• Clinical Nutrition Research
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• v.12 no.3
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• pp.199-217
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• 2023

People with higher genetic predisposition to obesity are more susceptible to cardiovascular diseases (CVDs) and healthy plant-based foods may be associated with reduced risks of obesity and other metabolic markers. We investigated whether healthy plant-foods-rich dietary patterns might have inverse associations with cardiometabolic risk factors in participants at genetically elevated risk of obesity. For this cross-sectional study, 377 obese and overweight women were chosen from health centers in Tehran, Iran. We calculated a healthy plant-based diet index (h-PDI) in which healthy plant foods received positive scores, and unhealthy plant and animal foods received reversed scores. A genetic risk score (GRS) was developed based on 3 polymorphisms. The interaction between GRS and h-PDI on cardiometabolic traits was analyzed using a generalized linear model (GLM). We found significant interactions between GRS and h-PDI on body mass index (BMI) (p = 0.02), body fat mass (p = 0.04), and waist circumference (p = 0.056). There were significant gene-diet interactions for healthful plant-derived diets and BMI-GRS on high-sensitivity C-reactive protein (p = 0.03), aspartate aminotransferase (p = 0.04), alanine transaminase (p = 0.05), insulin (p = 0.04), and plasminogen activator inhibitor 1 (p = 0.002). Adherence to h-PDI was more strongly related to decreased levels of the aforementioned markers among participants in the second or top tertile of GRS than those with low GRS. These results highlight that following a plant-based dietary pattern considering genetics appears to be a protective factor against the risks of cardiometabolic abnormalities.

• Sleep Medicine and Psychophysiology
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• v.24 no.1
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• pp.38-45
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• 2017

Objectives: Sleep disturbance is a very rapidly growing disease with aging. The purpose of this study was to investigate the prevalence of sleep disturbances and its predictive factors in a three-year cohort study of people aged 60 years and over in Korea. Methods: In 2012 and 2014, we obtained data from a survey of the Korean Social Life, Health, and Aging Project. We asked participants if they had been diagnosed with stroke, myocardial infarction, angina pectoris, arthritis, pulmonary tuberculosis, asthma, cataract, glaucoma, hepatitis B, urinary incontinence, prostate hypertrophy, cancer, osteoporosis, hypertension, diabetes, hyperlipidemia, or metabolic syndrome. Cognitive function was assessed using the Mini-Mental State Examination for dementia screening in 2012, and depression was assessed using the Center for Epidemiologic Studies Depression Scale in 2012 and 2014. In 2015, a structured clinical interview for Axis I psychiatric disorders was administered to 235 people, and sleep disturbance was assessed using the Pittsburgh Sleep Quality Index. The perceived stress scale and the State-trait Anger Expression Inventory were also administered. Logistic regression analysis was used to predict sleep disturbance by gender, age, education, depression score, number of coexisting diseases in 2012 and 2014, current anger score, and perceived stress score. Results: Twenty-seven percent of the participants had sleep disturbances. Logistic regression analysis showed that the number of medical diseases three years ago, the depression score one year ago, and the current perceived stress significantly predicted sleep disturbances. Conclusion: Comorbid medical disease three years previous and depressive symptoms evaluated one year previous were predictive of current sleep disturbances. Further studies are needed to determine whether treatment of medical disease and depressive symptoms can improve sleep disturbances.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.163-171
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• 2021

Various records on health, food and treatment are written in ancient documents of Korea such as old recipe books, Korean medical books and history books, through these records, the principle of Sikchi can be discovered. Sikchi includes the meaning of medicine and food work on the same principle, and it is not only as traditional knowledge but also affecting modern food culture. Based on this principle of Sikchi, this study tried to lay a foundation that can be used as a modern health food material through scientific verification of foods recorded in the ancient literature. For this purpose, Omigalsu, a traditional drink made from omija, soybean, and honey, which is related to blood glucose control, which is one of the representative metabolic diseases of modern people, was selected as the subject of this study. In order to compensate for the agglomeration of beverages caused by honey or the rise in postprandial blood glucose, which occurs when the traditional Omigalsu recorded in the ancient literature is reproduced, the raw material that can be substituted for honey was discovered. The health promotion and quality improvement effects of newly prepared Omigalsu using honey substitutive raw material were confirmed through a comparative test with traditional Omigalsu. Based on this study, through scientific research using the principle of Sikchi, we intend to lay a foundation that can be used as various contents in the medical and food fields such as food bio and healthcare in modern society.

• Journal of Life Science
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• v.33 no.10
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• pp.783-790
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• 2023

Modern people have an increased incidence of metabolic diseases due to changed eating habits, and diabetes is considered the most significant metabolic disease. Given that existing diabetes treatments are accompanied by side effects, the aim of this study was to identify traditional natural products that have anti-diabetic activity. The potential anti-diabetic and antioxidant activities of natural products were examined using 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assay, α-glucosidase assay, and protein tyrosine phosphatase 1B (PTP1B) inhibition assay. Methanol extracts of Ulmus davidiana var. japonica, Acer tegmentosum branches, Nelumbo nucifera seeds, and Carthamus tinctorius seeds were found to have high anti-diabetic activity and further fractionated with solvents using ethyl acetate and butanol. Consequently, the ethyl acetate fraction of C. tinctorius seeds (MG-11-E) with high α-glucosidase and PTP1B inhibitory activity was selected. MG-11-E was subjected to preparative thin layer chromatography, and fraction #6 showed high α-glucosidase and PTP1B inhibitory activity. Fraction #6 was analyzed and fractionated via high performance liquid chromatography with 50% methanol as the mobile phase, and anti-diabetic activity was observed in the sample that eluted after 4 min as a single peak. The α-glucosidase inhibitory activity exhibited by this sample seemed to be greater than the PTP1B inhibitory activity; thus, it was concluded that a greater anti-diabetic therapeutic effect may be achieved by combining this agent with natural products that inhibit PTP1B activity.

• Clinical and Experimental Pediatrics
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• v.53 no.3
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• pp.300-306
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• 2010

Purpose : Recent changes in the population structure of Korea, such as rapid decline in birth rate and exponential increase in old-aged people, prompted us to prepare a new health improvement program in children and adolescents. Methods : We reviewed current health screenings applied for children and adolescents in Korea and other developed countries. We collected and reviewed population-based data focused on mortality and morbidity, and other health-related statistical data. We generated problem lists in current systems and developed new principles. Results : Current health screening programs for children and adolescents were usually based on laboratory tests, such as blood tests, urinalysis, and radiologic tests. Almost all of these programs lacked evidence based on population data or controlled studies. In most developed countries, laboratory tests are used only very selectively, and they usually focus on primary prevention of diseases and health improvement using anticipatory guidance. In Korea, statistics on mortality and morbidity reveal that diseases related to lifestyle, such as obesity and metabolic syndrome, are increasing in all generations. Conclusion : We recommend a periodic health screening program with anticipatory guidance, which is focused on growth and developmental surveillance in infants and children. We no longer recommend old programs that are based on laboratory and radiologic examinations. School health screening programs should also be changed to meet current health issues, such as developing a healthier lifestyle to minimize risk behaviors—or example, good mental health, balanced nutrition, and more exercise.

• 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.

• The Journal of Bigdata
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• v.6 no.1
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• pp.83-89
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• 2021

Lifestyle is changing rapidly, and food consumption patterns vary widely among households as dietary and food processing technologies evolve. This paper reclassified the food group of consumer panel data established by the Rural Development Administration, which contains information on purchasing agricultural products by household unit, and compared the consumption characteristics of agricultural products by age group. The criteria for age classification were divided into groups in their 60s and older with a prevalence of 20% or more metabolic diseases and groups in their 30s and 40s with less than 10%. Using the LightGBM algorithm, we classified the differences in food consumption patterns in their 30s and 50s and 60s and found that the precision was 0.85, the reproducibility was 0.71, and F1_score was 0.77. The results of variable importance were confectionery, folio, seasoned vegetables, fruit vegetables, and marine products, followed by the top five values of the SHAP indicator: confectionery, marine products, seasoned vegetables, fruit vegetables, and folio vegetables. As a result of binary classification of consumption patterns as a median instead of the average sensitive to outliers, confectionery showed that those in their 30s and 40s were more than twice as high as those in their 60s. Other variables also showed significant differences between those in their 30s and 40s and those in their 60s and older. According to the study, people in their 30s and 40s consumed more than twice as much confectionery as those in their 60s, while those in their 60s consumed more than twice as much marine products, seasoned vegetables, fruit vegetables, and folioce or logistics as much as those in their 30s and 40s. In addition to the top five items, consumption of 30s and 40s in wheat-processed snacks, breads and noodles was high, which differed from food consumption patterns in their 60s.