Sex differences in factors associated with metabolic syndrome among Korean adults without diabetes mellitus: results from the Korea National Health and Nutrition Examination Survey from 2010 to 2013

Sex differences in factors associated with metabolic syndrome among Korean adults without... Abstract Objectives The purpose of this study was to examine sex differences in factors associated with metabolic syndrome in Korean adults without diabetes mellitus. Study design Cross-sectional design. Methods The dataset of Korea National Health and Nutrition Examination Survey from 2010 to 2013 was used. Among a total of 33552 adults aged ≥30, subjects who (i) were diagnosed or had been treated for diabetes mellitus, (ii) had a fasting blood glucose level of 126 mg/dL or higher or (iii) had a glycosylated haemoglobin level of 6.5% or higher were excluded. Subjects who had anaemia or were pregnant were also excluded. Finally, 9406 subjects were included in this study. Sex differences in subjects’ characteristics were assessed with Student’s t-test and chi-square test. Logistic regressions were used to examine factors associated with metabolic syndrome by sex. Results The prevalence of metabolic syndrome in Korean adults overall without diabetes mellitus was 12.2%. Glycosylated haemoglobin from 5.7 to 6.5 and increased body mass index were independently associated with metabolic syndrome in both men and women. Current smoking, age and age square were significantly associated with metabolic syndrome in men, whereas age and illiteracy were significantly associated with it in women. Conclusions This study confirmed that glycosylated haemoglobin and body mass index can be important indicators of metabolic syndrome in Korean adults without diabetes mellitus. Diabetes, health risk behaviours, metabolic syndrome, obesity, public health Introduction Metabolic syndrome is a global health problem and affects approximately 20% to 30% of the adult population worldwide (1). Based on Korean National Health and Nutrition Survey data from 2008 to2013, the prevalence of metabolic syndrome in adults aged 20 years old and older was 28.9% and was 30.8% and 26.3% in men and women, respectively (2). The diagnostic criteria for metabolic syndrome by the National Cholesterol Education Program (3) are as follows: (i) blood pressure ≥130/85 mmHg, (ii) waist circumference >90 cm in men and >80 cm in women (4), (iii) fasting blood glucose ≥110 mg/dL, (iv) triglycerides ≥150 mg/dL, and (v) high-density lipoprotein cholesterol <40 mg/dL in men and <50 mg/dL in women. Metabolic syndrome is called insulin resistance syndrome because metabolic syndrome is significantly related to insulin resistance (5). Indeed, metabolic syndrome is a known precursor of diabetes mellitus (6). For example, in one study, the prevalence of metabolic syndrome in the normal fasting glucose group was 25.8%, whereas that in the diabetes mellitus group was 86.0% (7). Diabetes mellitus is usually diagnosed using fasting glucose, an oral glucose tolerance test and typical symptoms of diabetes mellitus (8). In 2010, the American Diabetes Association added glycosylated haemoglobin to the diagnostic criteria of diabetes mellitus; the diagnostic level of glycosylated haemoglobin is ≥6.5%. According to the American Diabetes Association, people within the range of 5.5% to 6.5% of glycosylated haemoglobin were more likely to have diabetes mellitus compared with people who had a glycosylated haemoglobin <5.5%. The close relationship between diabetes mellitus and metabolic syndrome has already been revealed in the previous study (6,7). The relationship between metabolic syndrome and from normal to prediabetic population is unclear. This result implies that people having a glycosylated haemoglobin from 5.5% to 6.5% should heed preventative strategies for metabolic syndrome and diabetes mellitus. In the literature, sex differences in factors associated with metabolic syndrome have been reported but the results have been inconsistent. Some studies found that glycosylated haemoglobin (9), increased body mass index (10,11), age (11), smoking status (11) and physical activity (12,13) were significant factors of metabolic syndrome in both men and women. On the other hand, other studies found that alcohol consumption (14) and physical activity (15) were significantly associated with metabolic syndrome in men only, and low education level (16,17), low socioeconomic status (16,17) and current smoking (14) were significantly associated with metabolic syndrome in women only (16,17). Therefore, the purpose of the current study was to examine sex differences in factors associated with metabolic syndrome in Korean adults without diabetes mellitus. This study provides evidence for the development and application of sex-specific interventions for preventing metabolic syndrome in Korean adults without diabetes mellitus. Methods Study design This study involved secondary data analysis of nationwide data from the Korea National Health and Nutrition Examination Survey from 2010 to 2013. Study participants Korea National Health and Nutrition Examination Survey is an ongoing series of cross-sectional surveys designed to assess health and nutrition status from a nationally representative sample of the civilian non-institutionalized South Korean population. For the analysis, we used the Korea National Health and Nutrition Examination Survey dataset from 2010 to 2013 (18,19). Among the total of 33552 adults aged 30 years and older, we excluded all subjects who had diabetes mellitus because the purpose of the current study was to examine factors associated with metabolic syndrome among adults without diabetes mellitus. Therefore, subjects who (i) were diagnosed or had been treated for diabetes mellitus and (ii) had a fasting blood glucose level of 126 mg/dL or higher were excluded based on the definition of diabetes mellitus in Korea National Health and Nutrition Examination Survey. In addition, subjects who had a glycosylated haemoglobin level of 6.5% or greater, which is the diagnostic criteria for diabetes mellitus in American Diabetes Association, were excluded. In addition, subjects who had anaemia or were pregnant were excluded because these conditions can affect glycosylated haemoglobin. Finally, 9406 subjects were included in this study. This study was approved by the Institutional Review Board of Yonsei University in Korea (Study No. 2016-0031). Variables Metabolic syndrome According to the guidelines presented in the National Cholesterol Education Program Adult Treatment Panel Ⅲ, metabolic syndrome was defined as the presence of three or more complex signs of the five risk factors as described above. Glycosylated haemoglobin According to the Classification and Diagnosis of Diabetes presented by the American Diabetes Association in 2014 (8), pre-diabetes stages were divided into three groups by glycosylated haemoglobin level: (i) group 1: <5.3%; (ii) group 2: ≥5.3% and <5.7% and (iii) group 3: ≥5.7% and <6.5%. Body mass index Body mass index was categorized as underweight (<18.5 kg/m2), normal (≥18.5, <23.0 kg/m2), overweight (≥23.0 kg/m2 and <25.0 kg/m2) or obese (≥25.0 kg/m2) based on the Asia-Pacific Criteria (4). Education Education was divided into four groups based on years of education: illiteracy (non-formal education); 1 to 9 years (elementary to middle school); 10 to 12 years (high school) or 13 years or more (college or above). Smoking Smoking status was divided into three groups: non-smokers, past smokers or current smokers (20). Non-smokers meant that subjects reported that they had ‘never smoked in their lifetime’ or ‘they smoked before and the amount of cigarettes smoked in their lifetime was less than 5 packs’. Subjects who responded ‘I smoked more than 5 packs before but in the past year I have not smoked’ were considered past smokers. Current smokers were defined as subjects who answered that they ‘smoke now’ or ‘smoke sometimes’. Drinking Drinking was classified as past drinker, light drinker or heavy drinker. Past drinkers were subjects who had experience drinking at one point in their lives but have not been drinking for the last year. Subjects who drink two to four times a month or less and five to six glasses per time for men (six to four glasses for women) were considered light drinkers. Heavy drinkers were defined as subjects who drink more than twice a week and seven glasses or more per time for men (five glasses or more for women). Physical activity Physical activity in the Korea National Health and Nutrition Examination Survey was assessed using the International Physical Activity Questionnaire (21), which contains the items related to the moderate- and high-intensity physical activity and walking. The physical activity time was converted to the metabolic equivalent of task (MET) based on the International Physical Activity Questionnaire score conversion guidelines (22). The subjects’ physical activity was divided into three levels according to the International Physical Activity Questionnaire scoring proposal (low, moderate and high physical activity) (22). High physical activity was defined as any one of the following two criteria: (i) vigorous-intensity activity on at least 3 days and accumulating at least 1500 MET-minutes/week and (ii) 7 or more days of any combination of walking, moderate- or vigorous-intensity activities accumulating at least 3000 MET-minutes/week. Moderate physical activity was defined as either of the following three criteria: (i) 3 or more days of vigorous activity of at least 20 minutes per day, (ii) 5 or more days of moderate-intensity activity and/or walking of at least 30 minutes per day and (iii) 5 or more days of any combination of walking, moderate- or vigorous-intensity activities achieving a minimum of at least 600 MET-minutes/week. Low physical activity group was defined as follows: no activity or some activity is reported but not enough to meet the categories of moderate or high physical activity. Statistical analysis All statistical analysis was performed using SAS 9.4 (Cary, NC, USA). Descriptive statistics including mean, standard deviation (SD), frequency and percent were performed for general characteristics of the subjects. Sex differences in the subjects’ characteristics were assessed with Student’s t-test for continuous variables and the chi-squared test for categorical variables. To examine the multicollinearity between variables, variance inflation factor (VIF) and condition index (CI) were examined and the all values of VIF and CI were less than 2.0. Logistic regressions were performed to identify factors associated with metabolic syndrome by sex. The variables of glycosylated haemoglobin, age, body mass index, education, smoking status, drinking and physical activity, which were selected based on previous literature, were included in the logistic regressions. All P ≤ 0.05 were considered statistically significant. Results The characteristics of the subjects by sex are presented in Table 1. Among the 9406 subjects, there were 4780 men and 4626 women. There were significant differences between men and women in most variables except total cholesterol and metabolic syndrome. Overall, men were older, more educated, more overweight or obese, had a greater waist circumference and had higher blood pressure than women. Similarly, in blood tests, men showed significantly higher triglycerides, lower low-density lipoprotein (LDL), lower high-density lipoprotein (HDL), higher fasting glucose and higher glycosylated haemoglobin compared with women. Regarding lifestyle variables, the proportion of current smokers and heavy drinkers and high physical activity in men were significantly greater than those in women. Table 1. General characteristics of Korean adults without diabetes mellitus of Korea National Health and Nutrition Examination Survey from 2010 to 2013 Variables Total (n = 9406) Men (n = 4780) Women (n = 4626) Age in years, mean (SD) 45.55 (15.25) 46.45 (15.57) 44.63 (14.86) Group, n (%)  30–39 2097 (22.29) 1035 (21.65) 1062 (22.96)  40–49 1981 (21.06) 996 (20.84) 985 (21.29)  50–59 1836 (19.52) 904 (18.91) 932 (20.15)  ≥60 3492 (37.13) 1845 (38.60) 1647 (35.60) Education in years, n (%)  0 (illiteracy) 185 (1.97) 39 (0.82) 146 (3.16)  1–9 (elementary to middle school) 1940 (20.63) 856 (17.91) 1084 (23.43)  10–12 (high school) 2911 (30.95) 1467 (30.69) 1444 (31.21)  ≥ 13 (college or above) 4370 (46.45) 2418 (50.59) 1952 (42.20)  Body mass index (kg/m2), mean (SD) 23.60 (3.31) 24.12 (3.15) 23.06 (3.39) Group, n (%)  Under (<18.5) 415 (4.41) 118 (2.47) 297 (6.42)  Normal (≥18.5 and <23.0) 3862 (41.06) 1688 (35.31) 2174 (47.00)  Overweight (≥23 and <25.0) 2231 (23.72) 1245 (26.05) 986 (21.31)  Obese (≥25.0) 2898 (30.81) 1729 (36.17) 1169 (25.27)  Waist circumference (cm), mean (SD) 80.55 (9.72) 84.06 (8.72) 76.92 (9.35) Blood pressure, mean (SD)  Systolic BP (mmHg) 116.9 (15.87) 120.1 (14.80) 113.5 (16.23)  Diastolic BP (mmHg) 76.22 (10.44) 78.83 (10.62) 73.53 (9.52) Blood tests, mean (SD)  Total cholesterol (mg/dl) 189.1 (34.90) 188.9 (34.55) 189.3 (35.26)  Triglyceride (mg/dl) 130.3 (104.8) 152.6 (124.8) 107.2 (72.07)  High-density lipoprotein (mg/dl) 50.08 (11.36) 47.20 (10.66) 53.07 (11.29)  Low-density lipoprotein (mg/dl) 109.5 (32.77) 108.0 (34.04) 111.0 (31.33)  Fasting glucose (mg/dl) 93.15 (9.63) 94.68 (9.99) 91.58 (8.97)  Glycosylated haemoglobin (%) 5.55 (0.34) 5.57 (0.33) 5.52 (0.35)  Metabolic syndrome, n (%) 1146 (12.18) 596 (12.47) 550 (11.89) Smoking status, n (%)  Non-smoker 5144 (54.69) 1041 (21.78) 4103 (88.69)  Past smoker 1815 (19.30) 1640 (34.31) 175 (3.78)  Current smoker 2447 (26.01) 2099 (43.91) 348 (7.52) Drinking status, n (%)  Past drinker 2843 (30.23) 1771 (37.05) 1072 (23.17)  Light drinker 5139 (54.64) 1893 (39.60) 3246 (70.17)  Heavy drinker 1424 (15.13) 1116 (23.35) 308 (6.66) Physical activity, n (%)  Low 4058 (43.14) 1861 (38.93) 2197 (47.49)  Moderate 2929 (31.14) 1417 (29.64) 1512 (32.68)  High 2419 (25.72) 1502 (31.42) 917 (19.81) Variables Total (n = 9406) Men (n = 4780) Women (n = 4626) Age in years, mean (SD) 45.55 (15.25) 46.45 (15.57) 44.63 (14.86) Group, n (%)  30–39 2097 (22.29) 1035 (21.65) 1062 (22.96)  40–49 1981 (21.06) 996 (20.84) 985 (21.29)  50–59 1836 (19.52) 904 (18.91) 932 (20.15)  ≥60 3492 (37.13) 1845 (38.60) 1647 (35.60) Education in years, n (%)  0 (illiteracy) 185 (1.97) 39 (0.82) 146 (3.16)  1–9 (elementary to middle school) 1940 (20.63) 856 (17.91) 1084 (23.43)  10–12 (high school) 2911 (30.95) 1467 (30.69) 1444 (31.21)  ≥ 13 (college or above) 4370 (46.45) 2418 (50.59) 1952 (42.20)  Body mass index (kg/m2), mean (SD) 23.60 (3.31) 24.12 (3.15) 23.06 (3.39) Group, n (%)  Under (<18.5) 415 (4.41) 118 (2.47) 297 (6.42)  Normal (≥18.5 and <23.0) 3862 (41.06) 1688 (35.31) 2174 (47.00)  Overweight (≥23 and <25.0) 2231 (23.72) 1245 (26.05) 986 (21.31)  Obese (≥25.0) 2898 (30.81) 1729 (36.17) 1169 (25.27)  Waist circumference (cm), mean (SD) 80.55 (9.72) 84.06 (8.72) 76.92 (9.35) Blood pressure, mean (SD)  Systolic BP (mmHg) 116.9 (15.87) 120.1 (14.80) 113.5 (16.23)  Diastolic BP (mmHg) 76.22 (10.44) 78.83 (10.62) 73.53 (9.52) Blood tests, mean (SD)  Total cholesterol (mg/dl) 189.1 (34.90) 188.9 (34.55) 189.3 (35.26)  Triglyceride (mg/dl) 130.3 (104.8) 152.6 (124.8) 107.2 (72.07)  High-density lipoprotein (mg/dl) 50.08 (11.36) 47.20 (10.66) 53.07 (11.29)  Low-density lipoprotein (mg/dl) 109.5 (32.77) 108.0 (34.04) 111.0 (31.33)  Fasting glucose (mg/dl) 93.15 (9.63) 94.68 (9.99) 91.58 (8.97)  Glycosylated haemoglobin (%) 5.55 (0.34) 5.57 (0.33) 5.52 (0.35)  Metabolic syndrome, n (%) 1146 (12.18) 596 (12.47) 550 (11.89) Smoking status, n (%)  Non-smoker 5144 (54.69) 1041 (21.78) 4103 (88.69)  Past smoker 1815 (19.30) 1640 (34.31) 175 (3.78)  Current smoker 2447 (26.01) 2099 (43.91) 348 (7.52) Drinking status, n (%)  Past drinker 2843 (30.23) 1771 (37.05) 1072 (23.17)  Light drinker 5139 (54.64) 1893 (39.60) 3246 (70.17)  Heavy drinker 1424 (15.13) 1116 (23.35) 308 (6.66) Physical activity, n (%)  Low 4058 (43.14) 1861 (38.93) 2197 (47.49)  Moderate 2929 (31.14) 1417 (29.64) 1512 (32.68)  High 2419 (25.72) 1502 (31.42) 917 (19.81) View Large Table 1. General characteristics of Korean adults without diabetes mellitus of Korea National Health and Nutrition Examination Survey from 2010 to 2013 Variables Total (n = 9406) Men (n = 4780) Women (n = 4626) Age in years, mean (SD) 45.55 (15.25) 46.45 (15.57) 44.63 (14.86) Group, n (%)  30–39 2097 (22.29) 1035 (21.65) 1062 (22.96)  40–49 1981 (21.06) 996 (20.84) 985 (21.29)  50–59 1836 (19.52) 904 (18.91) 932 (20.15)  ≥60 3492 (37.13) 1845 (38.60) 1647 (35.60) Education in years, n (%)  0 (illiteracy) 185 (1.97) 39 (0.82) 146 (3.16)  1–9 (elementary to middle school) 1940 (20.63) 856 (17.91) 1084 (23.43)  10–12 (high school) 2911 (30.95) 1467 (30.69) 1444 (31.21)  ≥ 13 (college or above) 4370 (46.45) 2418 (50.59) 1952 (42.20)  Body mass index (kg/m2), mean (SD) 23.60 (3.31) 24.12 (3.15) 23.06 (3.39) Group, n (%)  Under (<18.5) 415 (4.41) 118 (2.47) 297 (6.42)  Normal (≥18.5 and <23.0) 3862 (41.06) 1688 (35.31) 2174 (47.00)  Overweight (≥23 and <25.0) 2231 (23.72) 1245 (26.05) 986 (21.31)  Obese (≥25.0) 2898 (30.81) 1729 (36.17) 1169 (25.27)  Waist circumference (cm), mean (SD) 80.55 (9.72) 84.06 (8.72) 76.92 (9.35) Blood pressure, mean (SD)  Systolic BP (mmHg) 116.9 (15.87) 120.1 (14.80) 113.5 (16.23)  Diastolic BP (mmHg) 76.22 (10.44) 78.83 (10.62) 73.53 (9.52) Blood tests, mean (SD)  Total cholesterol (mg/dl) 189.1 (34.90) 188.9 (34.55) 189.3 (35.26)  Triglyceride (mg/dl) 130.3 (104.8) 152.6 (124.8) 107.2 (72.07)  High-density lipoprotein (mg/dl) 50.08 (11.36) 47.20 (10.66) 53.07 (11.29)  Low-density lipoprotein (mg/dl) 109.5 (32.77) 108.0 (34.04) 111.0 (31.33)  Fasting glucose (mg/dl) 93.15 (9.63) 94.68 (9.99) 91.58 (8.97)  Glycosylated haemoglobin (%) 5.55 (0.34) 5.57 (0.33) 5.52 (0.35)  Metabolic syndrome, n (%) 1146 (12.18) 596 (12.47) 550 (11.89) Smoking status, n (%)  Non-smoker 5144 (54.69) 1041 (21.78) 4103 (88.69)  Past smoker 1815 (19.30) 1640 (34.31) 175 (3.78)  Current smoker 2447 (26.01) 2099 (43.91) 348 (7.52) Drinking status, n (%)  Past drinker 2843 (30.23) 1771 (37.05) 1072 (23.17)  Light drinker 5139 (54.64) 1893 (39.60) 3246 (70.17)  Heavy drinker 1424 (15.13) 1116 (23.35) 308 (6.66) Physical activity, n (%)  Low 4058 (43.14) 1861 (38.93) 2197 (47.49)  Moderate 2929 (31.14) 1417 (29.64) 1512 (32.68)  High 2419 (25.72) 1502 (31.42) 917 (19.81) Variables Total (n = 9406) Men (n = 4780) Women (n = 4626) Age in years, mean (SD) 45.55 (15.25) 46.45 (15.57) 44.63 (14.86) Group, n (%)  30–39 2097 (22.29) 1035 (21.65) 1062 (22.96)  40–49 1981 (21.06) 996 (20.84) 985 (21.29)  50–59 1836 (19.52) 904 (18.91) 932 (20.15)  ≥60 3492 (37.13) 1845 (38.60) 1647 (35.60) Education in years, n (%)  0 (illiteracy) 185 (1.97) 39 (0.82) 146 (3.16)  1–9 (elementary to middle school) 1940 (20.63) 856 (17.91) 1084 (23.43)  10–12 (high school) 2911 (30.95) 1467 (30.69) 1444 (31.21)  ≥ 13 (college or above) 4370 (46.45) 2418 (50.59) 1952 (42.20)  Body mass index (kg/m2), mean (SD) 23.60 (3.31) 24.12 (3.15) 23.06 (3.39) Group, n (%)  Under (<18.5) 415 (4.41) 118 (2.47) 297 (6.42)  Normal (≥18.5 and <23.0) 3862 (41.06) 1688 (35.31) 2174 (47.00)  Overweight (≥23 and <25.0) 2231 (23.72) 1245 (26.05) 986 (21.31)  Obese (≥25.0) 2898 (30.81) 1729 (36.17) 1169 (25.27)  Waist circumference (cm), mean (SD) 80.55 (9.72) 84.06 (8.72) 76.92 (9.35) Blood pressure, mean (SD)  Systolic BP (mmHg) 116.9 (15.87) 120.1 (14.80) 113.5 (16.23)  Diastolic BP (mmHg) 76.22 (10.44) 78.83 (10.62) 73.53 (9.52) Blood tests, mean (SD)  Total cholesterol (mg/dl) 189.1 (34.90) 188.9 (34.55) 189.3 (35.26)  Triglyceride (mg/dl) 130.3 (104.8) 152.6 (124.8) 107.2 (72.07)  High-density lipoprotein (mg/dl) 50.08 (11.36) 47.20 (10.66) 53.07 (11.29)  Low-density lipoprotein (mg/dl) 109.5 (32.77) 108.0 (34.04) 111.0 (31.33)  Fasting glucose (mg/dl) 93.15 (9.63) 94.68 (9.99) 91.58 (8.97)  Glycosylated haemoglobin (%) 5.55 (0.34) 5.57 (0.33) 5.52 (0.35)  Metabolic syndrome, n (%) 1146 (12.18) 596 (12.47) 550 (11.89) Smoking status, n (%)  Non-smoker 5144 (54.69) 1041 (21.78) 4103 (88.69)  Past smoker 1815 (19.30) 1640 (34.31) 175 (3.78)  Current smoker 2447 (26.01) 2099 (43.91) 348 (7.52) Drinking status, n (%)  Past drinker 2843 (30.23) 1771 (37.05) 1072 (23.17)  Light drinker 5139 (54.64) 1893 (39.60) 3246 (70.17)  Heavy drinker 1424 (15.13) 1116 (23.35) 308 (6.66) Physical activity, n (%)  Low 4058 (43.14) 1861 (38.93) 2197 (47.49)  Moderate 2929 (31.14) 1417 (29.64) 1512 (32.68)  High 2419 (25.72) 1502 (31.42) 917 (19.81) View Large Factors associated with metabolic syndrome in men Results of multivariate logistic regression analysis in men are presented in Table 2. Regarding glycosylated haemoglobin, group 3 was significantly associated with metabolic syndrome (OR = 1.88; 95% CI = 1.34–2.65). Age and age square significantly associated with metabolic syndrome in men (age: OR = 1.14; 95% CI = 1.09–1.20, age (2): OR = 0.99; 95% CI = 0.998–0.999), indicating that the relationship between metabolic syndrome and age was not linear. Being overweight (OR = 3.11; 95% CI = 2.08–4.67) and obese (OR = 17.18; 95% CI = 12.04–24.52) were also significantly associated with metabolic syndrome. Current smoking (OR = 1.42; 95% CI = 1.08–1.86) was significantly associated with metabolic syndrome. Table 2. Result of multivariable logistic regression for factors associated with metabolic syndrome in men (n = 4780) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.13 (0.80–1.60) Group 3 (≥5.7 and <6.5) 1.88 (1.34–2.65) Age (year) 1.14 (1.09–1.20) Age2 (year2) 0.99 (0.998–0.999) Body mass index (kg/m2) Under (<18.5) 0.49 (0.07–3.58) Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 3.11 (2.08–4.67) Obese (≥25.0) 17.18 (12.04–24.52) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 1.00 (0.26–3.79) 10–12 (high school) 0.89 (0.24–3.39) ≥ 13 (college or above) 0.72 (0.19–2.76) Drinking Non-drinker Reference Past drinker 1.06 (0.85–1.33) Current drinker 1.24 (0.98–1.58) Smoking Non-smoker Reference Past smoker 1.17 (0.88–1.56) Current smoker 1.42 (1.08–1.86) Physical activity Low Reference Moderate 1.14 (0.91–1.42) High 0.85 (0.68–1.07) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.13 (0.80–1.60) Group 3 (≥5.7 and <6.5) 1.88 (1.34–2.65) Age (year) 1.14 (1.09–1.20) Age2 (year2) 0.99 (0.998–0.999) Body mass index (kg/m2) Under (<18.5) 0.49 (0.07–3.58) Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 3.11 (2.08–4.67) Obese (≥25.0) 17.18 (12.04–24.52) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 1.00 (0.26–3.79) 10–12 (high school) 0.89 (0.24–3.39) ≥ 13 (college or above) 0.72 (0.19–2.76) Drinking Non-drinker Reference Past drinker 1.06 (0.85–1.33) Current drinker 1.24 (0.98–1.58) Smoking Non-smoker Reference Past smoker 1.17 (0.88–1.56) Current smoker 1.42 (1.08–1.86) Physical activity Low Reference Moderate 1.14 (0.91–1.42) High 0.85 (0.68–1.07) OR, odd ratio; CI, confidence interval. Data source: Korea National Health and Nutrition Examination survey from 2010 to 2013. View Large Table 2. Result of multivariable logistic regression for factors associated with metabolic syndrome in men (n = 4780) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.13 (0.80–1.60) Group 3 (≥5.7 and <6.5) 1.88 (1.34–2.65) Age (year) 1.14 (1.09–1.20) Age2 (year2) 0.99 (0.998–0.999) Body mass index (kg/m2) Under (<18.5) 0.49 (0.07–3.58) Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 3.11 (2.08–4.67) Obese (≥25.0) 17.18 (12.04–24.52) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 1.00 (0.26–3.79) 10–12 (high school) 0.89 (0.24–3.39) ≥ 13 (college or above) 0.72 (0.19–2.76) Drinking Non-drinker Reference Past drinker 1.06 (0.85–1.33) Current drinker 1.24 (0.98–1.58) Smoking Non-smoker Reference Past smoker 1.17 (0.88–1.56) Current smoker 1.42 (1.08–1.86) Physical activity Low Reference Moderate 1.14 (0.91–1.42) High 0.85 (0.68–1.07) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.13 (0.80–1.60) Group 3 (≥5.7 and <6.5) 1.88 (1.34–2.65) Age (year) 1.14 (1.09–1.20) Age2 (year2) 0.99 (0.998–0.999) Body mass index (kg/m2) Under (<18.5) 0.49 (0.07–3.58) Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 3.11 (2.08–4.67) Obese (≥25.0) 17.18 (12.04–24.52) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 1.00 (0.26–3.79) 10–12 (high school) 0.89 (0.24–3.39) ≥ 13 (college or above) 0.72 (0.19–2.76) Drinking Non-drinker Reference Past drinker 1.06 (0.85–1.33) Current drinker 1.24 (0.98–1.58) Smoking Non-smoker Reference Past smoker 1.17 (0.88–1.56) Current smoker 1.42 (1.08–1.86) Physical activity Low Reference Moderate 1.14 (0.91–1.42) High 0.85 (0.68–1.07) OR, odd ratio; CI, confidence interval. Data source: Korea National Health and Nutrition Examination survey from 2010 to 2013. View Large Factors associated with metabolic syndrome in women Results of multivariate logistic regression analysis in women are presented in Table 3. Group 3 of glycosylated haemoglobin was significantly associated with metabolic syndrome (OR = 2.01; 95% CI = 1.42–2.84). Women’s age (OR = 1.02; 95% CI = 1.01–1.03) was also significantly associated with metabolic syndrome. Being overweight (OR = 4.30; 95% CI = 3.12–5.92) and obese (OR = 10.91; 95% CI = 8.13–14.62) were also significantly associated with metabolic syndrome. Regarding education, women had high school education (OR = 0.58; 95% CI = 0.36–0.93) or a college or above education (OR = 0.47; 95% CI = 0.28–0.80) were less likely to have metabolic syndrome compared with women who were illiterate. Table 3. Result of multivariable logistic regression for factors associated with metabolic syndrome in women (n = 4626) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.04 (0.73–1.48) Group 3 (≥5.7 and <6.5) 2.01 (1.42–2.84) Age (year) 1.02 (1.01–1.03) Body mass index (kg/m2) Under (<18.5) <0.001(<0.001–>999.999)a Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 4.30 (3.12–5.92) Obese (≥25.0) 10.91 (8.13–14.62) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 0.74 (0.49–1.13) 10–12 (high school) 0.58 (0.36–0.93) ≥ 13 (college or above) 0.47 (0.28–0.80) Drinking Non-drinker Reference Past drinker 1.03 (0.79–1.34) Current drinker 1.08 (0.68–1.74) Smoking Non-smoker Reference Past smoker 0.81 (0.46–1.43) Current smoker 1.48 (1.00–2.19) Physical Activity Low Reference Moderate 0.86 (0.68–1.08) High 0.79 (0.60–1.02) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.04 (0.73–1.48) Group 3 (≥5.7 and <6.5) 2.01 (1.42–2.84) Age (year) 1.02 (1.01–1.03) Body mass index (kg/m2) Under (<18.5) <0.001(<0.001–>999.999)a Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 4.30 (3.12–5.92) Obese (≥25.0) 10.91 (8.13–14.62) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 0.74 (0.49–1.13) 10–12 (high school) 0.58 (0.36–0.93) ≥ 13 (college or above) 0.47 (0.28–0.80) Drinking Non-drinker Reference Past drinker 1.03 (0.79–1.34) Current drinker 1.08 (0.68–1.74) Smoking Non-smoker Reference Past smoker 0.81 (0.46–1.43) Current smoker 1.48 (1.00–2.19) Physical Activity Low Reference Moderate 0.86 (0.68–1.08) High 0.79 (0.60–1.02) OR, odd ratio; CI, confidence interval. aQuasi-complete separation of data points were detected because no one having metabolic syndrome were <18.5 of body mass index. Data source: Korea National Health and Nutrition Examination survey from 2010 to 2013. View Large Table 3. Result of multivariable logistic regression for factors associated with metabolic syndrome in women (n = 4626) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.04 (0.73–1.48) Group 3 (≥5.7 and <6.5) 2.01 (1.42–2.84) Age (year) 1.02 (1.01–1.03) Body mass index (kg/m2) Under (<18.5) <0.001(<0.001–>999.999)a Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 4.30 (3.12–5.92) Obese (≥25.0) 10.91 (8.13–14.62) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 0.74 (0.49–1.13) 10–12 (high school) 0.58 (0.36–0.93) ≥ 13 (college or above) 0.47 (0.28–0.80) Drinking Non-drinker Reference Past drinker 1.03 (0.79–1.34) Current drinker 1.08 (0.68–1.74) Smoking Non-smoker Reference Past smoker 0.81 (0.46–1.43) Current smoker 1.48 (1.00–2.19) Physical Activity Low Reference Moderate 0.86 (0.68–1.08) High 0.79 (0.60–1.02) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.04 (0.73–1.48) Group 3 (≥5.7 and <6.5) 2.01 (1.42–2.84) Age (year) 1.02 (1.01–1.03) Body mass index (kg/m2) Under (<18.5) <0.001(<0.001–>999.999)a Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 4.30 (3.12–5.92) Obese (≥25.0) 10.91 (8.13–14.62) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 0.74 (0.49–1.13) 10–12 (high school) 0.58 (0.36–0.93) ≥ 13 (college or above) 0.47 (0.28–0.80) Drinking Non-drinker Reference Past drinker 1.03 (0.79–1.34) Current drinker 1.08 (0.68–1.74) Smoking Non-smoker Reference Past smoker 0.81 (0.46–1.43) Current smoker 1.48 (1.00–2.19) Physical Activity Low Reference Moderate 0.86 (0.68–1.08) High 0.79 (0.60–1.02) OR, odd ratio; CI, confidence interval. aQuasi-complete separation of data points were detected because no one having metabolic syndrome were <18.5 of body mass index. Data source: Korea National Health and Nutrition Examination survey from 2010 to 2013. View Large Discussion The major findings of the current study were that the prevalence of metabolic syndrome in Korean adults without diabetes mellitus was 12.2%, and glycosylated haemoglobin (≥5.7 and <6.5) and increased body mass index (overweight and obese) were independently associated with metabolic syndrome in both men and women. Men who currently smoked were more likely to have metabolic syndrome, whereas illiterate women were more likely to have metabolic syndrome. Firstly, the prevalence of metabolic syndrome in Korean adults without diabetes mellitus (overall 12.2%, 12.5% for men and 11.9% for women) appears to be lower than those in other ethnic groups. Limited information on the prevalence of metabolic syndrome in non-diabetic adults exists. Alexander and colleagues (7) analyzed NHANES III data and found that the prevalence of metabolic syndrome in non-diabetic Americans who were 50 years old or older was 28.7%. Specifically, the prevalence of metabolic syndrome in their normal fasting glucose group was 25.8% and that in the diabetes mellitus group was 86.0%. Similarly, Leite et al. (23) found that the prevalence of metabolic syndrome in participants aged 40 to 74 years old without diabetes mellitus was 26.8% and 23.7% for Italian men and women, respectively, and 25.9% and 40.9% for Brazilian men and women, respectively. In addition, there was a significant association between age and metabolic syndrome in both men and women. Specifically, age and age square was significantly associated with metabolic syndrome in men. This result was similar with Xu et al.’s study (24). Unlike men, age and metabolic syndrome had a linear relationship in women. Secondly, glycosylated haemoglobin (≥5.7 and <6.5) and increased body mass index (overweight and obese) were independently associated with metabolic syndrome in both men and women in the current study. Similarly, in a cohort study among non-diabetic Americans, the glycosylated haemoglobin level (≥5.8 and <6.5) was highly correlated with metabolic syndrome (10). This association implies that glycosylated haemoglobin can be considered a surrogate marker for metabolic syndrome in non-diabetic adults (10). Regarding the association between body mass index and metabolic syndrome, the result of current study was consistent with previous studies (25–27). In Leite and colleague’s study (23), 40.8% of people with a body mass index over 30 kg/m2 had more than three risk factors for metabolic syndrome. A significant association between increased body mass index and metabolic syndrome could result from increased insulin resistance and waist circumference overweight or obese adults (28–31). Lastly, the current study found sex-specific factors associated with metabolic syndrome. The association between smoking and metabolic syndrome results from the fact that smoking increases insulin resistance (32). Lee and colleagues (33) found that the risk of developing the metabolic syndrome was 1.9-fold higher in the group who smoked over 20 years than in the non-smoker group in men and women. Similarly, in a study by Park et al. (11), they observed a significant association between smoking and metabolic syndrome in men and women. In contrast, in the current study, smoking was associated with metabolic syndrome in men only. A possible reason for the lack of relevance of the metabolic syndrome to female smokers could be a low smoking rate in women (7.5%). Regarding physical activity, the current study found that metabolic syndrome was not associated with physical activity in both men and women. The results of sex-specific association between physical activity and metabolic syndrome were inconsistent in the literature. Two previous studies found that inactivity was associated with metabolic syndrome in both men and women (13,34). A study by Clarke and Janssen (13) reported that the relative odds ratio for metabolic syndrome was 4.43-fold higher in physically inactive participants than physically active participants. Moreover, Glazer et al. (34) found that moderate to vigorous physical activity was associated with lower triglycerides and waist circumference, which are components of metabolic syndrome. In different, other studies found that the association between physical activity and metabolic syndrome was specific to men (15,35). Zhu et al. (15) found that physically active men less likely to have metabolic syndrome after controlling lifestyle-related factors, demographic factors and other modifiable factors such as total caloric intake. Similarly, Brien and Katzmaryk (35) found that physical activity was significantly associated lower odds of metabolic syndrome, particularly in men (OR = 0.45, 95% CI = 0.29–0.69). Regarding education levels, the current study found that being illiterate was associated with metabolic syndrome in women only, which was consistent with previous studies (16,17). In Zhan’s study, women having more than 12 years education were 0.83 times less likely to have metabolic syndrome compared with women having less than 7 years education after adjusting age, marital status, smoking, drinking, physical activity, body mass index, and community type (16). In addition, in a study of healthy women in Sweden, education level was significantly correlated with waist-to-hip ratio, blood pressure, serum triglyceride and HDL, which are all components of metabolic syndrome. The risk of developing metabolic syndrome was also 2.7-fold higher in participants with a low education level in Swedish women (17). Possible reasons for the association between education level and metabolic syndrome might be that women with low education levels tend to not have healthy dietary patterns and frequently consume high-calorie/low-nutrition foods (36,37), which can lead to metabolic syndrome (36,38–40). Limitations The current study has limitations. Due to the secondary data analysis using the cross-sectional dataset from Korea National Health and Nutrition Examination Survey, the causal relationship between risk factors and metabolic syndrome cannot be determined. Another limitation can be related to recall or report bias for the lifestyle variables such as smoking, drinking and physical activity. Therefore, further study using a longitudinal design is needed to examine the association of various factors with the criteria of metabolic syndrome for adults without diabetes mellitus. Conclusions This study supports prior data suggesting that glycosylated haemoglobin and body mass index can be important indicators of metabolic syndrome in Korean adults without diabetes mellitus. Therefore, the addition of glycosylated haemoglobin and body mass index to the diagnostic criteria for metabolic syndrome should be considered. In addition, demographic factors such as age and education and lifestyle factors such as smoking should be considered to develop prevention programs for metabolic syndrome. Specifically, smoking cessation for men without diabetes mellitus and tailored health education for illiterate for non-diabetes mellitus women can be important components of metabolic syndrome preventive programs. Declaration Funding: none. Ethical approval: Institutional Review Board of Yonsei University in Korea (Study No. 2016-0031). Conflict of interests: none. References 1. Grundy SM . Metabolic syndrome pandemic . Arterioscler Thromb Vasc Biol 2008 ; 28 : 629 – 36 . Google Scholar CrossRef Search ADS PubMed 2. Tran BT , Jeong BY , Oh JK . The prevalence trend of metabolic syndrome and its components and risk factors in Korean adults: results from the Korean National Health and Nutrition Examination Survey 2008-2013 . BMC Public Health 2017 ; 17 : 71 . Google Scholar CrossRef Search ADS PubMed 3. Expert panel on detection E, and treatment of high blood cholesterol in adults . Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) . JAMA 2001 ; 285 : 2486 - 97 . CrossRef Search ADS PubMed 4. World Health Organization, Regional Office for the Western Pacific . The Asia-Pacific perspective: redefining obesity and its treatment . Sydney : Health Communications Australia , 2000 . 5. Lorenzo C , Okoloise M , Williams K , Stern MP , Haffner SM ; San Antonio Heart Study . The metabolic syndrome as predictor of type 2 diabetes: the San Antonio heart study . Diabetes Care 2003 ; 26 : 3153 – 9 . Google Scholar CrossRef Search ADS PubMed 6. Wilson PW , D’Agostino RB , Parise H , Sullivan L , Meigs JB . Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus . Circulation 2005 ; 112 : 3066 – 72 . Google Scholar CrossRef Search ADS PubMed 7. Alexander CM , Landsman PB , Teutsch SM , Haffner SM ; Third National Health and Nutrition Examination Survey (NHANES III); National Cholesterol Education Program (NCEP) . NCEP-defined metabolic syndrome, diabetes, and prevalence of coronary heart disease among NHANES III participants age 50 years and older . Diabetes 2003 ; 52 : 1210 – 4 . Google Scholar CrossRef Search ADS PubMed 8. American Diabetes Association . Diagnosis and classification of diabetes mellitus . Diabetes Care 2014 ; 37 ( Suppl 1 ): S81 – 90 . CrossRef Search ADS PubMed 9. Ong KL , Tso AW , Lam KS , Cherny SS , Sham PC , Cheung BM . Using glycosylated hemoglobin to define the metabolic syndrome in United States adults . Diabetes Care 2010 ; 33 : 1856 – 8 . Google Scholar CrossRef Search ADS PubMed 10. Veeranna V , Ramesh K , Zalawadiya SK et al. Glycosylated hemoglobin and prevalent metabolic syndrome in nondiabetic multiethnic U.S. adults . Metab Syndr Relat Disord 2011 ; 9 : 361 – 7 . Google Scholar CrossRef Search ADS PubMed 11. Park YW , Zhu S , Palaniappan L , Heshka S , Carnethon MR , Heymsfield SB . The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988-1994 . Arch Intern Med 2003 ; 163 : 427 – 36 . Google Scholar CrossRef Search ADS PubMed 12. Stelmach W , Kaczmarczyk-Chałas K , Bielecki W , Drygas W . How education, income, control over life and life style contribute to risk factors for cardiovascular disease among adults in a post-communist country . Public Health 2005 ; 119 : 498 – 508 . Google Scholar CrossRef Search ADS PubMed 13. Clarke J , Janssen I . Is the frequency of weekly moderate-to-vigorous physical activity associated with the metabolic syndrome in Canadian adults ? Appl Physiol Nutr Metab 2013 ; 38 : 773 – 8 . Google Scholar CrossRef Search ADS PubMed 14. Kang DR , Ha Y , Hwang WJ . Prevalence and associated risk factors of the metabolic syndrome in the Korean workforce . Ind Health 2013 ; 51 : 256 – 65 . Google Scholar CrossRef Search ADS PubMed 15. Zhu S , St-Onge MP , Heshka S , Heymsfield SB . Lifestyle behaviors associated with lower risk of having the metabolic syndrome . Metabolism 2004 ; 53 : 1503 – 11 . Google Scholar CrossRef Search ADS PubMed 16. Zhan Y , Yu J , Chen R et al. Socioeconomic status and metabolic syndrome in the general population of China: a cross-sectional study . BMC Public Health 2012 ; 12 : 921 . Google Scholar CrossRef Search ADS PubMed 17. Wamala SP , Lynch J , Horsten M , Mittleman MA , Schenck-Gustafsson K , Orth-Gomér K . Education and the metabolic syndrome in women . Diabetes Care 1999 ; 22 : 1999 – 2003 . Google Scholar CrossRef Search ADS PubMed 18. Korea Centers for Disease Control and Prevention . The Fifth Korea National Health and Nutrition Examination Survey (KNHANES V) . Osong : Korea Centers for Disease Control and Prevention ; 2012 . 19. Korea Centers for Disease Control and Prevention . The Sixth Korea National Health and Nutrition Examination Survey (KNHANES Ⅵ-1) . Osong : Korea Centers for Disease Control and Prevention , 2013 . 20. World Health Organization . Guidelines for controlling and monitoring the tobacco epidemic . Geneva : World Health Organization , 1998 . 21. Craig CL , Marshall AL , Sjöström M et al. International physical activity questionnaire: 12-country reliability and validity . Med Sci Sports Exerc 2003 ; 35 : 1381 – 95 . Google Scholar CrossRef Search ADS PubMed 22. Committee IR . Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)-short and long forms . http://www.ipaq.ki.se/scoring.pdf 2005 (accessed on May 16, 2018 ). 23. Leite ML , Nicolosi A , Firmo JO , Lima-Costa MF . Features of metabolic syndrome in non-diabetic Italians and Brazilians: a discriminant analysis . Int J Clin Pract 2007 ; 61 : 32 – 8 . Google Scholar CrossRef Search ADS PubMed 24. Tian X , Xu X , Zhang K , Wang H . Gender difference of metabolic syndrome and its association with dietary diversity at different ages . Oncotarget 2017 ; 8 : 73568 – 78 . Google Scholar PubMed 25. Slagter SN , van Waateringe RP , van Beek AP , van der Klauw MM , Wolffenbuttel BHR , van Vliet-Ostaptchouk JV . Sex, BMI and age differences in metabolic syndrome: the Dutch Lifelines Cohort Study . Endocr Connect 2017 ; 6 : 278 – 88 . Google Scholar CrossRef Search ADS PubMed 26. Ervin RB . Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index: United States, 2003–2006 . Natl Health Stat Report 2009 ; 13 : 1 – 7 . 27. Park HS , Park CY , Oh SW , Yoo HJ . Prevalence of obesity and metabolic syndrome in Korean adults . Obes Rev 2008 ; 9 : 104 – 7 . Google Scholar CrossRef Search ADS PubMed 28. Després JP , Lemieux I . Abdominal obesity and metabolic syndrome . Nature 2006 ; 444 : 881 – 7 . Google Scholar CrossRef Search ADS PubMed 29. Bray GA , Ryan D. Overweight and the metabolic syndrome: from bench to bedside . Boston, MA : Springer , 2007 . 30. Yin XY , Zheng FP , Zhou JQ et al. Central obesity and metabolic risk factors in middle-aged Chinese . Biomed Environ Sci 2014 ; 27 : 343 – 52 . Google Scholar PubMed 31. Okosun IS , Cooper RS , Rotimi CN , Osotimehin B , Forrester T . Association of waist circumference with risk of hypertension and type 2 diabetes in Nigerians, Jamaicans, and African-Americans . Diabetes Care 1998 ; 21 : 1836 – 42 . Google Scholar CrossRef Search ADS PubMed 32. Facchini FS , Hollenbeck CB , Jeppesen J , Chen YD , Reaven GM . Insulin resistance and cigarette smoking . Lancet 1992 ; 339 : 1128 – 30 . Google Scholar CrossRef Search ADS PubMed 33. Lee WY , Jung CH , Park JS , Rhee EJ , Kim SW . Effects of smoking, alcohol, exercise, education, and family history on the metabolic syndrome as defined by the ATP III . Diabetes Res Clin Pract 2005 ; 67 : 70 – 7 . Google Scholar CrossRef Search ADS PubMed 34. Glazer NL , Lyass A , Esliger DW et al. Sustained and shorter bouts of physical activity are related to cardiovascular health . Med Sci Sports Exerc 2013 ; 45 : 109 – 15 . Google Scholar CrossRef Search ADS PubMed 35. Brien SE , Katzmarzyk PT . Physical activity and the metabolic syndrome in Canada . Appl Physiol Nutr Metab 2006 ; 31 : 40 – 7 . Google Scholar CrossRef Search ADS PubMed 36. Andreeva VA , Allès B , Feron G et al. Sex-specific sociodemographic correlates of dietary patterns in a large sample of French elderly individuals . Nutrients 2016 ; 8 : 484 . Google Scholar CrossRef Search ADS 37. Wang M , Heck K , Winkleby M , Cubbin C . Social disparities in dietary habits among women: geographic Research on Wellbeing (GROW) Study . Public Health Nutr 2016 ; 19 : 1666 – 73 . Google Scholar CrossRef Search ADS PubMed 38. Suliga E , Kozieł D , Cieśla E , Głuszek S . Association between dietary patterns and metabolic syndrome in individuals with normal weight: a cross-sectional study . Nutr J 2015 ; 14 : 55 . Google Scholar CrossRef Search ADS PubMed 39. de Oliveira EP , McLellan KC , Vaz de Arruda Silveira L , Burini RC . Dietary factors associated with metabolic syndrome in Brazilian adults . Nutr J 2012 ; 11 : 13 . Google Scholar CrossRef Search ADS PubMed 40. Baik I , Lee M , Jun NR , Lee JY , Shin C . A healthy dietary pattern consisting of a variety of food choices is inversely associated with the development of metabolic syndrome . Nutr Res Pract 2013 ; 7 : 233 – 41 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Family Practice Oxford University Press

Sex differences in factors associated with metabolic syndrome among Korean adults without diabetes mellitus: results from the Korea National Health and Nutrition Examination Survey from 2010 to 2013

Loading next page...
 
/lp/ou_press/sex-differences-in-factors-associated-with-metabolic-syndrome-among-CQax96B5m8
Publisher
Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
ISSN
0263-2136
eISSN
1460-2229
D.O.I.
10.1093/fampra/cmy053
Publisher site
See Article on Publisher Site

Abstract

Abstract Objectives The purpose of this study was to examine sex differences in factors associated with metabolic syndrome in Korean adults without diabetes mellitus. Study design Cross-sectional design. Methods The dataset of Korea National Health and Nutrition Examination Survey from 2010 to 2013 was used. Among a total of 33552 adults aged ≥30, subjects who (i) were diagnosed or had been treated for diabetes mellitus, (ii) had a fasting blood glucose level of 126 mg/dL or higher or (iii) had a glycosylated haemoglobin level of 6.5% or higher were excluded. Subjects who had anaemia or were pregnant were also excluded. Finally, 9406 subjects were included in this study. Sex differences in subjects’ characteristics were assessed with Student’s t-test and chi-square test. Logistic regressions were used to examine factors associated with metabolic syndrome by sex. Results The prevalence of metabolic syndrome in Korean adults overall without diabetes mellitus was 12.2%. Glycosylated haemoglobin from 5.7 to 6.5 and increased body mass index were independently associated with metabolic syndrome in both men and women. Current smoking, age and age square were significantly associated with metabolic syndrome in men, whereas age and illiteracy were significantly associated with it in women. Conclusions This study confirmed that glycosylated haemoglobin and body mass index can be important indicators of metabolic syndrome in Korean adults without diabetes mellitus. Diabetes, health risk behaviours, metabolic syndrome, obesity, public health Introduction Metabolic syndrome is a global health problem and affects approximately 20% to 30% of the adult population worldwide (1). Based on Korean National Health and Nutrition Survey data from 2008 to2013, the prevalence of metabolic syndrome in adults aged 20 years old and older was 28.9% and was 30.8% and 26.3% in men and women, respectively (2). The diagnostic criteria for metabolic syndrome by the National Cholesterol Education Program (3) are as follows: (i) blood pressure ≥130/85 mmHg, (ii) waist circumference >90 cm in men and >80 cm in women (4), (iii) fasting blood glucose ≥110 mg/dL, (iv) triglycerides ≥150 mg/dL, and (v) high-density lipoprotein cholesterol <40 mg/dL in men and <50 mg/dL in women. Metabolic syndrome is called insulin resistance syndrome because metabolic syndrome is significantly related to insulin resistance (5). Indeed, metabolic syndrome is a known precursor of diabetes mellitus (6). For example, in one study, the prevalence of metabolic syndrome in the normal fasting glucose group was 25.8%, whereas that in the diabetes mellitus group was 86.0% (7). Diabetes mellitus is usually diagnosed using fasting glucose, an oral glucose tolerance test and typical symptoms of diabetes mellitus (8). In 2010, the American Diabetes Association added glycosylated haemoglobin to the diagnostic criteria of diabetes mellitus; the diagnostic level of glycosylated haemoglobin is ≥6.5%. According to the American Diabetes Association, people within the range of 5.5% to 6.5% of glycosylated haemoglobin were more likely to have diabetes mellitus compared with people who had a glycosylated haemoglobin <5.5%. The close relationship between diabetes mellitus and metabolic syndrome has already been revealed in the previous study (6,7). The relationship between metabolic syndrome and from normal to prediabetic population is unclear. This result implies that people having a glycosylated haemoglobin from 5.5% to 6.5% should heed preventative strategies for metabolic syndrome and diabetes mellitus. In the literature, sex differences in factors associated with metabolic syndrome have been reported but the results have been inconsistent. Some studies found that glycosylated haemoglobin (9), increased body mass index (10,11), age (11), smoking status (11) and physical activity (12,13) were significant factors of metabolic syndrome in both men and women. On the other hand, other studies found that alcohol consumption (14) and physical activity (15) were significantly associated with metabolic syndrome in men only, and low education level (16,17), low socioeconomic status (16,17) and current smoking (14) were significantly associated with metabolic syndrome in women only (16,17). Therefore, the purpose of the current study was to examine sex differences in factors associated with metabolic syndrome in Korean adults without diabetes mellitus. This study provides evidence for the development and application of sex-specific interventions for preventing metabolic syndrome in Korean adults without diabetes mellitus. Methods Study design This study involved secondary data analysis of nationwide data from the Korea National Health and Nutrition Examination Survey from 2010 to 2013. Study participants Korea National Health and Nutrition Examination Survey is an ongoing series of cross-sectional surveys designed to assess health and nutrition status from a nationally representative sample of the civilian non-institutionalized South Korean population. For the analysis, we used the Korea National Health and Nutrition Examination Survey dataset from 2010 to 2013 (18,19). Among the total of 33552 adults aged 30 years and older, we excluded all subjects who had diabetes mellitus because the purpose of the current study was to examine factors associated with metabolic syndrome among adults without diabetes mellitus. Therefore, subjects who (i) were diagnosed or had been treated for diabetes mellitus and (ii) had a fasting blood glucose level of 126 mg/dL or higher were excluded based on the definition of diabetes mellitus in Korea National Health and Nutrition Examination Survey. In addition, subjects who had a glycosylated haemoglobin level of 6.5% or greater, which is the diagnostic criteria for diabetes mellitus in American Diabetes Association, were excluded. In addition, subjects who had anaemia or were pregnant were excluded because these conditions can affect glycosylated haemoglobin. Finally, 9406 subjects were included in this study. This study was approved by the Institutional Review Board of Yonsei University in Korea (Study No. 2016-0031). Variables Metabolic syndrome According to the guidelines presented in the National Cholesterol Education Program Adult Treatment Panel Ⅲ, metabolic syndrome was defined as the presence of three or more complex signs of the five risk factors as described above. Glycosylated haemoglobin According to the Classification and Diagnosis of Diabetes presented by the American Diabetes Association in 2014 (8), pre-diabetes stages were divided into three groups by glycosylated haemoglobin level: (i) group 1: <5.3%; (ii) group 2: ≥5.3% and <5.7% and (iii) group 3: ≥5.7% and <6.5%. Body mass index Body mass index was categorized as underweight (<18.5 kg/m2), normal (≥18.5, <23.0 kg/m2), overweight (≥23.0 kg/m2 and <25.0 kg/m2) or obese (≥25.0 kg/m2) based on the Asia-Pacific Criteria (4). Education Education was divided into four groups based on years of education: illiteracy (non-formal education); 1 to 9 years (elementary to middle school); 10 to 12 years (high school) or 13 years or more (college or above). Smoking Smoking status was divided into three groups: non-smokers, past smokers or current smokers (20). Non-smokers meant that subjects reported that they had ‘never smoked in their lifetime’ or ‘they smoked before and the amount of cigarettes smoked in their lifetime was less than 5 packs’. Subjects who responded ‘I smoked more than 5 packs before but in the past year I have not smoked’ were considered past smokers. Current smokers were defined as subjects who answered that they ‘smoke now’ or ‘smoke sometimes’. Drinking Drinking was classified as past drinker, light drinker or heavy drinker. Past drinkers were subjects who had experience drinking at one point in their lives but have not been drinking for the last year. Subjects who drink two to four times a month or less and five to six glasses per time for men (six to four glasses for women) were considered light drinkers. Heavy drinkers were defined as subjects who drink more than twice a week and seven glasses or more per time for men (five glasses or more for women). Physical activity Physical activity in the Korea National Health and Nutrition Examination Survey was assessed using the International Physical Activity Questionnaire (21), which contains the items related to the moderate- and high-intensity physical activity and walking. The physical activity time was converted to the metabolic equivalent of task (MET) based on the International Physical Activity Questionnaire score conversion guidelines (22). The subjects’ physical activity was divided into three levels according to the International Physical Activity Questionnaire scoring proposal (low, moderate and high physical activity) (22). High physical activity was defined as any one of the following two criteria: (i) vigorous-intensity activity on at least 3 days and accumulating at least 1500 MET-minutes/week and (ii) 7 or more days of any combination of walking, moderate- or vigorous-intensity activities accumulating at least 3000 MET-minutes/week. Moderate physical activity was defined as either of the following three criteria: (i) 3 or more days of vigorous activity of at least 20 minutes per day, (ii) 5 or more days of moderate-intensity activity and/or walking of at least 30 minutes per day and (iii) 5 or more days of any combination of walking, moderate- or vigorous-intensity activities achieving a minimum of at least 600 MET-minutes/week. Low physical activity group was defined as follows: no activity or some activity is reported but not enough to meet the categories of moderate or high physical activity. Statistical analysis All statistical analysis was performed using SAS 9.4 (Cary, NC, USA). Descriptive statistics including mean, standard deviation (SD), frequency and percent were performed for general characteristics of the subjects. Sex differences in the subjects’ characteristics were assessed with Student’s t-test for continuous variables and the chi-squared test for categorical variables. To examine the multicollinearity between variables, variance inflation factor (VIF) and condition index (CI) were examined and the all values of VIF and CI were less than 2.0. Logistic regressions were performed to identify factors associated with metabolic syndrome by sex. The variables of glycosylated haemoglobin, age, body mass index, education, smoking status, drinking and physical activity, which were selected based on previous literature, were included in the logistic regressions. All P ≤ 0.05 were considered statistically significant. Results The characteristics of the subjects by sex are presented in Table 1. Among the 9406 subjects, there were 4780 men and 4626 women. There were significant differences between men and women in most variables except total cholesterol and metabolic syndrome. Overall, men were older, more educated, more overweight or obese, had a greater waist circumference and had higher blood pressure than women. Similarly, in blood tests, men showed significantly higher triglycerides, lower low-density lipoprotein (LDL), lower high-density lipoprotein (HDL), higher fasting glucose and higher glycosylated haemoglobin compared with women. Regarding lifestyle variables, the proportion of current smokers and heavy drinkers and high physical activity in men were significantly greater than those in women. Table 1. General characteristics of Korean adults without diabetes mellitus of Korea National Health and Nutrition Examination Survey from 2010 to 2013 Variables Total (n = 9406) Men (n = 4780) Women (n = 4626) Age in years, mean (SD) 45.55 (15.25) 46.45 (15.57) 44.63 (14.86) Group, n (%)  30–39 2097 (22.29) 1035 (21.65) 1062 (22.96)  40–49 1981 (21.06) 996 (20.84) 985 (21.29)  50–59 1836 (19.52) 904 (18.91) 932 (20.15)  ≥60 3492 (37.13) 1845 (38.60) 1647 (35.60) Education in years, n (%)  0 (illiteracy) 185 (1.97) 39 (0.82) 146 (3.16)  1–9 (elementary to middle school) 1940 (20.63) 856 (17.91) 1084 (23.43)  10–12 (high school) 2911 (30.95) 1467 (30.69) 1444 (31.21)  ≥ 13 (college or above) 4370 (46.45) 2418 (50.59) 1952 (42.20)  Body mass index (kg/m2), mean (SD) 23.60 (3.31) 24.12 (3.15) 23.06 (3.39) Group, n (%)  Under (<18.5) 415 (4.41) 118 (2.47) 297 (6.42)  Normal (≥18.5 and <23.0) 3862 (41.06) 1688 (35.31) 2174 (47.00)  Overweight (≥23 and <25.0) 2231 (23.72) 1245 (26.05) 986 (21.31)  Obese (≥25.0) 2898 (30.81) 1729 (36.17) 1169 (25.27)  Waist circumference (cm), mean (SD) 80.55 (9.72) 84.06 (8.72) 76.92 (9.35) Blood pressure, mean (SD)  Systolic BP (mmHg) 116.9 (15.87) 120.1 (14.80) 113.5 (16.23)  Diastolic BP (mmHg) 76.22 (10.44) 78.83 (10.62) 73.53 (9.52) Blood tests, mean (SD)  Total cholesterol (mg/dl) 189.1 (34.90) 188.9 (34.55) 189.3 (35.26)  Triglyceride (mg/dl) 130.3 (104.8) 152.6 (124.8) 107.2 (72.07)  High-density lipoprotein (mg/dl) 50.08 (11.36) 47.20 (10.66) 53.07 (11.29)  Low-density lipoprotein (mg/dl) 109.5 (32.77) 108.0 (34.04) 111.0 (31.33)  Fasting glucose (mg/dl) 93.15 (9.63) 94.68 (9.99) 91.58 (8.97)  Glycosylated haemoglobin (%) 5.55 (0.34) 5.57 (0.33) 5.52 (0.35)  Metabolic syndrome, n (%) 1146 (12.18) 596 (12.47) 550 (11.89) Smoking status, n (%)  Non-smoker 5144 (54.69) 1041 (21.78) 4103 (88.69)  Past smoker 1815 (19.30) 1640 (34.31) 175 (3.78)  Current smoker 2447 (26.01) 2099 (43.91) 348 (7.52) Drinking status, n (%)  Past drinker 2843 (30.23) 1771 (37.05) 1072 (23.17)  Light drinker 5139 (54.64) 1893 (39.60) 3246 (70.17)  Heavy drinker 1424 (15.13) 1116 (23.35) 308 (6.66) Physical activity, n (%)  Low 4058 (43.14) 1861 (38.93) 2197 (47.49)  Moderate 2929 (31.14) 1417 (29.64) 1512 (32.68)  High 2419 (25.72) 1502 (31.42) 917 (19.81) Variables Total (n = 9406) Men (n = 4780) Women (n = 4626) Age in years, mean (SD) 45.55 (15.25) 46.45 (15.57) 44.63 (14.86) Group, n (%)  30–39 2097 (22.29) 1035 (21.65) 1062 (22.96)  40–49 1981 (21.06) 996 (20.84) 985 (21.29)  50–59 1836 (19.52) 904 (18.91) 932 (20.15)  ≥60 3492 (37.13) 1845 (38.60) 1647 (35.60) Education in years, n (%)  0 (illiteracy) 185 (1.97) 39 (0.82) 146 (3.16)  1–9 (elementary to middle school) 1940 (20.63) 856 (17.91) 1084 (23.43)  10–12 (high school) 2911 (30.95) 1467 (30.69) 1444 (31.21)  ≥ 13 (college or above) 4370 (46.45) 2418 (50.59) 1952 (42.20)  Body mass index (kg/m2), mean (SD) 23.60 (3.31) 24.12 (3.15) 23.06 (3.39) Group, n (%)  Under (<18.5) 415 (4.41) 118 (2.47) 297 (6.42)  Normal (≥18.5 and <23.0) 3862 (41.06) 1688 (35.31) 2174 (47.00)  Overweight (≥23 and <25.0) 2231 (23.72) 1245 (26.05) 986 (21.31)  Obese (≥25.0) 2898 (30.81) 1729 (36.17) 1169 (25.27)  Waist circumference (cm), mean (SD) 80.55 (9.72) 84.06 (8.72) 76.92 (9.35) Blood pressure, mean (SD)  Systolic BP (mmHg) 116.9 (15.87) 120.1 (14.80) 113.5 (16.23)  Diastolic BP (mmHg) 76.22 (10.44) 78.83 (10.62) 73.53 (9.52) Blood tests, mean (SD)  Total cholesterol (mg/dl) 189.1 (34.90) 188.9 (34.55) 189.3 (35.26)  Triglyceride (mg/dl) 130.3 (104.8) 152.6 (124.8) 107.2 (72.07)  High-density lipoprotein (mg/dl) 50.08 (11.36) 47.20 (10.66) 53.07 (11.29)  Low-density lipoprotein (mg/dl) 109.5 (32.77) 108.0 (34.04) 111.0 (31.33)  Fasting glucose (mg/dl) 93.15 (9.63) 94.68 (9.99) 91.58 (8.97)  Glycosylated haemoglobin (%) 5.55 (0.34) 5.57 (0.33) 5.52 (0.35)  Metabolic syndrome, n (%) 1146 (12.18) 596 (12.47) 550 (11.89) Smoking status, n (%)  Non-smoker 5144 (54.69) 1041 (21.78) 4103 (88.69)  Past smoker 1815 (19.30) 1640 (34.31) 175 (3.78)  Current smoker 2447 (26.01) 2099 (43.91) 348 (7.52) Drinking status, n (%)  Past drinker 2843 (30.23) 1771 (37.05) 1072 (23.17)  Light drinker 5139 (54.64) 1893 (39.60) 3246 (70.17)  Heavy drinker 1424 (15.13) 1116 (23.35) 308 (6.66) Physical activity, n (%)  Low 4058 (43.14) 1861 (38.93) 2197 (47.49)  Moderate 2929 (31.14) 1417 (29.64) 1512 (32.68)  High 2419 (25.72) 1502 (31.42) 917 (19.81) View Large Table 1. General characteristics of Korean adults without diabetes mellitus of Korea National Health and Nutrition Examination Survey from 2010 to 2013 Variables Total (n = 9406) Men (n = 4780) Women (n = 4626) Age in years, mean (SD) 45.55 (15.25) 46.45 (15.57) 44.63 (14.86) Group, n (%)  30–39 2097 (22.29) 1035 (21.65) 1062 (22.96)  40–49 1981 (21.06) 996 (20.84) 985 (21.29)  50–59 1836 (19.52) 904 (18.91) 932 (20.15)  ≥60 3492 (37.13) 1845 (38.60) 1647 (35.60) Education in years, n (%)  0 (illiteracy) 185 (1.97) 39 (0.82) 146 (3.16)  1–9 (elementary to middle school) 1940 (20.63) 856 (17.91) 1084 (23.43)  10–12 (high school) 2911 (30.95) 1467 (30.69) 1444 (31.21)  ≥ 13 (college or above) 4370 (46.45) 2418 (50.59) 1952 (42.20)  Body mass index (kg/m2), mean (SD) 23.60 (3.31) 24.12 (3.15) 23.06 (3.39) Group, n (%)  Under (<18.5) 415 (4.41) 118 (2.47) 297 (6.42)  Normal (≥18.5 and <23.0) 3862 (41.06) 1688 (35.31) 2174 (47.00)  Overweight (≥23 and <25.0) 2231 (23.72) 1245 (26.05) 986 (21.31)  Obese (≥25.0) 2898 (30.81) 1729 (36.17) 1169 (25.27)  Waist circumference (cm), mean (SD) 80.55 (9.72) 84.06 (8.72) 76.92 (9.35) Blood pressure, mean (SD)  Systolic BP (mmHg) 116.9 (15.87) 120.1 (14.80) 113.5 (16.23)  Diastolic BP (mmHg) 76.22 (10.44) 78.83 (10.62) 73.53 (9.52) Blood tests, mean (SD)  Total cholesterol (mg/dl) 189.1 (34.90) 188.9 (34.55) 189.3 (35.26)  Triglyceride (mg/dl) 130.3 (104.8) 152.6 (124.8) 107.2 (72.07)  High-density lipoprotein (mg/dl) 50.08 (11.36) 47.20 (10.66) 53.07 (11.29)  Low-density lipoprotein (mg/dl) 109.5 (32.77) 108.0 (34.04) 111.0 (31.33)  Fasting glucose (mg/dl) 93.15 (9.63) 94.68 (9.99) 91.58 (8.97)  Glycosylated haemoglobin (%) 5.55 (0.34) 5.57 (0.33) 5.52 (0.35)  Metabolic syndrome, n (%) 1146 (12.18) 596 (12.47) 550 (11.89) Smoking status, n (%)  Non-smoker 5144 (54.69) 1041 (21.78) 4103 (88.69)  Past smoker 1815 (19.30) 1640 (34.31) 175 (3.78)  Current smoker 2447 (26.01) 2099 (43.91) 348 (7.52) Drinking status, n (%)  Past drinker 2843 (30.23) 1771 (37.05) 1072 (23.17)  Light drinker 5139 (54.64) 1893 (39.60) 3246 (70.17)  Heavy drinker 1424 (15.13) 1116 (23.35) 308 (6.66) Physical activity, n (%)  Low 4058 (43.14) 1861 (38.93) 2197 (47.49)  Moderate 2929 (31.14) 1417 (29.64) 1512 (32.68)  High 2419 (25.72) 1502 (31.42) 917 (19.81) Variables Total (n = 9406) Men (n = 4780) Women (n = 4626) Age in years, mean (SD) 45.55 (15.25) 46.45 (15.57) 44.63 (14.86) Group, n (%)  30–39 2097 (22.29) 1035 (21.65) 1062 (22.96)  40–49 1981 (21.06) 996 (20.84) 985 (21.29)  50–59 1836 (19.52) 904 (18.91) 932 (20.15)  ≥60 3492 (37.13) 1845 (38.60) 1647 (35.60) Education in years, n (%)  0 (illiteracy) 185 (1.97) 39 (0.82) 146 (3.16)  1–9 (elementary to middle school) 1940 (20.63) 856 (17.91) 1084 (23.43)  10–12 (high school) 2911 (30.95) 1467 (30.69) 1444 (31.21)  ≥ 13 (college or above) 4370 (46.45) 2418 (50.59) 1952 (42.20)  Body mass index (kg/m2), mean (SD) 23.60 (3.31) 24.12 (3.15) 23.06 (3.39) Group, n (%)  Under (<18.5) 415 (4.41) 118 (2.47) 297 (6.42)  Normal (≥18.5 and <23.0) 3862 (41.06) 1688 (35.31) 2174 (47.00)  Overweight (≥23 and <25.0) 2231 (23.72) 1245 (26.05) 986 (21.31)  Obese (≥25.0) 2898 (30.81) 1729 (36.17) 1169 (25.27)  Waist circumference (cm), mean (SD) 80.55 (9.72) 84.06 (8.72) 76.92 (9.35) Blood pressure, mean (SD)  Systolic BP (mmHg) 116.9 (15.87) 120.1 (14.80) 113.5 (16.23)  Diastolic BP (mmHg) 76.22 (10.44) 78.83 (10.62) 73.53 (9.52) Blood tests, mean (SD)  Total cholesterol (mg/dl) 189.1 (34.90) 188.9 (34.55) 189.3 (35.26)  Triglyceride (mg/dl) 130.3 (104.8) 152.6 (124.8) 107.2 (72.07)  High-density lipoprotein (mg/dl) 50.08 (11.36) 47.20 (10.66) 53.07 (11.29)  Low-density lipoprotein (mg/dl) 109.5 (32.77) 108.0 (34.04) 111.0 (31.33)  Fasting glucose (mg/dl) 93.15 (9.63) 94.68 (9.99) 91.58 (8.97)  Glycosylated haemoglobin (%) 5.55 (0.34) 5.57 (0.33) 5.52 (0.35)  Metabolic syndrome, n (%) 1146 (12.18) 596 (12.47) 550 (11.89) Smoking status, n (%)  Non-smoker 5144 (54.69) 1041 (21.78) 4103 (88.69)  Past smoker 1815 (19.30) 1640 (34.31) 175 (3.78)  Current smoker 2447 (26.01) 2099 (43.91) 348 (7.52) Drinking status, n (%)  Past drinker 2843 (30.23) 1771 (37.05) 1072 (23.17)  Light drinker 5139 (54.64) 1893 (39.60) 3246 (70.17)  Heavy drinker 1424 (15.13) 1116 (23.35) 308 (6.66) Physical activity, n (%)  Low 4058 (43.14) 1861 (38.93) 2197 (47.49)  Moderate 2929 (31.14) 1417 (29.64) 1512 (32.68)  High 2419 (25.72) 1502 (31.42) 917 (19.81) View Large Factors associated with metabolic syndrome in men Results of multivariate logistic regression analysis in men are presented in Table 2. Regarding glycosylated haemoglobin, group 3 was significantly associated with metabolic syndrome (OR = 1.88; 95% CI = 1.34–2.65). Age and age square significantly associated with metabolic syndrome in men (age: OR = 1.14; 95% CI = 1.09–1.20, age (2): OR = 0.99; 95% CI = 0.998–0.999), indicating that the relationship between metabolic syndrome and age was not linear. Being overweight (OR = 3.11; 95% CI = 2.08–4.67) and obese (OR = 17.18; 95% CI = 12.04–24.52) were also significantly associated with metabolic syndrome. Current smoking (OR = 1.42; 95% CI = 1.08–1.86) was significantly associated with metabolic syndrome. Table 2. Result of multivariable logistic regression for factors associated with metabolic syndrome in men (n = 4780) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.13 (0.80–1.60) Group 3 (≥5.7 and <6.5) 1.88 (1.34–2.65) Age (year) 1.14 (1.09–1.20) Age2 (year2) 0.99 (0.998–0.999) Body mass index (kg/m2) Under (<18.5) 0.49 (0.07–3.58) Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 3.11 (2.08–4.67) Obese (≥25.0) 17.18 (12.04–24.52) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 1.00 (0.26–3.79) 10–12 (high school) 0.89 (0.24–3.39) ≥ 13 (college or above) 0.72 (0.19–2.76) Drinking Non-drinker Reference Past drinker 1.06 (0.85–1.33) Current drinker 1.24 (0.98–1.58) Smoking Non-smoker Reference Past smoker 1.17 (0.88–1.56) Current smoker 1.42 (1.08–1.86) Physical activity Low Reference Moderate 1.14 (0.91–1.42) High 0.85 (0.68–1.07) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.13 (0.80–1.60) Group 3 (≥5.7 and <6.5) 1.88 (1.34–2.65) Age (year) 1.14 (1.09–1.20) Age2 (year2) 0.99 (0.998–0.999) Body mass index (kg/m2) Under (<18.5) 0.49 (0.07–3.58) Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 3.11 (2.08–4.67) Obese (≥25.0) 17.18 (12.04–24.52) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 1.00 (0.26–3.79) 10–12 (high school) 0.89 (0.24–3.39) ≥ 13 (college or above) 0.72 (0.19–2.76) Drinking Non-drinker Reference Past drinker 1.06 (0.85–1.33) Current drinker 1.24 (0.98–1.58) Smoking Non-smoker Reference Past smoker 1.17 (0.88–1.56) Current smoker 1.42 (1.08–1.86) Physical activity Low Reference Moderate 1.14 (0.91–1.42) High 0.85 (0.68–1.07) OR, odd ratio; CI, confidence interval. Data source: Korea National Health and Nutrition Examination survey from 2010 to 2013. View Large Table 2. Result of multivariable logistic regression for factors associated with metabolic syndrome in men (n = 4780) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.13 (0.80–1.60) Group 3 (≥5.7 and <6.5) 1.88 (1.34–2.65) Age (year) 1.14 (1.09–1.20) Age2 (year2) 0.99 (0.998–0.999) Body mass index (kg/m2) Under (<18.5) 0.49 (0.07–3.58) Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 3.11 (2.08–4.67) Obese (≥25.0) 17.18 (12.04–24.52) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 1.00 (0.26–3.79) 10–12 (high school) 0.89 (0.24–3.39) ≥ 13 (college or above) 0.72 (0.19–2.76) Drinking Non-drinker Reference Past drinker 1.06 (0.85–1.33) Current drinker 1.24 (0.98–1.58) Smoking Non-smoker Reference Past smoker 1.17 (0.88–1.56) Current smoker 1.42 (1.08–1.86) Physical activity Low Reference Moderate 1.14 (0.91–1.42) High 0.85 (0.68–1.07) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.13 (0.80–1.60) Group 3 (≥5.7 and <6.5) 1.88 (1.34–2.65) Age (year) 1.14 (1.09–1.20) Age2 (year2) 0.99 (0.998–0.999) Body mass index (kg/m2) Under (<18.5) 0.49 (0.07–3.58) Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 3.11 (2.08–4.67) Obese (≥25.0) 17.18 (12.04–24.52) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 1.00 (0.26–3.79) 10–12 (high school) 0.89 (0.24–3.39) ≥ 13 (college or above) 0.72 (0.19–2.76) Drinking Non-drinker Reference Past drinker 1.06 (0.85–1.33) Current drinker 1.24 (0.98–1.58) Smoking Non-smoker Reference Past smoker 1.17 (0.88–1.56) Current smoker 1.42 (1.08–1.86) Physical activity Low Reference Moderate 1.14 (0.91–1.42) High 0.85 (0.68–1.07) OR, odd ratio; CI, confidence interval. Data source: Korea National Health and Nutrition Examination survey from 2010 to 2013. View Large Factors associated with metabolic syndrome in women Results of multivariate logistic regression analysis in women are presented in Table 3. Group 3 of glycosylated haemoglobin was significantly associated with metabolic syndrome (OR = 2.01; 95% CI = 1.42–2.84). Women’s age (OR = 1.02; 95% CI = 1.01–1.03) was also significantly associated with metabolic syndrome. Being overweight (OR = 4.30; 95% CI = 3.12–5.92) and obese (OR = 10.91; 95% CI = 8.13–14.62) were also significantly associated with metabolic syndrome. Regarding education, women had high school education (OR = 0.58; 95% CI = 0.36–0.93) or a college or above education (OR = 0.47; 95% CI = 0.28–0.80) were less likely to have metabolic syndrome compared with women who were illiterate. Table 3. Result of multivariable logistic regression for factors associated with metabolic syndrome in women (n = 4626) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.04 (0.73–1.48) Group 3 (≥5.7 and <6.5) 2.01 (1.42–2.84) Age (year) 1.02 (1.01–1.03) Body mass index (kg/m2) Under (<18.5) <0.001(<0.001–>999.999)a Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 4.30 (3.12–5.92) Obese (≥25.0) 10.91 (8.13–14.62) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 0.74 (0.49–1.13) 10–12 (high school) 0.58 (0.36–0.93) ≥ 13 (college or above) 0.47 (0.28–0.80) Drinking Non-drinker Reference Past drinker 1.03 (0.79–1.34) Current drinker 1.08 (0.68–1.74) Smoking Non-smoker Reference Past smoker 0.81 (0.46–1.43) Current smoker 1.48 (1.00–2.19) Physical Activity Low Reference Moderate 0.86 (0.68–1.08) High 0.79 (0.60–1.02) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.04 (0.73–1.48) Group 3 (≥5.7 and <6.5) 2.01 (1.42–2.84) Age (year) 1.02 (1.01–1.03) Body mass index (kg/m2) Under (<18.5) <0.001(<0.001–>999.999)a Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 4.30 (3.12–5.92) Obese (≥25.0) 10.91 (8.13–14.62) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 0.74 (0.49–1.13) 10–12 (high school) 0.58 (0.36–0.93) ≥ 13 (college or above) 0.47 (0.28–0.80) Drinking Non-drinker Reference Past drinker 1.03 (0.79–1.34) Current drinker 1.08 (0.68–1.74) Smoking Non-smoker Reference Past smoker 0.81 (0.46–1.43) Current smoker 1.48 (1.00–2.19) Physical Activity Low Reference Moderate 0.86 (0.68–1.08) High 0.79 (0.60–1.02) OR, odd ratio; CI, confidence interval. aQuasi-complete separation of data points were detected because no one having metabolic syndrome were <18.5 of body mass index. Data source: Korea National Health and Nutrition Examination survey from 2010 to 2013. View Large Table 3. Result of multivariable logistic regression for factors associated with metabolic syndrome in women (n = 4626) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.04 (0.73–1.48) Group 3 (≥5.7 and <6.5) 2.01 (1.42–2.84) Age (year) 1.02 (1.01–1.03) Body mass index (kg/m2) Under (<18.5) <0.001(<0.001–>999.999)a Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 4.30 (3.12–5.92) Obese (≥25.0) 10.91 (8.13–14.62) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 0.74 (0.49–1.13) 10–12 (high school) 0.58 (0.36–0.93) ≥ 13 (college or above) 0.47 (0.28–0.80) Drinking Non-drinker Reference Past drinker 1.03 (0.79–1.34) Current drinker 1.08 (0.68–1.74) Smoking Non-smoker Reference Past smoker 0.81 (0.46–1.43) Current smoker 1.48 (1.00–2.19) Physical Activity Low Reference Moderate 0.86 (0.68–1.08) High 0.79 (0.60–1.02) Variables OR (95% CI) Glycosylated haemoglobin (%) Group 1 (<5.3) Reference Group 2 (≥5.3 and <5.7) 1.04 (0.73–1.48) Group 3 (≥5.7 and <6.5) 2.01 (1.42–2.84) Age (year) 1.02 (1.01–1.03) Body mass index (kg/m2) Under (<18.5) <0.001(<0.001–>999.999)a Normal (≥18.5 and <23.0) Reference Overweight (≥23 and <25.0) 4.30 (3.12–5.92) Obese (≥25.0) 10.91 (8.13–14.62) Education (year) 0 (illiteracy) Reference 1–9 (elementary to middle school) 0.74 (0.49–1.13) 10–12 (high school) 0.58 (0.36–0.93) ≥ 13 (college or above) 0.47 (0.28–0.80) Drinking Non-drinker Reference Past drinker 1.03 (0.79–1.34) Current drinker 1.08 (0.68–1.74) Smoking Non-smoker Reference Past smoker 0.81 (0.46–1.43) Current smoker 1.48 (1.00–2.19) Physical Activity Low Reference Moderate 0.86 (0.68–1.08) High 0.79 (0.60–1.02) OR, odd ratio; CI, confidence interval. aQuasi-complete separation of data points were detected because no one having metabolic syndrome were <18.5 of body mass index. Data source: Korea National Health and Nutrition Examination survey from 2010 to 2013. View Large Discussion The major findings of the current study were that the prevalence of metabolic syndrome in Korean adults without diabetes mellitus was 12.2%, and glycosylated haemoglobin (≥5.7 and <6.5) and increased body mass index (overweight and obese) were independently associated with metabolic syndrome in both men and women. Men who currently smoked were more likely to have metabolic syndrome, whereas illiterate women were more likely to have metabolic syndrome. Firstly, the prevalence of metabolic syndrome in Korean adults without diabetes mellitus (overall 12.2%, 12.5% for men and 11.9% for women) appears to be lower than those in other ethnic groups. Limited information on the prevalence of metabolic syndrome in non-diabetic adults exists. Alexander and colleagues (7) analyzed NHANES III data and found that the prevalence of metabolic syndrome in non-diabetic Americans who were 50 years old or older was 28.7%. Specifically, the prevalence of metabolic syndrome in their normal fasting glucose group was 25.8% and that in the diabetes mellitus group was 86.0%. Similarly, Leite et al. (23) found that the prevalence of metabolic syndrome in participants aged 40 to 74 years old without diabetes mellitus was 26.8% and 23.7% for Italian men and women, respectively, and 25.9% and 40.9% for Brazilian men and women, respectively. In addition, there was a significant association between age and metabolic syndrome in both men and women. Specifically, age and age square was significantly associated with metabolic syndrome in men. This result was similar with Xu et al.’s study (24). Unlike men, age and metabolic syndrome had a linear relationship in women. Secondly, glycosylated haemoglobin (≥5.7 and <6.5) and increased body mass index (overweight and obese) were independently associated with metabolic syndrome in both men and women in the current study. Similarly, in a cohort study among non-diabetic Americans, the glycosylated haemoglobin level (≥5.8 and <6.5) was highly correlated with metabolic syndrome (10). This association implies that glycosylated haemoglobin can be considered a surrogate marker for metabolic syndrome in non-diabetic adults (10). Regarding the association between body mass index and metabolic syndrome, the result of current study was consistent with previous studies (25–27). In Leite and colleague’s study (23), 40.8% of people with a body mass index over 30 kg/m2 had more than three risk factors for metabolic syndrome. A significant association between increased body mass index and metabolic syndrome could result from increased insulin resistance and waist circumference overweight or obese adults (28–31). Lastly, the current study found sex-specific factors associated with metabolic syndrome. The association between smoking and metabolic syndrome results from the fact that smoking increases insulin resistance (32). Lee and colleagues (33) found that the risk of developing the metabolic syndrome was 1.9-fold higher in the group who smoked over 20 years than in the non-smoker group in men and women. Similarly, in a study by Park et al. (11), they observed a significant association between smoking and metabolic syndrome in men and women. In contrast, in the current study, smoking was associated with metabolic syndrome in men only. A possible reason for the lack of relevance of the metabolic syndrome to female smokers could be a low smoking rate in women (7.5%). Regarding physical activity, the current study found that metabolic syndrome was not associated with physical activity in both men and women. The results of sex-specific association between physical activity and metabolic syndrome were inconsistent in the literature. Two previous studies found that inactivity was associated with metabolic syndrome in both men and women (13,34). A study by Clarke and Janssen (13) reported that the relative odds ratio for metabolic syndrome was 4.43-fold higher in physically inactive participants than physically active participants. Moreover, Glazer et al. (34) found that moderate to vigorous physical activity was associated with lower triglycerides and waist circumference, which are components of metabolic syndrome. In different, other studies found that the association between physical activity and metabolic syndrome was specific to men (15,35). Zhu et al. (15) found that physically active men less likely to have metabolic syndrome after controlling lifestyle-related factors, demographic factors and other modifiable factors such as total caloric intake. Similarly, Brien and Katzmaryk (35) found that physical activity was significantly associated lower odds of metabolic syndrome, particularly in men (OR = 0.45, 95% CI = 0.29–0.69). Regarding education levels, the current study found that being illiterate was associated with metabolic syndrome in women only, which was consistent with previous studies (16,17). In Zhan’s study, women having more than 12 years education were 0.83 times less likely to have metabolic syndrome compared with women having less than 7 years education after adjusting age, marital status, smoking, drinking, physical activity, body mass index, and community type (16). In addition, in a study of healthy women in Sweden, education level was significantly correlated with waist-to-hip ratio, blood pressure, serum triglyceride and HDL, which are all components of metabolic syndrome. The risk of developing metabolic syndrome was also 2.7-fold higher in participants with a low education level in Swedish women (17). Possible reasons for the association between education level and metabolic syndrome might be that women with low education levels tend to not have healthy dietary patterns and frequently consume high-calorie/low-nutrition foods (36,37), which can lead to metabolic syndrome (36,38–40). Limitations The current study has limitations. Due to the secondary data analysis using the cross-sectional dataset from Korea National Health and Nutrition Examination Survey, the causal relationship between risk factors and metabolic syndrome cannot be determined. Another limitation can be related to recall or report bias for the lifestyle variables such as smoking, drinking and physical activity. Therefore, further study using a longitudinal design is needed to examine the association of various factors with the criteria of metabolic syndrome for adults without diabetes mellitus. Conclusions This study supports prior data suggesting that glycosylated haemoglobin and body mass index can be important indicators of metabolic syndrome in Korean adults without diabetes mellitus. Therefore, the addition of glycosylated haemoglobin and body mass index to the diagnostic criteria for metabolic syndrome should be considered. In addition, demographic factors such as age and education and lifestyle factors such as smoking should be considered to develop prevention programs for metabolic syndrome. Specifically, smoking cessation for men without diabetes mellitus and tailored health education for illiterate for non-diabetes mellitus women can be important components of metabolic syndrome preventive programs. Declaration Funding: none. Ethical approval: Institutional Review Board of Yonsei University in Korea (Study No. 2016-0031). Conflict of interests: none. References 1. Grundy SM . Metabolic syndrome pandemic . Arterioscler Thromb Vasc Biol 2008 ; 28 : 629 – 36 . Google Scholar CrossRef Search ADS PubMed 2. Tran BT , Jeong BY , Oh JK . The prevalence trend of metabolic syndrome and its components and risk factors in Korean adults: results from the Korean National Health and Nutrition Examination Survey 2008-2013 . BMC Public Health 2017 ; 17 : 71 . Google Scholar CrossRef Search ADS PubMed 3. Expert panel on detection E, and treatment of high blood cholesterol in adults . Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) . JAMA 2001 ; 285 : 2486 - 97 . CrossRef Search ADS PubMed 4. World Health Organization, Regional Office for the Western Pacific . The Asia-Pacific perspective: redefining obesity and its treatment . Sydney : Health Communications Australia , 2000 . 5. Lorenzo C , Okoloise M , Williams K , Stern MP , Haffner SM ; San Antonio Heart Study . The metabolic syndrome as predictor of type 2 diabetes: the San Antonio heart study . Diabetes Care 2003 ; 26 : 3153 – 9 . Google Scholar CrossRef Search ADS PubMed 6. Wilson PW , D’Agostino RB , Parise H , Sullivan L , Meigs JB . Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus . Circulation 2005 ; 112 : 3066 – 72 . Google Scholar CrossRef Search ADS PubMed 7. Alexander CM , Landsman PB , Teutsch SM , Haffner SM ; Third National Health and Nutrition Examination Survey (NHANES III); National Cholesterol Education Program (NCEP) . NCEP-defined metabolic syndrome, diabetes, and prevalence of coronary heart disease among NHANES III participants age 50 years and older . Diabetes 2003 ; 52 : 1210 – 4 . Google Scholar CrossRef Search ADS PubMed 8. American Diabetes Association . Diagnosis and classification of diabetes mellitus . Diabetes Care 2014 ; 37 ( Suppl 1 ): S81 – 90 . CrossRef Search ADS PubMed 9. Ong KL , Tso AW , Lam KS , Cherny SS , Sham PC , Cheung BM . Using glycosylated hemoglobin to define the metabolic syndrome in United States adults . Diabetes Care 2010 ; 33 : 1856 – 8 . Google Scholar CrossRef Search ADS PubMed 10. Veeranna V , Ramesh K , Zalawadiya SK et al. Glycosylated hemoglobin and prevalent metabolic syndrome in nondiabetic multiethnic U.S. adults . Metab Syndr Relat Disord 2011 ; 9 : 361 – 7 . Google Scholar CrossRef Search ADS PubMed 11. Park YW , Zhu S , Palaniappan L , Heshka S , Carnethon MR , Heymsfield SB . The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988-1994 . Arch Intern Med 2003 ; 163 : 427 – 36 . Google Scholar CrossRef Search ADS PubMed 12. Stelmach W , Kaczmarczyk-Chałas K , Bielecki W , Drygas W . How education, income, control over life and life style contribute to risk factors for cardiovascular disease among adults in a post-communist country . Public Health 2005 ; 119 : 498 – 508 . Google Scholar CrossRef Search ADS PubMed 13. Clarke J , Janssen I . Is the frequency of weekly moderate-to-vigorous physical activity associated with the metabolic syndrome in Canadian adults ? Appl Physiol Nutr Metab 2013 ; 38 : 773 – 8 . Google Scholar CrossRef Search ADS PubMed 14. Kang DR , Ha Y , Hwang WJ . Prevalence and associated risk factors of the metabolic syndrome in the Korean workforce . Ind Health 2013 ; 51 : 256 – 65 . Google Scholar CrossRef Search ADS PubMed 15. Zhu S , St-Onge MP , Heshka S , Heymsfield SB . Lifestyle behaviors associated with lower risk of having the metabolic syndrome . Metabolism 2004 ; 53 : 1503 – 11 . Google Scholar CrossRef Search ADS PubMed 16. Zhan Y , Yu J , Chen R et al. Socioeconomic status and metabolic syndrome in the general population of China: a cross-sectional study . BMC Public Health 2012 ; 12 : 921 . Google Scholar CrossRef Search ADS PubMed 17. Wamala SP , Lynch J , Horsten M , Mittleman MA , Schenck-Gustafsson K , Orth-Gomér K . Education and the metabolic syndrome in women . Diabetes Care 1999 ; 22 : 1999 – 2003 . Google Scholar CrossRef Search ADS PubMed 18. Korea Centers for Disease Control and Prevention . The Fifth Korea National Health and Nutrition Examination Survey (KNHANES V) . Osong : Korea Centers for Disease Control and Prevention ; 2012 . 19. Korea Centers for Disease Control and Prevention . The Sixth Korea National Health and Nutrition Examination Survey (KNHANES Ⅵ-1) . Osong : Korea Centers for Disease Control and Prevention , 2013 . 20. World Health Organization . Guidelines for controlling and monitoring the tobacco epidemic . Geneva : World Health Organization , 1998 . 21. Craig CL , Marshall AL , Sjöström M et al. International physical activity questionnaire: 12-country reliability and validity . Med Sci Sports Exerc 2003 ; 35 : 1381 – 95 . Google Scholar CrossRef Search ADS PubMed 22. Committee IR . Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)-short and long forms . http://www.ipaq.ki.se/scoring.pdf 2005 (accessed on May 16, 2018 ). 23. Leite ML , Nicolosi A , Firmo JO , Lima-Costa MF . Features of metabolic syndrome in non-diabetic Italians and Brazilians: a discriminant analysis . Int J Clin Pract 2007 ; 61 : 32 – 8 . Google Scholar CrossRef Search ADS PubMed 24. Tian X , Xu X , Zhang K , Wang H . Gender difference of metabolic syndrome and its association with dietary diversity at different ages . Oncotarget 2017 ; 8 : 73568 – 78 . Google Scholar PubMed 25. Slagter SN , van Waateringe RP , van Beek AP , van der Klauw MM , Wolffenbuttel BHR , van Vliet-Ostaptchouk JV . Sex, BMI and age differences in metabolic syndrome: the Dutch Lifelines Cohort Study . Endocr Connect 2017 ; 6 : 278 – 88 . Google Scholar CrossRef Search ADS PubMed 26. Ervin RB . Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index: United States, 2003–2006 . Natl Health Stat Report 2009 ; 13 : 1 – 7 . 27. Park HS , Park CY , Oh SW , Yoo HJ . Prevalence of obesity and metabolic syndrome in Korean adults . Obes Rev 2008 ; 9 : 104 – 7 . Google Scholar CrossRef Search ADS PubMed 28. Després JP , Lemieux I . Abdominal obesity and metabolic syndrome . Nature 2006 ; 444 : 881 – 7 . Google Scholar CrossRef Search ADS PubMed 29. Bray GA , Ryan D. Overweight and the metabolic syndrome: from bench to bedside . Boston, MA : Springer , 2007 . 30. Yin XY , Zheng FP , Zhou JQ et al. Central obesity and metabolic risk factors in middle-aged Chinese . Biomed Environ Sci 2014 ; 27 : 343 – 52 . Google Scholar PubMed 31. Okosun IS , Cooper RS , Rotimi CN , Osotimehin B , Forrester T . Association of waist circumference with risk of hypertension and type 2 diabetes in Nigerians, Jamaicans, and African-Americans . Diabetes Care 1998 ; 21 : 1836 – 42 . Google Scholar CrossRef Search ADS PubMed 32. Facchini FS , Hollenbeck CB , Jeppesen J , Chen YD , Reaven GM . Insulin resistance and cigarette smoking . Lancet 1992 ; 339 : 1128 – 30 . Google Scholar CrossRef Search ADS PubMed 33. Lee WY , Jung CH , Park JS , Rhee EJ , Kim SW . Effects of smoking, alcohol, exercise, education, and family history on the metabolic syndrome as defined by the ATP III . Diabetes Res Clin Pract 2005 ; 67 : 70 – 7 . Google Scholar CrossRef Search ADS PubMed 34. Glazer NL , Lyass A , Esliger DW et al. Sustained and shorter bouts of physical activity are related to cardiovascular health . Med Sci Sports Exerc 2013 ; 45 : 109 – 15 . Google Scholar CrossRef Search ADS PubMed 35. Brien SE , Katzmarzyk PT . Physical activity and the metabolic syndrome in Canada . Appl Physiol Nutr Metab 2006 ; 31 : 40 – 7 . Google Scholar CrossRef Search ADS PubMed 36. Andreeva VA , Allès B , Feron G et al. Sex-specific sociodemographic correlates of dietary patterns in a large sample of French elderly individuals . Nutrients 2016 ; 8 : 484 . Google Scholar CrossRef Search ADS 37. Wang M , Heck K , Winkleby M , Cubbin C . Social disparities in dietary habits among women: geographic Research on Wellbeing (GROW) Study . Public Health Nutr 2016 ; 19 : 1666 – 73 . Google Scholar CrossRef Search ADS PubMed 38. Suliga E , Kozieł D , Cieśla E , Głuszek S . Association between dietary patterns and metabolic syndrome in individuals with normal weight: a cross-sectional study . Nutr J 2015 ; 14 : 55 . Google Scholar CrossRef Search ADS PubMed 39. de Oliveira EP , McLellan KC , Vaz de Arruda Silveira L , Burini RC . Dietary factors associated with metabolic syndrome in Brazilian adults . Nutr J 2012 ; 11 : 13 . Google Scholar CrossRef Search ADS PubMed 40. Baik I , Lee M , Jun NR , Lee JY , Shin C . A healthy dietary pattern consisting of a variety of food choices is inversely associated with the development of metabolic syndrome . Nutr Res Pract 2013 ; 7 : 233 – 41 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

Family PracticeOxford University Press

Published: Jun 5, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off