U-shaped associations between sleep duration and Framingham risk score in Korean adults

Introduction

Sleep is a fundamental biological process essential for human health across the life course. It plays a critical role in early brain development and is indispensable for cognitive function and memory consolidation (1,2). In addition, sleep is closely linked to immune regulation, contributing to host defense against infectious diseases (3). Insufficient sleep has been shown to increase susceptibility to infections, including the common cold (4), while chronic sleep deprivation is associated with metabolic and cardiovascular conditions such as diabetes and atherosclerosis (3).

Despite the recognized importance of sleep, average sleep duration in Korea is among the shortest in Organization for Economic Co-operation and Development (OECD) countries (5). In Korea, 40.5% of adults reported sleeping less than 7 hours (6). In addition, the prevalence of insomnia has steadily increased in recent years (7). This pattern may be attributable to Korea’s characteristically long working hours and commuting times (5). Furthermore, advances in objective sleep assessment methods, including polysomnography (PSG), have accelerated scientific and clinical interest in sleep research (8). These trends underscore the growing public health relevance of sleep-related health outcomes in Korea.

Cardiovascular disease (CVD), encompassing conditions such as ischemic heart disease, myocardial infarction, and stroke, remains a leading cause of morbidity and mortality worldwide. In 2022, an estimated 19.8 million deaths were attributed to CVD, accounting for approximately 32% of all global deaths (9). In Korea, CVD consistently ranks among the top three causes of death, with mortality continuing to rise (10). Notably, deaths due to heart failure have increased in recent years (11). Although lifestyle modifications—including healthy diet, regular physical activity, and smoking cessation—are effective in reducing CVD risk (12). However, the management of major contributing conditions such as diabetes and hypertension remain suboptimal in Korea (13).

Sleep is an important factor associated with cardiovascular health. Short sleep duration has been related to hormonal imbalance, increased heart rate and blood pressure, and elevated catecholamine levels. It is also associated with increased inflammatory markers, such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6). These changes may be associated with the development of coronary artery disease (CAD) (14). Long sleep duration has also been reported to show similar associations (15). Accordingly, previous studies have suggested that the association between sleep duration and CVD risk follows a U-shaped or J-shaped pattern, with both short and long sleep durations linked to elevated risk (16,17). Given the relatively short sleep duration observed in Korea, this pattern may be associated with an increased risk of CVD in the Korean population. However, population-based evidence from Korea remains limited, and few studies have adequately accounted for sample weighting and population representativeness. Therefore, the present study aimed to examine the association between sleep duration and cardiovascular risk among Korean adults using weighted, nationally representative data. By doing so, this study seeks to provide updated epidemiological evidence to inform future CVD prevention strategies. We present this article in accordance with the STROBE reporting checklist (available at https://jphe.amegroups.com/article/view/10.21037/jphe-2026-1-0014/rc).

Methods

Study design

A nationally representative, population-based cross-sectional study was conducted among Korean adults.

Data

Data for this study were drawn from the Korea National Health and Nutrition Examination Survey (KNHANES) conducted between 2020 and 2022 by the Korea Disease Control and Prevention Agency (KDCA). KNHANES is a nationwide, population-based cross-sectional survey that was first conducted in 1998 and has been conducted annually since 2007. Participants were selected using a two-stage stratified cluster sampling method based on national census data. Stratification was performed by region (province or metropolitan area), residential area (urban or rural), and housing type. The survey includes detailed information from the household, health interview, health examination, and nutrition surveys. It provides data on sociodemographic characteristics and health behaviors, such as smoking and alcohol consumption. It also includes clinical measurements, such as systolic blood pressure (SBP) and high-density lipoprotein cholesterol (HDL), as well as information on the diagnosis of CVD.

Study population

Sampling weights were applied according to the guidelines of the KNHANES (18,19). The initial analytic sample consisted of 20,714 participants, representing a weighted population of 51,458,183 individuals. Based on the age criteria of the Framingham risk score (FRS) (20), those younger than 30 years (n=15,076,844) were excluded. In accordance with previous studies (21), individuals with a history of CVD were excluded. This included those with ischemic heart disease (n=588,778), stroke (n=424,058), or both conditions (n=60,126).

Among the remaining 35,308,377 individuals, participants with missing or unknown data for the variables of interest were excluded (n=11,184,523). These variables included CVD history, sleep duration, HDL cholesterol, total cholesterol, SBP adjusted for antihypertensive medication use, smoking status, diabetes diagnosis, education level, personal income, obesity, physical activity, and obstructive sleep apnea. As a result, a total of 24,123,854 individuals were included in the final analysis. The flowchart of participant selection is presented in Figure 1.

Figure 1 Flowchart of sample selection process. CVD, cardiovascular disease; HDL, high-density lipoprotein; KNHANES, Korea National Health and Nutrition Examination Survey; OSA, obstructive sleep apnea; SBP, systolic blood pressure; TC, total cholesterol.

Variables

Sleep duration was assessed using different survey questions depending on the year. In 2020 and 2022, participants were asked, “On average, how many hours do you sleep per day?”. In 2021, the questions included, “On weekdays (or working days), what time do you usually go to bed and wake up?” and “On weekends (or non-working days), what time do you usually go to bed and wake up?” (18,19). Following the official guidelines (18,19), sleep duration was calculated as the difference between bedtime and wake-up time on weekdays and weekends. The two values were then averaged. Weekday and weekend sleep durations were treated equally and averaged without weighting. Sleep duration primarily reflected nighttime sleep patterns. Based on previous studies (21-24), sleep duration was categorized into three groups: less than 6 hours, 6–8 hours, and more than 8 hours.

The FRS is a tool developed in the United States to estimate and manage the risk of CVD among individuals without a history of CVD (25). It is calculated by summing scores based on sex, age, total cholesterol, HDL, SBP, treatment for hypertension, smoking status, and diabetes status (20). Higher total scores are associated with a greater estimated 10-year CVD probability. Because it integrates multiple risk factors, the FRS is widely used to estimate CVD risk. (25). It is therefore commonly used to identify individuals at elevated cardiovascular risk (26). In this study, participants with an FRS greater than 20% were classified as the high-risk group, while those with an FRS of 20% or less were classified as the low-/intermediate-risk group.

Risk factor definitions were as follows. SBP was defined as the average of the second and third measurements, according to the KNHANES guidelines (18,19). Hypertension treatment was determined based on responses to the question, “Are you currently taking medication to control your blood pressure?”. Smoking status was classified as current smoker or non-smoker (20). Current smokers included those who reported smoking conventional cigarettes, heated tobacco products, or e-cigarettes at the time of the survey. Non-smokers included both former and never smokers. Diabetes was defined as having a fasting glucose level ≥126 mg/dL, a diagnosis by a physician, the use of glucose-lowering medication or insulin, or a glycosylated hemoglobin (HbA1c) level ≥6.5% (18,19).

Based on previous studies, various social and biological factors—including education level, income, marital status, depression, obesity, obstructive sleep apnea, alcohol consumption, physical activity, and sedentary time—were included as confounders. These factors are associated with both sleep duration and cardiovascular risk (21,27-29). Obesity was defined as a body mass index (BMI) ≥25 kg/m². Obstructive sleep apnea was determined based on a physician’s diagnosis. High-risk alcohol consumption was defined as consuming ≥7 drinks per occasion for men and ≥5 for women, at least twice per week. Physical activity was assessed using the Global Physical Activity Questionnaire (GPAQ) and classified according to the World Health Organization (WHO) threshold of 600 MET-minutes per week (30). Sedentary time was measured as the self-reported average of daily sitting or lying time. Education level, income, and marital status were categorized according to the KNHANES guidelines. Depression was assessed using the Patient Health Questionnaire-9 (PHQ-9).

Statistical analysis

Population characteristics by sleep duration and FRS were analyzed using the Rao-Scott chi-square test and complex sample analysis of variance (ANOVA). Categorical variables were analyzed with the Rao-Scott chi-square test and presented as frequencies and percentages. Continuous variables were analyzed with complex sample ANOVA and presented as means and standard deviations (SDs). The association between sleep duration and FRS was examined using complex sample logistic regression analysis. Confounding variables were first evaluated and then adjusted for in the model. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Model 1 was unadjusted. Model 2 was adjusted for social factors, including education level, income level, and marital status. Model 3 was adjusted for biological factors such as depression, alcohol consumption, obesity, average daily sedentary time, and obstructive sleep apnea. Model 4 was adjusted for all variables included in both Models 2 and 3. Age was not included as a covariate because it is already incorporated into the FRS. All models used sleep duration as the primary exposure variable. Both the complex sample logistic regression and the Rao-Scott chi-square test were conducted using two-tailed tests. Statistical significance was defined as P<0.05. All statistical analyses were conducted using IBM SPSS Statistics version 29.0.

Ethical considerations

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The KNHANES was reviewed and approved by the Institutional Review Board (IRB) of the KDCA prior to implementation. As the survey was conducted by the government for the purpose of public welfare under the Bioethics and Safety Act, and the data are publicly available and fully anonymized, this study was exempt from further review by the IRB.

Results

Characteristics of participants

Participant characteristics by sleep duration group are presented in Table 1. The mean age was 60.7 years (SD =11.4 years) in the group sleeping less than 6 hours, 56.5 years (SD =10.7 years) in the 6–8 hours group, and 56.0 years (SD =11.9 years) in the group sleeping more than 8 hours (P<0.001).

Distribution of FRS categories by sleep duration group

The distribution of FRS categories across sleep duration groups is shown in Table 2. In all sleep duration groups, most participants were classified as having an FRS ≤20% (<6 hours: 74.9%, 6–8 hours: 81.4%, >8 hours: 78.6%).

Association between sleep duration and FRS

Table 3 and Figure 2 present the association between sleep duration and high FRS (>20%), before and after adjustment for potential confounders. After adjusting for all confounders (Model 4), compared with the reference group (6–8 hours), participants sleeping less than 6 hours had higher odds of having a high FRS (>20%) (OR =1.161; 95% CI: 1.157–1.165). Participants sleeping more than 8 hours also had higher odds of high FRS compared with the reference group (OR =1.030; 95% CI: 1.026–1.034). Overall, a U-shaped association was observed between sleep duration and high FRS, with the highest odds observed in the <6-hour sleep group.

Figure 2 Sleep duration and high Framingham cardiovascular risk: a U-shaped association. Model 1: unadjusted; Model 2: adjusted for education level, personal income, and marital status; Model 3: adjusted for depression, alcohol drinking, obesity, sitting time, physical activity, and obstructive sleep apnea; Model 4: fully adjusted for education level, personal income, marital status, depression severity, alcohol drinking, obesity, sitting time, physical activity, and obstructive sleep apnea. OR, odds ratio.

Discussion

A U-shaped relationship was observed between sleep duration and elevated 10-year CVD risk (above 20%). The association was stronger for short sleep duration than for long sleep duration. This association remained statistically significant after adjusting for potential confounders. These confounders included education level, income, marital status, depression, sedentary time, alcohol consumption, physical activity, obstructive sleep apnea, and obesity. These findings suggest that non-optimal sleep duration, particularly short sleep duration, may be associated with increased CVD risk. Because CVD is multifactorial in nature, modest associations are commonly observed in population-based epidemiological studies. Nevertheless, exposures with high population prevalence, such as insufficient sleep, may still contribute substantially to the overall population burden of cardiovascular risk. Although the observed effect sizes were modest, this finding should be interpreted in the context of the relatively short sleep duration observed in the Korean population. Even small increases in cardiovascular risk may therefore have important public health implications. In addition, cardiovascular risk is influenced by multiple factors, and modest associations are commonly observed in population-based studies.

Possible biological mechanisms underlying the relationship between sleep and CVD are complex. They involve multiple physiological systems, including hormonal regulation and immune function. Inadequate sleep has been associated with chronic conditions that increase the risk of CVD. Sleep deprivation has been shown to reduce the phosphorylation of protein kinase B (PKB). This reduction is linked to reduced insulin sensitivity and may play a role in the development of diabetes and, ultimately, CVD (31). Additionally, decreased leptin and increased ghrelin levels are associated with increased appetite, which may contribute to obesity and CVD (31). Patients with insomnia accompanied by sleep deprivation exhibit exaggerated blood pressure responses to sympathetic stimulation. This response may be associated with a higher risk of hypertension and CVD (32).

Sleep disturbances have long been linked to increased CVD risk (33-35). In response, preventive strategies—including blood pressure and lipid control—have been proposed for CVD risk management. Predictive algorithms such as FRS and Systematic Coronary Risk Evaluation (SCORE) were subsequently developed to estimate 10-year cardiovascular risk (36). This has been accompanied by a growing body of research exploring the relationship between sleep patterns and CVD risk prediction models. For instance, a study conducted in Iran found that longer sleep duration in men was associated with a lower likelihood of being classified as moderate or high-risk for CVD. However, no significant association was observed in women (37). In São Paulo, Brazil, higher FRS values were linked to lower sleep efficiency and older age (38). A study conducted in Turkey suggested a U-shaped association between sleep duration and 10-year coronary heart disease risk, whereby both short and long sleep durations were linked to elevated risk (17). Consistent results were observed in a United States-based study focusing on middle-aged women (16). However, the FRS was primarily developed using data from White and African American populations. Its applicability to Asian populations has not been thoroughly validated (39). Cultural, behavioral, and genetic differences between Western and Asian populations may influence cardiovascular risk profiles and the calibration of risk prediction models. In particular, differences in sleep patterns, dietary habits, obesity distribution, and metabolic characteristics may affect the applicability of FRS in Korean populations. Previous studies have also suggested that the FRS may overestimate absolute cardiovascular risk when applied to Asian populations, including Koreans. Therefore, caution is warranted when interpreting absolute risk estimates derived from FRS in non-Western populations. Nevertheless, the primary aim of the present study was to evaluate the relative association between sleep duration and cardiovascular risk distribution within a nationally representative Korean population, rather than to provide precise individual-level cardiovascular risk prediction. Against this backdrop, several studies in Asian countries have examined the association between sleep duration and FRS. These include research conducted in China (40) and South Korea. Korean studies have reported that shorter sleep duration is linked to a higher prevalence of CVD (21). Nevertheless, these findings were largely derived from relatively outdated datasets. In addition, recent research that uses up-to-date data and specifically focuses on Asian populations—particularly Koreans—remains limited.

Sleep disorders and CVD are not merely individual health issues. They also pose substantial societal challenges, contributing to increasing economic burdens and reduced productivity. Therefore, these conditions require continuous and systematic public health interventions. In Korea, the prevalence of sleep disorders nearly doubled from 3,867,975 individuals (7.62%) in 2011 to 7,446,846 (14.41%) in 2020. This rise has been accompanied by increased healthcare utilization and higher personal and societal medical expenditures. The average age of individuals diagnosed with sleep disorders was 53.5 years. This may reflect age-related declines in sleep quality, including reductions in slow-wave sleep (7). Korea’s aging population continues to rise (41). As a result, the socioeconomic burden of sleep disorders is expected to keep increasing. CVD also remains a major source of economic burden in Korea, national healthcare expenditure for circulatory system diseases reached Korean won (KRW) 13.4 trillion in 2023. This amount exceeded the expenditure for cancer—the leading cause of death—which totaled KRW 10.1 trillion. Among individual diseases, hypertension accounted for the highest cost (KRW 4.4 trillion, 4.1%), followed by type 2 diabetes (KRW 3.1 trillion, 3.3%) (42). Given these trends, the prevention and management of sleep disorders and CVD may be important. They may have important implications for socioeconomic costs, productivity, and mortality. In this context, identifying modifiable factors, such as sleep duration, that are associated with cardiovascular risk can help inform effective prevention strategies. Accordingly, our findings on sleep duration and the FRS may inform future public health strategies in Korea.

This study has several strengths. First, this study is notable for confirming the widely reported U- or J-shaped association between sleep duration and cardiovascular risk in a recent sample of Korean adults. Sleep patterns and their health effects vary across ethnic groups (43). In particular, the FRS was originally developed in the United States. When applied to Asian populations, it may overestimate or underestimate cardiovascular risk (44). Although population-specific studies focusing on Koreans are necessary, such research has been relatively limited. Previous Korean studies often used outdated data (21). To address these limitations, the present study utilized the most recent nationally representative data. Sampling weights were applied to ensure population representativeness and to correct potential sampling errors, such as non-response and unequal selection probabilities. These adjustments enhanced the accuracy and generalizability of the estimates. As a result, the U-shaped association between sleep duration and cardiovascular risk previously reported in Western populations was also observed among Korean adults. This finding suggests that the association may also be relevant in the current Korean context.

Second, multivariable logistic regression analyses were conducted to examine the association while controlling for major potential confounders. These included education level, personal income, marital status, depression, alcohol consumption, obesity, sedentary time, physical activity, and obstructive sleep apnea. Even after these adjustments, the U-shaped association between sleep duration and the FRS remained statistically significant. This suggests that sleep duration may be independently associated with cardiovascular risk.

Third, the FRS was used in this study. It is a validated and widely used tool for assessing CVD risk (25). Unlike many prior studies focusing solely on the clinical presence of CVD, this approach provides valuable insights by enabling intervention before the onset of disease. Moreover, this study shows that sleep duration—a modifiable lifestyle factor—is associated with an early predictive marker, the FRS. This suggests that sleep may serve not only as a general health habit but also as a potential factor relevant to CVD risk. Fourth, the FRS is calculated using readily available health examination variables such as age, sex, total cholesterol, HDL, SBP, treatment for hypertension, smoking status, and diabetes status. Incorporating a simple question on sleep duration into routine health examinations may provide a cost-effective approach for identifying individuals at elevated risk of CVD. This illustrates the potential for linking lifestyle factors such as sleep to health screening data in a meaningful way. Such an approach holds considerable promise for practical application in community-based health promotion initiatives and lifestyle intervention programs. Ultimately, it could serve as a foundation for targeted interventions aimed at individuals at high-risk, contributing to the prevention of CVD and a reduction in mortality.

Lastly, diabetes and hypertension, major contributors to CVD, remain inadequately managed in Korea (13). Incorporating sleep as an additional lifestyle factor—one that is simple and low-cost—may serve as a practical adjunct to complex clinical procedures for assessing cardiovascular risk and supporting early risk identification. Such an approach may be more accessible and feasible than conventional clinical approaches, as it can be easily understood and adopted by the general population. Considering the current limitations in CVD management, promoting health behaviors may be an important component of public health strategies. Such behaviors also may have potential for application in healthcare policies and community health promotion programs.

However, there are also limitations.

First, the sleep measurement was collected through self-report, and no data on sleep quality was gathered. Sleep can be measured using both objective and subjective methods. Objective methods include PSG and actigraphy, while subjective methods involve self-reported measurements such as sleep diaries and questionnaires (45). PSG utilizes electrophysiological signals, including electromyogram, electrocardiogram, electroencephalogram, and electro-oculogram recordings (46). Actigraphy estimates sleep parameters by using methods comparable to PSG measurements (47). However, PSG is not easily used in daily life and is time- and cost-intensive. Actigraphy, while less expensive, does not directly measure sleep itself and is further limited by variability in devices and algorithms used for indirect measurement (45). Sleep diaries are the most commonly used subjective method, where sleep estimates are recorded every morning. However, they rely on daily records and may be less reliable among older adults due to memory difficulties (45). On the other hand, the self-report measure used in this study is low-cost, easily administered online across populations, and requires no supervision. It also demands minimal time from healthcare professionals, making it suitable for nationwide surveys such as the KNHANES. However, the self-reported method carries the risk of overestimating sleep duration. This may have led to non-differential misclassification and attenuation of the observed association. Moreover, not only the quantity but also the quality and regularity of sleep are important for overall sleep health (48). Studies have also shown that the relative risk of hypertension and diabetes is higher when sleep quality is analyzed than when sleep duration alone is considered (49). Therefore, further in-depth research considering both the quantity and quality of sleep is necessary.

Second, there may also be errors arising from the nature of the data collected. The KNHANES is a cross-sectional survey, which limits the ability to infer causality between explanatory and outcome variables. Additionally, inconsistencies in survey items across years may have introduced errors. Although data were collected using standardized protocols, supporting overall comparability across years, minor inconsistencies may still have influenced the results. For example, in contrast to the 2020 and 2021 surveys, the 2022 dataset did not include information on stroke, myocardial infarction, or angina. This may have introduced bias during the selection of study participants, potentially affecting the magnitude of the observed associations. Nevertheless, this analysis was conducted using the raw data from KNHANES, and the study was therefore limited to variables available in the dataset. For more accurate assessment, future research should utilize data that more thoroughly capture CVD history.

In addition, a substantial number of participants were excluded due to missing data across variables included in the analysis. This may have introduced selection bias, as the included participants could differ systematically from those excluded. If the missingness is not completely at random, the observed associations may be biased. Therefore, the findings of this study should be interpreted with caution. Additionally, residual confounding from unmeasured factors, such as diet quality and stress levels, cannot be ruled out.

Conclusions

Both short and long sleep durations were independently associated with elevated 10-year cardiovascular risk among Korean adults. These findings suggest that sleep duration may represent a modifiable lifestyle factor relevant to cardiovascular risk stratification and prevention.

In conclusion, both short and long sleep durations were associated with elevated 10-year cardiovascular risk among Korean adults, demonstrating a U-shaped relationship between sleep duration and cardiovascular risk. Korea is characterized by chronically short sleep duration compared with other OECD countries, reflecting unique sociocultural and occupational environments such as long working hours, extended commuting time, and high psychosocial stress. In this context, the present study provides important population-based evidence on the association between sleep duration and cardiovascular risk within a chronically sleep-deprived society, extending the existing literature beyond simple replication in Western populations.

Unlike many previous studies that focused primarily on established CVD outcomes, this study evaluated cardiovascular risk at a preclinical stage using the FRS. This approach highlights sleep duration as a potentially modifiable upstream lifestyle factor associated with early cardiovascular risk accumulation and prevention.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jphe.amegroups.com/article/view/10.21037/jphe-2026-1-0014/rc

Peer Review File: Available at https://jphe.amegroups.com/article/view/10.21037/jphe-2026-1-0014/prf

Funding: This work was supported by research fund of Chungnam National University. The university had no role in the design of this study, analysis of the data, in the writing of the manuscript, or in the decision to submit the manuscript for publication.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jphe.amegroups.com/article/view/10.21037/jphe-2026-1-0014/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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