• Biotechnology and Bioprocess Engineering:BBE
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• v.7 no.5
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• pp.237-251
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• 2002

The recent progress on metabolic systems engineering was reviewed based on our recent research results in terms of (1) metabolic signal flow diagram approach, (2) metabolic flux analysis (MFA) in particular with intracellular isotopomer distribution using NMR and/or GC-MS, (3) synthesis and optimization of metabolic flux distribution (MFD), (4) modification of MFD by gene manipulation and by controlling culture environment, (5) metabolic control analysis (MCA), (6) design of metabolic regulation structure, and (7) identification of unknown pathways with isotope tracing by NMR. The main characteristics of metabolic engineering is to treat metabolism as a network or entirety instead of individual reactions. The applications were made for poly-3-hydroxybutyrate (PHB) production using Ralstonia eutropha and recombinant Escherichia coli, lactate production by recombinant Saccharomyces cerevisiae, pyruvate production by vitamin auxotrophic yeast Toluropsis glabrata, lysine production using Corynebacterium glutamicum, and energetic analysis of photosynthesic microorganisms such as Cyanobateria. The characteristics of each approach were reviewed with their applications. The approach based on isotope labeling experiments gives reliable and quantitative results for metabolic flux analysis. It should be recognized that the next stage should be toward the investigation of metabolic flux analysis with gene and protein expressions to uncover the metabolic regulation in relation to genetic modification and/ or the change in the culture condition.

• BMB Reports
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• v.52 no.1
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• pp.5-12
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• 2019

Organismal aging is accompanied by a host of progressive metabolic alterations and an accumulation of senescent cells, along with functional decline and the appearance of multiple diseases. This implies that the metabolic features of cell senescence may contribute to the organism's metabolic changes and be closely linked to age-associated diseases, especially metabolic syndromes. However, there is no clear understanding of senescent metabolic characteristics. Here, we review key metabolic features and regulators of cellular senescence, focusing on mitochondrial dysfunction and anabolic deregulation, and their link to other senescence phenotypes and aging. We further discuss the mechanistic involvement of the metabolic regulators mTOR, AMPK, and GSK3, proposing them as key metabolic switches for modulating senescence.

• Clinical and Experimental Pediatrics
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• v.51 no.12
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• pp.1290-1294
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• 2008

Seizures are a frequent symptom in metabolic disorders, although metabolic disorders are rarely found to be the cause of epilepsy. A precise diagnosis might not only influence treatment, but it might also call for counseling of the family, even if there are no direct therapeutic consequences. We review the main characteristics of epilepsy in metabolic disorders with regard to energy metabolism, toxic effects, neurotransmitters, and vitamins.

• The Journal of Korean Society for School & Community Health Education
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• v.16 no.2
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• pp.17-31
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• 2015

Objectives: This study aimed to analyze the relationship between metabolic syndrome and cataract in a middle-aged men over the age of 40's by using the data from the 5th Korean National Health and Nutrition Examination Survey(2010~2012). Methods: The subjects for the analysis were comprised of 5,808 patients who were at least 40 years or more with metabolic syndrome and cataract. The comparison between the metabolic syndrome and ophthalmic diseases according to the health demographic characteristics, health behavior and health conditions was analyzed with ${\chi}^2-test$. The complex samples logistic regression analysis was used to calculate the odds ratio of cataract according to the health demographic characteristics, health behavior and health conditions and the combination of the comprising factors of the metabolic syndrome. Results: After controlling the confounding factors, the odds ratio of cataract in the metabolic syndrome group was OR 1.30, which was significantly high (p<0.01). Furthermore, the odds ratio of cataract was significantly high in the case of males, as the age increased, educational level decreased, with exercising habits of less than 3 days a week, sun exposure of at least 5 hours, with hypertension and diabetes (p<0.01). The results of analyzing the odds ratio for the occurrence of cataract with the components of metabolic syndrome were OR 1.36 (concurrent high fasting blood glucose, high triglycerides, low HDL cholesterol), OR 1.63 (concurrent high fasting blood glucose, high triglycerides, high waist circumference) and OR 1.33 (high triglycerides, low HDL cholesterol, high waist circumference), which showed significantly high odds ratio for cataract occurrence (p<0.05). Conclusions: We come to know that the relation between metabolic syndrome and components are associate with cataract. Therefore the thorough management of metabolic syndrome and components is needed to prevent cataract.

• Korean Journal of Biological Psychiatry
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• v.15 no.3
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• pp.186-193
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• 2008

Objectives : The authors tried to investigate the characteristics of Heart Rate Variability(HRV) among the subjects with metabolic syndrome for testing the possibility of clinical application of HRV in evaluating metabolic syndrome. Methods : We examined the difference of the means of HRV between 122 subjects of metabolic syndrome and 1057 healthy controls using T-test, and the change of means in HRV according to increasing risk factors of metabolic syndrome. Using multiple regression analysis, we examined the association of HRV with the risk factors of metabolic syndrome. Results : The HRV indices including SDNN(Standard Deviation of all normal NN intervals), RMSSD(the square Root of the Mean Squared Differences of successive all normal NN interval), TP(Total Power), LF(Low Freuency), and HF(High Frequency) were significantly lower in the metabolic syndrome group than in the normal control group, and the means of all HRV indices except LF/HF were decreased consistently according to increasing risk factors of metabolic syndrome. The fasting glucose, triglyceride, and waist circumference were dependent factors that contributed significantly to the change of HRV. Conclusion : These findings suggest metabolic syndrome adversely affects cardiac autonomic system and HRV could be a useful method for evaluating metabolic syndrome.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.823-829
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• 2005

We have newly constructed an in silico model of fermentative metabolism for Lactococcus lactis in order to analyze the characteristics of metabolite flux for dynamic network. A rigorous mathematical model for metabolic flux has been developed and simulation researches have been performed by using GEPASI program. In this simulation task, we were able to predict the whole flux distribution trend for lactate metabolism and analyze the flux ratio on the pyruvate branch point by using metabolic flux analysis(MFA). And we have studied flux control coefficients of key reaction steps in the model by using metabolic control analysis(MCA). The role of pyruvate branch seems to be essential for the secretion of lactate and other organic byproducts. Then we have made an effort to elucidate its metabolic regulation characteristics and key reaction steps, and find an optimal condition for the production of lactate.

• Korean Journal of Agricultural Science
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• v.45 no.1
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• pp.50-57
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• 2018

Meat comes from the skeletal muscles of farm animals, such as pigs, chickens, and cows. Skeletal muscles are composed of many muscle fibers. Muscle fibers are categorized into three types, fiber type I, IIA, and IIB, based on their contractile speed and metabolic properties. Different muscle fiber types have different biochemical, physiological, and biophysical characteristics. Especially, the characteristics of muscle fiber type I and IIB are opposite to each other. Muscle fiber type I has a relatively strong oxidative metabolic trait and a higher content of lipids. In contrast to fiber type I, muscle fiber type IIB has a strong glycolytic metabolic trait and a relatively lower content of lipids and a higher content of glycogen. Muscle fiber type IIA has intermediate properties between fiber type I and IIB. Thus, muscles with different fiber type compositions exhibit different ante- and post-mortem muscle characteristics. In particular, the different metabolic traits of muscles due to the different compositions of the fiber types strongly affect the biochemical and physiological processes during the conversion of muscle to meat and subsequently influence the quality of the meat. Therefore, understating muscle metabolism and muscle fiber characteristics is very important when discussing the traits of meat quality. This review is an overview on basic muscle metabolism, muscle fiber characteristics, and their influence on meat quality and finally provides a comprehensive understanding about the fundamental traits of muscles and meat quality.

• Communications for Statistical Applications and Methods
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• v.30 no.1
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• pp.21-35
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• 2023

Metabolic syndrome is a serious disease that can eventually lead to various complications, such as stroke and cardiovascular disease. In this study, we aimed to identify the risk factors related to metabolic syndrome for its prevention and recognition and propose a nomogram that visualizes and predicts the probability of the incidence of metabolic syndrome. We conducted an analysis using data from the Korea National Health and Nutrition Survey (KNHANES VII) and identified 10 risk factors affecting metabolic syndrome by using the Rao-Scott chi-squared test, considering the characteristics of the complex sample. A naïve Bayesian classifier was used to build a nomogram for metabolic syndrome. We then predicted the incidence of metabolic syndrome using the nomogram. Finally, we verified the nomogram using a receiver operating characteristic curve and a calibration plot.

• Journal of dental hygiene science
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• v.20 no.3
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• pp.178-186
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• 2020

Background: The purpose of this study was to evaluate the association between metabolic syndrome and periodontal disease in Korean adults. Methods: This study analyzed the screening data of 12,686 adults aged ≥19 years, including demographic characteristics, lifestyle habits, and each component of metabolic syndrome, obtained from the sixth Korea National Health and Nutrition Examination Survey database. Periodontal health status was measured by the community periodontal index. Subjects with three or more risk factors were considered as having metabolic syndrome. The Rao-Scott chi-square test was performed to assess the relationships between demographic characteristics, lifestyle habits, components of metabolic syndrome, and periodontal disease. Logistic regression analysis was performed based on the complex sample to evaluate the relationship between metabolic syndrome and periodontal disease. Results: The prevalence of periodontal disease was higher among the subjects with advancing age, lower average household income and education level, those working in production, residents of eup-myeon areas, in past or current smoker, those with excessive alcohol consumption habit in a week, and reduced brushing frequency and the use of oral care products (p<0.001). Each component of metabolic syndrome was associated with higher prevalence of periodontitis in the subjects with abnormal than in those with normal levels (p<0.001). The prevalence of periodontal disease in subjects with metabolic syndrome was approximately 1.443 times higher than that in normal subjects (odds ratio,1.443; p<0.001). Conclusion: This study confirmed the association between metabolic syndrome and periodontitis in Korean adults, and further studies will be needed to determine the causal relationship between the two conditions.

• Korean Journal of Health Education and Promotion
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• v.26 no.1
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• pp.81-92
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• 2009

Objectives: This study set out to investigate the relationship among the factors of metabolic syndrome diagnosis criteria, their risk factors including general characteristics, and the distribution of the diagnosis criteria and risk among the adult residents of a rural community. Methods: Among 1,968 residents, those who had three or more of the risk factors of metabolic syndrome, which include blood pressure, blood glucose, triglyceride, abdominal obesity, and HDL-C, were categorized as the metabolic syndrome group. And their correlations were analyzed. Results: As for the risk ratio with five factors of the metabolic syndrome diagnosis criteria, it was high according to age and smoking. In addition, the results show that body fat percentage, hs-CRP, insulin, BMI, PP2, total cholesterol, and W/Ht also had much impact on increasing the risk ratio of the metabolic syndrome diagnosis criteria. It turned out that metabolic syndrome was affected by the body mass index(BMI), insulin, waist to height ratio(W/Ht), and hs-CRP. It was 2.51 times crude odds ratio that BMI over the 25kg/m2 in the ratio of the fact of metabolic syndrome and adjusted for sex odds ratio 2.50times and W/Ht was 3.31times, adjusted for sex odds ratio 3.25 times. Conclusion: BMI, W/Ht and smoking of the general characteristics seem to have close relationships with high correlations between the metabolic syndrome diagnosis criteria and the risk factors. Thus there is an urgent need to evaluate them and take interventions and monitoring measures for the clustering of risk factors.