Skip Navigation
Skip to contents

Diabetes Metab J : Diabetes & Metabolism Journal

Search
OPEN ACCESS

Search

Page Path
HOME > Search
46 "Diabetes mellitus, type 1"
Filter
Filter
Article category
Keywords
Publication year
Authors
Funded articles
Original Articles
Technology/Device
Article image
Current Status of Continuous Glucose Monitoring Use in South Korean Type 1 Diabetes Mellitus Population–Pronounced Age-Related Disparities: Nationwide Cohort Study
Ji Yoon Kim, Seohyun Kim, Jae Hyeon Kim
Received December 9, 2024  Accepted February 3, 2025  Published online April 28, 2025  
DOI: https://doi.org/10.4093/dmj.2024.0804    [Epub ahead of print]
  • 132 View
  • 18 Download
AbstractAbstract PDF
Background
This study aims to identify the status of continuous glucose monitoring (CGM) use among individuals with type 1 diabetes mellitus (T1DM) in South Korea and to investigate whether age-related disparities exist.
Methods
Individuals with T1DM receiving intensive insulin therapy were identified from the Korean National Health Insurance Cohort (2019–2022). Characteristics of CGM users and non-users were compared, and the prescription rates of CGM and sensor- augmented pump (SAP) or automated insulin delivery (AID) systems according to age groups (<19, 19–39, 40–59, and ≥60 years) were analyzed using chi-square tests. Glycosylated hemoglobin (HbA1c) levels and coefficients of variation (CV) among CGM users were also examined.
Results
Among the 56,908 individuals with T1DM, 10,822 (19.0%) used CGM at least once, and 6,073 (10.7%) used CGM continuously. Only 241 (0.4%) individuals utilized either SAP or AID systems. CGM users were younger than non-users. The continuous prescription rate of CGM was highest among individuals aged <19 years (37.0%), followed by those aged 19–39 years (15.8%), 40–59 years (10.7%), and ≥60 years (3.9%) (P<0.001 for between-group differences). Among CGM users, HbA1c levels decreased from 8.7%±2.4% at baseline to 7.2%±1.2% at 24 months, and CV decreased from 36.6%±11.9% at 3 months to 34.1%±12.7% at 24 months.
Conclusion
Despite national reimbursement for CGM devices, the prescription rates of CGM remain low, particularly among older adults. Given the improvements in HbA1c and CV following CGM initiation, more efforts are needed to increase CGM utilization and reduce age-related disparities.
Basic Research
Article image
Effects of CXCR1/2 Blockade with Ladarixin on Streptozotocin-Induced Type 1 Diabetes Mellitus and Peripheral Neuropathy and Retinopathy in Rat
Serena Boccella, Andrea Maria Morace, Cristina Giorgio, Francesca Guida, Michela Perrone, Iolanda Manzo, Carmela Belardo, Meghan Jones, Sabatino Maione, Andrea Aramini, Marcello Allegretti, Livio Luongo, Laura Brandolini
Received August 25, 2024  Accepted November 15, 2024  Published online March 12, 2025  
DOI: https://doi.org/10.4093/dmj.2024.0504    [Epub ahead of print]
  • 632 View
  • 50 Download
AbstractAbstract PDFPubReader   ePub   
Background
The CXC motif chemokine ligand 8 (CXCL8)-CXC motif chemokine receptor 1/2 (CXCR1/2) axis has been implicated in type 1 diabetes mellitus (T1DM). Its actions on non-immune cells may also contribute to T1DM-associated complications, including painful diabetic peripheral neuropathy (DPN) and diabetic retinopathy (DR).
Methods
We assessed the efficacy of early (4–8 weeks) or late (8–12 weeks) daily ladarixin (LDX) for the treatment of streptozotocin (STZ)-induced T1DM and the related complications of DPN or DR in male rats.
Results
Early LDX mitigated STZ-induced dysmetabolism (i.e., blood glucose, insulin), inflammation in dorsal root ganglion/ sciatic nerve (interleukin-1β and tumor necrosis factor-α expression) and mechanical allodynia and thermal hyperalgesia, indicative of DPN. Moreover, vitreous citrullinated histone H3 (CitH3) and plasma GRO/CINC1 (CXCL8) increase were attenuated. Late LDX failed to reverse STZ-induced changes in metabolic parameters (i.e., blood glucose, insulin, C-peptide, pancreatic β-cell number and function). Strikingly, even in the absence of an effect on glycemic control, late LDX mitigated STZ-induced mechanical allodynia and thermal hyperalgesia and vitreous (CXCL8, CitH3) and retinal (CXCL8, CXCR1/2, myeloperoxidase, CitH3) inflammatory/pro-angiogenic (vascular endothelial growth factor, CD34) signs of DR.
Conclusion
These data confirm the efficacy of LDX in STZ-induced T1DM and provide evidence of a protective effect also against DPN and onset of DR which is independent of its effect on β-cell functionality preservation and glycemic control.
Type 1 Diabetes
Article image
Global Burden of Type 1 Diabetes Mellitus Related Chronic Kidney Disease among Adolescents and Young Adults, and Projections to 2035: Results from the Global Burden of Disease Study 2021
Xiaoli Qu, Chongbin Liu, Lin Sun, Zhifeng Sheng
Received September 4, 2024  Accepted December 12, 2024  Published online March 10, 2025  
DOI: https://doi.org/10.4093/dmj.2024.0544    [Epub ahead of print]
  • 909 View
  • 78 Download
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Type 1 diabetes mellitus related chronic kidney disease (T1DM-CKD) presents a global health challenge, with unclear trends and patterns among adolescents and young adults. This study analyzed the burden and risk factors of T1DM-CKD in individuals aged 15 to 39 from 1990 to 2021 and predicted future trends.
Methods
Using data from the Global Burden of Disease (GBD) study 2021, we analyzed the prevalence, incidence, mortality, disability-adjusted life years (DALYs), and average annual percentage change (AAPC) of T1DM-CKD among youth across gender, sociodemographic index (SDI) areas, and data from 21 regions and 204 countries and territories. Risk factors were assessed and future trends were projected.
Results
Between 1990 and 2021, the global prevalence of T1DM-CKD aged 15 to 39 increased by 107.5% to 3.32 million, with an age-standardized prevalence rate (ASPR) of 111.44 per 100,000 (AAPC 1.33%). Incidence rose by 165.4% to 14,200, with an agestandardized incidence rate of 0.48 per 100,000 (AAPC 2.19%). However, age-standardized mortality rate (0.50 per 100,000, AAPC –0.87%) and age-standardized DALYs rate (30.61 per 100,000, AAPC –0.83%) decreased. ASPR increased across all SDI regions, especially in high-SDI countries. High fasting glucose remained the major risk factor influencing DALYs. By 2035, T1DM-CKD prevalence was projected to decrease to 2.86 million (ASPR 89.67 per 100,000).
Conclusion
The research revealed a global increase in T1DM-CKD among youth, with a shift towards younger onset and significant variations based on gender and location, emphasizing the importance of early prevention and management strategies for this demographic.
Technology/Device
Article image
Comparison of Real-Time and Intermittently-Scanned Continuous Glucose Monitoring for Glycemic Control in Type 1 Diabetes Mellitus: Nationwide Cohort Study
Ji Yoon Kim, Seohyun Kim, Jae Hyeon Kim
Received March 28, 2024  Accepted October 30, 2024  Published online February 27, 2025  
DOI: https://doi.org/10.4093/dmj.2024.0160    [Epub ahead of print]
  • 754 View
  • 53 Download
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
This study compares the association between real-time continuous glucose monitoring (rtCGM) and intermittently- scanned CGM (isCGM) and glycemic control in individuals with type 1 diabetes mellitus (T1DM) in a real-world setting.
Methods
Using data from the Korean National Health Insurance Service Cohort, individuals with T1DM managed by intensive insulin therapy were followed at 3-month intervals for 2 years after the initiation of CGM. The glycosylated hemoglobin (HbA1c) levels and coefficients of variation (CVs) of rtCGM and isCGM users were compared using independent two-sample t-test and a linear mixed model.
Results
The analyses considered 7,786 individuals (5,875 adults aged ≥19 years and 1,911 children and adolescents aged <19 years). Overall, a significant reduction in HbA1c level was observed after 3 months of CGM, and the effect was sustained for 2 years. The mean HbA1c level at baseline was higher in rtCGM users than in isCGM users (8.9%±2.7% vs. 8.6%±2.2%, P<0.001). However, from 3 to 24 months, rtCGM users had lower HbA1c levels than isCGM users at every time point (7.1%±1.2% vs. 7.5%±1.3% at 24 months, P<0.001 for all time points). In both adults and children, the greater reduction in HbA1c with rtCGM remained significant after adjusting for the baseline characteristics of the users. The CV also showed greater decrease with rtCGM than with isCGM.
Conclusion
In this large nationwide cohort study, the use of rtCGM was associated with a greater improvement in glycemic control, including HbA1c reduction, than the use of isCGM in both adults and children with T1DM.
Technology/Device
Article image
Efficacy and Safety of Automated Insulin Delivery Systems in Patients with Type 1 Diabetes Mellitus: A Systematic Review and Meta-Analysis
Wenqi Fan, Chao Deng, Ruoyao Xu, Zhenqi Liu, Richard David Leslie, Zhiguang Zhou, Xia Li
Diabetes Metab J. 2025;49(2):235-251.   Published online November 13, 2024
DOI: https://doi.org/10.4093/dmj.2024.0130
  • 2,929 View
  • 251 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Automated insulin delivery (AID) systems studies are upsurging, half of which were published in the last 5 years. We aimed to evaluate the efficacy and safety of AID systems in patients with type 1 diabetes mellitus (T1DM).
Methods
We searched PubMed, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov until August 31, 2023. Randomized clinical trials that compared AID systems with other insulin-based treatments in patients with T1DM were considered eligible. Studies characteristics and glycemic metrics was extracted by three researchers independently.
Results
Sixty-five trials (3,623 patients) were included. The percentage of time in range (TIR) was 11.74% (95% confidence interval [CI], 9.37 to 14.12; P<0.001) higher with AID systems compared with control treatments. Patients on AID systems had more pronounced improvement of time below range when diabetes duration was more than 20 years (–1.80% vs. –0.86%, P=0.031) and baseline glycosylated hemoglobin lower than 7.5% (–1.93% vs. –0.87%, P=0.033). Dual-hormone full closed-loop systems revealed a greater improvement in TIR compared with hybrid closed-loop systems (–19.64% vs. –10.87%). Notably, glycemia risk index (GRI) (–3.74; 95% CI, –6.34 to –1.14; P<0.01) was also improved with AID therapy.
Conclusion
AID systems showed significant advantages compared to other insulin-based treatments in improving glucose control represented by TIR and GRI in patients with T1DM, with more favorable effect in euglycemia by dual-hormone full closedloop systems as well as less hypoglycemia for patients who are within target for glycemic control and have longer diabetes duration.

Citations

Citations to this article as recorded by  
  • Transitioning between automated insulin delivery systems: A focus on personalisation
    Pilar Isabel Beato-Víbora, Ana Chico, Jesus Moreno-Fernandez, Sharona Azriel-Mira, Lia Nattero-Chávez, Rosario Vallejo Mora, Núria Alonso-Carril, Olga Simó-Servat, Eva Aguilera-Hurtado, Luz María Reyes Céspedes, Marisol Ruiz de Adana, Marta Domínguez, Ros
    Diabetes Research and Clinical Practice.2025; 222: 112070.     CrossRef
Brief Report
Technology/Device
Article image
Effectiveness of Predicted Low-Glucose Suspend Pump Technology in the Prevention of Hypoglycemia in People with Type 1 Diabetes Mellitus: Real-World Data Using DIA:CONN G8
Jee Hee Yoo, Ji Yoon Kim, Jae Hyeon Kim
Diabetes Metab J. 2025;49(1):144-149.   Published online August 28, 2024
DOI: https://doi.org/10.4093/dmj.2024.0039
  • 1,895 View
  • 132 Download
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
We evaluated the effectiveness of the predictive low-glucose suspend (PLGS) algorithm in the DIA:CONN G8. Forty people with type 1 diabetes mellitus (T1DM) who used a DIA:CONN G8 for at least 2 months with prior experience using pumps without and with PLGS were retrospectively analyzed. The objective was to assess the changes in time spent in hypoglycemia (percent of time below range [%TBR]) before and after using PLGS. The mean age, sensor glucose levels, glucose threshold for suspension, and suspension time were 31.1±22.8 years, 159.7±23.2 mg/dL, 81.1±9.1 mg/dL, and 111.9±79.8 min/day, respectively. Overnight %TBR <70 mg/dL was significantly reduced after using the algorithm (differences=0.3%, from 1.4%±1.5% to 1.1%±1.2%, P=0.045). The glycemia risk index (GRI) improved significantly by 4.2 (from 38.8±20.9 to 34.6±19.0, P=0.002). Using the PLGS did not result in a change in the hyperglycemia metric (all P>0.05). Our findings support the PLGS in DIA:CONN G8 as an effective algorithm to improve night-time hypoglycemia and GRI in people with T1DM.
Original Articles
Type 1 Diabetes
Article image
A New Tool to Identify Pediatric Patients with Atypical Diabetes Associated with Gene Polymorphisms
Sophie Welsch, Antoine Harvengt, Paola Gallo, Manon Martin, Dominique Beckers, Thierry Mouraux, Nicole Seret, Marie-Christine Lebrethon, Raphaël Helaers, Pascal Brouillard, Miikka Vikkula, Philippe A. Lysy
Diabetes Metab J. 2024;48(5):949-959.   Published online March 22, 2024
DOI: https://doi.org/10.4093/dmj.2023.0166
  • 3,108 View
  • 199 Download
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Recent diabetes subclassifications have improved the differentiation between patients with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus despite several overlapping features, yet without considering genetic forms of diabetes. We sought to facilitate the identification of monogenic diabetes by creating a new tool that we validated in a pediatric maturity-onset diabetes of the young (MODY) cohort.
Methods
We first created the DIAgnose MOnogenic DIAbetes (DIAMODIA) criteria based on the pre-existing, but incomplete, MODY calculator. This new score is composed of four strong and five weak criteria, with patients having to display at least one weak and one strong criterion.
Results
The effectiveness of the DIAMODIA criteria was evaluated in two patient cohorts, the first consisting of patients with confirmed MODY diabetes (n=34) and the second of patients with T1DM (n=390). These DIAMODIA criteria successfully detected 100% of MODY patients. Multiple correspondence analysis performed on the MODY and T1DM cohorts enabled us to differentiate MODY patients from T1DM. The three most relevant variables to distinguish a MODY from T1DM profile were: lower insulin-dose adjusted A1c score ≤9, glycemic target-adjusted A1c score ≤4.5, and absence of three anti-islet cell autoantibodies.
Conclusion
We validated the DIAMODIA criteria, as it effectively identified all monogenic diabetes patients (MODY cohort) and succeeded to differentiate T1DM from MODY patients. The creation of this new and effective tool is likely to facilitate the characterization and therapeutic management of patients with atypical diabetes, and promptly referring them for genetic testing which would markedly improve clinical care and counseling, as well.
Type 1 Diabetes
Article image
Optimal Coefficient of Variance Threshold to Minimize Hypoglycemia Risk in Individuals with Well-Controlled Type 1 Diabetes Mellitus
Jee Hee Yoo, Seung Hee Yang, Sang-Man Jin, Jae Hyeon Kim
Diabetes Metab J. 2024;48(3):429-439.   Published online March 4, 2024
DOI: https://doi.org/10.4093/dmj.2023.0083
  • 3,873 View
  • 264 Download
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
This study investigated the optimal coefficient of variance (%CV) for preventing hypoglycemia based on real-time continuous glucose monitoring (rt-CGM) data in people with type 1 diabetes mellitus (T1DM) already achieving their mean glucose (MG) target.
Methods
Data from 172 subjects who underwent rt-CGM for at least 90 days and for whom 439 90-day glycemic profiles were available were analyzed. Receiver operator characteristic analysis was conducted to determine the cut-off value of %CV to achieve time below range (%TBR)<54 mg/dL <1 and =0.
Results
Overall mean glycosylated hemoglobin was 6.8% and median %TBR<54 mg/dL was 0.2%. MG was significantly higher and %CV significantly lower in profiles achieving %TBR<54 mg/dL <1 compared to %TBR<54 mg/dL ≥1 (all P<0.001). The cut-off value of %CV for achieving %TBR<54 mg/dL <1 was 37.5%, 37.3%, and 31.0%, in the whole population, MG >135 mg/dL, and ≤135 mg/dL, respectively. The cut-off value for %TBR<54 mg/dL=0% was 29.2% in MG ≤135 mg/dL. In profiles with MG ≤135 mg/dL, 94.2% of profiles with a %CV <31 achieved the target of %TBR<54 mg/dL <1, and 97.3% with a %CV <29.2 achieved the target of %TBR<54 mg/ dL=0%. When MG was >135 mg/dL, 99.4% of profiles with a %CV <37.3 achieved %TBR<54 mg/dL <1.
Conclusion
In well-controlled T1DM with MG ≤135 mg/dL, we suggest a %CV <31% to achieve the %TBR<54 mg/dL <1 target. Furthermore, we suggest a %CV <29.2% to achieve the target of %TBR<54 mg/dL =0 for people at high risk of hypoglycemia.
Complications
Article image
Does the Relationship of the Autonomic Symptoms Questionnaire COMPASS 31 with Cardiovascular Autonomic Tests Differ between Type 1 and Type 2 Diabetes Mellitus?
Ilenia D’Ippolito, Marika Menduni, Cinzia D’Amato, Aikaterini Andreadi, Davide Lauro, Vincenza Spallone
Diabetes Metab J. 2024;48(6):1114-1125.   Published online February 26, 2024
DOI: https://doi.org/10.4093/dmj.2023.0301
  • 3,672 View
  • 181 Download
  • 1 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
The aim was to investigate if autonomic symptoms questionnaire Composite Autonomic Symptom Score (COMPASS) 31 has different association with cardiovascular autonomic neuropathy (CAN) and diagnostic performance between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM).
Methods
Seventy-nine participants with T1DM and 140 with T2DM completed COMPASS 31 before cardiovascular reflex tests (CARTs) for CAN, and assessment of symptoms, signs, vibration, and thermal perception thresholds for diabetic polyneuropathy (DPN) diagnosis.
Results
COMPASS 31 total weighted score (TWS) was similar in the two groups, but significantly associated with confirmed CAN only in T1DM (P=0.0056) and not T2DM group (P=0.1768) and correlated with CARTs score more strongly in T1DM (rho=0.356, P=0.0016) than in T2DM group (rho=0.084, P=0.3218) (P=0.016). Only in T1DM and not T2DM group, the area under the receiver operating characteristic curve (AUC) reached a fair diagnostic accuracy (>0.7) for confirmed CAN (0.73±0.07 vs. 0.61±0.08) and DPN (0.75±0.06 vs. 0.68±0.05), although without a significant difference. COMPASS 31 TWS (cut-off 16.44) reached acceptable diagnostic performance in T1DM, with sensitivity for confirmed CAN 81.2% and sensitivity and specificity for DPN 76.3% and 78%, compared to T2DM group (all <70%). AUC for DPN of orthostatic intolerance domain was higher in T1DM compared to T2DM group (0.73±0.05 vs. 0.58±0.04, P=0.027).
Conclusion
COMPASS 31 is more weakly related to CAN in T2DM than in T1DM, with a fair diagnostic accuracy for confirmed CAN only in T1DM. This difference supports a multifactorial origin of symptoms and should be considered when using COMPASS 31.

Citations

Citations to this article as recorded by  
  • Corneal confocal microscopy identifies early and definite diabetic cardiac autonomic neuropathy
    Shazli Azmi, Maryam Ferdousi, Alise Kalteniece, Ioannis N Petropoulos, Uazman Alam, Georgios Ponirakis, Omar Asghar, Andrew Marshall, Andrew JM Boulton, Nathan Efron, Rayaz A Malik
    Diabetes Research and Clinical Practice.2025; 224: 112172.     CrossRef
  • Frequency and severity of autonomic dysfunction assessed by objective hemodynamic responses and patient-reported symptoms in individuals with myasthenia gravis
    Monika Zawadka-Kunikowska, Mirosława Cieślicka, Jacek J. Klawe, Małgorzata Tafil-Klawe, Wojciech Kaźmierczak, Łukasz Rzepiński
    Frontiers in Neuroscience.2024;[Epub]     CrossRef
Review
Pathophysiology
Article image
Immune-Checkpoint Inhibitors-Induced Type 1 Diabetes Mellitus: From Its Molecular Mechanisms to Clinical Practice
Yun Kyung Cho, Chang Hee Jung
Diabetes Metab J. 2023;47(6):757-766.   Published online July 24, 2023
DOI: https://doi.org/10.4093/dmj.2023.0072
  • 7,940 View
  • 561 Download
  • 13 Web of Science
  • 17 Crossref
AbstractAbstract PDFPubReader   ePub   
With the increasing use of immune-checkpoint inhibitors (ICIs), such as anti-cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) and anti-programmed cell death-1 (PD-1), for the treatment of malignancies, cases of ICI-induced type 1 diabetes mellitus (ICI-T1DM) have been reported globally. This review focuses on the features and pathogenesis of this disease. T1DM is an immune-related adverse event that occurs following the administration of anti-PD-1 or anti-programmed death ligand-1 (PDL1) alone or in combination with anti-CTLA-4. More than half of the reported cases presented as abrupt-onset diabetic ketoacidosis. The primary mechanism of ICI-T1DM is T-cell stimulation, which results from the loss of interaction between PD-1 and PD-L1 in pancreatic islet. The similarities and differences between ICI-T1DM and classical T1DM may provide insights into this disease entity. ICI-T1DM is a rare but often life-threatening medical emergency that healthcare professionals and patients need to be aware of. Early detection of and screening for this disease is imperative. At present, the only known treatment for ICI-T1DM is insulin injection. Further research into the mechanisms and risk factors associated with ICI-T1DM development may contribute to a better understanding of this disease entity and the identification of possible preventive strategies.

Citations

Citations to this article as recorded by  
  • Diabetic ketoacidosis and hyperglycaemic hyperosmolar syndrome in patients with cancer: A multicentre study
    Rabia K. Shahid, Qasem Haider, Sunil Yadav, Duc Le, Shahid Ahmed
    Clinical Medicine.2025; 25(1): 100262.     CrossRef
  • Immune checkpoint inhibitor‐related type 1 diabetes incidence, risk, and survival association
    Fumika Kamitani, Yuichi Nishioka, Miyuki Koizumi, Hiroki Nakajima, Yukako Kurematsu, Sadanori Okada, Shinichiro Kubo, Tomoya Myojin, Tatsuya Noda, Tomoaki Imamura, Yutaka Takahashi
    Journal of Diabetes Investigation.2025; 16(2): 334.     CrossRef
  • Management of Immunotherapy-Induced Type 1 Diabetes
    Veronica Brady
    Critical Care Nursing Clinics of North America.2025; 37(1): 93.     CrossRef
  • Type I Diabetes—A Rare Adverse Event Described in Patients Receiving Immunotherapy Versus a Side Effect from SARS-CoV-2 Infection
    Raluca-Ileana Pătru, Miruna Ghigeanu, Maria-Alexandra Barbu, Andreea Iuliana Ionescu, Antone-Iordache Ionuț-Lucian
    Reports.2025; 8(1): 31.     CrossRef
  • Immune Checkpoint Inhibitor-Induced Pancreatic Injury (ICI-PI) in Adult Cancer Patients: A Systematic Review and Meta-Analysis
    Cha Len Lee, Israt Jahan Riya, Ifrat Jahan Piya, Thiago Pimentel Muniz, Marcus Otho Butler, Samuel David Saibil
    Cancers.2025; 17(7): 1080.     CrossRef
  • Cemiplimab and diabetic ketoacidosis: a case report of a rare endocrinopathy associated with immune checkpoint inhibitors
    Anna Arecco, Cristian Petolicchio, Alessandro Pastorino, Enrica Teresa Tanda, Lara Vera, Mara Boschetti, Francesco Cocchiara, Davide Carlo Maggi, Diego Ferone, Federico Gatto
    Frontiers in Endocrinology.2025;[Epub]     CrossRef
  • Efficacy and adverse events of immune checkpoint inhibitors: evidence from non-small cell lung cancer and gastric cancer in Korea and Japan
    Mc Neil Valencia, Zeeshan Abbas, Seung Won Lee
    Precision and Future Medicine.2025; 9(1): 15.     CrossRef
  • Durvalumab‑induced type 1 diabetes mellitus in lung adenocarcinoma: A case report and literature review
    Huijing Dong, Shengfu Li, Yanmei Peng, Xu Zhang, Jiabin Zheng, Chongxiang Xue, Yumin Zheng, Yixuan Yu, Xingyu Lu, Zixin Hu, Huijuan Cui
    Oncology Letters.2025; 29(6): 1.     CrossRef
  • Research Advances of Immune Checkpoint Inhibitors Related Endocrine Adverse Events
    晶晶 王
    Advances in Clinical Medicine.2024; 14(02): 2706.     CrossRef
  • Immune checkpoint inhibitor‑associated diabetes mellitus in patients with HCC: Report of three cases and literature review
    Gaocheng Wang, Jingjing Wang, Shuilin Dong, Zhanguo Zhang, Wanguang Zhang, Jianping Zhao
    Experimental and Therapeutic Medicine.2024;[Epub]     CrossRef
  • Type 1 diabetes: immune pathology and novel therapeutic approaches
    Eleanor M. Ling, Joana R. N. Lemos, Khemraj Hirani, Matthias von Herrath
    Diabetology International.2024; 15(4): 761.     CrossRef
  • Pathophysiology, diagnosis, and management of immune checkpoint inhibitor-induced diabetes mellitus
    Eleni-Rafaela Kani, Eleftheria Karaviti, Dimitra Karaviti, Eleni Gerontiti, Ioanna A. Paschou, Katerina Saltiki, Katerina Stefanaki, Theodora Psaltopoulou, Stavroula A. Paschou
    Endocrine.2024; 87(3): 875.     CrossRef
  • Use pembrolizumab may cause acute and severe adverse reactions: a case report and review
    Linna Ouyang, Huixing Liu, Zhixiang Tang, Rui Wu, Yujie Zhong, Haibin Chen
    International Journal of Surgery Oncology.2024; 9(3): 40.     CrossRef
  • Pembrolizumab-Induced Insulin-Dependent Diabetes Mellitus in a Patient With Triple-Negative Breast Cancer: A Rare Immune-Related Adverse Event
    Mirac Burak Tak, Zaid Munir, Ahmet Aydin
    Cureus.2024;[Epub]     CrossRef
  • Diabetes and the associated complications: The role of antioxidants in diabetes therapy and care
    Lowell Dilworth, Dewayne Stennett, Aldeam Facey, Felix Omoruyi, Shada Mohansingh, Felix O. Omoruyi
    Biomedicine & Pharmacotherapy.2024; 181: 117641.     CrossRef
  • Fulminant type 1 diabetes mellitus: a neglected but high-risk adverse event associated with immune checkpoint inhibitors
    Kelin Meng, Shengling Fu, Yaochen Huang, Wei Chen, Wenbin Zou
    Expert Opinion on Drug Safety.2024; : 1.     CrossRef
  • Fulminant Type 1 Diabetes Mellitus Leading to Diabetic Ketoacidosis and Mesenteric Ischemia With Necrosis Following Pembrolizumab Administration: A Case Report
    Mayu Ueki, Takeshi Fukuda, Kenta Oue, Takuma Wada, Toshiyuki Sumi
    Cureus.2024;[Epub]     CrossRef
Original Articles
Lifestyle
Article image
Clinical Effects of a Home Care Pilot Program for Patients with Type 1 Diabetes Mellitus: A Retrospective Cohort Study
Sejeong Lee, KyungYi Kim, Ji Eun Kim, Yura Hyun, Minyoung Lee, Myung-Il Hahm, Sang Gyu Lee, Eun Seok Kang
Diabetes Metab J. 2023;47(5):693-702.   Published online June 22, 2023
DOI: https://doi.org/10.4093/dmj.2022.0170
  • 4,192 View
  • 189 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Given the importance of continuous self-care for people with type 1 diabetes mellitus (T1DM), the Ministry of Health and Welfare of Korea launched a pilot program for chronic disease management. Herein, we applied a home care pilot program to people with T1DM to investigate its effects.
Methods
This retrospective cohort study was conducted at a single tertiary hospital (January 2019 to October 2021). A multidisciplinary team comprising doctors, nurses, and clinical nutritionists provided specialized education and periodically assessed patients’ health status through phone calls or text messages. A linear mixed model adjusting for age, sex, and body mass index was used to analyze the glycemic control changes before and after implementing the program between the intervention and control groups.
Results
Among 408 people with T1DM, 196 were enrolled in the intervention group and 212 in the control group. The reduction in glycosylated hemoglobin (HbA1c) after the program was significantly greater in the intervention group than in the control group (estimated marginal mean, –0.57% vs. –0.23%, P=0.008); the same trend was confirmed for glycoalbumin (GA) (–3.2% vs. –0.39%, P<0.001). More patients achieved the target values of HbA1c (<7.0%) and GA (<20%) in the intervention group than in the control group at the 9-month follow-up (34.5% vs. 19.6% and 46.7% vs. 28.0%, respectively).
Conclusion
The home care program for T1DM was clinically effective in improving glycemic control and may provide an efficient care option for people with T1DM, and positive outcomes are expected to expand the program to include more patients.

Citations

Citations to this article as recorded by  
  • Glycemic outcomes and patient satisfaction and self-management improves in transition from standard to virtual multidisciplinary care
    Noga Minsky, Liat Arnon Klug, Tatyana Kolobov, Elizabeth Tarshish, Yuval Shalev Many, Aviva Lipsitz, Amna Jabarin, Nicole Morozov, Dania Halperin, Moshe Shalom, Rachel Nissanholtz-Gannot, Genya Aharon-Hananel, Amir Tirosh, Orly Tamir
    Diabetes Research and Clinical Practice.2024; 209: 111587.     CrossRef
Guideline/Fact Sheet
Article image
Insulin Fact Sheet in Type 1 and 2 Diabetes Mellitus and Trends of Antidiabetic Medication Use in Insulin Users with Type 2 Diabetes Mellitus: 2002 to 2019
Jiyun Park, Gyuri Kim, Bong-Sung Kim, Kyung-Do Han, So Yoon Kwon, So Hee Park, You-Bin Lee, Sang-Man Jin, Jae Hyeon Kim
Diabetes Metab J. 2023;47(2):211-219.   Published online February 7, 2023
DOI: https://doi.org/10.4093/dmj.2022.0346
  • 8,600 View
  • 386 Download
  • 11 Web of Science
  • 12 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
This study investigated the trends of insulin use among Korean patients with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Changes in prescription of antidiabetic medications in T2DM patients taking insulin therapy were evaluated.
Methods
We analyzed data from the National Health Insurance Service database in Korea to evaluate the prevalence of insulin users and trends of insulin use in T1DM and T2DM patients from January 2002 to December 2019. We also investigated numbers and types of antidiabetic medications in insulin users with T2DM.
Results
The overall total number of insulin users increased from 2002 to 2019, reaching 348,254 for T2DM and 20,287 for T1DM in 2019 compared with 109,974 for T2DM and 34,972 for T1DM in 2002. The proportion of patients using basal analogs and short acting analogs have increased and those using human insulin, premixed insulin, or biphasic human insulin have decreased (rapid acting analogs: 71.85% and 24.12% in T1DM and T2DM, respectively, in 2019; basal analogs: 76.75% and 75.09% in T1DM and T2DM, respectively, in 2019). The use of other antidiabetic medication in addition to insulin increased for T2DM, especially in dual therapy, reaching up to 52.35% in 2019 compared with 16.72% in 2002.
Conclusion
The proportion of the patients using basal or rapid acting analogs increased among all insulin users in both T1DM and T2DM patients. Among patients with T2DM, the proportion of patients using antidiabetic medications in addition to insulin was significantly increased compared to those who used insulin alone.

Citations

Citations to this article as recorded by  
  • New-Onset Type 1 and Type 2 Diabetes Among Korean Youths During the COVID-19 Pandemic
    Da Hye Lee, Hwa Young Kim, Ji Young Park, Jaehyun Kim, Jae Hyeon Park
    JAMA Pediatrics.2025; 179(2): 155.     CrossRef
  • Improved Glycemic Control in Insulin-Treated Individuals With Poorly Controlled Type 2 Diabetes Through Combined Structured Education With Real-Time Continuous Glucose Monitoring
    Jee Hee Yoo, Ji Eun Jun, Soo Heon Kwak, Jae Hyeon Kim
    Journal of Diabetes Science and Technology.2025;[Epub]     CrossRef
  • In vitro α–Glucosidase Inhibition, Cytotoxicity, SAR, Swiss ADME Prediction and Molecular Docking Study of New N–Substituted Hydantoin Derivatives
    Ndivhuwo R. Tshiluka, Dakalo T. Mbedzi, Mpelegeng V. Bvumbi, Simon S. Mnyakeni‐Moleele
    ChemistryOpen.2025;[Epub]     CrossRef
  • Real-World Continuous Glucose Monitoring Data from a Population with Type 1 Diabetes in South Korea: Nationwide Single-System Analysis
    Ji Yoon Kim, Sang-Man Jin, Sarah B. Andrade, Boyang Chen, Jae Hyeon Kim
    Diabetes Technology & Therapeutics.2024; 26(6): 394.     CrossRef
  • Continuous glucose monitoring for glycaemic control and cardiovascular risk reduction in patients with type 2 diabetes not on insulin therapy: A clinical trial
    Joseph Reed, Tony Dong, Elke Eaton, Janice Friswold, Jodie Porges, Sadeer G. Al‐Kindi, Sanjay Rajagopalan, Ian J. Neeland
    Diabetes, Obesity and Metabolism.2024; 26(7): 2881.     CrossRef
  • Gastrointestinal safety evaluation of semaglutide for the treatment of type 2 diabetes mellitus: A meta-analysis
    Xiaoyan Huang, Miaohui Wu, Jiaojiao Lin, Lunpan Mou, Yaping Zhang, Jianjia Jiang
    Medicine.2024; 103(21): e38236.     CrossRef
  • Once-Weekly Insulin Icodec in Diabetes Mellitus: A Systematic Review and Meta-Analysis of Randomized Clinical Trials (ONWARDS Clinical Program)
    Giuseppe Lisco, Anna De Tullio, Vincenzo De Geronimo, Vito Angelo Giagulli, Edoardo Guastamacchia, Giuseppina Piazzolla, Olga Eugenia Disoteo, Vincenzo Triggiani
    Biomedicines.2024; 12(8): 1852.     CrossRef
  • Changes in insulin utilization in China from 2020 to 2022
    Chen Chen, Xingyu Liu, Jieqiong Zhang, Shuchen Hu, Jinwei Zhang, Xiaoyong Liu, Caijun Yang, Yu Fang
    Diabetes, Obesity and Metabolism.2024; 26(12): 5681.     CrossRef
  • Changes in the Epidemiological Landscape of Diabetes in South Korea: Trends in Prevalence, Incidence, and Healthcare Expenditures
    Kyoung Hwa Ha, Dae Jung Kim
    Endocrinology and Metabolism.2024; 39(5): 669.     CrossRef
  • Adherence to the nutritional recommendations according to diabetes status in Korean adults: a cross-sectional study
    Jong Han Choi, Chen Lulu, Seon-Joo Park, Hae-Jeung Lee
    BMC Public Health.2024;[Epub]     CrossRef
  • Evaluation of pharmacokinetic interactions between lobeglitazone, empagliflozin, and metformin in healthy subjects
    Heeyoung Kim, Choon Ok Kim, Hyeonsoo Park, Min Soo Park, Dasohm Kim, Taegon Hong, Yesong Shin, Byung Hak Jin
    Translational and Clinical Pharmacology.2023; 31(1): 59.     CrossRef
  • Smart Insulin Pen: Managing Insulin Therapy for People with Diabetes in the Digital Era
    Jee Hee Yoo, Jae Hyeon Kim
    The Journal of Korean Diabetes.2023; 24(4): 190.     CrossRef
Review
Technology/Device
Article image
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 Metab J. 2023;47(1):27-41.   Published online January 12, 2023
DOI: https://doi.org/10.4093/dmj.2022.0271
  • 15,211 View
  • 601 Download
  • 28 Web of Science
  • 36 Crossref
AbstractAbstract PDFPubReader   ePub   
Continuous glucose monitoring (CGM) technology has evolved over the past decade with the integration of various devices including insulin pumps, connected insulin pens (CIPs), automated insulin delivery (AID) systems, and virtual platforms. CGM has shown consistent benefits in glycemic outcomes in type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) treated with insulin. Moreover, the combined effect of CGM and education have been shown to improve glycemic outcomes more than CGM alone. Now a CIP is the expected future technology that does not need to be worn all day like insulin pumps and helps to calculate insulin doses with a built-in bolus calculator. Although only a few clinical trials have assessed the effectiveness of CIPs, they consistently show benefits in glycemic outcomes by reducing missed doses of insulin and improving problematic adherence. AID systems and virtual platforms made it possible to achieve target glycosylated hemoglobin in diabetes while minimizing hypoglycemia, which has always been challenging in T1DM. Now fully automatic AID systems and tools for diabetes decisions based on artificial intelligence are in development. These advances in technology could reduce the burden associated with insulin treatment for diabetes.

Citations

Citations to this article as recorded by  
  • Inter-temporal dynamic joint learning model considering intra- and inter-day mutable correlations for blood glucose level prediction
    Shuang Wen, Hongru Li, Yinghua Yang
    Biomedical Signal Processing and Control.2025; 101: 107204.     CrossRef
  • Glycemia Risk Index is Associated With Risk of Albuminuria Among Individuals With Type 1 Diabetes
    Ji Yoon Kim, Jee Hee Yoo, Nam Hoon Kim, Jae Hyeon Kim
    Journal of Diabetes Science and Technology.2025;[Epub]     CrossRef
  • Real-World Life Analysis of a Continuous Glucose Monitoring and Smart Insulin Pen System in Type 1 Diabetes: A Cohort Study
    Paola Pantanetti, Giovanni Cangelosi, Sara Morales Palomares, Gaetano Ferrara, Federico Biondini, Stefano Mancin, Gabriele Caggianelli, Mauro Parozzi, Marco Sguanci, Fabio Petrelli
    Diabetology.2025; 6(1): 7.     CrossRef
  • Abuse-deterrent wearable device with potential for extended delivery of opioid drugs
    Myoung Ju Kim, Jae Min Park, Jun Su Lee, Ji Yang Lee, Juhui Lee, Chang Hee Min, Min Ji Kim, Jae Hoon Han, Eun Jung Kwon, Young Bin Choy
    Biomedical Engineering Letters.2025; 15(2): 427.     CrossRef
  • Unlocking Real-Time Data Access in Diabetes Management: Toward an Interoperability Model
    Pietro Randine, Miriam Kopperstad Wolff, Matthias Pocs, Ian R. O. Connell, Joseph A. Cafazzo, Eirik Årsand
    Journal of Diabetes Science and Technology.2025;[Epub]     CrossRef
  • Harnessing Machine Learning, a Subset of Artificial Intelligence, for Early Detection and Diagnosis of Type 1 Diabetes: A Systematic Review
    Rahul Mittal, Matthew B. Weiss, Alexa Rendon, Shirin Shafazand, Joana R N Lemos, Khemraj Hirani
    International Journal of Molecular Sciences.2025; 26(9): 3935.     CrossRef
  • The usefulness of continuous glucose monitoring in the diagnostic approach to hypoglycemia after metabolic surgery
    Diana Cristina Henao, Ana María Gómez, Sofía Robledo, Ricardo Rosero
    Mini-invasive Surgery.2025;[Epub]     CrossRef
  • Recent advances in artificial intelligence-assisted endocrinology and diabetes
    Ioannis T. Oikonomakos, Ranjit M. Anjana, Viswanathan Mohan, Charlotte Steenblock, Stefan R. Bornstein
    Exploration of Endocrine and Metabolic Disease.2024; 1(1): 16.     CrossRef
  • Accuracy and Safety of the 15-Day CareSens Air Continuous Glucose Monitoring System
    Kyung-Soo Kim, Seung-Hwan Lee, Won Sang Yoo, Cheol-Young Park
    Diabetes Technology & Therapeutics.2024; 26(4): 222.     CrossRef
  • Real-World Continuous Glucose Monitoring Data from a Population with Type 1 Diabetes in South Korea: Nationwide Single-System Analysis
    Ji Yoon Kim, Sang-Man Jin, Sarah B. Andrade, Boyang Chen, Jae Hyeon Kim
    Diabetes Technology & Therapeutics.2024; 26(6): 394.     CrossRef
  • Recent advances in the precision control strategy of artificial pancreas
    Wuyi Ming, Xudong Guo, Guojun Zhang, Yinxia Liu, Yongxin Wang, Hongmei Zhang, Haofang Liang, Yuan Yang
    Medical & Biological Engineering & Computing.2024; 62(6): 1615.     CrossRef
  • Digital Health in Diabetes and Cardiovascular Disease
    Dorothy Avoke, Abdallah Elshafeey, Robert Weinstein, Chang H. Kim, Seth S. Martin
    Endocrine Research.2024; 49(3): 124.     CrossRef
  • Continuous glucose monitoring with structured education in adults with type 2 diabetes managed by multiple daily insulin injections: a multicentre randomised controlled trial
    Ji Yoon Kim, Sang-Man Jin, Kang Hee Sim, Bo-Yeon Kim, Jae Hyoung Cho, Jun Sung Moon, Soo Lim, Eun Seok Kang, Cheol-Young Park, Sin Gon Kim, Jae Hyeon Kim
    Diabetologia.2024; 67(7): 1223.     CrossRef
  • Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring
    Anubhuti Juyal, Shradha Bisht, Mamta F. Singh
    Blood Pressure Monitoring.2024; 29(5): 260.     CrossRef
  • Emerging trends in functional molecularly imprinted polymers for electrochemical detection of biomarkers
    Sanjida Yeasmin, Li-Jing Cheng
    Biomicrofluidics.2024;[Epub]     CrossRef
  • Continuous glucose monitoring in pregnant women with pregestational type 2 diabetes: a narrative review
    Sylvia Ye, Ibrahim Shahid, Christopher J Yates, Dev Kevat, I-Lynn Lee
    Obstetric Medicine.2024; 17(4): 194.     CrossRef
  • Advancements in nanohybrid material-based acetone gas sensors relevant to diabetes diagnosis: A comprehensive review
    Arpit Verma, Deepankar Yadav, Subramanian Natesan, Monu Gupta, Bal Chandra Yadav, Yogendra Kumar Mishra
    Microchemical Journal.2024; 201: 110713.     CrossRef
  • Current treatment options of diabetes mellitus type 1 in pediatric population
    Petr Polák, Renata Pomahačová, Karel Fiklík, Petra Paterová, Josef Sýkora
    Pediatrie pro praxi.2024; 25(3): 161.     CrossRef
  • Efectividad de un sistema híbrido de circuito cerrado en pacientes con diabetes tipo 1 durante el ejercicio físico: un estudio descriptivo en la vida real
    Ruben Martin-Payo, Maria del Mar Fernandez-Alvarez, Rebeca García-García, Ángela Pérez-Varela, Shelini Surendran, Isolina Riaño-Galán
    Anales de Pediatría.2024; 101(3): 183.     CrossRef
  • Effectiveness of a hybrid closed-loop system for children and adolescents with type 1 diabetes during physical exercise: A cross-sectional study in real life
    Ruben Martin-Payo, Maria del Mar Fernandez-Alvarez, Rebeca García-García, Ángela Pérez-Varela, Shelini Surendran, Isolina Riaño-Galán
    Anales de Pediatría (English Edition).2024; 101(3): 183.     CrossRef
  • Real-time continuous glucose monitoring vs. self-monitoring of blood glucose: cost-utility in South Korean type 2 diabetes patients on intensive insulin
    Ji Yoon Kim, Sabrina Ilham, Hamza Alshannaq, Richard F. Pollock, Waqas Ahmed, Gregory J. Norman, Sang-Man Jin, Jae Hyeon Kim
    Journal of Medical Economics.2024; 27(1): 1245.     CrossRef
  • Impact of missed insulin doses on glycaemic parameters in people with diabetes using smart insulin pens
    Malavika Varma, David J T Campbell
    Evidence Based Nursing.2024; : ebnurs-2024-104109.     CrossRef
  • Clinical Trial Protocol for Porcine Islet Xenotransplantation in South Korea
    Byung-Joon Kim, Jun-Seop Shin, Byoung-Hoon Min, Jong-Min Kim, Chung-Gyu Park, Hee-Jung Kang, Eung Soo Hwang, Won-Woo Lee, Jung-Sik Kim, Hyun Je Kim, Iov Kwon, Jae Sung Kim, Geun Soo Kim, Joonho Moon, Du Yeon Shin, Bumrae Cho, Heung-Mo Yang, Sung Joo Kim,
    Diabetes & Metabolism Journal.2024; 48(6): 1160.     CrossRef
  • Long-Term Benefits of Continuous Glucose Monitoring Combined with Insulin Pump Therapy
    Rukhsana Zulfiqar, Komal Abbas, Saeeda Khan, Kanwal Fatima, Adnan Manzoor, Muhammad Awais
    Indus Journal of Bioscience Research.2024; 2(2): 785.     CrossRef
  • Continuous Glucose Monitoring—New Diagnostic Tool in Complex Pathophysiological Disorder of Glucose Metabolism in Children and Adolescents with Obesity
    Marko Simunovic, Marko Kumric, Doris Rusic, Martina Paradzik Simunovic, Josko Bozic
    Diagnostics.2024; 14(24): 2801.     CrossRef
  • Innovations in Diabetes Management for Pregnant Women: Artificial Intelligence and the Internet of Medical Things
    Ellen M. Murrin, Antonio F. Saad, Scott Sullivan, Yuri Millo, Menachem Miodovnik
    American Journal of Perinatology.2024;[Epub]     CrossRef
  • Glycemic Outcomes During Early Use of the MiniMed™ 780G Advanced Hybrid Closed-Loop System with Guardian™ 4 Sensor
    Toni L. Cordero, Zheng Dai, Arcelia Arrieta, Fang Niu, Melissa Vella, John Shin, Andrew S. Rhinehart, Jennifer McVean, Scott W. Lee, Robert H. Slover, Gregory P. Forlenza, Dorothy I. Shulman, Rodica Pop-Busui, James R. Thrasher, Mark S. Kipnes, Mark P. Ch
    Diabetes Technology & Therapeutics.2023; 25(9): 652.     CrossRef
  • Navigating the Seas of Glycemic Control: The Role of Continuous Glucose Monitoring in Type 1 Diabetes Mellitus
    Jun Sung Moon
    Diabetes & Metabolism Journal.2023; 47(3): 345.     CrossRef
  • APSec1.0: Innovative Security Protocol Design with Formal Security Analysis for the Artificial Pancreas System
    Jiyoon Kim, Jongmin Oh, Daehyeon Son, Hoseok Kwon, Philip Virgil Astillo, Ilsun You
    Sensors.2023; 23(12): 5501.     CrossRef
  • Advances and Development of Electronic Neural Interfaces
    Xue Jiaxiang, Liu Zhixin
    Journal of Computing and Natural Science.2023; : 147.     CrossRef
  • Continuous Glucose Monitoring (CGM) and Metabolic Control in a Cohort of Patients with Type 1 Diabetes and Coeliac Disease
    Flavia Amaro, Maria Alessandra Saltarelli, Marina Primavera, Marina Cerruto, Stefano Tumini
    Endocrines.2023; 4(3): 595.     CrossRef
  • Comparison of Glycemia Risk Index with Time in Range for Assessing Glycemic Quality
    Ji Yoon Kim, Jee Hee Yoo, Jae Hyeon Kim
    Diabetes Technology & Therapeutics.2023; 25(12): 883.     CrossRef
  • The Benefits Of Continuous Glucose Monitoring In Pregnancy
    Jee Hee Yoo, Jae Hyeon Kim
    Endocrinology and Metabolism.2023; 38(5): 472.     CrossRef
  • The Growing Challenge of Diabetes Management in an Aging Society
    Seung-Hwan Lee
    Diabetes & Metabolism Journal.2023; 47(5): 630.     CrossRef
  • An Observational Pilot Study of a Tailored Environmental Monitoring and Alert System for Improved Management of Chronic Respiratory Diseases
    Mohammed Alotaibi, Fady Alnajjar, Badr A Alsayed, Tareq Alhmiedat, Ashraf M Marei, Anas Bushnag, Luqman Ali
    Journal of Multidisciplinary Healthcare.2023; Volume 16: 3799.     CrossRef
  • Smart Insulin Pen: Managing Insulin Therapy for People with Diabetes in the Digital Era
    Jee Hee Yoo, Jae Hyeon Kim
    The Journal of Korean Diabetes.2023; 24(4): 190.     CrossRef
Original Articles
Type 1 Diabetes
Article image
Performance of Fast-Acting Aspart Insulin as Compared to Aspart Insulin in Insulin Pump for Managing Type 1 Diabetes Mellitus: A Meta-Analysis
Deep Dutta, Ritin Mohindra, Kunal Mahajan, Meha Sharma
Diabetes Metab J. 2023;47(1):72-81.   Published online June 24, 2022
DOI: https://doi.org/10.4093/dmj.2022.0035
  • 7,320 View
  • 292 Download
  • 4 Web of Science
  • 6 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
No meta-analysis has analysed efficacy and safety of fast-acting aspart insulin (FIAsp) with insulin pump in type 1 diabetes mellitus (T1DM).
Methods
Electronic databases were searched for randomised controlled trials (RCTs) involving T1DM patients on insulin pump receiving FIAsp in intervention arm, and placebo/active comparator insulin in control arm. Primary outcome was to evaluate changes in 1- and 2-hour post-prandial glucose (1hPPG and 2hPPG). Secondary outcomes were to evaluate alterations in percentage time with blood glucose <3.9 mmol/L (hypoglycaemia), time in range (TIR) blood glucose 3.9 to 10 mmol/L, insulin requirements and adverse events.
Results
Data from four RCTs involving 640 patients was analysed. FIAsp use in insulin pump was associated with significantly greater lowering of 1hPPG (mean difference [MD], –1.35 mmol/L; 95% confidence interval [CI], –1.72 to –0.98; P<0.01; I2=63%) and 2hPPG (MD, –1.19 mmol/L; 95% CI, –1.38 to –1.00; P<0.01; I2=0%) as compared to controls. TIR was comparable among groups (MD, 1.06%; 95% CI, –3.84 to 5.96; P=0.67; I2=70%). Duration of blood glucose <3.9 mmol/L was lower in FIAsp group, approaching significance (MD, –0.91%; 95% CI, –1.84 to 0.03; P=0.06; I2=0%). Total hypoglycaemic episodes (risk ratio [RR], 1.35; 95% CI, 0.55 to 3.31; P=0.51; I2=0%), severe hypoglycaemia (RR, 2.26; 95% CI, 0.77 to 6.66; P=0.14), infusion site reactions (RR, 1.35; 95% CI, 0.63 to 2.93; P=0.77; I2=0%), and treatment-emergent adverse events (RR, 1.13; 95% CI, 0.80 to 1.60; P=0.50; I2=0%) were comparable.
Conclusion
FIAsp use in insulin pump is associated with better post-prandial glycaemic control with no increased hypoglycaemia or glycaemic variability.

Citations

Citations to this article as recorded by  
  • Tips for successful use of commercial automated insulin delivery systems: An expert paper of the Italian working group on diabetes and technology
    Sergio Di Molfetta, Antonio Rossi, Roberta Assaloni, Roberto Franceschi, Valeria Grancini, Vincenzo Guardasole, Andrea Enzo Scaramuzza, Antonietta Maria Scarpitta, Maddalena Trombetta, Angela Zanfardino, Riccardo Candido, Angelo Avogaro, Valentino Cherubi
    Diabetes Research and Clinical Practice.2025; 223: 112117.     CrossRef
  • Glycemic Management Across the Lifespan for People With Type 1 Diabetes: A Clinical Practice Guideline
    Ilana J. Halperin, Brandy Wicklow, Shazhan Amed, Alanna Chambers, Charlotte Courage, Elizabeth Cummings, Patricia Kirkland, Dylan MacKay, Meranda Nakhla, Zubin Punthakee, Paul M. Ryan, Lindsay Sawatsky, Peter A. Senior, Bikrampal S. Sidhu, Alanna Weisman
    Canadian Journal of Diabetes.2025; 49(1): 5.     CrossRef
  • Burden and Coping Strategies of Hypoglycemia in People with Diabetes
    Aris Liakos, Thomas Karagiannis, Ioannis Avgerinos, Apostolos Tsapas, Eleni Bekiari
    Current Diabetes Reviews.2024;[Epub]     CrossRef
  • Ultrafast-acting insulin: pharmacological properties and their impact on clinical aspects
    L. A. Suplotova, A. Sh. Tilkiyan
    Meditsinskiy sovet = Medical Council.2024; (13): 146.     CrossRef
  • Unveiling the Spectrum of Glucose Variability: A Novel Perspective on FreeStyle Libre Monitoring Data
    Adrian H. Heald, Mike Stedman, John Warner-Levy, Lleyton Belston, Angela Paisley, Aleksandra Jotic, Nebojsa Lalic, Martin Gibson, Hellena H. Habte-Asres, Martin Whyte, Angus Forbes
    Diabetes Therapy.2024; 15(12): 2475.     CrossRef
  • Efficacy and Safety of Ultra-rapid Lispro Insulin in Managing Type-1 and Type-2 Diabetes: A Systematic Review and Meta-Analysis
    Deep Dutta, Lakshmi Nagendra, Saptarshi Bhattacharya, Meha Sharma
    Indian Journal of Endocrinology and Metabolism.2023; 27(6): 467.     CrossRef
Type 1 Diabetes
Article image
Identification of Key Genes and Pathways in Peripheral Blood Mononuclear Cells of Type 1 Diabetes Mellitus by Integrated Bioinformatics Analysis
Xing Li, Mingyu Liao, Jiangheng Guan, Ling Zhou, Rufei Shen, Min Long, Jiaqing Shao
Diabetes Metab J. 2022;46(3):451-463.   Published online April 1, 2022
DOI: https://doi.org/10.4093/dmj.2021.0018
  • 9,578 View
  • 348 Download
  • 8 Web of Science
  • 11 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
The onset and progression of type 1 diabetes mellitus (T1DM) is closely related to autoimmunity. Effective monitoring of the immune system and developing targeted therapies are frontier fields in T1DM treatment. Currently, the most available tissue that reflects the immune system is peripheral blood mononuclear cells (PBMCs). Thus, the aim of this study was to identify key PBMC biomarkers of T1DM.
Methods
Common differentially expressed genes (DEGs) were screened from the Gene Expression Omnibus (GEO) datasets GSE9006, GSE72377, and GSE55098, and PBMC mRNA expression in T1DM patients was compared with that in healthy participants by GEO2R. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) network analyses of DEGs were performed using the Cytoscape, DAVID, and STRING databases. The vital hub genes were validated by reverse transcription-polymerase chain reaction using clinical samples. The disease-gene-drug interaction network was built using the Comparative Toxicogenomics Database (CTD) and Drug Gene Interaction Database (DGIdb).
Results
We found that various biological functions or pathways related to the immune system and glucose metabolism changed in PBMCs from T1DM patients. In the PPI network, the DEGs of module 1 were significantly enriched in processes including inflammatory and immune responses and in pathways of proteoglycans in cancer. Moreover, we focused on four vital hub genes, namely, chitinase-3-like protein 1 (CHI3L1), C-X-C motif chemokine ligand 1 (CXCL1), matrix metallopeptidase 9 (MMP9), and granzyme B (GZMB), and confirmed them in clinical PBMC samples. Furthermore, the disease-gene-drug interaction network revealed the potential of key genes as reference markers in T1DM.
Conclusion
These results provide new insight into T1DM pathogenesis and novel biomarkers that could be widely representative reference indicators or potential therapeutic targets for clinical applications.

Citations

Citations to this article as recorded by  
  • BNT162b2 mRNA vaccine elicits robust virus-specific antibodies but poor cross-protective CD8+ memory T cell responses in adolescents with type 1 diabetes
    Ching-Fen Shen, Pei-De Chang, Yen-Yin Chou, Shih-Wei Wang, Yu-Wen Pan, Chih-An Chen, Ching-Wei Lin, Bo-Yang Tsai, Pei-Jane Tsai, Ching-Chuan Liu, Chao-Min Cheng, Wen-Chien Ko, Chi-Chang Shieh
    Journal of Microbiology, Immunology and Infection.2025;[Epub]     CrossRef
  • Identification of crucial extracellular genes as potential biomarkers in newly diagnosed Type 1 diabetes via integrated bioinformatics analysis
    Ming Gao, Qing Liu, Lingyu Zhang, Fatema Tabak, Yifei Hua, Wei Shao, Yangyang Li, Li Qian, Yu Liu
    PeerJ.2025; 13: e18660.     CrossRef
  • Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer
    Zhihao Zhao, Qilin Wang, Fang Zhao, Junnan Ma, Xue Sui, Hyok Chol Choe, Peng Chen, Xue Gao, Lin Zhang
    BMC Genomics.2024;[Epub]     CrossRef
  • Bioinformatics analysis identifies TGF-β signaling pathway-associated molecular subtypes and gene signature in diabetic foot
    Guanggang Du, Jie Chen, Xuezhu Zhu, Zongdong Zhu
    iScience.2024; 27(3): 109094.     CrossRef
  • Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID‐19 Using Bioinformatics Approaches
    Shudeb Babu Sen Omit, Salma Akhter, Humayan Kabir Rana, A. R. M. Mahamudul Hasan Rana, Nitun Kumar Podder, Mahmudul Islam Rakib, Ashadun Nobi, Ali Imran
    BioMed Research International.2023;[Epub]     CrossRef
  • Advanced Delivery Strategies for Immunotherapy in Type I Diabetes Mellitus
    Mingshu Huang, Weixing Chen, Min Wang, Yisheng Huang, Hongyu Liu, Yue Ming, Yuanxin Chen, Zhengming Tang, Bo Jia
    BioDrugs.2023; 37(3): 331.     CrossRef
  • Identification of the key genes of tuberculosis and construction of a diagnostic model via weighted gene co-expression network analysis
    Baiying Li, Lifang Sun, Yaping Sun, Libo Zhen, Qi Qi, Ting Mo, Huijie Wang, Meihua Qiu, Qingshan Cai
    Journal of Infection and Chemotherapy.2023; 29(11): 1046.     CrossRef
  • Probing biological network in concurrent carcinomas and Type-2 diabetes for potential biomarker screening: An advanced computational paradigm
    Abdullah Al Marzan, Shatila Shahi, Md Sakil Arman, Md Zafrul Hasan, Ajit Ghosh
    Advances in Biomarker Sciences and Technology.2023; 5: 89.     CrossRef
  • Transcriptional analysis of human peripheral blood mononuclear cells stimulated by Mycobacterium tuberculosis antigen
    Jing Wei, Fangzheng Guo, Yamin Song, Kun Xu, Feiyang Lin, Kangsheng Li, Baiqing Li, Zhongqing Qian, Xiaojing Wang, Hongtao Wang, Tao Xu
    Frontiers in Cellular and Infection Microbiology.2023;[Epub]     CrossRef
  • Combining bioinformatics and machine learning algorithms to identify and analyze shared biomarkers and pathways in COVID-19 convalescence and diabetes mellitus
    Jinru Shen, Yaolou Wang, Xijin Deng, Si Ri Gu Leng Sana
    Frontiers in Endocrinology.2023;[Epub]     CrossRef
  • Transcriptome analysis of peripheral blood mononuclear cells in patients with type 1 diabetes mellitus
    Zhaoxiang Wang, Li Zhang, Fengyan Tang, Zhongming Yang, Mengzhu Wang, Jue Jia, Dong Wang, Ling Yang, Shao Zhong, Guoyue Yuan
    Endocrine.2022; 78(2): 270.     CrossRef

Diabetes Metab J : Diabetes & Metabolism Journal
Close layer
TOP