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Brief Report
Diabetes, obesity and metabolism
Association of Steatotic Liver Disease with Retinal Vascular Occlusion: The Influence of Obesity in a Large Health Screening Cohort
Younjin Oh, Su Jeong Song
Endocrinol Metab. 2025;40(2):299-303.   Published online February 12, 2025
DOI: https://doi.org/10.3803/EnM.2024.2181
  • 4,002 View
  • 97 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
In this cross-sectional study, we aimed to investigate the relationship between steatotic liver disease (SLD) and retinal abnormalities in a cohort undergoing health screening. Our study included 353,607 participants who underwent fundus photography and abdominal ultrasonography at least once at the Kangbuk Samsung Health Promotion Center from 2002 to 2022. After adjusting for age and sex, the risk of retinal vein occlusion (RVO) significantly increased with the presence of non-alcoholic fatty liver disease, metabolic dysfunction-associated fatty liver disease, and metabolic dysfunction-associated SLD, with odds ratios of 1.259 (95% confidence interval [CI], 1.050 to 1.510), 1.498 (95% CI, 1.249 to 1.796), and 1.342 (95% CI, 1.121 to 1.605), respectively. However, these associations weakened after adjusting for body mass index. No statistically significant associations were observed with other retinal disorders after adjusting for age, sex, and other confounding factors. Our findings suggest that obesity may mediate the relationship between SLD and RVO, while other retinal abnormalities may be more closely associated with known risk factors rather than SLD itself.

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Citations to this article as recorded by  
  • Association Between Baseline Smoking Exposure and Incident Branch Retinal Vein Occlusion: A Retrospective Cohort Study
    EunAh Kim, Nak Gyeong Ko, Mihyeon Jin
    Ophthalmic Epidemiology.2026; : 1.     CrossRef
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Review Articles
Diabetes, obesity and metabolism
Artificial Intelligence Applications in Diabetic Retinopathy: What We Have Now and What to Expect in the Future
Mingui Kong, Su Jeong Song
Endocrinol Metab. 2024;39(3):416-424.   Published online June 10, 2024
DOI: https://doi.org/10.3803/EnM.2023.1913
  • 22,364 View
  • 470 Download
  • 22 Web of Science
  • 33 Crossref
AbstractAbstract PDFPubReader   ePub   
Diabetic retinopathy (DR) is a major complication of diabetes mellitus and is a leading cause of vision loss globally. A prompt and accurate diagnosis is crucial for ensuring favorable visual outcomes, highlighting the need for increased access to medical care. The recent remarkable advancements in artificial intelligence (AI) have raised high expectations for its role in disease diagnosis and prognosis prediction across various medical fields. In addition to achieving high precision comparable to that of ophthalmologists, AI-based diagnosis of DR has the potential to improve medical accessibility, especially through telemedicine. In this review paper, we aim to examine the current role of AI in the diagnosis of DR and explore future directions.

Citations

Citations to this article as recorded by  
  • Hardware-aware deep learning for multi-stage diabetic retinopathy screening on edge devices
    Shijida Shain, J Alfred Daniel
    Biomedical Signal Processing and Control.2027; 129: 111406.     CrossRef
  • Emerging innovations in ophthalmic drug delivery for diabetic retinopathy: a translational perspective
    Souvik Adak, Vaishnavi Suresh Jadhav, Dharmendra Kumar Khatri
    Drug Delivery and Translational Research.2026; 16(4): 1064.     CrossRef
  • Investigating the Correlation Between Ocular Diseases for Retinal Layer Fractal Dimensions Analysis Using Multiclass Segmentation With Attention U‐Net
    M. Saranya, K. A. Sunitha, A. Asuntha, Pratyusha Ganne
    International Journal of Imaging Systems and Technology.2026;[Epub]     CrossRef
  • Research Status of Diabetic Retinopathy Prediction Models: From Traditional Risk Factors to Artificial Intelligence
    银娟 李
    Journal of Clinical Personalized Medicine.2026; 05(01): 332.     CrossRef
  • The Burden of Delayed Diabetic Retinopathy Management and Use of Artificial Intelligence-Driven Screening Tools: A Systematic Literature Review
    Firas Rahhal, Jun Zhang, Munia Mukherjee
    Ophthalmology and Therapy.2026; 15(3): 925.     CrossRef
  • Research Advances in OCT Biomarkers for Predicting Visual Outcomes in DME
    越遆 孔
    Advances in Clinical Medicine.2026; 16(02): 1761.     CrossRef
  • BigEye: a clinically interpretable deep learning framework for diabetic retinopathy detection and stage prediction
    Hunter Mathias Gill, Doaa Hassan Salem, Okiemute Beatrice Omoru, Frank Dash Bogan, Jeffrey Xiao Liu, Michael Happe, Amir Reza Hajrasouliha, Sarath Chandra Janga
    Scientific Reports.2026;[Epub]     CrossRef
  • COMPARISON OF ARTIFICIAL INTELLIGENCE SYSTEMS FOR THE SCREENING OF DIABETIC RETINOPATHY. A REVIEW
    Oksana P. Vitovska, Liudmyla S. Vasylieva, Mariana M. Semehen
    Clinical and Preventive Medicine.2026; (1): 178.     CrossRef
  • Lesion-Aware Ordinal Transformer for Diabetic Retinopathy Classification from Fundus Images
    Ambuj Kumar Agarwal, Abu Bakar Bin Abdul Hamid, Danish Ather, Raj Gaurang Tiwari, Indrajit De, Kunchanapaalli Rama Krishna
    Biomedical & Pharmacology Journal.2026; 1(19): 233.     CrossRef
  • Pregnancy and Retinal Disorders: Pathophysiology, Clinical Features, and Management
    Minsub Lee, Hyeong Min Kim, Hyungwoo Lee, Hyewon Chung
    Journal of Retina.2026; 11(1): 1.     CrossRef
  • From pixels to precision: Artificial intelligence in diabetic eye disease screening and management
    Francesco Cappellani, Matteo Capobianco, Federico Visalli, Marieme Khouyyi, Mutali Musa, Alessandro Avitabile, Inferrera Leandro, Rosa Giglio, Daniele Tognetto, Caterina Gagliano, Fabiana D’Esposito, Marco Zeppieri
    World Journal of Diabetes.2026;[Epub]     CrossRef
  • Harnessing technology and AI-driven innovations in diabetes management: addressing clinical challenges and the rising global burden
    Parvinder Nagar, Mohammad Rashid, Swamita Arora, Sanjar Alam, Mohit Agrawal, Parakh Basist
    International Journal of Diabetes in Developing Countries.2026;[Epub]     CrossRef
  • The evolving role of artificial intelligence in optimizing treatment and patient selection in diabetic macular edema
    Dhanashree Ratra, Ramachandran Rajalakshmi, M Suchetha, Devanjali Relan, J S Deepikasri, Sundaramoorthy Sathishkumar, Aashna Ratra
    Indian Journal of Ophthalmology.2026; 74(5): 690.     CrossRef
  • AI-driven healthcare: a trend toward better healthcare or the emergence of public health burden
    Virak Sorn, Techly San, Sokchan Lorn
    Frontiers in Digital Health.2026;[Epub]     CrossRef
  • Artificial intelligence in diabetic macular edema: Expanding clinical vision
    Aditya Barigali
    Indian Journal of Ophthalmology.2026; 74(6): 929.     CrossRef
  • Two contrasting cases of adrenal insufficiency in adults: a case report
    Yingqi Guo
    Journal of Medical Case Reports.2026;[Epub]     CrossRef
  • Artificial intelligence for early detection of diabetic retinopathy: A vision transformer-based approach
    Asma ElAdel, Imen Filali, Mourad Zaied, Anirban Bhowmick
    PLOS One.2026; 21(7): e0350854.     CrossRef
  • Revolutionizing diabetic retinopathy screening and management: The role of artificial intelligence and machine learning
    Mona Mohamed Ibrahim Abdalla, Jaiprakash Mohanraj
    World Journal of Clinical Cases.2025;[Epub]     CrossRef
  • Retinal Biomarkers in Diabetic Retinopathy: From Early Detection to Personalized Treatment
    Georgios Chondrozoumakis, Eleftherios Chatzimichail, Oussama Habra, Efstathios Vounotrypidis, Nikolaos Papanas, Zisis Gatzioufas, Georgios D. Panos
    Journal of Clinical Medicine.2025; 14(4): 1343.     CrossRef
  • Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence
    David B. Olawade, Kusal Weerasinghe, Mathugamage Don Dasun Eranga Mathugamage, Aderonke Odetayo, Nicholas Aderinto, Jennifer Teke, Stergios Boussios
    Medicina.2025; 61(3): 433.     CrossRef
  • Advancing Diabetic Retinopathy Screening: A Systematic Review of Artificial Intelligence and Optical Coherence Tomography Angiography Innovations
    Alireza Hayati, Mohammad Reza Abdol Homayuni, Reza Sadeghi, Hassan Asadigandomani, Mohammad Dashtkoohi, Sajad Eslami, Mohammad Soleimani
    Diagnostics.2025; 15(6): 737.     CrossRef
  • What do You Need to Know after Diabetes and before Diabetic Retinopathy?
    Shiyu Zhang, Jia Liu, Heng Zhao, Yuan Gao, Changhong Ren, Xuxiang Zhang
    Aging and disease.2025;[Epub]     CrossRef
  • OCT Angiography Assessment of Type 1 Diabetes Mellitus Patients Without Diabetic Retinopathy: A 3-Year Follow-Up Study
    Alexandra Oltea Dan, Carmen Luminița Mocanu, Alin Ștefan Ștefănescu-Dima, Andreea Cornelia Tănasie, Veronica Elena Maria, Anca Elena Târtea, Andrei Theodor Bălășoiu
    Diagnostics.2025; 15(13): 1703.     CrossRef
  • The Role of Artificial Intelligence in the Diagnosis and Management of Diabetic Retinopathy
    Areeb Ansari, Nabiha Ansari, Usman Khalid, Daniel Markov, Kristian Bechev, Vladimir Aleksiev, Galabin Markov, Elena Poryazova
    Journal of Clinical Medicine.2025; 14(14): 5150.     CrossRef
  • Computer-aided diagnosis of colorectal polyps: assisted or autonomous?
    Yuichi Mori, Cesare Hassan
    Clinical Endoscopy.2025; 58(4): 514.     CrossRef
  • RetinoDeep: Leveraging Deep Learning Models for Advanced Retinopathy Diagnostics
    Sachin Kansal, Bajrangi Kumar Mishra, Saniya Sethi, Kanika Vinayak, Priya Kansal, Jyotindra Narayan
    Sensors.2025; 25(16): 5019.     CrossRef
  • XMal-CNN: An Explainable Deep Neural Model for Automated Malaria Detection from Blood Smear Images
    RiddhiKumari Patel, Safvan Vahora
    Journal of Innovative Image Processing.2025; 7(3): 739.     CrossRef
  • Artificial Intelligence–Enhanced Multi-Algorithm R Shiny Application for Predictive Modeling and Analytics: Case Study of Alzheimer Disease Diagnostics
    Han Wenzheng, Edmund F Agyemang, Sudesh K Srivastav, Jeffrey G Shaffer, Samuel Kakraba
    JMIR Aging.2025; 8: e70272.     CrossRef
  • Effectiveness of AI-Based Tools in Detecting Diabetic Retinopathy in Low- and Middle-Income Countries: A Systematic Review of Diagnostic Performance and Implementation Feasibility
    Nneoma Onyeze, Sami Sartawi, Zain Nayyer
    Cureus.2025;[Epub]     CrossRef
  • Diagnostic Accuracy of Artificial Intelligence in Predicting Anti-VEGF Treatment Response in Diabetic Macular Edema: A Systematic Review and Meta-Analysis
    Faisal A. Al-Harbi, Mohanad A. Alkuwaiti, Meshari A. Alharbi, Ahmed A. Alessa, Ajwan A. Alhassan, Elan A. Aleidan, Fatimah Y. Al-Theyab, Mohammed Alfalah, Sajjad M. AlHaddad, Ahmed Y. Azzam
    Journal of Clinical Medicine.2025; 14(22): 8177.     CrossRef
  • Precision Medicine in Diabetic Retinopathy: The Role of Genetic and Epigenetic Biomarkers
    Snježana Kaštelan, Tamara Nikuševa-Martić, Daria Pašalić, Tomislav Matejić, Antonela Gverović Antunica
    Journal of Clinical Medicine.2025; 14(24): 8778.     CrossRef
  • ARTIFICIAL INTELLIGENCE–ENHANCED RETINAL IMAGING IN DIABETIC RETINOPATHY: OPPORTUNITIES AND LIMITATIONS
    Julia Pawłowska, Kinga Szyszka, Anna Baranowska, Marta Cieślak, Laura Kurczoba, Aleksandra Oparcik, Anastazja Orłowa, Anita Pakuła, Klaudia Martyna Patrzykąt, Kamil Turlej
    International Journal of Innovative Technologies in Social Science.2025;[Epub]     CrossRef
  • Artificial intelligence and community ophthalmology: Challenges and the way forward
    Shiva Prasad Sahoo
    Odisha Journal of Ophthalmology.2024; 31(1): 1.     CrossRef
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Effects of Incretin-Based Therapies on Diabetic Microvascular Complications
Yu Mi Kang, Chang Hee Jung
Endocrinol Metab. 2017;32(3):316-325.   Published online September 18, 2017
DOI: https://doi.org/10.3803/EnM.2017.32.3.316
  • 9,393 View
  • 74 Download
  • 16 Web of Science
  • 16 Crossref
AbstractAbstract PDFPubReader   

The morbidity and mortality associated with diabetic complications impose a huge socioeconomic burden worldwide. Therefore, the ultimate goal of managing diabetes mellitus (DM) is to lower the risk of macrovascular complications and highly morbid microvascular complications such as diabetic nephropathy (DN) and diabetic retinopathy (DR). Potential benefits of incretin-based therapies such as glucagon-like peptide 1 receptor agonists (GLP-1 RAs) and dipeptidyl peptidase-4 (DPP-4) inhibitors on the diabetic macrovascular complications have been recently suggested, owing to their pleiotropic effects on multiple organ systems. However, studies primarily investigating the role of these therapies in diabetic microvascular complications are rare. Nevertheless, preclinical and limited clinical data suggest the potential protective effect of incretin-based agents against DN and DR via their anti-inflammatory, antioxidative, and antiapoptotic properties. Evidence also suggests that these incretin-dependent and independent beneficial effects are not necessarily associated with the glucose-lowering properties of GLP-1 RAs and DPP-4 inhibitors. Hence, in this review, we revisit the preclinical and clinical evidence of incretin-based therapy for DR and DN, the two most common, morbid complications in individuals with DM. In addition, the review discusses a few recent studies raising concerns of aggravating DR with the use of incretin-based therapies.

Citations

Citations to this article as recorded by  
  • Risk of Sight-Threatening Diabetic Retinopathy with Glucagon-Like Peptide-1 Receptor Agonist Use in Routine Clinical Practice
    Andrew J. Barkmeier, Yihong Deng, Kavya Sindhu Swarna, Jeph Herrin, Eric C. Polley, Guillermo E. Umpierrez, Rodolfo J. Galindo, Joseph S. Ross, Mindy M. Mickelson, Rozalina G. McCoy
    Ophthalmology Retina.2026; 10(2): 142.     CrossRef
  • Redox-Driven Blood–Nerve Barrier Dysfunction in Diabetic Peripheral Neuropathy: Mechanisms and Therapeutic Opportunities
    Wei-Hsiu Huang, Chih-Shung Wong
    Antioxidants.2026; 15(6): 670.     CrossRef
  • Glucagon-Like Peptide-1 Receptor Agonists and Risk of Neovascular Age-Related Macular Degeneration
    Reut Shor, Andrew Mihalache, Atefeh Noori, Renana Shor, Radha P. Kohly, Marko M. Popovic, Rajeev H. Muni
    JAMA Ophthalmology.2025; 143(7): 587.     CrossRef
  • Efficacy and Safety of the Utilization of Dipeptidyl Peptidase IV Inhibitors in Diabetic Patients with Chronic Kidney Disease: A Meta-Analysis of Randomized Clinical Trials
    Moeber Mahzari, Muhannad Alqirnas, Moustafa Alhamadh, Faisal Alrasheed, Abdulrahman Alhabeeb, Wedad Al Madani, Hussain Aldera
    Diabetes, Metabolic Syndrome and Obesity.2024; Volume 17: 1425.     CrossRef
  • Comparative Effectiveness of Glucagon-Like Peptide-1 Receptor Agonists, Sodium-Glucose Cotransporter 2 Inhibitors, Dipeptidyl Peptidase-4 Inhibitors, and Sulfonylureas for Sight-Threatening Diabetic Retinopathy
    Andrew J. Barkmeier, Jeph Herrin, Kavya Sindhu Swarna, Yihong Deng, Eric C. Polley, Guillermo E. Umpierrez, Rodolfo J. Galindo, Joseph S. Ross, Mindy M. Mickelson, Rozalina G. McCoy
    Ophthalmology Retina.2024; 8(10): 943.     CrossRef
  • Incretin-based therapy: a new horizon in diabetes management
    Malek Zarei, Navideh Sahebi Vaighan, Mohammad Hadi Farjoo, Soosan Talebi, Mohammad Zarei
    Journal of Diabetes & Metabolic Disorders.2024; 23(2): 1665.     CrossRef
  • Anti-Inflammatory Effects of GLP-1R Activation in the Retina
    Alessandra Puddu, Davide Maggi
    International Journal of Molecular Sciences.2022; 23(20): 12428.     CrossRef
  • Diabetes and Its Complications: Therapies Available, Anticipated and Aspired
    Anu Grover, Komal Sharma, Suresh Gautam, Srishti Gautam, Monica Gulati, Sachin Kumar Singh
    Current Diabetes Reviews.2021; 17(4): 397.     CrossRef
  • SGLT2 Inhibitors, GLP-1 Agonists, and DPP-4 Inhibitors in Diabetes and Microvascular Complications: A Review
    Christopher El Mouhayyar, Ruba Riachy, Abir Bou Khalil, Asaad Eid, Sami Azar
    International Journal of Endocrinology.2020; 2020: 1.     CrossRef
  • Novel therapeutic agents for the treatment of diabetic kidney disease
    Rachel E. Hartman, P.S.S. Rao, Mariann D. Churchwell, Susan J. Lewis
    Expert Opinion on Investigational Drugs.2020; 29(11): 1277.     CrossRef
  • Nationwide Trends in Pancreatitis and Pancreatic Cancer Risk Among Patients With Newly Diagnosed Type 2 Diabetes Receiving Dipeptidyl Peptidase 4 Inhibitors
    Minyoung Lee, Jiyu Sun, Minkyung Han, Yongin Cho, Ji-Yeon Lee, Chung Mo Nam, Eun Seok Kang
    Diabetes Care.2019; 42(11): 2057.     CrossRef
  • Effects of Dipeptidyl Peptidase-4 Inhibitors on Renal Outcomes in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis
    Jae Hyun Bae, Sunhee Kim, Eun-Gee Park, Sin Gon Kim, Seokyung Hahn, Nam Hoon Kim
    Endocrinology and Metabolism.2019; 34(1): 80.     CrossRef
  • Serum adipocytokines are associated with microalbuminuria in patients with type 1 diabetes and incipient chronic complications
    Tomislav Bulum, Marijana Vučić Lovrenčić, Martina Tomić, Sandra Vučković-Rebrina, Vinko Roso, Branko Kolarić, Vladimir Vuksan, Lea Duvnjak
    Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2019; 13(1): 496.     CrossRef
  • Protective Effects of Incretin Against Age-Related Diseases
    Di Zhang, Mingzhu Ma, Yueze Liu
    Current Drug Delivery.2019; 16(9): 793.     CrossRef
  • Dipeptidyl Peptidase-4 Inhibitors and Inflammation: Dpp-4 Inhibitors Improve Mean Pleatelet Volume and Gamma Glutamyl Transferase Level
    Deniz Avcı
    Journal of Biosciences and Medicines.2019; 07(02): 42.     CrossRef
  • The role of dipeptidylpeptidase-4 inhibitors in management of cardiovascular disease in diabetes; focus on linagliptin
    Annayya R. Aroor, Camila Manrique-Acevedo, Vincent G. DeMarco
    Cardiovascular Diabetology.2018;[Epub]     CrossRef
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Diabetes, Obesity and Metabolism
Current Challenges in Diabetic Retinopathy: Are We Really Doing Better?
Jae Hyuck Lee, Su Jeong Song
Endocrinol Metab. 2016;31(2):254-257.   Published online June 10, 2016
DOI: https://doi.org/10.3803/EnM.2016.31.2.254
  • 7,542 View
  • 39 Download
  • 7 Web of Science
  • 7 Crossref
AbstractAbstract PDFPubReader   

Management of diabetic complications has been a worldwide major global health issue for decades. Recent studies from many parts of the world indicate improvement in this area. However, it is unknown if such an improvement is being realized in Koreans. Although there is limited information regarding diabetic retinopathy management among Koreans, recent epidemiologic studies have indicated improved screening rates and less frequent visual impairment among type 2 diabetics. Moreover, results achieved with new diagnostic and treatment modalities aimed to improve diabetic retinopathy management are encouraging for both physicians and patients.

Citations

Citations to this article as recorded by  
  • A meta-analysis of prevalence of diabetic retinopathy in Asia
    Clyve Y. YAOW, Snow Y. LIN, Jieling XIAO, Jin H. KOH, Jie N. YONG, Phoebe W. TAY, See T. TAN
    Minerva Endocrinology.2025;[Epub]     CrossRef
  • Plasma amino acids and oxylipins as potential multi-biomarkers for predicting diabetic macular edema
    Sang Youl Rhee, Eun Sung Jung, Dong Ho Suh, Su Jin Jeong, Kiyoung Kim, Suk Chon, Seung-Young Yu, Jeong-Taek Woo, Choong Hwan Lee
    Scientific Reports.2021;[Epub]     CrossRef
  • Urine protein: Urine creatinine ratio correlation with diabetic retinopathy
    Samya Mujeeb, Gladys R Rodrigues, Rajesh R Nayak, Ajay R Kamath, Sumana J Kamath, Gurudutt Kamath
    Indian Journal of Ophthalmology.2021; 69(11): 3359.     CrossRef
  • Diabetic Retinopathy in the Asia-Pacific

    Asia-Pacific Journal of Ophthalmology.2019;[Epub]     CrossRef
  • Plasma glutamine and glutamic acid are potential biomarkers for predicting diabetic retinopathy
    Sang Youl Rhee, Eun Sung Jung, Hye Min Park, Su Jin Jeong, Kiyoung Kim, Suk Chon, Seung-Young Yu, Jeong-Taek Woo, Choong Hwan Lee
    Metabolomics.2018;[Epub]     CrossRef
  • Articles inEndocrinology and Metabolismin 2016
    Won-Young Lee
    Endocrinology and Metabolism.2017; 32(1): 62.     CrossRef
  • Normal-to-mildly increased albuminuria predicts the risk for diabetic retinopathy in patients with type 2 diabetes
    Min-Kyung Lee, Kyung-Do Han, Jae-Hyuk Lee, Seo-Young Sohn, Oak-Kee Hong, Jee-Sun Jeong, Mee-Kyoung Kim, Ki-Hyun Baek, Ki-Ho Song, Hyuk-Sang Kwon
    Scientific Reports.2017;[Epub]     CrossRef
Close layer
Original Article
Clinical Study
Association between Bsm1 Polymorphism in Vitamin D Receptor Gene and Diabetic Retinopathy of Type 2 Diabetes in Korean Population
Yong Joo Hong, Eun Seok Kang, Myoung Jin Ji, Hyung Jin Choi, Taekeun Oh, Sung-Soo Koong, Hyun Jeong Jeon
Endocrinol Metab. 2015;30(4):469-474.   Published online December 31, 2015
DOI: https://doi.org/10.3803/EnM.2015.30.4.469
  • 8,253 View
  • 57 Download
  • 30 Web of Science
  • 30 Crossref
AbstractAbstract PDFPubReader   
Background

Type 2 diabetes is one of the most common diseases with devastating complications. However, genetic susceptibility of diabetic complications has not been clarified. The vitamin D endocrine system is related with calcification and lipolysis, insulin secretion, and may be associated with many complicated disease including diabetes and cardiovascular disease. Recent studies reported that single nucleotide polymorphisms of vitamin D receptor (VDR) gene were associated with diabetic complications.

Methods

In present study, we evaluated the association of BsmI polymorphism of VDR with diabetic complications in Korean diabetes patients. Total of 537 type 2 diabetic subjects from the Endocrinology Clinic of Chungbuk National University Hospital were investigated. Polymerase chain reaction-restriction fragment length polymorphism was used to test the genotype and allele frequency of BsmI (rs1544410; BB, Bb, bb) polymorphisms.

Results

Mean age was 62.44±10.64 years and mean disease duration was 13.65±7.39 years. Patients with B allele (BB or Bb) was significantly associated with lower risk of diabetic retinopathy (severe non-proliferative diabetic retinopathy or proliferative retinopathy; 7.4%, 5/68) compared with patients without B allele (bb; 17.3%, 81/469; P=0.035). This association was also significant after adjusting for hemoglobin A1c level, body mass index, age, sex, and diabetes mellitus duration, concurrent dyslipidemia and hypertension (odds ratio, 2.99; 95% confidence interval, 1.08 to 8.29; P=0.035) in logistic regression analysis.

Conclusion

Our findings suggest that B allele of Bsm1 polymorphism in VDR gene is associated with lower risk of diabetic retinopathy in type 2 diabetic patients. Bsm1 genotype could be used as a susceptibility marker to predict the risk of diabetes complication.

Citations

Citations to this article as recorded by  
  • Meta-analysis of genes and genetic variants implicated in Type II diabetes mellitus, diabetic retinopathy, and diabetic nephropathy
    A.N. Rizza, Nethra Lenin, Yazhini Ramaswamy, Deepak Kumar Sundaramoorthy, Rajiv Raman, Sinnakaruppan Mathavan
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    Mehar Darukhshan Kalim, Sachidananda Behera, Niyamat Ali Siddiqui, Krishna Pandey, Vahab Ali
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    Rakesh Kumar Gupta, Sonal Tiwari, Sakshi Agarwal, Amita Diwakar, Pawan K. Dubey
    Diabetology & Metabolic Syndrome.2025;[Epub]     CrossRef
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    Feifei Li, Qiujing Chen, Yang Dai, Lin Lu
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    Serkan YILDIZ, Serbülent YİĞİT, Ayşe Feyda NURSAL, Nevin KARAKUŞ, Mehmet Kemal TÜMER
    ADO Klinik Bilimler Dergisi.2024; 13(1): 100.     CrossRef
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    Pramod Kumar Sahu, Priyanka Gautam, Gopal Krushna Das, Priyanka Gogoi, Nitika Beri, Rahul Bhatia
    Journal of Family Medicine and Primary Care.2024; 13(8): 3298.     CrossRef
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    Nilufer Kuruca, Aynur Atilla, Muhammed Taha Kaya, Sedat Gokmen, Ayse Feyda Nursal, Ozgur Kilic, Tuba Kuruoglu, Fatih Temocin, Tolga Guvenc, Serbulent Yigit, Dilek Guvenc
    Journal of Investigative Medicine.2024; 72(8): 876.     CrossRef
  • Vitamin D Deficiency as a Risk Factor for Diabetic Retinopathy: A Systematic Review and Meta-Analysis
    Claudia Elena Petrea, Laura Andreea Ghenciu, Roxana Iacob, Emil Robert Stoicescu, Dorel Săndesc
    Biomedicines.2024; 13(1): 68.     CrossRef
  • Association analysis between the VDR gene variants and type 2 diabetes
    Shabnam Salehizadeh, Sara Ramezani, Mojgan Asadi, Mahdi Afshari, Seyed Hamid Jamaldini, Farhad Adhami Moghadam, Mandana Hasanzad
    Journal of Diabetes & Metabolic Disorders.2023; 23(1): 633.     CrossRef
  • Metabolic impact of the VDR rs1544410 in diabetic retinopathy
    Caroline Severo de Assis, Tainá Gomes Diniz, João Otávio Scarano Alcântara, Vanessa Polyana Alves de Sousa Brito, Rayner Anderson Ferreira do Nascimento, Mayara Karla dos Santos Nunes, Alexandre Sérgio Silva, Isabella Wanderley de Queiroga Evangelista, Ma
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    Miłosz Caban, Urszula Lewandowska
    Nutrients.2022; 14(11): 2353.     CrossRef
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    Paula González Rojo, Cristina Pérez Ramírez, José María Gálvez Navas, Laura Elena Pineda Lancheros, Susana Rojo Tolosa, María del Carmen Ramírez Tortosa, Alberto Jiménez Morales
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Review Article
Diabetes, Obesity and Metabolism
Genetic Studies on Diabetic Microvascular Complications: Focusing on Genome-Wide Association Studies
Soo Heon Kwak, Kyong Soo Park
Endocrinol Metab. 2015;30(2):147-158.   Published online June 30, 2015
DOI: https://doi.org/10.3803/EnM.2015.30.2.147
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AbstractAbstract PDFPubReader   

Diabetes is a common metabolic disorder with a worldwide prevalence of 8.3% and is the leading cause of visual loss, end-stage renal disease and amputation. Recently, genome-wide association studies (GWASs) have identified genetic risk factors for diabetic microvascular complications of retinopathy, nephropathy, and neuropathy. We summarized the recent findings of GWASs on diabetic microvascular complications and highlighted the challenges and our opinion on future directives. Five GWASs were conducted on diabetic retinopathy, nine on nephropathy, and one on neuropathic pain. The majority of recent GWASs were underpowered and heterogeneous in terms of study design, inclusion criteria and phenotype definition. Therefore, few reached the genome-wide significance threshold and the findings were inconsistent across the studies. Recent GWASs provided novel information on genetic risk factors and the possible pathophysiology of diabetic microvascular complications. However, further collaborative efforts to standardize phenotype definition and increase sample size are necessary for successful genetic studies on diabetic microvascular complications.

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