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Original Article
Complications
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To Determine the Risk-Based Screening Interval for Diabetic Retinopathy: Development and Validation of Risk Algorithm from a Retrospective Cohort Study
Jinxiao Lian, Ching So, Sarah Morag McGhee, Thuan-quoc Thach, Cindy Lo Kuen Lam, Colman Siu Cheung Fung, Alfred Siu Kei Kwong, Jonathan Cheuk Hung Chan
Received March 21, 2024  Accepted July 24, 2024  Published online October 31, 2024  
DOI: https://doi.org/10.4093/dmj.2024.0142    [Epub ahead of print]
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
The optimal screening interval for diabetic retinopathy (DR) remains controversial. This study aimed to develop a risk algorithm to predict the individual risk of referable sight-threatening diabetic retinopathy (STDR) in a mainly Chinese population and to provide evidence for risk-based screening intervals.
Methods
The retrospective cohort data from 117,418 subjects who received systematic DR screening in Hong Kong between 2010 and 2016 were included to develop and validate the risk algorithm using a parametric survival model. The risk algorithm can be used to predict the individual risk of STDR within a specific time interval, or the time to reach a specific risk margin and thus to allocate a screening interval. The calibration performance was assessed by comparing the cumulative STDR events versus predicted risk over 2 years, and discrimination by using receiver operative characteristics (ROC) curve.
Results
Duration of diabetes, glycosylated hemoglobin, systolic blood pressure, presence of chronic kidney disease, diabetes medication, and age were included in the risk algorithm. The validation of prediction performance showed that there was no significant difference between predicted and observed STDR risks in males (5.6% vs. 5.1%, P=0.724) or females (4.8% vs. 4.6%, P=0.099). The area under the receiver operating characteristic curve was 0.80 (95% confidence interval [CI], 0.78 to 0.81) for males and 0.81 (95% CI, 0.79 to 0.83) for females.
Conclusion
The risk algorithm has good prediction performance for referable STDR. Using a risk-based screening interval allows us to allocate screening visits disproportionally more to those at higher risk, while reducing the frequency of screening of lower risk people.
Review
Guideline/Fact Sheet
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Screening for Prediabetes and Diabetes in Korean Nonpregnant Adults: A Position Statement of the Korean Diabetes Association, 2022
Kyung Ae Lee, Dae Jung Kim, Kyungdo Han, Suk Chon, Min Kyong Moon, on Behalf of the Committee of Clinical Practice Guideline of Korean Diabetes Association
Diabetes Metab J. 2022;46(6):819-826.   Published online November 24, 2022
DOI: https://doi.org/10.4093/dmj.2022.0364
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AbstractAbstract PDFPubReader   ePub   
Diabetes screening serves to identify individuals at high-risk for diabetes who have not yet developed symptoms and to diagnose diabetes at an early stage. Globally, the prevalence of diabetes is rapidly increasing. Furthermore, obesity and/or abdominal obesity, which are major risk factors for type 2 diabetes mellitus (T2DM), are progressively increasing, particularly among young adults. Many patients with T2DM are asymptomatic and can accompany various complications at the time of diagnosis, as well as chronic complications develop as the duration of diabetes increases. Thus, proper screening and early diagnosis are essential for diabetes care. Based on reports on the changing epidemiology of diabetes and obesity in Korea, as well as growing evidence from new national cohort studies on diabetes screening, the Korean Diabetes Association has updated its clinical practice recommendations regarding T2DM screening. Diabetes screening is now recommended in adults aged ≥35 years regardless of the presence of risk factors, and in all adults (aged ≥19) with any of the risk factors. Abdominal obesity based on waist circumference (men ≥90 cm, women ≥85 cm) was added to the list of risk factors.

Citations

Citations to this article as recorded by  
  • Oxidative Balance Score and New-Onset Type 2 Diabetes Mellitus in Korean Adults without Non-Alcoholic Fatty Liver Disease: Korean Genome and Epidemiology Study-Health Examinees (KoGES-HEXA) Cohort
    Mid-Eum Moon, Dong Hyuk Jung, Seok-Jae Heo, Byoungjin Park, Yong Jae Lee
    Antioxidants.2024; 13(1): 107.     CrossRef
  • Efficacy and Safety of Once-Weekly Semaglutide Versus Once-Daily Sitagliptin as Metformin Add-on in a Korean Population with Type 2 Diabetes
    Byung-Wan Lee, Young Min Cho, Sin Gon Kim, Seung-Hyun Ko, Soo Lim, Amine Dahaoui, Jin Sook Jeong, Hyo Jin Lim, Jae Myung Yu
    Diabetes Therapy.2024; 15(2): 547.     CrossRef
  • Triglyceride-glucose index predicts type 2 diabetes mellitus more effectively than oral glucose tolerance test-derived insulin sensitivity and secretion markers
    Min Jin Lee, Ji Hyun Bae, Ah Reum Khang, Dongwon Yi, Mi Sook Yun, Yang Ho Kang
    Diabetes Research and Clinical Practice.2024; 210: 111640.     CrossRef
  • Association of sleep fragmentation with general and abdominal obesity: a population-based longitudinal study
    Yu-xiang Xu, Shan-shan Wang, Yu-hui Wan, Pu-yu Su, Fang-biao Tao, Ying Sun
    International Journal of Obesity.2024; 48(9): 1258.     CrossRef
  • Oxidative balance score as a useful predictive marker for new-onset type 2 diabetes mellitus in Korean adults aged 60 years or older: The Korean Genome and Epidemiologic Study–Health Examination (KoGES-HEXA) cohort
    Mid-Eum Moon, Dong Hyuk Jung, Seok-Jae Heo, Byoungjin Park, Yong Jae Lee
    Experimental Gerontology.2024; 193: 112475.     CrossRef
  • The optimal dose of metformin to control conversion to diabetes in patients with prediabetes: A meta-analysis
    Xiaoyan Yi, Yongliang Pan, Huan Peng, Mengru Ren, Qin Jia, Bing Wang
    Journal of Diabetes and its Complications.2024; 38(10): 108846.     CrossRef
  • Changes in the Epidemiological Landscape of Diabetes in South Korea: Trends in Prevalence, Incidence, and Healthcare Expenditures
    Kyoung Hwa Ha, Dae Jung Kim
    Endocrinology and Metabolism.2024; 39(5): 669.     CrossRef
  • Development and validation of machine learning models to predict prediabetes using dietary intake data in young adults in Korea: a cross-sectional study
    Myoung-Lyun Heo
    Journal of Korean Biological Nursing Science.2024; 26(4): 300.     CrossRef
  • Cumulative muscle strength and risk of diabetes: A prospective cohort study with mediation analysis
    Shanhu Qiu, Xue Cai, Yan Liang, Wenji Chen, Duolao Wang, Zilin Sun, Bo Xie, Tongzhi Wu
    Diabetes Research and Clinical Practice.2023; 197: 110562.     CrossRef
  • Revisiting the Diabetes Crisis in Korea: Call for Urgent Action
    Jun Sung Moon
    The Journal of Korean Diabetes.2023; 24(1): 1.     CrossRef
  • 2023 Clinical Practice Guidelines for Diabetes Mellitus of the Korean Diabetes Association
    Jong Han Choi, Kyung Ae Lee, Joon Ho Moon, Suk Chon, Dae Jung Kim, Hyun Jin Kim, Nan Hee Kim, Ji A Seo, Mee Kyoung Kim, Jeong Hyun Lim, YoonJu Song, Ye Seul Yang, Jae Hyeon Kim, You-Bin Lee, Junghyun Noh, Kyu Yeon Hur, Jong Suk Park, Sang Youl Rhee, Hae J
    Diabetes & Metabolism Journal.2023; 47(5): 575.     CrossRef
  • 2023 Clinical Practice Guidelines for Diabetes
    Min Kyong Moon
    The Journal of Korean Diabetes.2023; 24(3): 120.     CrossRef
Original Articles
Complications
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
DOI: https://doi.org/10.4093/dmj.2021.0099
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  • 4 Web of Science
  • 5 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
We investigated the prevalence of diabetic retinopathy (DR) in patients with undiagnosed diabetes through a nationwide survey, compared to those with known diabetes.
Methods
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.”
Results
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.
Conclusion
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

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; 61(11): 8656.     CrossRef
  • Trends and Barriers in Diabetic Retinopathy Screening: Korea National Health and Nutritional Examination Survey 2016–2021
    Min Seok Kim, Sang Jun Park, Kwangsic Joo, Se Joon Woo
    Journal of Korean Medical Science.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
Complications
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SUDOSCAN in Combination with the Michigan Neuropathy Screening Instrument Is an Effective Tool for Screening Diabetic Peripheral Neuropathy
Tae Jung Oh, Yoojung Song, Hak Chul Jang, Sung Hee Choi
Diabetes Metab J. 2022;46(2):319-326.   Published online September 16, 2021
DOI: https://doi.org/10.4093/dmj.2021.0014
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Graphical AbstractGraphical Abstract AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Screening for diabetic peripheral neuropathy (DPN) is important to prevent severe foot complication, but the detection rate of DPN is unsatisfactory. We investigated whether SUDOSCAN combined with Michigan Neuropathy Screening Instrument (MNSI) could be an effective tool for screening for DPN in people with type 2 diabetes mellitus (T2DM) in clinical practice.
Methods
We analysed the data for 144 people with T2DM without other cause of neuropathy. The presence of DPN was confirmed according to the Toronto Consensus criteria. Electrochemical skin conductance (ESC) of the feet was assessed using SUDOSCAN. We compared the discrimination power of following methods, MNSI only vs. SUDOSCAN only vs. MNSI plus SUDOSCAN vs. MNSI plus 10-g monofilament test.
Results
Confirmed DPN was detected in 27.8% of the participants. The optimal cut-off value of feet ESC to distinguish DPN was 56 μS. We made the DPN screening scores using the corresponding odds ratios for MNSI-Questionnaire, MNSI-Physical Examination, SUDOSCAN, and 10-g monofilament test. For distinguishing the presence of DPN, the MNSI plus SUDOSCAN model showed higher areas under the receiver operating characteristic curve (AUC) than MNSI only model (0.717 vs. 0.638, P=0.011), and SUDOSCAN only model or MNSI plus 10-g monofilament test showed comparable AUC with MNSI only model.
Conclusion
The screening model for DPN that includes both MNSI and SUDOSCAN can detect DPN with acceptable discrimination power and it may be useful in Korean patients with T2DM.

Citations

Citations to this article as recorded by  
  • Association of sudomotor dysfunction with risk of diabetic retinopathy in patients with type 2 diabetes
    Ming Wang, Niuniu Chen, Yaxin Wang, Jiaying Ni, Jingyi Lu, Weijing Zhao, Yating Cui, Ronghui Du, Wei Zhu, Jian Zhou
    Endocrine.2024; 84(3): 951.     CrossRef
  • Vitamin D deficiency increases the risk of diabetic peripheral neuropathy in elderly type 2 diabetes mellitus patients by predominantly increasing large-fiber lesions
    Sijia Fei, Jingwen Fan, Jiaming Cao, Huan Chen, Xiaoxia Wang, Qi Pan
    Diabetes Research and Clinical Practice.2024; 209: 111585.     CrossRef
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    Okti Sri Purwanti, Nursalam Nursalam, Moses Glorino Rumambo Pandin
    Frontiers in Endocrinology.2024;[Epub]     CrossRef
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    Benjamin Vittrant, Hanna Ayoub, Philippe Brunswick
    Frontiers in Neuroanatomy.2024;[Epub]     CrossRef
  • Diagnostic Accuracy of Screening Tests for Diabetic Peripheral Neuropathy: An Umbrella Review
    María Mogilevskaya, Mariana Gaviria-Carrillo, John Edwin Feliciano-Alfonso, Ana M. Barragan, Carlos A. Calderon-Ospina, Mauricio O. Nava-Mesa, Syed Anees Ahmed
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    Raffaele Galiero, Alfredo Caturano, Erica Vetrano, Domenico Beccia, Chiara Brin, Maria Alfano, Jessica Di Salvo, Raffaella Epifani, Alessia Piacevole, Giuseppina Tagliaferri, Maria Rocco, Ilaria Iadicicco, Giovanni Docimo, Luca Rinaldi, Celestino Sardu, T
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    Ken Munene Nkonge, Dennis Karani Nkonge, Teresa Njeri Nkonge
    Diabetology & Metabolic Syndrome.2023;[Epub]     CrossRef
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    Jean-Pascal Lefaucheur
    Neurophysiologie Clinique.2023; 53(2): 102859.     CrossRef
  • Electrochemical skin conductances values and clinical factors affecting sudomotor dysfunction in patients with prediabetes, type 1 diabetes, and type 2 diabetes: A single center experience
    Bedia Fulya Calikoglu, Selda Celik, Cemile Idiz, Elif Bagdemir, Halim Issever, Jean-Henri Calvet, Ilhan Satman
    Primary Care Diabetes.2023; 17(5): 499.     CrossRef
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    Heung Yong Jin, Tae Sun Park
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    Jean-Pierre Riveline, Roberto Mallone, Clarisse Tiercelin, Fetta Yaker, Laure Alexandre-Heymann, Lysa Khelifaoui, Florence Travert, Claire Fertichon, Jean-Baptiste Julla, Tiphaine Vidal-Trecan, Louis Potier, Jean-Francois Gautier, Etienne Larger, Jean-Pas
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Technology/Device
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
DOI: https://doi.org/10.4093/dmj.2020.0081
  • 9,908 View
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  • 9 Web of Science
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Graphical AbstractGraphical Abstract AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
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.
Methods
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.
Results
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.
Conclusion
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

Citations to this article as recorded by  
  • Interpretable time-series neural turing machine for prognostic prediction of patients with type 2 diabetes in physician-pharmacist collaborative clinics
    Jie Xiao, Bin Chen, Lei Chen, Qing Wang, Shenglan Tan, Haiyan Yuan, Daxiong Xiang, Bikui Zhang, Xia Li, Shuting Huang, Yuhan Tan, Yining Cheng, Wenzheng Xie, Ping Xu
    International Journal of Medical Informatics.2025; 195: 105737.     CrossRef
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    Hyunji Sang, Hojae Lee, Myeongcheol Lee, Jaeyu Park, Sunyoung Kim, Ho Geol Woo, Masoud Rahmati, Ai Koyanagi, Lee Smith, Sihoon Lee, You-Cheol Hwang, Tae Sun Park, Hyunjung Lim, Dong Keon Yon, Sang Youl Rhee
    Scientific Reports.2024;[Epub]     CrossRef
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    Oana Vîrgolici, Laura Gabriela Tănăsescu
    Proceedings of the International Conference on Business Excellence.2024; 18(1): 246.     CrossRef
  • Machine Learning–Based Prediction of Neurodegenerative Disease in Patients With Type 2 Diabetes by Derivation and Validation in 2 Independent Korean Cohorts: Model Development and Validation Study
    Hyunji Sang, Hojae Lee, Jaeyu Park, Sunyoung Kim, Ho Geol Woo, Ai Koyanagi, Lee Smith, Sihoon Lee, You-Cheol Hwang, Tae Sun Park, Hyunjung Lim, Dong Keon Yon, Sang Youl Rhee
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    Ji Hye Huh, Eun Roh, Seong Jin Lee, Sung-Hee Ihm, Kyung-Do Han, Jun Goo Kang
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    Juyoung Shin, Jaewon Kim, Chanjung Lee, Joon Young Yoon, Seyeon Kim, Seungjae Song, Hun-Sung Kim
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Brief Report
Complications
Article image
Diabetic Retinopathy and Related Clinical Practice for People with Diabetes in Korea: A 10-Year Trend Analysis
Yoo-Ri Chung, Kyoung Hwa Ha, Kihwang Lee, Dae Jung Kim
Diabetes Metab J. 2020;44(6):928-932.   Published online July 10, 2020
DOI: https://doi.org/10.4093/dmj.2020.0096
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   

We performed a retrospective cohort study including people diagnosed with diabetes from 2006 to 2015 according to the Korean National Health Insurance Service-National Sample Cohort database, to analyze the changes in the prevalence, screening rate, and treatment patterns for diabetic retinopathy (DR) over 10 years. The proportion of people who underwent fundus screening for DR steadily increased over the past decade. The prevalence of DR increased from 13.4% in 2006 to 15.9% in 2015, while that of proliferative DR steadily decreased from 1.29% in 2006 to 1.16% in 2015. The proportion of patients undergoing retinal photocoagulation constantly decreased. The prevalence of DR increased over the past decade, while its severity seemed to have improved, with a decreased rate of proliferative DR and retinal photocoagulation. A higher proportion of patients underwent ophthalmic screening using fundus examination, but still less than 30% of patients with diabetes underwent comprehensive examination in 2015.

Citations

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  • Ten-Year Trends of Fluorescein Angiography in Korea Using Data From the Health Insurance Review and Assessment Service
    Dong Woo Kim, Min Seok Kim
    Journal of Korean Medical Science.2025;[Epub]     CrossRef
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    Min Seok Kim, Sang Jun Park, Kwangsic Joo, Se Joon Woo
    Journal of Korean Medical Science.2024;[Epub]     CrossRef
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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 & Metabolism Journal.2022; 46(4): 620.     CrossRef
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    Ihn Sook Jeong, Chan Mi Kang
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  • Time to Reach Target Glycosylated Hemoglobin Is Associated with Long-Term Durable Glycemic Control and Risk of Diabetic Complications in Patients with Newly Diagnosed Type 2 Diabetes Mellitus: A 6-Year Observational Study (Diabetes Metab J 2021;45:368-78)
    Ja Young Jeon
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Original Article
Metabolic Risk/Epidemiology
Article image
A Comparison of Predictive Performances between Old versus New Criteria in a Risk-Based Screening Strategy for Gestational Diabetes Mellitus
Subeen Hong, Seung Mi Lee, Soo Heon Kwak, Byoung Jae Kim, Ja Nam Koo, Ig Hwan Oh, Sohee Oh, Sun Min Kim, Sue Shin, Won Kim, Sae Kyung Joo, Errol R. Norwitz, Souphaphone Louangsenlath, Chan-Wook Park, Jong Kwan Jun, Joong Shin Park
Diabetes Metab J. 2020;44(5):726-736.   Published online April 13, 2020
DOI: https://doi.org/10.4093/dmj.2019.0126
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background

The definition of the high-risk group for gestational diabetes mellitus (GDM) defined by the American College of Obstetricians and Gynecologists was changed from the criteria composed of five historic/demographic factors (old criteria) to the criteria consisting of 11 factors (new criteria) in 2017. To compare the predictive performances between these two sets of criteria.

Methods

This is a secondary analysis of a large prospective cohort study of non-diabetic Korean women with singleton pregnancies designed to examine the risk of GDM in women with nonalcoholic fatty liver disease. Maternal fasting blood was taken at 10 to 14 weeks of gestation and measured for glucose and lipid parameters. GDM was diagnosed by the two-step approach.

Results

Among 820 women, 42 (5.1%) were diagnosed with GDM. Using the old criteria, 29.8% (n=244) of women would have been identified as high risk versus 16.0% (n=131) using the new criteria. Of the 42 women who developed GDM, 45.2% (n=19) would have been mislabeled as not high risk by the old criteria versus 50.0% (n=21) using the new criteria (1-sensitivity, 45.2% vs. 50.0%, P>0.05). Among the 778 patients who did not develop GDM, 28.4% (n=221) would have been identified as high risk using the old criteria versus 14.1% (n=110) using the new criteria (1-specificity, 28.4% vs. 14.1%, P<0.001).

Conclusion

Compared with the old criteria, use of the new criteria would have decreased the number of patients identified as high risk and thus requiring early GDM screening by half (from 244 [29.8%] to 131 [16.0%]).

Citations

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Review
Complications
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
DOI: https://doi.org/10.4093/dmj.2019.0177
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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.

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Original Articles
Epidemiology
Performance of the Achutha Menon Centre Diabetes Risk Score in Identifying Prevalent Diabetes in Tamil Nadu, India
Anu Mary Oommen, Vinod Joseph Abraham, Thirunavukkarasu Sathish, V. Jacob Jose, Kuryan George
Diabetes Metab J. 2017;41(5):386-392.   Published online August 25, 2017
DOI: https://doi.org/10.4093/dmj.2017.41.5.386
  • 3,552 View
  • 36 Download
  • 3 Web of Science
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AbstractAbstract PDFPubReader   
Background

The Achutha Menon Centre Diabetes Risk Score (AMCDRS), which was developed in rural Kerala State, South India, had not previously been externally validated. We examined the performance of the AMCDRS in urban and rural areas in the district of Vellore in the South Indian state of Tamil Nadu, and compared it with other diabetes risk scores developed from India.

Methods

We used the data from 4,896 participants (30 to 64 years) of a cross-sectional study conducted in Vellore (2010 to 2012), to calculate the AMCDRS scores using age, family history, and waist circumference. Sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV), and the area under the receiver operating characteristic curve (AROC) were calculated for undiagnosed and total diabetes.

Results

Of the 4,896 individuals surveyed, 274 (5.6%) had undiagnosed diabetes and 759 (15.5%) had total diabetes. The AMCDRS, with an optimum cut-point of ≥4, identified 45.0% for further testing with 59.5% sensitivity, 60.5% specificity, 9.1% PPV, 95.8% NPV, and an AROC of 0.639 (95% confidence interval [CI], 0.608 to 0.670) for undiagnosed diabetes. The corresponding figures for total diabetes were 75.1%, 60.5%, 25.9%, 93.0%, and 0.731 (95% CI, 0.713 to 0.750), respectively. The AROC for the AMCDRS was not significantly different from that of the Indian Diabetes Risk Score, the Ramachandran or the Chaturvedi risk scores for total diabetes, but was significantly lower than the AROC of the Chaturvedi score for undiagnosed diabetes.

Conclusion

The AMCDRS is a simple diabetes risk score that can be used to screen for undiagnosed and total diabetes in low-resource primary care settings in India. However, it probably requires recalibration to improve its performance for undiagnosed diabetes.

Citations

Citations to this article as recorded by  
  • Evaluating the Performance of the Indian Diabetes Risk Score in Different Ethnic Groups
    Manjula D. Nugawela, Sobha Sivaprasad, Viswanathan Mohan, Ramachandran Rajalakshmi, Gopalakrishnan Netuveli
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    Thirunavukkarasu Sathish, Jonathan E. Shaw, Robyn J. Tapp, Rory Wolfe, Kavumpurathu R. Thankappan, Sajitha Balachandran, Brian Oldenburg
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    Elizabeth Jacob, Vivek Koshy Varghese, Tittu Oommen
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Role of HbA1c in the Screening of Diabetes Mellitus in a Korean Rural Community
Jae Hyun Kim, Gun Woo Kim, Mi Young Lee, Jang Yel Shin, Young Goo Shin, Sang Baek Koh, Choon Hee Chung
Diabetes Metab J. 2012;36(1):37-42.   Published online February 17, 2012
DOI: https://doi.org/10.4093/dmj.2012.36.1.37
  • 4,583 View
  • 46 Download
  • 11 Crossref
AbstractAbstract PDFPubReader   
Background

Recently, the measurement of glycated hemoglobin (HbA1c) was recommended as an alternative to fasting plasma glucose or oral glucose tolerance tests for diagnosing diabetes mellitus (DM). In this study, we analyzed HbA1c levels for diabetes mellitus screening in a Korean rural population.

Methods

We analyzed data from 10,111 subjects from a Korean Rural Genomic Cohort study and generated a receiver operating characteristic curve to determine an appropriate HbA1c cutoff value for diabetes.

Results

The mean age of the subjects was 56.3±8.1 years. Fasting plasma glucose and 2-hour plasma glucose after 75 g oral glucose tolerance tests were 97.5±25.6 and 138.3±67.1 mg/dL, respectively. The mean HbA1c level of the subjects was 5.7±0.9%. There were 8,809 non-DM patients (87.1%) and 1,302 DM patients (12.9%). A positive relationship between HbA1c and plasma glucose levels and between HbA1c and 2-hour plasma glucose levels after oral glucose tolerance tests was found in a scatter plot of the data. Using Youden's index, the proper cutoff level of HbA1c for diabetes mellitus screening was 5.95% (sensitivity, 77%; specificity, 89.4%).

Conclusion

Our results suggest that the optimal HbA1c level for DM screening is 5.95%.

Citations

Citations to this article as recorded by  
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