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. 2025 Jul 9;57:103171. doi: 10.1016/j.pmedr.2025.103171

Sleep patterns and risk of new-onset hypertension and cardiovascular disease in prehypertensive adults: The UK Biobank Study

Wanqing Yan a,b,1, Menglin Fan c,d,1, Jin Lv a,b, Shaoyong Xu c,d,⁎, Yong Ren b,⁎⁎
PMCID: PMC12281244  PMID: 40704108

Abstract

Objective

This study aimed to investigate the relationship between comprehensive sleep patterns and the incidence of new-onset hypertension (HTN) and cardiovascular disease (CVD) in individuals with prehypertension.

Methods

This analysis included 118,523 baseline participants from the UK Biobank (2006–2010) with follow-up through 31 December 2021. A sleep pattern included chronotype, sleep duration, insomnia, snoring, and excessive daytime sleepiness. Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95 % confidence intervals (CIs).

Results

Over a median follow-up period of 12.5 years, 10,276 participants (8.7 %) developed HTN, and 7665 participants (6.5 %) experienced CVD events. Participants adhering to healthy sleep patterns had a 27 % lower risk of developing HTN (HR = 0.73; 95 % CI: 0.69–0.77) and a 23 % lower risk of CVD (HR = 0.77; 95 % CI: 0.72–0.82) compared with those with unhealthy sleep patterns. When analyzed as a continuous variable, higher healthy sleep scores were associated with a progressive reduction in disease risks.

Conclusions

Healthy sleep patterns are significantly associated with reduced risk the risk of new-onset HTN and CVD in people with prehypertension, emphasizing the importance of assessing and optimizing sleep health as part of clinical primary prevention strategies for CVD in prehypertensive populations.

Keywords: Prehypertension, Sleep, Cardiovascular disease, Coronary heart disease, Primary prevention

Highlights

  • •

    Exploring primary prevention through sleep in prehypertensive populations.

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    Healthy sleep pattern reduce cardiovascular disease risk in prehypertensive adults.

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    Higher sleep scores progressively decrease cardiovascular disease risks.

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    Sleep assessment should be popularized for prehypertensive populations.

1. Introduction

Cardiovascular disease (CVD) remain a significant global public health challenge, affecting approximately 523 million individuals worldwide in 2019 and resulting in 6.2 million deaths. This underscores the urgent need to identify modifiable risk factors and implement effective prevention strategies to reduce the global burden of disease (Roth et al., 2020; GBD 2019 Diseases and Injuries Collaborators, 2020). While traditional interventions, such as improving diet and increasing physical activity, have long been central to CVD prevention, emerging evidence highlights the critical role of sleep as an essential lifestyle factor in mitigating cardiovascular risk.

Globally, it is estimated that one in five individuals experience sleep disturbances (Zhang et al., 2019). Numerous epidemiological studies have demonstrated significant associations between sleep behaviors and adverse cardiovascular outcomes, including hypertension (HTN). For instance, a meta-analysis of 395,641 participants revealed a 21 % increased risk of HTN among individuals with insomnia (relative risk = 1.21) (Li et al., 2021a). However, sleep health is a multidimensional construct requiring comprehensive evaluation that encompasses various factors, including chronotype, sleep duration, insomnia, snoring, and daytime sleepiness. A large cross-sectional study reported that unhealthy sleep patterns were associated with a significantly elevated risk of HTN in the general population (odds ratio = 2.47) (Li et al., 2023). Furthermore, in 2022, Makarem et al. established sleep as an independent predictor of cardiovascular events (Makarem et al., 2022).

In recent years, the prevention of CVD has increasingly emphasized early detection and management of prehypertension, defined as a systolic blood pressure (SBP) of 120–139 mmHg and/or a diastolic blood pressure (DBP) of 80–89 mmHg (Chobanian et al., 2003) Normal blood pressure is defined as SBP <120 mmHg and DBP <80 mmHg. Individuals with prehypertension face a 3.57-fold higher incidence of developing HTN compared with those with normal blood pressure (Yukiko et al., 2017). Sustained elevations in blood pressure are strongly linked to adverse cardiovascular outcomes, including left ventricular hypertrophy, early atherosclerosis (Cuspidi et al., 2019; Celik et al., 2013), and a 40 % increased risk of cardiovascular events within four years (Han et al., 2019). Globally, approximately one-third of cardiovascular events are attributable to elevated blood pressure within the prehypertension range (Egan and Stevens-Fabry, 2015). International guidelines consistently emphasize the importance of lifestyle interventions in this population (Mancia et al., 2023; McEvoy et al., 2024). However, the preventive potential of healthy sleep patterns in individuals with prehypertension remains poorly understood.

Using data from the UK Biobank, this prospective study enrolled participants free of HTN and CVD at baseline (2006–2010) with follow-up until 31 December 2021. It examined the association between sleep patterns and risks of new-onset HTN and CVD in prehypertensive individuals.

2. Methods

2.1. Study population

The UK Biobank is a large-scale, prospective cohort study comprising over 500,000 volunteers aged 37–69 years across the United Kingdom. At baseline, participants completed touch-screen questionnaires and standardized interviews, underwent physical examinations, and provided biological samples. Subsequently, participants regularly contributed blood, urine, and saliva samples with informed consent, alongside detailed lifestyle data. Additionally, healthcare system data were integrated into the study database. The UK Biobank study was approved by the North West Multi-centre Research Ethics Committee, and all participants provided written informed consent. Therefore, no additional ethical review was required for this study. The data underlying this article are available in UK Biobank at https://www.ukbiobank.ac.uk/, and can be accessed with reasonable request. Throughout the study, researchers had no access to personal identifiers or any information that could compromise participant confidentiality. The present study was performed under application number 92014.

According to the 2023 European Society of Hypertension guidelines (Mancia et al., 2023), blood pressure in the prehypertensive range is categorized as: (1) normal (SBP 120–129 mmHg and DBP 80–84 mmHg); or (2) high-normal (SBP 130–139 mmHg and/or DBP 85–89 mmHg). For this investigation, a total of 185,018 individuals with SBP of 120–139 mmHg and/or DBP of 80–89 mmHg at baseline were included. Subsequently, individuals were excluded if they had a pre-baseline diagnosis of HTN or were taking antihypertensive medications (n = 37,881), had a history of CVD (n = 3859), or lacked sleep behavior data (n = 24,755). After applying these exclusion criteria, a final cohort of 118,523 participants was retained for analysis. A flowchart detailing the inclusion and exclusion criteria is provided in Fig. 1.

Fig. 1.

Fig. 1

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Flowchart of participant selection and incident cardiovascular disease outcomes in UK Biobank adults with prehypertension (2006–2021). SBP, systolic blood pressure; DBP, diastolic blood pressure; HTN, hypertension; CVD, cardiovascular disease; CHD, coronary heart disease;

2.2. Assessment of sleep behaviors and definition of sleep score

Sleep behaviors were assessed via a self-administered touch-screen questionnaire. Five specific sleep factors were incorporated into a composite sleep score: chronotype, sleep duration, insomnia, snoring, and daytime sleepiness. Healthy sleep behaviors were defined as follows: early chronotype, sleep duration of 7–9 h within 24 h, “never/rarely” experiencing insomnia, absence of self-reported snoring, and “never/rarely” or “sometimes” experiencing excessive daytime sleepiness was assigned a score of 1, while unhealthy behaviors were scored as 0 (Table S1). The total scores ranged from zero to five, and participants were categorized into three groups: “healthy sleep pattern” (scores 4–5), “intermediate sleep pattern” (scores 2–3), and “unhealthy sleep pattern” (scores 0–1).

2.3. Assessment of outcomes

The primary outcomes of this study were the incidence of new-onset HTN and CVD, including coronary heart disease (CHD), heart failure, and stroke. Outcome data were derived from a combination of self-reports, hospital records, and primary care records. Dates of hospitalizations or consultations and corresponding diagnoses were obtained through linkage with the Hospital Episode Statistics for England, Scottish Morbidity Record, and Patient Episode Database for Wales. These data were recorded in accordance with the International Classification of Diseases, Tenth Revision (ICD-10). The diagnosis of HTN was coded I10; CVD outcomes included CHD (I20–I25), heart failure (I50), and stroke (I60–I64).

2.4. Covariate evaluation

Baseline data were obtained through a self-administered touch-screen questionnaire. Sociodemographic characteristics at the time of recruitment included age, sex, educational attainment (categorized as College/University, Upper secondary, Lower secondary, Vocational, and Other), ethnicity (classified as White, Asian or Asian British, Black or Black British, or other), and average annual household gross income (<£18,000, £18,000–£51,999, £52,000–£100,000, >£100,000, or none). Lifestyle variables encompassed smoking status (classified as “never,” “previously,” or “currently”), alcohol drinking (“never,” “occasionally,” “weekly,” or “daily”), physical activity, tea drinking (cups/day), and coffee drinking (cups/day). Additionally, participants self-reported their family history of CVD and HTN (both categorized as yes or no). Physical measurements included body mass index (BMI), SBP, and DBP. BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m2). Age was derived from the participant's date of birth and the date of their initial assessment center visit. Blood pressure was measured twice at baseline using an Omron HEM-7015IT electronic sphygmomanometer by a nurse trained in the procedure, with readings taken a few minutes apart. Average SBP and DBP were calculated from these two measurements. Physical activity was assessed using a modified version of the abbreviated International Physical Activity Questionnaire (Bassett, 2003), which evaluates the duration and frequency of leisure-time physical activity. Weekly physical activity was quantified by calculating the weekly metabolic equivalent task (MET), determined as the product of the MET value for each activity and the number of minutes of activity performed per week (MET-min/week).

2.5. Statistical analysis

Continuous variables were presented as mean(standard deviations, SD), with group comparisons conducted using analysis of variance. Categorical variables were expressed as frequencies and percentages, with comparisons performed using chi-square tests. Follow-up time for each participant was calculated from baseline to the first occurrence of HTN, CVD, death, or the censoring date (31 December 2021). Hazard ratios (HRs) and 95 % confidence intervals (CIs) for the risk of incident disease were estimated using Cox proportional hazards regression models, with analyses conducted under two conditions: Model 1 (unadjusted) and Model 2 (adjusted for age, sex, smoking status, alcohol drinking, education level, household income, BMI, physical activity, tea drinking, coffee drinking, and family history of heart disease and HTN). Missing covariate data were imputed using mean interpolation. To evaluate the relationship between sleep-related risk factors and multivariable-adjusted outcome incidence, cumulative estimates were plotted, and individual sleep factors were analyzed to determine their specific effects on new-onset HTN and CVD. Healthy sleep scores were also examined as continuous variables to assess linear trends.

Sensitivity analyses were conducted to mitigate potential reverse causation. These included excluding participants who developed HTN within the first two years of follow-up and excluding individuals with missing covariate information due to responses of “don't know” or “prefer not to answer” on baseline questionnaires. Statistical tests were two-sided, and a p-value <0.05 was considered statistically significant. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC) and R software.

3. Results

3.1. Baseline information for participants

Baseline characteristics of participants, stratified by unhealthy, intermediate, and healthy sleep patterns, are summarized in Table 1. The study cohort included 118,523 participants with a mean age (SD) of 54.3 (8.0) years, of whom 42.8 % were male. Among the participants, 43.6 % and 51.5 % reported a family history of HTN and CVD, respectively. Overall, 62.9 % (n = 74,564) of participants reported healthy sleep patterns, 26.0 % (n = 30,838) intermediate sleep patterns, and 11.1 % (n = 13,121) unhealthy sleep patterns. No statistically significant differences were observed in age or baseline systolic blood pressure across sleep pattern categories (P = 0.07 and P = 0.49). Healthy sleep patterns were more frequently reported among Caucasians and nonsmokers. By contrast, participants with unhealthy sleep patterns exhibited higher BMI and DBP, lower education levels, reduced physical activity, and greater consumption of coffee and tea.

Table 1.

Baseline characteristics of UK Biobank adults with prehypertension (2006–2010) according to sleep patterns categories.

Characteristics All participants
N = 118,523		 n(%)/mean(SD)
 P
Healthy sleep pattern
N = 74,564		 Intermediate sleep pattern
N = 30,838		 Poor
sleep pattern
N = 13,121
Age, year 54.3(8.0) 54.3(8.1) 54.4(7.9) 54.2(7.8) 0.07
Male 5068(42.8) 30,350 (40.7) 14,370 (46.6) 5967 (45.5) <0.01
BMI, kg/m2 26.5(4.2) 26.1(4.0) 27.0(4.3) 27.7(4.6) <0.01
SBP, mmHg 128.9(6.0) 128.95(6.0) 128.97(6.0) 129.0(6.0) 0.49
DBP, mmHg 78.8(5.9) 78.7(5.9) 79.0(5.8) 79.3(5.8) <0.01
Education <0.01
 College/University 43,774 (36.9) 28,972 (38.9) 10,792 (35.0) 4010 (30.6)
 Upper secondary 14,488 (12.2) 9233 (12.4) 3709 (12.0) 1546 (11.8)
 Lower secondary 33,118 (27.9) 20,212 (27.1) 8868 (28.8) 4038 (30.8)
 Vocational 6922 (5.8) 4144 (5.6) 1894 (6.1) 884 (6.7)
 Other 20,221 (17.1) 12,003 (16.1) 5575 (18.1) 2643 (20.1)
Ethnicity <0.01
 White 107,989 (91.1) 68,072 (91.3) 28,074 (91.0) 11,843 (90.3)
 Asian or Asian British 2505 (2.1) 1580 (2.1) 626 (2.0) 299 (2.3)
 Black or Black British 1558 (1.3) 861 (1.2) 466 (1.5) 231 (1.8)
 Other 6471 (5.5) 4051 (5.4) 1672 (5.4) 748 (5.7)
Income*, £ <0.01
 <18,000 17,185 (14.5) 10,255 (13.8) 4559 (14.8) 2371 (18.1)
 18,000–51,999 53,398 (45.1) 33,584 (45.0) 13,959 (45.3) 5855 (44.6)
 52,000–100,000 26,326 (22.2) 17,114 (23.0) 6625 (21.5) 2587 (19.7)
 >100,000 7391 (6.2) 4916 (6.6) 1860 (6.0) 615 (4.7)
 Not clear 14,223 (12.0) 8695 (11.7) 3835 (12.4) 1693 (12.9)
Smoking <0.01
 Never 67,872 (57.3) 45,028 (60.4) 16,551 (53.7) 6293 (48.0)
 Previously 37,494 (31.6) 22,854 (30.7) 10,069 (32.7) 4571 (34.8)
 Currently 12,880 (10.9) 6522 (8.8) 4129 (13.4) 2229 (17.0)
 Not clear 277 (2.3) 160 (2.1) 89 (2.9) 28 (2.1)
Alcohol <0.01
 Never 8193 (6.9) 5091 (6.8) 2091 (6.8) 1011 (7.7)
 Occasionally 25,850 (21.8) 16,088 (21.6) 6604 (21.4) 3158 (24.1)
 Weekly 61,136 (51.6) 39,149 (52.5) 15,752 (51.1) 6235 (47.5)
 Daily 23,285 (19.6) 14,207 (19.1) 6371 (20.7) 2707 (20.6)
 Not clear 59 (0.5) 29 (0.4) 20 (0.6) 10 (0.8)
Physical activity, MET-min/week 2691(2702) 2744(2666) 2617(2723) 2554(2849) <0.01
Tea, cups/day 3.4(2.9) 3.4(2.7) 3.5(2.9) 3.6(3.2) <0.01
Coffee, cups/day 2.1(2.0) 2.0(2.0) 2.1(2.2) 2.3(2.4) <0.01
Family history of heart diseases 61,015 (51.5) 38,044 (51.0) 16,021 (52.0) 6950 (53.0) <0.01
Family history of hypertension 51,627 (43.6) 32,324 (43.4) 13,459 (43.6) 5844 (44.5) <0.01
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Sleep pattern definition: sleep duration, insomnia, daytime sleepiness, chronotype, and snoring. Each behavior received dichotomous scoring (0 = unhealthy, 1 = healthy); total scores categorized as: unhealthy (0–1), intermediate (2–3), or healthy (4–5). P-values generated by analysis of variance for continuous variables and chi-square tests for categorical variables. *Income indicates average annual household gross income, SD, standard deviations; BMI, body mass index; kg, kilograms; m, meters; SBP, systolic blood pressure; DBP, diastolic blood pressure; MET, metabolic equivalent of tasks.

3.2. Association between sleep patterns and hypertension and cardiovascular disease

Over a median follow-up period of 12.5 years, 10,276 (8.7 %) cases of new-onset HTN and 7665 (6.5 %) new CVD events were recorded. These events included 5614 (4.7 %) cases of CHD, 1518 (1.3 %) cases of heart failure, and 1487 (1.3 %) cases of stroke.

Participants adhering to healthy sleep patterns exhibited a 27 % reduction in the risk of HTN (HR = 0.73; 95 % CI: 0.69–0.77) and a 23 % reduction in the risk of CVD (HR = 0.77; 95 % CI: 0.72–0.82) compared with those with unhealthy sleep patterns. For specific CVD composite outcomes, healthy sleep patterns were associated with a 23 % lower risk of CHD (95 % CI: 0.71–0.83), a 22 % lower risk of heart failure (95 % CI: 0.77–0.90), and a 17 % lower risk of stroke (95 % CI: 0.71–0.96) (Fig. 2). Further analysis revealed that participants with a sleep score of 5 (the highest score) experienced a 40 % reduction in the risk of new-onset HTN (HR = 0.60; 95 % CI: 0.53–0.67) and a 38 % reduction in the risk of CVD (HR = 0.62; 95 % CI: 0.54–0.72) compared with those with a sleep score of 0–1. Among CVD component outcomes, rates of CHD, heart failure, and stroke were reduced by 37 % (HR = 0.63; 95 %CI: 0.53–0.73), 41 % (HR = 0.59; 95 %CI: 0.44–0.78), and 38 % (HR = 0.62; 95 %CI: 0.45–0.86) (Fig. 3). When analyzed as a continuous variable, each one-point increase in sleep score was associated with an 11 % reduction in the risk of HTN and an 8 % reduction in the risk of CVD (Table S2). Over time, participants with healthy sleep patterns demonstrated significantly lower cumulative incidence rates of new-onset HTN, CVD, and CVD composite outcomes compared with individuals with unhealthy sleep patterns (Fig. S1). Regarding individual sleep behaviors, sleep duration of 7–9 h, “Never/rarely” insomnia, infrequent or no daytime sleepiness, and absence of snoring were all independently associated with reduced risks of new-onset HTN and CVD (Table S3).

Fig. 2.

Fig. 2

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Adjusted hazard ratios for cardiovascular outcomes associated with sleep patterns in UK Biobank adults with prehypertension (2006–2021). Sleep pattern definition: sleep duration, insomnia, daytime sleepiness, chronotype, and snoring. Each behavior received dichotomous scoring (0 = unhealthy, 1 = healthy); total scores categorized as: unhealthy (0–1), intermediate (2–3), or healthy (4–5). Multivariable models are adjusted for age, sex, smoking status, alcohol drinking, education level, household income, BMI, physical activity, tea drinking, coffee drinking, and family history of heart disease and hypertension. HTN, hypertension; CVD, cardiovascular disease; CHD, coronary heart disease; HR, hazard ratio; CI, confidence interval.

Fig. 3.

Fig. 3

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Adjusted hazard ratios for cardiovascular outcomes by discrete sleep score categories in UK Biobank adults with prehypertension (2006–2021). (A) HTN, (B) CVD, (C) CHD, (D)Heart failure, (E) Stroke. Sleep pattern definition: sleep duration, insomnia, daytime sleepiness, chronotype, and snoring. Each behavior received dichotomous scoring (0 = unhealthy, 1 = healthy); total scores categorized as: unhealthy (0–1), intermediate (2–3), or healthy (4–5). Multivariable models are adjusted for age, sex, smoking status, alcohol drinking, education level, household income, BMI, physical activity, tea drinking, coffee drinking, and family history of heart disease and hypertension. HTN, hypertension; CVD, cardiovascular disease; CHD, coronary heart disease; HR, hazard ratio; CI, confidence interval.

Sensitivity analyses confirmed the robustness of these findings. Excluding participants who developed HTN or cardiovascular events within the first two years of follow-up yielded results consistent with the main analyses. Similarly, the exclusion of participants with missing covariate data did not alter the significant associations between healthy sleep patterns and reduced risks of new-onset HTN and CVD (Table S4).

4. Discussion

This study, leveraging the extensive UK Biobank database, investigates the relationship between composite sleep patterns and the incidence of new-onset HTN and CVD in individuals with prehypertension. By integrating five sleep behaviors into a composite framework, this research represents a novel exploration of the preventive potential of healthy sleep patterns within this high-risk cohort. Key findings demonstrate that adherence to favorable sleep characteristics was significantly associated with reduced risks of both new-onset HTN (HR = 0.73; 95 % CI: 0.69–0.77) and CVD (HR = 0.77; 95 % CI: 0.72–0.82) in prehypertensive individuals, with sensitivity analyses confirming consistency of results. Each 1-point increase in the healthy sleep score was associated with a graded reduction in the risks of HTN and CVD. This trend suggests that improvements in sleep health may exert continuous and incremental protective effects. Specific protective sleep components included sleep duration of 7–9 h, “never/rarely” insomnia, infrequent or no daytime sleepiness, and absence of snoring. Building on this foundational work, our study focuses specifically on the prehypertension population—a critical window for intervention—and highlights the preventive value of optimizing sleep behaviors in mitigating cardiovascular risk within this group.

American Heart Association has emphasized the importance of comprehensive assessments of sleep behaviors to better understand their impact on health outcomes (Lloyd-Jones et al., 2022). In 2020, Mengyu Fan introduced the healthy sleep pattern framework, which encompassed multiple sleep factors and used data from over 300,000 participants in the UK Biobank. This study demonstrated that nearly 10 % of cardiovascular events could be attributed to poor sleep patterns (Fan et al., 2020). Building on this foundational work, our study focuses specifically on the prehypertension population—a critical window for intervention—and highlights the preventive value of optimizing sleep behaviors in mitigating cardiovascular risk within this group.

Cumulatively, prior research has established that individual sleep behaviors can exert additive effects on health outcomes. In the general population, each 1-point increase in composite sleep score has been associated with a 9 %–12 % reduction in HTN risk, while a maximum score of 5 compared with scores of 0–1 corresponds to a 33 %–42 % reduction in HTN risk (Li et al., 2021b, Lv et al., 2022) and a 9 % reduction in CVD risk (Song et al., 2022). Consistent with these findings, our study observed similar trends, demonstrating that improvements in sleep quality and behaviors may possibly lower cardiovascular event risks in prehypertensive individuals.

Although further research is needed to strengthen the evidence base, implementing routine sleep screening within prehypertension management is crucial, requiring scaled public sleep health education initiatives; integration of standardized sleep assessments with personalized recommendations into clinical workflows through electronic systems featuring embedded decision support; and deployment of intensive screening alongside risk-stratified interventions for high-risk populations.

Non-rapid eye movement sleep, which constitutes the majority of total sleep duration, is characterized by vagus nerve predominance, leading to reductions in blood pressure and heart rate (Somers et al., 1993). However, disturbances in sleep frequently result in excessive activation of the sympathetic nervous system (Floam et al., 2015), which induces heightened vasoconstriction and elevated blood pressure. This sympathetic overactivation triggers the sympathetic-adrenal axis, promoting the release of catecholamines and norepinephrine, which accelerate heart rate, increase vasoconstriction (Castro-Diehl et al., 2015), and activate the renin–angiotensin–aldosterone system, thereby raising vascular pressure and cardiac workload. Additionally, sleep disturbances can disrupt the circadian rhythm of hormonal secretion, including melatonin, which plays a critical role in cardiovascular health through its antioxidative, anti-inflammatory, and antihypertensive properties (Covassin and Somers, 2023). Sleep deficiency can also enhance the migratory capacity of monocytes (Huynh et al., 2024) and reduce hypothalamic wake-promoting factor levels, subsequently increasing colony-stimulating factor 1 in the bone marrow (McAlpine et al., 2019), thereby amplifying the inflammatory response. The activation of inflammatory pathways, including nuclear factor kappa B, activator protein-1, and signal transducer and activator of transcription family proteins, further elevates mRNA expression of pro-inflammatory cytokines, collectively establishing an inflammatory microenvironment (Irwin et al., 2015; Irwin, 2019). In addition, snoring and sleep apnea can lead to recurrent nocturnal hypoxemia, which exacerbates oxidative stress and promotes hypertension and arterial atherosclerosis (Cowie et al., 2021). Sleep restriction and poor sleep quality also disrupt appetite-regulating hormones, such as leptin and ghrelin, resulting in increased daily energy intake, weight gain, and visceral fat accumulation, alongside reduced insulin sensitivity (Covassin et al., 2022).

This study represents the first investigation into the association between comprehensive sleep patterns and the risk of hypertension and CVD in individuals with prehypertension. Using a cohort of over 100,000 participants with a follow-up period spanning 12 years, this study incorporated self-reported, sociodemographic, medical, and lifestyle data. The dataset was comprehensive, complete, and deemed reliable. Despite these strengths, several limitations warrant consideration. First, sleep patterns were assessed only at baseline, without accounting for subsequent changes over time. This limitation may not fully capture the dynamic nature of sleep behaviors and their relationship with cardiovascular outcomes. However, prior research by Nambiema et al. demonstrated that baseline sleep patterns exerted a stronger effect on CVD risk compared to longitudinal changes, with 63.7 % of participants maintaining stable, favorable sleep patterns over time. Second, this cohort was predominantly composed of White individuals (91.1 %). Previous studies found a 10.7 % higher prevalence of short sleep (95 % CI: 8.1–13.2) in Black and Hispanic populations versus Whites (Caraballo et al., 2022), alongside a greater burden of cardiovascular risk factors and mortality in non-Whites (Post et al., 2022), this association requires further validation. Finally, although the study adjusted for a wide range of confounding variables, the possibility of residual confounding due to unmeasured or unknown factors cannot be entirely excluded.

5. Conclusion

This study demonstrates that adherence to a healthy sleep pattern in individuals with prehypertension is significantly associated with a lower risk of developing hypertension and CVD, with risk reductions further amplified by improvements in sleep behaviors. The findings underscore the importance of evaluating sleep patterns and providing tailored sleep recommendations for individuals with prehypertension.

CRediT authorship contribution statement

Wanqing Yan: Writing – original draft, Software, Investigation, Data curation. Menglin Fan: Writing – original draft, Software, Data curation. Jin Lv: Validation, Investigation. Shaoyong Xu: Supervision, Methodology, Conceptualization. Yong Ren: Writing – review & editing, Methodology, Conceptualization.

Ethical approval and consent to participate

The UK Biobank study was approved by the North West Multi-centre Research Ethics Committee, and all participants provided written informed consent.

Disclosure of funding

The study was supported by the Hubei Provincial Natural Science Foundation (No. 2019CFB822); the Hubei Provincial Natural Science Foundation (Key Project of Joint Fund) (No. 2023AFD031); and Xiangyang Central Hospital - level Projects (No. 2023YZ08).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We thank Phoebe Chi, MD, from Liwen Bianji (Edanz) (www.liwenbianji.cn), for editing a draft of this manuscript.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pmedr.2025.103171.

Contributor Information

Shaoyong Xu, Email: yoji_xu@hotmail.com.

Yong Ren, Email: Renyong@hbuas.edu.cn.

Appendix A. Supplementary data

Supplementary material 1 Ren, Yong (2025), “Supplementary Data:Download the document”, Mendeley Data, V1, doi: 10.17632/k74xtrg299.1

mmc1.docx (325.5KB, docx)

Data availability

Data are available in the public, open-access repository. This research has been conducted using the UK Biobank (application number 92014). The UK Biobank data are available on application to the UK Biobank (www.ukbiobank.ac.uk/) with access fees.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material 1 Ren, Yong (2025), “Supplementary Data:Download the document”, Mendeley Data, V1, doi: 10.17632/k74xtrg299.1

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Data Availability Statement

Data are available in the public, open-access repository. This research has been conducted using the UK Biobank (application number 92014). The UK Biobank data are available on application to the UK Biobank (www.ukbiobank.ac.uk/) with access fees.

Articles from Preventive Medicine Reports are provided here courtesy of Elsevier

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