Endocrinology and Metabolism

Original Articles

Diabetes, obesity and metabolism
Effects of an Electronic Medical Records-Linked Diabetes Self-Management System on Treatment Targets in Real Clinical Practice: Retrospective, Observational Cohort Study: So Jung Yang, Sun-Young Lim, Yoon Hee Choi, Jin Hee Lee, Kun-Ho Yoon; Endocrinol Metab. 2024;39(2):364-374. Published online March 21, 2024; DOI: https://doi.org/10.3803/EnM.2023.1878

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Abstract PDF Supplementary Material PubReader ePub: Background
This study evaluated the effects of a mobile diabetes management program called “iCareD” (College of Medicine, The Catholic University of Korea) which was integrated into the hospital’s electronic medical records system to minimize the workload of the healthcare team in the real clinical practice setting.
Methods
In this retrospective observational study, we recruited 308 patients. We categorized these patients based on their compliance regarding their use of the iCareD program at home; compliance was determined through self-monitored blood glucose inputs and message subscription rates. We analyzed changes in the ABC (hemoglobin A1c, blood pressure, and low-density lipoprotein cholesterol) levels from the baseline to 12 months thereafter, based on the patients’ iCareD usage patterns.
Results
The patients comprised 92 (30%) non-users, 170 (55%) poor-compliance users, and 46 (15%) good-compliance users; the ABC target achievement rate showed prominent changes in good-compliance groups from baseline to 12 months (10.9% vs. 23.9%, P<0.05), whereas no significant changes were observed for poor-compliance users and non-users (13.5% vs. 18.8%, P=0.106; 20.7% vs. 14.1%, P=0.201; respectively).
Conclusion
Implementing the iCareD can improve the ABC levels of patients with diabetes with minimal efforts of the healthcare team in real clinical settings. However, the improvement of patients’ compliance concerning the use of the system without the vigorous intervention of the healthcare team needs to be solved in the future.

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.

Citations

Citations to this article as recorded by

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.

Citations

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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.2023;[Epub] 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

Clinical Study Big Data Articles (National Health Insurance Service Database)
Cumulative Exposure to Metabolic Syndrome Components and the Risk of Dementia: A Nationwide Population-Based Study: Yunjung Cho, Kyungdo Han, Da Hye Kim, Yong-Moon Park, Kun-Ho Yoon, Mee Kyoung Kim, Seung-Hwan Lee; Endocrinol Metab. 2021;36(2):424-435. Published online April 14, 2021; DOI: https://doi.org/10.3803/EnM.2020.935

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Background
Metabolic disturbances are modifiable risk factors for dementia. Because the status of metabolic syndrome (MetS) and its components changes over time, we aimed to investigate the association of the cumulative exposure to MetS and its components with the risk of dementia.
Methods
Adults (n=1,492,776; ≥45-years-old) who received health examinations for 4 consecutive years were identified from a nationwide population-based cohort in Korea. Two exposure-weighted scores were calculated: cumulative number of MetS diagnoses (MetS exposure score, range of 0 to 4) and the composite of its five components (MetS component exposure score, range of 0 to 20). Hazard ratio (HR) and 95% confidence interval (CI) values for dementia were analyzed using the multivariable Cox proportional-hazards model.
Results
Overall, 47.1% of subjects were diagnosed with MetS at least once, and 11.5% had persistent MetS. During the mean 5.2 years of follow-up, there were 7,341 cases (0.5%) of incident dementia. There was a stepwise increase in the risk of all-cause dementia, Alzheimer’s disease, and vascular dementia with increasing MetS exposure score and MetS component exposure score (each P for trend <0.0001). The HR of all-cause dementia was 2.62 (95% CI, 1.87 to 3.68) in subjects with a MetS component exposure score of 20 compared with those with a score of 0. People fulfilling only one MetS component out of 20 already had an approximately 40% increased risk of all-cause dementia and Alzheimer’s disease.
Conclusion
More cumulative exposure to metabolic disturbances was associated with a higher risk of dementia. Of note, even minimal exposure to MetS components had a significant effect on the risk of dementia.

Citations

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Association between metabolic syndrome and risk of incident dementia in UK Biobank
Danial Qureshi, Jennifer Collister, Naomi E. Allen, Elżbieta Kuźma, Thomas Littlejohns
Alzheimer's & Dementia.2024; 20(1): 447. CrossRef
Cumulative exposure to metabolic syndrome affects the risk of psoriasis differently according to age group: a nationwide cohort study in South Korea
Se Young Jung, Kyungdo Han, Jin Hyung Jung, Hyunsun Park, Dong Wook Shin
British Journal of Dermatology.2024; 190(3): 447. CrossRef
Electroacupuncture stimulation improves cognitive ability and regulates metabolic disorders in Alzheimer’s disease model mice: new insights from brown adipose tissue thermogenesis
Ting Li, Junjian Tian, Meng Wu, Yuanshuo Tian, Zhigang Li
Frontiers in Endocrinology.2024;[Epub] CrossRef
Investigating the nexus of metabolic syndrome, serum uric acid, and dementia risk: a prospective cohort study
Tara SR Chen, Ning-Ning Mi, Hubert Yuenhei Lao, Chen-Yu Wang, Wai Leung Ambrose Lo, Yu-Rong Mao, Yan Tang, Zhong Pei, Jin-Qiu Yuan, Dong-Feng Huang
BMC Medicine.2024;[Epub] CrossRef
Clustering of Cardiometabolic Risk Factors and Dementia Incidence in Older Adults: A Cross-Country Comparison in England, the United States, and China
Panagiota Kontari, Chris Fife-Schaw, Kimberley Smith, Lewis A Lipsitz
The Journals of Gerontology: Series A.2023; 78(6): 1035. 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
Metabolic syndrome and the risk of postoperative delirium and postoperative cognitive dysfunction: a multi-centre cohort study
Insa Feinkohl, Jürgen Janke, Arjen J.C. Slooter, Georg Winterer, Claudia Spies, Tobias Pischon
British Journal of Anaesthesia.2023; 131(2): 338. CrossRef
Is metabolic-healthy obesity associated with risk of dementia? An age-stratified analysis of the Whitehall II cohort study
Marcos D. Machado-Fragua, Séverine Sabia, Aurore Fayosse, Céline Ben Hassen, Frank van der Heide, Mika Kivimaki, Archana Singh-Manoux
BMC Medicine.2023;[Epub] CrossRef
Cumulative effect of impaired fasting glucose on the risk of dementia in middle-aged and elderly people: a nationwide cohort study
Jin Yu, Kyu-Na Lee, Hun-Sung Kim, Kyungdo Han, Seung-Hwan Lee
Scientific Reports.2023;[Epub] CrossRef
Early metabolic impairment as a contributor to neurodegenerative disease: Mechanisms and potential pharmacological intervention
Walaa Fakih, Ralph Zeitoun, Ibrahim AlZaim, Ali H. Eid, Firas Kobeissy, Khaled S. Abd‐Elrahman, Ahmed F. El‐Yazbi
Obesity.2022; 30(5): 982. 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
Association of Metabolic Syndrome With Incident Dementia: Role of Number and Age at Measurement of Components in a 28-Year Follow-up of the Whitehall II Cohort Study
Marcos D. Machado-Fragua, Aurore Fayosse, Manasa Shanta Yerramalla, Thomas T. van Sloten, Adam G. Tabak, Mika Kivimaki, Séverine Sabia, Archana Singh-Manoux
Diabetes Care.2022; 45(9): 2127. CrossRef
Risk of Neurodegenerative Diseases in Patients With Acromegaly
Sangmo Hong, Kyungdo Han, Kyung-Soo Kim, Cheol-Young Park
Neurology.2022;[Epub] CrossRef

Endocrine Research
Suppression of Fibrotic Reactions of Chitosan-Alginate Microcapsules Containing Porcine Islets by Dexamethasone Surface Coating: Min Jung Kim, Heon-Seok Park, Ji-Won Kim, Eun-Young Lee, Marie Rhee, Young-Hye You, Gilson Khang, Chung-Gyu Park, Kun-Ho Yoon; Endocrinol Metab. 2021;36(1):146-156. Published online February 24, 2021; DOI: https://doi.org/10.3803/EnM.2021.879

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Background
The microencapsulation is an ideal solution to overcome immune rejection without immunosuppressive treatment. Poor biocompatibility and small molecular antigens secreted from encapsulated islets induce fibrosis infiltration. Therefore, the aims of this study were to improve the biocompatibility of microcapsules by dexamethasone coating and to verify its effect after xenogeneic transplantation in a streptozotocin-induced diabetes mice.
Methods
Dexamethasone 21-phosphate (Dexa) was dissolved in 1% chitosan and was cross-linked with the alginate microcapsule surface. Insulin secretion and viability assays were performed 14 days after microencapsulation. Dexa-containing chitosan-coated alginate (Dexa-chitosan) or alginate microencapsulated porcine islets were transplanted into diabetic mice. The fibrosis infiltration score was calculated from the harvested microcapsules. The harvested microcapsules were stained with trichrome and for insulin and macrophages.
Results
No significant differences in glucose-stimulated insulin secretion and islet viability were noted among naked, alginate, and Dexa-chitosan microencapsulated islets. After transplantation of microencapsulated porcine islets, nonfasting blood glucose were normalized in both the Dexa-chitosan and alginate groups until 231 days. The average glucose after transplantation were lower in the Dexa-chitosan group than the alginate group. Pericapsular fibrosis and inflammatory cell infiltration of microcapsules were significantly reduced in Dexa-chitosan compared with alginate microcapsules. Dithizone and insulin were positive in Dexa-chitosan capsules. Although fibrosis and macrophage infiltration was noted on the surface, some alginate microcapsules were stained with insulin.
Conclusion
Dexa coating on microcapsules significantly suppressed the fibrotic reaction on the capsule surface after transplantation of xenogenic islets containing microcapsules without any harmful effects on the function and survival of the islets.

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Engineering superstable islets-laden chitosan microgels with carboxymethyl cellulose coating for long-term blood glucose regulation in vivo
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Carbohydrate Polymers.2024; 323: 121425. CrossRef
Investigation of encapsulation of pancreatic beta cells and curcumin within alginate microcapsules
Zahra Hosseinzadeh, Iran Alemzadeh, Manouchehr Vossoughi
The Canadian Journal of Chemical Engineering.2024; 102(2): 561. CrossRef
Advancements in innate immune regulation strategies in islet transplantation
Kehang Duan, Jiao Liu, Jian Zhang, Tongjia Chu, Huan Liu, Fengxiang Lou, Ziyu Liu, Bing Gao, Shixiong Wei, Feng Wei
Frontiers in Immunology.2024;[Epub] CrossRef
A Case for Material Stiffness as a Design Parameter in Encapsulated Islet Transplantation
Courtney D. Johnson, Helim Aranda-Espinoza, John P. Fisher
Tissue Engineering Part B: Reviews.2023; 29(4): 334. CrossRef
Improved membrane stability of alginate-chitosan microcapsules by crosslinking with tannic acid
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Advances in alginate encapsulation of pancreatic islets for immunoprotection in type 1 diabetes
Dinesh Chaudhary, Tiep Tien Nguyen, Simmyung Yook, Jee-Heon Jeong
Journal of Pharmaceutical Investigation.2023; 53(5): 601. CrossRef
Emerging strategies for beta cell transplantation to treat diabetes
Jesus Paez-Mayorga, Izeia Lukin, Dwaine Emerich, Paul de Vos, Gorka Orive, Alessandro Grattoni
Trends in Pharmacological Sciences.2022; 43(3): 221. CrossRef
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Wenyan Li, Xuejiao Lei, Hua Feng, Bingyun Li, Jiming Kong, Malcolm Xing
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Journal of Diabetes Investigation.2022; 13(11): 1798. CrossRef
Modern pancreatic islet encapsulation technologies for the treatment of type 1 diabetes
P. S. Ermakova, E. I. Cherkasova, N. A. Lenshina, A. N. Konev, M. A. Batenkin, S. A. Chesnokov, D. M. Kuchin, E. V. Zagainova, V. E. Zagainov, A. V. Kashina
Russian Journal of Transplantology and Artificial Organs.2021; 23(4): 95. CrossRef

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
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
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Diabetology & Metabolic Syndrome.2021;[Epub] CrossRef

Review Articles

Diabetes
Lessons from Use of Continuous Glucose Monitoring Systems in Digital Healthcare: Hun-Sung Kim, Kun-Ho Yoon; Endocrinol Metab. 2020;35(3):541-548. Published online September 22, 2020; DOI: https://doi.org/10.3803/EnM.2020.675

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We live in a digital world where a variety of wearable medical devices are available. These technologies enable us to measure our health in our daily lives. It is increasingly possible to manage our own health directly through data gathered from these wearable devices. Likewise, healthcare professionals have also been able to indirectly monitor patients’ health. Healthcare professionals have accepted that digital technologies will play an increasingly important role in healthcare. Wearable technologies allow better collection of personal medical data, which healthcare professionals can use to improve the quality of healthcare provided to the public. The use of continuous glucose monitoring systems (CGMS) is the most representative and desirable case in the adoption of digital technology in healthcare. Using the case of CGMS and examining its use from the perspective of healthcare professionals, this paper discusses the necessary adjustments required in clinical practices. There is a need for various stakeholders, such as medical staff, patients, industry partners, and policy-makers, to utilize and harness the potential of digital technology.

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Current status of remote collaborative care for hypertension in medically underserved areas
Seo Yeon Baik, Kyoung Min Kim, Hakyoung Park, Jiwon Shinn, Hun-Sung Kim
Cardiovascular Prevention and Pharmacotherapy.2024; 6(1): 33. CrossRef
Using medical big data for clinical research and legal considerations for the protection of personal information: the double-edged sword
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Cardiovascular Prevention and Pharmacotherapy.2024; 6(1): 8. CrossRef
Diverse perspectives on remote collaborative care for chronic disease management
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Patent analysis of digital sensors for continuous glucose monitoring
Olena Litvinova, Magdalena Eitenberger, Aylin Bilir, Andy Wai Kan Yeung, Emil D. Parvanov, ArunSundar MohanaSundaram, Jarosław Olav Horbańczuk, Atanas G. Atanasov, Harald Willschke
Frontiers in Public Health.2023;[Epub] CrossRef
Personalized Nutrition 2020: Proceedings from the American Nutrition Association’s 61st Annual Summit
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Towards Telemedicine Adoption in Korea: 10 Practical Recommendations for Physicians
Hun-Sung Kim
Journal of Korean Medical Science.2021;[Epub] CrossRef
Perceptron: Basic Principles of Deep Neural Networks
Eung-Hee Kim, Hun-Sung Kim
Cardiovascular Prevention and Pharmacotherapy.2021; 3(3): 64. CrossRef
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
Digital health and diabetes: experience from India
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Therapeutic Advances in Endocrinology and Metabolism.2021; 12: 204201882110546. CrossRef

Miscellaneous
Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration: Hun-Sung Kim, Dai-Jin Kim, Kun-Ho Yoon; Endocrinol Metab. 2019;34(4):349-354. Published online December 23, 2019; DOI: https://doi.org/10.3803/EnM.2019.34.4.349

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Most people are now familiar with the concepts of big data, deep learning, machine learning, and artificial intelligence (AI) and have a vague expectation that AI using medical big data can be used to improve the quality of medical care. However, the expectation that big data could change the field of medicine is inconsistent with the current reality. The clinical meaningfulness of the results of research using medical big data needs to be examined. Medical staff needs to be clear about the purpose of AI that utilizes medical big data and to focus on the quality of this data, rather than the quantity. Further, medical professionals should understand the necessary precautions for using medical big data, as well as its advantages. No doubt that someday, medical big data will play an essential role in healthcare; however, at present, it seems too early to actively use it in clinical practice. The field continues to work toward developing medical big data and making it appropriate for healthcare. Researchers should continue to engage in empirical research to ensure that appropriate processes are in place to empirically evaluate the results of its use in healthcare.

Citations

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Original Articles

Association between Body Weight Changes and Menstrual Irregularity: The Korea National Health and Nutrition Examination Survey 2010 to 2012: Kyung Min Ko, Kyungdo Han, Youn Jee Chung, Kun-Ho Yoon, Yong Gyu Park, Seung-Hwan Lee; Endocrinol Metab. 2017;32(2):248-256. Published online June 23, 2017; DOI: https://doi.org/10.3803/EnM.2017.32.2.248

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Background

Menstrual irregularity is an indicator of endocrine disorders and reproductive health status. It is associated with various diseases and medical conditions, including obesity and underweight. We aimed to assess the association between body weight changes and menstrual irregularity in Korean women.

Methods

A total of 4,621 women 19 to 54 years of age who participated in the 2010 to 2012 Korea National Health and Nutrition Examination Survey were included in this study. Self-reported questionnaires were used to collect medical information assessing menstrual health status and body weight changes. Odds ratios (ORs) and 95% confidence interval (CI) were calculated to evaluate the association between body weight changes and menstrual irregularity.

Results

Significantly higher ORs (95% CI) were observed in the association between menstrual irregularity and both weight loss (OR, 1.74; 95% CI, 1.22 to 2.48) and weight gain (OR, 1.45; 95% CI, 1.13 to 1.86) after adjusting for age, body mass index, current smoking, heavy alcohol drinking, regular exercise, calorie intake, education, income, metabolic syndrome, age of menarche, parity, and stress perception. Of note, significant associations were only observed in subjects with obesity and abdominal obesity, but not in non-obese or non-abdominally obese subjects. U-shaped patterns were demonstrated in both obese and abdominally obese subjects, indicating that greater changes in body weight are associated with higher odds of menstrual irregularity.

Conclusion

We found a U-shaped pattern of association between body weight changes and menstrual irregularity among obese women in the general Korean population. This result indicates that not only proper weight management but also changes in body weight may influence the regulation of the menstrual cycle.

Citations

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Development of Clinical Data Mart of HMG-CoA Reductase Inhibitor for Varied Clinical Research: Hun-Sung Kim, Hyunah Kim, Yoo Jin Jeong, Tong Min Kim, So Jung Yang, Sun Jung Baik, Seung-Hwan Lee, Jae Hyoung Cho, In Young Choi, Kun-Ho Yoon; Endocrinol Metab. 2017;32(1):90-98. Published online February 28, 2017; DOI: https://doi.org/10.3803/EnM.2017.32.1.90

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Background

The increasing use of electronic medical record (EMR) systems for documenting clinical medical data has led to EMR data being increasingly accessed for clinical trials. In this study, a database of patients who were prescribed statins for the first time was developed using EMR data. A clinical data mart (CDM) was developed for cohort study researchers.

Methods

Seoul St. Mary's Hospital implemented a clinical data warehouse (CDW) of data for ~2.8 million patients, 47 million prescription events, and laboratory results for 150 million cases. We developed a research database from a subset of the data on the basis of a study protocol. Data for patients who were prescribed a statin for the first time (between the period from January 1, 2009 to December 31, 2015), including personal data, laboratory data, diagnoses, and medications, were extracted.

Results

We extracted initial clinical data of statin from a CDW that was established to support clinical studies; the data was refined through a data quality management process. Data for 21,368 patients who were prescribed statins for the first time were extracted. We extracted data every 3 months for a period of 1 year. A total of 17 different statins were extracted. It was found that statins were first prescribed by the endocrinology department in most cases (69%, 14,865/21,368).

Conclusion

Study researchers can use our CDM for statins. Our EMR data for statins is useful for investigating the effectiveness of treatments and exploring new information on statins. Using EMR is advantageous for compiling an adequate study cohort in a short period.

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Review Article

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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