Diabetes & Metabolism Journal

Review

Guideline/Fact Sheet
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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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.

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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
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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
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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
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Association of sleep fragmentation with general and abdominal obesity: a population-based longitudinal study
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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
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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
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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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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.

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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
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
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Archives of Osteoporosis.2023;[Epub] CrossRef

Complications
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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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.

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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
Early detection of diabetic neuropathy based on health belief model: a scoping review
Okti Sri Purwanti, Nursalam Nursalam, Moses Glorino Rumambo Pandin
Frontiers in Endocrinology.2024;[Epub] CrossRef
Whether coagulation dysfunction influences the onset and progression of diabetic peripheral neuropathy: A multicenter study in middle‐aged and aged patients with type 2 diabetes
Jiali Xie, Xinyue Yu, Luowei Chen, Yifan Cheng, Kezheng Li, Mengwan Song, Yinuo Chen, Fei Feng, Yunlei Cai, Shuting Tong, Yuqin Qian, Yiting Xu, Haiqin Zhang, Junjie Yang, Zirui Xu, Can Cui, Huan Yu, Binbin Deng
CNS Neuroscience & Therapeutics.2024;[Epub] CrossRef
Peripheral Neuropathy in Diabetes Mellitus: Pathogenetic Mechanisms and Diagnostic Options
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International Journal of Molecular Sciences.2023; 24(4): 3554. CrossRef
Screening for diabetic peripheral neuropathy in resource-limited settings
Ken Munene Nkonge, Dennis Karani Nkonge, Teresa Njeri Nkonge
Diabetology & Metabolic Syndrome.2023;[Epub] CrossRef
The value of electrochemical skin conductance measurement by Sudoscan® for assessing autonomic dysfunction in peripheral neuropathies beyond diabetes
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
Autonomic Nerve Function Tests in Patients with Diabetes
Heung Yong Jin, Tae Sun Park
The Journal of Korean Diabetes.2023; 24(2): 71. CrossRef
Validation of the Body Scan®, a new device to detect small fiber neuropathy by assessment of the sudomotor function: agreement with the Sudoscan®
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
Frontiers in Neurology.2023;[Epub] CrossRef
Electrochemical Skin Conductance by Sudoscan in Non-Dialysis Chronic Kidney Disease Patients
Liang-Te Chiu, Yu-Li Lin, Chih-Hsien Wang, Chii-Min Hwu, Hung-Hsiang Liou, Bang-Gee Hsu
Journal of Clinical Medicine.2023; 13(1): 187. CrossRef
The Presence of Clonal Hematopoiesis Is Negatively Associated with Diabetic Peripheral Neuropathy in Type 2 Diabetes
Tae Jung Oh, Han Song, Youngil Koh, Sung Hee Choi
Endocrinology and Metabolism.2022; 37(2): 243. CrossRef
Case report: Significant relief of linezolid-induced peripheral neuropathy in a pre-XDR-TB case after acupuncture treatment
Yuping Mo, Zhu Zhu, Jie Tan, Zhilin Liang, Jiahui Wu, Xingcheng Chen, Ming Hu, Peize Zhang, Guofang Deng, Liang Fu
Frontiers in Neurology.2022;[Epub] CrossRef
Detection of sudomotor alterations evaluated by Sudoscan in patients with recently diagnosed type 2 diabetes
Ana Cristina García-Ulloa, Paloma Almeda-Valdes, Teresa Enedina Cuatecontzi-Xochitiotzi, Jorge Alberto Ramírez-García, Michelle Díaz-Pineda, Fernanda Garnica-Carrillo, Alejandra González-Duarte, K M Venkat Narayan, Carlos Alberto Aguilar-Salinas, Sergio H
BMJ Open Diabetes Research & Care.2022; 10(6): e003005. CrossRef

Technology/Device
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

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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.

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Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts
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
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Farida Mohsen, Hamada R. H. Al-Absi, Noha A. Yousri, Nady El Hajj, Zubair Shah
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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
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Juyoung Shin, Joonyub Lee, Taehoon Ko, Kanghyuck Lee, Yera Choi, Hun-Sung Kim
Journal of Personalized Medicine.2022; 12(11): 1899. CrossRef

Brief Report

Complications
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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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.

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Variations in Electronic Health Record-Based Definitions of Diabetic Retinopathy Cohorts
Jimmy S. Chen, Ivan A. Copado, Cecilia Vallejos, Fritz Gerald P. Kalaw, Priyanka Soe, Cindy X. Cai, Brian C. Toy, Durga Borkar, Catherine Q. Sun, Jessica G. Shantha, Sally L. Baxter
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Present and future screening programs for diabetic retinopathy: a narrative review
Andreas Abou Taha, Sebastian Dinesen, Anna Stage Vergmann, Jakob Grauslund
International Journal of Retina and Vitreous.2024;[Epub] 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
Chronic disease management program applied to type 2 diabetes patients and prevention of diabetic complications: a retrospective cohort study using nationwide data
Min Kyung Hyun, Jang Won Lee, Seung-Hyun Ko
BMC Public Health.2023;[Epub] CrossRef
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Sang-Yeob Kim, Byeong-Yeon Moon, Hyun-Gug Cho, Dong-Sik Yu
International Journal of Environmental Research and Public Health.2022; 19(14): 8689. CrossRef
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
Prevalence and Risk Factors of Diabetic Retinopathy in Diabetes People using Korean National Health and Nutrition Examination Survey VII
Ihn Sook Jeong, Chan Mi Kang
Journal of Korean Academy of Community Health Nursing.2022; 33(4): 408. CrossRef
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
Diabetes & Metabolism Journal.2021; 45(4): 613. CrossRef

Original Article

Metabolic Risk/Epidemiology
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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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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Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea
Seung-Hwan Lee, Jin Yu, Kyungdo Han, Seung Woo Lee, Sang Youn You, Hun-Sung Kim, Jae-Hyoung Cho, Kun-Ho Yoon, Mee Kyoung Kim
Endocrinology and Metabolism.2023; 38(1): 129. CrossRef
Metabolic Dysfunction-Associated Fatty Liver Disease and Subsequent Development of Adverse Pregnancy Outcomes
Seung Mi Lee, Young Mi Jung, Eun Saem Choi, Soo Heon Kwak, Ja Nam Koo, Ig Hwan Oh, Byoung Jae Kim, Sun Min Kim, Sang Youn Kim, Gyoung Min Kim, Sae Kyung Joo, Bo Kyung Koo, Sue Shin, Errol R. Norwitz, Chan-Wook Park, Jong Kwan Jun, Won Kim, Joong Shin Park
Clinical Gastroenterology and Hepatology.2022; 20(11): 2542. CrossRef
Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods
Seung Mi Lee, Suhyun Hwangbo, Errol R. Norwitz, Ja Nam Koo, Ig Hwan Oh, Eun Saem Choi, Young Mi Jung, Sun Min Kim, Byoung Jae Kim, Sang Youn Kim, Gyoung Min Kim, Won Kim, Sae Kyung Joo, Sue Shin, Chan-Wook Park, Taesung Park, Joong Shin Park
Clinical and Molecular Hepatology.2022; 28(1): 105. CrossRef
Nonalcoholic fatty liver disease-based risk prediction of adverse pregnancy outcomes: Ready for prime time?
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So-Yeon Kim, Young Shin Song, Soo-Kyung Kim, Yong-Wook Cho, Kyung-Soo Kim
Diabetes & Metabolism Journal.2022; 46(1): 140. CrossRef
Effect of Different Types of Diagnostic Criteria for Gestational Diabetes Mellitus on Adverse Neonatal Outcomes: A Systematic Review, Meta-Analysis, and Meta-Regression
Fahimeh Ramezani Tehrani, Marzieh Saei Ghare Naz, Razieh Bidhendi-Yarandi, Samira Behboudi-Gandevani
Diabetes & Metabolism Journal.2022; 46(4): 605. CrossRef
Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning
Seung Mi Lee, Yonghyun Nam, Eun Saem Choi, Young Mi Jung, Vivek Sriram, Jacob S. Leiby, Ja Nam Koo, Ig Hwan Oh, Byoung Jae Kim, Sun Min Kim, Sang Youn Kim, Gyoung Min Kim, Sae Kyung Joo, Sue Shin, Errol R. Norwitz, Chan-Wook Park, Jong Kwan Jun, Won Kim,
Scientific Reports.2022;[Epub] CrossRef
The Clinical Characteristics of Gestational Diabetes Mellitus in Korea: A National Health Information Database Study
Kyung-Soo Kim, Sangmo Hong, Kyungdo Han, Cheol-Young Park
Endocrinology and Metabolism.2021; 36(3): 628. CrossRef
The risk of pregnancy‐associated hypertension in women with nonalcoholic fatty liver disease
Young Mi Jung, Seung Mi Lee, Subeen Hong, Ja Nam Koo, Ig Hwan Oh, Byoung Jae Kim, Sun Min Kim, Sang Youn Kim, Gyoung Min Kim, Sae Kyung Joo, Sue Shin, Errol R. Norwitz, Chan‐Wook Park, Jong Kwan Jun, Won Kim, Joong Shin Park
Liver International.2020; 40(10): 2417. CrossRef

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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Abstract PDF PubReader

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

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Abstract PDF PubReader

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
Diabetes Technology & Therapeutics.2020; 22(4): 285. CrossRef
Targeted screening for prediabetes and undiagnosed diabetes in a community setting in India
Thirunavukkarasu Sathish, Jonathan E. Shaw, Robyn J. Tapp, Rory Wolfe, Kavumpurathu R. Thankappan, Sajitha Balachandran, Brian Oldenburg
Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2019; 13(3): 1785. CrossRef
OBSERVATIONAL STUDY EVALUATING ASSOCIATION OF TYPE 2 DIABETES MELLITUS AND THYROID DYSFUNCTION
Elizabeth Jacob, Vivek Koshy Varghese, Tittu Oommen
Journal of Evidence Based Medicine and Healthcare.2018; 5(31): 2285. CrossRef

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

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46 Download
11 Crossref

Abstract PDF PubReader

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

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