Endocrinology and Metabolism

Original Articles

Diabetes, obesity and metabolism
Prevalence of Mortality and Vascular Complications in Older Patients with Diabetes in Korea: Kwang Joon Kim, Jeongmin Lee, Yang Sun Park, Yong-ho Lee, Kyeong Hye Park, Hee-Won Jung, Chang Oh Kim, Man Young Park, Hun-Sung Kim, Bong-Soo Cha; Endocrinol Metab. 2025;40(3):448-458. Published online February 18, 2025; DOI: https://doi.org/10.3803/EnM.2024.2173

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Background
This study investigated the prevalence of diabetes mellitus (DM) and impaired fasting glucose, as well as their management and comorbidities among older Korean adults.
Methods
Data from 269,447 individuals aged 65 years and older from the Korean National Health Insurance Service between 2000 and 2019 were analyzed to evaluate trends in DM prevalence, healthcare utilization, mortality, and complications.
Results
Among 269,447 individuals, 18.6% (n=50,159/269,447) were diagnosed with DM and 27.0% (n=72,670/269,447) had impaired fasting glucose. The DM group had the highest body mass index, waist circumference, and prevalence of current smokers (P<0.001) but not the highest hypertension prevalence. From 2010 to 2019, the prevalence of DM and impaired fasting glucose increased from 15.5% to 21.9% and from 26.0% to 30.6%, respectively. Cancer-related mortality in DM was 1.15 times higher than in those with normal glucose tolerance (P<0.001), and cardiovascular disease-related mortality was 1.32 times higher (P<0.001); all mortalities were higher in female participants. Myocardial infarction (hazard ratio [HR], 1.34; P<0.001), stroke (HR, 1.24; P<0.001), and heart failure (HR, 1.13; P<0.001) were significantly higher in those with DM.
Conclusion
This is the first study to investigate the prevalence of DM and related complications in older individuals based on longterm representative data in Korea. These results highlight the necessity for targeted interventions to enhance management and outcomes in this population.

Citations

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Efficacy and safety of switching to ezetimibe 10 mg/rosuvastatin 2.5 mg in Korean patients with type 2 diabetes mellitus and dyslipidaemia: A multicentre, prospective study (EROICA study)
Sangmo Hong, Won J. Kim, Sungrae Kim, Jung H. Park, Eun S. Kang, Min K. Moon, Jae T. Kim, Ji‐Oh Mok, Ki Y. Lee, Cheol‐Young Park, Chang B. Lee
Diabetes, Obesity and Metabolism.2026; 28(2): 906. CrossRef

Miscellaneous
Protective Effect of Delta-Like 1 Homolog Against Muscular Atrophy in a Mouse Model: Ji Young Lee, Minyoung Lee, Dong-Hee Lee, Yong-ho Lee, Byung-Wan Lee, Eun Seok Kang, Bong-Soo Cha; Endocrinol Metab. 2022;37(4):684-697. Published online August 29, 2022; DOI: https://doi.org/10.3803/EnM.2022.1446

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Background
Muscle atrophy is caused by an imbalance between muscle growth and wasting. Delta-like 1 homolog (DLK1), a protein that modulates adipogenesis and muscle development, is a crucial regulator of myogenic programming. Thus, we investigated the effect of exogenous DLK1 on muscular atrophy.
Methods
We used muscular atrophy mouse model induced by dexamethasone (Dex). The mice were randomly divided into three groups: (1) control group, (2) Dex-induced muscle atrophy group, and (3) Dex-induced muscle atrophy group treated with DLK1. The effects of DLK1 were also investigated in an in vitro model using C2C12 myotubes.
Results
Dex-induced muscular atrophy in mice was associated with increased expression of muscle atrophy markers and decreased expression of muscle differentiation markers, while DLK1 treatment attenuated these degenerative changes together with reduced expression of the muscle growth inhibitor, myostatin. In addition, electron microscopy revealed that DLK1 treatment improved mitochondrial dynamics in the Dex-induced atrophy model. In the in vitro model of muscle atrophy, normalized expression of muscle differentiation markers by DLK1 treatment was mitigated by myostatin knockdown, implying that DLK1 attenuates muscle atrophy through the myostatin pathway.
Conclusion
DLK1 treatment inhibited muscular atrophy by suppressing myostatin-driven signaling and improving mitochondrial biogenesis. Thus, DLK1 might be a promising candidate to treat sarcopenia, characterized by muscle atrophy and degeneration.

Citations

Citations to this article as recorded by

Advancements in the study of DLK1 in the pathogenesis of diabetes
Min Li, Yanqiu Peng, Yuke Shi, Yunfei Liu, Jian Zhang
Life Sciences.2025; 369: 123535. CrossRef
Molecular mechanism of Activin receptor inhibition by DLK1
Daniel Antfolk, Qianqian Ming, Anna Manturova, Erich J. Goebel, Thomas B. Thompson, Vincent C. Luca
Nature Communications.2025;[Epub] CrossRef

Review Article

Diabetes, Obesity and Metabolism
State-of-the-Art Overview of the Pharmacological Treatment of Non-Alcoholic Steatohepatitis: Yongin Cho, Yong-ho Lee; Endocrinol Metab. 2022;37(1):38-52. Published online February 28, 2022; DOI: https://doi.org/10.3803/EnM.2022.102

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Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide, and non-alcoholic steatohepatitis (NASH), a subtype of NAFLD, can progress to cirrhosis, hepatocellular carcinoma, and death. Nevertheless, the current treatment for NAFLD/NASH is limited to lifestyle modifications, and no drugs are currently officially approved as treatments for NASH. Many global pharmaceutical companies are pursuing the development of medications for the treatment of NASH, and results from phase 2 and 3 clinical trials have been published in recent years. Here, we review data from these recent clinical trials and reports on the efficacy of newly developed antidiabetic drugs in NASH treatment.

Citations

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Impact of physical activities in metabolic dysfunction associated steatotic liver disease, sarcopenia, and cardiovascular disease
Eugene Han, Sin Yung Woo, Justin Y. Jeon, Eun Seok Kang, Bong-Soo Cha, Byung-Wan Lee, Yong-ho Lee
Diabetes Research and Clinical Practice.2025; 224: 112209. CrossRef
Association of non-alcoholic fatty liver disease with cardiovascular disease and all cause death in patients with type 2 diabetes mellitus: nationwide population based study
Kyung-Soo Kim, Sangmo Hong, Kyungdo Han, Cheol-Young Park
BMJ.2024; 384: e076388. CrossRef
Insulin Resistance, Non-Alcoholic Fatty Liver Disease and Type 2 Diabetes Mellitus: Clinical and Experimental Perspective
Inha Jung, Dae-Jeong Koo, Won-Young Lee
Diabetes & Metabolism Journal.2024; 48(3): 327. CrossRef
Mitochondrial Quality Control: Its Role in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)
Soyeon Shin, Jaeyoung Kim, Ju Yeon Lee, Jun Kim, Chang-Myung Oh
Journal of Obesity & Metabolic Syndrome.2023; 32(4): 289. CrossRef
Sodium-glucose cotransporter 2 inhibitors for non-alcoholic fatty liver disease in patients with type 2 diabetes mellitus: A nationwide propensity-score matched cohort study
Jinyoung Kim, Kyungdo Han, Bongsung Kim, Ki-Hyun Baek, Ki-Ho Song, Mee Kyoung Kim, Hyuk-Sang Kwon
Diabetes Research and Clinical Practice.2022; 194: 110187. CrossRef

Original Articles

Diabetes, Obesity and Metabolism
The Leg Fat to Total Fat Ratio Is Associated with Lower Risks of Non-Alcoholic Fatty Liver Disease and Less Severe Hepatic Fibrosis: Results from Nationwide Surveys (KNHANES 2008–2011): Hyun Min Kim, Yong-ho Lee; Endocrinol Metab. 2021;36(6):1232-1242. Published online November 23, 2021; DOI: https://doi.org/10.3803/EnM.2021.1087

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Background
The prevalence of non-alcoholic fatty liver disease (NAFLD) has rapidly increased worldwide. The aim of this study was to investigate whether there is an independent relationship between regional fat distribution, especially leg fat mass, and the presence of NAFLD using nationally representative data in Korea.
Methods
This cross-sectional study analyzed data from 14,502 participants in the Korea National Health and Nutrition Examination Survey 2008 to 2011. Total fat mass, leg fat mass, and appendicular skeletal muscle mass were measured by dual-energy X-ray absorptiometry. Validated NAFLD prediction models and scoring systems for hepatic fibrosis were used.
Results
The leg fat to total fat (LF/TF) ratio showed a negative relationship with many factors, including body mass index, waist circumference, blood pressure, fasting blood glucose, and liver enzyme levels. When the LF/TF ratio and indices of hepatic steatosis were stratified by quartiles, the LF/TF ratio showed a negative correlation with the scoring systems that were used. The LF/TF ratio showed better accuracy in predicting NAFLD than total fat mass or leg fat mass alone. After adjusting for various traditional and lifestyle factors, a low LF/TF ratio remained a risk factor for NAFLD. Among NAFLD subjects, the LF/TF ratio showed a negative relationship with hepatic fibrosis.
Conclusion
A lower LF/TF ratio was markedly associated with a higher risk of hepatic steatosis and advanced hepatic fibrosis using various predictive models in a Korean population. Therefore, the LF/TF ratio could be a useful anthropometric parameter to predict NAFLD or advanced hepatic fibrosis.

Citations

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Low leg fat mass is associated with low insulin sensitivity, inflammatory markers, and β-cell dysfunction in non-obese Japanese people
Satomi Minato-Inokawa, Mari Honda, Ayaka Tsuboi-Kaji, Mika Takeuchi, Kaori Kitaoka, Miki Kurata, Bin Wu, Tsutomu Kazumi, Keisuke Fukuo
Scientific Reports.2025;[Epub] CrossRef
Body composition mediates the association between fatty acids and NAFLD risk: a prospective cohort study
Qishan Yang, Xinming Xu, Yue Chen, Zhicheng Zhang, Berty Ruping Song, Liang Sun, Xiang Gao
Journal of Health, Population and Nutrition.2025;[Epub] CrossRef
Waistline to thigh circumference ratio as a predictor of MAFLD: a health care worker study with 2-year follow-up
Xiaoyan Hao, Honghai He, Liyuan Tao, Wei Zhao, Peng Wang
BMC Gastroenterology.2024;[Epub] CrossRef
Regional fat distribution and hepatic fibrosis and steatosis severity in patients with nonalcoholic fatty liver disease and type 2 diabetes
Asieh Mansour, Saeed Pourhassan, Hadis Gerami, Mohammad Reza Mohajeri‐Tehrani, Marziye Salahshour, Ali Abbasi, Elham Madreseh, Sayed Mahmoud Sajjadi‐Jazi
Obesity Science & Practice.2024;[Epub] CrossRef
Insulin Resistance, Non-Alcoholic Fatty Liver Disease and Type 2 Diabetes Mellitus: Clinical and Experimental Perspective
Inha Jung, Dae-Jeong Koo, Won-Young Lee
Diabetes & Metabolism Journal.2024; 48(3): 327. CrossRef
Adipose tissue insulin resistance index was inversely associated with gluteofemoral fat and skeletal muscle mass in Japanese women
Satomi Minato-Inokawa, Mari Honda, Ayaka Tsuboi-Kaji, Mika Takeuchi, Kaori Kitaoka, Miki Kurata, Bin Wu, Tsutomu Kazumi, Keisuke Fukuo
Scientific Reports.2024;[Epub] CrossRef
A greater ratio of thigh subcutaneous fat to abdominal fat is associated with protection against non-alcoholic fatty liver disease
Yebei Liang, Peizhu Chen, Siyu Chen, Dan Liu, Fusong Jiang, Zhijun Zhu, Keqing Dong, Li Wei, Xuhong Hou
JHEP Reports.2023; 5(7): 100730. CrossRef
Association between Alcohol Consumption and Metabolic Dysfunction-Associated Steatotic Liver Disease Based on Alcohol Flushing Response in Men: The Korea National Health and Nutrition Examination Survey 2019–2021
Dae Eon Kang, Si Nae Oh
Nutrients.2023; 15(18): 3901. CrossRef

Diabetes, Obesity and Metabolism
Non-Laboratory-Based Simple Screening Model for Nonalcoholic Fatty Liver Disease in Patients with Type 2 Diabetes Developed Using Multi-Center Cohorts: Jiwon Kim, Minyoung Lee, Soo Yeon Kim, Ji-Hye Kim, Ji Sun Nam, Sung Wan Chun, Se Eun Park, Kwang Joon Kim, Yong-ho Lee, Joo Young Nam, Eun Seok Kang; Endocrinol Metab. 2021;36(4):823-834. Published online August 27, 2021; DOI: https://doi.org/10.3803/EnM.2021.1074

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Background
Nonalcoholic fatty liver disease (NAFLD) is the most prevalent cause of chronic liver disease worldwide. Type 2 diabetes mellitus (T2DM) is a risk factor that accelerates NAFLD progression, leading to fibrosis and cirrhosis. Thus, here we aimed to develop a simple model to predict the presence of NAFLD based on clinical parameters of patients with T2DM.
Methods
A total of 698 patients with T2DM who visited five medical centers were included. NAFLD was evaluated using transient elastography. Univariate logistic regression analyses were performed to identify potential contributors to NAFLD, followed by multivariable logistic regression analyses to create the final prediction model for NAFLD.
Results
Two NAFLD prediction models were developed, with and without serum biomarker use. The non-laboratory model comprised six variables: age, sex, waist circumference, body mass index (BMI), dyslipidemia, and smoking status. For a cutoff value of ≥60, the prediction accuracy was 0.780 (95% confidence interval [CI], 0.743 to 0.817). The second comprehensive model showed an improved discrimination ability of up to 0.815 (95% CI, 0.782 to 0.847) and comprised seven variables: age, sex, waist circumference, BMI, glycated hemoglobin, triglyceride, and alanine aminotransferase to aspartate aminotransferase ratio. Our non-laboratory model showed non-inferiority in the prediction of NAFLD versus previously established models, including serum parameters.
Conclusion
The new models are simple and user-friendly screening methods that can identify individuals with T2DM who are at high-risk for NAFLD. Additional studies are warranted to validate these new models as useful predictive tools for NAFLD in clinical practice.

Citations

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An interpretable machine learning model for predicting metabolic dysfunction‐associated steatotic liver disease in patients with type 2 diabetes
Zhuolin Zhou, Nan Gao, Jiaojiao Liu, Xuerong Ma, Zhijuan Ge, Cheng Ji
Diabetes, Obesity and Metabolism.2026; 28(1): 122. CrossRef
Prevalence, Sonographic Characteristics, and Metabolic Predictors of Nonalcoholic Fatty Liver Disease in Adults With Type 2 Diabetes in Tanzania
Zubeir Zubeir, Zuhura Nkrumbih, Salama Ally, Yasser H. Hadi
Dr. Sulaiman Al Habib Medical Journal.2025; 7(3): 180. CrossRef
Insulin Resistance, Non-Alcoholic Fatty Liver Disease and Type 2 Diabetes Mellitus: Clinical and Experimental Perspective
Inha Jung, Dae-Jeong Koo, Won-Young Lee
Diabetes & Metabolism Journal.2024; 48(3): 327. CrossRef
Non-Alcoholic Fatty Liver Disease or Type 2 Diabetes Mellitus—The Chicken or the Egg Dilemma
Marcin Kosmalski, Agnieszka Śliwińska, Józef Drzewoski
Biomedicines.2023; 11(4): 1097. CrossRef

Clinical Study
Current Management of Type 2 Diabetes Mellitus in Primary Care Clinics in Korea: Da Hea Seo, Shinae Kang, Yong-ho Lee, Jung Yoon Ha, Jong Suk Park, Byoung-Wan Lee, Eun Seok Kang, Chul Woo Ahn, Bong-Soo Cha; Endocrinol Metab. 2019;34(3):282-290. Published online September 26, 2019; DOI: https://doi.org/10.3803/EnM.2019.34.3.282

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Background

This study investigated the overall status of diabetes control and screening for diabetic microvascular complications in patients with type 2 diabetes mellitus attending primary care clinics in Korea.

Methods

In this cross-sectional observational study, 191 primary care clinics were randomly selected across Korea from 2015 to 2016. In total, 3,227 subjects were enrolled in the study.

Results

The patients followed at the primary care clinics were relatively young, with a mean age of 61.4±11.7 years, and had a relatively short duration of diabetes (mean duration, 7.6±6.5 years). Approximately 14% of subjects had diabetic microvascular complications. However, the patients treated at the primary care clinics had suboptimal control of hemoglobin A1c levels, blood pressure, and serum lipid levels, along with a metabolic target achievement rate of 5.9% according to the Korean Diabetes Association guidelines. The screening rates for diabetic nephropathy, retinopathy, and neuropathy within the past 12 months were 28.4%, 23.3%, and 13.3%, respectively.

Conclusion

The overall status of diabetes management, including the frequency of screening for microvascular complications, was suboptimal in the primary care clinics. More efforts should be made and more resources need to be allocated for primary care physicians to promote adequate healthcare delivery, which would result in stricter diabetes control and improved management of diabetic complications.

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Evidence of Overlapping Roles Between Clinics and Hospitals in Primary Care
Boram Sim, Jihye Shin, Hyun Woo Kim, Jin Yong Lee, Min-Woo Jo
Journal of Korean Medical Science.2025;[Epub] CrossRef
Efficacy and safety in tirzepatide‐treated Korean adults with type 2 diabetes—A post hoc analysis of SURPASS‐AP‐combo and SURPASS‐3
Byung Wan Lee, Chang Beom Lee, Soo Lim, Sin Gon Kim, Nan Hee Kim, Jong Chul Won, Woo Je Lee, Min Ju Kang, Ju Young Yuh, Li Ying Du, Hyojin Lim, Kyu Jeung Ahn
Diabetes, Obesity and Metabolism.2025; 27(12): 7110. CrossRef
Digital Healthcare and Diabetic Retinopathy: Patient Experience Innovation and Support for Diabetes Care
Sang Jun Park
The Journal of Korean Diabetes.2025; 26(4): 208. CrossRef
Efficacy and Safety of Pioglitazone/Metformin Fixed-Dose Combination Versus Uptitrated Metformin in Patients with Type 2 Diabetes without Adequate Glycemic Control: A Randomized Clinical Trial
Li-xin Guo, Lian-wei Wang, De-zeng Tian, Feng-mei Xu, Wei Huang, Xiao-hong Wu, Wei Zhu, Jun-Qiu Chen, Xin Zheng, Hai-Yan Zhou, Hong-Mei Li, Zhong-Chen He, Wen-Bo Wang, Li-Zhen Ma, Jun-Ting Duan
Diabetes Therapy.2024; 15(11): 2351. CrossRef
Analytical techniques for determination of metformin-thiazolidinediones combination antidiabetic drug
Imad Osman Abu Reid, Sayda Mohamed Osman, Somia Mohammed Bakheet
Journal of Pharmacy and Allied Medicine.2024; 2(2): 82. 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
Comparison of on-Statin Lipid and Lipoprotein Levels for the Prediction of First Cardiovascular Event in Type 2 Diabetes Mellitus
Ji Yoon Kim, Jimi Choi, Sin Gon Kim, Nam Hoon Kim
Diabetes & Metabolism Journal.2023; 47(6): 837. CrossRef
Effectiveness of quality of care for patients with type 2 diabetes in China: findings from the Shanghai Integration Model (SIM)
Chun Cai, Yuexing Liu, Yanyun Li, Yan Shi, Haidong Zou, Yuqian Bao, Yun Shen, Xin Cui, Chen Fu, Weiping Jia
Frontiers of Medicine.2022; 16(1): 126. CrossRef
Comparison of Health Outcomes by Care Provider Type for Newly Diagnosed Mild Type 2 Diabetes Patients in South Korea: A Retrospective Cohort Study
Hee-Chung Kang, Jae-Seok Hong
Healthcare.2022; 10(2): 334. CrossRef
Management Status of Patients with Type 2 Diabetes Mellitus at General Hospitals in Korea: A 5-Year Follow-Up Study
Jin Hee Jung, Jung Hwa Lee, Hyang Mi Jang, Young Na, Hee Sun Choi, Yeon Hee Lee, Yang Gyo Kang, Na Rae Kim, Jeong Rim Lee, Bok Rye Song, Kang Hee Sim
The Journal of Korean Diabetes.2022; 23(1): 64. CrossRef
Type 2 Diabetes Mellitus with Early Dry Skin Disorder: A Comparison Study Between Primary and Tertiary Care in Indonesia
Lili Legiawati, Kusmarinah Bramono, Wresti Indriatmi, Em Yunir, Aditya Indra Pratama
Current Diabetes Reviews.2022;[Epub] CrossRef
Long-Term Changes in HbA1c According to Blood Glucose Control Status During the First 3 Months After Visiting a Tertiary University Hospital
Hyunah Kim, Da Young Jung, Seung-Hwan Lee, Jae-Hyoung Cho, Hyeon Woo Yim, Hun-Sung Kim
Journal of Korean Medical Science.2022;[Epub] CrossRef
Differences in health behavior and nutrient intake status between diabetes-aware and unaware Korean adults based on the Korea national health and nutrition examination survey 2016–18 data: A cross-sectional study
Anshul Sharma, Chen Lulu, Kee-Ho Song, Hae-Jeung Lee
Frontiers in Public Health.2022;[Epub] CrossRef
Effects of Diabetes Quality Assessment on Diabetes Management Behaviors Based on a Nationwide Survey
Chang Kyun Choi, Jungho Yang, Ji-An Jeong, Min-Ho Shin
International Journal of Environmental Research and Public Health.2022; 19(23): 15781. CrossRef
The Impact of the Indonesian Chronic Disease Management Program (PROLANIS) on Metabolic Control and Renal Function of Type 2 Diabetes Mellitus Patients in Primary Care Setting
Firas Farisi Alkaff, Fauzan Illavi, Sovia Salamah, Wiwit Setiyawati, Ristra Ramadhani, Elly Purwantini, Dicky L. Tahapary
Journal of Primary Care & Community Health.2021;[Epub] CrossRef
Questionnaire-based Survey of Demographic and Clinical Characteristics, Health Behaviors, and Mental Health of Young Korean Adults with Early-Onset Diabetes
Ji In Park, Hyunjeong Baek, Sang-Wook Kim, Ji Yun Jeong, Kee-Ho Song, Ji Hee Yu, Il Sung Nam-Goong, Eun-Hee Cho
Journal of Korean Medical Science.2021;[Epub] CrossRef
Sodium–Glucose Cotransporter 2 Inhibitors and Risk of Retinal Vein Occlusion Among Patients With Type 2 Diabetes: A Propensity Score–Matched Cohort Study
Min-Kyung Lee, Bongsung Kim, Kyungdo Han, Jae-Hyuk Lee, Minhee Kim, Mee Kyoung Kim, Ki-Hyun Baek, Ki-Ho Song, Hyuk-Sang Kwon, Young-Jung Roh
Diabetes Care.2021; 44(10): 2419. CrossRef
Challenges in the Management of Diabetes in Primary Care
Yeon Kyung Lee
The Journal of Korean Diabetes.2020; 21(3): 161. CrossRef
Does Diabetes Increase the Risk of Contracting COVID-19? A Population-Based Study in Korea
Sung-Youn Chun, Dong Wook Kim, Sang Ah Lee, Su Jung Lee, Jung Hyun Chang, Yoon Jung Choi, Seong Woo Kim, Sun Ok Song
Diabetes & Metabolism Journal.2020; 44(6): 897. CrossRef
Comprehensive Efforts Are Needed to Improve the Quality of Primary Diabetes Care in Korea
Chan-Hee Jung
Endocrinology and Metabolism.2019; 34(3): 265. CrossRef

Clinical Study
Trends in Hyperglycemic Crisis Hospitalizations and in- and out-of-Hospital Mortality in the Last Decade Based on Korean National Health Insurance Claims Data: Ji Hong You, Sun Ok Song, Se Hee Park, Kyoung Hye Park, Joo Young Nam, Dong Wook Kim, Hyun Min Kim, Dong-Jun Kim, Yong-ho Lee, Byung-Wan Lee; Endocrinol Metab. 2019;34(3):275-281. Published online September 26, 2019; DOI: https://doi.org/10.3803/EnM.2019.34.3.275

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Background

Hyperglycemic crisis is a metabolic emergency associated with diabetes mellitus. However, accurate epidemiologic information on cases of hyperglycemic crisis in Korea remains scarce. We evaluated trends in hyperglycemic crisis hospitalizations and in- and out-of-hospital mortality in Korea. We also predicted future trends.

Methods

We extracted claims data with hyperglycemic crisis as the principal diagnosis from the National Health Insurance Service database in Korea from January 2004 to December 2013. We investigated the numbers of claims with hyperglycemic crisis and identified trends in hyperglycemic crisis based on those claims data. We predicted future trends by statistical estimation.

Results

The total annual number of claims of hyperglycemic crisis increased from 2,674 in 2004 to 5,540 in 2013. Statistical analysis revealed an increasing trend in hyperglycemic crisis hospitalizations (P for trend <0.01). In contrast, the hospitalization rate per 1,000 diabetes cases showed a decreasing trend (P for trend <0.01) during this period. The mortality rate per 1,000 diabetes cases also showed a decreasing trend (P for trend <0.0001). However, no distinct linear trend in the case-related fatality rate at <60 days over the last decade was observed. The predicted number of annual claims of hyperglycemic crisis will increase by 2030.

Conclusion

The number of hyperglycemic crisis hospitalizations in Korea increased in the last decade, although the hospitalization rate per 1,000 diabetes cases and mortality rate decreased. Also, the predicted number of annual claims will increase in the future. Thus, it is necessary to establish long-term healthcare policies to prevent hyperglycemic crisis.

Citations

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Trends in admissions for hyperglycaemic emergencies and associated clinical factors in adults with type 2 diabetes in Singapore, 2013–2022
Gerald Gui Ren Sng, Gek Hsiang Lim, Kristy Jia Yi Tian, Yong Mong Bee, Ming Ming Teh
Diabetic Medicine.2026;[Epub] CrossRef
Enhancing outcome prediction by applying the 2019 WHO DM classification to adults with hyperglycemic crises: A single-center cohort in Thailand
Chatchon Kaewkrasaesin, Weerapat Kositanurit, Phawinpon Chotwanvirat, Nitchakarn Laichuthai
Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2024; 18(4): 103012. CrossRef
Prevalence of hyperglycaemic crisis among diabetes mellitus patients in Ethiopia, systematic review and meta-analysis
A Getie, T Ayenew, G Yilak, M Gedfew, BT Amlak, A Wondmieneh, M Bimerew
Journal of Endocrinology, Metabolism and Diabetes of South Africa.2024; 29(2): 61. CrossRef
Obesity and 30-day case fatality after hyperglycemic crisis hospitalizations in Korea: a national cohort study
Hojun Yoon, Hyun Ho Choi, Giwoong Choi, Sun Ok Song, Kyoung Hwa Ha, Dae Jung Kim
Cardiovascular Prevention and Pharmacotherapy.2023; 5(3): 74. CrossRef
Interpreting global trends in type 2 diabetes complications and mortality
Mohammed K. Ali, Jonathan Pearson-Stuttard, Elizabeth Selvin, Edward W. Gregg
Diabetologia.2022; 65(1): 3. CrossRef
Comparison of the clinical characteristics and outcomes of pediatric patients with and without diabetic ketoacidosis at the time of type 1 diabetes diagnosis
Young-Jun Seo, Chang Dae Kum, Jung Gi Rho, Young Suk Shim, Hae Sang Lee, Jin Soon Hwang
Annals of Pediatric Endocrinology & Metabolism.2022; 27(2): 126. CrossRef
Clinical characteristics and outcomes of care in patients hospitalized with diabetic ketoacidosis
Mohsen S. Eledrisi, Haifaa Alkabbani, Malk Aboawon, Aya Ali, Imad Alabdulrazzak, Maab Elhaj, Ashraf Ahmed, Hazim Alqahwachi, Joanne Daghfal, Salem A. Beshyah, Rayaz A. Malik
Diabetes Research and Clinical Practice.2022; 192: 110041. CrossRef
Hyperglycemic Crisis Characteristics and Outcome of Care in Adult Patients without and with a History of Diabetes in Tigrai, Ethiopia: Comparative Study
Getachew Gebremedhin, Fikre Enqueselassie, Helen Yifter, Negussie Deyessa
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2021; Volume 14: 547. CrossRef
Increased Incidence of Pediatric Diabetic Ketoacidosis After COVID-19: A Two-Center Retrospective Study in Korea
Min Jeong Han, Jun Ho Heo
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2021; Volume 14: 783. CrossRef
Acute Hyperglycemic Crises with Coronavirus Disease-19: Case Reports
Na-young Kim, Eunyeong Ha, Jun Sung Moon, Yong-Hoon Lee, Eun Young Choi
Diabetes & Metabolism Journal.2020; 44(2): 349. CrossRef
Letter: Trends in Hyperglycemic Crisis Hospitalizations and in- and out-of-Hospital Mortality in the Last Decade Based on Korean National Health Insurance Claims Data (Endocrinol Metab 2019;34:275–81, Ji Hong You et al.)
Jang Won Son
Endocrinology and Metabolism.2019; 34(4): 422. CrossRef
Response: Trends in Hyperglycemic Crisis Hospitalizations and in- and out-of-Hospital Mortality in the Last Decade Based on Korean National Health Insurance Claims Data (Endocrinol Metab 2019;34:275–81, Ji Hong You et al.)
Ji Hong You, Sun Ok Song
Endocrinology and Metabolism.2019; 34(4): 424. CrossRef

Obesity and Metabolism
Comparison of the Effects of Ezetimibe-Statin Combination Therapy on Major Adverse Cardiovascular Events in Patients with and without Diabetes: A Meta-Analysis: Namki Hong, Yong-ho Lee, Kenichi Tsujita, Jorge A. Gonzalez, Christopher M. Kramer, Tomas Kovarnik, George N. Kouvelos, Hiromichi Suzuki, Kyungdo Han, Chan Joo Lee, Sung Ha Park, Byung-Wan Lee, Bong-Soo Cha, Eun Seok Kang; Endocrinol Metab. 2018;33(2):219-227. Published online May 4, 2018; DOI: https://doi.org/10.3803/EnM.2018.33.2.219

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Background

Ezetimibe-statin combination therapy has been found to reduce low density lipoprotein cholesterol levels and the risk of major adverse cardiovascular events (MACEs) in large trials. We sought to examine the differential effect of ezetimibe on MACEs when added to statins according to the presence of diabetes.

Methods

Randomized clinical trials with a sample size of at least 50 participants and at least 24 weeks of follow-up that compared ezetimibe-statin combination therapy with a statin- or placebo-controlled arm and reported at least one MACE, stratified by diabetes status, were included in the meta-analysis and meta-regression.

Results

A total of seven trials with 28,191 enrolled patients (mean age, 63.6 years; 75.1% men; 7,298 with diabetes [25.9%]; mean follow-up, 5 years) were analysed. MACEs stratified by diabetes were obtained from the published data (two trials) or through direct contact (five trials). No significant heterogeneity was observed among studies (I²=14.7%, P=0.293). Ezetimibe was associated with a greater reduction of MACE risk in subjects with diabetes than in those without diabetes (pooled relative risk, 0.84 vs. 0.93; P_{heterogeneity}=0.012). In the meta-regression analysis, the presence of diabetes was associated with a greater reduction of MACE risk when ezetimibe was added to statins (β=0.87, P=0.038).

Conclusion

Ezetimibe-statin combination therapy was associated with greater cardiovascular benefits in patients with diabetes than in those without diabetes. Our findings suggest that ezetimibe-statin combination therapy might be a useful strategy in patients with diabetes at a residual risk of MACEs.

Citations

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Miscellaneous
Effects of Serum Albumin, Calcium Levels, Cancer Stage and Performance Status on Weight Loss in Parathyroid Hormone-Related Peptide Positive or Negative Patients with Cancer: Ji-Yeon Lee, Namki Hong, Hye Ryun Kim, Byung Wan Lee, Eun Seok Kang, Bong-Soo Cha, Yong-ho Lee; Endocrinol Metab. 2018;33(1):97-104. Published online March 21, 2018; DOI: https://doi.org/10.3803/EnM.2018.33.1.97

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Background

A recent animal study showed that parathyroid hormone-related peptide (PTHrP) is associated with cancer cachexia by promoting adipose tissue browning, and we previously demonstrated that PTHrP predicts weight loss (WL) in patients with cancer. In this study, we investigated whether prediction of WL by PTHrP is influenced by clinical factors such as serum albumin, corrected calcium levels, cancer stage, and performance status (PS).

Methods

A cohort of 219 patients with cancer whose PTHrP level was measured was enrolled and followed for body weight (BW) changes. Subjects were divided into two groups by serum albumin (cutoff value, 3.7 g/dL), corrected calcium (cutoff value, 10.5 mg/dL), cancer stage (stage 1 to 3 or 4), or PS (Eastern Cooperative Oncology Group 0 to 1 or 2 to 4), respectively. Clinically significant WL was defined as either percent of BW change (% BW) <−5% or % BW <−2% plus body mass index (BMI) <20 kg/m².

Results

After a median follow-up of 327 days, 74 patients (33.8%) experienced clinically significant WL. A positive PTHrP level was associated with a 2-fold increased risk of WL after adjusting for age, baseline BMI, serum albumin, corrected calcium level, cancer stage, and PS. The effect of PTHrP on WL remained significant in patients with low serum albumin, stage 4 cancer, and good PS. Regardless of calcium level, the effect of PTHrP on WL was maintained, although there was an additive effect of higher calcium and PTHrP levels.

Conclusion

Early recognition of patients with advanced cancer who are PTHrP positive with hypercalcemia or hypoalbuminemia is needed for their clinical management.

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Cancer knocks you out by fasting: Cachexia as a consequence of metabolic alterations in cancer
Salvatore Cortellino, Margherita D'Angelo, Massimiliano Quintiliani, Antonio Giordano
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Development and Characterization of a Cancer Cachexia Rat Model Transplanted with Cells of the Rat Lung Adenocarcinoma Cell Line Sato Lung Cancer (SLC)
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Comparison between Atorvastatin and Rosuvastatin in Renal Function Decline among Patients with Diabetes: Eugene Han, Gyuri Kim, Ji-Yeon Lee, Yong-ho Lee, Beom Seok Kim, Byung-Wan Lee, Bong-Soo Cha, Eun Seok Kang; Endocrinol Metab. 2017;32(2):274-280. Published online June 23, 2017; DOI: https://doi.org/10.3803/EnM.2017.32.2.274

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Background

Although the beneficial effects of statin treatment in dyslipidemia and atherosclerosis have been well studied, there is limited information regarding the renal effects of statins in diabetic nephropathy. We aimed to investigate whether, and which, statins affected renal function in Asian patients with diabetes.

Methods

We enrolled 484 patients with diabetes who received statin treatment for more than 12 months. We included patients treated with moderate-intensity dose statin treatment (atorvastatin 10 to 20 mg/day or rosuvastatin 5 to 10 mg/day). The primary outcome was a change in estimated glomerular filtration rate (eGFR) during the 12-month statin treatment, and rapid renal decline was defined as a >3% reduction in eGFR in a 1-year period.

Results

In both statin treatment groups, patients showed improved serum lipid levels and significantly reduced eGFRs (from 80.3 to 78.8 mL/min/1.73 m² for atorvastatin [P=0.012], from 79.1 to 76.1 mL/min/1.73 m² for rosuvastatin [P=0.001]). A more rapid eGFR decline was observed in the rosuvastatin group than in the atorvastatin group (48.7% vs. 38.6%, P=0.029). Multiple logistic regression analyses demonstrated more rapid renal function loss in the rosuvastatin group than in the atorvastatin group after adjustment for other confounding factors (odds ratio, 1.60; 95% confidence interval, 1.06 to 2.42).

Conclusion

These results suggest that a moderate-intensity dose of atorvastatin has fewer detrimental effects on renal function than that of rosuvastatin.

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Clinical Study
Obesity and Hyperglycemia in Korean Men with Klinefelter Syndrome: The Korean Endocrine Society Registry: Seung Jin Han, Kyung-Soo Kim, Wonjin Kim, Jung Hee Kim, Yong-ho Lee, Ji Sun Nam, Ji A Seo, Bu Kyung Kim, Jihyun Lee, Jin Ook Chung, Min-Hee Kim, Tae-Seo Sohn, Han Seok Choi, Seong Bin Hong, Yoon-Sok Chung; Endocrinol Metab. 2016;31(4):598-603. Published online December 20, 2016; DOI: https://doi.org/10.3803/EnM.2016.31.4.598

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Background

The aim of this study was to investigate the prevalence of obesity in Korean men with Klinefelter syndrome (KS) and the associated risk factors for obesity and hyperglycemia.

Methods

Data were collected retrospectively from medical records from 11 university hospitals in Korea between 1994 and 2014. Subjects aged ≥18 years with newly diagnosed KS were enrolled. The following parameters were recorded at baseline before treatment: chief complaint, height, weight, fasting glucose level, lipid panel, blood pressure, testosterone, luteinizing hormone, follicle-stimulating hormone, karyotyping patterns, and history of hypertension, diabetes, and dyslipidemia.

Results

Data were analyzed from 376 of 544 initially enrolled patients. The rate of the 47 XXY chromosomal pattern was 94.1%. The prevalence of obesity (body mass index ≥25 kg/m²) in Korean men with KS was 42.6%. The testosterone level was an independent risk factor for obesity and hyperglycemia.

Conclusion

Obesity is common in Korean men with KS. Hypogonadism in patients with KS was associated with obesity and hyperglycemia.

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New Horizons in Klinefelter Syndrome: Current Evidence, Gaps, and Research Priorities
Angela K Lucas-Herald, Lise Aksglaede, Ida Dyhr Caspersen, S Faisal Ahmed, Francesco Carlomagno, Andrea M Isidori
Endocrine Reviews.2025; 46(4): 447. CrossRef
A dual-center study of predictive factors for sperm retrieval through microdissection testicular sperm extraction and intracytoplasmic sperm injection outcomes in men with non-mosaic Klinefelter syndrome
Jong Hyeun Baeck, Tae Jin Kim, Tae Heon Kim, Seung-Ryeol Lee, Dong Soo Park, Hwang Kwon, Ji Eun Shin, Dong Hyeon Lee, Young Dong Yu
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Cardiorespiratory fitness in adolescents and young adults with Klinefelter syndrome – a pilot study
Julia Spiekermann, Kathrin Sinningen, Beatrice Hanusch, Michaela Kleber, Michael M. Schündeln, Cordula Kiewert, Heide Siggelkow, Jakob Höppner, Corinna Grasemann
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Metabolic Profile in a Cohort of Young Sicilian Patients with Klinefelter’s Syndrome: The Role of Irisin
Stefano Radellini, Valentina Guarnotta, Vincenzo Sciabica, Giuseppe Pizzolanti, Carla Giordano, Vito Angelo Giagulli
International Journal of Endocrinology.2022; 2022: 1. CrossRef
Metabolic and Nutritional Aspects in Paediatric Patients with Klinefelter Syndrome: A Narrative Review
Chiara Mameli, Giulia Fiore, Arianna Sangiorgio, Marta Agostinelli, Giulia Zichichi, Gianvincenzo Zuccotti, Elvira Verduci
Nutrients.2022; 14(10): 2107. CrossRef
Klinefelter syndrome in an adolescent with severe obesity, insulin resistance, and hyperlipidemia, successfully treated with testosterone replacement therapy
Shota Fukuhara, Jun Mori, Hisakazu Nakajima
Clinical Pediatric Endocrinology.2021; 30(3): 127. CrossRef
Glucose metabolic disorder in Klinefelter syndrome: a retrospective analysis in a single Chinese hospital and literature review
Shixuan Liu, Tao Yuan, Shuoning Song, Shi Chen, Linjie Wang, Yong Fu, Yingyue Dong, Yan Tang, Weigang Zhao
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What Every Internist-Endocrinologist Should Know about Rare Genetic Syndromes in Order to Prevent Needless Diagnostics, Missed Diagnoses and Medical Complications: Five Years of ‘Internal Medicine for Rare Genetic Syndromes’
Anna G. W. Rosenberg, Minke R. A. Pater, Karlijn Pellikaan, Kirsten Davidse, Anja A. Kattentidt-Mouravieva, Rogier Kersseboom, Anja G. Bos-Roubos, Agnies van Eeghen, José M. C. Veen, Jiske J. van der Meulen, Nina van Aalst-van Wieringen, Franciska M. E. H
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Mark J. O’Connor, Emma A. Snyder, Frances J. Hayes
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Adriana Herrera Lizarazo, Michelle McLoughlin, Maria G. Vogiatzi
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Claus H Gravholt, Simon Chang, Mikkel Wallentin, Jens Fedder, Philip Moore, Anne Skakkebæk
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Temeka Zore, Maria Palafox, Karen Reue
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Andrea Salzano, Roberta D’Assante, Liam M. Heaney, Federica Monaco, Giuseppe Rengo, Pietro Valente, Daniela Pasquali, Eduardo Bossone, Daniele Gianfrilli, Andrea Lenzi, Antonio Cittadini, Alberto M. Marra, Raffaele Napoli
Endocrine.2018; 61(2): 194. CrossRef
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Articles inEndocrinology and Metabolismin 2016
Won-Young Lee
Endocrinology and Metabolism.2017; 32(1): 62. CrossRef
Sex differences in obesity: X chromosome dosage as a risk factor for increased food intake, adiposity and co-morbidities
Karen Reue
Physiology & Behavior.2017; 176: 174. CrossRef
Klinefelter Syndrome with Morbid Obesity Before Bariatric Surgery: A Case Report
Parisa Janmohammadi, Gholamreza Mohammadi-Farsani, Hana Arghavani, Mahmoud Arshad, Tayebeh Mokhber
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Klinefelter Syndrome and Metabolic Disorder
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Clinical Study
Trends in Diabetes Incidence in the Last Decade Based on Korean National Health Insurance Claims Data: Sun Ok Song, Yong-ho Lee, Dong Wook Kim, Young Duk Song, Joo Young Nam, Kyoung Hye Park, Dae Jung Kim, Seok Won Park, Hyun Chul Lee, Byung-Wan Lee; Endocrinol Metab. 2016;31(2):292-299. Published online June 10, 2016; DOI: https://doi.org/10.3803/EnM.2016.31.2.292

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Background

Epidemiological data is useful to estimate the necessary manpower and resources used for disease control and prevention of prevalent chronic diseases. We aimed to evaluate the incidence of diabetes and identify its trends based on the claims data from the National Health Insurance Service database over the last decade.

Methods

We extracted claims data on diabetes as the principal and first additional diagnoses of National Health Insurance from January 2003 to December 2012. We investigated the number of newly claimed subjects with diabetes codes, the number of claims and the demographic characteristics of this population.

Results

Total numbers of claimed cases and populations with diabetes continuously increased from 1,377,319 in 2003 to 2,571,067 by 2012. However, the annual number of newly claimed diabetic subjects decreased in the last decade. The total number of new claim patients with diabetes codes decreased as 30.9% over 2005 to 2009. Since 2009, the incidence of new diabetes claim patients has not experienced significant change. The 9-year average incidence rate was 0.98% and 1.01% in men and women, respectively. The data showed an increasing proportion of new diabetic subjects of younger age (<60 years) combined with a sharply decreasing proportion of subjects of older age (≥60 years).

Conclusion

There were increasing numbers of newly claimed subjects with diabetes codes of younger age over the last 10 years. This increasing number of diabetic patients will require management throughout their life courses because Korea is rapidly becoming an aging society.

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Review Article

Adrenal gland
How to Establish Clinical Prediction Models: Yong-ho Lee, Heejung Bang, Dae Jung Kim; Endocrinol Metab. 2016;31(1):38-44. Published online March 16, 2016; DOI: https://doi.org/10.3803/EnM.2016.31.1.38

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Abstract PDF PubReader

A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.

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Journal of Personalized Medicine.2022; 12(11): 1899. CrossRef
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Development and validation of a risk assessment nomogram for venous thromboembolism associated with hospitalized postoperative Chinese breast cancer patients
Jing Li, Wan‐Min Qiang, Yan Wang, Xiao‐Yuan Wang
Journal of Advanced Nursing.2021; 77(1): 473. CrossRef
The role of behaviour problems in screening for mental ill-health in adults with intellectual disability
F. Westlake, A. Hassiotis, G. Unwin, V. Totsika
The European Journal of Psychiatry.2021; 35(2): 122. CrossRef
Deep learning model for classifying endometrial lesions
YunZheng Zhang, ZiHao Wang, Jin Zhang, CuiCui Wang, YuShan Wang, Hao Chen, LuHe Shan, JiaNing Huo, JiaHui Gu, Xiaoxin Ma
Journal of Translational Medicine.2021;[Epub] CrossRef
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Ru Xin Wong, Ho Shirlynn, Yen Sin Koh, Stella Goh Seow Lin, Daniel Quah, Qingyuan Zhuang
Palliative Medicine Reports.2021;[Epub] CrossRef
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Sarah Denzler, Diem Vuong, Marta Bogowicz, Matea Pavic, Thomas Frauenfelder, Sandra Thierstein, Eric Innocents Eboulet, Britta Maurer, Janine Schniering, Hubert Szymon Gabryś, Isabelle Schmitt-Opitz, Miklos Pless, Robert Foerster, Matthias Guckenberger, S
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An empirical analysis of dealing with patients who are lost to follow-up when developing prognostic models using a cohort design
Jenna M. Reps, Peter Rijnbeek, Alana Cuthbert, Patrick B. Ryan, Nicole Pratt, Martijn Schuemie
BMC Medical Informatics and Decision Making.2021;[Epub] CrossRef
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Qing Zheng, Wen Liu, Yu Huang, Zhenyu Gao, Yuanhui Wu, Xiaohong Wang, Meimei Cai, Yan He, Shiju Chen, Bin Wang, Lingyu Liu, Shuqiang Chen, Hongjie Huang, Ling Zheng, Rihui Kang, Xiaohong Zeng, Jing Chen, Huaning Chen, Junmin Chen, Zhibin Li, Guixiu Shi
Rheumatology and Therapy.2021; 8(1): 411. CrossRef
Development of a model for predicting the 4-year risk of symptomatic knee osteoarthritis in China: a longitudinal cohort study
Limin Wang, Han Lu, Hongbo Chen, Shida Jin, Mengqi Wang, Shaomei Shang
Arthritis Research & Therapy.2021;[Epub] CrossRef
Digital Communication Biomarkers of Mood and Diagnosis in Borderline Personality Disorder, Bipolar Disorder, and Healthy Control Populations
George Gillett, Niall M. McGowan, Niclas Palmius, Amy C. Bilderbeck, Guy M. Goodwin, Kate E. A. Saunders
Frontiers in Psychiatry.2021;[Epub] CrossRef
Prediction of Multiple Organ Failure Complicated by Moderately Severe or Severe Acute Pancreatitis Based on Machine Learning: A Multicenter Cohort Study
Fumin Xu, Xiao Chen, Chenwenya Li, Jing Liu, Qiu Qiu, Mi He, Jingjing Xiao, Zhihui Liu, Bingjun Ji, Dongfeng Chen, Kaijun Liu, Mirella Giovarelli
Mediators of Inflammation.2021;[Epub] CrossRef
Comparison of multiple statistical models for the development of clinical prediction scores to detect advanced colorectal neoplasms in asymptomatic Thai patients
Kamonwan Soonklang, Boonying Siribumrungwong, Bunchorn Siripongpreeda, Chirayu Auewarakul
Medicine.2021; 100(20): e26065. CrossRef
European Childhood Obesity Risk Evaluation (CORE) index based on perinatal factors and maternal sociodemographic characteristics: the Feel4Diabetes-study
Christina Mavrogianni, George Moschonis, Eva Karaglani, Greet Cardon, Violeta Iotova, Pilar De Miguel-Etayo, Esther M. González-Gil, Κaloyan Tsochev, Tsvetalina Tankova, Imre Rurik, Patrick Timpel, Emese Antal, Stavros Liatis, Konstantinos Makrilakis, Geo
European Journal of Pediatrics.2021; 180(8): 2549. CrossRef
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Yun Peng, Shangrong Wu, Zihan Xu, Dingkun Hou, Nan Li, Zheyu Zhang, Lili Wang, Haitao Wang
Cancer Medicine.2021; 10(16): 5499. CrossRef
Individual 5-Year Lung Cancer Risk Prediction Model in Korea Using a Nationwide Representative Database
Yohwan Yeo, Dong Wook Shin, Kyungdo Han, Sang Hyun Park, Keun-Hye Jeon, Jungkwon Lee, Junghyun Kim, Aesun Shin
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Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort
Sang Youl Rhee, Ji Min Sung, Sunhee Kim, In-Jeong Cho, Sang-Eun Lee, Hyuk-Jae Chang
Diabetes & Metabolism Journal.2021; 45(4): 515. CrossRef
A nomogram for predicting lymph node metastasis in superficial esophageal squamous cell carcinoma
Weifeng Zhang, Han Chen, Guoxin Zhang, Guangfu Jin
The Journal of Biomedical Research.2021; 35(5): 361. CrossRef
Relationship Between Sensibility Tests and Functional Outcomes in Patients With Traumatic Upper Limb Nerve Injuries: A Systematic Review
Liheng Chen, Emmanuel Ogalo, Chloe Haldane, Sean G. Bristol, Michael J. Berger
Archives of Rehabilitation Research and Clinical Translation.2021; 3(4): 100159. CrossRef
Personalized 5-Year Prostate Cancer Risk Prediction Model in Korea Based on Nationwide Representative Data
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Journal of Personalized Medicine.2021; 12(1): 2. CrossRef
Utility of prediction model score: a proposed tool to standardize the performance and generalizability of clinical predictive models based on systematic review
Jeff Ehresman, Daniel Lubelski, Zach Pennington, Bethany Hung, A. Karim Ahmed, Tej D. Azad, Kurt Lehner, James Feghali, Zorica Buser, James Harrop, Jefferson Wilson, Shekar Kurpad, Zoher Ghogawala, Daniel M. Sciubba
Journal of Neurosurgery: Spine.2021; 34(5): 779. CrossRef
Prodromal clinical, demographic, and socio-ecological correlates of asthma in adults: a 10-year statewide big data multi-domain analysis
Jennifer N. Fishe, Jiang Bian, Zhaoyi Chen, Hui Hu, Jae Min, Francois Modave, Mattia Prosperi
Journal of Asthma.2020; 57(11): 1155. CrossRef
Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis
Shannon Wongvibulsin, Katherine C. Wu, Scott L. Zeger
BMC Medical Research Methodology.2020;[Epub] CrossRef
Prognostic models for predicting overall survival in metastatic castration-resistant prostate cancer: a systematic review
M. Pinart, F. Kunath, V. Lieb, I. Tsaur, B. Wullich, Stefanie Schmidt
World Journal of Urology.2020; 38(3): 613. CrossRef
Peripheral Nerve Field Stimulation for Chronic Back Pain: Therapy Outcome Predictive Factors
Eric‐Jan van Gorp, Sam Eldabe, Konstantin V. Slavin, Philippe Rigoard, Stefaan Goossens, Dorothee Mielke, Giancarlo Barolat, Christ Declerck, Chris Gilmore, Ismaïl Gültuna, Kris C.P. Vissers, Jennifer Tinsley, Rudolf Likar, Pierre‐Philippe Luyet
Pain Practice.2020; 20(5): 522. CrossRef
Biomarker Score in Risk Prediction: Beyond Scientific Evidence and Statistical Performance
Heejung Bang
Diabetes & Metabolism Journal.2020; 44(2): 245. CrossRef
Variable selection strategies and its importance in clinical prediction modelling
Mohammad Ziaul Islam Chowdhury, Tanvir C Turin
Family Medicine and Community Health.2020; 8(1): e000262. CrossRef
Reply to the Letter to the Editor: Derivation and Internal Validation of a Clinical Prediction Tool to Predict Nonalcoholic Fatty Liver Disease in Patients With Crohn’s Disease
Scott McHenry, Matthew A Ciorba, Parakkal Deepak
Inflammatory Bowel Diseases.2020; 26(6): e46. CrossRef
The application of unsupervised deep learning in predictive models using electronic health records
Lei Wang, Liping Tong, Darcy Davis, Tim Arnold, Tina Esposito
BMC Medical Research Methodology.2020;[Epub] CrossRef
Development and evaluation of an osteoarthritis risk model for integration into primary care health information technology
Jason E. Black, Amanda L. Terry, Daniel J. Lizotte
International Journal of Medical Informatics.2020; 141: 104160. CrossRef
An Individualized Prediction Model for Long-term Lung Function Trajectory and Risk of COPD in the General Population
Wenjia Chen, Don D. Sin, J. Mark FitzGerald, Abdollah Safari, Amin Adibi, Mohsen Sadatsafavi
Chest.2020; 157(3): 547. CrossRef
Optimization of the management of pregnant women at high risk of miscarriage and premature birth
Yu. A. Semenov, V. F. Dolgushina, M. G. Moscvicheva, V. S. Chulkov
Rossiiskii vestnik akushera-ginekologa.2020; 20(1): 54. CrossRef
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Merhawi T Gebrezgi, Kristopher P Fennie, Diana M Sheehan, Boubakari Ibrahimou, Sandra G Jones, Petra Brock, Robert A Ladner, Mary Jo Trepka
International Journal of STD & AIDS.2020; 31(3): 244. CrossRef
The impact of age and comorbidity on the postoperative outcomes after emergency surgical management of complicated intra-abdominal infections
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Scientific Reports.2020;[Epub] CrossRef
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Mohammad Z. I. Chowdhury, Tanvir C. Turin
The Journal of Primary Health Care.2020; 12(1): 3. CrossRef
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Scott D Nelson, Colin G Walsh, Casey A Olsen, Andrew J McLaughlin, Joseph R LeGrand, Nick Schutz, Thomas A Lasko
American Journal of Health-System Pharmacy.2020; 77(19): 1556. CrossRef
Association does not imply prediction: the accuracy of birthweight in predicting child mortality and anthropometric failure
Akshay Swaminathan, Rockli Kim, S.V. Subramanian
Annals of Epidemiology.2020; 50: 7. CrossRef
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Peter R. Farrell, Lindsey Hornung, Peter Farmer, Angelica W. DesPain, Esther Kim, Ryan Pearman, Beemnet Neway, Ashley Serrette, Sona Sehgal, James E. Heubi, Tom K. Lin, Jaimie D. Nathan, David S. Vitale, Maisam Abu‐El‐Haija
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Biomarkers of Fabry Nephropathy: Review and Future Perspective
Tina Levstek, Bojan Vujkovac, Katarina Trebusak Podkrajsek
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Coledocolitiasis y pancreatitis: las dificultades de la predicción
David Benigno Páramo Hernández
Revista Colombiana de Gastroenterología.2020; 35(3): 266. CrossRef
Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar
Yan Li, Matthew Sperrin, Darren M Ashcroft, Tjeerd Pieter van Staa
BMJ.2020; : m3919. CrossRef
Outcome prediction with serial neuron-specific enolase and machine learning in anoxic-ischaemic disorders of consciousness
Emily Muller, Jonathan P. Shock, Andreas Bender, Julian Kleeberger, Tobias Högen, Martin Rosenfelder, Bubacarr Bah, Alex Lopez-Rolon
Computers in Biology and Medicine.2019; 107: 145. CrossRef
Machine learning models to predict disease progression among veterans with hepatitis C virus
Monica A. Konerman, Lauren A. Beste, Tony Van, Boang Liu, Xuefei Zhang, Ji Zhu, Sameer D. Saini, Grace L. Su, Brahmajee K. Nallamothu, George N. Ioannou, Akbar K. Waljee, Davide Bacciu
PLOS ONE.2019; 14(1): e0208141. CrossRef
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European Journal of Cancer.2019; 107: 28. CrossRef
Big Data Research in Neuro-Ophthalmology: Promises and Pitfalls
Heather E. Moss, Charlotte E. Joslin, Daniel S. Rubin, Steven Roth
Journal of Neuro-Ophthalmology.2019; 39(4): 480. CrossRef
Nonalcoholic Fatty Liver Disease in Diabetes. Part I: Epidemiology and Diagnosis
Yong-ho Lee, Yongin Cho, Byung-Wan Lee, Cheol-Young Park, Dae Ho Lee, Bong-Soo Cha, Eun-Jung Rhee
Diabetes & Metabolism Journal.2019; 43(1): 31. CrossRef
The use of rigorous methods was strongly warranted among prognostic prediction models for obstetric care
Jing Tan, Yana Qi, Chunrong Liu, Yiquan Xiong, Qiao He, Guiting Zhang, Meng Chen, Guolin He, Wen Wang, Xinghui Liu, Xin Sun
Journal of Clinical Epidemiology.2019; 115: 98. CrossRef
Support Vector Machines and logistic regression to predict temporal artery biopsy outcomes
Edsel Ing, Wanhua Su, Matthias Schonlau, Nurhan Torun
Canadian Journal of Ophthalmology.2019; 54(1): 116. CrossRef
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Yasuaki Saijo, Yoshiya Ito, Eiji Yoshioka, Yukihiro Sato, Machiko Minatoya, Atsuko Araki, Chihiro Miyashita, Reiko Kishi
Clinical Pediatric Endocrinology.2019; 28(3): 81. CrossRef
Development and performance evaluation of the Medicines Optimisation Assessment Tool (MOAT): a prognostic model to target hospital pharmacists’ input to prevent medication-related problems
Cathy Geeson, Li Wei, Bryony Dean Franklin
BMJ Quality & Safety.2019; 28(8): 645. CrossRef
Machine Learning Accurately Predicts Short-Term Outcomes Following Open Reduction and Internal Fixation of Ankle Fractures
Robert K. Merrill, Rocco M. Ferrandino, Ryan Hoffman, Gene W. Shaffer, Anthony Ndu
The Journal of Foot and Ankle Surgery.2019; 58(3): 410. CrossRef
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Elke Veirman, Dimitri M. L. Van Ryckeghem, Annick De Paepe, Olivia J. Kirtley, Geert Crombez
PAIN Reports.2019; 4(5): e775. CrossRef
iHealthcare: Predictive Model Analysis Concerning Big Data Applications for Interactive Healthcare Systems †
Md. Ataur Rahman Bhuiyan, Md. Rifat Ullah, Amit Kumar Das
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M.V. Kabyshev, S.V. Kovalchuk
Procedia Computer Science.2019; 156: 123. CrossRef
Validation of childhood asthma predictive tools: A systematic review
Silvia Colicino, Daniel Munblit, Cosetta Minelli, Adnan Custovic, Paul Cullinan
Clinical & Experimental Allergy.2019; 49(4): 410. CrossRef
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Scientific Reports.2019;[Epub] CrossRef
Predicting treatment response using pharmacy register in migraine
Thomas Folkmann Hansen, Mona Ameri Chalmer, Thilde Marie Haspang, Lisette Kogelman, Jes Olesen
The Journal of Headache and Pain.2019;[Epub] CrossRef
Development and Evaluation of Electronic Health Record Data-Driven Predictive Models for Pressure Ulcers
Seul Ki Park, Hyeoun-Ae Park, Hee Hwang
Journal of Korean Academy of Nursing.2019; 49(5): 575. CrossRef
Characteristics and outcome of acute heart failure patients according to the severity of peripheral oedema
Ahmad Shoaib, Mamas A. Mamas, Qazi S. Ahmad, Theresa M. McDonagh, Suzanna M.C. Hardman, Muhammad Rashid, Robert Butler, Simon Duckett, Duwarakan Satchithananda, James Nolan, Henry J. Dargie, Andrew L. Clark, John G.F. Cleland
International Journal of Cardiology.2019; 285: 40. CrossRef
Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature
Laura E. Cowley, Daniel M. Farewell, Sabine Maguire, Alison M. Kemp
Diagnostic and Prognostic Research.2019;[Epub] CrossRef
Development and validation of a nomogram to predict the prognosis of patients with squamous cell carcinoma of the bladder
Mei-Di Hu, Si-Hai Chen, Yuan Liu, Ling-Hua Jia
Bioscience Reports.2019;[Epub] CrossRef
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N. de'Angelis, F. Pigneur, A. Martínez‐Pérez, G. C. Vitali, F. Landi, S. A. Gómez‐Abril, M. Assalino, E. Espin, F. Ris, A. Luciani, F. Brunetti
Colorectal Disease.2019; 21(3): 277. CrossRef
External Validation of START nomogram to predict 3-Month unfavorable outcome in Chinese acute stroke patients
BaiLi Song, XiangLiang Chen, Dan Tang, Mako Ibrahim, YuKai Liu, Linda Nyame, Teng Jiang, Wei Wang, Xiang Li, Chao Sun, Zheng Zhao, Jie Yang, JunShan Zhou, JianJun Zou
Journal of Stroke and Cerebrovascular Diseases.2019; 28(6): 1618. CrossRef
Development and Validation of the Korean Diabetes Risk Score: A 10-Year National Cohort Study
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Diabetes & Metabolism Journal.2018; 42(5): 402. CrossRef
Clinical relevance and validity of tools to predict infant, childhood and adulthood obesity: a systematic review
Oliver J Canfell, Robyn Littlewood, Olivia RL Wright, Jacqueline L Walker
Public Health Nutrition.2018; 21(17): 3135. CrossRef
Letter to Editor
Laura E Cowley, Sabine A Maguire, Daniel M Farewell, Alison M Kemp
Law, Probability and Risk.2018; 17(3): 275. CrossRef
Self‐report assessment of severe periodontitis: Periodontal screening score development
Maria Clotilde Carra, Alice Gueguen, Frédérique Thomas, Bruno Pannier, Giuseppina Caligiuri, Philippe Gabriel Steg, Marie Zins, Philippe Bouchard
Journal of Clinical Periodontology.2018; 45(7): 818. CrossRef
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European Geriatric Medicine.2018; 9(6): 891. CrossRef
Prediction of Drug-Related Risks Using Clinical Context Information in Longitudinal Claims Data
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Value in Health.2018; 21(12): 1390. CrossRef
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Endocrinology and Metabolism.2017; 32(1): 62. CrossRef
Development of Clinical Data Mart of HMG-CoA Reductase Inhibitor for Varied Clinical Research
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Endocrinology and Metabolism.2017; 32(1): 90. CrossRef
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Diabetes Care.2017; 40(9): e136. CrossRef
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The Journal of Thoracic and Cardiovascular Surgery.2017; 154(6): 2038. CrossRef
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Autoimmunity Reviews.2016; 15(8): 833. CrossRef
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Diabetes Research and Clinical Practice.2016; 118: 146. CrossRef

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