Evaluation of the association between mortality and economic status in patients with metabolic syndrome in Korea: a retrospective cohort study using the National Health Screening cohort
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The disease burden of metabolic syndrome (MetS) is increasing with increasing prevalence. Economic deprivation is a risk factor for MetS and contributes to the overall mortality. Therefore, this study aimed to investigate the association between economic status and mortality in patients with MetS.
Methods
Overall, 83,786 patients with MetS were included from the Korean National Health Insurance Service-Health Screening Database. They were divided into three economic levels (low, medium, and high) based on the health insurance premiums charged according to the annual household income. Adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) for all-cause mortality were calculated using Cox proportional hazards regression models.
Results
The median follow-up duration was 10.0 years. Kaplan-Meier plots showed that the mortality rate was highest in males with a low economic status (P<0.001, log-rank test). Compared with that of the high economic status group, unadjusted HRs (95% CIs) of the middle and low economic status groups for all-cause mortality were 1.44 (1.32–1.57) and 1.88 (1.72–2.06), respectively, in males, and 0.84 (0.76–0.93) and 0.99 (0.89–1.10), respectively, in females. However, in the fully adjusted model, the corresponding HRs (95% CIs) were 1.23 (1.13–1.48) and 1.35 (1.23–1.48), respectively, in males and 1.17 (1.06–1.30) and 1.25 (1.12–1.39), respectively, in females.
Conclusion
Among South Korean adults with MetS, the economically deprived population was significantly associated with higher mortality rates than those of wealthier groups.
Metabolic syndrome (MetS) is a cluster of conditions including obesity, dyslipidemia, hypertension, and hyperglycemia [1]. These conditions, independently or in combination, are risk factors for cardiovascular disease (CVD) [1,2]. The pathophysiology of MetS has not been fully elucidated but is thought to be related to insulin resistance [2]. The global prevalence of MetS varies from 12.5% to 31.4%, depending on the diagnostic criteria; however, it is common and its overall prevalence is increasing [3]. This increasing prevalence can lead to CVDs, morbidity, hospitalization, and mortality, resulting in disease burden [4].
Social determinants of health (SDH) are important nonmedical factors that affect health outcomes [5,6]. These factors significantly affect health disparities, resulting in unfair and avoidable health gaps. A low socioeconomic status (SES) is associated with poor health outcomes. SDHs, as defined by the World Health Organization, include income and social protection, education, unemployment and job insecurity, working conditions, food insecurity, and housing [6]. Economic status (ES) is inversely associated with MetS risk [7,8]. Individuals with a high SES have a lower risk of developing MetS than those with a low SES [7,8]. Economic deprivation is also a risk factor for mortality in individuals with diseases such as cancer and CVDs [9]. However, evidence on the association between ES and mortality in individuals with MetS is lacking. Thus, this study aimed to determine whether economic deprivation confers an additional mortality risk among individuals already at high cardiometabolic risk due to MetS, using nationally representative South Korean cohort data. In addition, we attempted to raise awareness about the need to manage MetS and increase interest in the influence of a low ES on MetS management.
Methods
Data source and study population
This study used data from the Korean National Health Insurance Service-Health Screening (NHIS-HEALS) Cohort Study database. The NHIS-HEALS database included 514,886 individuals randomly selected from 10% of South Korean national health insurance holders aged 40 to 79 years as of 2002, among national health screening examinees from 2002 to 2003. The database contains sociodemographic characteristics, laboratory data from health checkups, medical institute usage, lifestyle behaviors, disease codes, and personal prescription records from the claims data.
Figure 1 shows a flowchart of the participant selection for this study. As data on the triglyceride (TG) and high-density lipoprotein (HDL) cholesterol levels required to define MetS have been available since 2009, the baseline for this study was set between 2009 and 2010 and not in 2002 when this cohort was constructed. First, 159,993 participants who met the criteria for MetS among the health screening examinees between 2009 and 2010 were selected. Of these initial participants, 76,207 who met any of the following criteria were excluded: (1) participants diagnosed with ischemic heart diseases (International Classification of Diseases, 10th Revision [ICD-10] codes, I20–I25; n=33,792), cerebrovascular diseases (ICD-10 codes, I60–I69; n=24,864), and malignancy (ICD-10 codes, C00–C97 and D00–48; n=39,454); (2) participants who died between 2009 and 2011 (n=67); (3) participants for whom complete data were unavailable (n=2,372); and (4) participants for whom the total study duration was <30 days (n=19). Notably, the small number of early deaths (n=67) indicated that most deaths during this period overlapped with participants already excluded due to preexisting CVD or malignancy, thereby minimizing duplicate counting across the exclusion categories. Finally, 83,786 participants were included in this study.
Ethical considerations
This study was approved by the Institutional Review Board of Chungbuk National University Hospital (CBNUH 2024-01-003) and was conducted according to the tenets of the 1975 Declaration of Helsinki. The need for informed consent was waived by the NHIS ethics committee because this study was conducted using de-identified data.
Operational definition and study duration
In the initial participant selection process, MetS was diagnosed based on compliance with three or more of the following five modified National Cholesterol Education Program Adult Treatment Panel III (ATP III) criteria for Asians: (1) waist circumference of ≥90 cm in males and ≥80 cm in females; (2) serum TG level of ≥150 mg/dL or use of TG-lowering drugs due to elevated TG levels; (3) HDL cholesterol level of <40 mg/dL in males and <50 mg/dL in females or use of related drug treatment; (4) systolic blood pressure (SBP) of ≥130 mm Hg, diastolic blood pressure of ≥85 mm Hg, or use of antihypertensives to reduce blood pressure; and (5) fasting blood glucose level of ≥100 mg/dL or use of antidiabetic agents for elevated glucose levels [10].
For high TG levels, treatment was defined as the use of lipid-lowering agents primarily targeting hypertriglyceridemia, including fibrates and omega-3 fatty acid derivatives (e.g., Omacor), for more than 30 days. Patients were considered to meet the high TG level criterion by medication only if they had not already met the low HDL cholesterol criteria described below. For low HDL cholesterol levels, because pharmacological therapy specifically aimed at increasing HDL levels is rare and often indirect in clinical practice, treatment was defined as the use of nicotinic acid (niacin) or statins, as a widely used proxy for general MetS-related dyslipidemia management in claims data, for more than 30 days. Patients receiving statins or niacin therapy were considered to satisfy the low HDL cholesterol level criteria based on medication use. Importantly, to prevent double counting, patients receiving statin or niacin therapy could satisfy the high TG level criterion only through an abnormal laboratory value and not through concurrent medication use. For blood pressure, treatment was defined as the use of any type of antihypertensive drugs including angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, alpha-blockers, beta-blockers, calcium channel blockers, or diuretics for more than 30 days. For elevated blood glucose levels, treatment was defined as the use of glucose-lowering agents, including metformin, sulfonylureas, alpha-glucosidase inhibitors, thiazolidinedione, dipeptidyl peptidase 4-inhibitors, and sodium-glucose cotransporter 2 inhibitors, for more than 30 days or more than one use of insulin or glucagon-like peptide 1 receptor agonists.
The primary-outcome measure was all-cause mortality. The study period for each participant started on the first health examination date between 2009 and 2010 and ended on the date of death. If death did not occur during the study period, the end of the study period was defined as the last health examination or hospital visit, whichever occurred later. ES was assessed based on monthly health insurance premiums (HIPs) calculated and managed by the NHIS. For employee-insured individuals, HIPs were determined primarily by reported salaries, whereas for self-employed individuals, they were assessed based on a composite of the reported income, property value, and other assets. Thus, HIPs served as an integrated proxy for the overall household economic capacity, rather than income alone. Participants were categorized into three ES groups based on the HIP distribution within the study population: low (0–30th percentile), middle (31st–70th percentile), and high (71st–100th percentile). This classification approach followed previous NHIS-based cohort studies that used the same criteria to ensure comparability across studies on socioeconomic disparities in South Korea [11,12].
Confounding variables
The following variables were considered for the occurrence of death according to ES in patients with MetS: age, body mass index (BMI), smoking status, alcohol intake, physical activity, residential area, and Charlson comorbidity index (CCI).
The BMI was calculated as the body weight (kg) divided by the height squared (m2). Participants were categorized based on their smoking status: never smokers (never smoked before), former smokers (history of smoking ≥100 cigarettes but had quit before the study), or current smokers (smoked cigarettes at the time of the study). Alcohol consumption was categorized based on the number of drinks consumed per week: rare (less than once a week), sometimes (2–3 times a week), or often (≥4 times a week). The degree of physical activity was classified according to the average number of physical activities per week: rare (less than once a week), sometimes (1–4 times a week), or often (≥5 times a week). Residential areas were divided into metropolitan and nonmetropolitan areas according to the population and the criteria for urban classification in South Korea. The metropolitan areas included Seoul, six major metropolitan cities (Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan), and Sejong Special Self-Governing City as defined by the Ministry of the Interior and Safety of Korea. All other regions were classified as nonmetropolitan [13]. The CCI was calculated by classifying each of the 18 comorbidities using the relevant ICD-10 codes and scoring them accordingly (Supplement 1).
Statistical analysis
Statistical analysis results are presented as mean±standard deviation for continuous variables and as counts with proportions (%) for categorical variables. To compare the baseline characteristics between the groups, analysis of variance and chi-square tests were performed for continuous and categorical variables, respectively. To compare the cumulative occurrence of death, survival probabilities were analyzed using Kaplan-Meier estimates and log-rank tests.
To minimize the effects of confounding variables, the following three Cox proportional hazards regression models with 95% confidence intervals (CIs) were applied sequentially: Model 1, unadjusted; Model 2, adjusted for age and BMI; and Model 3, Model 2 adjusted for smoking status, alcohol intake, physical activity, and CCI. All tests were two-sided, and statistical significance was set at P-value <0.05. Statistical analyses were conducted using SAS ver. 7.1 (SAS Inc.) and R studio ver. 3.3.3 (The R Foundation). This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, and the completed checklist is provided in Supplement 2.
Results
This study included 83,786 participants, including 44,662 males (53.3%) and 39,124 females (46.7%). Table 1 shows the baseline participant characteristics. The high-, middle-, and low-ES groups included 22,784, 13,879, and 7,999 males, respectively, and 14,707, 13,856, and 10,561 females, respectively. Males in the high-ES group were the youngest, whereas females in the high-ES group were the oldest. Among males, the waist circumference, SBP, and fasting blood glucose levels were the highest in the low-ES group; the TG level was the highest in the middle-ES group; and the HDL cholesterol level was the lowest in the high-ES group. Among females, only two components of MetS (waist circumference and fasting blood glucose level) were significantly different between the groups, whereas SBP, TG, and HDL cholesterol levels were not different. In terms of lifestyle factors, both sexes had the lowest percentage of current smokers and frequent drinkers and the highest proportion of regular physical activities in the high-ES group. The proportion of participants with a CCI score of ≥3 was the highest in the low-ES group among males and was the highest in the high-ES group among females.
The median follow-up duration was 10.0 years. Figure 2 shows the survival probability according to ES by sex. Among males, survival probability increased with higher ES (P<0.001). Among females, the unadjusted Kaplan–Meier curves showed the highest survival probability in the middle ES group and the lowest in the high ES group (P=0.001).
Table 2 shows the hazard ratios (HRs) for all-cause mortality according to ES. Compared to that of the high-ES group, the unadjusted HRs (95% CIs) of the middle- and low-ES groups were 1.44 (1.32–1.57) and 1.88 (1.72–2.06), respectively, in males and 0.84 (0.76–0.93) and 0.99 (0.89–1.10), respectively, in females (Model 1). After fully adjusting for age, BMI, smoking status, alcohol intake, physical activity, residential area, and CCI, the HRs (95% CIs) of the middle- and low-ES groups were 1.23 (1.13–1.48) and 1.35 (1.23–1.48), respectively, among males and 1.17 (1.06–1.30) and 1.25 (1.12–1.39), respectively, among females (Model 3).
Discussion
This study makes a distinct contribution by demonstrating that even among South Korean adults already defined as high-risk due to MetS [1], economic deprivation remains positively associated with a higher risk of all-cause mortality. While the components of MetS, including obesity, hyperglycemia, dyslipidemia, and hypertension [14,15], are well-established risk factors for increased cardiovascular and all-cause mortality [16], our findings reveal that SES is a powerful and independent prognostic factor in this specific patient group.
Our analysis indicates that economic deprivation exerts an additive effect on mortality risk, extending beyond the established metabolic burden of MetS. This finding addresses the critical question of whether SES-mortality associations remain strong, strengthen, or attenuate within disease-specific populations. The relationship is well documented in general cohorts. However, our data show that it persists robustly in individuals with MetS, suggesting that MetS may act as a modifier but does not nullify the profound influence of socioeconomic disparities on long-term survival [17-19].
The influence of SES on health outcomes is consistent across various chronic cardiometabolic diseases, which further contextualizes our findings. Evidence from patients with established diabetes, CVD, and chronic kidney disease (CKD) consistently shows that a lower SES is linked to poorer treatment adherence, reduced healthcare access, and higher mortality [19-21]. Furthermore, recent studies examining socioeconomic factors in MetS populations, such as a cohort study by Ma et al. [17] and the Dutch Lifelines analysis [22], also confirm that socioeconomic disadvantages remain potent predictors of adverse outcomes, despite adjustments for behavioral and biological confounders. Collectively, these findings highlight that SES exerts an independent and additive influence on mortality beyond metabolic mechanisms, underscoring the importance of integrating social determinants in chronic disease management.
Importantly, this study focused on individuals who had already developed MetS, thereby reflecting the prognostic rather than incidence-related effects of SES. Thus, the SES-based mortality disparities represent differences in disease progression and survival after the onset of MetS and do not represent the risk of developing the condition itself [19,20]. A lower SES contributes to a poorer prognosis through several mechanisms, including limited access to regular follow-ups and specialist care, lower adherence to lifestyle modifications or medications, and higher levels of psychosocial stress and inflammation that exacerbate metabolic and cardiovascular risks [23,24]. This disparity in survival is likely explained by socioeconomic inequalities in disease management after the onset of MetS. A lower SES is associated with poorer therapeutic adherence and lower intensity of pharmacological and non-pharmacological management due to financial barriers [18]. Chronic stress following diagnosis may aggravate central obesity, insulin resistance, and inflammation, accelerating cardiovascular deterioration [25,26]. In addition, limited access to timely follow-ups and complication screenings can delay interventions, resulting in a poor overall prognosis among economically disadvantaged patients [17]. These findings highlight the need to address the SDH not only for disease prevention but also for the long-term management of chronic conditions [19,27].
Interestingly, a nonlinear pattern was observed among females, where the middle-ES group exhibited a higher survival probability than the high-ES group. This unexpected finding may reflect sex-specific differences in health behaviors, healthcare utilization, and psychosocial stress, which were not fully captured by our variables [20]. Females with a higher income in South Korea may face greater work-related stress and caregiving responsibilities, potentially offsetting the expected health advantages of economic affluence [24,28]. Similar paradoxical SES-health gradients have been observed among female subgroups in previous studies [17,29].
No single mechanism explains the complex relationship between SES and health [30,31], because multiple interacting factors are involved. Economically deprived individuals are more likely to adopt unhealthy lifestyles, such as smoking, heavy drinking, poor diet, and low physical activity, and experience psychosocial stress, including depression, financial hardship, and limited social support [20,27,31]. Chronic stress related to financial insecurity may promote maladaptive coping behaviors such as overeating and smoking, while social distinction mechanisms may drive higher-SES individuals to adopt health-promoting behaviors such as exercise and smoking avoidance [25]. In addition, limited health literacy, restricted purchasing power for health-related goods, and weaker social networks may further exacerbate health inequalities [23,27].
These potential mechanisms help explain the link between a low SES, the onset and management of MetS, and the subsequent mortality [19,20]. From a healthcare perspective, fewer opportunities for preventive health checkups, the limited use of medical facilities, and barriers to high-quality care may contribute to poor outcomes in low-SES groups after the onset of MetS [17].
This study had several strengths compared with previous studies. First, we aimed to directly confirm the ES-associated differences in mortality in patients with MetS. By specifically examining the relationship between SES and mortality in patients with an existing high-risk condition (MetS), we assessed the additive prognostic value of socioeconomic factors, which is a rare focus in the literature. By examining the relationship between ES and mortality in patients with MetS, we emphasized the need for more active interventions in high-risk groups. Second, the cohort was representative of the entire South Korean population. Most South Koreans are obligated to subscribe to the national health insurance provided by the NHIS. In addition, the NHIS provides biennial health-screening programs for individuals aged ≥40 years. Therefore, this database comprised a cohort representing the South Korean population because approximately 510,000 examinees were extracted from the entire examined population using a health-screening program. Third, because most of the population was registered in this single health insurance system, missing or biased data were rare. Fourth, the possibility of distortion of the results due to diagnostic errors was low because the MetS diagnosis and medication records were checked using health insurance claims and laboratory data.
Despite these strengths, this study had several limitations. First, MetS was defined using medication use, to identify participants who were actively treated for dyslipidemia, hypertension, or hyperglycemia. However, the inclusion of statins as a lipid-lowering therapy may have introduced minor misclassifications. Second, monthly HIPs are a widely used and validated proxy for ES in South Korean population-based studies but may not fully capture unreported or non-salary income and may reflect different weighting criteria for employees and self-employed individuals. Therefore, this variable may not completely represent or encompass all forms of household economic capacity. Third, racial sensitivity was not considered when evaluating the outcomes. In South Korea, which is mostly racially homogeneous, racial diversity is increasing due to recent international marriages and the influx of foreign workers. However, the Korean NHIS-HEALS database does not contain race information. Therefore, we could not conduct a sensitivity analysis after stratification according to ethnicity. Fourth, the classification of ES based on reported income may not have fully reflected the unreported earnings or total household wealth. In South Korea, HIPs are determined by the income level. However, the income level used in this study was based on reported income, and the possibility of an incorrect categorization into the low-ES group could not be ruled out if the actual income level was high but the reported income was low. Furthermore, other factors that determined ES in addition to the income level, such as the education level, employment status, housing environment, and expenditure, were reflected. Finally, although adjustments were made for multiple confounders, unmeasured residual confounding factors could not be entirely excluded. In conclusion, economic deprivation increased the risk of all-cause mortality in South Korean adults with MetS.
Notes
Conflict of interest
No potential conflict of interest relevant to this article was reported.
Funding
None.
Data availability
The data utilized in this study were obtained from the Korean National Health Insurance Service (NHISS) data-sharing platform. Access to NHISS data is restricted to authorized users. Researchers must submit a formal application for research use, which is subject to review and approval by NHISS before access to the database in granted.
Author contribution
Conceptualization: HSY, HTK. Data curation: YHK, JK. Formal analysis: YHK, JK. Interpretation: HSY, YHK, JK, HTK. Supervision: HTK. Writing–original draft: HSY. Writing–review & editing: JK, HTK. Final approval of the manuscript: all authors.
Flowchart of participant selection and inclusion and exclusion criteria. ICD, International Classification of Diseases.
Figure. 2.
Kaplan-Meier estimation for all-cause mortality according to economic status by sex during the follow-up period. Differences between groups were assessed using the log-rank test.
Table 1.
Participant characteristics according to economic status
Characteristic
Economic status
P-value
High
Middle
Low
Male
No. of participants
22,784
13,879
7,999
Age (y)
55.6±7.8
57.4±7.6
59.4±7.6
<0.001
Body mass index (kg/m2)
25.4±2.6
25.2±2.7
25.2±2.7
<0.001
Waist circumference (cm)
88.1±6.8
87.9±7.1
88.3±7.0
<0.001
Systolic blood pressure (mm Hg)
130.5±13.6
132.1±14.4
132.5±14.7
<0.001
Fasting blood glucose (mg/dL)
112.7±29.7
114.7±32.8
116.0±35.5
<0.001
Triglyceride (mg/dL)
204.4±118.9
209.4±124.7
205.6±123.0
<0.001
HDL cholesterol (mg/dL)
48.2±21.7
49.4±23.9
49.5±20.9
<0.001
Smoking
<0.001
Never-smoker
7,307 (32.1)
4,545 (32.7)
2,752 (34.4)
Former smoker
8,308 (36.5)
4,431 (31.9)
2,381 (29.8)
Current smoker
7,169 (31.5)
4,903 (35.3)
2,866 (35.8)
Alcohol consumption
<0.001
Rarely
12,286 (53.9)
7,263 (52.3)
4,372 (54.7)
Sometimes
7,799 (34.2)
4,401 (31.7)
2,419 (30.2)
Often
2,699 (11.8)
2,215 (16.0)
1,208 (15.1)
Physical activity
<0.001
Rarely
16,405 (72.0)
10,383 (74.8)
5,982 (74.8)
Sometimes
856 (3.8)
447 (3.2)
269 (3.4)
Often
5,523 (24.2)
3,049 (22.0)
1,748 (21.8)
Residential area
<0.001
Non-metropolitan
12,122 (53.2)
8,091 (58.3)
4,202 (52.5)
Metropolitan
10,662 (46.8)
5,788 (41.7)
3,797 (47.5)
CCI
<0.001
0
11,703 (51.4)
6,520 (47.0)
3,737 (46.7)
1
6,565 (28.8)
4,181 (30.1)
2,354 (29.4)
2
2,636 (11.6)
1,958 (14.1)
1,114 (13.9)
Female
No. of participants
14,707
13,856
10,561
Age (y)
62.5±9.0
59.9±8.5
60.4±8.8
<0.001
Body mass index (kg/m2)
25.1±3.0
25.3±3.0
25.2±3.0
<0.001
Waist circumference (cm)
82.8±7.4
82.9±7.5
82.6±7.4
0.047
Systolic blood pressure (mm Hg)
129.6±15.2
129.7±15.2
130.0±15.3
0.086
Fasting blood glucose (mg/dL)
105.5±26.1
106.4±27.6
105.6±25.9
0.006
Triglyceride (mg/dL)
162.3±89.4
163.4±90.6
163.6±91.3
0.416
HDL cholesterol (mg/dL)
52.9±27.7
53.2±28.2
53.1±22.3
0.525
Smoking
<0.001
Never-smoker
14,446 (98.2)
13,467 (97.2)
10,304 (97.6)
Former smoker
94 (0.6)
113 (0.8)
66 (0.6)
Current smoker
167 (1.1)
276 (2.0)
191 (1.8)
Alcohol consumption
<0.001
Rarely
14,129 (96.1)
13,171 (95.1)
10,028 (95.0)
Sometimes
442 (3.0)
518 (3.7)
404 (3.8)
Physical activity
<0.001
Rarely
11,612 (79.0)
11,417 (82.4)
8,683 (82.2)
Sometimes
483 (3.3)
414 (3.0)
356 (3.4)
Often
2,612 (17.8)
2,025 (14.6)
1,522 (14.4)
Residential area
<0.001
Non-metropolitan
7,851 (53.4)
8,623 (62.2)
6,225 (58.9)
Metropolitan
6,856 (46.6)
5,233 (37.8)
4,336 (41.1)
CCI
<0.001
0
5,169 (35.1)
5,055 (36.5)
4,052 (38.4)
1
4,865 (33.1)
4,529 (32.7)
3,425 (32.4)
2
2,624 (17.8)
2,522 (18.2)
1,826 (17.3)
3+
2,049 (13.9)
1,750 (12.6)
1,258 (11.9)
Values are presented as mean±standard deviation or number (%) unless otherwise stated.
Cox proportional hazards regression models for all-cause mortality according to economic status
Variable
Economic status
High
Middle
Low
Male
Model 1
1
1.44 (1.32–1.57)
1.88 (1.72–2.06)
Model 2
1
1.27 (1.17–1.39)
1.39 (1.27–1.53)
Model 3
1
1.23 (1.13–1.48)
1.35 (1.23–1.48)
Female
Model 1
1
0.84 (0.76–0.93)
0.99 (0.89–1.10)
Model 2
1
1.19 (1.07–1.32)
1.26 (1.13–1.40)
Model 3
1
1.17 (1.06–1.30)
1.25 (1.12–1.39)
Values are presented as hazard ratio (95% confidence interval).
Model 1: unadjusted; Model 2: adjusted for age and body mass index; Model 3: adjusted for smoking status, alcohol intake, physical activity, residential area, and Charlson comorbidity index, in addition to the variables in Model 2.
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Evaluation of the association between mortality and economic status in patients with metabolic syndrome in Korea: a retrospective cohort study using the National Health Screening cohort
Figure. 1. Flowchart of participant selection and inclusion and exclusion criteria. ICD, International Classification of Diseases.
Figure. 2. Kaplan-Meier estimation for all-cause mortality according to economic status by sex during the follow-up period. Differences between groups were assessed using the log-rank test.
Graphical abstract
Figure. 1.
Figure. 2.
Graphical abstract
Evaluation of the association between mortality and economic status in patients with metabolic syndrome in Korea: a retrospective cohort study using the National Health Screening cohort
Characteristic
Economic status
P-value
High
Middle
Low
Male
No. of participants
22,784
13,879
7,999
Age (y)
55.6±7.8
57.4±7.6
59.4±7.6
<0.001
Body mass index (kg/m2)
25.4±2.6
25.2±2.7
25.2±2.7
<0.001
Waist circumference (cm)
88.1±6.8
87.9±7.1
88.3±7.0
<0.001
Systolic blood pressure (mm Hg)
130.5±13.6
132.1±14.4
132.5±14.7
<0.001
Fasting blood glucose (mg/dL)
112.7±29.7
114.7±32.8
116.0±35.5
<0.001
Triglyceride (mg/dL)
204.4±118.9
209.4±124.7
205.6±123.0
<0.001
HDL cholesterol (mg/dL)
48.2±21.7
49.4±23.9
49.5±20.9
<0.001
Smoking
<0.001
Never-smoker
7,307 (32.1)
4,545 (32.7)
2,752 (34.4)
Former smoker
8,308 (36.5)
4,431 (31.9)
2,381 (29.8)
Current smoker
7,169 (31.5)
4,903 (35.3)
2,866 (35.8)
Alcohol consumption
<0.001
Rarely
12,286 (53.9)
7,263 (52.3)
4,372 (54.7)
Sometimes
7,799 (34.2)
4,401 (31.7)
2,419 (30.2)
Often
2,699 (11.8)
2,215 (16.0)
1,208 (15.1)
Physical activity
<0.001
Rarely
16,405 (72.0)
10,383 (74.8)
5,982 (74.8)
Sometimes
856 (3.8)
447 (3.2)
269 (3.4)
Often
5,523 (24.2)
3,049 (22.0)
1,748 (21.8)
Residential area
<0.001
Non-metropolitan
12,122 (53.2)
8,091 (58.3)
4,202 (52.5)
Metropolitan
10,662 (46.8)
5,788 (41.7)
3,797 (47.5)
CCI
<0.001
0
11,703 (51.4)
6,520 (47.0)
3,737 (46.7)
1
6,565 (28.8)
4,181 (30.1)
2,354 (29.4)
2
2,636 (11.6)
1,958 (14.1)
1,114 (13.9)
Female
No. of participants
14,707
13,856
10,561
Age (y)
62.5±9.0
59.9±8.5
60.4±8.8
<0.001
Body mass index (kg/m2)
25.1±3.0
25.3±3.0
25.2±3.0
<0.001
Waist circumference (cm)
82.8±7.4
82.9±7.5
82.6±7.4
0.047
Systolic blood pressure (mm Hg)
129.6±15.2
129.7±15.2
130.0±15.3
0.086
Fasting blood glucose (mg/dL)
105.5±26.1
106.4±27.6
105.6±25.9
0.006
Triglyceride (mg/dL)
162.3±89.4
163.4±90.6
163.6±91.3
0.416
HDL cholesterol (mg/dL)
52.9±27.7
53.2±28.2
53.1±22.3
0.525
Smoking
<0.001
Never-smoker
14,446 (98.2)
13,467 (97.2)
10,304 (97.6)
Former smoker
94 (0.6)
113 (0.8)
66 (0.6)
Current smoker
167 (1.1)
276 (2.0)
191 (1.8)
Alcohol consumption
<0.001
Rarely
14,129 (96.1)
13,171 (95.1)
10,028 (95.0)
Sometimes
442 (3.0)
518 (3.7)
404 (3.8)
Physical activity
<0.001
Rarely
11,612 (79.0)
11,417 (82.4)
8,683 (82.2)
Sometimes
483 (3.3)
414 (3.0)
356 (3.4)
Often
2,612 (17.8)
2,025 (14.6)
1,522 (14.4)
Residential area
<0.001
Non-metropolitan
7,851 (53.4)
8,623 (62.2)
6,225 (58.9)
Metropolitan
6,856 (46.6)
5,233 (37.8)
4,336 (41.1)
CCI
<0.001
0
5,169 (35.1)
5,055 (36.5)
4,052 (38.4)
1
4,865 (33.1)
4,529 (32.7)
3,425 (32.4)
2
2,624 (17.8)
2,522 (18.2)
1,826 (17.3)
3+
2,049 (13.9)
1,750 (12.6)
1,258 (11.9)
Variable
Economic status
High
Middle
Low
Male
Model 1
1
1.44 (1.32–1.57)
1.88 (1.72–2.06)
Model 2
1
1.27 (1.17–1.39)
1.39 (1.27–1.53)
Model 3
1
1.23 (1.13–1.48)
1.35 (1.23–1.48)
Female
Model 1
1
0.84 (0.76–0.93)
0.99 (0.89–1.10)
Model 2
1
1.19 (1.07–1.32)
1.26 (1.13–1.40)
Model 3
1
1.17 (1.06–1.30)
1.25 (1.12–1.39)
Table 1. Participant characteristics according to economic status
Values are presented as mean±standard deviation or number (%) unless otherwise stated.
Table 2. Cox proportional hazards regression models for all-cause mortality according to economic status
Values are presented as hazard ratio (95% confidence interval).
Model 1: unadjusted; Model 2: adjusted for age and body mass index; Model 3: adjusted for smoking status, alcohol intake, physical activity, residential area, and Charlson comorbidity index, in addition to the variables in Model 2.
