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Prevalence of Diabetic Retinopathy in Undiagnosed Diabetic Patients: A Nationwide Population-Based Study
Han Na Jang, Min Kyong Moon, Bo Kyung Koo
Diabetes Metab J. 2022;46(4):620-629.   Published online February 23, 2022
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  • 219 Download
  • 3 Web of Science
  • 4 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
We investigated the prevalence of diabetic retinopathy (DR) in patients with undiagnosed diabetes through a nationwide survey, compared to those with known diabetes.
Among the participants of the Korean National Health and Nutrition Examination Surveys (KNHANES) from 2017 to 2018, individuals aged ≥40 years with diabetes and fundus exam results were enrolled. Sampling weights were applied to represent the entire Korean population. Newly detected diabetes patients through KNHANES were classified under “undiagnosed diabetes.”
Among a total of 9,108 participants aged ≥40 years, 951 were selected for analysis. Of them, 31.3% (standard error, ±2.0%) were classified under “undiagnosed diabetes.” The prevalence of DR in patients with known and undiagnosed diabetes was 24.5%±2.0% and 10.7%±2.2%, respectively (P<0.001). The DR prevalence increased with rising glycosylated hemoglobin (HbA1c) levels in patients with known and undiagnosed diabetes (P for trend=0.001 in both). Among those with undiagnosed diabetes, the prevalence of DR was 6.9%±2.1%, 8.0%±3.4%, 5.6%±5.7%, 16.7%±9.4%, and 42.6%±14.8% for HbA1c levels of <7.0%, 7.0%–7.9%, 8.0%–8.9%, 9.0%–9.9%, and ≥10.0% respectively. There was no difference in the prevalence of hypertension, dyslipidemia, hypertriglyceridemia, or obesity according to the presence or absence of DR.
About one-third of patients with diabetes were unaware of their diabetes, and 10% of them have already developed DR. Considering increasing the prevalence of DR according to HbA1c level was found in patients with undiagnosed diabetes like those with known diabetes, screening and early detection of diabetes and DR are important.


Citations to this article as recorded by  
  • Risk factors of peripheral occlusive arterial disease in patients with diabetic retinopathy due to type 2 diabetes
    Milos Maksimovic
    Srpski arhiv za celokupno lekarstvo.2024; 152(1-2): 50.     CrossRef
  • Gene Expression Analysis in T2DM and Its Associated Microvascular Diabetic Complications: Focus on Risk Factor and RAAS Pathway
    Laxmipriya Jena, Prabhsimran Kaur, Tashvinder Singh, Kangan Sharma, Sushil Kotru, Anjana Munshi
    Molecular Neurobiology.2024;[Epub]     CrossRef
  • Novel Asian-Specific Visceral Adiposity Indices Are Associated with Chronic Kidney Disease in Korean Adults
    Jonghwa Jin, Hyein Woo, Youngeun Jang, Won-Ki Lee, Jung-Guk Kim, In-Kyu Lee, Keun-Gyu Park, Yeon-Kyung Choi
    Diabetes & Metabolism Journal.2023; 47(3): 426.     CrossRef
  • Prevalence of osteosarcopenic obesity and related factors among Iranian older people: Bushehr Elderly Health (BEH) program
    Mozhgan Ahmadinezhad, Mohammad Ali Mansournia, Noushin Fahimfar, Gita Shafiee, Iraj Nabipour, Mahnaz Sanjari, Kazem Khalagi, Mohammad Javad Mansourzadeh, Bagher Larijani, Afshin Ostovar
    Archives of Osteoporosis.2023;[Epub]     CrossRef
Article image
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 Metab J. 2021;45(4):515-525.   Published online February 25, 2021
  • 8,835 View
  • 258 Download
  • 6 Web of Science
  • 5 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Previously developed prediction models for type 2 diabetes mellitus (T2DM) have limited performance. We developed a deep learning (DL) based model using a cohort representative of the Korean population.
This study was conducted on the basis of the National Health Insurance Service-Health Screening (NHIS-HEALS) cohort of Korea. Overall, 335,302 subjects without T2DM at baseline were included. We developed the model based on 80% of the subjects, and verified the power in the remainder. Predictive models for T2DM were constructed using the recurrent neural network long short-term memory (RNN-LSTM) network and the Cox longitudinal summary model. The performance of both models over a 10-year period was compared using a time dependent area under the curve.
During a mean follow-up of 10.4±1.7 years, the mean frequency of periodic health check-ups was 2.9±1.0 per subject. During the observation period, T2DM was newly observed in 8.7% of the subjects. The annual performance of the model created using the RNN-LSTM network was superior to that of the Cox model, and the risk factors for T2DM, derived using the two models were similar; however, certain results differed.
The DL-based T2DM prediction model, constructed using a cohort representative of the population, performs better than the conventional model. After pilot tests, this model will be provided to all Korean national health screening recipients in the future.


Citations to this article as recorded by  
  • Remnant Cholesterol Is an Independent Predictor of Type 2 Diabetes: A Nationwide Population-Based Cohort Study
    Ji Hye Huh, Eun Roh, Seong Jin Lee, Sung-Hee Ihm, Kyung-Do Han, Jun Goo Kang
    Diabetes Care.2023; 46(2): 305.     CrossRef
  • A scoping review of artificial intelligence-based methods for diabetes risk prediction
    Farida Mohsen, Hamada R. H. Al-Absi, Noha A. Yousri, Nady El Hajj, Zubair Shah
    npj Digital Medicine.2023;[Epub]     CrossRef
  • Development of Various Diabetes Prediction Models Using Machine Learning Techniques
    Juyoung Shin, Jaewon Kim, Chanjung Lee, Joon Young Yoon, Seyeon Kim, Seungjae Song, Hun-Sung Kim
    Diabetes & Metabolism Journal.2022; 46(4): 650.     CrossRef
  • Prediction Model for Hypertension and Diabetes Mellitus Using Korean Public Health Examination Data (2002–2017)
    Yong Whi Jeong, Yeojin Jung, Hoyeon Jeong, Ji Hye Huh, Ki-Chul Sung, Jeong-Hun Shin, Hyeon Chang Kim, Jang Young Kim, Dae Ryong Kang
    Diagnostics.2022; 12(8): 1967.     CrossRef
  • Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness
    Juyoung Shin, Joonyub Lee, Taehoon Ko, Kanghyuck Lee, Yera Choi, Hun-Sung Kim
    Journal of Personalized Medicine.2022; 12(11): 1899.     CrossRef
Diabetes and Cancer: Cancer Should Be Screened in Routine Diabetes Assessment
Sunghwan Suh, Kwang-Won Kim
Diabetes Metab J. 2019;43(6):733-743.   Published online December 23, 2019
  • 9,060 View
  • 265 Download
  • 88 Web of Science
  • 94 Crossref
AbstractAbstract PDFPubReader   

Cancer incidence appears to be increased in both type 1 and type 2 diabetes mellitus (DM). DM represents a risk factor for cancer, particularly hepatocellular, hepatobiliary, pancreas, breast, ovarian, endometrial, and gastrointestinal cancers. In addition, there is evidence showing that DM is associated with increased cancer mortality. Common risk factors such as age, obesity, physical inactivity and smoking may contribute to increased cancer risk in patients with DM. Although the mechanistic process that may link diabetes to cancer is not completely understood yet, biological mechanisms linking DM and cancer are hyperglycemia, hyperinsulinemia, increased bioactivity of insulin-like growth factor 1, oxidative stress, dysregulations of sex hormones, and chronic inflammation. However, cancer screening rate is significantly lower in people with DM than that in people without diabetes. Evidence from previous studies suggests that some medications used to treat DM are associated with either increased or reduced risk of cancer. However, there is no strong evidence supporting the association between the use of anti-hyperglycemic medication and specific cancer. In conclusion, all patients with DM should be undergo recommended age- and sex appropriate cancer screenings to promote primary prevention and early detection. Furthermore, cancer should be screened in routine diabetes assessment.


Citations to this article as recorded by  
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    Nutrients.2022; 14(12): 2521.     CrossRef
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  • Improvement in Age at Mortality and Changes in Causes of Death in the Population with Diabetes: An Analysis of Data from the Korean National Health Insurance and Statistical Information Service, 2006 to 2018
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  • Editorial: The relationship between diabetes and cancers and its underlying mechanisms
    Qiang Huo, Jing Wang, Nannan Zhang, Long Xie, Heshan Yu, Tao Li
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
  • Oxidatively Damaged Nucleic Acid: Linking Diabetes and Cancer
    Xiujuan Hong, Yiqiu Hu, Zhijun Yuan, Zhihao Fang, Xiaoxiao Zhang, Ying Yuan, Cheng Guo
    Antioxidants & Redox Signaling.2022; 37(16-18): 1153.     CrossRef
  • Diabetes mellitus and endometrial carcinoma: Risk factors and etiological links
    Ya Wang, Xinling Zeng, Jie Tan, Yi Xu, Cunjian Yi
    Medicine.2022; 101(34): e30299.     CrossRef
  • The Good, the Bad and the New about Uric Acid in Cancer
    Simone Allegrini, Mercedes Garcia-Gil, Rossana Pesi, Marcella Camici, Maria Grazia Tozzi
    Cancers.2022; 14(19): 4959.     CrossRef
  • Family cancer history and smoking habit associated with sarcoma in a Japanese population study
    Yoshihiro Araki, Norio Yamamoto, Yoshikazu Tanzawa, Takahiro Higashi, Aya Kuchiba, Katsuhiro Hayashi, Akihiko Takeuchi, Shinji Miwa, Kentaro Igarashi, Makoto Endo, Eisuke Kobayashi, Hiroyuki Tsuchiya, Akira Kawai
    Scientific Reports.2022;[Epub]     CrossRef
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    Jin Ho Choi, Woo Hyun Paik, Dong Kee Jang, Min Kyu Kim, Ji Kon Ryu, Yong-Tae Kim, Kyungdo Han, Sang Hyub Lee
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Diabetes Metab J : Diabetes & Metabolism Journal
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