• Journal of The Korean Society of Inherited Metabolic disease
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• v.22 no.2
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• pp.58-62
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• 2022

Pompe disease (PD) is an autosomal recessive genetic disorder caused by a deficiency of the lysosomal enzyme acid α-glucosidase (GAA). It is easy to hastily diagnose as patients if they have two pathogenic variants. Clinical pathologists misdiagnosed our infant and her mother as PD. Here, we report a case of pseudodeficiency in a potential late-onset Pompe disease (LOPD) carrier with a double dual variant, each in cis formation in a 3-month infant. The person who has two pathogenic variants was diagnosed as a carrier, not a patient. It was first reported in Korea. The patient had: two likely pathogenic heterozygous mutations on exon #4: c.752C>T (p.Ser251Leu), c.761C>T (p.Ser254Leu), and a heterozygous mutation on exon #12: c.1726G>A (p.Gly576Ser), also with a heterozygous mutation on exon #15: c.2065G>A (p.Glu689Lys). By presenting this case we emphasize the possibility of cis formation of genes which may cause pseudodeficiency, and potential LOPD carrier form. Hereby we suggest that thorough evaluation of GAA gene is essential among whom initially diagnosed as PD.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.24 no.1
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• pp.26-36
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• 2024

Lesch-Nyhan syndrome (LNS) is an Clinical symptoms can range from mild to severe depending on residual enzyme activity and genetic mutations. In Korea, 27 cases of LNS have been reported. We report the results of an 11-year comparative follow-up of two cases of children who visited because of pink diapers, one who died from LNS with no residual enzymes and one case with partial residual enzymes. Case 1: During follow-up, seizures, developmental delay, and regression were observed. The boy experienced insomnia and severe constipation. He exhibited self-mutilating behavior, a grand mal seizure, scoliosis with severe spasticity, truncal hypotonia, choreoathetoid movement, and ataxia. After prolonged emaciation, staghorn calculi, and recurrent pneumonia, the patient died suddenly at the age of 11 years. Genetic testing revealed a hemizygous HPRT1 variant (c.151C>T (p.Arg51Ter)). Uric acid level was 10.5 mg/dL (normal range: ~3.5-7.9) and HPRT activity 0.02 nmol/hr/spot (10-23.8 nmol/hr/spot). Case 2: During follow-up, the patient remained underweight. He has normal intelligence attending primary school. Self-mutilation symptoms were not observed. Regular renal ultrasonography did not reveal urolithiasis. The patient had a hemizygous HPRT1 variant (c.35A>C (p.Asp12Ala)). Uric acid level and HPRT activity were 11 mg/dL and 0.56 nmol/hr/spot. Pink diapers after the neonatal period and severe protein aversion, neurological problems, and kidney stones, differentiation for LNS is necessary. When suspected, serum uric acid levels, HPRT enzyme activity, and molecular biological tests may be helpful in predicting the prognosis of LNS.

• 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.

• Journal of Nutrition and Health
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• v.41 no.1
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• pp.65-76
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• 2008

This study was performed to identify association between nutritional status and health risks of the elderly. This was a cross-sectional study involving low income elderly women in Gwangju, Korea (${\geq}$65y, n = 92). Socio-demographics, life style characteristics, health conditions, dietary intakes based on 24h-recall method, anthropometric measures, and clinical biochemistry parameters were examined. Anthropometric and clinical parameters included wt, ht, waist, hip, body protein, body fat, abdominal fat, total cholesterol, HDL-cholesterol, triglyceride, total protein, albumin, hemoglobin, hematocrit, fasting blood glucose, ferritin, IL-2, IL-6, TNF-${\alpha}$, CRP, TAS, TBARS, systolic blood pressure, and diastolic blood pressure. The subjects were divided into three groups based on age (65-74y, 75-84y, 85y${\leq}$) and were divided into two groups according to the sum of the Nutrition Screening Initiative (NSI) checklist score (adequate nutritional status, NSI score ${\leq}$3; at risk of malnutrition, NSI score >3). Mean and frequency of variables were estimated. Analysis of Variance, Tukey test, Chi-square test, and Multiple linear regression analyses were performed. Mean BMI and body fat were 25.1 $kg/m^2$ and 40.0%, respectively. However, for over 80% of subjects, the intakes of energy, fiber, thiamin, riboflavin, niacin, folate, Ca, K, and Zn were less than the Korean DRI (EAR or AI). The subjects who had lower NSI score tended to have better health status, eat meals frequently, have less depression, and exercise regularly. The subjects who had higher NSI score tended to have tooth problems, to eat alone most of time, and to be physically unable to cook or feed. Serum IL-6 and TNF-${\alpha}$ were significantly related with nutritional status which suggested higher tendency of inflammatory response. Serum IL-2, TAS, and glucose were significantly correlated with body fat (%) or abdominal fat (%). These results suggest that improving the nutritional status, increasing regular exercise, maintaining normal weight are beneficial to health care of low income elderly women.

• Tuberculosis and Respiratory Diseases
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• v.44 no.6
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• pp.1343-1352
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• 1997

Background : Heat-treated cells are known to be protected from lysis by TNF, which is considered to play a central role in the pathogenesis of sepsis-induced acute lung injury. The objective of the study was to investigate the effect of heat shock response by heat-pretreatment on the acute lung injury of the rats induced by intratracheally administered TNF-$\alpha$, Methods : We intratracheally instilled either saline or TNF (R&D, 500ng) with and without heat pretreatment in Sprague-Dawley rats weighing 250~350 g. The heated rats were raised their rectal temperature to $41^{\circ}C$ and was maintained thereafter for 13 minutes at 18 h before intratracheal administration of saline or TNF. After 5 h of intratracheal treatment, lung leak, lung myeloperoxidase activity (MPO) and heat shock proteins were measured in rats. Lung leak index was defined as counts per minute of $I^{25}$ in the right lung divided by counts per minutes of $I^{25}$ in 1.0 ml of blood. All data are expressed as means ${\pm}$SE. Results : There is no difference in acute lung leak index ($0.099{\pm}0.024$ vs $0.123{\pm}0.005$) among the rats given saline intratracheally with and without heat pretreatment, but MPO activity showed a decreased tendency in heat-pretreated rats ($4.58{\pm}0.79\;U/g$) compared with heat-unpretreated rats ($7.32{\pm}0.97\;U/g$) (P=0.064). Rats administered TNF intratracheally with heat-pretreatment had decreased lung leak index ($0.137{\pm}0.012$) and lung MPO activity ($5.51{\pm}1.04\;U/g$) compared with those of heat-unpretreated and TNF-administered rats ($0.186{\pm}0.016$, $14.34{\pm}1.22\;U/g$) (P<0.05 in each). There were no significant difference of lung leak index and MPO activity between TNF-treated rats with heat-pretreatment and saline-treated rats with and without heat-pretreatment. Conclusion : The heat shock response attenuated neutrophil recruitment and acute lung leak induced by intratracheal instillation of TNF-in rats.