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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
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    Yu-xiang Xu, Shan-shan Wang, Yu-hui Wan, Pu-yu Su, Fang-biao Tao, Ying Sun
    International Journal of Obesity.2024;[Epub]     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
  • 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
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    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
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    Min Kyong Moon
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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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  • 219 Download
  • 3 Web of Science
  • 4 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

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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
  • 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
Article image
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

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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;[Epub]     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
  • Peripheral Neuropathy in Diabetes Mellitus: Pathogenetic Mechanisms and Diagnostic Options
    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
  • 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
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    Heung Yong Jin, Tae Sun Park
    The Journal of Korean Diabetes.2023; 24(2): 71.     CrossRef
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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
    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
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    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
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
  • 8,828 View
  • 258 Download
  • 6 Web of Science
  • 5 Crossref
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

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  • Remnant Cholesterol Is an Independent Predictor of Type 2 Diabetes: A Nationwide Population-Based Cohort Study
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    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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    Journal of Personalized Medicine.2022; 12(11): 1899.     CrossRef
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
  • 5,442 View
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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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  • 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
    Ophthalmology Science.2024; 4(4): 100468.     CrossRef
  • 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
  • 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
  • Visual Acuity Outcomes in Diseases Associated with Reduced Visual Acuity: An Analysis of the National Health Insurance Service Database in Korea
    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
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
  • 6,784 View
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  • 9 Web of Science
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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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  • 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
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    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
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    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
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    Seung Mi Lee, Won Kim
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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
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    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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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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  • High Glucose Induced Upregulation of Cyclin a Associating with a Short Survival of Patients with Cholangiocarcinoma: A Potential Target for Treatment of Patients with Diabetes Mellitus
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  • Metformin and survival: Is there benefit in a cohort limited to diabetic women with endometrial, breast, or ovarian cancer?
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  • The High Prevalence of Short-Term Elevation of Tumor Markers Due to Hyperglycemia in Diabetic Patients
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    Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2022; Volume 15: 1113.     CrossRef
  • Stationäre Patienten mit der Nebendiagnose Diabetes mellitus: klinische Relevanz
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  • Utility of Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio in Evaluating Incident Diabetes Risk
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  • The Association of Dietary Fiber Intake in Three Meals with All-Cause and Disease-Specific Mortality among Adults: The U.S. National Health and Nutrition Examination Survey, 2003–2014
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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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  • Novel insights into the pathogenic impact of diabetes on the gastrointestinal tract
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  • Oxidatively Damaged Nucleic Acid: Linking Diabetes and Cancer
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  • The Good, the Bad and the New about Uric Acid in Cancer
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  • Family cancer history and smoking habit associated with sarcoma in a Japanese population study
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  • Acute Pancreatitis Increases the Risk of Gastrointestinal Cancer in Type 2 Diabetic Patients: A Korean Nationwide Cohort Study
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  • Diabetes and Cancer: Metabolic Association, Therapeutic Challenges, and the Role of Natural Products
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  • Evaluation of the Efficacy of the Hospital Glycemic Management System for Patients with Malignant Tumors and Hyperglycemia
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    Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2021; Volume 14: 2717.     CrossRef
  • Insulin-Like Growth Factor 1 (IGF-1) Signaling in Glucose Metabolism in Colorectal Cancer
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  • GLP-1 Receptor Agonists: Beyond Their Pancreatic Effects
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  • The Aneugenicity of Ketone Bodies in Colon Epithelial Cells Is Mediated by Microtubule Hyperacetylation and Is Blocked by Resveratrol
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  • EFFECT OF DIABETES MELLITUS ON THE LEVEL OF GROWTH FACTORS IN GUERIN CARCINOMA IN RATS OF BOTH SEXES
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  • Clinical Significance of Screening Differential Metabolites in Ovarian Cancer Tissue and Ascites by LC/MS
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  • Obesity, Diabetes, and Increased Cancer Progression
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  • Simple Serum Pancreatic Ductal Adenocarcinoma (PDAC) Protein Biomarkers—Is There Anything in Sight?
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  • Chronic non-communicable diseases, risk factors, and quality of life in patients with malignancies of various localizations
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  • Survival after breast cancer in women with type 2 diabetes using antidiabetic medication and statins: a retrospective cohort study
    Mayu Hosio, Elina Urpilainen, Ari Hautakoski, Mikko Marttila, Martti Arffman, Reijo Sund, Anne Ahtikoski, Ulla Puistola, Peeter Karihtala, Arja Jukkola, Esa Läärä
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  • Transcriptional Profiling and Biological Pathway(s) Analysis of Type 2 Diabetes Mellitus in a Pakistani Population
    Zarish Noreen, Christopher A. Loffredo, Attya Bhatti, Jyothirmai J. Simhadri, Gail Nunlee-Bland, Thomas Nnanabu, Peter John, Jahangir S. Khan, Somiranjan Ghosh
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  • Screening Strategy of Pancreatic Cancer in Patients with Diabetes Mellitus
    Suguru Mizuno, Yousuke Nakai, Kazunaga Ishigaki, Kei Saito, Hiroki Oyama, Tsuyoshi Hamada, Yukari Suzuki, Akiyuki Inokuma, Sachiko Kanai, Kensaku Noguchi, Tatsuya Sato, Ryunosuke Hakuta, Tomotaka Saito, Naminatsu Takahara, Hirofumi Kogure, Hiroyuki Isayam
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  • Changes in mortality rates and ratios in people with pharmacologically treated type 2 diabetes mellitus between 2001 and 2016 in Hungary
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    Diabetes Research and Clinical Practice.2020; 163: 108134.     CrossRef
  • Role of αVβ3 in Prostate Cancer: Metastasis Initiator and Important Therapeutic Target


    Lin Tang, Meng Xu, Long Zhang, Lin Qu, Xiaoyan Liu
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  • Arterial stiffness is an independent predictor for risk of mortality in patients with type 2 diabetes mellitus: the REBOUND study
    Jeong Mi Kim, Sang Soo Kim, In Joo Kim, Jong Ho Kim, Bo Hyun Kim, Mi Kyung Kim, Soon Hee Lee, Chang Won Lee, Min Chul Kim, Jun Hyeob Ahn, Jinmi Kim
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  • An Overview of Cancer Prevention: Chemoprevention and Immunoprevention
    Kyle J. Gu, Guojun Li
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  • Type 2 diabetes mellitus facilitates endometrial hyperplasia progression by activating the proliferative function of mucin O-glycosylating enzyme GALNT2
    Xueyan Zhou, Yinxue Xu, Di Yin, Feng Zhao, Zhixiang Hao, Ya’nan Zhong, Jingbo Zhang, Bei Zhang, Xiaoxing Yin
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  • Diabetes mellitus and the risk of ovarian cancer: a systematic review and meta-analysis of cohort and case–control studies
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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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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
    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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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

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