- Calcium & Bone Metabolism
Big Data Articles (National Health Insurance Service Database)
- Hip Fracture Risk According to Diabetic Kidney Disease Phenotype in a Korean Population
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Seung Eun Lee, Juhwan Yoo, Kyoung-Ah Kim, Kyungdo Han, Han Seok Choi
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Endocrinol Metab. 2022;37(1):148-158. Published online February 28, 2022
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DOI: https://doi.org/10.3803/EnM.2021.1315
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 Abstract
 PDF Supplementary Material PubReader  ePub
- Background
Diabetic kidney disease (DKD) is associated with an elevated risk of fractures. However, little is known about the association between proteinuric or non-proteinuric DKD and the risk of hip fracture. Thus, we investigated the incidence of hip fractures among Korean adults with type 2 diabetes mellitus (T2DM) stratified by DKD phenotype.
Methods
In this retrospective cohort study using the Korean National Health Insurance Service database, patients with T2DM who received at least one general health checkup between 2009 and 2012 were followed until the date of hip fracture, death, or December 31, 2018. We classified the DKD phenotype by proteinuria and estimated glomerular filtration rate (eGFR), as follows: no DKD (PU−GFR−), proteinuric DKD with normal eGFR (PU+GFR−), non-proteinuric DKD with reduced eGFR (PU−GFR+), and proteinuric DKD with reduced eGFR (PU+GFR+)
Results
The cumulative incidence of hip fractures was highest in the PU+GFR+ group, followed by the PU−GFR+ group and the PU+GFR− group. After adjustment for confounding factors, the hazard ratio (HR) for hip fracture was still highest in the PU+GFR+ group. However, the PU+GFR− group had a higher HR for hip fracture than the PU−GFR+ group (PU+GFR+ : HR, 1.69; 95% confidence interval [CI], 1.57 to 1.81; PU+GFR− : HR, 1.37; 95% CI, 1.30 to 1.46; PU−GFR+ : HR, 1.20; 95% CI, 1.16 to 1.24 using the PU−GFR− group as the reference category).
Conclusion
The present study demonstrated that DKD was significantly associated with a higher risk of hip fracture, with proteinuria as a major determinant.
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Citations
Citations to this article as recorded by  - Proteinuria screening and risk of bone fracture: a retrospective cohort study using a nationwide population-based database
Akira Okada, Akira Honda, Hideaki Watanabe, Yusuke Sasabuchi, Shotaro Aso, Kayo Ikeda Kurakawa, Masaomi Nangaku, Toshimasa Yamauchi, Hideo Yasunaga, Hirotaka Chikuda, Takashi Kadowaki, Satoko Yamaguchi
Clinical Kidney Journal.2024;[Epub] CrossRef - Fracture risks associated with sodium-glucose cotransporter-2 inhibitors in type 2 diabetes patients across eGFR and albuminuria categories: A population-based study in Hong Kong
David Tak Wai Lui, Tingting Wu, Eric Ho Man Tang, Ivan Chi Ho Au, Chi Ho Lee, Yu Cho Woo, Kathryn Choon Beng Tan, Carlos King Ho Wong
Diabetes Research and Clinical Practice.2023; 197: 110576. CrossRef - Diagnose und Management der Osteoporose bei Diabetes mellitus (Update 2023)
Christian Muschitz, Alexandra Kautzky-Willer, Yvonne Winhofer, Martina Rauner, Judith Haschka, Daniel Cejka, Robert Wakolbinger-Habel, Peter Pietschmann
Wiener klinische Wochenschrift.2023; 135(S1): 207. CrossRef - Association between exercise and risk of fractures in new-onset type 2 diabetes: a retrospective cohort study
Seung Eun Lee, Juhwan Yoo, Bong-Seong Kim, Kyoung-Ah Kim, Kyungdo Han, Han Seok Choi
Archives of Osteoporosis.2023;[Epub] CrossRef - Two-Year Changes in Diabetic Kidney Disease Phenotype and the Risk of Heart Failure: A Nationwide Population-Based Study in Korea
Seung Eun Lee, Juhwan Yoo, Han Seok Choi, Kyungdo Han, Kyoung-Ah Kim
Diabetes & Metabolism Journal.2023; 47(4): 523. CrossRef
- Adrenal Gland
- Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea
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Eu Jeong Ku, Chaelin Lee, Jaeyoon Shim, Sihoon Lee, Kyoung-Ah Kim, Sang Wan Kim, Yumie Rhee, Hyo-Jeong Kim, Jung Soo Lim, Choon Hee Chung, Sung Wan Chun, Soon-Jib Yoo, Ohk-Hyun Ryu, Ho Chan Cho, A Ram Hong, Chang Ho Ahn, Jung Hee Kim, Man Ho Choi
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Endocrinol Metab. 2021;36(5):1131-1141. Published online October 21, 2021
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DOI: https://doi.org/10.3803/EnM.2021.1149
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6,315
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227
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12
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11
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Abstract
PDFSupplementary MaterialPubReader ePub
- Background
Conventional diagnostic approaches for adrenal tumors require multi-step processes, including imaging studies and dynamic hormone tests. Therefore, this study aimed to discriminate adrenal tumors from a single blood sample based on the combination of liquid chromatography-mass spectrometry (LC-MS) and machine learning algorithms in serum profiling of adrenal steroids.
Methods
The LC-MS-based steroid profiling was applied to serum samples obtained from patients with nonfunctioning adenoma (NFA, n=73), Cushing’s syndrome (CS, n=30), and primary aldosteronism (PA, n=40) in a prospective multicenter study of adrenal disease. The decision tree (DT), random forest (RF), and extreme gradient boost (XGBoost) were performed to categorize the subtypes of adrenal tumors.
Results
The CS group showed higher serum levels of 11-deoxycortisol than the NFA group, and increased levels of tetrahydrocortisone (THE), 20α-dihydrocortisol, and 6β-hydroxycortisol were found in the PA group. However, the CS group showed lower levels of dehydroepiandrosterone (DHEA) and its sulfate derivative (DHEA-S) than both the NFA and PA groups. Patients with PA expressed higher serum 18-hydroxycortisol and DHEA but lower THE than NFA patients. The balanced accuracies of DT, RF, and XGBoost for classifying each type were 78%, 96%, and 97%, respectively. In receiver operating characteristics (ROC) analysis for CS, XGBoost, and RF showed a significantly greater diagnostic power than the DT. However, in ROC analysis for PA, only RF exhibited better diagnostic performance than DT.
Conclusion
The combination of LC-MS-based steroid profiling with machine learning algorithms could be a promising one-step diagnostic approach for the classification of adrenal tumor subtypes.
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Citations
Citations to this article as recorded by - Treating Primary Aldosteronism-Induced Hypertension: Novel Approaches and Future Outlooks
Nathan Mullen, James Curneen, Padraig T Donlon, Punit Prakash, Irina Bancos, Mark Gurnell, Michael C Dennedy
Endocrine Reviews.2024; 45(1): 125. CrossRef - Steroid profiling in adrenal disease
Danni Mu, Dandan Sun, Xia Qian, Xiaoli Ma, Ling Qiu, Xinqi Cheng, Songlin Yu
Clinica Chimica Acta.2024; 553: 117749. CrossRef - Plasma Steroid Profiling Combined With Machine Learning for the Differential Diagnosis in Mild Autonomous Cortisol Secretion From Nonfunctioning Adenoma in Patients With Adrenal Incidentalomas
Danni Mu, Xia Qian, Yichen Ma, Xi Wang, Yumeng Gao, Xiaoli Ma, Shaowei Xie, Lian Hou, Qi Zhang, Fang Zhao, Liangyu Xia, Liling Lin, Ling Qiu, Jie Wu, Songlin Yu, Xinqi Cheng
Endocrine Practice.2024; 30(7): 647. CrossRef - Mild autonomous cortisol secretion: pathophysiology, comorbidities and management approaches
Alessandro Prete, Irina Bancos
Nature Reviews Endocrinology.2024; 20(8): 460. CrossRef - Plasma Steroid Profiling Between Patients With and Without Diabetes Mellitus in Nonfunctioning Adrenal Incidentalomas
Yui Nakano, Maki Yokomoto-Umakoshi, Kohta Nakatani, Hironobu Umakoshi, Hiroshi Nakao, Masamichi Fujita, Hiroki Kaneko, Norifusa Iwahashi, Tatsuki Ogasawara, Tazuru Fukumoto, Yayoi Matsuda, Ryuichi Sakamoto, Yoshihiro Izumi, Takeshi Bamba, Yoshihiro Ogawa
Journal of the Endocrine Society.2024;[Epub] CrossRef - Serum and hair steroid profiles in patients with nonfunctioning pituitary adenoma undergoing surgery: A prospective observational study
Seung Shin Park, Yong Hwy Kim, Ho Kang, Chang Ho Ahn, Dong Jun Byun, Man Ho Choi, Jung Hee Kim
The Journal of Steroid Biochemistry and Molecular Biology.2023; 230: 106276. CrossRef - Recent Updates on the Management of Adrenal Incidentalomas
Seung Shin Park, Jung Hee Kim
Endocrinology and Metabolism.2023; 38(4): 373. CrossRef - LC-MS based simultaneous profiling of adrenal hormones of steroids, catecholamines, and metanephrines
Jongsung Noh, Chaelin Lee, Jung Hee Kim, Seung Woon Myung, Man Ho Choi
Journal of Lipid Research.2023; 64(11): 100453. CrossRef - 2023 Korean Endocrine Society Consensus Guidelines for the Diagnosis and Management of Primary Aldosteronism
Jeonghoon Ha, Jung Hwan Park, Kyoung Jin Kim, Jung Hee Kim, Kyong Yeun Jung, Jeongmin Lee, Jong Han Choi, Seung Hun Lee, Namki Hong, Jung Soo Lim, Byung Kwan Park, Jung-Han Kim, Kyeong Cheon Jung, Jooyoung Cho, Mi-kyung Kim, Choon Hee Chung
Endocrinology and Metabolism.2023; 38(6): 597. CrossRef - Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer
Aliya Lakhani, Da Hyun Kang, Yea Eun Kang, Junyoung O. Park
Endocrinology and Metabolism.2023; 38(6): 619. CrossRef - Prevalence and Characteristics of Adrenal Tumors in an Unselected Screening Population
Ying Jing, Jinbo Hu, Rong Luo, Yun Mao, Zhixiao Luo, Mingjun Zhang, Jun Yang, Ying Song, Zhengping Feng, Zhihong Wang, Qingfeng Cheng, Linqiang Ma, Yi Yang, Li Zhong, Zhipeng Du, Yue Wang, Ting Luo, Wenwen He, Yue Sun, Fajin Lv, Qifu Li, Shumin Yang
Annals of Internal Medicine.2022; 175(10): 1383. CrossRef
- Study Design and Protocol for a Randomized Controlled Trial to Assess Long-Term Efficacy and Safety of a Triple Combination of Ezetimibe, Fenofibrate, and Moderate-Intensity Statin in Patients with Type 2 Diabetes and Modifiable Cardiovascular Risk Factors (ENSEMBLE)
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Nam Hoon Kim, Juneyoung Lee, Suk Chon, Jae Myung Yu, In-Kyung Jeong, Soo Lim, Won Jun Kim, Keeho Song, Ho Chan Cho, Hea Min Yu, Kyoung-Ah Kim, Sang Soo Kim, Soon Hee Lee, Chong Hwa Kim, Soo Heon Kwak, Yong‐ho Lee, Choon Hee Chung, Sihoon Lee, Heung Yong Jin, Jae Hyuk Lee, Gwanpyo Koh, Sang-Yong Kim, Jaetaek Kim, Ju Hee Lee, Tae Nyun Kim, Hyun Jeong Jeon, Ji Hyun Lee, Jae-Han Jeon, Hye Jin Yoo, Hee Kyung Kim, Hyeong-Kyu Park, Il Seong Nam-Goong, Seongbin Hong, Chul Woo Ahn, Ji Hee Yu, Jong Heon Park, Keun-Gyu Park, Chan Ho Park, Kyong Hye Joung, Ohk-Hyun Ryu, Keun Yong Park, Eun-Gyoung Hong, Bong-Soo Cha, Kyu Chang Won, Yoon-Sok Chung, Sin Gon Kim
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Received April 1, 2024 Accepted June 12, 2024 Published online August 22, 2024
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DOI: https://doi.org/10.3803/EnM.2024.1995
[Epub ahead of print]
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Abstract
PDFPubReader ePub
- Background
Atherogenic dyslipidemia, which is frequently associated with type 2 diabetes (T2D) and insulin resistance, contributes to the development of vascular complications. Statin therapy is the primary approach to dyslipidemia management in T2D, however, the role of non-statin therapy remains unclear. Ezetimibe reduces cholesterol burden by inhibiting intestinal cholesterol absorption. Fibrates lower triglyceride levels and increase high-density lipoprotein cholesterol (HDL-C) levels via peroxisome proliferator- activated receptor alpha agonism. Therefore, when combined, these drugs effectively lower non-HDL-C levels. Despite this, few clinical trials have specifically targeted non-HDL-C, and the efficacy of triple combination therapies, including statins, ezetimibe, and fibrates, has yet to be determined.
Methods
This is a multicenter, prospective, randomized, open-label, active-comparator controlled trial involving 3,958 eligible participants with T2D, cardiovascular risk factors, and elevated non-HDL-C (≥100 mg/dL). Participants, already on moderate-intensity statins, will be randomly assigned to either Ezefeno (ezetimibe/fenofibrate) addition or statin dose-escalation. The primary end point is the development of a composite of major adverse cardiovascular and diabetic microvascular events over 48 months.
Conclusion
This trial aims to assess whether combining statins, ezetimibe, and fenofibrate is as effective as, or possibly superior to, statin monotherapy intensification in lowering cardiovascular and microvascular disease risk for patients with T2D. This could propose a novel therapeutic approach for managing dyslipidemia in T2D.
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