Endocrinology and Metabolism

Original Articles

Miscellaneous
Prediction of Cardiovascular Complication in Patients with Newly Diagnosed Type 2 Diabetes Using an XGBoost/GRU-ODE-Bayes-Based Machine-Learning Algorithm: Joonyub Lee, Yera Choi, Taehoon Ko, Kanghyuck Lee, Juyoung Shin, Hun-Sung Kim; Endocrinol Metab. 2024;39(1):176-185. Published online November 21, 2023; DOI: https://doi.org/10.3803/EnM.2023.1739

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Abstract PDF Supplementary Material PubReader ePub: Background
Cardiovascular disease is life-threatening yet preventable for patients with type 2 diabetes mellitus (T2DM). Because each patient with T2DM has a different risk of developing cardiovascular complications, the accurate stratification of cardiovascular risk is critical. In this study, we proposed cardiovascular risk engines based on machine-learning algorithms for newly diagnosed T2DM patients in Korea.
Methods
To develop the machine-learning-based cardiovascular disease engines, we retrospectively analyzed 26,166 newly diagnosed T2DM patients who visited Seoul St. Mary’s Hospital between July 2009 and April 2019. To accurately measure diabetes-related cardiovascular events, we designed a buffer (1 year), an observation (1 year), and an outcome period (5 years). The entire dataset was split into training and testing sets in an 8:2 ratio, and this procedure was repeated 100 times. The area under the receiver operating characteristic curve (AUROC) was calculated by 10-fold cross-validation on the training dataset.
Results
The machine-learning-based risk engines (AUROC XGBoost=0.781±0.014 and AUROC gated recurrent unit [GRU]-ordinary differential equation [ODE]-Bayes=0.812±0.016) outperformed the conventional regression-based model (AUROC=0.723± 0.036).
Conclusion
GRU-ODE-Bayes-based cardiovascular risk engine is highly accurate, easily applicable, and can provide valuable information for the individualized treatment of Korean patients with newly diagnosed T2DM.

Diabetes, obesity and metabolism Big Data Articles (National Health Insurance Service Database)
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, Committee of Big Data, Korean Endocrine Society; Endocrinol Metab. 2023;38(5):525-537. Published online September 7, 2023; DOI: https://doi.org/10.3803/EnM.2023.1765

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Background
This study investigated the risk of cause-specific mortality according to glucose tolerance status in elderly South Koreans.
Methods
A total of 1,292,264 individuals aged ≥65 years who received health examinations in 2009 were identified from the National Health Information Database. Participants were classified as normal glucose tolerance, impaired fasting glucose, newly-diagnosed diabetes, early diabetes (oral hypoglycemic agents ≤2), or advanced diabetes (oral hypoglycemic agents ≥3 or insulin). The risk of system-specific and disease-specific deaths was estimated using multivariate Cox proportional hazards analysis.
Results
During a median follow-up of 8.41 years, 257,356 deaths were recorded. Diabetes was associated with significantly higher risk of all-cause mortality (hazard ratio [HR], 1.58; 95% confidence interval [CI], 1.57 to 1.60); death due to circulatory (HR, 1.49; 95% CI, 1.46 to 1.52), respiratory (HR, 1.51; 95% CI, 1.47 to 1.55), and genitourinary systems (HR, 2.22; 95% CI, 2.10 to 2.35); and neoplasms (HR, 1.30; 95% CI, 1.28 to 1.32). Diabetes was also associated with a significantly higher risk of death due to ischemic heart disease (HR, 1.70; 95% CI, 1.63 to 1.76), cerebrovascular disease (HR, 1.46; 95% CI, 1.41 to 1.50), pneumonia (HR, 1.69; 95% CI, 1.63 to 1.76), and acute or chronic kidney disease (HR, 2.23; 95% CI, 2.09 to 2.38). There was a stepwise increase in the risk of death across the glucose spectrum (P for trend <0.0001). Stroke, heart failure, or chronic kidney disease increased the risk of all-cause mortality at every stage of glucose intolerance.
Conclusion
A dose-dependent association between the risk of mortality from various causes and severity of glucose tolerance was noted in the elderly population.

Citations

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The Characteristics and Risk of Mortality in the Elderly Korean Population
Sunghwan Suh
Endocrinology and Metabolism.2023; 38(5): 522. CrossRef

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

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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

Response

Diabetes, Obesity and Metabolism
Characteristics of Glycemic Control and Long-Term Complications in Patients with Young-Onset Type 2 Diabetes (Endocrinol Metab 2022;37:641-51, Han-sang Baek et al.): Han-sang Baek, Ji-Yeon Park, Jin Yu, Joonyub Lee, Yeoree Yang, Jeonghoon Ha, Seung Hwan Lee, Jae Hyoung Cho, Dong-Jun Lim, Hun-Sung Kim; Endocrinol Metab. 2022;37(6):945-946. Published online December 2, 2022; DOI: https://doi.org/10.3803/EnM.2022.602; [Original]

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Original Articles

Diabetes, Obesity and Metabolism
Characteristics of Glycemic Control and Long-Term Complications in Patients with Young-Onset Type 2 Diabetes: Han-sang Baek, Ji-Yeon Park, Jin Yu, Joonyub Lee, Yeoree Yang, Jeonghoon Ha, Seung Hwan Lee, Jae Hyoung Cho, Dong-Jun Lim, Hun-Sung Kim; Endocrinol Metab. 2022;37(4):641-651. Published online August 29, 2022; DOI: https://doi.org/10.3803/EnM.2022.1501

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Background
The prevalence of young-onset diabetes (YOD) has been increasing worldwide. As the incidence of YOD increases, it is necessary to determine the characteristics of YOD and the factors that influence its development and associated complications.
Methods
In this retrospective study, we recruited patients who were diagnosed with type 2 diabetes mellitus between June 2001 and December 2021 at a tertiary hospital. The study population was categorized according to age: YOD (age <40 years), middle-age-onset diabetes (MOD, 40≤ age <65 years), and late-onset diabetes (LOD, age ≥65 years). We examined trends in glycemic control by analyzing fasting glucose levels during the first year in each age group. A Cox proportional-hazards model was used to determine the relative risk of developing complications according to glycemic control trends.
Results
The fasting glucose level at the time of diagnosis was highest in the YOD group (YOD 149±65 mg/dL; MOD 143±54 mg/dL; and LOD 140±55 mg/dL; p=0.009). In the YOD group, glucose levels decreased at 3 months, but increased by 12 months. YOD patients and those with poor glycemic control in the first year were at a higher risk of developing complications, whereas the risk in patients with LOD was not statistically significant.
Conclusion
YOD patients had higher glucose levels at diagnosis, and their glycemic control was poorly maintained. As poor glycemic control can influence the development of complications, especially in young patients, intensive treatment is necessary for patients with YOD.

Citations

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Increased risk of incident mental disorders in adults with new-onset type 1 diabetes diagnosed after the age of 19: A nationwide cohort study
Seohyun Kim, Gyuri Kim, So Hyun Cho, Rosa Oh, Ji Yoon Kim, You-Bin Lee, Sang-Man Jin, Kyu Yeon Hur, Jae Hyeon Kim
Diabetes & Metabolism.2024; 50(1): 101505. CrossRef
Association between age at diagnosis of type 2 diabetes and cardiovascular morbidity and mortality risks: A nationwide population-based study
Da Hea Seo, Mina Kim, Young Ju Suh, Yongin Cho, Seong Hee Ahn, Seongbin Hong, So Hun Kim
Diabetes Research and Clinical Practice.2024; 208: 111098. CrossRef
Impact of diabetes distress on glycemic control and diabetic complications in type 2 diabetes mellitus
Hye-Sun Park, Yongin Cho, Da Hea Seo, Seong Hee Ahn, Seongbin Hong, Young Ju Suh, Suk Chon, Jeong-Taek Woo, Sei Hyun Baik, Kwan Woo Lee, So Hun Kim
Scientific Reports.2024;[Epub] CrossRef
Early onset type 2 diabetes mellitus: an update
Myrsini Strati, Melpomeni Moustaki, Theodora Psaltopoulou, Andromachi Vryonidou, Stavroula A. Paschou
Endocrine.2024;[Epub] CrossRef
Complications and Treatment of Early-Onset Type 2 Diabetes
Fahimeh Soheilipour, Naghmeh Abbasi Kasbi, Mahshid Imankhan, Delaram Eskandari
International Journal of Endocrinology and Metabolism.2023;[Epub] CrossRef
Characteristics of Glycemic Control and Long-Term Complications in Patients with Young-Onset Type 2 Diabetes (Endocrinol Metab 2022;37:641-51, Han-sang Baek et al.)
Han-sang Baek, Ji-Yeon Park, Jin Yu, Joonyub Lee, Yeoree Yang, Jeonghoon Ha, Seung Hwan Lee, Jae Hyoung Cho, Dong-Jun Lim, Hun-Sung Kim
Endocrinology and Metabolism.2022; 37(6): 945. CrossRef
ISPAD Clinical Practice Consensus Guidelines 2022: Management of the child, adolescent, and young adult with diabetes in limited resource settings
Anju Virmani, Stuart J. Brink, Angela Middlehurst, Fauzia Mohsin, Franco Giraudo, Archana Sarda, Sana Ajmal, Julia E. von Oettingen, Kuben Pillay, Supawadee Likitmaskul, Luis Eduardo Calliari, Maria E. Craig
Pediatric Diabetes.2022; 23(8): 1529. CrossRef
Characteristics of Glycemic Control and Long-Term Complications in Patients with Young-Onset Type 2 Diabetes (Endocrinol Metab 2022;37:641-51, Han-sang Baek et al.)
May Thu Hla Aye, Sajid Adhi Raja, Vui Heng Chong
Endocrinology and Metabolism.2022; 37(6): 943. 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.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

Review Article

Diabetes, Obesity and Metabolism
A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus: Suehyun Lee, Seongwoo Jeon, Hun-Sung Kim; Endocrinol Metab. 2022;37(2):195-207. Published online April 13, 2022; DOI: https://doi.org/10.3803/EnM.2022.1404

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Drug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in the future. Reports on drug repositioning, retrieved from PubMed from January 2011 to December 2020, were classified based on an analysis of the methodology and reviewed by experts. Among various drug repositioning methods, the network-based approach was the most common (38.0%, 186/490 cases), followed by machine learning/deep learningbased (34.3%, 168/490 cases), text mining-based (7.1%, 35/490 cases), semantic-based (5.3%, 26/490 cases), and others (15.3%, 75/490 cases). Although drug repositioning offers several advantages, its implementation is curtailed by the need for prior, conclusive clinical proof. This approach requires the construction of various databases, and a deep understanding of the process underlying repositioning is quintessential. An in-depth understanding of drug repositioning could reduce the time, cost, and risks inherent to early drug development, providing reliable scientific evidence. Furthermore, regarding patient safety, drug repurposing might allow the discovery of new relationships between drugs and diseases.

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The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use
Ji-Won Chun, Hun-Sung Kim
Journal of Korean Medical Science.2023;[Epub] CrossRef

Editorial

Diabetes, Obesity and Metabolism
Drug Repositioning: Exploring New Indications for Existing Drug-Disease Relationships: Hun-Sung Kim; Endocrinol Metab. 2022;37(1):62-64. Published online February 28, 2022; DOI: https://doi.org/10.3803/EnM.2022.1403

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Drug Repositioning Using Computer-aided Drug Design (CADD)
Sona Rawat, Kanmani Subramaniam, Selva Kumar Subramanian, Saravanan Subbarayan, Subramanian Dhanabalan, Sashik Kumar Madurai Chidambaram, Balasubramaniam Stalin, Arpita Roy, Nagaraj Nagaprasad, Mahalingam Aruna, Jule Leta Tesfaye, Bayissa Badassa, Ramaswa
Current Pharmaceutical Biotechnology.2024; 25(3): 301. CrossRef
Magic bullets, magic shields, and antimicrobials in between
Praveen Prathapan
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Original Articles

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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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

Clinical Study
Association of Hyperparathyroidism and Papillary Thyroid Cancer: A Multicenter Retrospective Study: Chaiho Jeong, Hye In Kwon, Hansang Baek, Hun-Sung Kim, Dong-Jun Lim, Ki-Hyun Baek, Jeonghoon Ha, Moo Il Kang; Endocrinol Metab. 2020;35(4):925-932. Published online December 10, 2020; DOI: https://doi.org/10.3803/EnM.2020.725

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Background
Concomitant papillary thyroid cancer (PTC) and hyperparathyroidism (HPT) have been reported in several studies. Our study aimed to investigate the incidence of concomitant PTC in HPT patients upon preoperative diagnosis and present a clinical opinion on detecting thyroid malignancy in case of parathyroidectomy.
Methods
Patients who underwent parathyroidectomy between January 2009 and December 2019 in two medical centers were included. Of the 279 participants 154 were diagnosed as primary hyperparathyroidism (pHPT) and 125 as secondary hyperparathyroidism (sHPT). The incidence of concomitant PTC and its clinical characteristics were compared with 98 patients who underwent thyroidectomy and were diagnosed with classical PTC during the same period.
Results
Concurrent PTC was detected in 14 patients (9.1%) with pHPT and in nine patients (7.2%) with sHPT. Ten (71.4%) and seven (77.8%) PTCs were microcarcinomas in the pHPT and sHPT cases respectively. In the pHPT patients, vitamin D was lower in the pHPT+PTC group (13.0±3.7 ng/mL) than in the pHPT-only group (18.5±10.4 ng/mL; P=0.01). Vitamin D levels were also lower in the sHPT+PTC group (12.3±5.6 ng/mL) than in the sHPT-only group (18.0±10.2 ng/mL; P=0.12). In the concomitant PTC group, lymph node ratio was higher than in the classical PTC group (P=0.00).
Conclusion
A high prevalence of concomitant PTC was seen in patients with pHPT and sHPT. Those concomitant PTCs were mostly microcarcinomas and had more aggressive features, suggesting that efforts should be made to detect concomitant malignancies in the preoperative parathyroidectomy evaluation.

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The unexpected effect of parathyroid adenoma on inflammation
Ahmet Tarik Harmantepe, Belma Kocer, Zulfu Bayhan, Emre Gonullu, Ugur Can Dulger
Updates in Surgery.2024; 76(2): 589. CrossRef
Evaluation of Nodular Goiter and Papillary Thyroid Cancer Coincidence in Patients with Primary Hyperparathyroidism
Mustafa ÇALIŞKAN, Hasret CENGİZ, Taner DEMİRCİ
Düzce Tıp Fakültesi Dergisi.2023; 25(2): 200. CrossRef
Papillary thyroid carcinoma coexisting with benign thyroid and parathyroid pathology: clinical and pathomorphological features
A. Dinets, M. Gorobeiko, V. Hoperia, A. Lovin, S. Tarasenko
INTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine).2023; 19(4): 274. CrossRef
The Nexus of Hyperparathyroidism and Thyroid Carcinoma: Insights into Pathogenesis and Diagnostic Challenges—A Narrative Review
Gregorio Scerrino, Nunzia Cinzia Paladino, Giuseppina Orlando, Giuseppe Salamone, Pierina Richiusa, Stefano Radellini, Giuseppina Melfa, Giuseppa Graceffa
Journal of Clinical Medicine.2023; 13(1): 147. CrossRef
Is preoperative parathyroid localization necessary for tertiary hyperparathyroidism?
Rongzhi Wang, Peter Abraham, Brenessa Lindeman, Herbert Chen, Jessica Fazendin
The American Journal of Surgery.2022; 224(3): 918. CrossRef
Papillary thyroid carcinoma prevalence and its predictors in patients with primary hyperparathyroidism
Elif Tutku DURMUŞ, Ayşegül ATMACA, Mehmet KEFELİ, Ramis ÇOLAK, Buğra DURMUŞ, Cafer POLAT
Journal of Health Sciences and Medicine.2022; 5(5): 1499. CrossRef
Association of Hyperparathyroidism and Papillary Thyroid Cancer: A Multicenter Retrospective Study (Endocrinol Metab 2020;35:925-32, Chaiho Jeong et al.)
Chaiho Jeong, Jeonghoon Ha, Moo Il Kang
Endocrinology and Metabolism.2021; 36(1): 205. CrossRef
Association of Hyperparathyroidism and Papillary Thyroid Cancer: A Multicenter Retrospective Study (Endocrinol Metab 2020;35:925-32, Chaiho Jeong et al.)
Burcu Candemir, Coşkun Meriç
Endocrinology and Metabolism.2021; 36(1): 203. 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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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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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.

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Original Article

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.

Citations

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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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