Substance Use & Misuse, 49:1426–1436, 2014 C 2014 Informa Healthcare USA, Inc. Copyright ISSN: 1082-6084 print / 1532-2491 online DOI: 10.3109/10826084.2014.912227
ORIGINAL ARTICLE
Dose-Related Association Between Urinary Cotinine-Verified Smoking Status and Dyslipidemia among Korean Men: The 2008–2010 Korea National Health and Nutrition Examination Survey Ga Eun Nam1 , Do Hoon Kim1 , Yong Gyu Park2 , Kyungdo Han2 , Youn Seon Choi1 , Seon Mee Kim1 , Byung Joon Ko1 , Yang Hyun Kim1 , Kyung Shik Lee3 and Sung Joon Baek1 1
Department of Family Medicine, Korea University College of Medicine, Seoul, Republic of Korea; 2 Department of Biostatistics, Catholic University College of Medicine, Seoul, Republic of Korea; 3 Department of Family Medicine, Wonkwang University College of Medicine, Kunpo-si, Republic of Korea cigarettes smoked and the change in lipid or lipoprotein variables (Craig, Palomaki, & Haddow, 1989). In addition, smoking may induce proatherogenic changes in lipid metabolism even in the absence of overt dyslipidemia (Zaratin et al., 2004). Several studies have shown that smoking cessation improves lipid profiles (Eliasson, Hjalmarson, Kruse, Landfeldt, & Westin, 2001; Nilsson, Lundgren, Soderstrom, Fagerstrom, & Nilsson-Ehle, 1996). However, studies examining whether smokers have worse lipid profiles than nonsmokers have reported inconsistent results. The results for each lipid profile may differ according to sex and ethnicity (Freedman et al., 1986; Halfon, Green, & Heiss, 1984; Hughes, Choo, Kuperan, Ong, & Aw, 1998; Kuzuya, Ando, Iguchi, & Shimokata, 2006; McKarns, Smith, Payne, & Doolittle, 1995). In addition, there are inconsistencies in reports about the effects of smoking cessation on lipid profiles (Benowitz et al., 2012). There have been few investigations into the relationship between smoking and non-HDL cholesterol (non-HDL-C) and corresponding ratios such as TC/HDLC, LDL-C/HDL-C, and TG/HDL-C, which are known to have better predictive value for coronary heart disease (Boekholdt et al., 2012; Fernandez & Webb, 2008; Hanak, Munoz, Teague, Stanley, & Bittner, 2004; Natarajan et al., 2003). Self-reports through questionnaires or interviews have been used as indicators of smoking status in the majority of studies on the relationship between cigarette smoking and dyslipidemia. They are noninvasive and convenient to use. However, self-reporting is subjective and the results may underestimate smoking rates. Nicotine is the main component in cigarettes, and can be quantified to correspond to the number of cigarettes smoked (Leone,
This cross-sectionally designed study was based on data collected during the 2008–2010 Korea National Health and Nutrition Examination Survey. A total 3231 South Korean men aged more than 19 years were included. Urinary cotinine concentrations were measured. Smoking status was defined using questionnaire responses and urinary cotinine concentrations. Hierarchical multivariate logistic regression analyses were used to assess the association of urinary cotinine concentrations with the prevalence of dyslipidemia and various parameters of dyslipidemia. There is a significant dose-related association between smoking as assessed by urinary cotinine concentration and dyslipidemia and various parameters of dyslipidemia among South Korean men. Keywords
dyslipidemia, smoking, urinary cotinine
INTRODUCTION
Cigarette smoking has been established as a major risk factor for and the most important preventable cause of cardiovascular disease (CVD) (Goldstein et al., 2001). The risk posed by smoking for CVD is estimated with an emphasis on lipid and lipoprotein involvement. The adverse effects of smoking on the cardiovascular system stem from atherosclerotic progression and atherogenic effects through abnormal lipid metabolism. Smokingrelated lipid metabolism disorders are characterized by increased serum total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C), with low levels of high-density lipoprotein cholesterol (HDLC) (Ambrose & Barua, 2004). A meta-analysis identified a dose-response relationship between the number of
Address correspondence to Do Hoon Kim, Department of Family Medicine, Korea University College of Medicine, Seoul, Republic of Korea; E-mail: [email protected]
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URINARY COTININE-VERIFIED SMOKING STATUS AND DYSLIPIDEMIA
2005). Therefore, differentiation based on self-reporting has more validity when used in conjunction with analyses of nicotine metabolites. Among the various biomarkers of exposure to tobacco smoking, cotinine, a major metabolite of nicotine, is acknowkedged as the most appropriate parameter to evaluate tobacco exposure and smoking status due to its higher stability and longer half-life (SRNT Subcommittee on Biochemical Verification, 2002). Many laboratories perform cotinine assays using urinary matrices because cotinine is present in sufficient amounts for measurement, and it has a long half-life. Therefore, urinary cotinine concentration is considered a useful index that can distinguish smokers from nonsmokers (Matsukura et al., 1984). However, there is no consensus on a cut-off urinary cotinine level to discriminate smokers from nonsmokers. The cut-off point could vary from 20 to 100 ng/ml (Haufroid & Lison, 1998). Therefore, this study aimed to examine the potential effects of cigarette smoking on serum lipid profiles by determining the relationships between urinary cotinine concentrations as an objective biological marker of smoking, and various parameters of dyslipidemia among Korean men based on data gathered from the 2008–2010 Korea National Health and Nutrition Examination Survey (KNHANES).
METHODS Overview of Survey and Study Participants
This study was based on data collected during the 2008–2010 KNHANES (Korea Centers for Disease Control and Prevention). Conducted by the Division of Chronic Disease Surveillance under the Korea Centers for Disease Control and Prevention since 1998, the KNHANES is a nationwide survey designed to assess national health and nutrition levels accurately. The overall survey consists of a health interview, a nutrition assessment, and a health examination. A complex, stratified, multistage cluster sampling design with proportional allocation is used for the selected household units that participate in the survey. The sample included 5656 males aged over 19 years whose urinary cotinine was measured. Two hundred and forty-eight men who had been examined at Seoul Clinical Laboratories were excluded to ensure that the data had been measured at a single center. We excluded 198 subjects suffering from several cancers, chronic kidney disease, chronic liver disease, and active infectious disease such as active tuberculosis, and 190 subjects who had not fasted for at least 8 hr prior to blood sampling. We excluded 1631 past smokers and 74 passive smokers based on their questionnaire responses, and questionnaire responses and urinary cotinine concentrations, respectively. We excluded a further 84 subjects who were current smokers and who had urinary cotinine concentrations 30 minutes per session, or who exercised vigorously more than three times weekly for >20 min per session were
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G. E. NAM ET Al.
FIGURE 1. Flow chart of included or excluded study subjects. KNHANES = Korea National Health and Nutrition Examination Survey [See text regarding smoking status].
classified as regular exercisers. Daily food intake was assessed using the 24-hr recall method, and the food intake frequency method was used to determine the foods consumed the previous day. Energy and fat intakes were based on the food database developed for the KNHANES and the food composition table published by the National Rural Living Science Institute under the Rural Development Administration. Anthropometric and Biochemical Measurements
Height and body weight were measured with subjects dressed in light indoor clothing without shoes, and body mass index (BMI) was calculated using the formula [body weight (kg)/height2 (m2 )]. Waist circumference was measured at the narrowest point between the costal margin and the iliac crest at the end of exhalation. Blood samples were obtained from the antecubital vein after ≥8-hr fasting and appropriately processed, immediately refrigerated, transported in cold storage to the Central Testing Institute in Seoul, Korea, and analyzed
within 24 hr of transportation. The serum concentrations of TC, HDL-C, and TG were measured with a Hitachi Automatic Analyzer 7600 (Hitachi, Tokyo, Japan) by enzymatic methods using commercially available kits (Daiichi, Tokyo, Japan). LDL-C levels were calculated using Friedewald’s formula (Friedewald, Levy, & Fredrickson, 1972) in subjects with TG 400 mg/dl. Non-HDL-C was calculated as serum level of TC minus HDL-C level. Definition of Dyslipidemia
The diagnosis of dyslipidemia was based on the presence of one or more of the following, according to the criteria of the National Cholesterol Education Program Adult Treatment Panel III (Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults, 2001): (1) hypercholesterolemia, TC levels ≥240 mg/dl in a blood test after fasting or the use of lipid-lowering
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URINARY COTININE-VERIFIED SMOKING STATUS AND DYSLIPIDEMIA
drugs; (2) hypo-HDL-cholesterolemia, HDL-C levels under ≤40 mg/dl; (3) hyper-LDL-cholesterolemia, LDL-C levels ≥160 mg/dl or the use of lipid-lowering drugs; (4) hypertriglyceridemia, TG levels ≥200 mg/dl. Additionally, we used hyper-non-HDL-cholesterolemia and high TC/HDL-C, LDL-C/HDL-C, and TG/HDL-C ratios as parameters of dyslipidemia, as these values are known as reliable predictors of CVD risk. We defined non-HDLC ≥160 mg/dl, TC/HDL-C >5, LDL-C/HDL-C >2.5, and TG/HDL-C >3.8 as lipid abnormalities (Boekholdt et al., 2012; Fernandez & Webb, 2008; Hanak et al., 2004; Natarajan et al., 2003). Statistical Analyses
Statistical analysis was carried out using SAS version 9.2 for Windows (SAS Institute, Cary, NC, USA) and two-sided p-values < .05 were considered statistically significant. The baseline characteristics of demography, lifestyle, anthropometry, and biochemistry of the participants are presented as means ± standard error of means (SEM) or percentages (standard error (SE)). Student’s t-test or one way analysis of variance was used to investigate the differences in lipid profiles according to socioeconomic and lifestyle variables. Current smokers were divided equally into three groups based on urinary cotinine concentrations. We used analysis of covariance to compare the mean lipid profiles across the urinary cotinine categories after adjusting for age, BMI, education level, alcohol intake, and physical activity. Hierarchical multivariate logistic regression analyses were used to assess the association of urinary cotinine concentrations with the prevalence of dyslipidemia and its individual components, hyper-non-HDL-cholesterolemia, and high TC/HDL-C, LDL-C/HDL-C, and TG/HDL-C ratios. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated after adjustment for potential confounders. In the multivariate analyses of each of the dyslipidemic profiles, we first adjusted for age and BMI (model 1) and then adjusted for the variables in model 1 plus education level, monthly income, alcohol intake, physical activity, and daily energy and fat intake (model 2).
RESULTS Baseline Characteristics of the Study Subjects
There were 3231 participants in the present study. The baseline characteristics of the study subjects are shown in Table 1. The mean age of the participants was 40.8 ± 0.4 years. Based on questionnaires only, never smokers and current smokers made up 32.5% and 67.5%, of the sample, respectively; after verification by urinary cotinine concentrations, they made up 32.1% and 67.9%, of the sample respectively. The overall mean urinary cotinine concentration was 1007.2 ± 26.7 ng/ml. The mean concentration of urinary cotinine in never smokers and current smokers was 9.5 ± 0.5 ng/ml and 1488.4 ± 31.1 ng/ml,
TABLE 1. Baseline characteristics of Korean men in the KNHANES 2008–2010 Mean ± SEM or percentage (SE) Unweighted n Age (years) Education level (%) ≤Elementary school Middle to high school ≥University Monthly income (USD) 2814 Never smoker (%) Alcohol drinking (%) Non drinker Mild to moderate drinker Heavy drinker Regular exerciser (%) Total energy intake (kcal) Total fat intake (%) Lipid lowering drug user (%) Height (cm) Weight (kg) BMI (kg/m2 ) WC (cm) Urinary cotinine (ng/ml) TC (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) TG (mg/dl) Non-HDL-C (mg/dl) TC/HDL-C ratio LDL-C/HDL-C ratio TG/HDL-C ratio
3231 40.8 ± 0.4 11.1 (0.9) 53.5 (1.2) 35.5 (1.2) 16.1 (1.1) 43.2 (1.4) 40.7 (1.4) 32.1 (1.1) 11.2 (0.8) 70.0 (1.1) 18.8 (0.9) 27.5 (1.1) 2340.6 ± 25.4 19.3 (0.2) 1.9 (0.3) 171.3 ± 0.2 70.3 ± 0.3 23.9 ± 0.1 83.5 ± 0.2 1007.2 ± 26.7 184.9 ± 0.9 49.6 ± 0.3 110.6 ± 0.8 154.1 ± 3.1 139.3 ± 0.9 4.24 ± 0.03 2.34 ± 0.02 3.8 ± 0.1
Note. KNHANES, Korean National Health and Nutrition Examination Survey; SEM, standard error of mean; SE, standard error; USD, United States dollar; BMI, body mass index; WC, waist circumference; TC, total cholesterol; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; TG, triglycerides.
respectively. The baseline mean values of the various parameters of dyslipidemia are listed in Table 1. Mean Lipid Profiles According to Demographic and Lifestyle Subgroups
Table 2 presents the mean lipid profiles according to the demographic and lifestyle subgroups of the subjects. TC levels were significantly different according to age (p < .001), education level (p = .001), and monthly household income (p = .002). HDL-C levels were different according to age groups (p < .001) and alcohol intake (p < .001). There were significant differences in LDL-C levels according to age (p < .001), education level (p = .001), monthly household income (p = .017), and alcohol intake (p < .001). TG levels were significantly different according to age (p < .001), monthly household income (p = .033), and alcohol intake (p < .001).
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TABLE 2. Mean parameters of dyslipidemia by demographic and lifestyle subgroups among Korean men in the KNHANES 2008–2010 Parameters of dyslipidemia (mg/dl)
Age (years) 20–39 40–59 ≥60 pa Education level ≤Elementary school Middle to high school ≥University pa Monthly income (USD) 2814 pa Alcohol drinking non-drinker mild to moderate-drinker heavy-drinker pa Regular physical exercise Yes No pa
Unweighted n
TC
HDL-C
LDL-C
TGb
1417 1199 615
179.0 ± 1.2 194.6 ± 1.5 180.1 ± 2.1
ORIGINAL ARTICLE
Dose-Related Association Between Urinary Cotinine-Verified Smoking Status and Dyslipidemia among Korean Men: The 2008–2010 Korea National Health and Nutrition Examination Survey Ga Eun Nam1 , Do Hoon Kim1 , Yong Gyu Park2 , Kyungdo Han2 , Youn Seon Choi1 , Seon Mee Kim1 , Byung Joon Ko1 , Yang Hyun Kim1 , Kyung Shik Lee3 and Sung Joon Baek1 1
Department of Family Medicine, Korea University College of Medicine, Seoul, Republic of Korea; 2 Department of Biostatistics, Catholic University College of Medicine, Seoul, Republic of Korea; 3 Department of Family Medicine, Wonkwang University College of Medicine, Kunpo-si, Republic of Korea cigarettes smoked and the change in lipid or lipoprotein variables (Craig, Palomaki, & Haddow, 1989). In addition, smoking may induce proatherogenic changes in lipid metabolism even in the absence of overt dyslipidemia (Zaratin et al., 2004). Several studies have shown that smoking cessation improves lipid profiles (Eliasson, Hjalmarson, Kruse, Landfeldt, & Westin, 2001; Nilsson, Lundgren, Soderstrom, Fagerstrom, & Nilsson-Ehle, 1996). However, studies examining whether smokers have worse lipid profiles than nonsmokers have reported inconsistent results. The results for each lipid profile may differ according to sex and ethnicity (Freedman et al., 1986; Halfon, Green, & Heiss, 1984; Hughes, Choo, Kuperan, Ong, & Aw, 1998; Kuzuya, Ando, Iguchi, & Shimokata, 2006; McKarns, Smith, Payne, & Doolittle, 1995). In addition, there are inconsistencies in reports about the effects of smoking cessation on lipid profiles (Benowitz et al., 2012). There have been few investigations into the relationship between smoking and non-HDL cholesterol (non-HDL-C) and corresponding ratios such as TC/HDLC, LDL-C/HDL-C, and TG/HDL-C, which are known to have better predictive value for coronary heart disease (Boekholdt et al., 2012; Fernandez & Webb, 2008; Hanak, Munoz, Teague, Stanley, & Bittner, 2004; Natarajan et al., 2003). Self-reports through questionnaires or interviews have been used as indicators of smoking status in the majority of studies on the relationship between cigarette smoking and dyslipidemia. They are noninvasive and convenient to use. However, self-reporting is subjective and the results may underestimate smoking rates. Nicotine is the main component in cigarettes, and can be quantified to correspond to the number of cigarettes smoked (Leone,
This cross-sectionally designed study was based on data collected during the 2008–2010 Korea National Health and Nutrition Examination Survey. A total 3231 South Korean men aged more than 19 years were included. Urinary cotinine concentrations were measured. Smoking status was defined using questionnaire responses and urinary cotinine concentrations. Hierarchical multivariate logistic regression analyses were used to assess the association of urinary cotinine concentrations with the prevalence of dyslipidemia and various parameters of dyslipidemia. There is a significant dose-related association between smoking as assessed by urinary cotinine concentration and dyslipidemia and various parameters of dyslipidemia among South Korean men. Keywords
dyslipidemia, smoking, urinary cotinine
INTRODUCTION
Cigarette smoking has been established as a major risk factor for and the most important preventable cause of cardiovascular disease (CVD) (Goldstein et al., 2001). The risk posed by smoking for CVD is estimated with an emphasis on lipid and lipoprotein involvement. The adverse effects of smoking on the cardiovascular system stem from atherosclerotic progression and atherogenic effects through abnormal lipid metabolism. Smokingrelated lipid metabolism disorders are characterized by increased serum total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C), with low levels of high-density lipoprotein cholesterol (HDLC) (Ambrose & Barua, 2004). A meta-analysis identified a dose-response relationship between the number of
Address correspondence to Do Hoon Kim, Department of Family Medicine, Korea University College of Medicine, Seoul, Republic of Korea; E-mail: [email protected]
1426
URINARY COTININE-VERIFIED SMOKING STATUS AND DYSLIPIDEMIA
2005). Therefore, differentiation based on self-reporting has more validity when used in conjunction with analyses of nicotine metabolites. Among the various biomarkers of exposure to tobacco smoking, cotinine, a major metabolite of nicotine, is acknowkedged as the most appropriate parameter to evaluate tobacco exposure and smoking status due to its higher stability and longer half-life (SRNT Subcommittee on Biochemical Verification, 2002). Many laboratories perform cotinine assays using urinary matrices because cotinine is present in sufficient amounts for measurement, and it has a long half-life. Therefore, urinary cotinine concentration is considered a useful index that can distinguish smokers from nonsmokers (Matsukura et al., 1984). However, there is no consensus on a cut-off urinary cotinine level to discriminate smokers from nonsmokers. The cut-off point could vary from 20 to 100 ng/ml (Haufroid & Lison, 1998). Therefore, this study aimed to examine the potential effects of cigarette smoking on serum lipid profiles by determining the relationships between urinary cotinine concentrations as an objective biological marker of smoking, and various parameters of dyslipidemia among Korean men based on data gathered from the 2008–2010 Korea National Health and Nutrition Examination Survey (KNHANES).
METHODS Overview of Survey and Study Participants
This study was based on data collected during the 2008–2010 KNHANES (Korea Centers for Disease Control and Prevention). Conducted by the Division of Chronic Disease Surveillance under the Korea Centers for Disease Control and Prevention since 1998, the KNHANES is a nationwide survey designed to assess national health and nutrition levels accurately. The overall survey consists of a health interview, a nutrition assessment, and a health examination. A complex, stratified, multistage cluster sampling design with proportional allocation is used for the selected household units that participate in the survey. The sample included 5656 males aged over 19 years whose urinary cotinine was measured. Two hundred and forty-eight men who had been examined at Seoul Clinical Laboratories were excluded to ensure that the data had been measured at a single center. We excluded 198 subjects suffering from several cancers, chronic kidney disease, chronic liver disease, and active infectious disease such as active tuberculosis, and 190 subjects who had not fasted for at least 8 hr prior to blood sampling. We excluded 1631 past smokers and 74 passive smokers based on their questionnaire responses, and questionnaire responses and urinary cotinine concentrations, respectively. We excluded a further 84 subjects who were current smokers and who had urinary cotinine concentrations 30 minutes per session, or who exercised vigorously more than three times weekly for >20 min per session were
1428
G. E. NAM ET Al.
FIGURE 1. Flow chart of included or excluded study subjects. KNHANES = Korea National Health and Nutrition Examination Survey [See text regarding smoking status].
classified as regular exercisers. Daily food intake was assessed using the 24-hr recall method, and the food intake frequency method was used to determine the foods consumed the previous day. Energy and fat intakes were based on the food database developed for the KNHANES and the food composition table published by the National Rural Living Science Institute under the Rural Development Administration. Anthropometric and Biochemical Measurements
Height and body weight were measured with subjects dressed in light indoor clothing without shoes, and body mass index (BMI) was calculated using the formula [body weight (kg)/height2 (m2 )]. Waist circumference was measured at the narrowest point between the costal margin and the iliac crest at the end of exhalation. Blood samples were obtained from the antecubital vein after ≥8-hr fasting and appropriately processed, immediately refrigerated, transported in cold storage to the Central Testing Institute in Seoul, Korea, and analyzed
within 24 hr of transportation. The serum concentrations of TC, HDL-C, and TG were measured with a Hitachi Automatic Analyzer 7600 (Hitachi, Tokyo, Japan) by enzymatic methods using commercially available kits (Daiichi, Tokyo, Japan). LDL-C levels were calculated using Friedewald’s formula (Friedewald, Levy, & Fredrickson, 1972) in subjects with TG 400 mg/dl. Non-HDL-C was calculated as serum level of TC minus HDL-C level. Definition of Dyslipidemia
The diagnosis of dyslipidemia was based on the presence of one or more of the following, according to the criteria of the National Cholesterol Education Program Adult Treatment Panel III (Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults, 2001): (1) hypercholesterolemia, TC levels ≥240 mg/dl in a blood test after fasting or the use of lipid-lowering
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URINARY COTININE-VERIFIED SMOKING STATUS AND DYSLIPIDEMIA
drugs; (2) hypo-HDL-cholesterolemia, HDL-C levels under ≤40 mg/dl; (3) hyper-LDL-cholesterolemia, LDL-C levels ≥160 mg/dl or the use of lipid-lowering drugs; (4) hypertriglyceridemia, TG levels ≥200 mg/dl. Additionally, we used hyper-non-HDL-cholesterolemia and high TC/HDL-C, LDL-C/HDL-C, and TG/HDL-C ratios as parameters of dyslipidemia, as these values are known as reliable predictors of CVD risk. We defined non-HDLC ≥160 mg/dl, TC/HDL-C >5, LDL-C/HDL-C >2.5, and TG/HDL-C >3.8 as lipid abnormalities (Boekholdt et al., 2012; Fernandez & Webb, 2008; Hanak et al., 2004; Natarajan et al., 2003). Statistical Analyses
Statistical analysis was carried out using SAS version 9.2 for Windows (SAS Institute, Cary, NC, USA) and two-sided p-values < .05 were considered statistically significant. The baseline characteristics of demography, lifestyle, anthropometry, and biochemistry of the participants are presented as means ± standard error of means (SEM) or percentages (standard error (SE)). Student’s t-test or one way analysis of variance was used to investigate the differences in lipid profiles according to socioeconomic and lifestyle variables. Current smokers were divided equally into three groups based on urinary cotinine concentrations. We used analysis of covariance to compare the mean lipid profiles across the urinary cotinine categories after adjusting for age, BMI, education level, alcohol intake, and physical activity. Hierarchical multivariate logistic regression analyses were used to assess the association of urinary cotinine concentrations with the prevalence of dyslipidemia and its individual components, hyper-non-HDL-cholesterolemia, and high TC/HDL-C, LDL-C/HDL-C, and TG/HDL-C ratios. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated after adjustment for potential confounders. In the multivariate analyses of each of the dyslipidemic profiles, we first adjusted for age and BMI (model 1) and then adjusted for the variables in model 1 plus education level, monthly income, alcohol intake, physical activity, and daily energy and fat intake (model 2).
RESULTS Baseline Characteristics of the Study Subjects
There were 3231 participants in the present study. The baseline characteristics of the study subjects are shown in Table 1. The mean age of the participants was 40.8 ± 0.4 years. Based on questionnaires only, never smokers and current smokers made up 32.5% and 67.5%, of the sample, respectively; after verification by urinary cotinine concentrations, they made up 32.1% and 67.9%, of the sample respectively. The overall mean urinary cotinine concentration was 1007.2 ± 26.7 ng/ml. The mean concentration of urinary cotinine in never smokers and current smokers was 9.5 ± 0.5 ng/ml and 1488.4 ± 31.1 ng/ml,
TABLE 1. Baseline characteristics of Korean men in the KNHANES 2008–2010 Mean ± SEM or percentage (SE) Unweighted n Age (years) Education level (%) ≤Elementary school Middle to high school ≥University Monthly income (USD) 2814 Never smoker (%) Alcohol drinking (%) Non drinker Mild to moderate drinker Heavy drinker Regular exerciser (%) Total energy intake (kcal) Total fat intake (%) Lipid lowering drug user (%) Height (cm) Weight (kg) BMI (kg/m2 ) WC (cm) Urinary cotinine (ng/ml) TC (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) TG (mg/dl) Non-HDL-C (mg/dl) TC/HDL-C ratio LDL-C/HDL-C ratio TG/HDL-C ratio
3231 40.8 ± 0.4 11.1 (0.9) 53.5 (1.2) 35.5 (1.2) 16.1 (1.1) 43.2 (1.4) 40.7 (1.4) 32.1 (1.1) 11.2 (0.8) 70.0 (1.1) 18.8 (0.9) 27.5 (1.1) 2340.6 ± 25.4 19.3 (0.2) 1.9 (0.3) 171.3 ± 0.2 70.3 ± 0.3 23.9 ± 0.1 83.5 ± 0.2 1007.2 ± 26.7 184.9 ± 0.9 49.6 ± 0.3 110.6 ± 0.8 154.1 ± 3.1 139.3 ± 0.9 4.24 ± 0.03 2.34 ± 0.02 3.8 ± 0.1
Note. KNHANES, Korean National Health and Nutrition Examination Survey; SEM, standard error of mean; SE, standard error; USD, United States dollar; BMI, body mass index; WC, waist circumference; TC, total cholesterol; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; TG, triglycerides.
respectively. The baseline mean values of the various parameters of dyslipidemia are listed in Table 1. Mean Lipid Profiles According to Demographic and Lifestyle Subgroups
Table 2 presents the mean lipid profiles according to the demographic and lifestyle subgroups of the subjects. TC levels were significantly different according to age (p < .001), education level (p = .001), and monthly household income (p = .002). HDL-C levels were different according to age groups (p < .001) and alcohol intake (p < .001). There were significant differences in LDL-C levels according to age (p < .001), education level (p = .001), monthly household income (p = .017), and alcohol intake (p < .001). TG levels were significantly different according to age (p < .001), monthly household income (p = .033), and alcohol intake (p < .001).
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TABLE 2. Mean parameters of dyslipidemia by demographic and lifestyle subgroups among Korean men in the KNHANES 2008–2010 Parameters of dyslipidemia (mg/dl)
Age (years) 20–39 40–59 ≥60 pa Education level ≤Elementary school Middle to high school ≥University pa Monthly income (USD) 2814 pa Alcohol drinking non-drinker mild to moderate-drinker heavy-drinker pa Regular physical exercise Yes No pa
Unweighted n
TC
HDL-C
LDL-C
TGb
1417 1199 615
179.0 ± 1.2 194.6 ± 1.5 180.1 ± 2.1
