep onset, and AS (pnon−linear > 0.05) (Fig. 2-b). We dichotomized each sleep regularity index based on the restricted cubic spline results. The analysis also revealed a significant negative association between regular sleepers (SRI ≥ 72.67) and AS compared to irregular sleepers in the fully adjusted model(OR = 0.54, 95%CI: 0.35–0.82, p = 0.004), consistent with the findings from the four-classification results(Fig. 2-c).

However, there was no significant association between sleep duration regularity, sleep onset regularity, and AS(p > 0.050) (Fig. 2-a, c). This may also suggest that SRI is more strongly associated with AS risk than sleep duration and onset time.

Subgroup analysis of the association between sleep regularity and AS

We further performed a subgroup analysis of the association between SRI and AS. In the male population, participants who sleep regular had a lower risk of AS compared to those who sleep irregular in the fully adjusted model (ORQ4 VS. Q1=0.28, 95%CI: 0.12–0.69, p = 0.005) (Fig. 3-a). And in the non-retired population, we also found that people who slept more regularly had a lower risk of AS compared to those who did not sleep regularly (ORQ4 VS. Q1=0.45, 95%CI: 0.22–0.92, p = 0.028) (Fig. 3-b). In addition, the association between SRI and AS also was particularly marked for snoring populations (ORQ4 VS. Q1=0.13, 95%CI: 0.04–0.48, p = 0.002) (Fig. 3-c).

Fig. 3 Subgroup analysis of the association between sleep regularity and AS. Note: Figures a-c are forest plots of subgroup analyses of the association of four categorical sleep regularity indicators with AS. (a: Sex, b: Snoring, c: Work States); Figures d-f are forest plots of dichotomous subgroup analysis for each sleep regularity indicator (d: SRI, e: Sleep duration, f: Sleep Onset Time)

When we categorized the results based on restricted cubic spline curves, we also observed similar associations between sleep regularity and AS in the male, non-retired and snoring populations(OR = 0.46, 95%CI: 0.25–0.84, p = 0.012; OR = 0.46, 95%CI: 0.28–0.74, p = 0.002; OR = 0.33, 95%CI: 0.14–0.76, p = 0.009)(Fig. 3-d, e, f).

Sensitivity analysis of the association between sleep regularity and AS

Results showed that the association between SRI and AS remained significant even after excluding the baPWV ≥ 1800 cm/s(n = 59) (ORQ4 VS. Q1=0.49, 95%CI: 0.26–0.92 p = 0.026) or diabetic population (ORQ4 VS. Q1=0.48, 95%CI: 0.25–0.90, p = 0.022). After excluding those with hypertension, the trend between SRI and AS remained consistent, although not statistically significant (Fig. 4-a).

Fig. 4 Sensitivity analysis of the association between sleep regularity and AS. Notes: Figure-a show a forest plot of the sensitivity analysis of the association between the four categorical sleep regularity indicators and AS; Figure-b shows a forest plot of the sensitivity analysis of the association between dichotomous sleep regularity indicators and AS

The dichotomous results showed that the association between SRI and AS was attenuated for participants with baPWV ≥ 1800 cm/s when they were excluded, but it was not significantly changed(OR = 0.60, 95%CI: 0.38–0.93, p = 0.024). And the association between SRI and AS remained robust after excluding patients with diabetes(OR = 0.58, 95%CI: 0.37–0.91, p = 0.017) (Fig. 4-b).

Discussion

In this study, we observed that individuals with higher SRI scores exhibited a reduced risk of developing AS. This association remained a similar trend even after sensitivity analysis. In addition, we found this effect to be more pronounced in men, non-retired, and snoring populations. These findings imply that sleep regularity, as assessed by the SRI, may possess a distinctive etiological connection to the atherogenic process.

Metrics used to capture sleep regularity were inconsistent across previous studies, comprising the SD of sleep duration, SD of sleep onset and SRI. Unlike the results of the SRI, no statistically significant associations between sleep duration regularity, sleep onset regularity, and AS were observed in this study. But the results also showed a trend toward a decreased risk of AS in the group with a standard deviation of sleep duration ≤ 60 min and a standard deviation of sleep onset ≤ 30 min compared with the sleep irregularity group. A possible explanation for the differences in results might be that, compared with other measures of regularity and variability such as the SD of sleep duration and sleep onset time, the SRI can capture day-to-day changes in sleep on a 24-hour timescale, rather than comparing each individual day to an average day. In addition, the prevalence of behaviors such as snoring, awakening, and nocturia increases with age [35]. And these behaviors may lead to a decline in sleep continuity in the population, which can lead to irregular sleep. As a result, it may be more sensitive to issues such as sleep fragmentation.

SRI is one of the indicators of sleep regularity, the association between SRI and health outcomes may be more comprehensive and sensitive. Previous studies have also concluded that lower SRI is associated with an increased risk of diabetes, obesity, hypertension, and mortality risk [36, 37]. So, more attention should be paid to the monitoring of this indicator in future studies on sleep regularity. Due to the complexity of the SRI calculation method, this module should be added to the daily sleep monitoring tools, which will be more conducive to the population to carry out self-health management of sleep.

In addition, we investigated the association between sleep regularity and AS across genders, snoring, and whether or not they were retired. The results showed stronger negative association between SRI and the development of AS in males, which was also verified in the dichotomized results. Previous studies have also suggested that there was a significant gender difference in the development of AS, with males having a higher risk of AS than females [38, 39]. This study also confirmed that the risk of AS is significantly higher in people with snoring behavior. Previous studies have found an association between severe sleep irregularity and the development of obstructive sleep apnea (OSA) [40]. Consistent with OSA, people who snore severely may also experience obstructive hypoventilation during sleep. And this respiratory depression during snoring causes the brain to experience a transient, non-memorized arousal response, increasing the chances of daytime sleepiness, which can lead to irregular sleep. Sleep disruption due to snoring may disrupt the regularity of the population’s sleep patterns and thus affect the occurrence of adverse health outcomes [41]. In addition, since the SRI reflects more changes in 24-hour sleep patterns, it includes not only sleep duration and time to fall asleep, but also takes into account daytime naps, nighttime awakenings, and fragmented sleep due to other reasons. Therefore, in the present study, sleep fragmentation due to the high incidence of snoring in the male population may have increased the exposure to SRI levels in this population thus making stronger association in the male population. Moreover, in the current study, a stronger association between SRI and AS was also found in the snoring population, which further supports that snoring may disrupt the regularity of sleep patterns, which in turn affects the development of AS. This also suggests that it is more important for people who snore frequently to maintain a regular sleep pattern to reduce the risk of AS.

This study also found that the effect between SRI and AS was stronger in non-retired populations. Firstly, working hours may be one of the most influential factors for sleep regularity for employees. For example, sleep/wake schedules tend to differ between the workday, which reflects the social clock, and the non-workday, which reflects the biological clock. This phenomenon is referred to as social jetlag [42] and affects workers health [43–45] and sleep regularity. Second, previous research revealed that work interval (WI) may contribute to irregular sleep [46]. It is the daily work interval between the end of one day’s working hours and the beginning of the next day’s working hours includes activities and/or behaviors normally performed outside of work hours, such as sleep, leisure time, and other non-work time. Some studies have shown that extended WI may allow workers to increase time in bed [47]. However, because everyone’s daily work schedule was different, their WI was constantly changing, which means their sleep schedule may also vary, leading to the occurrence of sleep irregularities. In addition, work stress may also affect sleep regularity in the working population, leading to the development of adverse health outcomes. Studies have shown that poor lifestyle behaviors are also more prevalent in people with work-related stress [48, 49], such as alcohol abuse and increased nocturnal activity, which further increase the risk of sleep irregularity as well as metabolic disorders in the working population [50, 51].

It may also suggest that the adverse effects of irregular sleep may be more pronounced in the non-retired population. Therefore, these results may also suggest a more significant benefit from interventions to improve sleep regularity in men, in the presence of snoring behaviors, and in the non-retired population, who may need to focus more on maintaining a healthy lifestyle and on occupation-related health management. However, in the context of this study, there is less evidence of the direct mechanisms leading to these effects, and more research is needed to elucidate these relationships and the roles of the different pathways.

There are some limitations to this study. First of all, the sleep data obtained in this study came from the sleep assessment system of the smart band. Compared with polysomnography, the sensitivity and accuracy of the smart band may be insufficient, but the smart band can provide a more objective method of sleep monitoring, which can intuitively display the objective sleep condition of the population in the state of daily life. In addition, this study utilizes the information of smart band sleep monitoring to measure the use of SRI and also provides some reference for the information utilization mode of home sleep monitoring, as well as provides some basis for the future development of the smart band sleep monitoring module and the daily sleep management for the population based on the smart band. Second, due to the small sample size of our study, we were unable to show statistically significant results when conducting subgroup and sensitivity analyses, and the exclusion of certain samples may also have led to insufficient robustness of the results. Nevertheless, our results still suggest an association between SRI and AS, and the results were verified in the dichotomous results. Finally, our study was limited in revealing a causal relationship between exposure and outcome; therefore, future studies on the association between SRI, other indicators of sleep regularity, and AS need to be confirmed in prospective studies with larger sample sizes.

In conclusion, this study found that regular sleep patterns were associated with a lower risk of AS. And compared to other indicators of sleep regularity, SRI may be more sensitive in the assessment of AS. sleep regularity as an emerging dimension of sleep and also as a modifiable health factor that encourages regular sleep, as well as improvement of factors that may influence sleep regularity and thus reduce the risk of AS, may be an essential component of lifestyle advice provided as prevention of cardiovascular disease. In addition, the proliferation of commercial wristwatches in the home environment will aid in the future assessment of sleep patterns. It will also provide a basis for self-management of sleep health in the population.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Acknowledgements

Particularly thanks to the Fuquan Center for Disease Control and Prevention, the First People’s Hospital of Fuquan City to assist and support the investigation work. The participants in the study willingly donated their time and energy, and for that the authors are grateful.

Author contributions

Yuan-hao Shang conceptualized and wrote the initial draft of the manuscript. Da-qiang Liang and Xiao-ling Song acquired the data, calculated the sleep variables. Xia Feng, Guang-yan Mao, Ting-ting Yang participated in the on-site survey to collect data. Jun-hua Wang and Zi-yun Wang revised and corrected the paper. All authors contributed to the final version of the paper and have read and approved the final manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 81960612) and Guizhou Provincial Science and Technology Program Project (Qiankehe Basic-ZK[2022] General 382). The funder played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Data availability

The database used to support this study are not freely available in view of participants’ privacy protection.

Declarations

Ethics approval and consent to participate

This study protocol was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of the Guizhou Medical University (2021[174]). And written informed consent was obtained from each subject.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Yuan-hao Shang and Da-qiang Liang contributed equally to this work.
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