Endocrinology and Metabolism

Original Articles

Mineral, Bone & Muscle Big Data Articles (National Health Insurance Service Database)
Increased Risk of Hip Fracture in Patients with Acromegaly: A Nationwide Cohort Study in Korea: Jiwon Kim, Namki Hong, Jimi Choi, Ju Hyung Moon, Eui Hyun Kim, Eun Jig Lee, Sin Gon Kim, Cheol Ryong Ku; Endocrinol Metab. 2023;38(6):690-700. Published online October 30, 2023; DOI: https://doi.org/10.3803/EnM.2023.1782

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Background
Acromegaly leads to various skeletal complications, and fragility fractures are emerging as a new concern in patients with acromegaly. Therefore, this study investigated the risk of fractures in Korean patients with acromegaly.
Methods
We used the Korean nationwide claims database from 2009 to 2019. A total of 931 patients with acromegaly who had never used an osteoporosis drug before and were treated with surgery alone were selected as study participants, and a 1:29 ratio of 26,999 age- and sex-matched osteoporosis drug-naïve controls without acromegaly were randomly selected from the database.
Results
The mean age was 46.2 years, and 50.0% were male. During a median follow-up of 54.1 months, there was no difference in the risks of all, vertebral, and non-vertebral fractures between the acromegaly and control groups. However, hip fracture risk was significantly higher (hazard ratio [HR], 2.73; 95% confidence interval [CI], 1.32 to 5.65), and non-hip and non-vertebral fractures risk was significantly lower (HR, 0.40; 95% CI, 0.17 to 0.98) in patients with acromegaly than in controls; these results remained robust even after adjustment for socioeconomic status and baseline comorbidities. Age, type 2 diabetes mellitus, cardio-cerebrovascular disease, fracture history, recent use of acid-suppressant medication, psychotropic medication, and opioids were risk factors for all fractures in patients with acromegaly (all P<0.05).
Conclusion
Compared with controls, patients surgically treated for acromegaly had a higher risk of hip fractures. The risk factors for fracture in patients with acromegaly were consistent with widely accepted risk factors in the general population.

Citations

Citations to this article as recorded by

Nationwide Big Data Studies of Endocrine Diseases Using the Korean National Health Information Database: Research Trends and Standardization of Operational Definitions
Sun Wook Cho, Jung Hee Kim, Kyoung Jin Kim, Beom-Jun Kim, Mee Kyoung Kim, Eun Jung Rhee
Endocrinology and Metabolism.2026; 41(1): 86. CrossRef
Impact of splenectomy on fracture risk via the bone-immune axis: A nationwide Korean cohort study
Seonghyun Kang, Nak-Jun Choi, Kyungdo Han, Yebin Park, Changsin Lee, Sejong Jin, Ga Eun Nam, Jae-Woo Cho
Osteoporosis International.2026;[Epub] CrossRef
Consensus on acromegaly complications: an update
Andrea Giustina, Luigi di Filippo, Maria Fleseriu, Rosario Pivonello, Stephan Petersenn, John Wass, Susan L. Samson, Alberto M. Pereira, Raúl M. Luque, Betina Biagetti, Maria Chiara Zatelli, Ken K Y Ho, Cesar L. Boguszewski, Aart Jan van der Lely, Mark Gu
Pituitary.2026;[Epub] CrossRef
Long-Term Prognosis and Systemic Impact of Acromegaly: Analyses Utilizing Korean National Health Insurance Data
Sangmo Hong, Kyungdo Han, Cheol-Young Park
Endocrinology and Metabolism.2025; 40(1): 1. CrossRef
Musculoskeletal disease in acromegaly—a population-based registry study
Christian Rosendal, Mai Christiansen Arlien-Søborg, Eigil Husted Nielsen, Claus Larsen Feltoft, Åse Krogh Rasmussen, Marianne Skovsager Andersen, Jens Otto Lunde Jørgensen, Jakob Dal
European Journal of Endocrinology.2025; 192(3): 308. CrossRef
Growth Hormone and Bone: Preclinical and Clinical Perspectives
Kevin C.J. Yuen
Endocrine Practice.2025; 31(9): 1197. CrossRef
Bone Health in Acromegaly
Maria Francesca Birtolo, Simona Jaafar, Giacomo Cristofolini, Gherardo Mazziotti, Andrea G. Lania
Endocrinology and Metabolism Clinics of North America.2025; 54(4): 637. CrossRef
Osteoporosis and fragility fractures in patients with acromegaly: A two-center cross-sectional study
Mauricio Alvarez, Juliana Rincon, Maria Mercedes Ulloa, Oswaldo Rincon, Liliana Mejia, Alejandra Alvarado, Andres Pereira, Mónica Bernal
World Journal of Orthopedics.2025;[Epub] CrossRef
Novel approach to bone comorbidity in resistant acromegaly
Stefano Frara, Matteo Acanfora, Vincenzo Franzese, Maria Luisa Brandi, Marco Losa, Andrea Giustina
Pituitary.2024; 27(6): 813. 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

Citations to this article as recorded by

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
Surveying the features of screening strategies for non-alcoholic fatty liver disease: A scoping review
Nazanin Mir, Hamid R Baradaran, Seidamir Pasha Tabaeian, Nasrin Abolhasanbeigi Gallehzan, Aziz Rezapour
Journal of Education and Health Promotion.2026;[Epub] 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

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