
Stroke is a condition in which brain cells are damaged or die due to an acute disruption of blood flow to the brain, making it one of the leading causes of death worldwide.1 Since the time from interruption of blood supply to brain cell damage or death is short, early intervention and prevention are crucial.2 With the aging population, the prevalence and mortality rates of stroke in South Korea are also steadily increasing, resulting in a growing socioeconomic burden.3 A prior study reported that among South Korea’s middle-aged population, stroke incidence rises sharply from 175 cases per 100,000 in the 45-54 age group to 473 cases per 100,000 in the 55-64 age group, underscoring the need for targeted prevention and early management.4
Stroke often leads to permanent cognitive and physical disabilities even after recovery, resulting in various types of sequelae, including loss of motor function, speech impairments, and sensory paralysis, which limit patients’ daily lives and social participation.5,6 Such disabilities make it challenging for patients to live independently, and the long-term medical and caregiving costs associated with rehabilitation add to the economic burden.3 Consequently, stroke is regarded not only as an individual issue but also as a societal concern, emphasizing the need for effective prevention and management efforts to reduce its prevalence.
Major risk factors for stroke include hypertension, diabetes, smoking, alcohol consumption, lack of physical activity, and obesity, which are reported to have a significant impact, particularly among the elderly.7-9 These risk factors are generally closely related to lifestyle, and if not properly managed, they can lead to an increased incidence of stroke.10 Therefore, it is important to identify risk factors for stroke and prevent disease through lifestyle improvements.
Most existing studies have been conducted mainly in Western countries or focused on specific age groups or sexes, such as the elderly, and studies on middle-aged Koreans are limited.11-13 In particular, there is a relative lack of studies that reflect the lifestyle and social factors specific to Koreans. Therefore, an analysis of stroke prevalence and risk factors based on domestic data encompassing middle-aged Koreans is necessary.
This study aims to analyze the prevalence of stroke and its major risk factors among middle-aged Koreans using data from the Korea National Health and Nutrition Examination Survey (KNHANES). Through this analysis, the study seeks to identify high-risk groups for stroke at an early stage and develop effective prevention and management strategies. Additionally, it will provide essential baseline data for developing stroke prevention programs tailored to the characteristics of middle-aged Koreans. The specific objectives of this study are as follows: First, to determine the prevalence of stroke among middle-aged Koreans; second, to analyze the differences in demographic, health status, and behavior characteristics between stroke patients and the general population; and third, to identify risk factors that influence stroke incidence. This research ultimately aims to provide scientific evidence for stroke prevention and health promotion among middle-aged Koreans.
This study utilized data from the Korea National Health and Nutrition Examination Survey (KNHANES) provided by the Korea Disease Control and Prevention Agency for the years 2016-2021. The subjects were selected from adults. Out of the 46,828 participants, those under 40 years old, 29,276 individuals aged 65 and older, 6,742 non-participants of the health survey, and 9,598 individuals with diseases such as myocardial infarction, angina, or cancer were excluded. Ultimately, 9,598 subjects were selected. The final selected subjects were classified into 9,456 healthy individuals and 142 individuals with a history of stroke (Figure 1).
Sociodemographic factors considered in this analysis included sex, age, education level, marital status, and personal income. Educational attainment was grouped by completion of elementary, middle, high school, or university. Marital status was assessed based on whether participants were currently living with a spouse. Personal income was divided into quartiles based on average monthly income.14
Smoking status was classified by grouping individuals who reported ‘daily smoking’ or ‘occasional smoking’ as current smokers, those who indicated ‘I smoked in the past but no longer do’ as former smokers, and those who selected ‘I never smoked’ as nonsmokers. Drinking status was defined by identifying current drinkers as those who consumed alcohol ‘at least once per month’ and nondrinkers as individuals who drank ‘less than once a month’ or ‘did not drink at all in the past year.’
Aerobic exercise was assessed through self-reported walking duration, with participants noting the number of days they walked for at least 10 minutes at a time over the previous week. Individuals meeting the criteria of 150 minutes or more of walking per week were considered to meet the aerobic exercise guideline. Resistance exercise frequency was assessed by asking participants how often they performed resistance exercises, such as push-ups, sit-ups, or weightlifting. Resistance exercise participation was categorized as ‘never,’ ‘1 to 3 days per week’ for moderate intensity, and ‘more than 4 days per week’ for high intensity.
The subjective health status was classified into three categories: responses of ‘Very Good’ or ‘Good’ were grouped as ‘Good,’ ‘Moderate’ responses as ‘Normal,’ and responses of ‘Poor’ or ‘Very Poor’ as ‘Bad’.15
The study examined a range of health and disease-related factors, including height, weight, body mass index (BMI), blood pressure, fasting glucose levels, triglycerides, high-density lipoprotein cholesterol (HDL-C), waist circumference (WC). BMI was determined by dividing weight in kilograms by height in meters squared (kg/m2) and was categorized into groups of underweight, normal weight, overweight, and obese. Hypertension was identified by a systolic blood pressure greater than 130mmHg, a diastolic pressure exceeding 85mmHg, or the ongoing use of antihypertensive medications. Diabetes was diagnosed with a fasting glucose level of 100mg/dL or higher, or by the current use of diabetes medications. Elevated triglyceride levels were defined as exceeding 150mg/dL, while low HDL-C was indicated by levels below 40mg/dL for men and under 50mg/dL for women. Abdominal obesity was determined based on a WC greater than 90cm for men and over 85cm for women.16
The data analysis for this study was conducted using SPSS version 28.0, with a statistical significance level set at 0.05. The analysis utilized data extracted from a complex stratified sampling method to ensure the KNHANES data accurately represented the population of South Korea. Integrated weights, stratification variables, and cluster variables were applied to achieve this representation. As a result, estimated frequencies and values applicable to the entire population were generated, allowing the study to focus on both the survey results and the estimated values.
The specific analysis methods employed were as follows: First, differences in characteristics between groups were analyzed using a complex sample general linear model and Chi-squared tests (χ2-test), with variance estimation based on standard error (SE). Second, to analyze the risk factors influencing stroke, a complex sample multiple logistic regression analysis was performed, and the statistical outputs were presented as odds ratios (OR) with 95% confidence intervals (CI).
The prevalence of stroke in this study was found to be 1.40%. Differences in the demographic characteristics of the study participants are presented in Table 1. Significant differences were observed in terms of age, education level, marital status, and personal income according to stroke prevalence. The average age of the stroke group was 55.3 years, while the control group had an average age of 51.3 years, showing a statistically significant difference. In terms of education level, 11.3% of the stroke group had elementary education, 16.0% had middle school education, 39.5% had high school education, and 33.2% had university education. These figures were compared to the control group, which had 4.9%, 8.1%, 39.5%, and 33.2% in each category, respectively, showing significant differences. Regarding marital status, the percentage of participants living with a spouse was 74.4% in the stroke group, which was significantly lower than the 83.9% in the control group. Personal income levels in the stroke group were as follows: 35.8% in Q1, 26.5% in Q2, 18.0% in Q3, and 19.7% in Q4, compared to 23.8%, 25.4%, 25.9%, and 24.8% in the control group, with significant differences observed.
Sociodemographic characteristics of participants with stroke
Factors | Categories | Normal (n= 9,456) | Stroke (n= 142) | p |
---|---|---|---|---|
Mean± SE or % | Mean± SE or % | |||
Prevalence | 1.40 | |||
Age | 51.3± 0.1 | 55.3± 0.7 | < 0.001** | |
Sex | Male | 62 | 71.1 | 0.052 |
Female | 38 | 28.9 | ||
Education | Elementary | 4.9 | 11.3 | < 0.001** |
Middle | 8.1 | 16.0 | ||
High | 39.5 | 39.5 | ||
University | 33.2 | 33.2 | ||
Marital status | With | 83.9 | 74.4 | 0.005* |
Without | 16.1 | 25.6 | ||
Personal income | Q1 (Lowest) | 23.8 | 35.8 | 0.019* |
Q2 | 25.4 | 26.5 | ||
Q3 | 25.9 | 18.0 | ||
Q4 (Highest) | 24.8 | 19.7 |
Mean±SE: mean±standard error. *p<0.05, **p<0.001.
participants
The health status and health behavior characteristics of the study participants are presented in Table 2. Significant differences were observed in subjective health status, hypertension, diabetes, and abdominal obesity based on stroke prevalence. In the stroke group, 42.0% reported bad health, 42.9% reported normal health, and 15.2% reported good health. These figures were significantly different when compared to the control group, where 14.2%, 54.7%, and 31.0% were reported, respectively. The stroke group also had higher rates of hypertension (49.9%), diabetes (60.7%), and abdominal obesity (46.3%) compared to the control group.
Health status and behavior characteristics of participants with stroke
Factors | Categories | Normal (n= 9,456) | Stroke (n= 142) | p | |
---|---|---|---|---|---|
M or % | M or % | ||||
BMI | Low | 2.2 | 3.1 | 0.187 | |
Normal | 57.7 | 50.2 | |||
Overweight | 34.0 | 42.4 | |||
Obesity | 6.1 | 4.2 | |||
Smoking status | Current | 26.9 | 23.3 | 0.127 | |
Past | 35.4 | 44.8 | |||
Non | 31.9 | 31.9 | |||
Drinking status | Yes | 63.2 | 64.9 | 0.714 | |
No | 36.8 | 35.1 | |||
Aerobic exercise | Yes | 49.5 | 48.6 | 0.853 | |
Resistance exercise | Never | 73.2 | 74.2 | 0.605 | |
Mid | 15.8 | 17.8 | |||
High | 10.9 | 8.1 | |||
Subjective health status | Bad | 14.2 | 42.0 | < 0.001** | |
Normal | 54.7 | 42.9 | |||
Good | 31.0 | 15.2 | |||
Comorbidities conditions | |||||
Hypertension | 33.3 | 49.9 | < 0.001** | ||
Diabetes | 45.3 | 60.7 | 0.002* | ||
High triglyceride | 37.6 | 44.6 | 0.150 | ||
Low HDL-C | 30.9 | 29.0 | 0.660 | ||
Abdominal obesity | 35.2 | 46.3 | 0.016* |
BMI: body mass index, HDL-C: high-density lipoprotein cholesterol, M: mean. *p<0.05, **p<0.001.
The risk factors influencing stroke prevalence are presented in Table 3. The results of the multiple logistic regression analysis, which adjusted for confounding factors, indicated significant associations with age, marital status, subjective health status, and hypertension. The risk of stroke increased by 1.077 times (95% CI, 1.039-1.117) for each year of age. When comparing participants living with a spouse to those not living with a spouse, the odds of stroke prevalence were 1.552 times (95% CI, 1.009-2.386) higher for those not cohabiting. Individuals with poor subjective health had a significantly higher risk of stroke, with an odds ratio of 5.293 (95% CI, 2.847-9.841), compared to those with good health; however, there was no significant difference between normal health and the other categories. Participants with hypertension had a 1.572 times (95% CI, 1.042-2.374) higher risk of stroke compared to those with normal blood pressure.
Multiple logistic regression for stroke risk factors
Factors | Categories | Crude | Adjusted | |||
---|---|---|---|---|---|---|
OR (95% CI) | p | OR (95% CI) | p | |||
Age | 1.088 (1.056–1.122) | < 0.001** | 1.077 (1.039–1.117) | < 0.001** | ||
Sex | Male | 1.509 (0.993–2.294) | 0.054 | 1.818 (0.973–3.399) | 0.061 | |
Female | 1 | 1 | ||||
Education | Elementary | 3.283 (1.805–5.972) | < 0.001** | 1.472 (0.679–3.187) | 0.327 | |
Middle | 2.837 (1.57–5.127) | < 0.001** | 1.602 (0.763–3.364) | 0.213 | ||
High | 1.454 (0.914–2.314) | 0.114 | 1.179 (0.713–1.950) | 0.521 | ||
University | 1 | 1 | ||||
Marital status | With | 1 | 1 | |||
Without | 1.793 (1.183–2.718) | 0.006* | 1.552 (1.009–2.386) | 0.045* | ||
Personal income | Q1 (Lowest) | 1.901 (1.116–3.24) | 0.018* | 1.306 (0.708–2.410) | 0.393 | |
Q2 | 1.316 (0.743–2.329) | 0.347 | 1.179 (0.638–2.178) | 0.599 | ||
Q3 | 0.878 (0.475–1.625) | 0.678 | 0.868 (0.453–1.664) | 0.670 | ||
Q4 (Highest) | 1 | 1 | ||||
BMI | Low | 1.651 (0.493–5.536) | 0.417 | 1.681 (0.481–5.878) | 0.416 | |
Normal | 1 | 1 | ||||
Overweight | 1.435 (0.97–2.123) | 0.071 | 0.954 (0.513–1.771) | 0.880 | ||
Obesity | 0.799 (0.41–1.557) | 0.508 | 0.379 (0.153–0.941) | 0.379 | ||
Smoking status | Current | 1.021 (0.612–1.703) | 0.937 | 0.554 (0.284–1.078) | 0.082 | |
Past | 1.489 (0.957–2.318) | 0.078 | 0.953 (0.527–1.723) | 0.872 | ||
Non | 1 | 1 | ||||
Drinking status | Yes | 1.075 (0.729–1.586) | 0.714 | 1.084 (0.719–1.634) | 0.700 | |
No | 1 | 1 | ||||
Aerobic exercise | Yes | 1 | 1 | |||
No | 0.964 (0.651–1.427) | 0.853 | 0.966 (0.658–1.421) | 0.862 | ||
Resistance exercise | Never | 1.11 (0.668–1.844) | 0.326 | 1.286 (0.627–2.642) | 0.492 | |
Mid | 0.73 (0.349–1.528) | 0.402 | 1.718 (0.757–3.899) | 0.195 | ||
High | 1 | 1 | ||||
Subjective health status | Bad | 6.045 (3.378–10.819) | < 0.001** | 5.293 (2.847–9.841) | < 0.001** | |
Normal | 1.605 (0.904–2.849) | 0.106 | 1.566 (0.873–2.810) | 0.132 | ||
Good | 1 | 1 | ||||
Comorbidities conditions | ||||||
Blood pressure | Normal | 1 | 1 | |||
Hypertension | 1.998 (1.348–2.961) | < 0.001** | 1.572 (1.042–2.374) | 0.031* | ||
Fasting glucose | Normal | 1 | 1 | |||
Diabetes | 1.866 (1.241–2.807) | 0.003* | 1.273 (0.821–1.974) | 0.281 | ||
TG | Normal | 1 | 1 | |||
High | 1.333 (0.900–1.975) | 0.151 | 1.098 (0.690–1.747) | 0.693 | ||
HDL-C | Normal | 1 | 1 | |||
Low | 0.912 (0.603–1.378) | 0.660 | 0.773 (0.496–1.206) | 0.256 | ||
WC (cm) | Normal | 1 | 1 | |||
Abdominal obesity | 1.589 (1.087–2.322) | 0.017* | 1.332 (0.726–2.444) | 0.354 |
BMI: body mass index, TG: triglycerides, HDL-C: high-density lipoprotein cholesterol, WC: waist circumference, OR: odds ratio, CI: confidence interval. *p<.05, **p<0.001.
This study analyzed the prevalence of stroke and its major risk factors among middle-aged adults in Korea. The stroke prevalence in this group was found to be 1.40%, with significant differences observed in terms of age, marital status, subjective health status, and hypertension. These findings provide important foundational data for understanding stroke occurrence and its associated risk factors among middle-aged Koreans.
In this study, the stroke prevalence among middle-aged adults in South Korea, estimated at 1.40%, was found to be significantly lower than the prevalence reported in some high-income countries, such as the United States, where the rate among adults aged 20 and older is approximately 2.7%.17 Differences in healthcare systems, dietary practices, and approaches to managing chronic diseases may account for this variation. These findings underscore the potential for South Korea to achieve further reductions in stroke prevalence through targeted public health initiatives and preventive measures.
The probability of stroke occurrence increased by 1.08 times for each additional year of age. As confirmed in previous studies, this result supports the idea that the incidence of stroke increases with age.18,19 One prior study reported that the risk of stroke doubles every 10 years after the age of 55, indicating a growing trend of stroke cases due to population aging and longer life expectancy.19 Aging brings about various physiological changes in the body. The reduction in vascular elasticity, increased blood pressure, and the progression of arteriosclerosis become more pronounced with age, all of which can increase the risk of stroke.20
The results of this study showed that individuals not living with a spouse had a 1.55 times higher risk of stroke. This finding reflects the close relationship between social and emotional support and health. According to previous studies, living with a spouse positively influences physical activity, stress management, and the maintenance of healthy eating habits.21 These health behaviors can play a crucial role in preventing cardiovascular diseases, such as stroke.22,23 On the other hand, individuals who live separately from their spouses or live alone are more likely to experience social isolation, which increases the chances of hypertension, depression, chronic stress, and difficulties in managing their health.24 Moreover, living with a spouse can lead to better recognition of emergency situations and quicker responses, ultimately leading to better treatment outcomes.25 Therefore, cohabitation with a spouse can be an important factor in stroke prevalence, and strengthening social support should be considered in strategies for improving stroke prevention and management.
Participants who rated their subjective health as ‘bad’ had a 5.29 times higher risk of stroke compared to those who rated it as ‘good.’ Subjective health status is considered an important indicator for predicting disease risk as it reflects one’s health behaviors and condition.26 Previous studies have shown that bad subjective health is associated with a higher incidence of chronic diseases.27 When individuals perceive their health as bad, they tend to engage less in health-promoting behaviors, which may further elevate the risk of stroke.28,29 Those who report bad subjective health often show increased inflammatory responses and higher stress levels, both of which are factors that contribute to an elevated risk of stroke. Therefore, it is essential to continuously assess and manage subjective health status as part of efforts to prevent stroke.
Participants with hypertension had a 1.57 times higher probability of experiencing a stroke. Hypertension is one of the most significant risk factors for stroke, as increased pressure within the blood vessels can damage the vessel walls and lead to arteriosclerosis.30,31 Studies have shown that hypertension increases the risk of both ischemic and hemorrhagic strokes, with uncontrolled hypertension particularly accelerating vascular damage and heightening stroke risk.32 Furthermore, prolonged hypertension triggers inflammatory responses within the blood vessels, reducing the elasticity of cerebral blood vessels and making individuals more susceptible to stroke.33 Previous research has shown that for every 10mmHg reduction in systolic blood pressure, the risk of stroke decreases by approximately 30%, supporting the importance of managing hypertension for stroke prevention.33,34 Therefore, hypertension is a modifiable risk factor, and regular blood pressure monitoring and early intervention are crucial for stroke prevention.
This study has several limitations. First, as a cross-sectional study, it is difficult to establish a clear temporal relationship between the risk factors and the occurrence of stroke in the participants. Therefore, future cohort studies are needed to analyze the temporal causality. Second, other potential risk factors that may influence stroke occurrence, such as hormonal changes, family history, and dietary habits, were not considered in this study. Further research may be needed to explore the impact of these factors on stroke occurrence. Third, the smoking survey used in this study did not account for the amount or duration of smoking, so subsequent studies should address this issue. Fourth, as this study evaluated health behaviors based on self-report, the results may be influenced by the participants’ subjective assessments or recall bias. Thus, future studies should use objective health indicators. Fifth, since this study was conducted with middle-aged adults in South Korea, the results may differ from those in other countries or age groups. Sixth, the data in this study did not distinguish between types of strokes, such as ischemic and hemorrhagic strokes, so future research should include an analysis that reflects these subtypes. Despite these limitations, this study provides valuable foundational data for stroke prevention in South Korea’s middle-aged population.
In conclusion, this study analyzed the prevalence and risk factors of stroke among middle-aged adults in South Korea. The results showed that the risk of stroke increases with age, and that not living with a spouse, having poor subjective health, and having hypertension are significantly associated with stroke occurrence. Therefore, national-level prevention policies and management programs are necessary for the middle-aged and older population in South Korea who are at risk of stroke. It is believed that the findings of this study will serve as valuable foundational data for these public health policies.
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