Skip Navigation
Skip to contents

Diabetes Metab J : Diabetes & Metabolism Journal

Search
OPEN ACCESS

Search

Page Path
HOME > Search
33 "Hypoglycemia"
Filter
Filter
Article category
Keywords
Publication year
Authors
Funded articles
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
Received January 24, 2024  Accepted March 29, 2024  Published online August 28, 2024  
DOI: https://doi.org/10.4093/dmj.2024.0039    [Epub ahead of print]
  • 661 View
  • 35 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
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
  • 2,319 View
  • 243 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.
Drug/Regimen
Article image
Efficacy and Safety of IDegAsp in a Real-World Korean Population with Type 2 Diabetes Mellitus
Shinae Kang, Yu-Bae Ahn, Tae Keun Oh, Won-Young Lee, Sung Wan Chun, Boram Bae, Amine Dahaoui, Jin Sook Jeong, Sungeun Jung, Hak Chul Jang
Diabetes Metab J. 2024;48(5):929-936.   Published online February 27, 2024
DOI: https://doi.org/10.4093/dmj.2023.0297
  • 2,500 View
  • 220 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
This study investigated the real-world efficacy and safety of insulin degludec/insulin aspart (IDegAsp) in Korean adults with type 2 diabetes mellitus (T2DM), whose insulin treatment was switched to IDegAsp.
Methods
This was a multicenter, retrospective, observational study comprising two 26-week treatment periods, before and after switching to IDegAsp, respectively. Korean adults with uncontrolled T2DM treated with basal or premix insulin (±oral antidiabetic drugs) were enrolled. The primary objective was to compare the degree of glycosylated hemoglobin (HbA1c) change in each 26-week observation period. The analyses included changes in HbA1c, fasting plasma glucose (FPG), body weight, proportion of participants achieving HbA1c <7.0%, hypoglycemic events, and total daily insulin dose (ClinicalTrials.gov, number NCT04656106).
Results
In total, 196 adults (mean age, 65.95 years; mean T2DM duration, 18.99 years) were analyzed. The change in both HbA1c and FPG were significantly different between the pre-switching and the post-switching period (0.28% vs. –0.51%, P<0.001; 5.21 mg/dL vs. –23.10 mg/dL, P=0.005), respectively. After switching, the rate of achieving HbA1c <7.0% was significantly improved (5.10% at baseline vs. 11.22% with IDegAsp, P=0.012). No significant differences (before vs. after switching) were observed in body weight change, and total daily insulin dose. The rates of overall and severe hypoglycemia were similar in the two periods.
Conclusion
In real-world clinical practice in Korea, the change of insulin regimen to IDegAsp was associated with an improvement in glycemic control without increase of hypoglycemia, supporting the use of IDegAsp for patients with T2DM uncontrolled with basal or premix insulin.

Citations

Citations to this article as recorded by  
  • Switching from Premixed Insulin to Insulin Degludec/Insulin Aspart for the Management of Type 2 Diabetes Mellitus: Implications of a Real-World Study on Insulin Degludec Dosing
    Yiming Wu, Junqing Zhang, Ang Li
    Diabetes Therapy.2024; 15(12): 2515.     CrossRef
Review
Cardiovascular Risk/Epidemiology
Article image
Intensified Multifactorial Intervention in Patients with Type 2 Diabetes Mellitus
Takayoshi Sasako, Toshimasa Yamauchi, Kohjiro Ueki
Diabetes Metab J. 2023;47(2):185-197.   Published online January 12, 2023
DOI: https://doi.org/10.4093/dmj.2022.0325
  • 7,383 View
  • 425 Download
  • 13 Web of Science
  • 14 Crossref
AbstractAbstract PDFPubReader   ePub   
In the management of diabetes mellitus, one of the most important goals is to prevent its micro- and macrovascular complications, and to that end, multifactorial intervention is widely recommended. Intensified multifactorial intervention with pharmacotherapy for associated risk factors, alongside lifestyle modification, was first shown to be efficacious in patients with microalbuminuria (Steno-2 study), then in those with less advanced microvascular complications (the Anglo-Danish-Dutch Study of Intensive Treatment In People with Screen Detected Diabetes in Primary Care [ADDITION]-Europe and the Japan Diabetes Optimal Treatment study for 3 major risk factors of cardiovascular diseases [J-DOIT3]), and in those with advanced microvascular complications (the Nephropathy In Diabetes-Type 2 [NID-2] study and Diabetic Nephropathy Remission and Regression Team Trial in Japan [DNETT-Japan]). Thus far, multifactorial intervention led to a reduction in cardiovascular and renal events, albeit not necessarily significant. It should be noted that not only baseline characteristics but also the control status of the risk factors and event rates during intervention among the patients widely varied from one trial to the next. Further evidence is needed for the efficacy of multifactorial intervention in a longer duration and in younger or elderly patients. Moreover, now that new classes of antidiabetic drugs are available, it should be addressed whether strict and safe glycemic control, alongside control of other risk factors, could lead to further risk reductions in micro- and macrovascular complications, thereby decreasing all-cause mortality in patients with type 2 diabetes mellitus.

Citations

Citations to this article as recorded by  
  • Exploring mechanisms underlying diabetes comorbidities and strategies to prevent vascular complications
    Takayoshi Sasako
    Diabetology International.2024; 15(1): 34.     CrossRef
  • Targeting ERS-mitophagy in hippocampal neurons to explore the improvement of memory by tea polyphenols in aged type 2 diabetic rats
    Wenjuan Feng, Chenhui Lv, Le Cheng, Xin Song, Xuemin Li, Haoran Xie, Shuangzhi Chen, Xi Wang, Lushan Xue, Cheng Zhang, Jie Kou, Lili Wang, Haifeng Zhao
    Free Radical Biology and Medicine.2024; 213: 293.     CrossRef
  • Risk of Dementia Among Patients With Diabetes in a Multidisciplinary, Primary Care Management Program
    Kailu Wang, Shi Zhao, Eric Kam-Pui Lee, Susan Zi-May Yau, Yushan Wu, Chi-Tim Hung, Eng-Kiong Yeoh
    JAMA Network Open.2024; 7(2): e2355733.     CrossRef
  • Causes of In-Hospital Death and Pharmaceutical Associations with Age of Death during a 10-Year Period (2011–2020) in Individuals with and without Diabetes at a Japanese Community General Hospital
    Minae Hosoki, Taiki Hori, Yousuke Kaneko, Kensuke Mori, Saya Yasui, Seijiro Tsuji, Hiroki Yamagami, Saki Kawata, Tomoyo Hara, Shiho Masuda, Yukari Mitsui, Kiyoe Kurahashi, Takeshi Harada, Shingen Nakamura, Toshiki Otoda, Tomoyuki Yuasa, Akio Kuroda, Itsur
    Journal of Clinical Medicine.2024; 13(5): 1283.     CrossRef
  • External validation of a minimal-resource model to predict reduced estimated glomerular filtration rate in people with type 2 diabetes without diagnosis of chronic kidney disease in Mexico: a comparison between country-level and regional performance
    Camilla Sammut-Powell, Rose Sisk, Ruben Silva-Tinoco, Gustavo de la Pena, Paloma Almeda-Valdes, Sonia Citlali Juarez Comboni, Susana Goncalves, Rory Cameron
    Frontiers in Endocrinology.2024;[Epub]     CrossRef
  • Gut Microbiota Targeted Approach by Natural Products in Diabetes Management: An Overview
    Priyanka Sati, Praveen Dhyani, Eshita Sharma, Dharam Chand Attri, Arvind Jantwal, Rajni Devi, Daniela Calina, Javad Sharifi-Rad
    Current Nutrition Reports.2024; 13(2): 166.     CrossRef
  • Lipids as potential mediators linking body mass index to diabetes: evidence from a mediation analysis based on the NAGALA cohort
    Song Lu, Qun Wang, Hengcheng Lu, Maobin Kuang, Min Zhang, Guotai Sheng, Yang Zou, Xiaoping Peng
    BMC Endocrine Disorders.2024;[Epub]     CrossRef
  • Potential of FGF21 in type 2 diabetes mellitus treatment based on untargeted metabolomics
    Shuai Li, Zilong Song, Chunxiang Fan, Weiwei Zhang, Tianyi Ma, Xu Li, Qi Zhang, Ming Zhao, Tianfei Yu, Shanshan Li
    Biochemical Pharmacology.2024; 225: 116306.     CrossRef
  • Exploring mechanisms of insulin action and strategies to treat diabetes
    Takayoshi Sasako
    Endocrine Journal.2024; 71(7): 651.     CrossRef
  • Modern Challenges in Type 2 Diabetes: Balancing New Medications with Multifactorial Care
    Alfredo Caturano, Raffaele Galiero, Maria Rocco, Giuseppina Tagliaferri, Alessia Piacevole, Davide Nilo, Giovanni Di Lorenzo, Celestino Sardu, Erica Vetrano, Marcellino Monda, Raffaele Marfella, Luca Rinaldi, Ferdinando Carlo Sasso
    Biomedicines.2024; 12(9): 2039.     CrossRef
  • The gaps between the guidelines definitions and routine of care adopted in primary health care regarding diabetes kidney disease: a real-life study
    Silvia Ferreira Bortoto, Jacira Xavier de Carvalho, Mozania Reis de Matos, Cristiane das Graças Dias Cavalcante, Elenilda Almeida Silva Andrade, Márcia Silva Queiroz
    Journal of Public Health.2024;[Epub]     CrossRef
  • Recent Glycemia Is a Major Determinant of β-Cell Function in Type 2 Diabetes Mellitus
    Ji Yoon Kim, Jiyoon Lee, Sin Gon Kim, Nam Hoon Kim
    Diabetes & Metabolism Journal.2024; 48(6): 1135.     CrossRef
  • Cardiovascular Risk Reduction in Type 2 Diabetes: Further Insights into the Power of Weight Loss and Exercise
    Seung-Hwan Lee
    Endocrinology and Metabolism.2023; 38(3): 302.     CrossRef
  • Sarcopenia: Loss of mighty armor against frailty and aging
    Takayoshi Sasako, Kohjiro Ueki
    Journal of Diabetes Investigation.2023; 14(10): 1145.     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
  • 6,314 View
  • 279 Download
  • 2 Web of Science
  • 4 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  
  • 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
Metabolic Risk/Epidemiology
Article image
Reproductive Life Span and Severe Hypoglycemia Risk in Postmenopausal Women with Type 2 Diabetes Mellitus
Soyeon Kang, Yong-Moon Park, Dong Jin Kwon, Youn-Jee Chung, Jeong Namkung, Kyungdo Han, Seung-Hyun Ko
Diabetes Metab J. 2022;46(4):578-591.   Published online January 24, 2022
DOI: https://doi.org/10.4093/dmj.2021.0135
  • 8,489 View
  • 254 Download
  • 4 Web of Science
  • 4 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Estrogen promotes glucose homeostasis, enhances insulin sensitivity, and maintains counterregulatory responses in recurrent hypoglycemia in women of reproductive age. Postmenopausal women with type 2 diabetes mellitus (T2DM) might be more vulnerable to severe hypoglycemia (SH) events. However, the relationship between reproductive factors and SH occurrence in T2DM remains unelucidated.
Methods
This study included data on 181,263 women with postmenopausal T2DM who participated in a national health screening program from January 1 to December 31, 2009, obtained using the Korean National Health Insurance System database. Outcome data were obtained until December 31, 2018. Associations between reproductive factors and SH incidence were assessed using Cox proportional hazards models.
Results
During the mean follow-up of 7.9 years, 11,279 (6.22%) postmenopausal women with T2DM experienced SH episodes. A longer reproductive life span (RLS) (≥40 years) was associated with a lower SH risk compared to a shorter RLS (<30 years) (adjusted hazard ratio [HR], 0.74; 95% confidence interval [CI], 0.69 to 0.80; P for trend <0.001) after multivariable adjustment. SH risk decreased with every 5-year increment of RLS (with <30 years as a reference [adjusted HR, 0.91; 95% CI, 0.86 to 0.95; P=0.0001 for 30−34 years], [adjusted HR, 0.80; 95% CI, 0.76 to 0.84; P<0.001 for 35−39 years], [adjusted HR, 0.74; 95% CI, 0.68 to 0.81; P<0.001 for ≥40 years]). The use of hormone replacement therapy (HRT) was associated with a lower SH risk than HRT nonuse.
Conclusion
Extended exposure to endogenous ovarian hormone during lifetime may decrease the number of SH events in women with T2DM after menopause.

Citations

Citations to this article as recorded by  
  • Association between serum copper level and reproductive health of Women in the United States: a cross-sectional study
    Yi Yuan, Tong-Yu Peng, Guang-Yuan Yu, Zhao Zou, Meng-Ze Wu, Ruofei Zhu, Shuang Wu, Zi Lv, Su-Xin Luo
    International Journal of Environmental Health Research.2024; 34(6): 2441.     CrossRef
  • Defining Continuous Glucose Monitor Time in Range in a Large, Community-Based Cohort Without Diabetes
    Nicole L Spartano, Naznin Sultana, Honghuang Lin, Huimin Cheng, Sophia Lu, David Fei, Joanne M Murabito, Maura E Walker, Howard A Wolpert, Devin W Steenkamp
    The Journal of Clinical Endocrinology & Metabolism.2024;[Epub]     CrossRef
  • Reproductive Lifespan and Motor Progression of Parkinson’s Disease
    Ruwei Ou, Qianqian Wei, Yanbing Hou, Lingyu Zhang, Kuncheng Liu, Junyu Lin, Tianmi Yang, Jing Yang, Zheng Jiang, Wei Song, Bei Cao, Huifang Shang
    Journal of Clinical Medicine.2022; 11(20): 6163.     CrossRef
  • Menopause and development of Alzheimer’s disease: Roles of neural glucose metabolism and Wnt signaling
    Paulina Villaseca, Pedro Cisternas, Nibaldo C. Inestrosa
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
Review
Technology/Device
Article image
Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence
Sun Joon Moon, Inha Jung, Cheol-Young Park
Diabetes Metab J. 2021;45(6):813-839.   Published online November 22, 2021
DOI: https://doi.org/10.4093/dmj.2021.0177
  • 18,264 View
  • 922 Download
  • 46 Web of Science
  • 45 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Since Banting and Best isolated insulin in the 1920s, dramatic progress has been made in the treatment of type 1 diabetes mellitus (T1DM). However, dose titration and timely injection to maintain optimal glycemic control are often challenging for T1DM patients and their families because they require frequent blood glucose checks. In recent years, technological advances in insulin pumps and continuous glucose monitoring systems have created paradigm shifts in T1DM care that are being extended to develop artificial pancreas systems (APSs). Numerous studies that demonstrate the superiority of glycemic control offered by APSs over those offered by conventional treatment are still being published, and rapid commercialization and use in actual practice have already begun. Given this rapid development, keeping up with the latest knowledge in an organized way is confusing for both patients and medical staff. Herein, we explore the history, clinical evidence, and current state of APSs, focusing on various development groups and the commercialization status. We also discuss APS development in groups outside the usual T1DM patients and the administration of adjunct agents, such as amylin analogues, in APSs.

Citations

Citations to this article as recorded by  
  • Integration of a Safety Module to Prevent Rebound Hypoglycemia in Closed-Loop Artificial Pancreas Systems
    María F. Villa-Tamayo, Patricio Colmegna, Marc D. Breton
    Journal of Diabetes Science and Technology.2024; 18(2): 318.     CrossRef
  • The effects of acute hyperglycaemia on sports and exercise performance in type 1 diabetes: A systematic review and meta-analysis
    Bonar McGuire, Hashim Dadah, Dominic Oliver
    Journal of Science and Medicine in Sport.2024; 27(2): 78.     CrossRef
  • A new approach to stabilize diabetes systems with time-varying delays and disturbance rejection
    S. Syafiie, Fahd Alharbi, Abdullah Ali Alshehri, Bassam Hasanain
    Journal of the Franklin Institute.2024; 361(1): 543.     CrossRef
  • Effects of Low-Dose Glucagon on Subcutaneous Insulin Absorption in Pigs
    Ingrid Anna Teigen, Marte Kierulf Åm, Misbah Riaz, Sverre Christian Christiansen, Sven Magnus Carlsen
    Current Therapeutic Research.2024; 100: 100736.     CrossRef
  • Enhancing equity in access to automated insulin delivery systems in an ethnically and socioeconomically diverse group of children with type 1 diabetes
    John Pemberton, Louise Collins, Lesley Drummond, Renuka P Dias, Ruth Krone, Melanie Kershaw, Suma Uday
    BMJ Open Diabetes Research & Care.2024; 12(3): e004045.     CrossRef
  • Robust Online Correlation Method for Identification of a Nonparametric Model of Type 1 Diabetes
    Martin Dodek, Eva Miklovičová
    IEEE Access.2024; 12: 35899.     CrossRef
  • Comparison between a tubeless, on-body automated insulin delivery system and a tubeless, on-body sensor-augmented pump in type 1 diabetes: a multicentre randomised controlled trial
    Ji Yoon Kim, Sang-Man Jin, Eun Seok Kang, Soo Heon Kwak, Yeoree Yang, Jee Hee Yoo, Jae Hyun Bae, Jun Sung Moon, Chang Hee Jung, Ji Cheol Bae, Sunghwan Suh, Sun Joon Moon, Sun Ok Song, Suk Chon, Jae Hyeon Kim
    Diabetologia.2024; 67(7): 1235.     CrossRef
  • Prevention and treatment of type 1 diabetes: in search of the ideal combination therapy targeting multiple immunometabolic pathways
    Marcelo Maia Pinheiro, Felipe Moura Maia Pinheiro, Maria Luisa Garo, Donatella Pastore, Francesca Pacifici, Camillo Ricordi, David Della-Morte, Marco Infante
    Metabolism and Target Organ Damage.2024;[Epub]     CrossRef
  • Mitigating diabetes associated with reactive oxygen species (ROS) and protein aggregation through pharmacological interventions
    Giulia Bennici, Hanan Almahasheer, Mawadda Alghrably, Daniela Valensin, Arian Kola, Chrysoula Kokotidou, Joanna Lachowicz, Mariusz Jaremko
    RSC Advances.2024; 14(25): 17448.     CrossRef
  • Real‐world glycaemic outcomes of automated insulin delivery in type 1 diabetes: A meta‐analysis
    Qin Yang, Baoqi Zeng, Jiayi Hao, Qingqing Yang, Feng Sun
    Diabetes, Obesity and Metabolism.2024; 26(9): 3753.     CrossRef
  • Efficacy of advanced hybrid closed loop systems in cystic fibrosis related diabetes: a pilot study
    Marta Bassi, Daniele Franzone, Francesca Dufour, Giordano Spacco, Federico Cresta, Giuseppe d’Annunzio, Giacomo Tantari, Maria Grazia Calevo, Carlo Castellani, Nicola Minuto, Rosaria Casciaro
    Frontiers in Endocrinology.2024;[Epub]     CrossRef
  • Emerging Technologies in Endocrine Drug Delivery: Innovations for Improved Patient Care
    Mahvish Renzu, Carly Hubers, Kendall Conway, Viktoriya Gibatova, Vidhi Mehta, Wael Taha
    Cureus.2024;[Epub]     CrossRef
  • A stochastic model-based control methodology for glycemic management in the intensive care unit
    Melike Sirlanci, George Hripcsak, Cecilia C. Low Wang, J. N. Stroh, Yanran Wang, Tellen D. Bennett, Andrew M. Stuart, David J. Albers
    Frontiers in Medical Engineering.2024;[Epub]     CrossRef
  • Advances in Type 1 Diabetes Mellitus Management in Children
    Mridu Bahal, Vineeta Pande, Jasleen Dua, Shailaja Mane
    Cureus.2024;[Epub]     CrossRef
  • Expansion of the Pancreas Transplant Recipient Pool: Appropriate for Most or Are There Limits?
    Jonathan A. Fridell, Robert J. Stratta
    Current Transplantation Reports.2024; 11(4): 276.     CrossRef
  • Reinforcement Learning: A Paradigm Shift in Personalized Blood Glucose Management for Diabetes
    Lehel Dénes-Fazakas, László Szilágyi, Levente Kovács, Andrea De Gaetano, György Eigner
    Biomedicines.2024; 12(9): 2143.     CrossRef
  • Implantable Fluorogenic DNA Biosensor for Stress Detection
    Irina Drachuk, Namrata Ramani, Svetlana Harbaugh, Chad A. Mirkin, Jorge L. Chávez
    ACS Applied Materials & Interfaces.2024;[Epub]     CrossRef
  • Diabetes Management: Herbal Remedies and Emerging Therapies
    Pratik Kumar Vishwakarma, Ankita Moharana, Snigdha Rani Behra, Priyabati Choudhury, Sonali Jayronia, Shivendra Mani Tripathi
    Current Nutraceuticals.2024;[Epub]     CrossRef
  • Impact of Hypoglycemia on Glucose Variability over Time for Individuals with Open-Source Automated Insulin Delivery Systems
    Arsalan Shahid, Dana M. Lewis
    Diabetology.2024; 5(5): 514.     CrossRef
  • Optimal model-based insulin dosing strategy with offline and online optimization
    Martin Dodek, Eva Miklovičová, Miroslav Halás
    Informatics in Medicine Unlocked.2024; 51: 101594.     CrossRef
  • Technological advancements in glucose monitoring and artificial pancreas systems for shaping diabetes care
    Neha Ghosh, Saurabh Verma
    Current Medical Research and Opinion.2024; : 1.     CrossRef
  • A novel pulse-modulated closed-loop artificial pancreas based on intravenous administration of insulin and glucagon
    Simon L. Goede, Victor H. Snels, Willem-Jan W. J. H. Berghuis, Jan P. C. Bernards, Urs Wyder
    Scientific Reports.2024;[Epub]     CrossRef
  • 100 Years of insulin: A chemical engineering perspective
    B. Wayne Bequette
    Korean Journal of Chemical Engineering.2023; 40(1): 1.     CrossRef
  • Efficacy of intermittent short‐term use of a real‐time continuous glucose monitoring system in non‐insulin–treated patients with type 2 diabetes: A randomized controlled trial
    Sun Joon Moon, Kyung‐Soo Kim, Woo Je Lee, Mi Yeon Lee, Robert Vigersky, Cheol‐Young Park
    Diabetes, Obesity and Metabolism.2023; 25(1): 110.     CrossRef
  • Identifiable prediction animal model for the bi-hormonal intraperitoneal artificial pancreas
    Karim Davari Benam, Hasti Khoshamadi, Marte Kierulf Åm, Øyvind Stavdahl, Sebastien Gros, Anders Lyngvi Fougner
    Journal of Process Control.2023; 121: 13.     CrossRef
  • 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
  • CGM accuracy: Contrasting CE marking with the governmental controls of the USA (FDA) and Australia (TGA): A narrative review
    John S Pemberton, Emma G Wilmot, Katharine Barnard‐Kelly, Lalantha Leelarathna, Nick Oliver, Tabitha Randell, Craig E Taplin, Pratik Choudhary, Peter Adolfsson
    Diabetes, Obesity and Metabolism.2023; 25(4): 916.     CrossRef
  • Evaluation of awareness and attitude of paediatric nursing students, nurses, and adolescents regarding type one diabetes advanced devices and virtual nursing
    Howaida Moawad Ahmed Ali
    Kontakt.2023; 25(2): 100.     CrossRef
  • Predicting the output error of the suboptimal state estimator to improve the performance of the MPC-based artificial pancreas
    Martin Dodek, Eva Miklovičová
    Control Theory and Technology.2023; 21(4): 541.     CrossRef
  • A Markov Model of Gap Occurrence in Continuous Glucose Monitoring Data for Realistic in Silico Clinical Trials
    Martina Vettoretti, Martina Drecogna, Simone Del Favero, Andrea Facchinetti, Giovanni Sparacino
    Computer Methods and Programs in Biomedicine.2023; 240: 107700.     CrossRef
  • Drug delivery breakthrough technologies – A perspective on clinical and societal impact
    Beate Bittner, Manuel Sánchez-Félix, Dennis Lee, Athanas Koynov, Joshua Horvath, Felix Schumacher, Simon Matoori
    Journal of Controlled Release.2023; 360: 335.     CrossRef
  • Importance of continuous glucose monitoring in the treatment of diabetes mellitus
    Sun Joon Moon, Won-Young Lee
    Journal of the Korean Medical Association.2023; 66(7): 432.     CrossRef
  • Constrained Versus Unconstrained Model Predictive Control for Artificial Pancreas
    Chiara Toffanin, Lalo Magni
    IEEE Transactions on Control Systems Technology.2023; 31(5): 2288.     CrossRef
  • Intelligent Insulin vs. Artificial Intelligence for Type 1 Diabetes: Will the Real Winner Please Stand Up?
    Valentina Maria Cambuli, Marco Giorgio Baroni
    International Journal of Molecular Sciences.2023; 24(17): 13139.     CrossRef
  • Artificial Intelligence in Efficient Diabetes Care
    Gopal Bhagwan Khodve, Sugato Banerjee
    Current Diabetes Reviews.2023;[Epub]     CrossRef
  • The artificial pancreas: two alternative approaches to achieve a fully closed-loop system with optimal glucose control
    M. K. Åm, I. A. Teigen, M. Riaz, A. L. Fougner, S. C. Christiansen, S. M. Carlsen
    Journal of Endocrinological Investigation.2023; 47(3): 513.     CrossRef
  • Multivariable Automated Insulin Delivery System for Handling Planned and Spontaneous Physical Activities
    Mohammad Reza Askari, Mohammad Ahmadasas, Andrew Shahidehpour, Mudassir Rashid, Laurie Quinn, Minsun Park, Ali Cinar
    Journal of Diabetes Science and Technology.2023; 17(6): 1456.     CrossRef
  • Advanced Technology (Continuous Glucose Monitoring and Advanced Hybrid Closed-Loop Systems) in Diabetes from the Perspective of Gender Differences
    Maria Grazia Nuzzo, Marciano Schettino
    Diabetology.2023; 4(4): 519.     CrossRef
  • Artificial Pancreas under a Zone Model Predictive Control based on Gaussian Process models: toward the personalization of the closed loop
    Marco Polver, Beatrice Sonzogni, Mirko Mazzoleni, Fabio Previdi, Antonio Ferramosca
    IFAC-PapersOnLine.2023; 56(2): 9642.     CrossRef
  • Personalized Constrained MPC for glucose regulation
    Chiara Toffanin, Lalo Magni
    IFAC-PapersOnLine.2023; 56(2): 9648.     CrossRef
  • Automated Insulin Delivery Systems in Children and Adolescents With Type 1 Diabetes: A Systematic Review and Meta-analysis of Outpatient Randomized Controlled Trials
    Baoqi Zeng, Le Gao, Qingqing Yang, Hao Jia, Feng Sun
    Diabetes Care.2023; 46(12): 2300.     CrossRef
  • Novel Glycemic Index Based on Continuous Glucose Monitoring to Predict Poor Clinical Outcomes in Critically Ill Patients: A Pilot Study
    Eun Yeong Ha, Seung Min Chung, Il Rae Park, Yin Young Lee, Eun Young Choi, Jun Sung Moon
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
  • Dual‐hormone artificial pancreas for glucose control in type 1 diabetes: A meta‐analysis
    Baoqi Zeng, Hao Jia, Le Gao, Qingqing Yang, Kai Yu, Feng Sun
    Diabetes, Obesity and Metabolism.2022; 24(10): 1967.     CrossRef
  • Dual-Hormone Insulin-and-Pramlintide Artificial Pancreas for Type 1 Diabetes: A Systematic Review
    Alezandra Torres-Castaño, Amado Rivero-Santana, Lilisbeth Perestelo-Pérez, Andrea Duarte-Díaz, Analia Abt-Sacks, Vanesa Ramos-García, Yolanda Álvarez-Pérez, Ana M. Wäagner, Mercedes Rigla, Pedro Serrano-Aguilar
    Applied Sciences.2022; 12(20): 10262.     CrossRef
  • History of insulin treatment of pediatric patients with diabetes in Korea
    Jae Hyun Kim, Choong Ho Shin, Sei Won Yang
    Annals of Pediatric Endocrinology & Metabolism.2021; 26(4): 237.     CrossRef
Original Article
Drug/Regimen
Article image
Efficacy and Safety of Self-Titration Algorithms of Insulin Glargine 300 units/mL in Individuals with Uncontrolled Type 2 Diabetes Mellitus (The Korean TITRATION Study): A Randomized Controlled Trial
Jae Hyun Bae, Chang Ho Ahn, Ye Seul Yang, Sun Joon Moon, Soo Heon Kwak, Hye Seung Jung, Kyong Soo Park, Young Min Cho
Diabetes Metab J. 2022;46(1):71-80.   Published online June 16, 2021
DOI: https://doi.org/10.4093/dmj.2020.0274
  • 10,741 View
  • 474 Download
  • 2 Web of Science
  • 4 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
To compare the efficacy and safety of two insulin self-titration algorithms, Implementing New Strategies with Insulin Glargine for Hyperglycemia Treatment (INSIGHT) and EDITION, for insulin glargine 300 units/mL (Gla-300) in Korean individuals with uncontrolled type 2 diabetes mellitus (T2DM).
Methods
In a 12-week, randomized, open-label trial, individuals with uncontrolled T2DM requiring basal insulin were randomized to either the INSIGHT (adjusted by 1 unit/day) or EDITION (adjusted by 3 units/week) algorithm to achieve a fasting self-monitoring of blood glucose (SMBG) in the range of 4.4 to 5.6 mmol/L. The primary outcome was the proportion of individuals achieving a fasting SMBG ≤5.6 mmol/L without noct urnal hypoglycemia at week 12.
Results
Of 129 individuals (age, 64.1±9.5 years; 66 [51.2%] women), 65 and 64 were randomized to the INSIGHT and EDITION algorithms, respectively. The primary outcome of achievement was comparable between the two groups (24.6% vs. 23.4%, P=0.876). Compared with the EDITION group, the INSIGHT group had a greater reduction in 7-point SMBG but a similar decrease in fasting plasma glucose and glycosylated hemoglobin. The increment of total daily insulin dose was significantly higher in the INSIGHT group than in the EDITION group (between-group difference: 5.8±2.7 units/day, P=0.033). However, body weight was significantly increased only in the EDITION group (0.6±2.4 kg, P=0.038). There was no difference in the occurrence of hypoglycemia between the two groups. Patient satisfaction was significantly increased in the INSIGHT group (P=0.014).
Conclusion
The self-titration of Gla-300 using the INSIGHT algorithm was effective and safe compared with that using the EDITION algorithm in Korean individuals with uncontrolled T2DM (ClinicalTrials.gov number: NCT03406663).

Citations

Citations to this article as recorded by  
  • Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? A Systematic Review of Basal Insulin Dose Guidance
    Camilla Heisel Nyholm Thomsen, Stine Hangaard, Thomas Kronborg, Peter Vestergaard, Ole Hejlesen, Morten Hasselstrøm Jensen
    Journal of Diabetes Science and Technology.2024; 18(5): 1185.     CrossRef
  • Comparative efficacy and safety of weekly tirzepatide versus weekly insulin in type 2 diabetes: A network meta‐analysis of randomized clinical trials
    Hazem Ayesh, Sajida Suhail, Suhail Ayesh, Kevin Niswender
    Diabetes, Obesity and Metabolism.2024; 26(9): 3801.     CrossRef
  • Basal insulin titration algorithms in patients with type 2 diabetes: the simplest is the best (?)
    V.I. Katerenchuk
    INTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine).2023; 19(1): 72.     CrossRef
  • Issues of insulin therapy for type 2 diabetes and ways to solve them
    V.I. Katerenchuk, A.V. Katerenchuk
    INTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine).2023; 19(3): 240.     CrossRef
Review
Type 1 Diabetes
Article image
Non-Insulin Antidiabetes Treatment in Type 1 Diabetes Mellitus: A Systematic Review and Meta-Analysis
Xiaoling Cai, Chu Lin, Wenjia Yang, Lin Nie, Linong Ji
Diabetes Metab J. 2021;45(3):312-325.   Published online March 15, 2021
DOI: https://doi.org/10.4093/dmj.2020.0171
  • 7,218 View
  • 300 Download
  • 5 Web of Science
  • 5 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
In order to evaluate the efficacy and side effects of the non-insulin antidiabetes medications as an adjunct treatment in type 1 diabetes mellitus (T1DM), we conducted systematic searches in MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials for randomized controlled trials published between the date of inception and March 2020 to produce a systematic review and meta-analysis. Overall, 57 studies were included. Compared with placebo, antidiabetes agents in adjunct to insulin treatment resulted in significant reduction in glycosylated hemoglobin (weighted mean difference [WMD], –0.30%; 95% confidence interval [CI], –0.34 to –0.25%; P<0.01) and body weight (WMD, –2.15 kg; 95% CI, –2.77 to –1.53 kg; P<0.01), and required a significantly lower dosage of insulin (WMD, –5.17 unit/day; 95% CI, –6.77 to –3.57 unit/day; P<0.01). Compared with placebo, antidiabetes agents in adjunct to insulin treatment increased the risk of hypoglycemia (relative risk [RR], 1.04; 95% CI, 1.01 to 1.08; P=0.02) and gastrointestinal side effects (RR, 1.99; 95% CI, 1.61 to 2.46; P<0.01) in patients with T1DM. Compared with placebo, the use of non-insulin antidiabetes agents in addition to insulin could lead to glycemic improvement, weight control and lower insulin dosage, while they might be associated with increased risks of hypoglycemia and gastrointestinal side effects in patients with T1DM.

Citations

Citations to this article as recorded by  
  • Prescribing patterns of adjunctive therapy for the treatment of type 1 diabetes mellitus among Australian endocrinologists
    Patrice Forner, Jennifer Snaith, Jerry R. Greenfield
    Internal Medicine Journal.2024; 54(5): 779.     CrossRef
  • Dioscin: Therapeutic potential for diabetes and complications
    Haoyang Gao, Ze Wang, Danlin Zhu, Linlin Zhao, Weihua Xiao
    Biomedicine & Pharmacotherapy.2024; 170: 116051.     CrossRef
  • The Impact of Body Mass Index, Residual Beta Cell Function and Estimated Glucose Disposal Rate on the Development of Double Diabetes and Microvascular Complications in Patients With Type 1 Diabetes Mellitus
    Rameez Raja Bhagadurshah, Subbiah Eagappan, Raghavan Kasthuri Santharam, Sridhar Subbiah
    Cureus.2023;[Epub]     CrossRef
  • Type 1 diabetes glycemic management: Insulin therapy, glucose monitoring, and automation
    Bruce A. Perkins, Jennifer L. Sherr, Chantal Mathieu
    Science.2021; 373(6554): 522.     CrossRef
  • Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence
    Sun Joon Moon, Inha Jung, Cheol-Young Park
    Diabetes & Metabolism Journal.2021; 45(6): 813.     CrossRef
Original Articles
Complications
Article image
Differences in Clinical Outcomes between Patients with and without Hypoglycemia during Hospitalization: A Retrospective Study Using Real-World Evidence
Jeongmin Lee, Tong Min Kim, Hyunah Kim, Seung-Hwan Lee, Jae Hyoung Cho, Hyunyong Lee, Hyeon Woo Yim, Kun-Ho Yoon, Hun-Sung Kim
Diabetes Metab J. 2020;44(4):555-565.   Published online May 8, 2020
DOI: https://doi.org/10.4093/dmj.2019.0064
  • 7,562 View
  • 121 Download
  • 8 Web of Science
  • 9 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background

Some patients admitted to hospitals for glycemic control experience hypoglycemia despite regular meals and despite adhering to standard blood glucose control protocols. Different factors can have a negative impact on blood glucose control and prognosis after discharge. This study investigated risk factors for hypoglycemia and its effects on glycemic control during the hospitalization of patients in the general ward.

Methods

This retrospective study included patients who were admitted between 2009 and 2018. Patients were provided regular meals at fixed times according to ideal body weights during hospitalization. We categorized the patients into two groups: those with and those without hypoglycemia during hospitalization.

Results

Of the 3,031 patients, 379 experienced at least one episode of hypoglycemia during hospitalization (HYPO group). Hypoglycemia occurred more frequently particularly in cases of premixed insulin therapy. Compared with the control group, the HYPO group was older (61.0±16.8 years vs. 59.1±16.5 years, P=0.035), with more females (60.4% vs. 49.6%, P<0.001), lower body mass index (BMI) (23.5±4.2 kg/m2 vs. 25.1±4.4 kg/m2, P<0.001), and higher prevalence of type 1 diabetes mellitus (6.1% vs. 2.6%, P<0.001), They had longer hospital stay (11.1±13.5 days vs. 7.6±4.6 days, P<0.001). After discharge the HYPO group had lower glycosylated hemoglobin reduction rate (−2.0%±0.2% vs. −2.5%±0.1%, P=0.003) and tended to have more frequent cases of cardiovascular disease.

Conclusion

Hypoglycemia occurred more frequently in older female patients with lower BMI and was associated with longer hospital stay and poorer glycemic control after discharge. Therefore, clinicians must carefully ensure that patients do not experience hypoglycemia during hospitalization.

Citations

Citations to this article as recorded by  
  • Hypoglycemia in hospitalized patients: A sleeping monster
    Swarna Deepak Kuragayala, Sumita Nayak, Khalid Khatib
    Muller Journal of Medical Sciences and Research.2024; 15(1): 5.     CrossRef
  • Acute kidney injury: a strong risk factor for hypoglycaemia in hospitalized patients with type 2 diabetes
    Ana Carreira, Pedro Castro, Filipe Mira, Miguel Melo, Pedro Ribeiro, Lèlita Santos
    Acta Diabetologica.2023; 60(9): 1179.     CrossRef
  • Adherence to healthy lifestyle behaviors as a preventable risk factor for severe hypoglycemia in people with type 2 diabetes: A longitudinal nationwide cohort study
    Jae‐Seung Yun, Kyungdo Han, Yong‐Moon Park, Eugene Han, Yong‐ho Lee, Seung‐Hyun Ko
    Journal of Diabetes Investigation.2022; 13(9): 1533.     CrossRef
  • Predicting hypoglycemia in hospitalized patients with diabetes: A derivation and validation study
    Michal Elbaz, Jeries Nashashibi, Shiri Kushnir, Leonard Leibovici
    Diabetes Research and Clinical Practice.2021; 171: 108611.     CrossRef
  • Hospital care: improving outcomes in type 1 diabetes
    Schafer Boeder, Kristen Kulasa
    Current Opinion in Endocrinology, Diabetes & Obesity.2021; 28(1): 14.     CrossRef
  • Data Pseudonymization in a Range That Does Not Affect Data Quality: Correlation with the Degree of Participation of Clinicians
    Soo-Yong Shin, Hun-Sung Kim
    Journal of Korean Medical Science.2021;[Epub]     CrossRef
  • Letter: Differences in Clinical Outcomes between Patients with and without Hypoglycemia during Hospitalization: A Retrospective Study Using Real-World Evidence (Diabetes Metab J 2020;44:555-65)
    Sung-Woo Kim
    Diabetes & Metabolism Journal.2020; 44(5): 775.     CrossRef
  • Response: Differences in Clinical Outcomes between Patients with and without Hypoglycemia during Hospitalization: A Retrospective Study Using Real-World Evidence (Diabetes Metab J 2020;44:555-65)
    Jeongmin Lee, Hun-Sung Kim
    Diabetes & Metabolism Journal.2020; 44(5): 779.     CrossRef
  • Hypoglycaemia and Cardiovascular Disease Risk in Patients with Diabetes
    Niki Katsiki, Kalliopi Kotsa, Anca P. Stoian, Dimitri P. Mikhailidis
    Current Pharmaceutical Design.2020; 26(43): 5637.     CrossRef
Drug/Regimen
Article image
Switching to Once-Daily Insulin Degludec/Insulin Aspart from Basal Insulin Improves Postprandial Glycemia in Patients with Type 2 Diabetes Mellitus: Randomized Controlled Trial
Kyu Yong Cho, Akinobu Nakamura, Chiho Oba-Yamamoto, Kazuhisa Tsuchida, Shingo Yanagiya, Naoki Manda, Yoshio Kurihara, Shin Aoki, Tatsuya Atsumi, Hideaki Miyoshi
Diabetes Metab J. 2020;44(4):532-541.   Published online November 22, 2019
DOI: https://doi.org/10.4093/dmj.2019.0093
  • 6,788 View
  • 177 Download
  • 8 Web of Science
  • 11 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background

To explore the efficacy and safety of switching from once-daily basal insulin therapy to once-daily pre-meal injection insulin degludec/insulin aspart (IDegAsp) with respect to the glycemic control of participants with type 2 diabetes mellitus (T2DM).

Methods

In this multicenter, open-label, prospective, randomized, parallel-group comparison trial, participants on basal insulin therapy were switched to IDegAsp (IDegAsp group; n=30) or continued basal insulin (Basal group; n=29). The primary endpoint was the superiority of IDegAsp in causing changes in the daily blood glucose profile, especially post-prandial blood glucose concentration after 12 weeks.

Results

Blood glucose concentrations after dinner and before bedtime were lower in the IDegAsp group, and the improvement in blood glucose before bedtime was significantly greater in the IDegAsp group than in the Basal group at 12 weeks (−1.7±3.0 mmol/L vs. 0.3±2.1 mmol/L, P<0.05). Intriguingly, glycemic control after breakfast was not improved by IDegAsp injection before breakfast, in contrast to the favorable effect of injection before dinner on blood glucose after dinner. Glycosylated hemoglobin significantly decreased only in the IDegAsp group (58 to 55 mmol/mol, P<0.05). Changes in daily insulin dose, body mass, and recorded adverse effects, including hypoglycemia, were comparable between groups.

Conclusion

IDegAsp was more effective than basal insulin at reducing blood glucose after dinner and before bedtime, but did not increase the incidence of hypoglycemia. Switching from basal insulin to IDegAsp does not increase the burden on the patient and positively impacts glycemic control in patients with T2DM.

Citations

Citations to this article as recorded by  
  • Efficacy, safety and treatment satisfaction of transition to a regimen of insulin degludec/aspart: A pilot study
    Na Yang, Lu Lv, Shu-Meng Han, Li-Yun He, Zi-Yi Li, Yu-Cheng Yang, Fan Ping, Ling-Ling Xu, Wei Li, Hua-Bing Zhang, Yu-Xiu Li
    World Journal of Diabetes.2025;[Epub]     CrossRef
  • Glycaemic outcomes in hospital with IDegAsp versus BIAsp30 premixed insulins
    Joshua R. Walt, Julie Loughran, Spiros Fourlanos, Rahul D. Barmanray, Jasmine Zhu, Suresh Varadarajan, Mervyn Kyi
    Internal Medicine Journal.2024; 54(8): 1329.     CrossRef
  • The efficacy and safety of iGlarLixi versus IDegAsp in Chinese people with type 2 diabetes suboptimally controlled with oral antidiabetic drugs: The Soli‐D randomized controlled trial
    Ming Liu, Weijun Gu, Li Chen, Yanbing Li, Hongyu Kuang, Jianling Du, Agustina Alvarez, Felipe Lauand, Elisabeth Souhami, Jiewen Zhang, Weiya Xu, Qin Du, Yiming Mu
    Diabetes, Obesity and Metabolism.2024; 26(9): 3791.     CrossRef
  • Cutting-edge microneedle innovations: Transforming the landscape of cardiovascular and metabolic disease management
    Xiaoning Zhang, Ming Li, Qiang Gao, Xiaoya Kang, Jingyao Sun, Yao Huang, Hong Xu, Jing Xu, Songren Shu, Jian Zhuang, Yuan Huang
    iScience.2024; 27(9): 110615.     CrossRef
  • Low fasting glucose‐to‐estimated average glucose ratio was associated with superior response to insulin degludec/aspart compared with basal insulin in patients with type 2 diabetes
    Han Na Jang, Ye Seul Yang, Tae Jung Oh, Bo Kyung Koo, Seong Ok Lee, Kyong Soo Park, Hak Chul Jang, Hye Seung Jung
    Journal of Diabetes Investigation.2022; 13(1): 85.     CrossRef
  • Comparing Time to Intensification between insulin Degludec/Insulin Aspart and Insulin Glargine
    Rajiv Kovil
    Journal of Diabetology.2022; 13(2): 171.     CrossRef
  • Use of Insulin Degludec/Insulin Aspart in the Management of Diabetes Mellitus: Expert Panel Recommendations on Appropriate Practice Patterns
    Tevfik Demir, Serap Turan, Kursad Unluhizarci, Oya Topaloglu, Tufan Tukek, Dilek Gogas Yavuz
    Frontiers in Endocrinology.2021;[Epub]     CrossRef
  • Pharmacoeconomic comparison of the second generation insulin analogs and insulins on their base
    I. N. Dyakov, S. K. Zyryanov
    Kachestvennaya Klinicheskaya Praktika = Good Clinical Practice.2021; 20(1): 4.     CrossRef
  • Efficacy and Safety of Insulin Degludec/Insulin Aspart Compared with a Conventional Premixed Insulin or Basal Insulin: A Meta-Analysis
    Shinje Moon, Hye-Soo Chung, Yoon-Jung Kim, Jae-Myung Yu, Woo-Ju Jeong, Jiwon Park, Chang-Myung Oh
    Metabolites.2021; 11(9): 639.     CrossRef
  • Insulin therapy in diabetic kidney disease
    Yan Liu, Chanyue Zhao, Xiaofen Xiong, Ming Yang, Lin Sun
    Diabetic Nephropathy.2021; 1(2): 67.     CrossRef
  • Indirect comparison of efficacy and safety of insulin glargine/lixisenatide and insulin degludec/insulin aspart in type 2 diabetes patients not controlled on basal insulin
    Anwar Ali Jammah
    Primary Care Diabetes.2020;[Epub]     CrossRef
Clinical Complications
Hypoglycemia and Dementia Risk in Older Patients with Type 2 Diabetes Mellitus: A Propensity-Score Matched Analysis of a Population-Based Cohort Study
Young-Gun Kim, Dong Gyu Park, So Young Moon, Ja Young Jeon, Hae Jin Kim, Dae Jung Kim, Kwan-Woo Lee, Seung Jin Han
Diabetes Metab J. 2020;44(1):125-133.   Published online October 23, 2019
DOI: https://doi.org/10.4093/dmj.2018.0260
  • 7,687 View
  • 225 Download
  • 32 Web of Science
  • 32 Crossref
AbstractAbstract PDFPubReader   
Background

Type 2 diabetes mellitus (T2DM) is associated with an increased risk for dementia. The effects of hypoglycemia on dementia are controversial. Thus, we evaluated whether hypoglycemia increases the risk for dementia in senior patients with T2DM.

Methods

We used the Korean National Health Insurance Service Senior cohort, which includes >10% of the entire senior population of South Korea. In total, 5,966 patients who had ever experienced at least one episode of hypoglycemia were matched with those who had not, using propensity score matching. The risk of dementia was assessed through a survival analysis of matched pairs.

Results

Patients with underlying hypoglycemic events had an increased risk for all-cause dementia, Alzheimer's dementia (AD), and vascular dementia (VaD) compared with those who had not experienced a hypoglycemic event (hazard ratio [HR], 1.254; 95% confidence interval [CI], 1.166 to 1.349; P<0.001 for all-cause dementia; HR, 1.264; 95% CI, 1.162 to 1.375; P<0.001 for AD; HR, 1.286; 95% CI, 1.110 to 1.490; P<0.001 for VaD). According to number of hypoglycemic episodes, the HRs of dementia were 1.170, 1.201, and 1.358 in patients with one hypoglycemic episode, two or three episodes, and more than three episodes, respectively. In the subgroup analysis, hypoglycemia was associated with an increased risk for dementia in both sexes with or without T2DM microvascular or macrovascular complications.

Conclusion

Our findings suggest that patients with a history of hypoglycemia have a higher risk for dementia. This trend was similar for AD and VaD, the two most important subtypes of dementia.

Citations

Citations to this article as recorded by  
  • Potential risk factors for mild cognitive impairment among patients with type 2 diabetes experiencing hypoglycemia
    Ruonan Gao, Menglan Zhan, Sujie Ke, Kejun Wu, Guanlian He, Liqin Qi, Xiaoying Liu, Xiaohong Liu, Lijing Wang, Libin Liu
    Diabetes Research and Clinical Practice.2024; 207: 111036.     CrossRef
  • Association of hypoglycemic events with cognitive impairment in patients with type 2 diabetes mellitus: Protocol for a dose-response meta-analysis
    Min Ye, Ai Hong Yuan, Qi Qi Yang, Qun Wei Li, Fei Yue Li, Yan Wei, Muhammad Shahzad Aslam
    PLOS ONE.2024; 19(2): e0296662.     CrossRef
  • AGE, DISEASE DURATION AND MULTIMORBIDITY AS PREDICTORS OF HYPOGLYCEMIA IN ELDERLY AND SENILE WOMEN WITH TYPE 2 DIABETES MELLITUS
    Polina Ya. Merzlova, Svetlana V. Bulgakova, Dmitry P. Kurmaev, Ekaterina V. Treneva
    Science and Innovations in Medicine.2024;[Epub]     CrossRef
  • A nomogram for predicting the risk of cancer-related cognitive impairment in breast cancer patients based on a scientific symptom model
    Zhongtao Zhou, Jiajia Ren, Qiankun Liu, Shuoshuo Li, Jiahui Xu, Xiaoyan Wu, Yuanxiang Xiao, Zipu Zhang, Wanchen Jia, Huaiyu Bai, Jing Zhang
    Scientific Reports.2024;[Epub]     CrossRef
  • Взаимосвязь гипогликемии и когнитивных нарушений у пожилых пациентов с сахарным диабетом 2 типа
    S. V. Bulgakova, P. Ya. Merzlova, D. P. Kurmaev, E. V. Treneva
    Russian Journal of Geriatric Medicine.2024; (2): 108.     CrossRef
  • Effect of hypoglycemic events on cognitive function in individuals with type 2 diabetes mellitus: a dose–response meta-analysis
    Min Ye, Qiqi Yang, Lele Zhang, Hudie Song, Qin Fu, Jun Qian, Hongyu Xie, Aihong Yuan
    Frontiers in Neurology.2024;[Epub]     CrossRef
  • Hypoglycaemia and the risk of dementia: a population-based cohort study using exposure density sampling
    Wajd Alkabbani, Colleen J Maxwell, Ruth Ann Marrie, Suzanne L Tyas, Iliana C Lega, John-Michael Gamble
    International Journal of Epidemiology.2023; 52(3): 908.     CrossRef
  • Type 2 diabetes mellitus and cognitive function: understanding the connections
    Lisa Dao, Sarah Choi, Matthew Freeby
    Current Opinion in Endocrinology, Diabetes & Obesity.2023; 30(1): 7.     CrossRef
  • Associations of Mid- and Late-Life Severe Hypoglycemic Episodes With Incident Dementia Among Patients With Type 2 Diabetes: A Population-Based Cohort Study
    Wajd Alkabbani, Colleen J. Maxwell, Ruth Ann Marrie, Suzanne L. Tyas, Iliana C. Lega, John-Michael Gamble
    Diabetes Care.2023; 46(2): 331.     CrossRef
  • Nocturnal hypoglycemia is underdiagnosed in older people with insulin‐treated type 2 diabetes: The HYPOAGE observational study
    Anne‐Sophie Boureau, Béatrice Guyomarch, Pierre Gourdy, Ingrid Allix, Cédric Annweiler, Nathalie Cervantes, Guillaume Chapelet, Isabelle Delabrière, Sophie Guyonnet, Rachel Litke, Marc Paccalin, Alfred Penfornis, Pierre‐Jean Saulnier, Matthieu Wargny, Sam
    Journal of the American Geriatrics Society.2023; 71(7): 2107.     CrossRef
  • Dementia in Diabetes: The Role of Hypoglycemia
    Khaled Hameed Husain, Saud Faisal Sarhan, Haya Khaled Ali Abdulla AlKhalifa, Asal Buhasan, Abu Saleh Md Moin, Alexandra E. Butler
    International Journal of Molecular Sciences.2023; 24(12): 9846.     CrossRef
  • Association Between Trajectory of Severe Hypoglycemia and Dementia in Patients With Type 2 Diabetes: A Population-based Study
    Chung-Yi Li, Chia-Lun Kuo, Ya-Hui Chang, Chin-Li Lu, Santi Martini, Wen-Hsuan Hou
    Journal of Epidemiology.2022; 32(9): 423.     CrossRef
  • Severe Hypoglycemia Increases Dementia Risk and Related Mortality: A Nationwide, Population-based Cohort Study
    Eugene Han, Kyung-do Han, Byung-Wan Lee, Eun Seok Kang, Bong-Soo Cha, Seung-Hyun Ko, Yong-ho Lee
    The Journal of Clinical Endocrinology & Metabolism.2022; 107(5): e1976.     CrossRef
  • Association between hypoglycemia and dementia in patients with diabetes: a systematic review and meta-analysis of 1.4 million patients
    Lifen Huang, Manlian Zhu, Jie Ji
    Diabetology & Metabolic Syndrome.2022;[Epub]     CrossRef
  • Potential Roles of Glucagon-Like Peptide-1 and Its Analogues in Dementia Targeting Impaired Insulin Secretion and Neurodegeneration
    Sidharth Mehan, Sonalika Bhalla, Ehraz Mehmood Siddiqui, Nidhi Sharma, Ambika Shandilya, Andleeb Khan
    Degenerative Neurological and Neuromuscular Disease.2022; Volume 12: 31.     CrossRef
  • Immortal time bias for life-long conditions in retrospective observational studies using electronic health records
    Freya Tyrer, Krishnan Bhaskaran, Mark J. Rutherford
    BMC Medical Research Methodology.2022;[Epub]     CrossRef
  • Glucose-lowering drugs, cognition, and dementia: The clinical evidence
    Che-Yuan Wu, Lila Shapiro, Michael Ouk, Bradley J. MacIntosh, Sandra E. Black, Baiju R. Shah, Walter Swardfager
    Neuroscience & Biobehavioral Reviews.2022; 137: 104654.     CrossRef
  • Switching insulin degludec to insulin glulisine improved nocturnal hypoglycemia and ventricular arrythmia in an elderly type 1 diabetes patient with chronic heart failure: A case report
    Yuriko Hajika, Yuji Kawaguchi, Takako Tanaka, Kenji Hamazaki, Yasuro Kumeda
    Nippon Ronen Igakkai Zasshi. Japanese Journal of Geriatrics.2022; 59(2): 237.     CrossRef
  • Diagnostic, Prognostic, and Mechanistic Biomarkers of Diabetes Mellitus-Associated Cognitive Decline
    Hanan Ehtewish, Abdelilah Arredouani, Omar El-Agnaf
    International Journal of Molecular Sciences.2022; 23(11): 6144.     CrossRef
  • Amyloid $$upbeta$$ (1–42) peptide impairs mitochondrial respiration in primary human brain microvascular endothelial cells: impact of dysglycemia and pre-senescence
    Siva S. V. P. Sakamuri, Venkata N. Sure, Xiaoying Wang, Gregory Bix, Vivian A. Fonseca, Ricardo Mostany, Prasad V. G. Katakam
    GeroScience.2022; 44(6): 2721.     CrossRef
  • Mechanistic Role of Jak3 in Obesity-Associated Cognitive Impairments
    Premranjan Kumar, Jayshree Mishra, Narendra Kumar
    Nutrients.2022; 14(18): 3715.     CrossRef
  • Assessing real-world effectiveness of therapies: what is the impact of incretin-based treatments on hospital use for patients with type 2 diabetes?
    Clémence Bussiere, Pauline Chauvin, Jean-Michel Josselin, Christine Sevilla-Dedieu
    Health Economics Review.2022;[Epub]     CrossRef
  • Fasting Glucose Variability and the Risk of Dementia in Individuals with Diabetes: A Nationwide Cohort Study
    Da Young Lee, Jaeyoung Kim, Sanghyun Park, So Young Park, Ji Hee Yu, Ji A Seo, Nam Hoon Kim, Hye Jin Yoo, Sin Gon Kim, Kyung Mook Choi, Sei Hyun Baik, Kyungdo Han, Nan Hee Kim
    Diabetes & Metabolism Journal.2022; 46(6): 923.     CrossRef
  • Meta-Analysis: Association Between Hypoglycemia and Serious Adverse Events in Older Patients Treated With Glucose-Lowering Agents
    Katharina Mattishent, Yoon K. Loke
    Frontiers in Endocrinology.2021;[Epub]     CrossRef
  • Hypoglycemia, Vascular Disease and Cognitive Dysfunction in Diabetes: Insights from Text Mining-Based Reconstruction and Bioinformatics Analysis of the Gene Networks
    Olga V. Saik, Vadim V. Klimontov
    International Journal of Molecular Sciences.2021; 22(22): 12419.     CrossRef
  • Muscle strength, an independent determinant of glycemic control in older adults with long-standing type 2 diabetes: a prospective cohort study
    Bo Kyung Koo, Seoil Moon, Min Kyong Moon
    BMC Geriatrics.2021;[Epub]     CrossRef
  • Optimal Type 2 Diabetes Mellitus Management and Active Ageing
    Alessia Maria Calabrese, Valeria Calsolaro, Sara Rogani, Chukwuma Okoye, Nadia Caraccio, Fabio Monzani
    Endocrines.2021; 2(4): 523.     CrossRef
  • Response: Hypoglycemia and Dementia Risk in Older Patients with Type 2 Diabetes Mellitus: A Propensity-Score Matched Analysis of a Population-Based Cohort Study (Diabetes Metab J 2020;44:125–33)
    Seung Jin Han
    Diabetes & Metabolism Journal.2020; 44(2): 360.     CrossRef
  • Severe hypoglycaemia and absolute risk of cause-specific mortality in individuals with type 2 diabetes: a UK primary care observational study
    Francesco Zaccardi, Suping Ling, Claire Lawson, Melanie J. Davies, Kamlesh Khunti
    Diabetologia.2020; 63(10): 2129.     CrossRef
  • Letter: Hypoglycemia and Dementia Risk in Older Patients with Type 2 Diabetes Mellitus: A Propensity-Score Matched Analysis of a Population-Based Cohort Study (Diabetes Metab J 2020;44:125–33)
    Jin Hwa Kim
    Diabetes & Metabolism Journal.2020; 44(2): 356.     CrossRef
  • Low‐glucose‐sensitive TRPC6 dysfunction drives hypoglycemia‐induced cognitive impairment in diabetes
    Chengkang He, Peng Gao, Yuanting Cui, Qiang Li, Yingsha Li, Zongshi Lu, Huan Ma, Yu Zhao, Li Li, Fang Sun, Xiaowei Chen, Hongbo Jia, Daoyan Liu, Gangyi Yang, Hongting Zheng, Zhiming Zhu
    Clinical and Translational Medicine.2020;[Epub]     CrossRef
  • Hypoglycaemia and Cardiovascular Disease Risk in Patients with Diabetes
    Niki Katsiki, Kalliopi Kotsa, Anca P. Stoian, Dimitri P. Mikhailidis
    Current Pharmaceutical Design.2020; 26(43): 5637.     CrossRef
Review
Others
Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications
Giacomo Cappon, Martina Vettoretti, Giovanni Sparacino, Andrea Facchinetti
Diabetes Metab J. 2019;43(4):383-397.   Published online July 25, 2019
DOI: https://doi.org/10.4093/dmj.2019.0121
  • 29,361 View
  • 1,241 Download
  • 213 Web of Science
  • 223 Crossref
AbstractAbstract PDFPubReader   

By providing blood glucose (BG) concentration measurements in an almost continuous-time fashion for several consecutive days, wearable minimally-invasive continuous glucose monitoring (CGM) sensors are revolutionizing diabetes management, and are becoming an increasingly adopted technology especially for diabetic individuals requiring insulin administrations. Indeed, by providing glucose real-time insights of BG dynamics and trend, and being equipped with visual and acoustic alarms for hypo- and hyperglycemia, CGM devices have been proved to improve safety and effectiveness of diabetes therapy, reduce hypoglycemia incidence and duration, and decrease glycemic variability. Furthermore, the real-time availability of BG values has been stimulating the realization of new tools to provide patients with decision support to improve insulin dosage tuning and infusion. The aim of this paper is to offer an overview of current literature and future possible developments regarding CGM technologies and applications. In particular, first, we outline the technological evolution of CGM devices through the last 20 years. Then, we discuss about the current use of CGM sensors from patients affected by diabetes, and, we report some works proving the beneficial impact provided by the adoption of CGM. Finally, we review some recent advanced applications for diabetes treatment based on CGM sensors.

Citations

Citations to this article as recorded by  
  • Continuous glucose monitoring metrics following sub-Tenon’s injection of triamcinolone acetonide for diabetic macular edema
    Rei Sotani-Ogawa, Sentaro Kusuhara, Yushi Hirota, Kyung Woo Kim, Wataru Matsumiya, Wataru Ogawa, Makoto Nakamura
    Graefe's Archive for Clinical and Experimental Ophthalmology.2024; 262(2): 449.     CrossRef
  • Identifying and mapping measures of medication safety during transfer of care in a digital era: a scoping literature review
    Catherine Leon, Helen Hogan, Yogini H Jani
    BMJ Quality & Safety.2024; 33(3): 173.     CrossRef
  • Highly sensitive and stable glucose sensing using N-type conducting polymer based organic electrochemical transistor
    Gang Zhou, Zhu Cao, Yangxuan Liu, Haoyu Zheng, Kai Xu
    Journal of Electroanalytical Chemistry.2024; 952: 117961.     CrossRef
  • Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial
    Caterina Bérubé, Vera Franziska Lehmann, Martin Maritsch, Mathias Kraus, Stefan Feuerriegel, Felix Wortmann, Thomas Züger, Christoph Stettler, Elgar Fleisch, A Baki Kocaballi, Tobias Kowatsch
    JMIR Human Factors.2024; 11: e42823.     CrossRef
  • Nafion based biosensors: a review of recent advances and applications
    Roya Mohammadzadeh Kakhki
    International Journal of Polymeric Materials and Polymeric Biomaterials.2024; 73(17): 1470.     CrossRef
  • Can Electrochemical Aptasensors Achieve the Commercial Success of Glucose Biosensors?
    Sina Ardalan, Anna Ignaszak
    Advanced Sensor Research.2024;[Epub]     CrossRef
  • Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes
    Huiqi Y. Lu, Xiaorong Ding, Jane E. Hirst, Yang Yang, Jenny Yang, Lucy Mackillop, David A. Clifton
    IEEE Reviews in Biomedical Engineering.2024; 17: 98.     CrossRef
  • Effects of Digitization of Self-Monitoring of Blood Glucose Records Using a Mobile App and the Cloud System on Outpatient Management of Diabetes: Single-Armed Prospective Study
    Tomoko Handa, Takeshi Onoue, Tomoko Kobayashi, Ryutaro Maeda, Keigo Mizutani, Ayana Yamagami, Tamaki Kinoshita, Yoshinori Yasuda, Shintaro Iwama, Takashi Miyata, Mariko Sugiyama, Hiroshi Takagi, Daisuke Hagiwara, Hidetaka Suga, Ryoichi Banno, Yoshinori Az
    JMIR Diabetes.2024; 9: e48019.     CrossRef
  • The Association of Macronutrient Consumption and BMI to Exhaled Carbon Dioxide in Lumen Users: Retrospective Real-World Study
    Shlomo Yeshurun, Tomer Cramer, Daniel Souroujon, Merav Mor
    JMIR mHealth and uHealth.2024; 12: e56083.     CrossRef
  • Generative adversarial network-based data augmentation for improving hypoglycemia prediction: A proof-of-concept study
    Wonju Seo, Namho Kim, Sung-Woon Park, Sang-Man Jin, Sung-Min Park
    Biomedical Signal Processing and Control.2024; 92: 106077.     CrossRef
  • Pre‐dinner walks may be superior to post‐dinner walks for glucose time in range in adults with type 1 diabetes on hybrid closed‐loop insulin delivery systems
    Lauren V. Turner, Michael C. Riddell
    Diabetes, Obesity and Metabolism.2024; 26(6): 2492.     CrossRef
  • Real-world effectiveness of GLP-1 receptor agonist-based treatment strategies on “time in range” in patients with type 2 diabetes
    Yongru Chen, Jingxian Chen, Shuo Zhang, Dan Zhu, Feiying Deng, Rui Zuo, Yufei Hu, Yue Zhao, Yale Duan, Benwei Lin, Fengwu Chen, Yun Liang, Jiaxiong Zheng, Barkat Ali Khan, Kaijian Hou
    Frontiers in Pharmacology.2024;[Epub]     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
  • Utility of Flash Glucose Monitoring to Determine Glucose Variation Induced by Different Doughs in Persons with Type 2 Diabetes
    Maria Antonietta Taras, Sara Cherchi, Ilaria Campesi, Valentina Margarita, Gavino Carboni, Paola Rappelli, Giancarlo Tonolo
    Diabetology.2024; 5(1): 129.     CrossRef
  • Facile chemiresistive biosensor functionalized with PANI/GOx and novel green synthesized silver nanoparticles for glucose sensing
    Jitendra B. Zalke, N.P. Narkhede, Dinesh R. Rotake, Shiv Govind Singh
    Microchemical Journal.2024; 200: 110339.     CrossRef
  • A novel questionnaire for evaluating digital tool use (DTUQ-D) among individuals with type 2 diabetes: exploring the digital landscape
    Ora Peleg, Efrat Hadar, Meyran Boniel-Nissim
    Frontiers in Public Health.2024;[Epub]     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
  • Global scientific trends on the islet transplantation in the 21st century: A bibliometric and visualized analysis
    Sheng Chen, PeiZhong Wu, Ting Zhang, Jianqiang Zhang, Hongjun Gao
    Medicine.2024; 103(17): e37945.     CrossRef
  • A method for evaluating the risks of glucose dysregulation in daily life by continuous measurement of glucose excursions under reduced glycemic load: a pilot study
    Yoshitada Katagiri, Hiromi Ozaku, Katsuhiko Kondo
    Frontiers in Sensors.2024;[Epub]     CrossRef
  • The Impact of Missing Continuous Blood Glucose Samples on Machine Learning Models for Predicting Postprandial Hypoglycemia: An Experimental Analysis
    Najib Ur Rehman, Ivan Contreras, Aleix Beneyto, Josep Vehi
    Mathematics.2024; 12(10): 1567.     CrossRef
  • Measures of nutrition status and health for weight‐inclusive patient care: A narrative review
    Kasuen Mauldin, Giselle A. P. Pignotti, John Gieng
    Nutrition in Clinical Practice.2024; 39(4): 751.     CrossRef
  • Long-Term Evaluation of Inserted Nanocomposite Hydrogel-Based Phosphorescent Oxygen Biosensors: Evolution of Local Tissue Oxygen Levels and Foreign Body Response
    David Chimene, Waqas Saleem, Nichole Longbottom, Brian Ko, Ananth Soundaram Jeevarathinam, Staci Horn, Michael J. McShane
    ACS Applied Bio Materials.2024; 7(6): 3964.     CrossRef
  • Comparison of Metabolic Control in Children and Adolescents Treated with Insulin Pumps
    Agnieszka Lejk, Karolina Myśliwiec, Arkadiusz Michalak, Barbara Pernak, Wojciech Fendler, Małgorzata Myśliwiec
    Children.2024; 11(7): 839.     CrossRef
  • Kontinuierliche Glukosemessung bei Menschen mit Typ-2-Diabetes ohne intensivierte Insulintherapie: eine Standortbestimmung
    Jochen Seufert, Ingrid Dänschel, Stefan Gölz, Nicola Haller, Bernhard Kulzer, Susanne Tan, Oliver Schnell, Tobias Wiesner, Jens Kroeger
    Diabetologie und Stoffwechsel.2024; 19(05): 356.     CrossRef
  • The Influence of Nickel Electrode’s Morphology on Glucose Detection
    Hailong Hu, Guohua Ma, Baogang Guo, Xingquan Zhang, Ruishi Xie, Haifeng Liu, Heyan Huang
    Electrocatalysis.2024; 15(5): 374.     CrossRef
  • Insertable Biosensors: Combining Implanted Sensing Materials with Wearable Monitors
    David Chimene, Kirstie M.K. Queener, Brian S. Ko, Mike McShane, Michael Daniele
    Annual Review of Biomedical Engineering .2024; 26(1): 197.     CrossRef
  • Development and Effectiveness of a Pattern Management Educational Program Using Continuous Glucose Monitoring for Type 2 Diabetic Patients in Korea: A Quasi-Experimental Study
    Seung-Yeon Kong, Mi-Kyoung Cho
    Healthcare.2024; 12(14): 1381.     CrossRef
  • Review of Microwave Near-Field Sensing and Imaging Devices in Medical Applications
    Cristina Origlia, David O. Rodriguez-Duarte, Jorge A. Tobon Vasquez, Jean-Charles Bolomey, Francesca Vipiana
    Sensors.2024; 24(14): 4515.     CrossRef
  • Diabetes Management in Transition: Market Insights and Technological Advancements in CGM and Insulin Delivery
    Tae Sang Yu, Soojeong Song, Junwoo Yea, Kyung‐In Jang
    Advanced Sensor Research.2024;[Epub]     CrossRef
  • How to Use Continuous Glucose Monitoring Efficiently in Diabetes Management: Opinions and Recommendations by German Experts on the Status and Open Questions
    Andreas Thomas, Thomas Haak, Astrid Tombek, Bernhard Kulzer, Dominic Ehrmann, Olga Kordonouri, Jens Kröger, Oliver Schubert-Olesen, Ralf Kolassa, Thorsten Siegmund, Nicola Haller, Lutz Heinemann
    Journal of Diabetes Science and Technology.2024;[Epub]     CrossRef
  • Patterned thin film enzyme electrodes via spincoating and glutaraldehyde vapor crosslinking: towards scalable fabrication of integrated sensor-on-CMOS devices
    Dvin Adalian, Xiomi Madero, Samson Chen, Musab Jilani, Richard D. Smith, Songtai Li, Christin Ahlbrecht, Juan Cardenas, Abhinav Agarwal, Azita Emami, Oliver Plettenburg, Peter A. Petillo, Axel Scherer
    Lab on a Chip.2024; 24(17): 4172.     CrossRef
  • Continuous Glucose, Insulin and Lifestyle Data Augmentation in Artificial Pancreas Using Adaptive Generative and Discriminative Models
    Deepjyoti Kalita, Hrishita Sharma, Khalid B. Mirza
    IEEE Journal of Biomedical and Health Informatics.2024; 28(8): 4963.     CrossRef
  • Management of Continuous Glucose Monitors in Radiation Oncology Patients
    Johnathan Zeng, Tara Kosak, Samir Malkani, Julie C. Hudson, Neil E. Martin, Roy B. Tishler, Itai M. Pashtan
    Practical Radiation Oncology.2024;[Epub]     CrossRef
  • Glycemic Outcomes and Nurse Perceptions of Continuous Glucose Monitoring for Hospitalized Patients
    Alexandra Thullen, Rebecca Gerber, Alyson Keen
    Journal of Nursing Care Quality.2024; 39(4): 310.     CrossRef
  • Advances in Type 1 Diabetes Mellitus Management in Children
    Mridu Bahal, Vineeta Pande, Jasleen Dua, Shailaja Mane
    Cureus.2024;[Epub]     CrossRef
  • Time‐in‐range derived from self‐measured blood glucose in people with type 2 diabetes advancing to iGlarLixi: A participant‐level pooled analysis of three phase 3 LixiLan randomized controlled trials
    Martin Haluzík, Mohammed E. Al‐Sofiani, Alice Y. Y. Cheng, Felipe Lauand, Lydie Melas‐Melt, Julio Rosenstock
    Diabetes, Obesity and Metabolism.2024; 26(11): 5046.     CrossRef
  • Evaluating the Impact of Continuous Glucose Monitoring on Erectile Dysfunction in Type 1 Diabetes: A Focus on Reducing Glucose Variability and Inflammation
    Nicola Tecce, Davide Menafra, Mattia Proganò, Mario Felice Tecce, Rosario Pivonello, Annamaria Colao
    Healthcare.2024; 12(18): 1823.     CrossRef
  • Indications, specifics and reimbursement of glucose sensors in type 2 diabetes patients
    Kateřina Štechová
    Vnitřní lékařství.2024; 70(6): 390.     CrossRef
  • Enzyme free highly sensitive glucose biosensor based on Ag/Cu2O nanostructures deposited on TiO2 nanotube arrays
    Bittu Kumar, Sudip Kumar Sinha
    Monatshefte für Chemie - Chemical Monthly.2024; 155(11): 1095.     CrossRef
  • The status of blood glucose monitoring and its influencing factors in Chinese patients with type 2 diabetes initiating premixed insulin: A prospective real-world study
    Si Chen, Jingyi Lu, Danfeng Peng, Fengjing Liu, Wei Lu, Wei Zhu, Yuqian Bao, Jian Zhou, Weiping Jia
    Diabetes Research and Clinical Practice.2024; 218: 111895.     CrossRef
  • Ag@Au core–shell nanoparticles modified glassy carbon electrode synthesized by simple displacement reaction for non-enzymatic electrochemical glucose sensing
    Huiling Huang, Tianyu Chen, Xinyu Qin, Bo Quan, Sun Ha Paek, Wang Zhang, Yuanzhe Piao
    Journal of Electroanalytical Chemistry.2024; 975: 118726.     CrossRef
  • Integrated Lab-on-a-Chip Optical Glucose Sensing System Using a Silicon Waveguide Bragg Grating Device
    Hongqiang Li, Fanglin Xie, Ming Han, Lizhen Zhang, Lu Cao, Zhiyue Zhu, Enbang Li
    IEEE Sensors Journal.2024; 24(20): 32034.     CrossRef
  • Noninvasive Blood Glucose Monitoring Using Spatiotemporal ECG and PPG Feature Fusion and Weight-Based Choquet Integral Multimodel Approach
    Jingzhen Li, Jingjing Ma, Olatunji Mumini Omisore, Yuhang Liu, Huajie Tang, Pengfei Ao, Yan Yan, Lei Wang, Zedong Nie
    IEEE Transactions on Neural Networks and Learning Systems.2024; 35(10): 14491.     CrossRef
  • Technology and Continuous Glucose Monitoring Access, Literacy, and Use Among Patients at the Diabetes Center of an Inner-City Safety-Net Hospital: Mixed Methods Study
    Gaëlle Sabben, Courtney Telfort, Marissa Morales, Wenjia Stella Zhang, Juan C Espinoza, Francisco J Pasquel, Kate Winskell
    JMIR Diabetes.2024; 9: e54223.     CrossRef
  • Riemannian manifold-based geometric clustering of continuous glucose monitoring to improve personalized diabetes management
    Jiafeng Song, Jocelyn McNeany, Yifei Wang, Tanicia Daley, Arlene Stecenko, Rishikesan Kamaleswaran
    Computers in Biology and Medicine.2024; 183: 109255.     CrossRef
  • Implantable Fluorogenic DNA Biosensor for Stress Detection
    Irina Drachuk, Namrata Ramani, Svetlana Harbaugh, Chad A. Mirkin, Jorge L. Chávez
    ACS Applied Materials & Interfaces.2024;[Epub]     CrossRef
  • Predicting Dysglycemia in Patients with Diabetes Using Electrocardiogram
    Ho-Jung Song, Ju-Hyuck Han, Sung-Pil Cho, Sung-Il Im, Yong-Suk Kim, Jong-Uk Park
    Diagnostics.2024; 14(22): 2489.     CrossRef
  • Computational Model-Assisted Development of a Nonenzymatic Fluorescent Glucose-Sensing Assay
    Lydia Colvin, Diana Al Husseini, Dandan Tu, Darin Dunlap, Tyler Lalonde, Muhammed Üçüncü, Alicia Megia-Fernandez, Mark Bradley, Wenshe Liu, Melissa A. Grunlan, Gerard L. Coté
    ACS Sensors.2024; 9(11): 6218.     CrossRef
  • 당뇨병 치료의 진화: 관해를 향해가는 혁신적 약물치료와 첨단 관리기기의 결합
    종한 최, 민경 문
    Public Health Weekly Report.2024; 17(44): 1905.     CrossRef
  • Continuous Glucose Monitoring: A Transformative Approach to the Detection of Prediabetes
    Xueen Liu, Jiale Zhang
    Journal of Multidisciplinary Healthcare.2024; Volume 17: 5513.     CrossRef
  • Continuous Glucose Monitoring Among Patients with Type 1 Diabetes in Rwanda (CAPT1D) Phase I: Feasibility Study (Preprint)
    Jason Baker, Giacomo Cappon, Jean Claude Habineza, Corey H Basch, Steve Mey, Diana L. Malkin-Washeim, Christian Schuetz, Simon P. Niyonsenga, Etienne Uwingabire, Alvera Mukamazimpaka, Paul Mbonyi, Sandhya Narayanan
    JMIR Formative Research.2024;[Epub]     CrossRef
  • Development of a Novel Insulin Sensor for Clinical Decision-Making
    Eva Vargas, Eleonora M. Aiello, Jordan E. Pinsker, Hazhir Teymourian, Farshad Tehrani, Mei Mei Church, Lori M. Laffel, Francis J. Doyle, Mary-Elizabeth Patti, Eyal Dassau, Joseph Wang
    Journal of Diabetes Science and Technology.2023; 17(4): 1029.     CrossRef
  • Diabetes technology and sexual health: which role?
    V. Zamponi, J. Haxhi, G. Pugliese, A. Faggiano, R. Mazzilli
    Journal of Endocrinological Investigation.2023; 47(6): 1315.     CrossRef
  • Discordance Between Glycated Hemoglobin A1c and the Glucose Management Indicator in People With Diabetes and Chronic Kidney Disease
    Philippe Oriot, Claire Viry, Antoine Vandelaer, Sébastien Grigioni, Malanie Roy, Jean Christophe Philips, Gaëtan Prévost
    Journal of Diabetes Science and Technology.2023; 17(6): 1553.     CrossRef
  • Expertenaustausch zum Einsatz von kontinuierlichem Glukosemonitoring (CGM) im Diabetesmanagement: Eine aktuelle Bestandsaufnahme und Blick in die Zukunft
    Andreas Thomas, Thomas Haak, Astrid Tombek, Bernhard Kulzer, Dominic Ehrmann, Olga Kordonouri, Jens Kroeger, Oliver Schubert-Olesen, Ralf Kolassa, Thorsten Siegmund, Nicola Haller, Lutz Heinemann
    Diabetologie und Stoffwechsel.2023; 18(01): 57.     CrossRef
  • Evaluation of the performance and usability of a novel continuous glucose monitoring system
    Li Yan, Qiang Li, Qingbo Guan, Mingsong Han, Yu Zhao, Junfei Fang, Jiajun Zhao
    International Journal of Diabetes in Developing Countries.2023; 43(4): 551.     CrossRef
  • Efficacy of intermittent short‐term use of a real‐time continuous glucose monitoring system in non‐insulin–treated patients with type 2 diabetes: A randomized controlled trial
    Sun Joon Moon, Kyung‐Soo Kim, Woo Je Lee, Mi Yeon Lee, Robert Vigersky, Cheol‐Young Park
    Diabetes, Obesity and Metabolism.2023; 25(1): 110.     CrossRef
  • Intermittent-scanned continuous glucose monitoring with low glucose alarms decreases hypoglycemia incidence in middle-aged adults with type 1 diabetes in real-life setting
    Philippe Oriot, Michel P. Hermans
    Journal of Diabetes and its Complications.2023; 37(2): 108385.     CrossRef
  • Applications of Microwaves in Medicine
    J.-C. Chiao, Changzhi Li, Jenshan Lin, Robert H. Caverly, James C. M. Hwang, Harel Rosen, Arye Rosen
    IEEE Journal of Microwaves.2023; 3(1): 134.     CrossRef
  • A Double-Needle Gold-Silver Electrodes Continuous Glucose Monitoring Device
    C. Ben Ali Hassine, A. Tekin
    IRBM.2023; 44(3): 100752.     CrossRef
  • Accuracy of Flash Glucose Monitoring in Hemodialysis Patients With and Without Diabetes Mellitus
    Michèle R. Weber, Matthias Diebold, Peter Wiesli, Andreas D. Kistler
    Experimental and Clinical Endocrinology & Diabetes.2023; 131(03): 132.     CrossRef
  • Minimally invasive electrochemical continuous glucose monitoring sensors: Recent progress and perspective
    Yuanyuan Zou, Zhengkang Chu, Jiuchuan Guo, Shan Liu, Xing Ma, Jinhong Guo
    Biosensors and Bioelectronics.2023; 225: 115103.     CrossRef
  • Continuous Glucose Monitoring in Enterally Fed Children with Severe Central Nervous System Impairment
    Marlena Górska, Joanna Kudzin, Anna Borkowska, Agnieszka Szlagatys-Sidorkiewicz, Agnieszka Szadkowska, Małgorzata Myśliwiec, Ewa Toporowska-Kowalska
    Nutrients.2023; 15(3): 513.     CrossRef
  • Prevalence of type 2 diabetes complications and its association with diet knowledge and skills and self‐care barriers in Tabriz, Iran: A cross‐sectional study
    Habib Jalilian, Elnaz Javanshir, Leila Torkzadeh, Saeedeh Fehresti, Nazanin Mir, Majid Heidari‐Jamebozorgi, Somayeh Heydari
    Health Science Reports.2023;[Epub]     CrossRef
  • Status of continuous glucose monitoring use and management in tertiary hospitals of China: a cross-sectional study
    Liping Chen, Xiaoqin Liu, Qin Lin, Hongmei Dai, Yong Zhao, Zumin Shi, Liping Wu
    BMJ Open.2023; 13(2): e066801.     CrossRef
  • Diboronic-Acid-Based Electrochemical Sensor for Enzyme-Free Selective and Sensitive Glucose Detection
    Joong-Hyun Kim, Hongsik Choi, Chul-Soon Park, Heung-Seop Yim, Dongguk Kim, Sungmin Lee, Yeonkeong Lee
    Biosensors.2023; 13(2): 248.     CrossRef
  • Artificial intelligence biosensors for continuous glucose monitoring
    Xiaofeng Jin, Andrew Cai, Tailin Xu, Xueji Zhang
    Interdisciplinary Materials.2023; 2(2): 290.     CrossRef
  • Continuous Glucose Monitoring in Dogs and Cats
    Francesca Del Baldo, Federico Fracassi
    Veterinary Clinics of North America: Small Animal Practice.2023; 53(3): 591.     CrossRef
  • Accurate Post-Calibration Predictions for Noninvasive Glucose Measurements in People Using Confocal Raman Spectroscopy
    Anders Pors, Kaspar G. Rasmussen, Rune Inglev, Nina Jendrike, Amalie Philipps, Ajenthen G. Ranjan, Vibe Vestergaard, Jan E. Henriksen, Kirsten Nørgaard, Guido Freckmann, Karl D. Hepp, Michael C. Gerstenberg, Anders Weber
    ACS Sensors.2023; 8(3): 1272.     CrossRef
  • Diabetes mellitus in der Akut- und Notfallmedizin
    Leo Benning, Julian Krehl, Felix Patricius Hans
    Notfallmedizin up2date.2023; 18(01): 45.     CrossRef
  • Empowering People with Diabetes: Role of Continuous Glucose Monitor Systems
    Sneha B Srivastava
    American Journal of Lifestyle Medicine.2023; 17(3): 359.     CrossRef
  • Diabétologie connectée : quelles sont les attentes des médecins et des patients ?
    Nicolas Naïditch, Jean-Pierre Riveline
    Médecine des Maladies Métaboliques.2023; 17(2): 2S3.     CrossRef
  • Association of Vibrotactile Biofeedback With Reduced Ergonomic Risk for Surgeons During Tonsillectomy
    Natalie A. Kelly, Abdulrahman Althubaiti, Aashika D. Katapadi, Adam G. Smith, Sarah C. Nyirjesy, Jane H. Yu, Amanda J. Onwuka, Tendy Chiang
    JAMA Otolaryngology–Head & Neck Surgery.2023; 149(5): 397.     CrossRef
  • The Evolution of Diabetes Technology – Options Toward Personalized Care
    Maleeha Zahid, Samaneh Dowlatshahi, Abhishek H. Kansara, Archana R. Sadhu
    Endocrine Practice.2023; 29(8): 653.     CrossRef
  • A Personalized and Adaptive Insulin Bolus Calculator Based on Double Deep Q- Learning to Improve Type 1 Diabetes Management
    Giulia Noaro, Taiyu Zhu, Giacomo Cappon, Andrea Facchinetti, Pantelis Georgiou
    IEEE Journal of Biomedical and Health Informatics.2023; 27(5): 2536.     CrossRef
  • Celebrating a Century of Insulin Discovery: A Critical Appraisal of the Emerging Alternative Insulin Delivery Systems
    Ntethelelo Sibiya, Bonisiwe Mbatha, Phikelelani Ngubane, Andile Khathi
    Current Drug Delivery.2023; 20(6): 656.     CrossRef
  • Machine Learning–Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study
    Nicholas Berin Chan, Weizi Li, Theingi Aung, Eghosa Bazuaye, Rosa M Montero
    JMIR AI.2023; 2: e45450.     CrossRef
  • Drug Delivery Systems for Personal Healthcare by Smart Wearable Patch System
    Bikram Khadka, Byeongmoon Lee, Ki-Taek Kim
    Biomolecules.2023; 13(6): 929.     CrossRef
  • Wearable Electrochemical Glucose Sensors in Diabetes Management: A Comprehensive Review
    Tamoghna Saha, Rafael Del Caño, Kuldeep Mahato, Ernesto De la Paz, Chuanrui Chen, Shichao Ding, Lu Yin, Joseph Wang
    Chemical Reviews.2023; 123(12): 7854.     CrossRef
  • Real-life 24-week changes in glycemic parameters among European users of flash glucose monitoring with type 1 and 2 diabetes and different levels of glycemic control
    Annel Lameijer, Julia J. Bakker, Kalvin Kao, Yongjin Xu, Rijk O.B. Gans, Henk J.G. Bilo, Timothy C. Dunn, Peter R. van Dijk
    Diabetes Research and Clinical Practice.2023; 201: 110735.     CrossRef
  • Les médicaments anti-diabétiques : ce que le médecin anesthésiste réanimateur doit savoir
    Michael Joubert
    Anesthésie & Réanimation.2023; 9(3): 251.     CrossRef
  • Glycemia control using remote technologies
    L. A. Suplotova, O. O. Alieva
    Meditsinskiy sovet = Medical Council.2023; 17(9): 81.     CrossRef
  • Data-enabled learning and control algorithms for intelligent glucose management: The state of the art
    Deheng Cai, Wenjing Wu, Marzia Cescon, Wei Liu, Linong Ji, Dawei Shi
    Annual Reviews in Control.2023; 56: 100897.     CrossRef
  • A Markov Model of Gap Occurrence in Continuous Glucose Monitoring Data for Realistic in Silico Clinical Trials
    Martina Vettoretti, Martina Drecogna, Simone Del Favero, Andrea Facchinetti, Giovanni Sparacino
    Computer Methods and Programs in Biomedicine.2023; 240: 107700.     CrossRef
  • Volumetric Electron Transfer from Metabolites to Chemically Doped Polymer Electrodes
    Siew Ting Melissa Tan, Gijun Lee, Kalee Rozylowicz, Adam Marks, Alberto Salleo
    Advanced Functional Materials.2023;[Epub]     CrossRef
  • Diabetes and hypertension MobileHealth systems: a review of general challenges and advancements
    Bliss Utibe-Abasi Stephen, Benedicta C. Uzoewulu, Phillip Michael Asuquo, Simeon Ozuomba
    Journal of Engineering and Applied Science.2023;[Epub]     CrossRef
  • THE ASSESSMENT OF COMPENSATION OF CARBOHYDRATE METABOLISM IN PATIENTS WITH TYPE 2 DIABETES MELLITUS WITH METABOLIC SYNDROME BEYOND THE LIMITS OF GLYCATED HEMOGLOBIN
    Taras V. Romaniv, Nadiya V. Skrypnyk, Ulyana V. Synko, Nataliia M. Voronych-Semchenko, Oleh V. Melnyk, Anna O. Hryb, Igor B. Boruchok
    Wiadomości Lekarskie.2023; 76(6): 1385.     CrossRef
  • Pros and cons of continous glucose monitoring
    Marcin Ciechański, Edyta Witkowska, Agnieszka Ostańska, Adrianna Szafran, Klaudia Wiśniewska, Laura Piasek, Grzegorz Godek, Kacper Więcław, Katarzyna Stańko, Wiktor Terelak
    Journal of Medical Science.2023;[Epub]     CrossRef
  • Continuous Glucose Monitoring by Insulin-Treated Pilots Flying Commercial Aircraft Within the ARA.MED.330 Diabetes Protocol: A Preliminary Feasibility Study
    Gillian L. Garden, Fariba Shojaee-Moradie, Ewan J. Hutchison, Brian M. Frier, Kenneth M. Shaw, Simon R. Heller, Gerd Koehler, Julia K. Mader, Declan Maher, Graham A. Roberts, David L. Russell-Jones
    Diabetes Technology & Therapeutics.2023; 25(8): 543.     CrossRef
  • Importance of continuous glucose monitoring in the treatment of diabetes mellitus
    Sun Joon Moon, Won-Young Lee
    Journal of the Korean Medical Association.2023; 66(7): 432.     CrossRef
  • DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions
    Temiloluwa Prioleau, Abigail Bartolome, Richard Comi, Catherine Stanger
    Scientific Data.2023;[Epub]     CrossRef
  • Testing the Real-World Accuracy of the Dexcom G6 Pro CGM During the Insulin-Only Bionic Pancreas Pivotal Trial
    Martin Chase Marak, Peter Calhoun, Edward R. Damiano, Steven J. Russell, Katrina J. Ruedy, Roy W. Beck
    Diabetes Technology & Therapeutics.2023; 25(11): 817.     CrossRef
  • Use of continuous glucose monitoring in pediatric gastroenterology allows for personalized nutrition support care – Potential for collaboration between pediatric endocrinologists and gastroenterologists
    Kathryn Hitchcock, Stephanie Oliveira
    Journal of Pediatric Endocrinology and Diabetes.2023; 3: 34.     CrossRef
  • Anti-biofouling strategies for implantable biosensors of continuous glucose monitoring systems
    Yan Zheng, Dunyun Shi, Zheng Wang
    Frontiers of Chemical Science and Engineering.2023; 17(12): 1866.     CrossRef
  • A novel strategy for therapeutic drug monitoring: application of biosensors to quantify antimicrobials in biological matrices
    Quanfang Wang, Sihan Li, Jiaojiao Chen, Luting Yang, Yulan Qiu, Qian Du, Chuhui Wang, Mengmeng Teng, Taotao Wang, Yalin Dong
    Journal of Antimicrobial Chemotherapy.2023; 78(11): 2612.     CrossRef
  • Hypoglycemic Effect of an Herbal Decoction (Modified Gangsimtang) in a Patient with Severe Type 2 Diabetes Mellitus Refusing Oral Anti-Diabetic Medication: A Case Report
    Sungjun Joo, Hyonjun Chun, Jisu Lee, Seungmin Seo, Jungmin Lee, Jungtae Leem
    Medicina.2023; 59(11): 1919.     CrossRef
  • GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks
    Taiyu Zhu, Kezhi Li, Pau Herrero, Pantelis Georgiou
    IEEE Journal of Biomedical and Health Informatics.2023; 27(10): 5122.     CrossRef
  • Millifluidic valves and pumps made of tape and plastic
    Josue U. Amador-Hernandez, Pablo E. Guevara-Pantoja, Diana F. Cedillo-Alcantar, Gabriel A. Caballero-Robledo, Jose L. Garcia-Cordero
    Lab on a Chip.2023; 23(20): 4579.     CrossRef
  • Offline Deep Reinforcement Learning and Off-Policy Evaluation for Personalized Basal Insulin Control in Type 1 Diabetes
    Taiyu Zhu, Kezhi Li, Pantelis Georgiou
    IEEE Journal of Biomedical and Health Informatics.2023; 27(10): 5087.     CrossRef
  • Flash Glucose Monitoring in Croatia: The Optimal Number of Scans per Day to Achieve Good Glycemic Control in Type 1 Diabetes
    Silvija Canecki-Varzic, Ivana Prpic-Krizevac, Maja Cigrovski Berkovic, Dario Rahelic, Ema Schonberger, Marina Gradiser, Ines Bilic-Curcic
    Medicina.2023; 59(11): 1893.     CrossRef
  • The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP
    Francesco Prendin, Jacopo Pavan, Giacomo Cappon, Simone Del Favero, Giovanni Sparacino, Andrea Facchinetti
    Scientific Reports.2023;[Epub]     CrossRef
  • SHMAD: A Smart Health Care System to Monitor Alzheimer’s Disease Patients
    Shabana R. Ziyad, May Altulyan, Meshal Alharbi
    Journal of Alzheimer's Disease.2023; 95(4): 1545.     CrossRef
  • Séquelles fonctionnelles après résection pancréatique carcinologique. Un sujet d’actualité pour les patients et les praticiens
    Andrea Mulliri, Michael Joubert, Marie-Astrid Piquet, Arnaud Alves, Benoît Dupont
    Journal de Chirurgie Viscérale.2023; 160(6): 470.     CrossRef
  • Functional sequelae after pancreatic resection for cancer
    Andrea Mulliri, Michael Joubert, Marie-Astrid Piquet, Arnaud Alves, Benoît Dupont
    Journal of Visceral Surgery.2023; 160(6): 427.     CrossRef
  • Characteristics of glucose change in diabetes mellitus generalized through continuous wavelet transform processing: A preliminary study
    Yoichi Nakamura, Shinya Furukawa
    World Journal of Diabetes.2023; 14(10): 1562.     CrossRef
  • Evaluating passive physiological data collection during Spravato treatment
    Todd M. Solomon, Matus Hajduk, Martin Majernik, Jamileh Jemison, Alexander Deschamps, Jenna Scoggins, Adam Kolar, Miguel Amável Pinheiro, Peter Dubec, Ondrej Skala, Owen Muir, Amanda Tinkelman, Daniel R. Karlin, Robert Barrow
    Frontiers in Digital Health.2023;[Epub]     CrossRef
  • Fabrication of conductive Ag/AgCl/Ag nanorods ink on Laser-induced graphene electrodes on flexible substrates for non-enzymatic glucose detection
    Rana Bagheri, Saeid Alikhani, Ebrahim Miri-Moghaddam
    Scientific Reports.2023;[Epub]     CrossRef
  • Co-design of Human-centered, Explainable AI for Clinical Decision Support
    Cecilia Panigutti, Andrea Beretta, Daniele Fadda, Fosca Giannotti, Dino Pedreschi, Alan Perotti, Salvatore Rinzivillo
    ACM Transactions on Interactive Intelligent Systems.2023; 13(4): 1.     CrossRef
  • Analysis of blood glucose monitoring – a review on recent advancements and future prospects
    Gayathri Priyadarshini R, Sathiya Narayanan
    Multimedia Tools and Applications.2023; 83(20): 58375.     CrossRef
  • Overview of modern sensors for continuous glucose monitoring
    K. T. Momynaliev, M. V. Prokopiev, I. V. Ivanov
    Diabetes mellitus.2023; 26(6): 575.     CrossRef
  • A Prospective Multicenter Clinical Performance Evaluation of the C-CGM System
    Mihailo Rebec, Kevin Cai, Ralph Dutt-Ballerstadt, Ellen Anderson
    Journal of Diabetes Science and Technology.2022; 16(2): 390.     CrossRef
  • Perceived Burdens and Benefits Associated With Continuous Glucose Monitor Use in Type 1 Diabetes Across the Lifespan
    Vidita Divan, Margaret Greenfield, Christopher P. Morley, Ruth S. Weinstock
    Journal of Diabetes Science and Technology.2022; 16(1): 88.     CrossRef
  • Technologies for Diabetes Self-Monitoring: A Scoping Review and Assessment Using the REASSURED Criteria
    Jessica Hanae Zafra-Tanaka, David Beran, Beatrice Vetter, Rangarajan Sampath, Antonio Bernabe-Ortiz
    Journal of Diabetes Science and Technology.2022; 16(4): 962.     CrossRef
  • Temporal Trends for Diabetes Management and Glycemic Control Between 2010 and 2019 in Korean Children and Adolescents with Type 1 Diabetes
    Jaewon Choe, Seung Hyun Won, Yunsoo Choe, Sang Hee Park, Yun Jeong Lee, Jieun Lee, Young Ah Lee, Han Hyuk Lim, Jae-Ho Yoo, Seong Yong Lee, Eun Young Kim, Choong Ho Shin, Jae Hyun Kim
    Diabetes Technology & Therapeutics.2022; 24(3): 201.     CrossRef
  • International comparison of glycaemic control in people with type 1 diabetes: an update and extension
    Regina Prigge, John A. McKnight, Sarah H. Wild, Aveni Haynes, Timothy W. Jones, Elizabeth A. Davis, Birgit Rami‐Merhar, Maria Fritsch, Christine Prchla, Astrid Lavens, Kris Doggen, Suchsia Chao, Ronnie Aronson, Ruth Brown, Else H. Ibfelt, Jannet Svensson,
    Diabetic Medicine.2022;[Epub]     CrossRef
  • Artificial intelligence perspective in the future of endocrine diseases
    Mandana Hasanzad, Hamid Reza Aghaei Meybodi, Negar Sarhangi, Bagher Larijani
    Journal of Diabetes & Metabolic Disorders.2022; 21(1): 971.     CrossRef
  • Telehealth Technologies and Their Benefits to People With Diabetes
    Chinenye O. Usoh, Kristine Kilen, Carolyn Keyes, Crystal Paige Johnson, Joseph A. Aloi
    Diabetes Spectrum.2022; 35(1): 8.     CrossRef
  • Acetylated Trifluoromethyl Diboronic Acid Anthracene with a Large Stokes Shift and Long Excitation Wavelength as a Glucose-Selective Probe
    Hongsik Choi, Inhyeok Song, Chul Soon Park, Heung-seop Yim, Joong Hyun Kim
    Applied Sciences.2022; 12(6): 2782.     CrossRef
  • Suitability of the Unified Theory of Acceptance and Use of Technology 2 Model for Predicting mHealth Acceptance Using Diabetes as an Example: Qualitative Methods Triangulation Study
    Patrik Schretzlmaier, Achim Hecker, Elske Ammenwerth
    JMIR Human Factors.2022; 9(1): e34918.     CrossRef
  • Continuous Glucose Monitoring System Based on Percutaneous Microneedle Array
    Ming-Nan Chien, Yu-Jen Chen, Chin-Han Bai, Jung-Tung Huang
    Micromachines.2022; 13(3): 478.     CrossRef
  • Impact of COVID-19 Lockdown on the Metabolic Control Parameters in Patients with Diabetes Mellitus: A Systematic Review and Meta-Analysis
    Ifan Ali Wafa, Nando Reza Pratama, Nurizzah Farahiyah Sofia, Elsha Stephanie Anastasia, Tiffany Konstantin, Maharani Ayuputeri Wijaya, M. Rifqi Wiyono, Lilik Djuari, Hermina Novida
    Diabetes & Metabolism Journal.2022; 46(2): 260.     CrossRef
  • Expert Roundtable on Continuous Glucose Monitoring
    Cheryl Rosenfeld, Thomas Blevins, Grazia Aleppo, Gregory Forlenza, Diana Isaacs, Javier Morales, Jane Seley, Jeffrey Unger
    Endocrine Practice.2022; 28(6): 622.     CrossRef
  • Glucose variability and predicted cardiovascular risk after gastrectomy
    Jun Shibamoto, Takeshi Kubota, Takuma Ohashi, Hirotaka Konishi, Atsushi Shiozaki, Hitoshi Fujiwara, Kazuma Okamoto, Eigo Otsuji
    Surgery Today.2022; 52(11): 1634.     CrossRef
  • Efficacy of once-weekly tirzepatide versus once-daily insulin degludec on glycaemic control measured by continuous glucose monitoring in adults with type 2 diabetes (SURPASS-3 CGM): a substudy of the randomised, open-label, parallel-group, phase 3 SURPASS
    Tadej Battelino, Richard M Bergenstal, Angel Rodríguez, Laura Fernández Landó, Ross Bray, Zhentao Tong, Katelyn Brown
    The Lancet Diabetes & Endocrinology.2022; 10(6): 407.     CrossRef
  • Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes
    Loïc Olçomendy, Louis Cassany, Antoine Pirog, Roberto Franco, Emilie Puginier, Manon Jaffredo, David Gucik-Derigny, Héctor Ríos, Alejandra Ferreira de Loza, Julien Gaitan, Matthieu Raoux, Yannick Bornat, Bogdan Catargi, Jochen Lang, David Henry, Sylvie Re
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
  • Effect of divergent continuous glucose monitoring technologies on glycaemic control in type 1 diabetes mellitus: A systematic review and meta‐analysis of randomised controlled trials
    Mona Elbalshy, Jillian Haszard, Hazel Smith, Sarahmarie Kuroko, Barbara Galland, Nick Oliver, Viral Shah, Martin I. de Bock, Benjamin J. Wheeler
    Diabetic Medicine.2022;[Epub]     CrossRef
  • Novel Glycemic Index Based on Continuous Glucose Monitoring to Predict Poor Clinical Outcomes in Critically Ill Patients: A Pilot Study
    Eun Yeong Ha, Seung Min Chung, Il Rae Park, Yin Young Lee, Eun Young Choi, Jun Sung Moon
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
  • Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms
    Brian Bogue-Jimenez, Xiaolei Huang, Douglas Powell, Ana Doblas
    Sensors.2022; 22(9): 3534.     CrossRef
  • Generation of post-meal insulin correction boluses in type 1 diabetes simulation models for in-silico clinical trials: More realistic scenarios obtained using a decision tree approach
    N. Camerlingo, M. Vettoretti, S. Del Favero, A. Facchinetti, P. Choudhary, G. Sparacino
    Computer Methods and Programs in Biomedicine.2022; 221: 106862.     CrossRef
  • A Miniaturized Optofluidic Glucose Monitoring System Based on Enzyme Colorimetry
    Qingmei Xu, Chongwei Zou, Chengtao Sun, Xingguo Zhang, Haixia Yu, Dachao Li
    IEEE Sensors Journal.2022; 22(10): 9246.     CrossRef
  • Use and Trends of Diabetes Self-Management Technologies: A Correlation-Based Study
    Jesús Fontecha, Iván González, Alfonso Barragán, Theodore Lim, Dario Pitocco
    Journal of Diabetes Research.2022; 2022: 1.     CrossRef
  • Nanotechnology in Diabetes Mellitus: Overview for Nurses
    R Priya, Baba Vajrala
    Pondicherry Journal of Nursing.2022; 15(1): 22.     CrossRef
  • Effect of Different Glucose Monitoring Methods on Bold Glucose Control: A Systematic Review and Meta-Analysis
    Yeling Wang, Congcong Zou, Han Na, Weixin Zeng, Xiaoyan Li, Xi Lou
    Computational and Mathematical Methods in Medicine.2022; 2022: 1.     CrossRef
  • Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology—A Review
    Aminah Hina, Wala Saadeh
    Sensors.2022; 22(13): 4855.     CrossRef
  • Performance of islets of Langerhans conformally coated via an emulsion cross-linking method in diabetic rodents and nonhuman primates
    Aaron A. Stock, Grisell C. Gonzalez, Sophia I. Pete, Teresa De Toni, Dora M. Berman, Alexander Rabassa, Waldo Diaz, James C. Geary, Melissa Willman, Joy M. Jackson, Noa H. DeHaseth, Noel M. Ziebarth, Anthony R. Hogan, Camillo Ricordi, Norma S. Kenyon, Ali
    Science Advances.2022;[Epub]     CrossRef
  • Review—Electrochemistry and Other Emerging Technologies for Continuous Glucose Monitoring Devices
    Saroj Kumar Das, Kavya K. Nayak, P. R. Krishnaswamy, Vinay Kumar, Navakanta Bhat
    ECS Sensors Plus.2022; 1(3): 031601.     CrossRef
  • Design Strategies and Prospects in Developing Wearable Glucose Monitoring System Using Printable Organic Transistor and Microneedle: A Review
    Fazliyatul Azwa Md Rezali, Norhayati Soin, Sharifah Fatmadiana Wan Muhamad Hatta, Mohamad Hazwan Mohd Daut, Muhammad Hafizuddin Al-Helmy Nouxman, Hanim Hussin
    IEEE Sensors Journal.2022; 22(14): 13785.     CrossRef
  • Review of Automated Insulin Delivery Systems for Type 1 Diabetes and Associated Time in Range Outcomes
    Armaan Nallicheri, Katherine M Mahoney, Hanna A Gutow, Natalie Bellini, Diana Isaacs
    Endocrinology.2022; 18(1): 27.     CrossRef
  • Evaluation of Mesoporous TiO2 Layers as Glucose Optical Sensors
    David Ortiz de Zárate, Sara Serna, Salvador Ponce-Alcántara, Jaime García-Rupérez
    Sensors.2022; 22(14): 5398.     CrossRef
  • A Prospective Study on Continuous Glucose Monitoring in Glycogen Storage Disease Type Ia: Toward Glycemic Targets
    Alessandro Rossi, Annieke Venema, Petra Haarsma, Lude Feldbrugge, Rob Burghard, David Rodriguez-Buritica, Giancarlo Parenti, Maaike H Oosterveer, Terry G J Derks
    The Journal of Clinical Endocrinology & Metabolism.2022; 107(9): e3612.     CrossRef
  • Continuous glucose monitoring as a close to real life alternative to meal studies – a pilot study with a functional drink containing amino acids and chromium
    Azat Samigullin, Per M. Humpert, Elin Östman
    Frontiers in Medical Technology.2022;[Epub]     CrossRef
  • An overview of recent advances in insulin delivery and wearable technology for effective management of diabetes
    Sujeet Kumar Raj, M. Ravindra Babu, Sukriti Vishwas, M.V.N.L. Chaitanya, Vancha Harish, Gaurav Gupta, Dinesh Kumar Chellappan, Kamal Dua, Sachin Kumar Singh
    Journal of Drug Delivery Science and Technology.2022; 75: 103728.     CrossRef
  • Medical Certification of Pilots Through the Insulin-Treated Diabetes Mellitus Protocol at the FAA
    Lynn K. Stanwyck, James R. DeVoll, Joyce Pastore, Zykevise Gamble, Anna Poe, Gabrielle V. Gui
    Aerospace Medicine and Human Performance.2022; 93(8): 627.     CrossRef
  • Rate of glycaemic control and associated factors in patients with type 2 diabetes mellitus treated with insulin-based therapy at selected hospitals in Northwest Ethiopia: a multicentre cross-sectional study
    Ashenafi Kibret Sendekie, Eyayaw Ashete Belachew, Ephrem Mebratu Dagnew, Adeladlew Kassie Netere
    BMJ Open.2022; 12(9): e065250.     CrossRef
  • Glucose Profiles Assessed by Intermittently Scanned Continuous Glucose Monitoring System during the Perioperative Period of Metabolic Surgery
    Kyuho Kim, Sung Hee Choi, Hak Chul Jang, Young Suk Park, Tae Jung Oh
    Diabetes & Metabolism Journal.2022; 46(5): 713.     CrossRef
  • Hypoglycemic events and glycemic control effects between NPH and premixed insulin in patients with type 2 diabetes mellitus: A real-world experience at a comprehensive specialized hospital in Ethiopia
    Ashenafi Kibret Sendekie, Adeladlew Kassie Netere, Eyayaw Ashete Belachew, Rekha Samuel
    PLOS ONE.2022; 17(9): e0275032.     CrossRef
  • Continuous Glucose Monitoring for the Diagnosis of Gestational Diabetes Mellitus: A Pilot Study
    Daria Di Filippo, Marrwah Ahmadzai, Melissa Han Yiin Chang, Ksana Horgan, Ru Min Ong, Justine Darling, Mahmood Akhtar, Amanda Henry, Alec Welsh, Daniela Foti
    Journal of Diabetes Research.2022; 2022: 1.     CrossRef
  • Caring for people with diabetes
    Martha M. Funnell, Katherine A. Kloss, Robin B. Nwankwo
    Nursing.2022; 52(11): 26.     CrossRef
  • Tackling the challenges of developing microneedle-based electrochemical sensors
    Hilmee Abdullah, Tonghathai Phairatana, Itthipon Jeerapan
    Microchimica Acta.2022;[Epub]     CrossRef
  • A Concise and Systematic Review on Non-Invasive Glucose Monitoring for Potential Diabetes Management
    Soumyasanta Laha, Aditi Rajput, Suvra S. Laha, Rohan Jadhav
    Biosensors.2022; 12(11): 965.     CrossRef
  • Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions
    Francesco Prendin, José-Luis Díez, Simone Del Favero, Giovanni Sparacino, Andrea Facchinetti, Jorge Bondia
    Sensors.2022; 22(22): 8682.     CrossRef
  • Wearable Sensor-Based Monitoring of Environmental Exposures and the Associated Health Effects: A Review
    Xueer Lin, Jiaying Luo, Minyan Liao, Yalan Su, Mo Lv, Qing Li, Shenglan Xiao, Jianbang Xiang
    Biosensors.2022; 12(12): 1131.     CrossRef
  • Acceptability and feasibility of continuous glucose monitoring in people with diabetes: protocol for a mixed-methods systematic review of quantitative and qualitative evidence
    Jennifer V. E. Brown, Ramzi Ajjan, Najma Siddiqi, Peter A. Coventry
    Systematic Reviews.2022;[Epub]     CrossRef
  • Utilization of Personalized Machine-Learning to Screen for Dysglycemia from Ambulatory ECG, toward Noninvasive Blood Glucose Monitoring
    I-Min Chiu, Chi-Yung Cheng, Po-Kai Chang, Chao-Jui Li, Fu-Jen Cheng, Chun-Hung Richard Lin
    Biosensors.2022; 13(1): 23.     CrossRef
  • Effect of Hydroxychloroquine on Glycemic Variability in Type 2 Diabetes Patients Uncontrolled on Glimepiride and Metformin Therapy
    Rajesh Rajput, Suyasha Saini, Siddhant Rajput, Parankush Upadhyay
    Indian Journal of Endocrinology and Metabolism.2022; 26(6): 537.     CrossRef
  • GESTATIONAL DIABETES MELLITUS: MODERN GLYCEMIA MONITORING SYSTEMS
    YU.A. DUDAREVA, V.A. GURYEVA, G.V. NEMTSEVA
    AVICENNA BULLETIN.2022; 24(1): 97.     CrossRef
  • Extraction With Sweat-Sebum Emulsion as a New Test Method for Leachables in Patch-Based Medical Devices, Illustrated by Assessment of Isobornylacrylate (IBOA) in Diabetes Products
    Herbert Fink, Nuno M. de Barros Fernandes, Jörg Weissmann, Manfred Frey
    Journal of Diabetes Science and Technology.2021; 15(4): 792.     CrossRef
  • Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator
    Nunzio Camerlingo, Martina Vettoretti, Simone Del Favero, Andrea Facchinetti, Giovanni Sparacino
    Journal of Diabetes Science and Technology.2021; 15(2): 346.     CrossRef
  • Fit‐for‐Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience
    Alan Godfrey, Benjamin Vandendriessche, Jessie P. Bakker, Cheryl Fitzer‐Attas, Ninad Gujar, Matthew Hobbs, Qi Liu, Carrie A. Northcott, Virginia Parks, William A. Wood, Vadim Zipunnikov, John A. Wagner, Elena S. Izmailova
    Clinical and Translational Science.2021; 14(1): 62.     CrossRef
  • Self-charging wearables for continuous health monitoring
    Jiyong Kim, Salman Khan, Peng Wu, Sungjin Park, Hwanjoo Park, Choongho Yu, Woochul Kim
    Nano Energy.2021; 79: 105419.     CrossRef
  • Impact of Switching from Intermittently Scanned to Real-Time Continuous Glucose Monitoring Systems in a Type 1 Diabetes Patient French Cohort: An Observational Study of Clinical Practices
    Yannis Préau, Martine Armand, Sébastien Galie, Pauline Schaepelynck, Denis Raccah
    Diabetes Technology & Therapeutics.2021; 23(4): 259.     CrossRef
  • Individualizing Time-in-Range Goals in Management of Diabetes Mellitus and Role of Insulin: Clinical Insights From a Multinational Panel
    Sanjay Kalra, Shehla Shaikh, Gagan Priya, Manas P. Baruah, Abhyudaya Verma, Ashok K. Das, Mona Shah, Sambit Das, Deepak Khandelwal, Debmalya Sanyal, Sujoy Ghosh, Banshi Saboo, Ganapathi Bantwal, Usha Ayyagari, Daphne Gardner, Cecilia Jimeno, Nancy E. Barb
    Diabetes Therapy.2021; 12(2): 465.     CrossRef
  • Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy
    Giulia Noaro, Giacomo Cappon, Martina Vettoretti, Giovanni Sparacino, Simone Del Favero, Andrea Facchinetti
    IEEE Transactions on Biomedical Engineering.2021; 68(1): 247.     CrossRef
  • Efficacy of telemedicine for persons with type 1 diabetes during Covid19 lockdown
    Federico Boscari, Sara Ferretto, Ambra Uliana, Angelo Avogaro, Daniela Bruttomesso
    Nutrition & Diabetes.2021;[Epub]     CrossRef
  • Technological innovation of Continuous Glucose Monitoring (CGM) as a tool for commercial aviation pilots with insulin-treated diabetes and stakeholders/regulators: A new chance to improve the directives?
    F. Strollo, A. Furia, P. Verde, A. Bellia, M. Grussu, A. Mambro, M.D. Petrelli, S. Gentile
    Diabetes Research and Clinical Practice.2021; 172: 108638.     CrossRef
  • Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges
    Omer Mujahid, Ivan Contreras, Josep Vehi
    Sensors.2021; 21(2): 546.     CrossRef
  • Time in range–A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study
    Marina Valenzano, Ivan Cibrario Bertolotti, Adriano Valenzano, Giorgio Grassi
    BMJ Open Diabetes Research & Care.2021; 9(1): e001045.     CrossRef
  • Machine learning for the diagnosis of early-stage diabetes using temporal glucose profiles
    Woo Seok Lee, Junghyo Jo, Taegeun Song
    Journal of the Korean Physical Society.2021; 78(5): 373.     CrossRef
  • Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only
    Francesco Prendin, Simone Del Favero, Martina Vettoretti, Giovanni Sparacino, Andrea Facchinetti
    Sensors.2021; 21(5): 1647.     CrossRef
  • A “Slide Rule” to Adjust Insulin Dose Using Trend Arrows in Adults with Type 1 Diabetes: Test in Silico and in Real Life
    Daniela Bruttomesso, Federico Boscari, Giuseppe Lepore, Giulia Noaro, Giacomo Cappon, Angela Girelli, Lutgarda Bozzetto, Andrea Tumminia, Giorgio Grassi, Giovanni Sparacino, Luigi Laviola, Andrea Facchinetti
    Diabetes Therapy.2021; 12(5): 1313.     CrossRef
  • Glycemic variability and cardiovascular disease in patients with type 2 diabetes
    Marcela Martinez, Jimena Santamarina, Adrian Pavesi, Carla Musso, Guillermo E Umpierrez
    BMJ Open Diabetes Research & Care.2021; 9(1): e002032.     CrossRef
  • Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments
    Omar Diouri, Monika Cigler, Martina Vettoretti, Julia K. Mader, Pratik Choudhary, Eric Renard
    Diabetes/Metabolism Research and Reviews.2021;[Epub]     CrossRef
  • Smartphone-based colorimetric detection systems for glucose monitoring in the diagnosis and management of diabetes
    Özlem Kap, Volkan Kılıç, John G. Hardy, Nesrin Horzum
    The Analyst.2021; 146(9): 2784.     CrossRef
  • The impact of hypoglycaemia on the quality of life of family members of adults with type 1 or type 2 diabetes: A qualitative systematic review
    Mette Valdersdorf Jensen, Melanie Broadley, Jane Speight, Alison Scope, Louise Preston, Simon Heller, Bastiaan E. de Galan, Frans Pouwer, Christel Hendrieckx
    Diabetic Medicine.2021;[Epub]     CrossRef
  • A review of biosensor technology and algorithms for glucose monitoring
    Yaguang Zhang, Jingxue Sun, Liansheng Liu, Hong Qiao
    Journal of Diabetes and its Complications.2021; 35(8): 107929.     CrossRef
  • Optical glucose biosensor built-in disposable strips and wearable electronic devices
    Abdullah Reda, Sherif A. El-Safty, Mahmoud M. Selim, Mohamed A. Shenashen
    Biosensors and Bioelectronics.2021; 185: 113237.     CrossRef
  • Advances, Challenges, and Cost Associated with Continuous Glucose Monitor Use in Adolescents and Young Adults with Type 1 Diabetes
    Karishma A. Datye, Daniel R. Tilden, Angelee M. Parmar, Eveline R. Goethals, Sarah S. Jaser
    Current Diabetes Reports.2021;[Epub]     CrossRef
  • Is HbA1c an ideal biomarker of well-controlled diabetes?
    Georgia Kaiafa, Stavroula Veneti, George Polychronopoulos, Dimitrios Pilalas, Stylianos Daios, Ilias Kanellos, Triantafyllos Didangelos, Stamatina Pagoni, Christos Savopoulos
    Postgraduate Medical Journal.2021; 97(1148): 380.     CrossRef
  • Technology in the management of type 2 diabetes: Present status and future prospects
    Aideen Daly, Roman Hovorka
    Diabetes, Obesity and Metabolism.2021; 23(8): 1722.     CrossRef
  • A Non-Invasive Flexible Glucose Monitoring Sensor Using a Broadband Reject Filter
    Moussa Bteich, Jessica Hanna, Joseph Costantine, Rouwaida Kanj, Youssef Tawk, Ali H. Ramadan, Assaad A. Eid
    IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology.2021; 5(2): 139.     CrossRef
  • Wearable patch delivery system for artificial pancreas health diagnostic-therapeutic application: A review
    Nur Farrahain Nadia Ahmad, Nik Nazri Nik Ghazali, Yew Hoong Wong
    Biosensors and Bioelectronics.2021; 189: 113384.     CrossRef
  • Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies
    Mary Katherine Ray, Alana McMichael, Maria Rivera-Santana, Jacob Noel, Tamara Hershey
    JMIR Diabetes.2021; 6(2): e27027.     CrossRef
  • Designing biomaterials for the modulation of allogeneic and autoimmune responses to cellular implants in Type 1 Diabetes
    Magdalena M. Samojlik, Cherie L. Stabler
    Acta Biomaterialia.2021; 133: 87.     CrossRef
  • Evaluation of a continuous glucose monitoring system in neonatal foals
    David Wong, Caitlin Malik, Katarzyna Dembek, Krista Estell, Megan Marchitello, Katie Wilson
    Journal of Veterinary Internal Medicine.2021; 35(4): 1995.     CrossRef
  • Flash Glucose Monitoring in the Netherlands: Increased monitoring frequency is associated with improvement of glycemic parameters
    Annel Lameijer, Nicole Lommerde, Timothy C. Dunn, Marion J. Fokkert, Mireille A. Edens, Kalvin Kao, Yongjin Xu, R.O.B. Gans, Henk J.G. Bilo, Peter R. van Dijk
    Diabetes Research and Clinical Practice.2021; 177: 108897.     CrossRef
  • Utilisation, access and recommendations regarding technologies for people living with type 1 diabetes: consensus statement of the ADS/ADEA/APEG/ADIPS Working Group
    Anthony J Pease, Sofianos Andrikopoulos, Mary B Abraham, Maria E Craig, Brett Fenton, Jane Overland, Sarah Price, David Simmons, Glynis P Ross
    Medical Journal of Australia.2021; 215(10): 473.     CrossRef
  • Catalytic effects of magnetic and conductive nanoparticles on immobilized glucose oxidase in skin sensors
    Lilian C Alarcón-Segovia, Amay J Bandodkar, John A Rogers, Ignacio Rintoul
    Nanotechnology.2021; 32(37): 375101.     CrossRef
  • Optical Glucose Sensor Using Pressure Sensitive Paint
    Jongwon Park
    Sensors.2021; 21(13): 4474.     CrossRef
  • Type 1 diabetes glycemic management: Insulin therapy, glucose monitoring, and automation
    Bruce A. Perkins, Jennifer L. Sherr, Chantal Mathieu
    Science.2021; 373(6554): 522.     CrossRef
  • Clinical Utilities of Continuous Glucose Monitoring and Insulin Pumps in Pediatric Patients with Type 1 Diabetes
    Jieun Lee, Jae Hyun Kim
    The Ewha Medical Journal.2021; 44(3): 55.     CrossRef
  • Personalized Postprandial Glucose Response–Targeting Diet Versus Mediterranean Diet for Glycemic Control in Prediabetes
    Orly Ben-Yacov, Anastasia Godneva, Michal Rein, Smadar Shilo, Dmitry Kolobkov, Netta Koren, Noa Cohen Dolev, Tamara Travinsky Shmul, Bat Chen Wolf, Noa Kosower, Keren Sagiv, Maya Lotan-Pompan, Niv Zmora, Adina Weinberger, Eran Elinav, Eran Segal
    Diabetes Care.2021; 44(9): 1980.     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
  • Benefits of a Switch from Intermittently Scanned Continuous Glucose Monitoring (isCGM) to Real-Time (rt) CGM in Diabetes Type 1 Suboptimal Controlled Patients in Real-Life: A One-Year Prospective Study §
    Yannis Préau, Sébastien Galie, Pauline Schaepelynck, Martine Armand, Denis Raccah
    Sensors.2021; 21(18): 6131.     CrossRef
  • A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems
    Aleix Beneyto, Vicenç Puig, B. Wayne Bequette, Josep Vehi
    Sensors.2021; 21(21): 7117.     CrossRef
  • Editors’ Choice—Review—From Polarography to Electrochemical Biosensors: The 100-Year Quest for Selectivity and Sensitivity
    William R. Heineman, Peter T. Kissinger, Kenneth R. Wehmeyer
    Journal of The Electrochemical Society.2021; 168(11): 116504.     CrossRef
  • Digital health and diabetes: experience from India
    Jothydev Kesavadev, Gopika Krishnan, Viswanathan Mohan
    Therapeutic Advances in Endocrinology and Metabolism.2021;[Epub]     CrossRef
  • Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence
    Sun Joon Moon, Inha Jung, Cheol-Young Park
    Diabetes & Metabolism Journal.2021; 45(6): 813.     CrossRef
  • Factors Associated with Adherence to Self-Monitoring of Blood Glucose Among Young People with Type 1 Diabetes in China: A Cross-Sectional Study
    Wencong Lv, Jiaxin Luo, Qing Long, Jundi Yang, Xin Wang, Jia Guo
    Patient Preference and Adherence.2021; Volume 15: 2809.     CrossRef
  • Continuous glucose monitoring devices: A brief presentation (Review)
    Doina Mihai, Diana Stefan, Daniela Stegaru, Georgiana Bernea, Ileana Vacaroiu, Toma Papacocea, Mircea Lupușoru, Adriana Nica, Ovidiu Stiru, Dorin Dragos, Octavian Olaru
    Experimental and Therapeutic Medicine.2021;[Epub]     CrossRef
  • Acute glycemic variability on admission predicts the prognosis in hospitalized patients with coronary artery disease: a meta-analysis
    Zhaokun Pu, Lihong Lai, Xishan Yang, Yanyu Wang, Pingshuan Dong, Dan Wang, Yingli Xie, Zesen Han
    Endocrine.2020; 67(3): 526.     CrossRef
  • Glycemic profile of women with normoglycemia and gestational diabetes mellitus during early pregnancy using continuous glucose monitoring system
    Charandeep Singh, Yashdeep Gupta, Alpesh Goyal, Mani Kalaivani, Vineeta Garg, Juhi Bharti, Seema Singhal, Garima Kachhawa, Vidushi Kulshrestha, Rajesh Kumari, Reeta Mahey, Jai B Sharma, Neerja Bhatla, Rajesh Khadgawat, Nandita Gupta, Nikhil Tandon
    Diabetes Research and Clinical Practice.2020; 169: 108409.     CrossRef
  • Efficacy of Intermittently Scanned Continuous Glucose Monitoring in the Prevention of Recurrent Severe Hypoglycemia
    Timothy M.E. Davis, Penny Dwyer, Michelle England, P. Gerry Fegan, Wendy A. Davis
    Diabetes Technology & Therapeutics.2020; 22(5): 367.     CrossRef
  • How was the Diabetes Metabolism Journal added to MEDLINE?
    Hye Jin Yoo
    Science Editing.2020; 7(2): 201.     CrossRef
  • Applying Nanomaterials to Modern Biomedical Electrochemical Detection of Metabolites, Electrolytes, and Pathogens
    Itthipon Jeerapan, Thitaporn Sonsa-ard, Duangjai Nacapricha
    Chemosensors.2020; 8(3): 71.     CrossRef
  • Clinical Opportunities for Continuous Biosensing and Closed-Loop Therapies
    Jason Li, Jia Y. Liang, Steven J. Laken, Robert Langer, Giovanni Traverso
    Trends in Chemistry.2020; 2(4): 319.     CrossRef
  • A single-blind, randomised, crossover study to reduce hypoglycaemia risk during postprandial exercise with closed-loop insulin delivery in adults with type 1 diabetes: announced (with or without bolus reduction) vs unannounced exercise strategies
    Sémah Tagougui, Nadine Taleb, Laurent Legault, Corinne Suppère, Virginie Messier, Inès Boukabous, Azadeh Shohoudi, Martin Ladouceur, Rémi Rabasa-Lhoret
    Diabetologia.2020; 63(11): 2282.     CrossRef
  • Bimetallic PtAu alloy nanomaterials for nonenzymatic selective glucose sensing at low potential
    Lingling Lin, Shaohuang Weng, Yanjie Zheng, Xiyao Liu, Shaoming Ying, Feng Chen, Donghong You
    Journal of Electroanalytical Chemistry.2020; 865: 114147.     CrossRef
  • Type 1 Diabetes in Youth and Technology-Based Advances in Management
    Christopher Ferber, Catherine S. Mao, Jennifer K. Yee
    Advances in Pediatrics.2020; 67: 73.     CrossRef
  • Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors
    Martina Vettoretti, Giacomo Cappon, Andrea Facchinetti, Giovanni Sparacino
    Sensors.2020; 20(14): 3870.     CrossRef
  • Efficacy and safety of evogliptin treatment in patients with type 2 diabetes: A multicentre, active‐controlled, randomized, double‐blind study with open‐label extension (the EVERGREEN study)
    Gyuri Kim, Soo Lim, Hyuk‐Sang Kwon, Ie B. Park, Kyu J. Ahn, Cheol‐Young Park, Su K. Kwon, Hye S. Kim, Seok W. Park, Sin G. Kim, Min K. Moon, Eun S. Kim, Choon H. Chung, Kang S. Park, Mikyung Kim, Dong J. Chung, Chang B. Lee, Tae H. Kim, Moon‐Kyu Lee
    Diabetes, Obesity and Metabolism.2020; 22(9): 1527.     CrossRef
  • Association Between Continuous Glucose Monitoring-Derived Time in Range, Other Core Metrics, and Albuminuria in Type 2 Diabetes
    Jee Hee Yoo, Min Sun Choi, Jiyeon Ahn, Sung Woon Park, Yejin Kim, Kyu Yeon Hur, Sang-Man Jin, Gyuri Kim, Jae Hyeon Kim
    Diabetes Technology & Therapeutics.2020; 22(10): 768.     CrossRef
  • A New Approach to Determining Liquid Concentration Using Multiband Annular Ring Microwave Sensor and Polarity Correlator
    Waleed Sethi, Ahmed Ibrahim, Khaled Issa, Ali Albishi, Saleh Alshebeili
    Electronics.2020; 9(10): 1616.     CrossRef
  • Estrategia terapéutica en el paciente diabético (I). Empoderamiento del paciente y formación. Objetivos terapéuticos. Estilo de vida, alimentación, vacunación y consejos al paciente diabético
    F.B. Rivas Sánchez, J. Sanz Cánovas, J. Martín Carmona, S. Jansen Chaparro
    Medicine - Programa de Formación Médica Continuada Acreditado.2020; 13(17): 943.     CrossRef
  • Current status of continuous glucose monitoring among Korean children and adolescents with type 1 diabetes mellitus
    Jae Hyun Kim
    Annals of Pediatric Endocrinology & Metabolism.2020; 25(3): 145.     CrossRef
  • Towards sensor-based calving detection in the rangelands: a systematic review of credible behavioral and physiological indicators
    Anita Z Chang, David L Swain, Mark G Trotter
    Translational Animal Science.2020;[Epub]     CrossRef
  • Electrochemical glucose sensors in diabetes management: an updated review (2010–2020)
    Hazhir Teymourian, Abbas Barfidokht, Joseph Wang
    Chemical Society Reviews.2020; 49(21): 7671.     CrossRef
  • An analytical approach to determine the optimal duration of continuous glucose monitoring data required to reliably estimate time in hypoglycemia
    Nunzio Camerlingo, Martina Vettoretti, Andrea Facchinetti, Giovanni Sparacino, Julia K. Mader, Pratik Choudhary, Simone Del Favero
    Scientific Reports.2020;[Epub]     CrossRef
  • Smartphone-Based Data Collection in Ophthalmology
    Florian Philipp Raber, Rokas Gerbutavicius, Armin Wolf, Karsten Kortüm
    Klinische Monatsblätter für Augenheilkunde.2020; 237(12): 1420.     CrossRef
  • Glycemic Status Assessment by the Latest Glucose Monitoring Technologies
    Ilaria Malandrucco, Benedetta Russo, Fabiana Picconi, Marika Menduni, Simona Frontoni
    International Journal of Molecular Sciences.2020; 21(21): 8243.     CrossRef
  • Medical Nutrition Therapy Using Continuous Glucose Monitoring System
    Mee Ra Kweon
    The Journal of Korean Diabetes.2020; 21(4): 216.     CrossRef
  • Use of Flash Glucose Monitoring in Patients on Intensive Insulin Treatment
    Jun Sung Moon
    The Journal of Korean Diabetes.2020; 21(4): 184.     CrossRef
  • Data Analysis and Accuracy Evaluation of a Continuous Glucose-Monitoring Device
    Lijun Cai, Wancheng Ge, Zhigang Zhu, Xueling Zhao, Zhanhong Li
    Journal of Sensors.2019; 2019: 1.     CrossRef
  • Development of an Error Model for a Factory-Calibrated Continuous Glucose Monitoring Sensor with 10-Day Lifetime
    Martina Vettoretti, Cristina Battocchio, Giovanni Sparacino, Andrea Facchinetti
    Sensors.2019; 19(23): 5320.     CrossRef
Original Articles
Clinical Diabetes & Therapeutics
Efficacy and Safety of Sodium-Glucose Cotransporter-2 Inhibitors in Korean Patients with Type 2 Diabetes Mellitus in Real-World Clinical Practice
A Ram Hong, Bo Kyung Koo, Sang Wan Kim, Ka Hee Yi, Min Kyong Moon
Diabetes Metab J. 2019;43(5):590-606.   Published online February 28, 2019
DOI: https://doi.org/10.4093/dmj.2018.0134
  • 7,718 View
  • 123 Download
  • 20 Web of Science
  • 20 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   
Background

This study aimed to evaluate the efficacy and safety of sodium-glucose cotransporter-2 (SGLT2) inhibitors in Korean patients who had inadequately controlled type 2 diabetes mellitus (T2DM) in real-world clinical practice.

Methods

We included 410 patients who started SGLT2 inhibitors (empagliflozin or dapagliflozin) as add-on therapy or switch therapy between February 2015 and June 2017. The primary efficacy endpoint was a change in glycosylated hemoglobin (HbA1c) from baseline to week 12. The secondary endpoints were patients achieving HbA1c <7.0% and changes in the fasting plasma glucose (FPG), lipid profiles, body weight, and blood pressure (BP).

Results

The mean HbA1c at baseline was 8.5% (8.6% in the add-on group and 8.4% in the switch group). At week 12, the mean adjusted HbA1c decreased by −0.68% in the overall patients (P<0.001), by −0.94% in the add-on group, and by −0.42% in the switch group. Significant reductions in FPG were also observed both in the add-on group and switch group (−30.3 and −19.8 mg/dL, respectively). Serum triglyceride (−16.5 mg/dL), body weight (−2.1 kg), systolic BP (−4.7 mm Hg), and diastolic BP (−1.3 mm Hg) were significantly improved in the overall patients. Approximately 18.3% of the patients achieved HbA1c <7.0% at week 12. A low incidence of hypoglycemia and genital tract infection was observed (6.3% and 2.2%, respectively).

Conclusion

SGLT2 inhibitors can be a suitable option as either add-on or switch therapy for Korean patients with inadequately controlled T2DM.

Citations

Citations to this article as recorded by  
  • Effectiveness and Safety of Sodium-Glucose Cotransporter 2 Inhibitors Added to Dual or Triple Treatment in Patients with Type 2 Diabetes Mellitus
    Yesol Hong, Yoomin Jeon, Yoona Choi, Tae Kyu Chung, Howard Lee
    Diabetes Therapy.2024; 15(2): 487.     CrossRef
  • Real-world assessment of effectiveness and safety profile of remogliflozin etabonate in management of type 2 diabetes mellitus
    Bipin Sethi, Subhankar Chowdhury, Supratik Bhattacharya, Sagar Katare, Sachin Suryawanshi, Hanmant Barkate
    International Journal of Diabetes in Developing Countries.2023; 43(2): 214.     CrossRef
  • Effects of dapagliflozin compared with glimepiride on body composition in Asian patients with type 2 diabetes inadequately controlled with metformin: The BEYOND study
    Hyeong Kyu Park, Kyoung‐Ah Kim, Kyung‐Wan Min, Tae‐Seo Sohn, In Kyung Jeong, Chul Woo Ahn, Nan‐Hee Kim, Ie Byung Park, Ho Chan Cho, Choon Hee Chung, Sung Hee Choi, Kang Seo Park, Seoung‐Oh Yang, Kwan Woo Lee
    Diabetes, Obesity and Metabolism.2023; 25(9): 2743.     CrossRef
  • Efficacy and Safety of Evogliptin Add-on Therapy to Dapagliflozin/Metformin Combinations in Patients with Poorly Controlled Type 2 Diabetes Mellitus: A 24-Week Multicenter Randomized Placebo-Controlled Parallel-Design Phase-3 Trial with a 28-Week Extensio
    Jun Sung Moon, Il Rae Park, Hae Jin Kim, Choon Hee Chung, Kyu Chang Won, Kyung Ah Han, Cheol-Young Park, Jong Chul Won, Dong Jun Kim, Gwan Pyo Koh, Eun Sook Kim, Jae Myung Yu, Eun-Gyoung Hong, Chang Beom Lee, Kun-Ho Yoon
    Diabetes & Metabolism Journal.2023; 47(6): 808.     CrossRef
  • Real-world Data of Glycemic Control in a Suburban Population in Northern India during the COVID-19 Pandemic
    Jaydip V. Revale, Preeti J. Revale
    International Journal of Diabetes and Technology.2023; 2(2): 60.     CrossRef
  • Sodium glucose co-transporter-2 inhibitor, Empagliflozin, is associated with significant reduction in weight, body mass index, fasting glucose, and A1c levels in Type 2 diabetic patients with established coronary heart disease: the SUPER GATE study
    Satilmis Bilgin, Ozge Kurtkulagi, Tuba Taslamacioglu Duman, Burcin Meryem Atak Tel, Gizem Kahveci, Murat Kiran, Eray Erge, Gulali Aktas
    Irish Journal of Medical Science (1971 -).2022; 191(4): 1647.     CrossRef
  • Efficacy and Safety of Empagliflozin as Add-On Therapy in Patients of Type-2 Diabetes Mellitus
    Nauman Wazir, Shafqat Ur Rehman
    Journal of Gandhara Medical and Dental Science.2022; 9(1): 24.     CrossRef
  • Five comparative cohorts to assess the risk of genital tract infections associated with sodium‐glucose cotransporter‐2 inhibitors initiation in type 2 diabetes mellitus
    Wajd Alkabbani, Arsène Zongo, Jasjeet K. Minhas‐Sandhu, Dean T. Eurich, Baiju R. Shah, Mhd. Wasem Alsabbagh, John‐Michael Gamble
    Diabetic Medicine.2022;[Epub]     CrossRef
  • Effect of Dapagliflozin in Combination with Lobeglitazone and Metformin in Korean Patients with Type 2 Diabetes in Real-World Clinical Practice
    Da Hea Seo, Young Ju Suh, Yongin Cho, Seong Hee Ahn, Seongha Seo, Seongbin Hong, Yong-ho Lee, Young Ju Choi, Eunjig Lee, So Hun Kim
    Yonsei Medical Journal.2022; 63(9): 825.     CrossRef
  • Using real-world data for supporting regulatory decision making: Comparison of cardiovascular and safety outcomes of an empagliflozin randomized clinical trial versus real-world data
    Ha Young Jang, In-Wha Kim, Jung Mi Oh
    Frontiers in Pharmacology.2022;[Epub]     CrossRef
  • Study comparing the efficacy and renal safety for patients with diabetes switching from dapagliflozin to empagliflozin
    Ai-Yu Yang, Hung-Chun Chen
    International Journal of Clinical Pharmacy.2021; 43(4): 1015.     CrossRef
  • Empagliflozin Regulates the AdipoR1/p-AMPK/p-ACC Pathway to Alleviate Lipid Deposition in Diabetic Nephropathy
    Zhiqin Zhang, Lihua Ni, Lian Zhang, Dongqing Zha, Chun Hu, Lingli Zhang, Huiling Feng, Xiaobao Wei, Xiaoyan Wu
    Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2021; Volume 14: 227.     CrossRef
  • Efficacy and Safety of Luseogliflozin in Patients with Type 2 Diabetes Complicated by Hepatic Dysfunction: A Single-Site, Single-Arm, Open-Label, Exploratory Trial
    Hiroaki Seino
    Diabetes Therapy.2021; 12(3): 863.     CrossRef
  • Sodium–Glucose Cotransporter 2 Inhibitors and Risk of Retinal Vein Occlusion Among Patients With Type 2 Diabetes: A Propensity Score–Matched Cohort Study
    Min-Kyung Lee, Bongsung Kim, Kyungdo Han, Jae-Hyuk Lee, Minhee Kim, Mee Kyoung Kim, Ki-Hyun Baek, Ki-Ho Song, Hyuk-Sang Kwon, Young-Jung Roh
    Diabetes Care.2021; 44(10): 2419.     CrossRef
  • Sodium-Glucose Cotransporter-2 Inhibitors Improve Cardiovascular Dysfunction in Type 2 Diabetic East Asians
    Muhammad Afzal, Fahad Al-Abbasi, Muhammad Nadeem, Sultan Alshehri, Mohammed Ghoneim, Syed Imam, Waleed Almalki, Imran Kazmi
    Metabolites.2021; 11(11): 794.     CrossRef
  • Sodium-Glucose Cotransporter-2 Inhibitor for Renal Function Preservation in Patients with Type 2 Diabetes Mellitus: A Korean Diabetes Association and Korean Society of Nephrology Consensus Statement
    Tae Jung Oh, Ju-Young Moon, Kyu Yeon Hur, Seung Hyun Ko, Hyun Jung Kim, Taehee Kim, Dong Won Lee, Min Kyong Moon
    Diabetes & Metabolism Journal.2020; 44(4): 489.     CrossRef
  • Sodium-glucose cotransporter-2 inhibitor for renal function preservation in patients with type 2 diabetes mellitus: A Korean Diabetes Association and Korean Society of Nephrology consensus statement
    Tae Jung Oh, Ju-Young Moon, Kyu Yeon Hur, Seung Hyun Ko, Hyun Jung Kim, Taehee Kim, Dong Won Lee, Min Kyong Moon
    Kidney Research and Clinical Practice.2020; 39(3): 269.     CrossRef
  • Use and effectiveness of dapagliflozin in patients with type 2 diabetes mellitus: a multicenter retrospective study in Taiwan
    Jung-Fu Chen, Yun-Shing Peng, Chung-Sen Chen, Chin-Hsiao Tseng, Pei-Chi Chen, Ting-I Lee, Yung-Chuan Lu, Yi-Sun Yang, Ching-Ling Lin, Yi-Jen Hung, Szu-Ta Chen, Chieh-Hsiang Lu, Chwen-Yi Yang, Ching-Chu Chen, Chun-Chuan Lee, Pi-Jung Hsiao, Ju-Ying Jiang, S
    PeerJ.2020; 8: e9998.     CrossRef
  • Long-Term Effectiveness and Safety of SGLT-2 Inhibitors in an Italian Cohort of Patients with Type 2 Diabetes Mellitus
    Maria Mirabelli, Eusebio Chiefari, Patrizia Caroleo, Raffaella Vero, Francesco Saverio Brunetti, Domenica Maria Corigliano, Biagio Arcidiacono, Daniela Patrizia Foti, Luigi Puccio, Antonio Brunetti
    Journal of Diabetes Research.2019; 2019: 1.     CrossRef
  • An Age of Sodium-Glucose Cotransporter-2 Inhibitor Priority: Are We Ready?
    Ji A Seo
    Diabetes & Metabolism Journal.2019; 43(5): 578.     CrossRef
Clinical Care/Education
Patient Understanding of Hypoglycemia in Tertiary Referral Centers
Nan Hee Cho, Nam Kyung Kim, Eugene Han, Jun Hwa Hong, Eon Ju Jeon, Jun Sung Moon, Mi Hae Seo, Ji Eun Lee, Hyun-Ae Seo, Mi-Kyung Kim, Hye Soon Kim
Diabetes Metab J. 2018;42(1):43-52.   Published online February 23, 2018
DOI: https://doi.org/10.4093/dmj.2018.42.1.43
  • 5,575 View
  • 82 Download
  • 5 Web of Science
  • 5 Crossref
AbstractAbstract PDFPubReader   
Background

Hypoglycemia is an important complication in the treatment of patients with diabetes. We surveyed the insight by patients with diabetes into hypoglycemia, their hypoglycemia avoidance behavior, and their level of worry regarding hypoglycemia.

Methods

A survey of patients with diabetes, who had visited seven tertiary referral centers in Daegu or Gyeongsangbuk-do, Korea, between June 2014 and June 2015, was conducted. The survey contained questions about personal history, symptoms, educational experience, self-management, and attitudes about hypoglycemia.

Results

Of 758 participants, 471 (62.1%) had experienced hypoglycemia, and 250 (32.9%) had experienced hypoglycemia at least once in the month immediately preceding the study. Two hundred and forty-two (31.8%) of the participants had received hypoglycemia education at least once, but only 148 (19.4%) knew the exact definition of hypoglycemia. Hypoglycemic symptoms identified by the participants were dizziness (55.0%), sweating (53.8%), and tremor (40.8%). They mostly chose candy (62.1%), chocolate (37.7%), or juice (36.8%) as food for recovering hypoglycemia. Participants who had experienced hypoglycemia had longer duration of diabetes and a higher proportion of insulin usage. The mean scores for hypoglycemia avoidance behavior and worry about hypoglycemia were 21.2±10.71 and 23.38±13.19, respectively. These scores tended to be higher for participants with higher than 8% of glycosylated hemoglobin, insulin use, and experience of emergency room visits.

Conclusion

Many patients had experienced hypoglycemia and worried about it. We recommend identifying patients that are anxious about hypoglycemia and educating them about what to do when they develop hypoglycemic symptoms, especially those who have a high risk of hypoglycemia.

Citations

Citations to this article as recorded by  
  • Severe Hypoglycemia Increases Dementia Risk and Related Mortality: A Nationwide, Population-based Cohort Study
    Eugene Han, Kyung-do Han, Byung-Wan Lee, Eun Seok Kang, Bong-Soo Cha, Seung-Hyun Ko, Yong-ho Lee
    The Journal of Clinical Endocrinology & Metabolism.2022; 107(5): e1976.     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
  • Severe hypoglycemia and the risk of end stage renal disease in type 2 diabetes
    Jae-Seung Yun, Yong-Moon Park, Kyungdo Han, Hyung-Wook Kim, Seon-Ah Cha, Yu-Bae Ahn, Seung-Hyun Ko
    Scientific Reports.2021;[Epub]     CrossRef
  • Response: Patient Understanding of Hypoglycemia in Tertiary Referral Centers (Diabetes Metab J 2018;42:43-52)
    Nan Hee Cho, Hye Soon Kim
    Diabetes & Metabolism Journal.2018; 42(2): 175.     CrossRef
  • Letter: Patient Understanding of Hypoglycemia in Tertiary Referral Centers (Diabetes Metab J 2018;42:43-52)
    Jae-Han Jeon
    Diabetes & Metabolism Journal.2018; 42(2): 173.     CrossRef

Diabetes Metab J : Diabetes & Metabolism Journal
Close layer
TOP