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Keywords:

  • Korea;
  • metabolic syndrome;
  • nursing;
  • older adults;
  • risk factors;
  • younger adults

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Background
  5. The study
  6. Results
  7. Discussion
  8. Conclusion
  9. Funding
  10. Conflict of interest
  11. Author contributions
  12. References

Aim

To determine the factors affecting the prevalence of metabolic syndrome in younger and older Koreans.

Background

Metabolic syndrome, in combination with other, interrelated predisposing factors, is a risk factor for cardiovascular disease. In Korea, the prevalence of this syndrome, like those of other chronic diseases, has increased continually in recent years.

Design

This is an analytic, descriptive cross-sectional study.

Methods

This survey targeted 690,283 examinees that had undergone a medical examination on a life transition period performed by the National Health Insurance Corporation from January–December 2008. For the purpose of this study, the diagnosis of metabolic syndrome was based on the criteria of the American Heart Association and the Heart, Lung, and Blood Institute. The relationship between the risk factors and prevalence rate was shown using a multiple logistic regression model.

Results

The prevalence of metabolic syndrome was 24·8% in the 40 year olds and 40·8% in the 66 year olds. Among the younger adults, the prevalence in women was only 0·57 times that in men. A multiple logistic regression analysis demonstrated that heavy obesity and family history of cardiovascular disease are the strongest independent predictors of metabolic syndrome among younger and older Koreans.

Conclusion

As a management strategy, a nursing intervention strategy for the improvement of lifestyle factors including self-care through proper diet and exercise should be developed and implemented.

What is already known about this topic

  • It has been reported that metabolic syndrome is related to obesity and that in obesity, blood pressure, blood glucose, and blood triglyceride values are high, while high density lipoprotein cholesterol values are low.
  • Because previous study populations in Korea have been composed of examinees in the comprehensive health screen centres in university hospitals, they may not represent the general population.
  • Any person with a strong family history of metabolic syndrome or type II diabetes should be careful to maintain a healthy life style.

What this paper adds

  • Metabolic syndrome is a disease needing active and careful nursing management because it causes type II diabetes mellitus and complications by cardiovascular and cerebrovascular diseases and consequently the risk of early death could increase if the patient goes without treatment.
  • For the effective management of metabolic syndrome, it is necessary to improve lifestyle factors including diet, nutrition, stress management, and social support and medication comprehensively and systematically.
  • The effect of obesity on metabolic syndrome prevalence was far higher than that of smoking, drinking, or exercise, confirming that weight control is important for reducing the risk of metabolic syndrome in Korea.

Implications for practice and/or policy

  • Women should be pre-educated about metabolic syndrome and should receive intervention before the age of 40.
  • Additional studies are required to investigate metabolic syndrome-related factors to set up nursing interventions differentiated by lifestyle according to life transition points.
  • Individuals with disease-related family history should be selected for preferential management for metabolic syndrome prevention and should receive lifestyle training for health and early testing for preventive management.

Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Background
  5. The study
  6. Results
  7. Discussion
  8. Conclusion
  9. Funding
  10. Conflict of interest
  11. Author contributions
  12. References

Metabolic syndrome (MetS) is a constellation of interrelated risk factors, including hypertension, dyslipidaemia, obesity, and elevated blood glucose, that predispose an individual to higher cardiovascular (CV) risks (Kirkendoll et al. 2010). MetS had originally been referred to as ‘syndrome X’ (Reaven 1988) in the USA in the 1980s to describe a syndrome comprising glucose intolerance along with high blood pressure, dyslipidaemia, and abdominal obesity. The International Diabetes Federation (2007) recently defined metabolic syndrome as the most dangerous constellation of cardiac risk factors: diabetes, prediabetes, abdominal obesity, elevated cholesterol level, and high blood pressure.

The prevalence of cardiovascular disease caused by obesity is increasing in Korea as dietary habits and lifestyle change rapidly to a more Western pattern. According to data from the Korea Ministry of Health and Welfare (2007), the percentage of people with body mass index (BMI, kg/m2) over 25·0 increased from 26·0% of men and 26·5 of women in 1998 to 32·4% of men and 29·4% of women in 2001 and to 35·2% of men and 28·3% of women in 2005. This prevalence is expected to continue to increase to a severe level unless active management is undertaken.

In Korea, life transition physical examinations have been performed in the 40 and 66 year old populations since 2007 because incidence rates of chronic diseases like cancer, cardiovascular, and cerebrovascular diseases are higher in the 40 year old group and the risks of geriatric disorders including a decrease in physical function, falls, and dementia, and chronic diseases are higher in the 66 year old group. Transferring the concept of a physical screening system from selective screening for finding disease to preventive management for health, the life turning point physical examination is designed to evaluate, prescribe, and consult with the patient about aspects of the lifestyle related to five risk factors including smoking, physical activity, nutrition, alcohol, and obesity in the 40 and 66 year old groups (Ministry of Health & Welfare 2011). Given the ongoing rapid progression of lifestyle westernization and population ageing, this study attempted to assess the effects of factors such as family history, health behaviours, and obesity on the prevalence of MetS in groups representing Koreans of 40 and 66 years of age.

Background

  1. Top of page
  2. Abstract
  3. Introduction
  4. Background
  5. The study
  6. Results
  7. Discussion
  8. Conclusion
  9. Funding
  10. Conflict of interest
  11. Author contributions
  12. References

Metabolic syndrome

Metabolic syndrome is a group of syndromes with clinical features such as obesity, high blood pressure, glucose intolerance, hypertriglyceridemia, low high density lipoprotein (HDL), and hypercholesterolaemia (Choi et al. 2009). Both the prevalence of MetS and the risk of cardiovascular disease increase with age and Korea has recently experienced a continually increasing prevalence of metabolic syndrome along with other chronic diseases (Lee et al. 2009).

In the USA, the prevalence of MetS among adults aged 20 or older was 24·1% from 1999–2000 (Ford et al. 2004) and the prevalence increased with age to over 40% for those aged 60–70. Ford (2004) reported that in the USA, MetS is present in one in four adults and in over 40% of those over the age of 60. The prevalence of MetS was reported to be around 31% among men in Japan (Miyatake et al. 2006). In Korea, the prevalence of MetS was 32·9% in men and 31·8% in women at 30 years of age. The prevalence decreased with age in men but increased with age in women: at age 70, the prevalence among women was 63·4%, almost twice the 34·1% prevalence in men (Ministry of Health, Welfare and Family, 2005 National Health and Nutrition Survey). Yoo et al. (2009) studied patients visiting the cardiovascular centre of a university hospital and reported that the prevalence of metabolic syndrome was 53·2% in this population and that of the lifestyle factors examined, dietary habits had a significant effect on MetS.

According to the NCEP-ATP III (2001) guidelines, diagnosis of MetS requires at least three of the following risk factors: obesity (especially abdominal obesity), high blood pressure, high fasting glucose or hyperglycaemia, and lipid abnormalities. According to the WHO criteria (World Health Organization (WHO) 1999), the diagnosis of MetS requires the presence of diabetes mellitus, impaired glucose tolerance, impaired fasting glucose or IR, and at least two more of the above factors.

Predictors of MetS

It has been reported that MetS is related to obesity, especially abdominal obesity and that blood pressure, blood glucose, and blood triglyceride values are high while HDL-cholesterol are low in obese individuals (Kim 2001). It is known that smoking reduces HDL-cholesterol and raises LDL-cholesterol and triglycerides, increasing the risk of cardiovascular disease, while consumption of a small amount of alcohol increases insulin sensitivity and HDL-cholesterol, showing a protective effect against cardiovascular disease (Ganziano et al. 1993).

The risk factors related to MetS include health-related behaviours such as smoking, drinking, and exercise (Choi et al. 2009) and endocrine factors such as total cholesterol, aspartate (AST), ALT, WBC counts, RBC count, uric acid, GGT, and TSH. As the prevention and treatment of MetS are closely related to lifestyle factors, it has been reported that modification of lifestyle-related risk factors, including dietary habits associated with arteriosclerosis, sedentary lifestyle, obesity, and weight gain, should precede medical treatment (Stone & Saxon 2005).

Most of the studies of MetS in Korea have focused only on younger adults, while a few clinical studies (Park et al. 2002, Son et al. 2004) examined the biomedical factors related to MetS, so few studies have compared and analysed MetS-related risk factors in both younger and older Korean adults. Therefore, this study reanalysed the data from life transition examinations conducted by the National Health and Insurance Corporation and determined the prevalence of and risk factors for MetS in both younger and older adults. This study was performed to develop life stage-appropriate MetS management strategies for younger and older Korean adults.

The study

  1. Top of page
  2. Abstract
  3. Introduction
  4. Background
  5. The study
  6. Results
  7. Discussion
  8. Conclusion
  9. Funding
  10. Conflict of interest
  11. Author contributions
  12. References

Aim

The purpose of this study was to analyse MetS prevalence by family history, health behaviours, and obesity among younger and older adults and thereby determine which factors have concrete effects on the prevalence of MetS. The goal was to give the basic materials for developing MetS prevention strategies targeting younger and older adults.

Design

This is an analytic, descriptive cross-sectional study for secondary analysis of the data from life transition health examinations conducted by the National Health and Insurance Corporation. It was performed to assess the risk factors for MetS prevalence among younger and older adults.

Sample

The survey targeted 690,671 examinees who underwent health examinations on life transitions conducted by the National Health and Insurance Corporation from January–December 2008. The target of the life transition health examination included those aged 40 years old, who need to improve health risk factors due to increased chronic disease, such as hypertension, diabetes, dyslipidaemia, and so on, and those aged 66 years old, who show an increased risk of geriatric diseases, including stroke and myocardial infarction and suffered from reduced stamina in general. The final analysis was performed on 690,238 individuals, excluding 388 for whom there was insufficient data.

Data collection

The data on family history, medical history (drug intake), and health behaviours, including smoking, drinking, and exercise, were collected and the results of physical examination (height, weight, waist line circumference, and blood pressure) and blood tests (HDL, triglycerides and blood glucose) were also used.

Ethical considerations

This study was conducted using inquiry sheets and health test results acquired during the health test of life transition. These records could not be individually identified due to the National Health Insurance Corporation's internal regulations concerning information provision.

Measurements

Diagnostic criteria for MetS

The presence of MetS was evaluated according to the criteria presented by the American Heart Association (AHA) and the Heart, Lung and Blood Institute (NHLBI) (Grundy 2005). Individuals meeting the following five criteria were judged as abnormal in terms of risk for MetS:

  • Waist measurement: ≥90 cm for men and ≥85 cm for women;
  • Low HDL hypercholesterolaemia: blood HDL-cholesterol <40 mg/dL for men and <50 mg/dL for women;
  • Hypertriglyceridemia: blood triglycerides ≥150 mg/dL;
  • High blood pressure: systolic blood pressure/diastolic blood pressure ≥130/85 mmHg; and
  • Elevated blood glucose: fasting blood glucose ≥100 mg/dL; Individuals under drug treatment for the following five criteria.

Each person meeting at least three of the above five criteria was included in the MetS group and each individual's risk of MetS was scored from 0–5 points.

Health behaviours

For health behaviour analysis, smoking, drinking, and exercise were examined. Smoking was categorized as smoking over five packs a week, currently smoking, and non-smoking. Drinking was defined as drinking more than 1 day per week and exercising as over 30 minutes at least 3 times weekly (National Health Insurance Corporation 2008).

Obesity

The body mass index (kg/m2) was calculated from the height and weight measured by the health test of the life transition period. Body mass index was classified as underweight for below 18·5 kg/m2; normal for 18·5–25 kg/m2; mildly obese for 25–30 kg/m2; and severely obese for over 30 kg/m2. Zheng et al. (2011) evaluated the relationship between BMI and the risk of death using data from 19 cohorts, involving more than 1 million participants. In the cohorts of East Asians, including Koreans, the lowest risk of death was seen among persons with a BMI in the range of 22·6–27·5. That result was used to define BMI cut-off points of more than 25·0 for mild obesity and more than 30·0 for heavy obesity.

Data analysis

The collected data were analysed using the spss/win 15·0 (Chicago, IL, USA) programme. Family history, health behaviour, obesity, and MetS prevalence risk characteristics among younger and older adults were presented as frequencies, percentages, and means and standard deviations. The differences in MetS prevalence according to the risk factors were analysed among younger and older adults using the chi-squared test and the factors that affected MetS prevalence were analysed through multiple logistic regression. To evaluate the risk factors for metabolic syndrome, multiple logistic regression analysis was performed to investigate the relation among the risk factors after correction of other variables like gender, family history, lifestyle, and BMI, which are metabolic syndrome risk factors.

Validity

The content validity of the diagnostic criteria for MetS, health behaviour, and obesity in health examinations on life transitions was checked by experts recruited from the NHIC. In NHIC, 13 metabolic physicians, 4 public health professors, and 1 nurse educator with expertise in health examination diagnosis methods or MetS were involved in evaluating each examination item and in considering whether all items adequately measured the dimensions of the content domain.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Background
  5. The study
  6. Results
  7. Discussion
  8. Conclusion
  9. Funding
  10. Conflict of interest
  11. Author contributions
  12. References

Differences in family histories, health behaviours, and obesity between younger and older adults

The differences in family histories, health behaviours, and obesity between 40 year old and 66 year old adults are shown in (Table 1). Of the 690,283 adults included in the final analysis, 433,119, or 62·7%, were 40 year olds and 257,164, or 37·3%, were 66 year olds. These numbers included 47·9% of the 904,071 40 year old adults targeted for the health test of life transition period in 2008 and 57·6% of the 446,813 66 year old adults targeted.

Table 1. Characteristics of the participants
Characteristics40 years of age (n = 433,119)66 years of age (n = 257,164)χ2P
n%n%
Gender
Male214,18849·5119,20046·4621·26<0·001
Female218,93150·5137,96453·6
Family history
Stroke92,64921·452,35320·4103·82<0·001
Cardiovascular disease85,47319·744,45617·3632·47<0·001
Hypertension121,40628·060,56723·61667·32<0·001
Diabetes112,37225·951,61520·13073·39<0·001
Smoking      
Non-smoker317,23973·2220,50085·714,639·95<0·001
Current smoker115,88026·836,66414·3
Drinking
Less than one time per week204,18047·1181,53870·635,991·50<0·001
Over one time per week228,93952·975,62629·4
Exercise
No262,40360·6162,62837·2480·54<0·001
Over three times per week170,71639·494,53636·8
Obesity
Normality (18·5–25 kg/m2)290,18467·0152,06059·15737·45<0·001
Underweight ( < 18·5 kg/m2)13,2983·059912·3
Mild obesity (25–30 kg/m2)115,03626·689,92635·0
Heavy obesity (≥30 kg/m2)14,6013·491873·6

The gender, family histories, health behaviours (smoking, drinking, and exercise), and obesity rates showed statistically significant differences between the younger and older adults.

Among the 40 year old adults, 218,931 (50·5%) were women. The family histories of disease were as follows: stroke, 21·4% (92,649); cardiovascular disease, 19·7% (85,473); high blood pressure, 28·0% (121,406); and diabetes, 25·9% (112,372). According to the health behaviour classification scheme, 26·8% (115,880) were current smokers, 52·9% (228,939) drank more than once a week, and 39·4% (170,716) were exercisers. The majority, 67% (290,184), had normal-BMI measurements (18·5 kg/m2 ≤ BMI < 25 kg/m2), 26·6% (115,036) were mildly obese (25 kg/m2 ≤ BMI < 30 kg/m2), 3·4% (14,601) were severely obese (30 kg/m≤ BMI), and 3·0% (13,298) were underweight (18·5 kg/m2 > BMI).

Among the 66 year old adults, 53·6% (137,964) were women. The family histories of disease were as follows: stroke, 20·4% (52,353); cardiovascular disease, 17·3% (44,456); high blood pressure, 23·6% (60,567); and diabetes, 20·1% (51,615). According to the health behaviour classification scheme, 14·3% (36,664) were current smokers, 29·4% (75,626) drank more than once a week, and 36·8% (94,536) exercised more than 3 times a week.

The majority, 59·1% (152,060), had normal-BMI measurements (18·5 kg/m2 ≤ BMI < 25 kg/m2), 35·0% (89,926) were mildly obese (25 kg/m2 ≤ BMI < 30 kg/m2), 3·6% (9187) were severely obese (30 kg/m2 ≤ BMI), and 2·3% (5991) were underweight (18·5 kg/m2 > BMI).

Differences in MetS prevalence risk factors among younger and older adults

The analysis found that the rates of the MetS risk factors using the criteria presented by AHA/NHLBI (waist measurement, HDL-cholesterol, triglycerides, blood pressure, blood glucose, MetS points and presence of MetS) (Grundy et al. 2005) were significantly different between younger and older adults (Table 2).

Table 2. Difference in MetS prevalence risk characteristics among younger adults and older ones (n = 690,283)
Variable40 years of age (n = 433,119)66 years of age (n = 257,164)χ2P
n%n%
  1. WC, waist circumference; MetS, metabolic syndrome; FBS, fasting blood glucose; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride.

WC (cm)
Normal365,59984·4167,46065·134145·99<0·001
Abdominal obesity67,52015·689,70434·9
HDL cholesterol (mg/dL)
Normal339,36978·4181,91770·75060·91<0·001
HDL-C < 4093,75021·675,24729·3
TG (mg/dL)
Normal266,64461·6139,07254·13730·54<0·001
TG ≥ 150166,47538·4118,09245·9
Blood pressure (mmHg)
Normal313,54772·4125,05248·639,338·17<0·001
Hypertension119,57227·6132,11251·4
FBS (mg/dL)
Normal274,82863·5122,18847·516,777·50<0·001
FBG ≥ 100158,29136·5134,97652·5
Number of metabolic risk factors
0144,37033·331,43212·254,054·25<0·001
1115,93026·857,67222·4
265,51615·163,23024·6
374,97617·361,95924·1
427,9176·434,23313·3
544101·086383·4
MetS
No325,81675·2152,33459·219,379·19<0·001
Yes107,30324·8104,83040·8

Among the younger adults, 15·6% (67,520) demonstrated abdominal obesity (waist circumference ≥ 90 cm for men and ≥ 85 cm for women), 21·6% (93,750) low HDL hypercholesterolaemia, 38·4% (166,475) elevated triglycerides, 27·6% (119,572) high blood pressure, and 36·5% (158,291) elevated blood glucose. The average numerical MetS risk factor score of the younger adults was 1·39 (sd 1·32), with 33·3% (144,370) scoring 0 points, 26·8% (115,930) 1 point, 15·1% (65,516) 2 points, 17·3% (74,976) 3 points, 6·4% (27,917) 4 points, and 1·0% (4410) 5 points. Overall, 24·8% (107,303) had MetS risk factor scores of 3 points or more.

Among the older adults, 34·9% (89,704) demonstrated abdominal obesity, 29·3% (75,247) low HDL hypercholesterolaemia, 45·9% (118,092) elevated triglycerides, 51·4% (132,112) high blood pressure, and 52·5% (134,976) elevated blood glucose. The average MetS risk factor score for the older adults was 2·13 (1·33), with 12·2% (31,432) scoring 0 points, 22·4% (57,672) 1 point, 24·6% (63,230) 2 points, 24·1% (61,959) 3 points, 13·3% (34,233) 4 points, and 3·4% (8638) 5 points. Overall, 40·8% (104,830) had MetS risk factor scores of 3 or more points. The MetS prevalence among the 66 year old adults was 40·8%, 16% higher than the 24·8% among 40 year old adults.

Effects of family history, health behaviours, and obesity on MetS prevalence in younger adults

The effects of family history, health behaviours, and obesity on MetS prevalence in younger adults are shown in Table 3. Among the 40 year old adults, the prevalence of MetS was 29·0% (62,168) in men and 20·6% (45,135) in women and the effect of gender was statistically significant (< 0·001). The MetS prevalence was 69·3% (64,219) among those with a family history of stroke and 12·7% (43,084) among those without. For family history of other diseases, MetS prevalence was 75·4% (64,460) among those with a family history of cardiovascular disease and 12·3% (42,843) among those without, 52·5% (63,791) among those with a family history of high blood pressure and 14·0% (43,512) among those without, and 57·3% (64,388) among those with a family history of diabetes and 13·4% (42,915) among those without. Therefore, family histories of stroke, cardiovascular disease, high blood pressure, and diabetes had statistically significant effects on MetS prevalence in younger adults.

Table 3. Difference in MetS prevalence according to family history, health behaviour, and obesity among younger adults (n = 433,119)
VariableMetSTotalχ2P
No (n = 325,816)Yes (n = 107,303)
n%n%n
Gender
Male152,02071·062,16829·0214,1884107·73<0·001
Female173,79679·445,13520·6218,931
Family history
Stroke
No297,38687·343,08412·7340,470125,457·32<0·001
Yes28,43030·764,21969·392,649
Cardiovascular disease
No304,80387·742,84312·3347,646146,533·24<0·001
Yes21,01324·664,46075·485,473
Hypertension
No268,20186·043,51214·0311,71369,798·26<0·001
Yes57,61547·563,79152·5121,406
Diabetes
No277,83286·642,91513·4320,74786,129·88<0·001
Yes47,98442·764,38857·3112,372
Smoking
Non-smoker244,58577·172,65422·9317,2392230·81<0·001
Current smoker81,23170·134,64929·9115,880
Drinking
Less than one time per week158,14477·546,03622·5204,1801028·60<0·001
Over one time per week167,67273·261,26726·8228,939
Exercise
No197,65275·364,75124·7262,4033·450·032
Over three times per week128,16475·142,55224·9170,716
Obesity
Normality (18·5–25 kg/m2)236,14981·454,03518·6290,18425,985·74<0·001
Underweight ( < 18·5 kg/m2)11,15583·9214316·113,298
Mild obesity (25–30 kg/m2)73,00263·542,03436·5115,036
Heavy obesity (≥30 kg/m2)551037·7909162·314,601

The MetS prevalence was 22·9% (72,654) among non-smokers and 29·9% (34,649) among smokers, a statistically significant difference (< 0·001); for drinking, the prevalence was 22·5% (46,036) among those drinking less than once a week and 26·8% (61,267) among those drinking more than once a week, also a significant difference (< 0·001). The effect of exercise was also significant, as the prevalence of MetS was 24·7% (64,751) among non-exercisers and 24·9% (42,552) among exercisers (< 0·001). Obesity had a significant effect as well: the prevalence of MetS was 18·6% (54,035) among normal-BMI patients, 62·3% (9091) among the severely obese, 36·5% (42,034) among the mildly obese and 16·1% (2143) among the underweight (< 0·001).

Effects of family history, health behaviours, and obesity on MetS prevalence among older adults

The effects of family history, health behaviours, and obesity on MetS prevalence among older adults are shown in Table 4. Among the 66 year old adults, the prevalence of MetS was 36·6% (43,599) in men and 44·4% (61,231) in women; this gender difference was statistically significant (< 0·001). The MetS prevalence was 74·1% (38,814) among those with a family history of stroke and 32·2% (66,016) among those without, 82·6% (36,726) among those with a family history of cardiovascular disease and 32·0% (68,104) among those without, 71·4% (43,216) among those with a family history of high blood pressure and 31·3% (61,614) among those without, and 77·0% (39,721) among those with a family history of diabetes and 31·7% (65,105) among those without. Therefore, family histories of stroke, cardiovascular disease, high blood pressure, and diabetes had statically significant effects on MetS prevalence in older adults.

Table 4. Difference in MetS prevalence according to family history, health behaviour, and obesity among older adults (n = 257,164)
VariableMetSTotalχ2P
No (n = 152,334)Yes (n = 104,830)
n%n%n
Gender
Male75,60163·443,59936·6119,2001613·51<0·001
Female76,73355·661,23144·4137,964
Family history
Stroke
No138,79567·866,01632·2204,81130,323·74<0·001
Yes13,53925·938,81474·152,353
Cardiovascular disease
No144,60468·068,10432·0212,70838,980·48<0·001
Yes773017·436,72682·644,456
Hypertension
No134,98368·761,61431·3196,59730,698·98<0·001
Yes17,35128·643,21671·460,567
Diabetes
No140,44068·365,10531·7205,54535,032·51<0·001
Yes11,89423·039,72177·051,615
Smoking
Non-smoker128,99558·591,50541·5220,500346·00<0·001
Current smoker23,33963·713,32536·336,664
Drinking
Less than one time per week105,58158·275,95741·8181,538296·51<0·001
Over one time per week46,75361·828,87338·275,626
Exercise
No95,03458·467,59441·6162,628117·16<0·001
Over three times per week57,30060·637,23639·494,536
Obesity
Normality (18·5–25 kg/m2)105,29869·246,76230·8152,06019,991·86<0·001
Underweight ( < 18·5 kg/m2)482980·6116219·45991
Mild obesity (25–30 kg/m2)39,82744·350,09955·789,926
Heavy obesity (≥30 kg/m2)238025·9680774·19187

The prevalence of MetS was 41·5% (91,505) in non-smokers and 36·3% (13,325) in smokers, a statistically significant difference (< 0·001); for drinking, the prevalence was 41·8% (75,957) in those who drank less than once a week and 38·2% (28,873) in those who drank more than once a week, also a significant difference (< 0·001). The effect of exercise was also significant, as the prevalence of MetS was 41·6% (67,594) in non-exercisers and 39·4% (37,236) in exercisers (< 0·001). Finally, obesity had a significant effect on the prevalence of MetS, which was 30·8% (46,762) in those of normal BMI, 74·1% (6807) in the severely obese, 55·7% (50,099) in the mildly obese, and 19·4% (1162) in the underweight (< 0·001).

Factors influencing MetS prevalence

The results of multiple logistic regression for evaluating the factors affecting MetS prevalence are shown in Table 5. The analysis model used in this study was suitable for estimating the causal relationships between dependent and independent variables. It showed a higher expression because the independent variables expressed the dependent ones by 37·6–55·8% among younger adults and by 24·3–32·8% among women.

Table 5. Multiple logistic regression model: factors influencing MetS prevalence (n = 690,283)
Comparative groupReference group40 years of age (n = 433,119)66 years of age (n = 257,164)
Metabolic syndrome (ref. not MS) Adj or (95 CI)Metabolic syndrome (ref. not MS) Adj or (95 CI)
Odd ratios95% CIOdd ratios95% CI
  1. a

    < 0·05.

  2. b

    < 0·001.

Gender
FemaleMale0·57b0·560·591·28b1·261·31
Family history
StrokeNo4·83b4·714·951·61b1·561·66
Cardiovascular diseaseNo8·74b8·528·973·28b3·163·42
HypertensionNo1·52b1·491·561·86b1·811·91
DiabetesNo2·45b2·402·512·25b2·172·32
Smoking
Current smokerNon-smoker1·26b1·231·291·12b1·081·15
Drinking
DrinkerNon-drinker1·000·981·031·02a1·001·05
Exercise
Non-exerciserExerciser1·14b1·121·161·10b1·081·12
Obesity
UnderweightNormal0·77b0·720·820·44b0·410·48
Mild obesityNormal4·47b4·384·573·41a3·353·48
Heavy obesityNormal19·46b18·6720·287·82b7·448·23
d.f. 1111
−2log l 280,916·84b276,037·28b
Cox and Snell R2 0·370·24
Nagelkerke R2 0·550·32
Model chi-square 204,041·31b71,634·87b

Among the younger adults, the prevalence of MetS in women was 0·57 times that in men (< 0·001) and was increased 4·83-fold (< 0·001) by family history of stroke, 8·74-fold (< 0·001) by family history of heart disease, 1·52-fold (< 0·001) by family history of high blood pressure and by 2·45-fold (< 0·001) by family history of diabetes. The prevalence was also 1·26 times higher (< 0·001) in smokers than in non-smokers and 1·14-fold higher (< 0·001) in non-exercisers than in exercisers. Obesity also had an effect, with the prevalence increased 4·47-fold (< 0·001) by mild obesity and 19·46-fold (< 0·001) by severe obesity compared with normal-BMI individuals, while the prevalence in underweight individuals was only 0·77 times (< 0·001) that in normal-BMI individuals. As for the effect of drinking on MetS prevalence, those drinking more than once a week showed a prevalence 1·00 times that of those drinking less than once a week, a statistically insignificant difference.

Among the older adults, the prevalence of MetS was 1·28-fold higher (< 0·001) in women than in men, in contrast to the effect of gender in the younger adults. When the effect of family disease history was examined, the prevalence was 1·61-fold higher (< 0·001) in those with a family history of stroke, 3·28-fold higher (< 0·001) in those with a family history of heart disease, 1·86-fold higher (< 0·001) in those with a family history of high blood pressure, and 2·25-fold higher (< 0·001) in those with a family history of diabetes compared with those without. Health behaviours also had significant effects, with the prevalence of MetS 1·12-fold higher (< 0·001) in smokers than in non-smokers, 1·02-fold higher (< 0·001) in drinkers than in non-drinkers, and 1·10-fold higher (< 0·001) in non-exercisers than in exercisers. Finally, the prevalence of MetS was 3·41-fold higher (< 0·001) in the mildly obese and 7·82-fold higher (< 0·001) in the severely obese than in the normal-BMI patients, while the prevalence in the underweight was only 0·44 times (< 0·001) that of the normal-BMI patients. Although smoking, drinking, and exercise are significant here, it may be related to large sample size.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Background
  5. The study
  6. Results
  7. Discussion
  8. Conclusion
  9. Funding
  10. Conflict of interest
  11. Author contributions
  12. References

The prevalence of MetS

This study detected MetS in 24·8% of the younger adults and 40·8% of the older adults examined. Studies related to the prevalence of MetS in Korea can be found beginning in the late 1990s. Several of these studies targeted examinees in comprehensive examination centres in one city or one university, but none examined prevalence on a nation-wide scale. In contrast, this survey targeted 690,671 examinees that underwent the health test on life transition periods conducted by the National Health Insurance Corporation, thereby more accurately reflecting the prevalence of MetS in Korea.

A study in Finland that targeted older adults over a period of 14 years reported that the prevalence of MetS (defined by NCEP criteria) was 42·1% (Wang et al. 2008). In the USA, 12·9% of a sample population that had been MetS-free for 15 years was diagnosed with MetS at a 20-year follow-up exam (Paul et al. 2008). In Germany, in a cohort study conducted over 10 years, the prevalence of MetS (using NCEP criteria) was 25·8% (Jacqueline et al. 2005). In Mexico, Rojas et al. (2009) reported that the prevalence was 36·8% (using NCEP-ATP III criteria), while this study showed 41·6% under a criteria presented by the AHA/NHLBI, similar to the prevalence of 40·8% among the 66 year old adults in this study.

Risk factors for metabolic syndrome

Among the 40 year old adults, the MetS prevalence was lower in women than in men, while the prevalence was 1·28-fold higher in women than in men in the 66 year old adults. According to the 2005 National Health and Nutrition Survey, the prevalence of MetS among Korean adults aged 30 or more was 32·9% in men and 31·8% in women; in men, this prevalence gradually increased with age during their 50s and thereafter decreased into their 70s, while the prevalence in women increased steadily with age: at age 70, the prevalence among women was 63·4%, almost twice the 34·1% prevalence in men (Lee et al. 2009). Park et al. (2003a, 2003b) pointed out that while the prevalence of MetS increased with age, the pattern of increase depended on gender. As in our study, Park et al. (2003a, 2003b) reported that men in their 50s had MetS with a higher frequency than did women, while after the age of 60 the opposite pattern was observed. This was attributed to increased risk factors such as abdominal obesity and cardiovascular disease in postmenopausal women (Park et al. 2003a, 2003b). Therefore, women should be pre-educated about MetS and receive intervention before the age of 40.

According to this study, a family history of cardiovascular disease was the most significant risk factor for MetS. Ahn et al. (2010) reported that a family history of high blood pressure had the greatest degree of correlation with MetS: 27·5% in men and 40·9% in women in these groups. Park et al. (2003a, 2003b) reported that women with a family history of high blood pressure, diabetes, hyperlipidaemia, angina, myocardial infarction, or stroke had a 1·4-fold higher risk of MetS (< 0·05). In our study, family history of stroke, cardiovascular disease, or high blood pressure had statistically significant effects on the prevalence of MetS. Therefore, individuals with such family histories should be screened to detect MetS in advance and should take steps to reduce the risk of MetS such as decreasing BMI, weight control, smoking cessation, and exercise training.

It is known that frequent smoking reduces HDL-cholesterol and increases LDL-cholesterol and triglycerides, resulting in elevated risk of cardiovascular disease; furthermore, abdominal obesity increases with the frequency of smoking. Several studies have reported that smoking may be closely related to MetS (Katano et al. 2010). In our study, the prevalence of MetS was lower in non-smokers than in smokers at the age of 40 but higher in non-smokers at age 66. We conservatively interpret this phenomenon to be due to former smokers being classified as non-smokers. In our study, the prevalence ratio of MetS among smokers was statistically significant at 1·26 and 1·12 at the ages of 40 and 66 years respectively. In previous studies, Choi et al. (2009) reported that the prevalence ratio of MetS among smokers was statistically insignificant at 1·5 (95% CI: 0·8–2·6) and Jung et al. (2002) reported that the comparative risk of MetS prevalence among smokers was 1·9 (95% CI: 1·1–3·7). Our results confirmed once again that smoking could be closely related to MetS.

Concerning drinking, among 40 year old adults, drinking more than once a week increased the prevalence of MetS, while 66 year old who drank less than once per week had a higher prevalence (41·8%) than those who drank more than once a week (38·2%). Previous studies have also reported the effect of drinking on MetS. Our study defined a person who drank more than once a week as a drinker; as with the discordant results seen for smoking, it is possible that this contradiction is due to the classification as non-drinkers of individuals who drank excessively when younger but no longer drank at the age of 66. In addition, the effect of drinking on MetS prevalence was not statistically significant among 40 year old adults. Among the 66 year old adults, however, the risk was 1·02 times higher in drinkers than in non-drinkers. According to a previous study, the prevalence of MetS increased with a cross ratio of 1·15 in those drinking more than two alcoholic beverages a day. It is known that moderate drinking (one alcoholic beverage per day) reduces the risk of MetS, largely by elevating HDL-cholesterol (Hong et al. 2007). Among 40 year old adults, drinking was the only variable that did not show a statistically significant effect on MetS prevalence, confirming once again that drinking has complex effects on cardiovascular disease. Therefore, further research using longitudinal rather than cross-sectional studies is needed to confirm the effects of drinking on the prevalence of MetS.

This study has the limitation that an analysis of the exact relationships among drinking, smoking, and exercise was measured not by collecting exact frequencies of the behaviours but only by distinguishing between ‘less than once a week’ or ‘more than once a week’. Further research that collects more specific measurements of smoking, drinking, and exercise is necessary. Another factor contributing to the prevalence of MetS is reduced physical activity. According to many previous studies, physical activity is somewhat important in preventing chronic diseases such as type II diabetes and cardiovascular disease (Jekal et al. 2009). According to a study conducted by Wamala et al. (1999) in Sweden, the risk of MetS was 3·3 times higher prior to adjustment and 2·8 after adjustment in non-exercisers relative to exercisers and Jung et al. (2002) reported that non-exercisers showed a comparative risk of MetS prevalence of 1·7 (95% CI: 0·9–2·8) compared with those who exercised more than 5 days a week, similar to the results of our study. In the 66 year old adults, the prevalence of MetS was significantly higher in non-exercisers (41·6%) than in exercisers (39·4%), a phenomenon likely due to inaccurate tracking and observation of exercise habits. Given the previous studies, the results indicate that the results for the 40 year old in Table 3, where the exercisers had a marginally higher prevalence of MetS, would be more noteworthy.

In this study, the prevalence of MetS increased with obesity. This was similar to the results from Park et al. (2003a, 2003b): among both younger and older adults, the greater the degree of obesity, the higher the prevalence of MetS. Park et al. (2002) reported that individuals with a normal BMI of 18·5–23 kg/m2 had a MetS prevalence of 10% and our study showed a prevalence of 18·6% in the normal-BMI group (18·5–25 kg/m2) among 40 year old adults and a higher prevalence of 30·8% among 66 year old adults with normal-BMI measurements. This study confirmed once again that in Asian populations, the risk of MetS can be high in Asians even in the presence of relatively low BMI compared with western populations. In other words, MetS risk can be high in this population even when BMI is low because the level of abdominal obesity is high relative to the BMI, similar to other Asian populations (McKeigue et al. 1991). As mentioned above, the effect of obesity on MetS prevalence was much greater than that of smoking, drinking, or exercise, confirming that weight control is an important suggestion for those at risk for MetS and that it is imperative to control weight to reduce the risk of MetS in Korea.

Study limitations

For this study, approximately 50% of the people, who were the subjects of the life transition health examination conducted by National Health and Insurance Corporation, were taken into account. As there is a possibility of having the people who had more interest in health, the generalizing of the study results is limited.

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Background
  5. The study
  6. Results
  7. Discussion
  8. Conclusion
  9. Funding
  10. Conflict of interest
  11. Author contributions
  12. References

This study was a secondary analysis of the data on all examinees that underwent the health test on life transition periods conducted by the National Health and Insurance Corporation from January–December 2008. The study was designed to assess the effects of various risk factors on the prevalence of MetS among younger and older adults, so the results can be assumed representative of the risk of MetS among younger and older adults in Korea. According to this study, gender, family history, smoking, exercise, drinking, and obesity level showed significant effects on MetS prevalence. These results indicate that it is imperative to educate the population about lifestyle factors closely related to the prevalence of MetS, including high blood pressure, obesity, and hyperlipidaemia, to prevent the syndrome among younger and older adults. In other words, a lifestyle intervention program including smoking cessation, moderation in drinking, exercise habits, and weight control must be developed and implemented. In addition, individuals with family histories of stroke, cardiovascular disease, high blood pressure, or diabetes should be preferentially managed for MetS prevention and lifestyle training for proper health and early screening for preventive nursing management should be conducted in this population.

Author contributions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Background
  5. The study
  6. Results
  7. Discussion
  8. Conclusion
  9. Funding
  10. Conflict of interest
  11. Author contributions
  12. References

All authors meet at least one of the following criteria (recommended by the ICMJE: http://www.icmje.org/ethical_1author.html) and have agreed on the final version:

  • Substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data;
  • Drafting the article or revising it critically for important intellectual content.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Background
  5. The study
  6. Results
  7. Discussion
  8. Conclusion
  9. Funding
  10. Conflict of interest
  11. Author contributions
  12. References
  • Ahn M.S., Kim J.Y., Young J.Y., Kim S.Y., Koh S.B., Lee K., Yoo B.S., Lee S.H., Yoon J., Park J.K. & Choe K.H. (2010) Cardiovascular parameters correlated with metabolic syndrome in a rural community cohort of Korea: the ARIRANG study. Cadiovascular Disorders 25, 10451052.
  • Choi S.M., Kim K.Y., Lee T.Y., Jung J.G. & Lee O.K. (2009) Incidence and related factors of the metabolic syndrome in a university hospital. Korean Journal of Health Education and Promotion 26(4), 3547.
  • Dekker J.M., Girman C., Rhodes T., Nijpels G., Stehouwer C.D., Bouter L.M., Heine R.J. (2005) Metabolic syndrome and 10-year cardiovascular disease risk in the Hoorn Study. Circulation 112(5), 666673.
  • Ford E.S. (2004) Prevalence of the metabolic syndrome in US populations. Endocrinology and Metabolism Clinics of North America 33, 333350.
  • Ford E.S., Giles W.H. & Mokdad A.H. (2004) Increasing prevalence of the metabolic syndrome among U S. adults. Diabetes Care 27(2), 24442449.
  • Ganziano J.M., Buring J.E., Breslow J.L., Goldhaber S.Z., Rosner B., VanDenburgh M., Willett W. & Hennekens C.H. (1993) Moderate alcoholl intake, increased levels of high density lipoprotein and its subfractions, and decreased risk of myocardial infarction. New England Journal of Medicine 329(25), 18291834.
  • Grundy S.M. (2005) Metabolic syndrome scientific statement by the American Heart Association and the National, Heart, Lung, and Blood Institute. Arteriosclerosis Thrombosis and Vascular Biology 25, 22432244.
  • Grundy S.M., Cleeman J.I., Daniels S.R., Donato K.A., Eekel R.H. & Franklin B.A. (2005) Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation 12, 27352752.
  • Hong W.K., Kim J.S., Jung J.K., Kim S.S., Park C.I., Kim K.B. & Jung I.W. (2007) Alcohol and metabolic syndrome in the Korean women. The Korean Journal of Family Medicine 28(2), 120126.
  • International Diabetes Federation (2007) Worldwide definition of the metabolic syndrome. Retrieved from http://www.idf.org/home on 14 February 2008.
  • Jekal Y., Kim E.S., Im J.A., Park J.H., Lee M.K., Lee S.H., Suh S.H., Chu S.H., Kang E.S., Lee H.C. & Jeon J.Y. (2009) Interaction between fatness and fitness on CVD risk factors in Asian youth. International Journal of Sports Medicine 30, 733740.
  • Jung C.H., Park J.S., Lee W.Y. & Kim S.W. (2002) Original Articles: Effects of smoking, alcohol, exercise, level of education, and family history on the metabolic syndrome in Korean adults. The Korean Journal of Medicine 63(6), 649659.
  • Katano S., Nakamura Y., Nakamura A., Murakami Y., Tanaka T., Nakagawa H., Takebayashi T., Yamato H., Okayama A., Miura K., Okamura T. & Ueshima H. (2010) Relationship among physical activity, smoking, drinking and clustering of the metabolic syndrome diagnostic components. Journal of Artherosclerosis and Thrombosis 17(6), 644650.
  • Kim B.S. (2001) Prevalence of metabolic syndrome for Koreans – among the clients of comprehensive medical examination center in one university hospital. Korean Society for Health Promotion and Disease Prevention 2(1), 1725.
  • Kirkendoll K.D., Clark P.C., Grossniklaus D.A., Lgho-Pemu P., Mullis R.M. & Dunbar S.B. (2010) Metabolic syndrome in African Americans: views on making lifestyle changes. Journal of Transcultural Nursing 21(2), 104113.
  • Lee E.H., Cho S., Kwon E.J., Park J.Y., Hyun S.M. & Kim M. (2009) Prevalence and related factors of metabolic syndrome among Korean older adults. Korean Journal of Health Education and Promotion 26(4), 129143.
  • McKeigue P.M., Shah B. & Marmot M.G. (1991) Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in South Asians. Lancet 337, 382386.
  • Ministry of Health and Welfare (2007) Trade statistics yearbook of Ministry of Health and Welfare, 2007. Ministry of Health and Welfare (2006) Korea National Health and Nutrition Examination Survey. Ministry of Health and Welfare, Seoul.
  • Ministry of Health and Welfare (2011) National Physical Examination Project Information. Ministry of Health and Welfare, Seoul, South Korea.
  • Miyatake N., Kawasaki Y., Nishikawa H., Takenami S. & Numata T. (2006) Prevalence of metabolic syndrome in Okayama prefecture, Japan. Internal Medicine 45, 107108.
  • National Health Insurance Corporation. (2008) 2008 National Health Insurance Statistical Yearbook. National Health Insurance Corporation, Seoul.
  • National Institutes of Health: third report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of high blood cholesterol in adults (Adult Treatment Panel III) (2001) Executive Summary. Bethesda, MD: National Institute of Health, National Heart, Lung, Blood Institute (NIH Publication no.01-3670).
  • Park J.S., Park H.D., Youn J.W., Jong C.W., Lee W.Y. & Kim S.W. (2002) Prevalence of the metabolic syndrome as defined by NCEP - ATPIII among the urban Korean population. The Korean Journal of Internal Medicine 63, 290298.
  • Park S.H., Lee W.Y. & Kim S.W. (2003a) The relative risk of the metabolic syndrome defined by adult treatment pannel 3 according to insulin resistance in Korean population. The Korean Journal of Internal Medicine 64(5), 552560.
  • Park H.S., Shin H.C., Kim B.S., Lee K.Y., Choi W.S., Shin J.A., Nam Y.D., Bae S.P. & Chun K.S. (2003b) Prevalence and associated factors of metabolic syndrome among adults in primary care. The Korean Journal of Obesity 12(2), 108123.
  • Paul H., Lee D.H., Michael S., Myron G., David R. & Jacobs J.R. (2008) Association between circulating oxidized low-density lipoprotein and incidence of hte metabolic syndrome. JAMA 21(19), 22872293.
  • Reaven G.M. Banting lecture (1988) Role of insuline resistance in human disease. Diabetes 37, 15951607.
  • Rojas R., Aguilar-Salinas C.A., Jimenez-Corona A., Shamah-Levy T., Rauda J., Avila-Burgos L., Villalpando S. & Ponce E.L. (2009) Metabolic syndrome in Mexican adults Results from the National Health and Nutrition Survey 2006. Salud Publica de Mexica 52(1), S11S18.
  • Son K.P., Chae Y.J., Lee T.Y., Jeong I.K., Hur M.N., Jo G.Y., Lee Y., Lee S.J., Park C.Y., Oh K.W., Hong E.K. & Kim H.K. (2004) The influence of metabolic syndrome on the intima-medial thickness and cardiovascular factors in type 2 diabetes. Diabetes 28(5), 115.
  • Stone N.J. & Saxon D. (2005) Approaches to treatment of the patient with metabolic syndrome; lifestyle therapy. The American Journal of Cardiology 9694(a), 15e21e.
  • Wamala S.P., Lynch J., Horsten M., Mittleman M.A., Schenck-Gustafsson K., Orth-Gomér K. (1999) Education and the metabolic syndrome in women. Diabetes Care 22(12), 19992003.
  • Wang J., Ruotsalainen S., Moilanen L., Lepisto P., Laakso M. & Kuusisto J. (2008) The metabolic syndrome predicts incident stroke: a 14-year follow-up study in elderly people in Finland. Journal of Cerebral Circulation 39, 10781083.
  • World Health Organization (WHO) (1999) Definition, diagnosis and classification of diabetes mellitus and its complications. Report of a WHO consultation.
  • Yoo J.S., Jeong J.I., Park C.G., Kang S.W. & Ahn J.A. (2009) Impact of life style characterisitics on prevalence risk of metabolic syndrome. Journal of Korean Academy of Nursing 39(4), 594601.
  • Zheng W., McLerran D.F., Rolland B., Zhang X., Inoue M., Matsuo K., He J., Gupta P.C., Ramadas K., Tsugane S., Irie F., Tamakoshi A., Gao Y.T., Wang R., Shu X.O., Tsuji I., Kuriyama S., Tanaka H., Satoh H., Chen C.J., Yuan J.M., Yoo K.Y., Ahsan H., Pan W.H., Gu D., Pednekar M.S., Sauvaget C., Sasazuki S., Sairenchi T., Yang G., Xiang Y.B., Nagai M., Suzuki T., Nishino Y., You S.L., Koh W.P., Park S.K., Chen Y., Shen C.Y., Thornquist M., Feng Z., Kang D., Boffetta P., Potter J.D. (2011) Association between body–mass index and risk of death in more than 1 million asians. The New England Journal of Medicine 364(8), 719729.