Endocrinology and Metabolism

Jae-Hyoung Cho (Cho JH)

6 Articles

Diabetes, Obesity and Metabolism Big Data Articles (National Health Insurance Service Database)
Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea: Seung-Hwan Lee, Jin Yu, Kyungdo Han, Seung Woo Lee, Sang Youn You, Hun-Sung Kim, Jae-Hyoung Cho, Kun-Ho Yoon, Mee Kyoung Kim; Endocrinol Metab. 2023;38(1):129-138. Published online January 27, 2023; DOI: https://doi.org/10.3803/EnM.2022.1609

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Background
The severity of gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes. We aimed to generate a risk model for predicting insulin-requiring GDM before pregnancy in Korean women.
Methods
A total of 417,210 women who received a health examination within 52 weeks before pregnancy and delivered between 2011 and 2015 were recruited from the Korean National Health Insurance database. The risk prediction model was created using a sample of 70% of the participants, while the remaining 30% were used for internal validation. Risk scores were assigned based on the hazard ratios for each risk factor in the multivariable Cox proportional hazards regression model. Six risk variables were selected, and a risk nomogram was created to estimate the risk of insulin-requiring GDM.
Results
A total of 2,891 (0.69%) women developed insulin-requiring GDM. Age, body mass index (BMI), current smoking, fasting blood glucose (FBG), total cholesterol, and γ-glutamyl transferase were significant risk factors for insulin-requiring GDM and were incorporated into the risk model. Among the variables, old age, high BMI, and high FBG level were the main contributors to an increased risk of insulin-requiring GDM. The concordance index of the risk model for predicting insulin-requiring GDM was 0.783 (95% confidence interval, 0.766 to 0.799). The validation cohort’s incidence rates for insulin-requiring GDM were consistent with the risk model’s predictions.
Conclusion
A novel risk engine was generated to predict insulin-requiring GDM among Korean women. This model may provide helpful information for identifying high-risk women and enhancing prepregnancy care.

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Establishment and validation of a nomogram to predict the neck contracture after skin grafting in burn patients: A multicentre cohort study
Rui Li, Yangyang Zheng, Xijuan Fan, Zilong Cao, Qiang Yue, Jincai Fan, Cheng Gan, Hu Jiao, Liqiang Liu
International Wound Journal.2023; 20(9): 3648. CrossRef
Predicting the Need for Insulin Treatment: A Risk-Based Approach to the Management of Women with Gestational Diabetes Mellitus
Anna S. Koefoed, H. David McIntyre, Kristen S. Gibbons, Charlotte W. Poulsen, Jens Fuglsang, Per G. Ovesen
Reproductive Medicine.2023; 4(3): 133. CrossRef
Prepregnancy Glucose Levels Within Normal Range and Its Impact on Obstetric Complications in Subsequent Pregnancy: A Population Cohort Study
Ho Yeon Kim, Ki Hoon Ahn, Geum Joon Cho, Soon-Cheol Hong, Min-Jeong Oh, Hai-Joong Kim
Journal of Korean Medical Science.2023;[Epub] CrossRef
Risk of Cause-Specific Mortality across Glucose Spectrum in Elderly People: A Nationwide Population-Based Cohort Study
Joonyub Lee, Hun-Sung Kim, Kee-Ho Song, Soon Jib Yoo, Kyungdo Han, Seung-Hwan Lee
Endocrinology and Metabolism.2023; 38(5): 525. CrossRef
The CHANGED Score—A New Tool for the Prediction of Insulin Dependency in Gestational Diabetes
Paul Rostin, Selina Balke, Dorota Sroka, Laura Fangmann, Petra Weid, Wolfgang Henrich, Josefine Theresia Königbauer
Journal of Clinical Medicine.2023; 12(22): 7169. CrossRef

Diabetes, Obesity and Metabolism Big Data Articles (National Health Insurance Service Database)
Cumulative Exposure to High γ-Glutamyl Transferase Level and Risk of Diabetes: A Nationwide Population-Based Study: Ji-Yeon Park, Kyungdo Han, Hun-Sung Kim, Jae-Hyoung Cho, Kun-Ho Yoon, Mee Kyoung Kim, Seung-Hwan Lee; Endocrinol Metab. 2022;37(2):272-280. Published online April 13, 2022; DOI: https://doi.org/10.3803/EnM.2022.1416

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Background
Elevated γ-glutamyl transferase (γ-GTP) level is associated with metabolic syndrome, impaired glucose tolerance, and insulin resistance, which are risk factors for type 2 diabetes. We aimed to investigate the association of cumulative exposure to high γ-GTP level with risk of diabetes.
Methods
Using nationally representative data from the Korean National Health Insurance system, 346,206 people who were free of diabetes and who underwent 5 consecutive health examinations from 2005 to 2009 were followed to the end of 2018. High γ-GTP level was defined as those in the highest quartile, and the number of exposures to high γ-GTP level ranged from 0 to 5. Hazard ratio (HR) and 95% confidence interval (CI) for diabetes were analyzed using the multivariable Cox proportional-hazards model.
Results
The mean follow-up duration was 9.2±1.0 years, during which 15,183 (4.4%) patients developed diabetes. There was a linear increase in the incidence rate and the risk of diabetes with cumulative exposure to high γ-GTP level. After adjusting for possible confounders, the HR of diabetes in subjects with five consecutive high γ-GTP levels were 2.60 (95% CI, 2.47 to 2.73) in men and 3.05 (95% CI, 2.73 to 3.41) in women compared with those who never had a high γ-GTP level. Similar results were observed in various subgroup and sensitivity analyses.
Conclusion
There was a linear relationship between cumulative exposure to high γ-GTP level and risk of diabetes. Monitoring and lowering γ-GTP level should be considered for prevention of diabetes in the general population.

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Validation of Estimated Small Dense Low-Density Lipoprotein Cholesterol Concentration in a Japanese General Population
Keisuke Endo, Ryo Kobayashi, Makito Tanaka, Marenao Tanaka, Yukinori Akiyama, Tatsuya Sato, Itaru Hosaka, Kei Nakata, Masayuki Koyama, Hirofumi Ohnishi, Satoshi Takahashi, Masato Furuhashi
Journal of Atherosclerosis and Thrombosis.2024; 31(6): 931. CrossRef
Long-Term Cumulative Exposure to High γ-Glutamyl Transferase Levels and the Risk of Cardiovascular Disease: A Nationwide Population-Based Cohort Study
Han-Sang Baek, Bongseong Kim, Seung-Hwan Lee, Dong-Jun Lim, Hyuk-Sang Kwon, Sang-Ah Chang, Kyungdo Han, Jae-Seung Yun
Endocrinology and Metabolism.2023; 38(6): 770. CrossRef
Elevated gamma‐glutamyl transferase to high‐density lipoprotein cholesterol ratio has a non‐linear association with incident diabetes mellitus: A second analysis of a cohort study
Haofei Hu, Yong Han, Mijie Guan, Ling Wei, Qijun Wan, Yanhua Hu
Journal of Diabetes Investigation.2022; 13(12): 2027. CrossRef
Gamma-glutamyl transferase to high-density lipoprotein cholesterol ratio: A valuable predictor of type 2 diabetes mellitus incidence
Wangcheng Xie, Bin Liu, Yansong Tang, Tingsong Yang, Zhenshun Song
Frontiers in Endocrinology.2022;[Epub] CrossRef

Diabetes, Obesity and Metabolism
Association between Lung Function and New-Onset Diabetes Mellitus in Healthy Individuals after a 6-Year Follow-up: Hwa Young Lee, Juyoung Shin, Hyunah Kim, Seung-Hwan Lee, Jae-Hyoung Cho, Sook Young Lee, Hun-Sung Kim; Endocrinol Metab. 2021;36(6):1254-1267. Published online December 13, 2021; DOI: https://doi.org/10.3803/EnM.2021.1249

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Background
We analyzed hemoglobin A1c (HbA1c) levels and various lung function test results in healthy individuals after a 6-year follow-up period to explore the influence of lung function changes on glycemic control.
Methods
Subjects whose HbA1c levels did not qualify as diabetes mellitus (DM) and who had at least two consecutive lung function tests were selected among the people who visited a health promotion center. Lung function parameters, including forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FEV/FVC ratio, and forced expiratory flow 25% to 75% (FEF_25%−75%), were divided into four groups based on their baseline quantiles. To evaluate future DM onset risk in relation to lung function changes, the correlation between baseline HbA1c levels and changes in lung function parameters after a 6-year follow-up period was analyzed.
Results
Overall, 17,568 individuals were included; 0.9% of the subjects were diagnosed with DM. The individuals included in the quartile with FEV1/FVC ratio values of 78% to 82% had lower risk of DM than those in the quartile with FEV1/FVC ratio values of ≥86% after adjusting for age, sex, and body mass index (P=0.04). Baseline percent predicted FEV1, FVC, FEV1/FVC ratio, and FEF_25%−75%, and differences in the FEV1/FVC ratio or FEF_25%−75%, showed negative linear correlations with baseline HbA1c levels.
Conclusion
Healthy subjects with FEV1/FVC ratio values between 78% and 82% had 40% lower risk for future DM. Smaller differences and lower baseline FEV1/FVC ratio or FEF_25%−75% values were associated with higher baseline HbA1c levels. These findings suggest that airflow limitation affects systemic glucose control and that the FEV1/FVC ratio could be one of the factors predicting future DM risk in healthy individuals.

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Glycemic Control and Lung Function in Younger and Older Patients with Diabetes
Nitita Piya-amornphan, Taweephol Sanpakdee, Chadaporn Permphet, António Raposo
Advances in Public Health.2024;[Epub] CrossRef
Effect modification of glycemic control on association of lung function with all-cause and cardiovascular mortality in persons with type 2 diabetes – A retrospective cohort study
Cheng-Chieh Lin, Chia-Ing Li, Chuan-Wei Yang, Chiu-Shong Liu, Chih-Hsueh Lin, Shing-Yu Yang, Tsai-Chung Li
Respiratory Medicine.2024; 234: 107804. CrossRef
The association of spirometric small airways obstruction with respiratory symptoms, cardiometabolic diseases, and quality of life: results from the Burden of Obstructive Lung Disease (BOLD) study
Ben Knox-Brown, Jaymini Patel, James Potts, Rana Ahmed, Althea Aquart-Stewart, Cristina Barbara, A. Sonia Buist, Hamid Hacene Cherkaski, Meriam Denguezli, Mohammed Elbiaze, Gregory E. Erhabor, Frits M. E. Franssen, Mohammed Al Ghobain, Thorarinn Gislason,
Respiratory Research.2023;[Epub] CrossRef
Diabetes-related perturbations in the integrity of physiologic barriers
Arshag D. Mooradian
Journal of Diabetes and its Complications.2023; 37(8): 108552. CrossRef
Association of MMP7 T > C Gene Variant (rs10502001) and Expression in Chronic Obstructive Pulmonary Disease
Saurabh Kumar, Suchit Swaroop, Akancha Sahu, Surya Kant, Monisha Banerjee
DNA and Cell Biology.2023; 42(9): 548. CrossRef
Association between glycated haemoglobin and the risk of chronic obstructive pulmonary disease: A prospective cohort study in UK biobank
Mengyao Li, Yanan Wan, Zheng Zhu, Pengfei Luo, Hao Yu, Jian Su, Dong Hang, Yan Lu, Ran Tao, Ming Wu, Jinyi Zhou, Xikang Fan
Diabetes, Obesity and Metabolism.2023; 25(12): 3599. CrossRef
Combined multi-omics analysis reveals oil mist particulate matter-induced lung injury in rats: Pathological damage, proteomics, metabolic disturbances, and lung dysbiosis
Huipeng Nie, Huanliang Liu, Yue Shi, Wenqing Lai, Xuan Liu, Zhuge Xi, Bencheng Lin
Ecotoxicology and Environmental Safety.2022; 241: 113759. CrossRef
Retrospective cohort analysis comparing changes in blood glucose level and body composition according to changes in thyroid‐stimulating hormone level
Hyunah Kim, Da Young Jung, Seung‐Hwan Lee, Jae‐Hyoung Cho, Hyeon Woo Yim, Hun‐Sung Kim
Journal of Diabetes.2022; 14(9): 620. CrossRef
Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness
Juyoung Shin, Joonyub Lee, Taehoon Ko, Kanghyuck Lee, Yera Choi, Hun-Sung Kim
Journal of Personalized Medicine.2022; 12(11): 1899. CrossRef

Diabetes, Obesity and Metabolism
Higher Weight Variability Could Bring You a Fatty Liver: Yeoree Yang, Jae-Hyoung Cho; Endocrinol Metab. 2021;36(4):766-768. Published online August 27, 2021; DOI: https://doi.org/10.3803/EnM.2021.403

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Clinical Study
Predicting the Development of Myocardial Infarction in Middle-Aged Adults with Type 2 Diabetes: A Risk Model Generated from a Nationwide Population-Based Cohort Study in Korea: Seung-Hwan Lee, Kyungdo Han, Hun-Sung Kim, Jae-Hyoung Cho, Kun-Ho Yoon, Mee Kyoung Kim; Endocrinol Metab. 2020;35(3):636-646. Published online September 22, 2020; DOI: https://doi.org/10.3803/EnM.2020.704

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Background
Most of the widely used prediction models for cardiovascular disease are known to overestimate the risk of this disease in Asians. We aimed to generate a risk model for predicting myocardial infarction (MI) in middle-aged Korean subjects with type 2 diabetes.
Methods
A total of 1,272,992 subjects with type 2 diabetes aged 40 to 64 who received health examinations from 2009 to 2012 were recruited from the Korean National Health Insurance database. Seventy percent of the subjects (n=891,095) were sampled to develop the risk prediction model, and the remaining 30% (n=381,897) were used for internal validation. A Cox proportional hazards regression model and Cox coefficients were used to derive a risk scoring system. Twelve risk variables were selected, and a risk nomogram was created to estimate the 5-year risk of MI.
Results
During 7.1 years of follow-up, 24,809 cases of MI (1.9%) were observed. Age, sex, smoking status, regular exercise, body mass index, chronic kidney disease, duration of diabetes, number of anti-diabetic medications, fasting blood glucose, systolic blood pressure, total cholesterol, and atrial fibrillation were significant risk factors for the development of MI and were incorporated into the risk model. The concordance index for MI prediction was 0.682 (95% confidence interval [CI], 0.678 to 0.686) in the development cohort and 0.669 (95% CI, 0.663 to 0.675) in the validation cohort.
Conclusion
A novel risk engine was generated for predicting the development of MI among middle-aged Korean adults with type 2 diabetes. This model may provide useful information for identifying high-risk patients and improving quality of care.

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A literature review of quality assessment and applicability to HTA of risk prediction models of coronary heart disease in patients with diabetes
Li Jiu, Junfeng Wang, Francisco Javier Somolinos-Simón, Jose Tapia-Galisteo, Gema García-Sáez, Mariaelena Hernando, Xinyu Li, Rick A. Vreman, Aukje K. Mantel-Teeuwisse, Wim G. Goettsch
Diabetes Research and Clinical Practice.2024; 209: 111574. CrossRef
Investigating the impact of extreme weather events and related indicators on cardiometabolic multimorbidity
Di Wu, Yu Shi, ChenChen Wang, Cheng Li, Yaoqin Lu, Chunfang Wang, Weidong Zhu, Tingting Sun, Junjie Han, Yanling Zheng, Liping Zhang
Archives of Public Health.2024;[Epub] CrossRef
Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea
Seung-Hwan Lee, Jin Yu, Kyungdo Han, Seung Woo Lee, Sang Youn You, Hun-Sung Kim, Jae-Hyoung Cho, Kun-Ho Yoon, Mee Kyoung Kim
Endocrinology and Metabolism.2023; 38(1): 129. CrossRef
Factors Affecting High Body Weight Variability
Kyungdo Han, Mee Kyoung Kim
Journal of Obesity & Metabolic Syndrome.2023; 32(2): 163. CrossRef
Coronary Artery Calcium Score as a Sensitive Indicator of Cardiovascular Disease in Patients with Type 2 Diabetes Mellitus: A Long-Term Cohort Study
Dae-Jeong Koo, Mi Yeon Lee, Sun Joon Moon, Hyemi Kwon, Sang Min Lee, Se Eun Park, Cheol-Young Park, Won-Young Lee, Ki Won Oh, Sung Rae Cho, Young-Hoon Jeong, Eun-Jung Rhee
Endocrinology and Metabolism.2023; 38(5): 568. CrossRef
Serum/plasma biomarkers and the progression of cardiometabolic multimorbidity: a systematic review and meta-analysis
Yichen Jin, Ziyuan Xu, Yuting Zhang, Yue Zhang, Danyang Wang, Yangyang Cheng, Yaguan Zhou, Muhammad Fawad, Xiaolin Xu
Frontiers in Public Health.2023;[Epub] CrossRef
Assessing the Validity of the Criteria for the Extreme Risk Category of Atherosclerotic Cardiovascular Disease: A Nationwide Population-Based Study
Kyung-Soo Kim, Sangmo Hong, Kyungdo Han, Cheol-Young Park
Journal of Lipid and Atherosclerosis.2022; 11(1): 73. CrossRef
Evaluating Triglyceride and Glucose Index as a Simple and Easy-to-Calculate Marker for All-Cause and Cardiovascular Mortality
Kyung-Soo Kim, Sangmo Hong, You-Cheol Hwang, Hong-Yup Ahn, Cheol-Young Park
Journal of General Internal Medicine.2022; 37(16): 4153. CrossRef
Effects of exercise initiation and smoking cessation after new-onset type 2 diabetes mellitus on risk of mortality and cardiovascular outcomes
Mee Kyoung Kim, Kyungdo Han, Bongsung Kim, Jinyoung Kim, Hyuk-Sang Kwon
Scientific Reports.2022;[Epub] CrossRef
Current Trends of Big Data Research Using the Korean National Health Information Database
Mee Kyoung Kim, Kyungdo Han, Seung-Hwan Lee
Diabetes & Metabolism Journal.2022; 46(4): 552. CrossRef
Lipid cutoffs for increased cardiovascular disease risk in non-diabetic young people
Mee Kyoung Kim, Kyungdo Han, Hun-Sung Kim, Kun-Ho Yoon, Seung-Hwan Lee
European Journal of Preventive Cardiology.2022; 29(14): 1866. CrossRef
Low-Density Lipoprotein Cholesterol Level, Statin Use and Myocardial Infarction Risk in Young Adults
Heekyoung Jeong, Kyungdo Han, Soon Jib Yoo, Mee Kyoung Kim
Journal of Lipid and Atherosclerosis.2022; 11(3): 288. CrossRef
Nonalcoholic fatty liver disease and the risk of insulin-requiring gestational diabetes
Sang Youn You, Kyungdo Han, Seung-Hawn Lee, Mee Kyoung Kim
Diabetology & Metabolic Syndrome.2021;[Epub] CrossRef

Obesity and Metabolism
New Directions in Chronic Disease Management: Hun-Sung Kim, Jae-Hyoung Cho, Kun-Ho Yoon; Endocrinol Metab. 2015;30(2):159-166. Published online June 30, 2015; DOI: https://doi.org/10.3803/EnM.2015.30.2.159

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A worldwide epidemic of chronic disease, and complications thereof, is underway, with no sign of abatement. Healthcare costs have increased tremendously, principally because of the need to treat chronic complications of non-communicable diseases including cardiovascular disease, blindness, end-stage renal disease, and amputation of extremities. Current healthcare systems fail to provide an appropriate quality of care to prevent the development of chronic complications without additional healthcare costs. A new paradigm for prevention and treatment of chronic disease and the complications thereof is urgently required. Several clinical studies have clearly shown that frequent communication between physicians and patients, based on electronic data transmission from medical devices, greatly assists in the management of chronic disease. However, for various reasons, these advantages have not translated effectively into real clinical practice. In the present review, we describe current relevant studies, and trends in the use of information technology for chronic disease management. We also discuss limitations and future directions.

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Lack of Acceptance of Digital Healthcare in the Medical Market: Addressing Old Problems Raised by Various Clinical Professionals and Developing Possible Solutions
Jong Il Park, Hwa Young Lee, Hyunah Kim, Jisan Lee, Jiwon Shinn, Hun-Sung Kim
Journal of Korean Medical Science.2021;[Epub] CrossRef
Lessons from Temporary Telemedicine Initiated owing to Outbreak of COVID-19
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Healthcare Informatics Research.2020; 26(2): 159. CrossRef
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Physical Activity for Prevention and Management of Sleep Disturbances
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Lessons from Use of Continuous Glucose Monitoring Systems in Digital Healthcare
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Recent Technology-Driven Advancements in Cardiovascular Disease Prevention in Korea
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