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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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  • 358 Download
  • 13 Web of Science
  • 14 Crossref
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
  • 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
    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
    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
Brief Report
Complications
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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
  • 6,011 View
  • 198 Download
  • 7 Web of Science
  • 8 Crossref
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

Citations to this article as recorded by  
  • 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
  • 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
  • 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
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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
  • 7,215 View
  • 128 Download
  • 9 Web of Science
  • 9 Crossref
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

Citations to this article as recorded by  
  • 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?
    Seung Mi Lee, Won Kim
    Clinical and Molecular Hepatology.2022; 28(1): 47.     CrossRef
  • Postprandial Free Fatty Acids at Mid-Pregnancy Increase the Risk of Large-for-Gestational-Age Newborns in Women with Gestational Diabetes Mellitus
    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

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