Diabetes & Metabolism Journal

Original Articles

Cardiovascular Risk/Epidemiology
Psychotic Disorders and the Risk of Type 2 Diabetes Mellitus, Atherosclerotic Cardiovascular Diseases, and All-Cause Mortality: A Population-Based Matched Cohort Study: You-Bin Lee, Hyewon Kim, Jungkuk Lee, Dongwoo Kang, Gyuri Kim, Sang-Man Jin, Jae Hyeon Kim, Hong Jin Jeon, Kyu Yeon Hur; Diabetes Metab J. 2024;48(1):122-133. Published online January 3, 2024; DOI: https://doi.org/10.4093/dmj.2022.0431

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Abstract PDF Supplementary Material PubReader ePub: Background
The effects of psychotic disorders on cardiometabolic diseases and premature death need to be determined in Asian populations.
Methods
In this population-based matched cohort study, the Korean National Health Insurance Service database (2002 to 2018) was used. The risk of type 2 diabetes mellitus (T2DM), acute myocardial infarction (AMI), ischemic stroke, composite of all cardiometabolic diseases, and all-cause death during follow-up was compared between individuals with psychotic disorders treated with antipsychotics (n=48,162) and 1:1 matched controls without psychiatric disorders among adults without cardiometabolic diseases before or within 3 months after baseline.
Results
In this cohort, 53,683 composite cases of all cardiometabolic diseases (during median 7.38 years), 899 AMI, and 1,216 ischemic stroke cases (during median 14.14 years), 7,686 T2DM cases (during median 13.26 years), and 7,092 deaths (during median 14.23 years) occurred. The risk of all outcomes was higher in subjects with psychotic disorders than matched controls (adjusted hazard ratios [95% confidence intervals]: 1.522 [1.446 to 1.602] for T2DM; 1.455 [1.251 to 1.693] for AMI; 1.568 [1.373 to 1.790] for ischemic stroke; 1.595 [1.565 to 1.626] for composite of all cardiometabolic diseases; and 2.747 [2.599 to 2.904] for all-cause mortality) during follow-up. Similar patterns of associations were maintained in subgroup analyses but more prominent in younger individuals (P for interaction <0.0001) when categorized as those aged 18–39, 40–64, or ≥65 years.
Conclusion
Patients with psychotic disorders treated with antipsychotics were associated with increased risk of premature allcause mortality and cardiometabolic outcomes in an Asian population. This relationship was more pronounced in younger individuals, especially aged 18 to 39 years.

Complications
Association of Muscle Mass Loss with Diabetes Development in Liver Transplantation Recipients: Sejeong Lee, Minyoung Lee, Young-Eun Kim, Hae Kyung Kim, Sook Jung Lee, Jiwon Kim, Yurim Yang, Chul Hoon Kim, Hyangkyu Lee, Dong Jin Joo, Myoung Soo Kim, Eun Seok Kang; Diabetes Metab J. 2024;48(1):146-156. Published online January 3, 2024; DOI: https://doi.org/10.4093/dmj.2022.0100

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Abstract PDF Supplementary Material PubReader ePub: Background
Post-transplant diabetes mellitus (PTDM) is one of the most significant complications after transplantation. Patients with end-stage liver diseases requiring transplantation are prone to sarcopenia, but the association between sarcopenia and PTDM remains to be elucidated. We aimed to investigate the effect of postoperative muscle mass loss on PTDM development.
Methods
A total of 500 patients who underwent liver transplantation at a tertiary care hospital between 2005 and 2020 were included. Skeletal muscle area at the level of the L3–L5 vertebrae was measured using computed tomography scans performed before and 1 year after the transplantation. The associations between the change in the muscle area after the transplantation and the incidence of PTDM was investigated using a Cox proportional hazard model.
Results
During the follow-up period (median, 4.9 years), PTDM occurred in 165 patients (33%). The muscle mass loss was greater in patients who developed PTDM than in those without PTDM. Muscle depletion significantly increased risk of developing PTDM after adjustment for other confounding factors (hazard ratio, 1.50; 95% confidence interval, 1.23 to 1.84; P=0.001). Of the 357 subjects who had muscle mass loss, 124 (34.7%) developed PTDM, whereas of the 143 patients in the muscle mass maintenance group, 41 (28.7%) developed PTDM. The cumulative incidence of PTDM was significantly higher in patients with muscle loss than in patients without muscle loss (P=0.034).
Conclusion
Muscle depletion after liver transplantation is associated with increased risk of PTDM development.

Others
Development of Various Diabetes Prediction Models Using Machine Learning Techniques: Juyoung Shin, Jaewon Kim, Chanjung Lee, Joon Young Yoon, Seyeon Kim, Seungjae Song, Hun-Sung Kim; Diabetes Metab J. 2022;46(4):650-657. Published online March 11, 2022; DOI: https://doi.org/10.4093/dmj.2021.0115

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Background
There are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create various DM prediction models using easily accessible health screening test parameters.
Methods
Two sets of variables were used to develop eight DM prediction models. One set comprised 62 easily accessible examination results of commonly used variables from a tertiary university hospital. The second set comprised 27 of the 62 variables included in the national routine health checkups. Gradient boosting and random forest algorithms were used to develop the models. Internal validation was performed using the stratified 10-fold cross-validation method.
Results
The area under the receiver operating characteristic curve (ROC-AUC) for the 62-variable DM model making 12-month predictions for subjects without diabetes was the largest (0.928) among those of the eight DM prediction models. The ROC-AUC dropped by more than 0.04 when training with the simplified 27-variable set but still showed fairly good performance with ROC-AUCs between 0.842 and 0.880. The accuracy was up to 11.5% higher (from 0.807 to 0.714) when fasting glucose was included.
Conclusion
We created easily applicable diabetes prediction models that deliver good performance using parameters commonly assessed during tertiary university hospital and national routine health checkups. We plan to perform prospective external validation, hoping that the developed DM prediction models will be widely used in clinical practice.

Citations

Citations to this article as recorded by

Predictive modeling for the development of diabetes mellitus using key factors in various machine learning approaches
Marenao Tanaka, Yukinori Akiyama, Kazuma Mori, Itaru Hosaka, Kenichi Kato, Keisuke Endo, Toshifumi Ogawa, Tatsuya Sato, Toru Suzuki, Toshiyuki Yano, Hirofumi Ohnishi, Nagisa Hanawa, Masato Furuhashi
Diabetes Epidemiology and Management.2024; 13: 100191. CrossRef
Validation of the Framingham Diabetes Risk Model Using Community-Based KoGES Data
Hye Ah Lee, Hyesook Park, Young Sun Hong
Journal of Korean Medical Science.2024;[Epub] CrossRef
Integrated Embedded system for detecting diabetes mellitus using various machine learning techniques
Rishita Konda, Anuraag Ramineni, Jayashree J, Niharika Singavajhala, Sai Akshaj Vanka
EAI Endorsed Transactions on Pervasive Health and Technology.2024;[Epub] CrossRef
The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use
Ji-Won Chun, Hun-Sung Kim
Journal of Korean Medical Science.2023;[Epub] CrossRef
Machine learning for predicting diabetic metabolism in the Indian population using polar metabolomic and lipidomic features
Nikita Jain, Bhaumik Patel, Manjesh Hanawal, Anurag R. Lila, Saba Memon, Tushar Bandgar, Ashutosh Kumar
Metabolomics.2023;[Epub] CrossRef
Retrospective cohort analysis comparing changes in blood glucose level and body composition according to changes in thyroid‐stimulating hormone level
Hyunah Kim, Da Young Jung, Seung‐Hwan Lee, Jae‐Hyoung Cho, Hyeon Woo Yim, Hun‐Sung Kim
Journal of Diabetes.2022; 14(9): 620. CrossRef
Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness
Juyoung Shin, Joonyub Lee, Taehoon Ko, Kanghyuck Lee, Yera Choi, Hun-Sung Kim
Journal of Personalized Medicine.2022; 12(11): 1899. CrossRef

Basic Research
Differentiation of Microencapsulated Neonatal Porcine Pancreatic Cell Clusters in Vitro Improves Transplant Efficacy in Type 1 Diabetes Mellitus Mice: Gyeong-Jin Cheon, Heon-Seok Park, Eun-Young Lee, Min Jung Kim, Young-Hye You, Marie Rhee, Ji-Won Kim, Kun-Ho Yoon; Diabetes Metab J. 2022;46(5):677-688. Published online February 7, 2022; DOI: https://doi.org/10.4093/dmj.2021.0202

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Background
Neonatal porcine pancreatic cell clusters (NPCCs) have been proposed as an alternative source of β cells for islet transplantation because of their low cost and growth potential after transplantation. However, the delayed glucose lowering effect due to the immaturity of NPCCs and immunologic rejection remain as a barrier to NPCC’s clinical application. Here, we demonstrate accelerated differentiation and immune-tolerant NPCCs by in vitro chemical treatment and microencapsulation.
Methods
NPCCs isolated from 3-day-old piglets were cultured in F-10 media and then microencapsulated with alginate on day 5. Differentiation of NPCCs is facilitated by media supplemented with activin receptor-like kinase 5 inhibitor II, triiodothyronine and exendin-4 for 2 weeks. Marginal number of microencapsulated NPCCs to cure diabetes with and without differentiation were transplanted into diabetic mice and observed for 8 weeks.
Results
The proportion of insulin-positive cells and insulin mRNA levels of NPCCs were significantly increased in vitro in the differentiated group compared with the undifferentiated group. Blood glucose levels decreased eventually after transplantation of microencapsulated NPCCs in diabetic mice and normalized after 7 weeks in the differentiated group. In addition, the differentiated group showed nearly normal glucose tolerance at 8 weeks after transplantation. In contrast, neither blood glucose levels nor glucose tolerance were improved in the undifferentiated group. Retrieved graft in the differentiated group showed greater insulin response to high glucose compared with the undifferentiated group.
Conclusion
in vitro differentiation of microencapsulated immature NPCCs increased the proportion of insulin-positive cells and improved transplant efficacy in diabetic mice without immune rejection.

Citations

Citations to this article as recorded by

Dual-targeted nano-encapsulation of neonatal porcine islet-like cell clusters with triiodothyronine-loaded bifunctional polymersomes
Sang Hoon Lee, Minse Kim, Eun-Jin Lee, Sun Mi Ahn, Yu-Rim Ahn, Jaewon Choi, Jung-Taek Kang, Hyun-Ouk Kim
Discover Nano.2024;[Epub] CrossRef
Long‐term efficacy of encapsulated xenogeneic islet transplantation: Impact of encapsulation techniques and donor genetic traits
Heon‐Seok Park, Eun Young Lee, Young‐Hye You, Marie Rhee, Jong‐Min Kim, Seong‐Soo Hwang, Poong‐Yeon Lee
Journal of Diabetes Investigation.2024;[Epub] CrossRef

Short Communication

Basic Research
GPR40 Agonism Modulates Inflammatory Reactions in Vascular Endothelial Cells: Joo Won Kim, Eun Roh, Kyung Mook Choi, Hye Jin Yoo, Hwan-Jin Hwang, Sei Hyun Baik; Diabetes Metab J. 2022;46(3):506-511. Published online January 24, 2022; DOI: https://doi.org/10.4093/dmj.2021.0092

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Endothelial dysfunction is strongly linked with inflammatory responses, which can impact cardiovascular disease. Recently, G protein-coupled receptor 40 (GPR40) has been investigated as a modulator of metabolic stress; however, the function of GPR40 in vascular endothelial cells has not been reported. We analyzed whether treatment of GPR40-specific agonists modulated the inflammatory responses in human umbilical vein endothelial cells (HUVECs). Treatment with LY2922470, a GPR40 agonist, significantly reduced lipopolysaccharide (LPS)-mediated nuclear factor-kappa B (NF-κB) phosphorylation and movement into the nucleus from the cytosol. However, treatment with another GPR40 agonist, TAK875, did not inhibit LPS-induced NF-κB activation. LPS treatment induced expression of adhesion molecules vascular cell adhesion molecule-1 (VCAM-1) and intercellular adhesion molecule-1 (ICAM-1) and attachment of THP-1 cells to HUVECs, which were all decreased by LY2922470 but not TAK875. Our results showed that ligand-dependent agonism of GPR40 is a promising therapeutic target for overcoming inflammatory reactions in the endothelium.

Citations

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Synthetic GPR40/FFAR1 agonists: An exhaustive survey on the most recent chemical classes and their structure-activity relationships
Abhik Paul, Sourin Nahar, Pankaj Nahata, Arnab Sarkar, Avik Maji, Ajeya Samanta, Sanmoy Karmakar, Tapan Kumar Maity
European Journal of Medicinal Chemistry.2024; 264: 115990. CrossRef
Metabolite-sensing GPCRs in rheumatoid arthritis
Xuezhi Yang, Wankang Zhang, Luping Wang, Yingjie Zhao, Wei Wei
Trends in Pharmacological Sciences.2024; 45(2): 118. CrossRef
GPR40 deficiency worsens metabolic syndrome‐associated periodontitis in mice
Yanchun Li, Zhongyang Lu, Cameron L. Kirkwood, Keith L. Kirkwood, Stephen A. Wank, Ai‐Jun Li, Maria F. Lopes‐Virella, Yan Huang
Journal of Periodontal Research.2023; 58(3): 575. CrossRef
Signaling pathways and intervention for therapy of type 2 diabetes mellitus
Rong Cao, Huimin Tian, Yu Zhang, Geng Liu, Haixia Xu, Guocheng Rao, Yan Tian, Xianghui Fu
MedComm.2023;[Epub] CrossRef
G Protein-Coupled Receptor 40 Agonist LY2922470 Alleviates Ischemic-Stroke-Induced Acute Brain Injury and Functional Alterations in Mice
Yingyu Lu, Wanlu Zhou, Qinghua Cui, Chunmei Cui
International Journal of Molecular Sciences.2023; 24(15): 12244. CrossRef
AM1638, a GPR40-Full Agonist, Inhibited Palmitate- Induced ROS Production and Endoplasmic Reticulum Stress, Enhancing HUVEC Viability in an NRF2-Dependent Manner
Hwan-Jin Hwang, Joo Won Kim, SukHwan Yun, Min Jeong Park, Eyun Song, Sooyeon Jang, Ahreum Jang, Kyung Mook Choi, Sei Hyun Baik, Hye Jin Yoo
Endocrinology and Metabolism.2023; 38(6): 760. CrossRef
Learn from failures and stay hopeful to GPR40, a GPCR target with robust efficacy, for therapy of metabolic disorders
Hong-Ping Guan, Yusheng Xiong
Frontiers in Pharmacology.2022;[Epub] CrossRef

Brief Report

Technology/Device
Do-It-Yourself Open Artificial Pancreas System in Children and Adolescents with Type 1 Diabetes Mellitus: Real-World Data: Min Sun Choi, Seunghyun Lee, Jiwon Kim, Gyuri Kim, Sung Min Park, Jae Hyeon Kim; Diabetes Metab J. 2022;46(1):154-159. Published online November 23, 2021; DOI: https://doi.org/10.4093/dmj.2021.0011

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Few studies have been conducted among Asian children and adolescents with type 1 diabetes mellitus (T1DM) using do-it-yourself artificial pancreas system (DIY-APS). We evaluated real-world data of pediatric T1DM patients using DIY-APS. Data were obtained for 10 patients using a DIY-APS with algorithms. We collected sensor glucose and insulin delivery data from each participant for a period of 4 weeks. Average glycosylated hemoglobin was 6.2%±0.3%. The mean percentage of time that glucose level remained in the target range of 70 to 180 mg/dL was 82.4%±7.8%. Other parameters including time above range, time below range and mean glucose were also within the recommended level, similar to previous commercial and DIY-APS studies. However, despite meeting the target range, unadjusted gaps were still observed between the median basal setting and temporary basal insulin, which should be handled by healthcare providers.

Citations

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Advances in Continuous Glucose Monitoring and Integrated Devices for Management of Diabetes with Insulin-Based Therapy: Improvement in Glycemic Control
Jee Hee Yoo, Jae Hyeon Kim
Diabetes & Metabolism Journal.2023; 47(1): 27. CrossRef
Open-source automated insulin delivery systems (OS-AIDs) in a pediatric population with type 1 diabetes in a real-life setting: the AWeSoMe study group experience
Judith Nir, Marianna Rachmiel, Abigail Fraser, Yael Lebenthal, Avivit Brener, Orit Pinhas-Hamiel, Alon Haim, Eve Stern, Noa Levek, Tal Ben-Ari, Zohar Landau
Endocrine.2023; 81(2): 262. CrossRef
Efficacy and safety of Android artificial pancreas system use at home among adults with type 1 diabetes mellitus in China: protocol of a 26-week, free-living, randomised, open-label, two-arm, two-phase, crossover trial
Mengyun Lei, Beisi Lin, Ping Ling, Zhigu Liu, Daizhi Yang, Hongrong Deng, Xubin Yang, Jing Lv, Wen Xu, Jinhua Yan
BMJ Open.2023; 13(8): e073263. CrossRef
Barriers to Uptake of Open-Source Automated Insulin Delivery Systems: Analysis of Socioeconomic Factors and Perceived Challenges of Caregivers of Children and Adolescents With Type 1 Diabetes From the OPEN Survey
Antonia Huhndt, Yanbing Chen, Shane O’Donnell, Drew Cooper, Hanne Ballhausen, Katarzyna A. Gajewska, Timothée Froment, Mandy Wäldchen, Dana M. Lewis, Klemens Raile, Timothy C. Skinner, Katarina Braune
Frontiers in Clinical Diabetes and Healthcare.2022;[Epub] CrossRef
Toward Personalized Hemoglobin A1c Estimation for Type 2 Diabetes
Namho Kim, Da Young Lee, Wonju Seo, Nan Hee Kim, Sung-Min Park
IEEE Sensors Journal.2022; 22(23): 23023. CrossRef

Original Articles

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

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

Basic Research
Notch1 Has an Important Role in β-Cell Mass Determination and Development of Diabetes: Young Sil Eom, A-Ryeong Gwon, Kyung Min Kwak, Jin-Young Youn, Heekyoung Park, Kwang-Won Kim, Byung-Joon Kim; Diabetes Metab J. 2021;45(1):86-96. Published online February 26, 2020; DOI: https://doi.org/10.4093/dmj.2019.0160

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Background

Notch signaling pathway plays an important role in regulating pancreatic endocrine and exocrine cell fate during pancreas development. Notch signaling is also expressed in adult pancreas. There are few studies on the effect of Notch on adult pancreas. Here, we investigated the role of Notch in islet mass and glucose homeostasis in adult pancreas using Notch1 antisense transgenic (NAS).

Methods

Western blot analysis was performed for the liver of 8-week-old male NAS mice. We also conducted an intraperitoneal glucose tolerance test (IPGTT) and intraperitoneal insulin tolerance test in 8-week-old male NAS mice and male C57BL/6 mice (control). Morphologic observation of pancreatic islet and β-cell was conducted in two groups. Insulin secretion capacity in islets was measured by glucose-stimulated insulin secretion (GSIS) and perifusion.

Results

NAS mice showed higher glucose levels and lower insulin secretion in IPGTT than the control mice. There was no significant difference in insulin resistance. Total islet and β-cell masses were decreased in NAS mice. The number of large islets (≥250 µm) decreased while that of small islets (<250 µm) increased. Reduced insulin secretion was observed in GSIS and perifusion. Neurogenin3, neurogenic differentiation, and MAF bZIP transcription factor A levels increased in NAS mice.

Conclusion

Our study provides that Notch1 inhibition decreased insulin secretion and decreased islet and β-cell masses. It is thought that Notch1 inhibition suppresses islet proliferation and induces differentiation of small islets. In conclusion, Notch signaling pathway may play an important role in β-cell mass determination and diabetes.

Citations

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N6-methylation of RNA-bound adenosine regulator HNRNPC promotes vascular endothelial dysfunction in type 2 diabetes mellitus by activating the PSEN1-mediated Notch pathway
Ying Cai, Tao Chen, Mingzhu Wang, Lihua Deng, Cui Li, Siqian Fu, Kangling Xie
Diabetes Research and Clinical Practice.2023; 197: 110261. CrossRef
Single‐cell RNA sequencing: Inhibited Notch2 signalling underlying the increased lens fibre cells differentiation in high myopia
Yunqian Yao, Ling Wei, Zhenhua Chen, Hao Li, Jiao Qi, Qingfeng Wu, Xingtao Zhou, Yi Lu, Xiangjia Zhu
Cell Proliferation.2023;[Epub] CrossRef
Micro ribonucleic acid‐363 regulates the phosphatidylinositol 3‐kinase/threonine protein kinase axis by targeting NOTCH1 and forkhead box C2, leading to hepatic glucose and lipids metabolism disorder in type 2 diabetes mellitus
Yu‐Huan Peng, Ping Wang, Xiao‐Qun He, Ming‐Zhao Hong, Feng Liu
Journal of Diabetes Investigation.2022; 13(2): 236. CrossRef
Soluble T-cadherin promotes pancreatic β-cell proliferation by upregulating Notch signaling
Tomonori Okita, Shunbun Kita, Shiro Fukuda, Keita Fukuoka, Emi Kawada-Horitani, Masahito Iioka, Yuto Nakamura, Yuya Fujishima, Hitoshi Nishizawa, Dan Kawamori, Taka-aki Matsuoka, Maeda Norikazu, Iichiro Shimomura
iScience.2022; 25(11): 105404. CrossRef
Comparison of islet isolation result and clinical applicability according to GMP‐grade collagenase enzyme blend in adult porcine islet isolation and culture
Kyungmin Kwak, Jae‐kyung Park, Joohyun Shim, Nayoung Ko, Hyoung‐Joo Kim, Yongjin Lee, Jun‐Hyeong Kim, Michael Alexander, Jonathan R. T. Lakey, Hyunil Kim, Kimyung Choi
Xenotransplantation.2021;[Epub] CrossRef
Genome-Wide Meta-analysis Identifies Genetic Variants Associated With Glycemic Response to Sulfonylureas
Adem Y. Dawed, Sook Wah Yee, Kaixin Zhou, Nienke van Leeuwen, Yanfei Zhang, Moneeza K. Siddiqui, Amy Etheridge, Federico Innocenti, Fei Xu, Josephine H. Li, Joline W. Beulens, Amber A. van der Heijden, Roderick C. Slieker, Yu-Chuan Chang, Josep M. Mercade
Diabetes Care.2021; 44(12): 2673. 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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Cancer incidence appears to be increased in both type 1 and type 2 diabetes mellitus (DM). DM represents a risk factor for cancer, particularly hepatocellular, hepatobiliary, pancreas, breast, ovarian, endometrial, and gastrointestinal cancers. In addition, there is evidence showing that DM is associated with increased cancer mortality. Common risk factors such as age, obesity, physical inactivity and smoking may contribute to increased cancer risk in patients with DM. Although the mechanistic process that may link diabetes to cancer is not completely understood yet, biological mechanisms linking DM and cancer are hyperglycemia, hyperinsulinemia, increased bioactivity of insulin-like growth factor 1, oxidative stress, dysregulations of sex hormones, and chronic inflammation. However, cancer screening rate is significantly lower in people with DM than that in people without diabetes. Evidence from previous studies suggests that some medications used to treat DM are associated with either increased or reduced risk of cancer. However, there is no strong evidence supporting the association between the use of anti-hyperglycemic medication and specific cancer. In conclusion, all patients with DM should be undergo recommended age- and sex appropriate cancer screenings to promote primary prevention and early detection. Furthermore, cancer should be screened in routine diabetes assessment.

Citations

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4. Comprehensive Medical Evaluation and Assessment of Comorbidities: Standards of Care in Diabetes—2024
Nuha A. ElSayed, Grazia Aleppo, Raveendhara R. Bannuru, Dennis Bruemmer, Billy S. Collins, Kenneth Cusi, Laya Ekhlaspour, Talya K. Fleming, Marisa E. Hilliard, Eric L. Johnson, Kamlesh Khunti, Ildiko Lingvay, Glenn Matfin, Rozalina G. McCoy, Nicola Napoli
Diabetes Care.2024; 47(Supplement): S52. CrossRef
Editorial: The relationship between diabetes and cancers and its underlying mechanisms, volume II
Qiang Huo, Shuo Wang, Ying Hou, Reginald M. Gorczynski, Yining Shen, Bin Wang, Hanyi Ge, Tao Li
Frontiers in Endocrinology.2024;[Epub] CrossRef
Factors associated with gastric and duodenal neuroendocrine tumors: A multicenter case-control study
Kwangwoo Nam, Su Youn Nam, Jun Chul Park, Young Sin Cho, Hyuk Soon Choi, Kyoungwon Jung, Seon-Young Park, Joon Hyun Cho, Hyonho Chun
Digestive and Liver Disease.2024;[Epub] CrossRef
Exploring Perspectives on Cancer Screening in People Aged 30-70: A Comparative Study of Those With and Without Type 2 Diabetes
Yunus GÜR, Egemen TURAL, Akın DAYAN
Konuralp Tıp Dergisi.2024; 16(1): 26. CrossRef
Prognostic significance of glucose‐lipid metabolic index in pancreatic cancer patients with diabetes mellitus
Hailiang Wang, Shiye Ruan, Zelong Wu, Qian Yan, Yubin Chen, Jinwei Cui, Zhongyan Zhang, Shanzhou Huang, Baohua Hou, Chuanzhao Zhang
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Original Articles

Clinical Care/Education
Impact of Socioeconomic Status on Health Behaviors, Metabolic Control, and Chronic Complications in Type 2 Diabetes Mellitus: So Hun Kim, Seung Youn Lee, Chei Won Kim, Young Ju Suh, Seongbin Hong, Seong Hee Ahn, Da Hae Seo, Moon-Suk Nam, Suk Chon, Jeong-Taek Woo, Sei Hyun Baik, Yongsoo Park, Kwan Woo Lee, Young Seol Kim; Diabetes Metab J. 2018;42(5):380-393. Published online June 29, 2018; DOI: https://doi.org/10.4093/dmj.2017.0102

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Abstract PDF Supplementary Material PubReader

Background

The aim of the study was to assess the impact of socioeconomic status (SES) on health behaviors, metabolic control, and chronic complications in people with type 2 diabetes mellitus (T2DM) from South Korea, a country with universal health insurance coverage and that has experienced rapid economic and social transition.

Methods

A total of 3,294 Korean men and women with T2DM aged 30 to 65 years, participating in the Korean National Diabetes Program (KNDP) cohort who reported their SES and had baseline clinical evaluation were included in the current cross-sectional analysis. SES included the level of education and monthly household income.

Results

Lower education level and lower income level were closely related, and both were associated with older age in men and women. Women and men with lower income and education level had higher carbohydrate and lower fat intake. After adjustment for possible confounding factors, higher education in men significantly lowered the odds of having uncontrolled hyperglycemia (glycosylated hemoglobin ≥7.5%) (odds ratio [OR], 0.63; 95% confidence interval [CI], 0.43 to 0.91 for highest education; P_trend=0.048), while higher household income in men significantly lowered the odds of having diabetic retinopathy (OR, 0.59; 95% CI, 0.37 to 0.95 for highest income level; P_trend=0.048). In women, lower income was associated with a higher stress level.

Conclusion

Men with lower SES had higher odds of having diabetic retinopathy and uncontrolled hyperglycemia, showing the need to improve care targeted to this population.

Citations

Citations to this article as recorded by

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Batoul Safieddine, Stefanie Sperlich, Johannes Beller, Karin Lange, Jelena Epping, Juliane Tetzlaff, Fabian Tetzlaff, Siegfried Geyer
SSM - Population Health.2020; 11: 100596. CrossRef
Thirteen-year trends in the prevalence of diabetes according to socioeconomic condition and cardiovascular risk factors in a Swiss population
Carlos de Mestral, Silvia Stringhini, Idris Guessous, François R Jornayvaz
BMJ Open Diabetes Research & Care.2020; 8(1): e001273. CrossRef
Dietary Habits and Dietary Antioxidant Intake Are Related to Socioeconomic Status in Polish Adults: A Nationwide Study
Małgorzata Elżbieta Zujko, Anna Waśkiewicz, Wojciech Drygas, Alicja Cicha-Mikołajczyk, Kinga Zujko, Danuta Szcześniewska, Krystyna Kozakiewicz, Anna Maria Witkowska
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Diabetes Fact Sheets in Korea, 2018: An Appraisal of Current Status
Bo-Yeon Kim, Jong Chul Won, Jae Hyuk Lee, Hun-Sung Kim, Jung Hwan Park, Kyoung Hwa Ha, Kyu Chang Won, Dae Jung Kim, Kyong Soo Park
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R. Lauretta, M. Sansone, A. Sansone, F. Romanelli, M. Appetecchia
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Others
Generation of Insulin-Expressing Cells in Mouse Small Intestine by Pdx1, MafA, and BETA2/NeuroD: So-Hyun Lee, Marie Rhee, Ji-Won Kim, Kun-Ho Yoon; Diabetes Metab J. 2017;41(5):405-416. Published online September 5, 2017; DOI: https://doi.org/10.4093/dmj.2017.41.5.405

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Background

To develop surrogate insulin-producing cells for diabetes therapy, adult stem cells have been identified in various tissues and studied for their conversion into β-cells. Pancreatic progenitor cells are derived from the endodermal epithelium and formed in a manner similar to gut progenitor cells. Here, we generated insulin-producing cells from the intestinal epithelial cells that induced many of the specific pancreatic transcription factors using adenoviral vectors carrying three genes: PMB (pancreatic and duodenal homeobox 1 [Pdx1], V-maf musculoaponeurotic fibrosarcoma oncogene homolog A [MafA], and BETA2/NeuroD).

Methods

By direct injection into the intestine through the cranial mesenteric artery, adenoviruses (Ad) were successfully delivered to the entire intestine. After virus injection, we could confirm that the small intestine of the mouse was appropriately infected with the Ad-Pdx1 and triple Ad-PMB.

Results

Four weeks after the injection, insulin mRNA was expressed in the small intestine, and the insulin gene expression was induced in Ad-Pdx1 and Ad-PMB compared to control Ad-green fluorescent protein. In addition, the conversion of intestinal cells into insulin-expressing cells was detected in parts of the crypts and villi located in the small intestine.

Conclusion

These data indicated that PMB facilitate the differentiation of mouse intestinal cells into insulin-expressing cells. In conclusion, the small intestine is an accessible and abundant source of surrogate insulin-producing cells.

Citations

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Harnessing gut cells for functional insulin production: Strategies and challenges
Kelvin Baafi, John C. March
Biotechnology Notes.2023; 4: 7. CrossRef
Differential Morphological Diagnosis of Various Forms of Congenital Hyperinsulinism in Children
Lubov Borisovna Mitrofanova, Anastasia Arkadyevna Perminova, Daria Viktorovna Ryzhkova, Anna Andreyevna Sukhotskaya, Vladimir Gireyevich Bairov, Irina Leorovna Nikitina
Frontiers in Endocrinology.2021;[Epub] CrossRef
Generation of iPSC-derived insulin-producing cells from patients with type 1 and type 2 diabetes compared with healthy control
Min Jung Kim, Eun Young Lee, Young-Hye You, Hae Kyung Yang, Kun-Ho Yoon, Ji-Won Kim
Stem Cell Research.2020; 48: 101958. CrossRef
ERK Regulates NeuroD1-mediated Neurite Outgrowth via Proteasomal Degradation
Tae-young Lee, In-Su Cho, Narayan Bashyal, Francisco J Naya, Ming-Jer Tsai, Jeong Seon Yoon, Jung-Mi Choi, Chang-Hwan Park, Sung-Soo Kim, Haeyoung Suh-Kim
Experimental Neurobiology.2020; 29(3): 189. CrossRef
Generation of a PDX1–EGFP reporter human induced pluripotent stem cell line, KSCBi005-A-3, using the CRISPR/Cas9 system
Youngsun Lee, Hye Young Choi, Ara Kwon, Hyeyeon Park, Mi-Hyun Park, Ji-Won Kim, Min Jung Kim, Yong-Ou Kim, Sungwook Kwak, Soo Kyung Koo
Stem Cell Research.2019; 41: 101632. CrossRef

Intensive Individualized Reinforcement Education Is Important for the Prevention of Hypoglycemia in Patients with Type 2 Diabetes: Yun-Mi Yong, Kyung-Mi Shin, Kang-Min Lee, Jae-Young Cho, Sun-Hye Ko, Min-Hyang Yoon, Tae-Won Kim, Jong-Hyun Jeong, Yong-Moon Park, Seung-Hyun Ko, Yu-Bae Ahn; Diabetes Metab J. 2015;39(2):154-163. Published online March 10, 2015; DOI: https://doi.org/10.4093/dmj.2015.39.2.154

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Background

We investigated whether an intensive individualized reinforcement education program could influence the prevention of hypoglycemic events in patients with type 2 diabetes.

Methods

From March 2013 to September 2013, patients aged 35 to 75 years with type 2 diabetes who had not previously participated in diabetes education, and treated with insulin or a sulfonylurea-containing regimen were included in the study. After structured group education, the patients assigned to the intensive individualized education group (IT) were requested to visit for reinforcement. All subjects in the IT were encouraged to self-manage dose adjustments. Participants in both groups (control group [CG, group education only; n=22] and IT [n=24]) attended follow-up visits at 2, 8, 12, and 24 weeks. At each visit, all patients were asked whether they had experienced hypoglycemia.

Results

The total study population consisted of 20 men (43.5%; mean age and diabetic duration of 55.9±11.0 and 5.1±7.3 years, respectively). At 24 weeks, there were no significant differences in hemoglobin A1c values between the CG and IT. The total number of hypoglycemic events per patient was 5.26±6.5 in the CG and 2.58±2.3 times in the IT (P=0.004). Adherence to lifestyle modification including frequency of exercise, self-monitoring of blood glucose, or dietary habit was not significantly different between the groups. However, adherence to hypoglycemia management, especially the dose adjustment of medication, was significantly higher in the IT compared with the CG.

Conclusion

Compared with the structured group education, additional IT resulted in additional benefits in terms of avoidance of hypoglycemia and treating hypoglycemia in patients with type 2 diabetes.

Citations

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Huda Y. Almomani, Carlos Rodriguez Pascual, Paul Grassby, Keivan Ahmadi
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A Cross-Sectional study on risk factors for severe hypoglycemia among Insulin-Treated elderly type 2 diabetes Mellitus (T2DM) patients in Singapore
Michelle Shi Min Ko, Wai Kit Lee, Li Chang Ang, Su-Yen Goh, Yong Mong Bee, Ming Ming Teh
Diabetes Research and Clinical Practice.2022; 185: 109236. CrossRef
Management Status of Patients with Type 2 Diabetes Mellitus at General Hospitals in Korea: A 5-Year Follow-Up Study
Jin Hee Jung, Jung Hwa Lee, Hyang Mi Jang, Young Na, Hee Sun Choi, Yeon Hee Lee, Yang Gyo Kang, Na Rae Kim, Jeong Rim Lee, Bok Rye Song, Kang Hee Sim
The Journal of Korean Diabetes.2022; 23(1): 64. CrossRef
Anti-hyperglycemic Medication Compliance: A Quality Assurance Project
Rayan Mamoon, Md Y Mamoon, Debbie Hermanstyne, Issac Sachmechi
Cureus.2022;[Epub] CrossRef
Randomised controlled trial of pharmacist-led patient counselling in controlling hypoglycaemic attacks in older adults with type 2 diabetes mellitus (ROSE-ADAM): A study protocol of the SUGAR intervention
Huda Y. Almomani, Carlos Rodriguez Pascual, Sayer I. Al-Azzam, Keivan Ahmadi
Research in Social and Administrative Pharmacy.2021; 17(5): 885. CrossRef
Severe hypoglycemia as a preventable risk factor for cardiovascular disease in patients with type 2 diabetes mellitus
Soo-Yeon Choi, Seung-Hyun Ko
The Korean Journal of Internal Medicine.2021; 36(2): 263. CrossRef
Type 2 diabetes patients’ views on prevention of hypoglycaemia – a mixed methods study investigating self-management issues and self-identified causes of hypoglycaemia
Stijn Crutzen, Tessa van den Born-Bondt, Petra Denig, Katja Taxis
BMC Family Practice.2021;[Epub] CrossRef
Cross‐sectional analysis of emergency hypoglycaemia and outcome predictors among people with diabetes in an urban population
Chukwuma Uduku, Valentina Pendolino, Ian Godsland, Nick Oliver, Monika Reddy, Rachael T. Fothergill
Diabetic Medicine.2021;[Epub] CrossRef
Short-term efficacy of high intensity group and individual education in patients with type 2 diabetes: a randomized single-center trial
R. Reale, A. Tumminia, L. Romeo, N. La Spina, R. Baratta, G. Padova, L. Tomaselli, L. Frittitta
Journal of Endocrinological Investigation.2019; 42(4): 403. CrossRef
The role of structured education in the management of hypoglycaemia
Ahmed Iqbal, Simon R. Heller
Diabetologia.2018; 61(4): 751. CrossRef
Association of diabetes therapy-related quality of life and physical activity levels in patients with type 2 diabetes receiving medication therapy: the Diabetes Distress and Care Registry at Tenri (DDCRT 17)
Yasuaki Hayashino, Satoru Tsujii, Hitoshi Ishii
Acta Diabetologica.2018; 55(2): 165. CrossRef
Insulin Therapy for Adult Patients with Type 2 Diabetes Mellitus: A Position Statement of the Korean Diabetes Association, 2017
Byung-Wan Lee, Jin Hwa Kim, Seung-Hyun Ko, Kyu-Yeon Hur, Nan-Hee Kim, Sang Youl Rhee, Hyun Jin Kim, Min Kyong Moon, Seok-O Park, Kyung Mook Choi
Diabetes & Metabolism Journal.2017; 41(5): 367. CrossRef
Antihyperglycemic Agent Therapy for Adult Patients with Type 2 Diabetes Mellitus 2017: A Position Statement of the Korean Diabetes Association
Seung-Hyun Ko, Kyu-Yeon Hur, Sang Youl Rhee, Nan-Hee Kim, Min Kyong Moon, Seok-O Park, Byung-Wan Lee, Hyun Jin Kim, Kyung Mook Choi, Jin Hwa Kim
Diabetes & Metabolism Journal.2017; 41(5): 337. CrossRef
Insulin therapy for adult patients with type 2 diabetes mellitus: a position statement of the Korean Diabetes Association, 2017
Byung-Wan Lee, Jin Hwa Kim, Seung-Hyun Ko, Kyu Yeon Hur, Nan-Hee Kim, Sang Youl Rhee, Hyun Jin Kim, Min Kyong Moon, Seok-O Park, Kyung Mook Choi
The Korean Journal of Internal Medicine.2017; 32(6): 967. CrossRef
Physician-Directed Diabetes Education without a Medication Change and Associated Patient Outcomes
Hun-Sung Kim, Hyunah Kim, Hae-Kyung Yang, Eun Young Lee, Yoo Jin Jeong, Tong Min Kim, So Jung Yang, Seo Yeon Baik, Seung-Hwan Lee, Jae Hyoung Cho, In Young Choi, Hyeon Woo Yim, Bong-Yun Cha
Diabetes & Metabolism Journal.2017; 41(3): 187. CrossRef
Antihyperglycemic agent therapy for adult patients with type 2 diabetes mellitus 2017: a position statement of the Korean Diabetes Association
Seung-Hyun Ko, Kyu-Yeon Hur, Sang Youl Rhee, Nan-Hee Kim, Min Kyong Moon, Seok-O Park, Byung-Wan Lee, Hyun Jin Kim, Kyung Mook Choi, Jin Hwa Kim
The Korean Journal of Internal Medicine.2017; 32(6): 947. CrossRef
Hypoglycemia and Health Costs
Yong-ho Lee, Gyuri Kim, Eun Seok Kang
The Journal of Korean Diabetes.2016; 17(1): 11. CrossRef
Association between estimated blood glucose levels and glycated hemoglobin levels
Seon-Ah Cha, Seung-Hyun Ko
The Korean Journal of Internal Medicine.2016; 31(3): 457. CrossRef
Characteristics of Hypoglycemia Pateints Visiting the Emergency Department of a University Hospital
Sang-Hyeon Choi, Deok-Ki Youn, Moon-Gi Choi, Ohk-Hyun Ryu
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Experiences of Diabetes Education among Educators of Diabetes : a content analysis approach
Soo Jin Kang, Soo Jung Chang
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Effectiveness of 3-Day Continuous Glucose Monitoring for Improving Glucose Control in Type 2 Diabetic Patients in Clinical Practice: Soo Kyoung Kim, Hye Jeong Kim, Taehun Kim, Kyu Yeon Hur, Sun Wook Kim, Moon-Kyu Lee, Yong-Ki Min, Kwang-Won Kim, Jae Hoon Chung, Jae Hyeon Kim; Diabetes Metab J. 2014;38(6):449-455. Published online December 15, 2014; DOI: https://doi.org/10.4093/dmj.2014.38.6.449

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Abstract PDF PubReader

Background

The aim of this study was to investigate whether adjusting diabetic treatment regimens according to the information obtained from a continuous glucose monitoring system (CGMS) might lead to improved glycemic control in patients with type 2 diabetes.

Methods

We reviewed the medical charts of 172 patients who used the CGMS for 1 year starting in December 2008 and the records of 1,500 patients who visited their regular outpatient clinics during December 2008. Of these patients, a total of 65 CGMS patients and 301 regular outpatients (control group) were enrolled in the study after propensity score matching. There were no differences in baseline glycated hemoglobin (HbA1c), age, and duration of diabetes between the CGMS and the control groups after propensity score matching. The changes in the HbA1c levels from baseline to 6 months were calculated.

Results

The CGMS group showed a significant improvement in the HbA1c level compared to the control group at 3 months (7.9%±1.6% vs. 7.4%±1.2%, P=0.001) and at 6 months (7.4%±1.2% vs. 7.9%±1.6%, P=0.010). There were significant differences in the treatment modality changes between the CGMS group and the control group.

Conclusion

Using a 3-day CGMS was advantageous for improving glucose control in patients with type 2 diabetes and may help these patients to optimize glycemic control in clinical practice.

Citations

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Biological and Clinical Impacts of Glucose Metabolism in Pancreatic Ductal Adenocarcinoma
Zhao Liu, Hiromitsu Hayashi, Kazuki Matsumura, Norio Uemura, Yuta Shiraishi, Hiroki Sato, Hideo Baba
Cancers.2023; 15(2): 498. CrossRef
Professional continuous glucose monitoring in patients with diabetes mellitus: A systematic review and meta‐analysis
Sergio Di Molfetta, Irene Caruso, Angelo Cignarelli, Annalisa Natalicchio, Sebastio Perrini, Luigi Laviola, Francesco Giorgino
Diabetes, Obesity and Metabolism.2023; 25(5): 1301. CrossRef
American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus
George Grunberger, Jennifer Sherr, Myriam Allende, Thomas Blevins, Bruce Bode, Yehuda Handelsman, Richard Hellman, Rosemarie Lajara, Victor Lawrence Roberts, David Rodbard, Carla Stec, Jeff Unger
Endocrine Practice.2021; 27(6): 505. CrossRef
Lack of Acceptance of Digital Healthcare in the Medical Market: Addressing Old Problems Raised by Various Clinical Professionals and Developing Possible Solutions
Jong Il Park, Hwa Young Lee, Hyunah Kim, Jisan Lee, Jiwon Shinn, Hun-Sung Kim
Journal of Korean Medical Science.2021;[Epub] CrossRef
A head‐to‐head comparison of personal and professional continuous glucose monitoring systems in people with type 1 diabetes: Hypoglycaemia remains the weak spot
Othmar Moser, Marlene Pandis, Felix Aberer, Harald Kojzar, Daniel Hochfellner, Hesham Elsayed, Melanie Motschnig, Thomas Augustin, Philipp Kreuzer, Thomas R. Pieber, Harald Sourij, Julia K. Mader
Diabetes, Obesity and Metabolism.2019; 21(4): 1043. CrossRef
Glucose monitoring in diabetes: from clinical studies to real‐world practice
Rebecca C Sagar, Afroze Abbas, Ramzi Ajjan
Practical Diabetes.2019; 36(2): 57. CrossRef
The Effectiveness of Continuous Glucose Monitoring in Patients with Type 2 Diabetes: A Systematic Review of Literature and Meta-analysis
Cindy Park, Quang A. Le
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Effects of Dapagliflozin on 24-Hour Glycemic Control in Patients with Type 2 Diabetes: A Randomized Controlled Trial
Robert R. Henry, Poul Strange, Rong Zhou, Jeremy Pettus, Leon Shi, Sergey B. Zhuplatov, Traci Mansfield, David Klein, Arie Katz
Diabetes Technology & Therapeutics.2018; 20(11): 715. CrossRef
Clinical and economic benefits of professional CGM among people with type 2 diabetes in the United States: analysis of claims and lab data
Joseph A. Sierra, Mona Shah, Max S. Gill, Zachery Flores, Hiten Chawla, Francine R. Kaufman, Robert Vigersky
Journal of Medical Economics.2018; 21(3): 225. CrossRef
Role of continuous glucose monitoring for type 2 in diabetes management and research
Robert Vigersky, Maneesh Shrivastav
Journal of Diabetes and its Complications.2017; 31(1): 280. CrossRef
Assessing the Therapeutic Utility of Professional Continuous Glucose Monitoring in Type 2 Diabetes Across Various Therapies: A Retrospective Evaluation
Jothydev Kesavadev, Robert Vigersky, John Shin, Pradeep Babu Sadasivan Pillai, Arun Shankar, Geethu Sanal, Gopika Krishnan, Sunitha Jothydev
Advances in Therapy.2017; 34(8): 1918. CrossRef
Use of Continuous Glucose Monitoring in Youth-Onset Type 2 Diabetes
Christine L. Chan
Current Diabetes Reports.2017;[Epub] CrossRef
The efficacy and safety of adding either vildagliptin or glimepiride to ongoing metformin therapy in patients with type 2 diabetes mellitus
Gyuri Kim, Sewon Oh, Sang-Man Jin, Kyu Yeon Hur, Jae Hyeon Kim, Moon-Kyu Lee
Expert Opinion on Pharmacotherapy.2017; 18(12): 1179. CrossRef
Morning Spot Urine Glucose-to-Creatinine Ratios Predict Overnight Urinary Glucose Excretion in Patients With Type 2 Diabetes
So Ra Kim, Yong-ho Lee, Sang-Guk Lee, Sun Hee Lee, Eun Seok Kang, Bong-Soo Cha, Hyun Chul Lee, Jeong-Ho Kim, Byung-Wan Lee
Annals of Laboratory Medicine.2017; 37(1): 9. CrossRef
The Contemporary Role of Masked Continuous Glucose Monitoring in a Real-Time World
Ian Blumer
Journal of Diabetes Science and Technology.2016; 10(3): 790. CrossRef
Glycemic Variability: How Do We Measure It and Why Is It Important?
Sunghwan Suh, Jae Hyeon Kim
Diabetes & Metabolism Journal.2015; 39(4): 273. CrossRef

Pattern of Stress-Induced Hyperglycemia according to Type of Diabetes: A Predator Stress Model: Jin-Sun Chang, Young-Hye You, Shin-Young Park, Ji-Won Kim, Hun-Sung Kim, Kun-Ho Yoon, Jae-Hyoung Cho; Diabetes Metab J. 2013;37(6):475-483. Published online December 12, 2013; DOI: https://doi.org/10.4093/dmj.2013.37.6.475

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Abstract PDF PubReader

Background

We aimed to quantify stress-induced hyperglycemia and differentiate the glucose response between normal animals and those with diabetes. We also examined the pattern in glucose fluctuation induced by stress according to type of diabetes.

Methods

To load psychological stress on animal models, we used a predator stress model by exposing rats to a cat for 60 minutes and measured glucose level from the beginning to the end of the test to monitor glucose fluctuation. We induced type 1 diabetes model (T1D) for ten Sprague-Dawley rats using streptozotocin and used five Otsuka Long-Evans Tokushima Fatty rats as obese type 2 diabetes model (OT2D) and 10 Goto-Kakizaki rats as nonobese type 2 diabetes model (NOT2D). We performed the stress loading test in both the normal and diabetic states and compared patterns of glucose fluctuation among the three models. We classified the pattern of glucose fluctuation into A, B, and C types according to speed of change in glucose level.

Results

Increase in glucose, total amount of hyperglycemic exposure, time of stress-induced hyperglycemia, and speed of glucose increase were significantly increased in all models compared to the normal state. While the early increase in glucose after exposure to stress was higher in T1D and NOT2D, it was slower in OT2D. The rate of speed of the decrease in glucose level was highest in NOT2D and lowest in OT2D.

Conclusion

The diabetic state was more vulnerable to stress compared to the normal state in all models, and the pattern of glucose fluctuation differed among the three types of diabetes. The study provides basic evidence for stress-induced hyperglycemia patterns and characteristics used for the management of diabetes patients.

Citations

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Stress hyperglycemia as first sign of asymptomatic type 1 diabetes: an instructive case
Wei-De Wang, Chun-Hao Chu, Chiung-Hsi Tien, Shuo-Yu Wang, Shih-Yao Liu, Chien-Ming Lin
BMC Pediatrics.2021;[Epub] CrossRef
Genetic determinants of obesity heterogeneity in type II diabetes
Somayeh Alsadat Hosseini Khorami, Mohd Sokhini Abd Mutalib, Mohammad Feili Shiraz, Joseph Anthony Abdullah, Zulida Rejali, Razana Mohd Ali, Huzwah Khaza’ai
Nutrition & Metabolism.2020;[Epub] CrossRef
Sex Dimorphic Responses of the Hypothalamus–Pituitary–Thyroid Axis to Maternal Separation and Palatable Diet
Lorraine Jaimes-Hoy, Fidelia Romero, Jean-Louis Charli, Patricia Joseph-Bravo
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Hesperidin protects against stress induced gastric ulcer through regulation of peroxisome proliferator activator receptor gamma in diabetic rats
Shimaa M. Elshazly, Dalia M. Abd El Motteleb, Islam A.A.E-H. Ibrahim
Chemico-Biological Interactions.2018; 291: 153. CrossRef
Physiology and Neurobiology of Stress and the Implications for Physical Health
B Sivaprakash
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Education as Prescription for Patients with Type 2 Diabetes Mellitus: Compliance and Efficacy in Clinical Practice: Mi Yeon Kim, Sunghwan Suh, Sang-Man Jin, Se Won Kim, Ji Cheol Bae, Kyu Yeon Hur, Sung Hye Kim, Mi Yong Rha, Young Yun Cho, Myung-Shik Lee, Moon Kyu Lee, Kwang-Won Kim, Jae Hyeon Kim; Diabetes Metab J. 2012;36(6):452-459. Published online December 12, 2012; DOI: https://doi.org/10.4093/dmj.2012.36.6.452

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Abstract PDF PubReader

Background

Diabetes self-management education has an important role in diabetes management. The efficacy of education has been proven in several randomized trials. However, the status of diabetes education programs in real Korean clinical practice has not yet been evaluated in terms of patient compliance with the education prescription.

Methods

We retrospectively analyzed clinical and laboratory data from all patients who were ordered to undergo diabetes education during 2009 at Samsung Medical Center, Seoul, Korea (n=2,291). After excluding ineligible subjects, 588 patients were included in the analysis.

Results

Among the 588 patients, 433 received education. The overall compliance rate was 73.6%, which was significantly higher in the subjects with a short duration or living in a rural area compared to those with a long duration (85.0% vs. 65.1%, respectively; P<0.001) or living in an urban area (78.2% vs. 70.4%, respectively; P=0.037). The hemoglobin A1c decreased greater in the compliant group (from 7.84±1.54 at baseline to 6.79±1.06 at 3 months and 6.97±1.20 at 12 months after prescription in the compliant group vs. from 7.74±1.25 to 7.14±1.02 and 7.24±1.24 in the non-compliant group; P=0.001). The decrease in hemoglobin A1c was greater in the subjects with a short duration (P=0.032).

Conclusion

In our study a large percent of patients refuse to get education despite having a prescription from their physician. This refusal rate was higher in the patients with long-standing diabetes or in urban residence. Furthermore, education was more effective in patients with a short duration of diabetes in clinical practice.

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Jin Hee Jung, Jung Hwa Lee, Hyang Mi Jang, Young Na, Hee Sun Choi, Yeon Hee Lee, Yang Gyo Kang, Na Rae Kim, Jeong Rim Lee, Bok Rye Song, Kang Hee Sim
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Emmanuel Kumah, Giulia Sciolli, Maria Laura Toraldo, Anna Maria Murante
Health Policy.2018; 122(8): 866. CrossRef
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So Young Park, Sun Young Kim, Hye Mi Lee, Kyu Yeon Hur, Jae Hyeon Kim, Moon-Kyu Lee, Kang-Hee Sim, Sang-Man Jin
Diabetes & Metabolism Journal.2017; 41(2): 99. CrossRef
Educational attainment moderates the associations of diabetes education with health outcomes
Su Hyun Kim
International Journal of Nursing Practice.2016; 22(5): 444. CrossRef
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Soo Jin Kang, Soo Jung Chang
Journal of Korean Public Health Nursing.2016; 30(2): 221. CrossRef
Barrier Factors to the Completion of Diabetes Education in Korean Diabetic Adult Patients: Korea National Health and Nutrition Examination Surveys 2007-2012
Hee-Tae Kim, Kiheon Lee, Se Young Jung, Seung-Min Oh, Su-Min Jeong, Yoon-Jung Choi
Korean Journal of Family Medicine.2015; 36(5): 203. CrossRef
Determinants of glycaemic control in a practice setting: the role of weight loss and treatment adherence (The DELTA Study)
C. McAdam‐Marx, B. K. Bellows, S. Unni, J. Mukherjee, G. Wygant, U. Iloeje, J. N. Liberman, X. Ye, F. J. Bloom, D. I. Brixner
International Journal of Clinical Practice.2014; 68(11): 1309. CrossRef
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Mohsen Saffari, Ghader Ghanizadeh, Harold G. Koenig
Primary Care Diabetes.2014; 8(4): 275. CrossRef
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Kang Hee Sim, Moon Sook Hwang, Sun Young Kim, Hye Mi Lee, Ji Yeun Chang, Moon Kyu Lee
Diabetes & Metabolism Journal.2014; 38(2): 120. CrossRef
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