• Journal of Preventive Medicine and Public Health
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• v.36 no.2
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• pp.93-100
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• 2003

Objectives : Because shipyard workers are involved with various manufacturing process, they are exposed to many kinds of hazardous materials. Welders especially, are exposed to bisphenol-A (BPA) during the welding and flame cutting of coated steel, This study was conducted to assess the exposure status of the endocrine disrupter based on the job-exposure matrix. The effects of the genetic polymorphism of xenobiotic enzyme metabolisms involved in the metabolism of BPA on the levels of urinary metabolite were investigated. Methods : The study population was recruited from a shipyard company in the f province. A total of 84 shipbuilding workers 47 and 37 in the exposed and control groups, respectively, were recruited for this study. The questionnaire variables included, age, sex, use of personal protective equipment, smoking, drinking and work duration. The urinary metabolite was collected in the afternoon and correction made for the urinary creatinine concentration. The of the CYP1A1, CYP2E1 and UGT1A6 genotypes were investigated using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methods with the DNA extracted from venous blood. Results : The urinary BPA level in the welders group was significantly higher than in the control group (p<0.05). The urinary BPA concentration with the wild type UGT1A6 was higher than the other UGT1A6 genotypes, but with no statistical significant. From themultiple regression analysis of the urinary BPA, the regression coefficient for job grade was statistically significant (p<0.05). Conclusions : The grade of exposure to BPA affected the urinary BPA concentration was statistically significant. However, the genetic polymorphisms of xenobiotics enzyme metabolism were not statistically significant. Further investigation of the genetic polymorphisms with a larger sample size is needed.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.7
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• pp.868-875
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• 2017

The aim of the present study was to investigate antioxidant nutrient intake and risk of metabolic syndrome based on stress level in rural Korean women. Subject were participants from the Multi-Rural Communities Cohort Study, which was a part of the Korean Genome and Epidemiology Study. According to scores of the Psychosocial Well-Being Index Short-Form, a total of 10,111 subjects were classified into 'low stress group (n=8,015)' from 0 to 26 points and 'high stress group (n=2,096)' above 27 points. Data were collected using self-administered questionnaires, anthropometric measurements, and blood chemical analysis. Dietary nutrient consumption was assessed by a semi-quantitative food frequency questionnaire. High stress group showed lower intake of antioxidant nutrients, such as vitamin A, vitamin C, vitamin E, folate, zinc, and carotene compared to the low stress group. Intake of fruits and vegetable was lower in the high stress group compared to the low stress group. Subjects with high stress showed higher risk of hypertension [odd ratio (OR), 95% confidence interval (CI)=1.226 (1.112~1.351)] and hypertriglyceridemia [OR, 95% CI=1.227 (1.110~1.356)] than subjects with low stress. High stress group had a significantly greater odds ratio for metabolic syndrome compared with the low stress group [OR, 95% CI=1.216 (1.101~1.342]). Thus, the present study suggests that high stress might be associated with low intake of antioxidant nutrients and high risk of metabolic syndrome in rural Korean women.

• Journal of Nutrition and Health
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• v.52 no.6
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• pp.569-580
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• 2019

Purpose: This study examined the associations of Korean fermented cabbage kimchi consumption with the incidence risk of metabolic syndrome and its components in Korean adults. Methods: We used the community-based cohort data from the 2001 ~ 2012 Korean Genome and Epidemiology Study (KoGES). General characteristics, food group frequencies and nutrient intakes at baseline from 3,560 healthy individuals aged 40 ~ 69 years and the incidence of metabolic syndrome and its component from 2,259 participants, after excluding the data with incomplete anthropometric and blood test, during 10-year follow-up were analyzed. The participants were classified into three groups according to their daily consumption frequency of Korean fermented cabbage kimchi: 'less than once (< 1/day)', 'once or twice (1 ~ 2/day)', and 'three times (3/day)'. Results: After controlling for potential confounders such as age, education, income, residence area, alcohol drinking and energy intake, the consumption frequencies of rice and legumes were significantly higher, and the consumption frequency of meat was significantly lower in the 3/day group compared to that of the other two groups in men and women. The average intakes of energy and most nutrients, except fat and cholesterol, were higher in the 3/day group compared to those of the other two groups in men and women. Frequent consumption of kimchi was associated with a lower incidence of metabolic syndrome in all the models (unadjusted, age-adjusted, and multivariable-adjusted models) in women. When examining the multivariable-adjusted model, the hazard ratio for metabolic syndrome was 0.63 (95% CI: 0.47 ~ 0.86) for the 3/day group compared to that of the < 1/day group in women. However, there was no significant association between kimchi consumption and the incidence of metabolic syndrome in men. Conclusion: Our results show that consumption of kimchi at every meal was significantly associated with a lower incidence of metabolic syndrome in women.

• Journal of Nutrition and Health
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• v.52 no.2
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• pp.206-216
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• 2019

Purpose: Management of the metabolic risk factors in diabetes patients is essential for preventing or delaying diabetic complications. This study compared the levels of the metabolic risk factors in diabetes patients according to the income levels, and examined the secular trends in recent decades. Methods: The data from the Korea National Health and Nutrition Examination Survey 1998 ~ 2014 were used. The diabetes patients were divided into three groups based on their household income levels. General information was obtained through self-administered questionnaires, and the blood biomarkers and blood pressure data were obtained from a health examination. Multivariable linear regression models were used to compare the metabolic biomarker levels according to the household income levels, adjusting for potential confounding factors. Results: The fasting blood glucose, hemoglobin A1c, and blood lipid (total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglyceride) levels were similar in the three groups. During the survey period of 16 years, the blood pressure showed a significant decreasing trend with time in all groups (p < 0.001). In contrast, the fasting blood glucose (p = 0.004), total cholesterol (p < 0.001), and LDL-cholesterol levels (p = 0.007) decreased significantly, and the HDL-cholesterol level (p < 0.001) increased significantly in the highest-income groups. In the lowest-income group, the fasting blood glucose (p = 0.02), total cholesterol (p < 0.001), and triglyceride (p = 0.003) levels showed a significant decreasing trend over time. On the other hand, the middle-income group showed no significant change in any of the metabolic risk factors except for blood pressure. Conclusion: The level of management of metabolic risk factors according to the income level of Korean diabetes patients was similar. On the other hand, the highest- and lowest-income groups showed positive trends of management of these factors during 16 years of observation, whereas the middle-income group did not show any improvement.

• Exercise Science
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• v.21 no.2
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• pp.231-242
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• 2012

The purpose of this study was to develop the prediction model of VO₂max using submaximal metabolic responses from the Bruce protocol, HR responses at several stages and 1,200 m running record, and to compare and analyse the validity of these prediction models. The subjects were consisted of 255 male(133 male for 1,200 m running). They were participated maximal exercise testing with Bruce protocol, and the metabolic responses were measured in the end of the first(3 minute), second stage(6 minute), and 1,200 m running record. Measurement items were VO₂(㎖/kg/min), VCO₂(㎖/kg/min), VE(L/min), HR(bpm) of 3 and 6 minute, time to HR 150 bpm and 170 bpm, HR difference between Bruce protocol 6 and 3 minute, 1,200 m running record. Analyzing with all variables using enter method, the multiple R of total variable model was 0.642(p<.01), SEE was 4.38 ㎖/kg/min, CV was 10.8 %, but multicolinearity was appeared. The multiple R of 3 minutes model 1 and model 2 were 0.341 and 0.461, SEE was 6.05 and 5.72 ㎖/kg/min, CV was 14.9 and 14.1%, and multicolinearity did not appeared. The multiple R of 6 minutes model 1 and model 2 were 0.350 and 0.456, SEE was 6.03 and 5.74 ㎖/kg/min, CV was 14.9 and 14.2%, and multicolinearity did not appeared. The R of HR 170 and HR 170 model were 0.151 and 0.154, SEE were 6.36~6.37 ㎖/kg/min, CV were 15.7%. The R of 1,200 m running model was 0.444, SEE was 4.82 ㎖/kg/min, CV were 11.9%. In conclusion, with considering usefulness and convenience through the validity of these prediction models, the prediction model of VO₂max recommended 6 and 3 minute model, and the validity of HR model and 1,200 m running model were moderately low.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.9
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• pp.5660-5667
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• 2014

The aim of this study was to assess the association of metabolic syndrome, metabolic syndrome score (MSS) and pulse pressure (PP) in Korean adults. The study subjects were Korean adults 20 years or older (n=5,889) who participated in the Korea National Health and Nutrition Examination Survey 2012. After adjusting for factors, such as year and gender and BMI, the mean PP increased with increasing MSS (MSS 0, $41.30{\pm}0.34mmHg$ and MSS 1, $42.16{\pm}0.31mmHg$ and MSS 2, $44.73{\pm}0.34mmHg$ and MSS, 3, $46.46{\pm}0.42mmHg$ and MSS 4, $48.62{\pm}0.58mmHg$ and MSS 5, $53.50{\pm}1.05mmHg$), and the mean PP for metabolic syndrome($47.25{\pm}0.34mmHg$) increased in comparison to Non-Metabolic syndrome ($42.77{\pm}0.19mmHg$). When logistic regression analysis was performed, the odds ratio (OR) of Hyper-PP (61> PP) for MSS 0 was 4.49 in MSS 1 (95% confidence interval[CI], 2.68-7.57) and 8.01 in MSS 2 (95% CI, 4.77-13.47) and 11.37 in MSS 3 (95% CI, 6.67-19.35) and 19.69 in MSS 4 (95% CI, 11.20-34.60) and 34.07 in MSS 5 (95% CI, 17.44-66.52), metabolic syndrome was associated with an increased Hyper-PP(OR 4.6, 95% CI, 2.0-10.4). Conclusion. These results suggest that an increase in MSS or metabolic syndrome might increase the pulse pressure.

• Journal of the Korean Chemical Society
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• v.53 no.6
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• pp.653-662
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• 2009

Developing effective tools for predicting absorption, distribution, metabolism, excretion properties and toxicity (ADME/T) of new chemical entities in the early stage of drug design is one of the most important tasks in drug discovery and development today. As one of these attempts, support vector machines (SVM) has recently been exploited for the prediction of ADME/T related properties. However, two problems in SVM modeling, i.e. feature selection and parameters setting, are still far from solved. The two problems have been shown to be crucial to the efficiency and accuracy of SVM classification. In particular, the feature selection and optimal SVM parameters setting influence each other, which indicates that they should be dealt with simultaneously. In this account, we present an integrated practical solution, in which genetic-based algorithm (GA) is used for feature selection and grid search (GS) method for parameters optimization. hERG ion-channel inhibitor classification models of ADME/T related properties has been built for assessing and testing the proposed GA-GS-SVM. We generated 6 different models that are 3 different single models and 3 different ensemble models using training set - 1891 compounds and validated with external test set - 175 compounds. We compared single model with ensemble model to solve data imbalance problems. It was able to improve accuracy of prediction to use ensemble model.

• Korean Journal of Food Science and Technology
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• v.53 no.2
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• pp.139-148
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• 2021

Atopic dermatitis is a chronic, recurrent, inflammatory skin disease that is a well-known allergic disease with severe itching, making daily life difficult. Since the immunomodulatory action of vitamin D has been reported, several researchers have attempted to determine the correlation between vitamin D and atopic dermatitis. In this review, 41 articles meeting the inclusion criteria were selected and reviewed from the articles published to date. Several studies have reported that low vitamin D levels are associated with the onset of atopic dermatitis and severe atopic dermatitis, but the opinions remain conflicting. Similarly, there are conflicting opinions on the improvement effect of oral vitamin D supplementation on atopic dermatitis, but some possibilities have been suggested. To apply vitamin D as a therapeutic agent for atopic dermatitis, a more systematically designed experiment should be conducted, and an appropriate intake dose for an immunomodulatory function should be obtained.

• Clinical and Experimental Pediatrics
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• v.49 no.3
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• pp.317-325
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• 2006

Purpose : This study was carried out to elucidate the effects of nitric oxide synthase(NOS) inhibitor, NG-monomethyl-L-arginine(L-NMMA) and nitric oxide precursor, L-arginine(L-Arg) on cerebral hemodynamics and energy metabolism during reoxygenation-reperfusion(RR) after hypoxia-ischemia(HI) in newborn piglets. Methods : Twenty-eight newborn piglets were divided into 4 groups; Sham normal control(NC), experimental control(EC), L-NMMA(HI & RR with L-NMMA), and L-Arg(HI & RR with L-Arg) groups. HI was induced by occlusion of bilateral common carotid arteries and simultaneously breathing with 8 percent oxygen for 30 mins, and followed RR by release of carotid occlusion and normoxic ventilation for one hour. All groups were monitored with cerebral hemodynamics and cytochrome $aa_3$ (Cyt $aa_3$) using near infrared spectroscopy(NIRS). $Na^+$, $K^+$-ATPase activity, lipid peroxidation products, and tissue high energy phosphate levels were determined biochemically in the cerebral cortex. Results : In experimental groups, mean arterial blood pressure, $PaO_2$, and pH decreased, and base excess and blood lactate level increased after HI compared to NC group(P<0.05). These variables subsequently returned to baseline after RR except pH. There were no differences among the experimental groups. In NIRS, oxidized hemoglobin($HbO_2$) decreased and hemoglobin(Hb) increased during HI(P<0.05) but returned to base line immediately after RR; 40 min after RR, the $HbO_2$ had decreased significantly compared to NC group(P<0.05). Changes of Cyt $aa_3$ decreased significantly compared to NC after HI and recovered at the end of the experiment. Significantly reduced cerebral cortical cell membrane $Na^+$, $K^+$-ATPase activity and increased lipid peroxidation products(P<0.05) were not improved with L-NMMA or L-Arg. Conclusion : These findings suggest that NO is not involved in the mechanism of HI and RR brain damage during the early acute phase of RR.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.