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Original Articles
Type 1 Diabetes
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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
  • 2,198 View
  • 239 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.
Technology/Device
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Clinical and Lifestyle Determinants of Continuous Glucose Monitoring Metrics in Insulin-Treated Patients with Type 2 Diabetes Mellitus
Da Young Lee, Namho Kim, Inha Jung, So Young Park, Ji Hee Yu, Ji A Seo, Jihee Kim, Kyeong Jin Kim, Nam Hoon Kim, Hye Jin Yoo, Sin Gon Kim, Kyung Mook Choi, Sei Hyun Baik, Sung-Min Park, Nan Hee Kim
Diabetes Metab J. 2023;47(6):826-836.   Published online August 24, 2023
DOI: https://doi.org/10.4093/dmj.2022.0273
  • 3,548 View
  • 235 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
There was limited evidence to evaluate the association between lifestyle habits and continuous glucose monitoring (CGM) metrics. Thus, we aimed to depict the behavioral and metabolic determinants of CGM metrics in insulin-treated patients with type 2 diabetes mellitus (T2DM).
Methods
This is a prospective observational study. We analyzed data from 122 insulin-treated patients with T2DM. Participants wore Dexcom G6 and Fitbit, and diet information was identified for 10 days. Multivariate-adjusted logistic regression analysis was performed for the simultaneous achievement of CGM-based targets, defined by the percentage of time in terms of hyper, hypoglycemia and glycemic variability (GV). Intake of macronutrients and fiber, step counts, sleep, postprandial C-peptide-to-glucose ratio (PCGR), information about glucose lowering medications and metabolic factors were added to the analyses. Additionally, we evaluated the impact of the distribution of energy and macronutrient during a day, and snack consumption on CGM metrics.
Results
Logistic regression analysis revealed that female, participants with high PCGR, low glycosylated hemoglobin (HbA1c) and daytime step count had a higher probability of achieving all targets based on CGM (odds ratios [95% confidence intervals] which were 0.24 [0.09 to 0.65], 1.34 [1.03 to 1.25], 0.95 [0.9 to 0.99], and 1.15 [1.03 to 1.29], respectively). And participants who ate snacks showed a shorter period of hyperglycemia and less GV compared to those without.
Conclusion
We confirmed that residual insulin secretion, daytime step count, HbA1c, and women were the most relevant determinants of adequate glycemic control in insulin-treated patients with T2DM. In addition, individuals with snack consumption were exposed to lower times of hyperglycemia and GV.

Citations

Citations to this article as recorded by  
  • Explanatory variables of objectively measured 24-h movement behaviors in people with prediabetes and type 2 diabetes: A systematic review
    Lotte Bogaert, Iris Willems, Patrick Calders, Eveline Dirinck, Manon Kinaupenne, Marga Decraene, Bruno Lapauw, Boyd Strumane, Margot Van Daele, Vera Verbestel, Marieke De Craemer
    Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2024; 18(4): 102995.     CrossRef
Technology/Device
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Glycemia according to the Use of Continuous Glucose Monitoring among Adults with Type 1 Diabetes Mellitus in Korea: A Real-World Study
You-Bin Lee, Minjee Kim, Jae Hyeon Kim
Diabetes Metab J. 2023;47(3):405-414.   Published online March 6, 2023
DOI: https://doi.org/10.4093/dmj.2022.0032
  • 4,387 View
  • 145 Download
  • 3 Web of Science
  • 4 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
We explored the association between continuous glucose monitoring (CGM) use and glycemia among adults with type 1 diabetes mellitus (T1DM) and determined the status of CGM metrics among adults with T1DM using CGM in the real-world.
Methods
For this propensity-matched cross-sectional study, individuals with T1DM who visited the outpatient clinic of the Endocrinology Department of Samsung Medical Center between March 2018 and February 2020 were screened. Among them, 111 CGM users (for ≥9 months) were matched based on propensity score considering age, sex, and diabetes duration in a 1:2 ratio with 203 CGM never-users. The association between CGM use and glycemic measures was explored. In a subpopulation of CGM users who had been using official applications (not “do-it-yourself” software) such that Ambulatory Glucose Profile data for ≥1 month were available (n=87), standardized CGM metrics were summarized.
Results
Linear regression analyses identified CGM use as a determining factor for log-transformed glycosylated hemoglobin. The fully-adjusted odds ratio (OR) and 95% confidence interval (CI) for uncontrolled glycosylated hemoglobin (>8%) were 0.365 (95% CI, 0.190 to 0.703) in CGM users compared to never-users. The fully-adjusted OR for controlled glycosylated hemoglobin (<7%) was 1.861 (95% CI, 1.119 to 3.096) in CGM users compared to never-users. Among individuals who had been using official applications for CGM, time in range (TIR) values within recent 30- and 90-day periods were 62.45%±16.63% and 63.08%±15.32%, respectively.
Conclusion
CGM use was associated with glycemic control status among Korean adults with T1DM in the real-world, although CGM metrics including TIR might require further improvement among CGM users.

Citations

Citations to this article as recorded by  
  • Real-World Continuous Glucose Monitoring Data from a Population with Type 1 Diabetes in South Korea: Nationwide Single-System Analysis
    Ji Yoon Kim, Sang-Man Jin, Sarah B. Andrade, Boyang Chen, Jae Hyeon Kim
    Diabetes Technology & Therapeutics.2024; 26(6): 394.     CrossRef
  • Accuracy and Safety of the 15-Day CareSens Air Continuous Glucose Monitoring System
    Kyung-Soo Kim, Seung-Hwan Lee, Won Sang Yoo, Cheol-Young Park
    Diabetes Technology & Therapeutics.2024; 26(4): 222.     CrossRef
  • Navigating the Seas of Glycemic Control: The Role of Continuous Glucose Monitoring in Type 1 Diabetes Mellitus
    Jun Sung Moon
    Diabetes & Metabolism Journal.2023; 47(3): 345.     CrossRef
  • Smart Insulin Pen: Managing Insulin Therapy for People with Diabetes in the Digital Era
    Jee Hee Yoo, Jae Hyeon Kim
    The Journal of Korean Diabetes.2023; 24(4): 190.     CrossRef
Review
Technology/Device
Article image
Advances in Continuous Glucose Monitoring and Integrated Devices for Management of Diabetes with Insulin-Based Therapy: Improvement in Glycemic Control
Jee Hee Yoo, Jae Hyeon Kim
Diabetes Metab J. 2023;47(1):27-41.   Published online January 12, 2023
DOI: https://doi.org/10.4093/dmj.2022.0271
  • 10,270 View
  • 491 Download
  • 17 Web of Science
  • 25 Crossref
AbstractAbstract PDFPubReader   ePub   
Continuous glucose monitoring (CGM) technology has evolved over the past decade with the integration of various devices including insulin pumps, connected insulin pens (CIPs), automated insulin delivery (AID) systems, and virtual platforms. CGM has shown consistent benefits in glycemic outcomes in type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) treated with insulin. Moreover, the combined effect of CGM and education have been shown to improve glycemic outcomes more than CGM alone. Now a CIP is the expected future technology that does not need to be worn all day like insulin pumps and helps to calculate insulin doses with a built-in bolus calculator. Although only a few clinical trials have assessed the effectiveness of CIPs, they consistently show benefits in glycemic outcomes by reducing missed doses of insulin and improving problematic adherence. AID systems and virtual platforms made it possible to achieve target glycosylated hemoglobin in diabetes while minimizing hypoglycemia, which has always been challenging in T1DM. Now fully automatic AID systems and tools for diabetes decisions based on artificial intelligence are in development. These advances in technology could reduce the burden associated with insulin treatment for diabetes.

Citations

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  • Recent advances in artificial intelligence-assisted endocrinology and diabetes
    Ioannis T. Oikonomakos, Ranjit M. Anjana, Viswanathan Mohan, Charlotte Steenblock, Stefan R. Bornstein
    Exploration of Endocrine and Metabolic Disease.2024; 1(1): 16.     CrossRef
  • Accuracy and Safety of the 15-Day CareSens Air Continuous Glucose Monitoring System
    Kyung-Soo Kim, Seung-Hwan Lee, Won Sang Yoo, Cheol-Young Park
    Diabetes Technology & Therapeutics.2024; 26(4): 222.     CrossRef
  • Real-World Continuous Glucose Monitoring Data from a Population with Type 1 Diabetes in South Korea: Nationwide Single-System Analysis
    Ji Yoon Kim, Sang-Man Jin, Sarah B. Andrade, Boyang Chen, Jae Hyeon Kim
    Diabetes Technology & Therapeutics.2024; 26(6): 394.     CrossRef
  • Recent advances in the precision control strategy of artificial pancreas
    Wuyi Ming, Xudong Guo, Guojun Zhang, Yinxia Liu, Yongxin Wang, Hongmei Zhang, Haofang Liang, Yuan Yang
    Medical & Biological Engineering & Computing.2024; 62(6): 1615.     CrossRef
  • Digital Health in Diabetes and Cardiovascular Disease
    Dorothy Avoke, Abdallah Elshafeey, Robert Weinstein, Chang H. Kim, Seth S. Martin
    Endocrine Research.2024; 49(3): 124.     CrossRef
  • Continuous glucose monitoring with structured education in adults with type 2 diabetes managed by multiple daily insulin injections: a multicentre randomised controlled trial
    Ji Yoon Kim, Sang-Man Jin, Kang Hee Sim, Bo-Yeon Kim, Jae Hyoung Cho, Jun Sung Moon, Soo Lim, Eun Seok Kang, Cheol-Young Park, Sin Gon Kim, Jae Hyeon Kim
    Diabetologia.2024; 67(7): 1223.     CrossRef
  • Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring
    Anubhuti Juyal, Shradha Bisht, Mamta F. Singh
    Blood Pressure Monitoring.2024; 29(5): 260.     CrossRef
  • Emerging trends in functional molecularly imprinted polymers for electrochemical detection of biomarkers
    Sanjida Yeasmin, Li-Jing Cheng
    Biomicrofluidics.2024;[Epub]     CrossRef
  • Continuous glucose monitoring in pregnant women with pregestational type 2 diabetes: a narrative review
    Sylvia Ye, Ibrahim Shahid, Christopher J Yates, Dev Kevat, I-Lynn Lee
    Obstetric Medicine.2024;[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
  • Current treatment options of diabetes mellitus type 1 in pediatric population
    Petr Polák, Renata Pomahačová, Karel Fiklík, Petra Paterová, Josef Sýkora
    Pediatrie pro praxi.2024; 25(3): 161.     CrossRef
  • Efectividad de un sistema híbrido de circuito cerrado en pacientes con diabetes tipo 1 durante el ejercicio físico: un estudio descriptivo en la vida real
    Ruben Martin-Payo, Maria del Mar Fernandez-Alvarez, Rebeca García-García, Ángela Pérez-Varela, Shelini Surendran, Isolina Riaño-Galán
    Anales de Pediatría.2024; 101(3): 183.     CrossRef
  • Effectiveness of a hybrid closed-loop system for children and adolescents with type 1 diabetes during physical exercise: A cross-sectional study in real life
    Ruben Martin-Payo, Maria del Mar Fernandez-Alvarez, Rebeca García-García, Ángela Pérez-Varela, Shelini Surendran, Isolina Riaño-Galán
    Anales de Pediatría (English Edition).2024; 101(3): 183.     CrossRef
  • Real-time continuous glucose monitoring vs. self-monitoring of blood glucose: cost-utility in South Korean type 2 diabetes patients on intensive insulin
    Ji Yoon Kim, Sabrina Ilham, Hamza Alshannaq, Richard F. Pollock, Waqas Ahmed, Gregory J. Norman, Sang-Man Jin, Jae Hyeon Kim
    Journal of Medical Economics.2024; 27(1): 1245.     CrossRef
  • Impact of missed insulin doses on glycaemic parameters in people with diabetes using smart insulin pens
    Malavika Varma, David J T Campbell
    Evidence Based Nursing.2024; : ebnurs-2024-104109.     CrossRef
  • Glycemic Outcomes During Early Use of the MiniMed™ 780G Advanced Hybrid Closed-Loop System with Guardian™ 4 Sensor
    Toni L. Cordero, Zheng Dai, Arcelia Arrieta, Fang Niu, Melissa Vella, John Shin, Andrew S. Rhinehart, Jennifer McVean, Scott W. Lee, Robert H. Slover, Gregory P. Forlenza, Dorothy I. Shulman, Rodica Pop-Busui, James R. Thrasher, Mark S. Kipnes, Mark P. Ch
    Diabetes Technology & Therapeutics.2023; 25(9): 652.     CrossRef
  • Navigating the Seas of Glycemic Control: The Role of Continuous Glucose Monitoring in Type 1 Diabetes Mellitus
    Jun Sung Moon
    Diabetes & Metabolism Journal.2023; 47(3): 345.     CrossRef
  • APSec1.0: Innovative Security Protocol Design with Formal Security Analysis for the Artificial Pancreas System
    Jiyoon Kim, Jongmin Oh, Daehyeon Son, Hoseok Kwon, Philip Virgil Astillo, Ilsun You
    Sensors.2023; 23(12): 5501.     CrossRef
  • Advances and Development of Electronic Neural Interfaces
    Xue Jiaxiang, Liu Zhixin
    Journal of Computing and Natural Science.2023; : 147.     CrossRef
  • Continuous Glucose Monitoring (CGM) and Metabolic Control in a Cohort of Patients with Type 1 Diabetes and Coeliac Disease
    Flavia Amaro, Maria Alessandra Saltarelli, Marina Primavera, Marina Cerruto, Stefano Tumini
    Endocrines.2023; 4(3): 595.     CrossRef
  • Comparison of Glycemia Risk Index with Time in Range for Assessing Glycemic Quality
    Ji Yoon Kim, Jee Hee Yoo, Jae Hyeon Kim
    Diabetes Technology & Therapeutics.2023; 25(12): 883.     CrossRef
  • The Benefits Of Continuous Glucose Monitoring In Pregnancy
    Jee Hee Yoo, Jae Hyeon Kim
    Endocrinology and Metabolism.2023; 38(5): 472.     CrossRef
  • The Growing Challenge of Diabetes Management in an Aging Society
    Seung-Hwan Lee
    Diabetes & Metabolism Journal.2023; 47(5): 630.     CrossRef
  • An Observational Pilot Study of a Tailored Environmental Monitoring and Alert System for Improved Management of Chronic Respiratory Diseases
    Mohammed Alotaibi, Fady Alnajjar, Badr A Alsayed, Tareq Alhmiedat, Ashraf M Marei, Anas Bushnag, Luqman Ali
    Journal of Multidisciplinary Healthcare.2023; Volume 16: 3799.     CrossRef
  • Smart Insulin Pen: Managing Insulin Therapy for People with Diabetes in the Digital Era
    Jee Hee Yoo, Jae Hyeon Kim
    The Journal of Korean Diabetes.2023; 24(4): 190.     CrossRef
Short Communication
Technology/Device
Comparison of Laser and Conventional Lancing Devices for Blood Glucose Measurement Conformance and Patient Satisfaction in Diabetes Mellitus
Jung A Kim, Min Jeong Park, Eyun Song, Eun Roh, So Young Park, Da Young Lee, Jaeyoung Kim, Ji Hee Yu, Ji A Seo, Kyung Mook Choi, Sei Hyun Baik, Hye Jin Yoo, Nan Hee Kim
Diabetes Metab J. 2022;46(6):936-940.   Published online March 30, 2022
DOI: https://doi.org/10.4093/dmj.2021.0293
  • 6,357 View
  • 273 Download
  • 3 Web of Science
  • 2 Crossref
AbstractAbstract PDFPubReader   ePub   
Self-monitoring of capillary blood glucose is important for controlling diabetes. Recently, a laser lancing device (LMT-1000) that can collect capillary blood without skin puncture was developed. We enrolled 150 patients with type 1 or 2 diabetes mellitus. Blood sampling was performed on the same finger on each hand using the LMT-1000 or a conventional lancet. The primary outcome was correlation between glucose values using the LMT-1000 and that using a lancet. And we compared the pain and satisfaction of the procedures. The capillary blood sampling success rates with the LMT-1000 and lancet were 99.3% and 100%, respectively. There was a positive correlation (r=0.974, P<0.001) between mean blood glucose levels in the LMT-1000 (175.8±63.0 mg/dL) and conventional lancet samples (172.5±63.6 mg/dL). LMT-1000 reduced puncture pain by 75.0% and increased satisfaction by 80.0% compared to a lancet. We demonstrated considerable consistency in blood glucose measurements between samples from the LMT-1000 and a lancet, but improved satisfaction and clinically significant pain reduction were observed with the LMT-1000 compared to those with a lancet.

Citations

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  • Comparison between a laser-lancing device and automatic incision lancet for capillary blood sampling from the heel of newborn infants: a randomized feasibility trial
    Chul Kyu Yun, Eui Kyung Choi, Hyung Jin Kim, Jaeyoung Kim, Byung Cheol Park, Kyuhee Park, Byung Min Choi
    Journal of Perinatology.2024; 44(8): 1193.     CrossRef
  • Comparison of laser and traditional lancing devices for capillary blood sampling in patients with diabetes mellitus and high bleeding risk
    Min Jeong Park, Soon Young Hwang, Ahreum Jang, Soo Yeon Jang, Eyun Song, So Young Park, Da Young Lee, Jaeyoung Kim, Byung Cheol Park, Ji Hee Yu, Ji A Seo, Kyung Mook Choi, Sei Hyun Baik, Hye Jin Yoo, Nan Hee Kim
    Lasers in Medical Science.2024;[Epub]     CrossRef
Original Article
Technology/Device
Article image
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 Metab J. 2022;46(5):713-721.   Published online January 24, 2022
DOI: https://doi.org/10.4093/dmj.2021.0164
  • 6,128 View
  • 347 Download
  • 5 Web of Science
  • 5 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Continuous glucose monitoring (CGM) has been widely used in the management of diabetes. However, the usefulness and detailed data during perioperative status were not well studied. In this study, we described the immediate changes of glucose profiles after metabolic surgery using intermittently scanned CGM (isCGM) in individuals with type 2 diabetes mellitus (T2DM).
Methods
This was a prospective, single-center, single-arm study including 20 participants with T2DM. The isCGM (FreeStyle Libre CGM) implantation was performed within 2 weeks before surgery. We compared CGM metrics of 3 days before surgery and 3 days after surgery, and performed the correlation analyses with clinical variables.
Results
The mean glucose significantly decreased after surgery (147.0±40.4 to 95.5±17.1 mg/dL, P<0.001). Time in range (TIR; 70 to 180 mg/dL) did not significantly change after surgery in total. However, it was significantly increased in a subgroup of individuals with glycosylated hemoglobin (HbA1c) ≥8.0%. Time above range (>250 or 180 mg/dL) was significantly decreased in total. In contrast, time below range (<70 or 54 mg/dL) was significantly increased in total and especially in a subgroup of individuals with HbA1c <8.0% after surgery. The coefficient of variation significantly decreased after surgery. Higher baseline HbA1c was correlated with greater improvement in TIR (rho=0.607, P=0.005).
Conclusion
The isCGM identified improvement of mean glucose and glycemic variability, and increase of hypoglycemia after metabolic surgery, but TIR was not significantly changed after surgery. We detected an increase of TIR only in individuals with HbA1c ≥8.0%.

Citations

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  • Comparative Effect of Glucose-Lowering Drugs for Type 2 Diabetes Mellitus on Stroke Prevention: A Systematic Review and Network Meta-Analysis
    Ji Soo Kim, Gyeongsil Lee, Kyung-Il Park, Seung-Won Oh
    Diabetes & Metabolism Journal.2024; 48(2): 312.     CrossRef
  • Continuous Glucose Monitoring Captures Glycemic Variability After Roux-en-Y Gastric Bypass in Patients with and Without Type 2 Diabetes Mellitus: A Prospective Cohort Study
    Raquel do A. P. Quevedo, Maria Edna de Melo, Cintia Cercato, Ariana E. Fernandes, Anna Carolina B. Dantas, Marco Aurélio Santo, Denis Pajecki, Marcio C. Mancini
    Obesity Surgery.2024; 34(8): 2789.     CrossRef
  • Perioperative Management of Adult Patients with Diabetes Wearing Devices: A Society for Perioperative Assessment and Quality Improvement (SPAQI) Expert Consensus Statement
    Adriana D. Oprea, Smita K. Kalra, Elizabeth W. Duggan, Linda L. Russell, Richard D. Urman, Basem B. Abdelmalak, Preethi Patel, Kurt J. Pfeifer, Paul J. Grant, Marina M. Charitou, Carlos E. Mendez, Jennifer L. Sherr, Guillermo E. Umpierrez, David C. Klonof
    Journal of Clinical Anesthesia.2024; 99: 111627.     CrossRef
  • Use of Continuous Glucose Monitoring in Patients Following Bariatric Surgery: A Scoping Review
    Yang Yu, Susan W. Groth
    Obesity Surgery.2023; 33(8): 2573.     CrossRef
  • Asymptomatic Hypoglycemia after Metabolic Surgery: New Insights from Perioperative Continuous Glucose Monitoring
    Sang-Man Jin
    Diabetes & Metabolism Journal.2022; 46(5): 675.     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
  • 17,821 View
  • 907 Download
  • 44 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

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  • 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;[Epub]     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;[Epub]     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
Short Communication
Type 1 Diabetes
Article image
Real-World Analysis of Therapeutic Outcome in Type 1 Diabetes Mellitus at a Tertiary Care Center
Antonia Kietaibl, Michaela Riedl, Latife Bozkurt
Diabetes Metab J. 2022;46(1):149-153.   Published online July 6, 2021
DOI: https://doi.org/10.4093/dmj.2020.0267
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AbstractAbstract PDFPubReader   ePub   
Insulin replacement in type 1 diabetes mellitus (T1DM) needs intensified treatment, which can either be performed by multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII). This retrospective analysis of a real-world scenario aimed to evaluate whether glycaemic and cardiovascular risk factors could be controlled with CSII outclass MDI as suggested by recent evidence. Data from patients with either insulin pump (n=68) or injection (n=224) therapy at an Austrian tertiary care centre were analysed between January 2016 and December 2017. There were no significant differences with regard to the latest glycosylated hemoglobin, cardiovascular risk factor control or diabetes-associated late complications. Hypoglycaemia was less frequent (P<0.001), sensor-augmented therapy was more common (P=0.003) and mean body mass index (BMI) was higher (P=0.002) with CSII treatment. This retrospective analysis of real-world data in T1DM did not demonstrate the superiority of insulin pump treatment with regard to glycaemic control or cardiovascular risk factor control.
Original Articles
Drug/Regimen
Article image
Effects of Teneligliptin on HbA1c levels, Continuous Glucose Monitoring-Derived Time in Range and Glycemic Variability in Elderly Patients with T2DM (TEDDY Study)
Ji Cheol Bae, Soo Heon Kwak, Hyun Jin Kim, Sang-Yong Kim, You-Cheol Hwang, Sunghwan Suh, Bok Jin Hyun, Ji Eun Cha, Jong Chul Won, Jae Hyeon Kim
Diabetes Metab J. 2022;46(1):81-92.   Published online June 16, 2021
DOI: https://doi.org/10.4093/dmj.2021.0016
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Graphical AbstractGraphical Abstract AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
To evaluate the effects of teneligliptin on glycosylated hemoglobin (HbA1c) levels, continuous glucose monitoring (CGM)-derived time in range, and glycemic variability in elderly type 2 diabetes mellitus patients.
Methods
This randomized, double-blinded, placebo-controlled study was conducted in eight centers in Korea (clinical trial registration number: NCT03508323). Sixty-five participants aged ≥65 years, who were treatment-naïve or had been treated with stable doses of metformin, were randomized at a 1:1 ratio to receive 20 mg of teneligliptin (n=35) or placebo (n=30) for 12 weeks. The main endpoints were the changes in HbA1c levels from baseline to week 12, CGM metrics-derived time in range, and glycemic variability.
Results
After 12 weeks, a significant reduction (by 0.84%) in HbA1c levels was observed in the teneligliptin group compared to that in the placebo group (by 0.08%), with a between-group least squares mean difference of –0.76% (95% confidence interval [CI], –1.08 to –0.44). The coefficient of variation, standard deviation, and mean amplitude of glycemic excursion significantly decreased in participants treated with teneligliptin as compared to those in the placebo group. Teneligliptin treatment significantly decreased the time spent above 180 or 250 mg/dL, respectively, without increasing the time spent below 70 mg/dL. The mean percentage of time for which glucose levels remained in the 70 to 180 mg/dL time in range (TIR70–180) at week 12 was 82.0%±16.0% in the teneligliptin group, and placebo-adjusted change in TIR70–180 from baseline was 13.3% (95% CI, 6.0 to 20.6).
Conclusion
Teneligliptin effectively reduced HbA1c levels, time spent above the target range, and glycemic variability, without increasing hypoglycemia in our study population.

Citations

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  • Comparison of teneligliptin and other gliptin-based regimens in addressing insulin resistance and glycemic control in type 2 diabetic patients: a cross-sectional study
    Harmanjit Singh, Ravi Rohilla, Shivani Jaswal, Mandeep Singla
    Expert Review of Endocrinology & Metabolism.2024; 19(1): 81.     CrossRef
  • Potential approaches using teneligliptin for the treatment of type 2 diabetes mellitus: current status and future prospects
    Harmanjit Singh, Jasbir Singh, Ravneet Kaur Bhangu, Mandeep Singla, Jagjit Singh, Farideh Javid
    Expert Review of Clinical Pharmacology.2023; 16(1): 49.     CrossRef
  • Mechanism of molecular interaction of sitagliptin with human DPP4 enzyme - New Insights
    Michelangelo Bauwelz Gonzatti, José Edvar Monteiro Júnior, Antônio José Rocha, Jonathas Sales de Oliveira, Antônio José de Jesus Evangelista, Fátima Morgana Pio Fonseca, Vânia Marilande Ceccatto, Ariclécio Cunha de Oliveira, José Ednésio da Cruz Freire
    Advances in Medical Sciences.2023; 68(2): 402.     CrossRef
  • A prospective multicentre open label study to assess effect of Teneligliptin on glycemic control through parameters of time in range (TIR) Metric using continuous glucose monitoring (TOP-TIR study)
    Banshi Saboo, Suhas Erande, A.G. Unnikrishnan
    Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2022; 16(2): 102394.     CrossRef
  • Association between Variability of Metabolic Risk Factors and Cardiometabolic Outcomes
    Min Jeong Park, Kyung Mook Choi
    Diabetes & Metabolism Journal.2022; 46(1): 49.     CrossRef
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
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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
Reviews
Type 1 Diabetes
Article image
Time in Range from Continuous Glucose Monitoring: A Novel Metric for Glycemic Control
Jee Hee Yoo, Jae Hyeon Kim
Diabetes Metab J. 2020;44(6):828-839.   Published online December 23, 2020
DOI: https://doi.org/10.4093/dmj.2020.0257
Correction in: Diabetes Metab J 2021;45(5):795
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AbstractAbstract PDFPubReader   ePub   
Glycosylated hemoglobin (HbA1c) has been the sole surrogate marker for assessing diabetic complications. However, consistently reported limitations of HbA1c are that it lacks detailed information on short-term glycemic control and can be easily interfered with by various clinical conditions such as anemia, pregnancy, or liver disease. Thus, HbA1c alone may not represent the real glycemic status of a patient. The advancement of continuous glucose monitoring (CGM) has enabled both patients and healthcare providers to monitor glucose trends for a whole single day, which is not possible with HbA1c. This has allowed for the development of core metrics such as time spent in time in range (TIR), hyperglycemia, or hypoglycemia, and glycemic variability. Among the 10 core metrics, TIR is reported to represent overall glycemic control better than HbA1c alone. Moreover, various evidence supports TIR as a predictive marker of diabetes complications as well as HbA1c, as the inverse relationship between HbA1c and TIR reveals. However, there are more complex relationships between HbA1c, TIR, and other CGM metrics. This article provides information about 10 core metrics with particular focus on TIR and the relationships between the CGM metrics for comprehensive understanding of glycemic status using CGM.

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  • Two-week continuous glucose monitoring-derived metrics and degree of hepatic steatosis: a cross-sectional study among Chinese middle-aged and elderly participants
    Haili Zhong, Ke Zhang, Lishan Lin, Yan Yan, Luqi Shen, Hanzu Chen, Xinxiu Liang, Jingnan Chen, Zelei Miao, Ju-Sheng Zheng, Yu-ming Chen
    Cardiovascular Diabetology.2024;[Epub]     CrossRef
  • Acute and Chronic Adverse Outcomes of Type 1 Diabetes
    Rachel Longendyke, Jody B. Grundman, Shideh Majidi
    Endocrinology and Metabolism Clinics of North America.2024; 53(1): 123.     CrossRef
  • La plongée sous-marine en scaphandre autonome avec un diabète de type 1. Une belle histoire du dernier millénaire
    Lise Dufaitre Patouraux, Agnès Sola-Gazagnes, Boris Lormeau, Corinne Lormeau
    Médecine des Maladies Métaboliques.2024;[Epub]     CrossRef
  • S100A9 exerts insulin-independent antidiabetic and anti-inflammatory effects
    Gloria Ursino, Giulia Lucibello, Pryscila D. S. Teixeira, Anna Höfler, Christelle Veyrat-Durebex, Soline Odouard, Florian Visentin, Luca Galgano, Emmanuel Somm, Claudia R. Vianna, Ariane Widmer, François R. Jornayvaz, Andreas Boland, Giorgio Ramadori, Rob
    Science Advances.2024;[Epub]     CrossRef
  • Hybrid Closed-Loop Versus Manual Insulin Delivery in Adults With Type 1 Diabetes: A Post Hoc Analysis Using the Glycemia Risk Index
    Melissa H. Lee, Sara Vogrin, Timothy W. Jones, David N. O’Neal
    Journal of Diabetes Science and Technology.2024; 18(4): 764.     CrossRef
  • Clinically relevant stratification of patients with type 2 diabetes by using continuous glucose monitoring data
    Xiaopeng Shao, Jingyi Lu, Rui Tao, Liang Wu, Yaxin Wang, Wei Lu, Hongru Li, Jian Zhou, Xia Yu
    Diabetes, Obesity and Metabolism.2024; 26(6): 2082.     CrossRef
  • Effects of a 2-Week Kinect-Based Mixed-Reality Exercise Program on Prediabetes: A Pilot Trial during COVID-19
    So Young Ahn, Si Woo Lee, Hye Jung Shin, Won Jae Lee, Jun Hyeok Kim, Hyun-Jun Kim, Wook Song
    Journal of Obesity & Metabolic Syndrome.2024; 33(1): 54.     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
  • 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
  • Psychometric Properties of the Automated Insulin Delivery: Benefits and Burdens Scale for Adults with Type 1 Diabetes
    Jenna B. Shapiro, Anthony T. Vesco, Michael S. Carroll, Jill Weissberg-Benchell
    Diabetes Technology & Therapeutics.2024; 26(11): 842.     CrossRef
  • Expert Consensus on Dipeptidyl Peptidase-4 Inhibitor-Based Therapies in the Modern Era of Type 2 Diabetes Mellitus Management in India
    Sanjay Kalra, Saptarshi Bhattacharya, A Dhingra, Sambit Das, Nitin Kapoor, Shehla Shaikh, Vivek Kolapkar, R V Lokesh Kumar, Kamlesh Patel, Rahul Kotwal
    Cureus.2024;[Epub]     CrossRef
  • Optimal hyperglycemia thresholds in patients undergoing chemotherapy: a cross sectional study of oncologists’ practices
    Teresa M. Salgado, Poorva B. Birari, Mona Alshahawey, Erin Hickey Zacholski, Emily Mackler, Tonya M. Buffington, Kerri T. Musselman, William J. Irvin, Jennifer M. Perkins, Trang N. Le, Dave L. Dixon, Karen B. Farris, Vanessa B. Sheppard, Resa M. Jones
    Supportive Care in Cancer.2024;[Epub]     CrossRef
  • Glycemic Risk Index in a Cohort of Patients with Type 1 Diabetes Mellitus Stratified by the Coefficient of Variation: A Real-Life Study
    Sandra Herranz-Antolín, Clara Coton-Batres, María Covadonga López-Virgos, Verónica Esteban-Monge, Visitación Álvarez-de Frutos, Leonel Pekarek, Miguel Torralba
    Diabetes Technology & Therapeutics.2024;[Epub]     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
  • Association between glucose levels of children with type 1 diabetes and parental economic status in mobile health application
    Wen-Hao Zhang, Chao-Fan Wang, Hao Wang, Jie Tang, Hong-Qiang Zhang, Jiang-Yu Zhu, Xue-Ying Zheng, Si-Hui Luo, Yu Ding
    World Journal of Diabetes.2024; 15(7): 1477.     CrossRef
  • Differences Between Glycated Hemoglobin and Glucose Management Indicator in Real-Time and Intermittent Scanning Continuous Glucose Monitoring in Adults With Type 1 Diabetes
    Jee Hee Yoo, Sun Joon Moon, Cheol-Young Park, Jae Hyeon Kim
    Journal of Diabetes Science and Technology.2024;[Epub]     CrossRef
  • Impact of diverse aerobic exercise plans on glycemic control, lipid levels, and functional activity in stroke patients with type 2 diabetes mellitus
    Kangcheng Chen, Yulong Wang, Dongxia Li, Jun Li, Yong Huang, Meiling Huang, Haifeng Ma
    Frontiers in Endocrinology.2024;[Epub]     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
  • A Review of Third-Trimester Complications in Pregnancies Complicated by Diabetes Mellitus
    Shaun R. Welsey, Jessica Day, Scott Sullivan, Sarah D. Crimmins
    American Journal of Perinatology.2024;[Epub]     CrossRef
  • Anagliptin twice‐daily regimen improves glycaemic variability in subjects with type 2 diabetes: A double‐blind, randomized controlled trial
    Yong‐ho Lee, Doo‐Man Kim, Jae Myung Yu, Kyung Mook Choi, Sin Gon Kim, Kang Seo Park, Hyun‐Shik Son, Choon Hee Chung, Kyu Jeung Ahn, Soon Hee Lee, Ki‐Ho Song, Su Kyoung Kwon, Hyeong Kyu Park, Kyu Chang Won, Hak Chul Jang
    Diabetes, Obesity and Metabolism.2023; 25(5): 1174.     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
  • 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
  • Real-world outcomes of continuous glucose monitoring in adults with diabetes mellitus attending an Irish tertiary hospital
    Aoife Courtney, Diarmuid Smith, Hannah Forde
    Irish Journal of Medical Science (1971 -).2023; 192(6): 2763.     CrossRef
  • Insight into continuous glucose monitoring: from medical basics to commercialized devices
    Ayman Chmayssem, Małgorzata Nadolska, Emily Tubbs, Kamila Sadowska, Pankaj Vadgma, Isao Shitanda, Seiya Tsujimura, Youssef Lattach, Martin Peacock, Sophie Tingry, Stéphane Marinesco, Pascal Mailley, Sandrine Lablanche, Pierre Yves Benhamou, Abdelkader Zeb
    Microchimica Acta.2023;[Epub]     CrossRef
  • Efficacy of polyethylene glycol loxenatide versus insulin glargine on glycemic control in patients with type 2 diabetes: a randomized, open-label, parallel-group trial
    Shuo Zhang, Chuanyan Zhang, Jingxian Chen, Feiying Deng, Zezhen Wu, Dan Zhu, Fengwu Chen, Yale Duan, Yue Zhao, Kaijian Hou
    Frontiers in Pharmacology.2023;[Epub]     CrossRef
  • Impact of continuous glucose monitoring on glycemic control and its derived metrics in type 1 diabetes: a longitudinal study
    So Hyun Cho, Seohyun Kim, You-Bin Lee, Sang-Man Jin, Kyu Yeon Hur, Gyuri Kim, Jae Hyeon Kim
    Frontiers in Endocrinology.2023;[Epub]     CrossRef
  • Association Between Continuous Glucose Monitoring-Derived Glycemia Risk Index and Albuminuria in Type 2 Diabetes
    Jee Hee Yoo, Ji Yoon Kim, Jae Hyeon Kim
    Diabetes Technology & Therapeutics.2023; 25(10): 726.     CrossRef
  • Acute Glycemic Variability and Early Outcomes After Cardiac Surgery: A Meta-Analysis
    Shuo Chang, Mian Xu, Yu Wang, Yanbo Zhang
    Hormone and Metabolic Research.2023; 55(11): 771.     CrossRef
  • Comparison of Glycemia Risk Index with Time in Range for Assessing Glycemic Quality
    Ji Yoon Kim, Jee Hee Yoo, Jae Hyeon Kim
    Diabetes Technology & Therapeutics.2023; 25(12): 883.     CrossRef
  • Correlação entre tempo no alvo e hemoglobina glicada de pessoas com diabetes mellitus: revisão sistemática
    Rafael Aparecido Dias Lima, Daiane Rubinato Fernandes, Rute Aparecida Casas Garcia, Lucas Ariel da Rocha Carvalho, Renata Cristina de Campos Pereira Silveira, Carla Regina de Souza Teixeira
    Revista Latino-Americana de Enfermagem.2023;[Epub]     CrossRef
  • Correlación entre tiempo en rango y hemoglobina glicosilada en personas con diabetes mellitus: revisión sistemática
    Rafael Aparecido Dias Lima, Daiane Rubinato Fernandes, Rute Aparecida Casas Garcia, Lucas Ariel da Rocha Carvalho, Renata Cristina de Campos Pereira Silveira, Carla Regina de Souza Teixeira
    Revista Latino-Americana de Enfermagem.2023;[Epub]     CrossRef
  • Correlation between time on target and glycated hemoglobin in people with diabetes mellitus: systematic review
    Rafael Aparecido Dias Lima, Daiane Rubinato Fernandes, Rute Aparecida Casas Garcia, Lucas Ariel da Rocha Carvalho, Renata Cristina de Campos Pereira Silveira, Carla Regina de Souza Teixeira
    Revista Latino-Americana de Enfermagem.2023;[Epub]     CrossRef
  • Smart Insulin Pen: Managing Insulin Therapy for People with Diabetes in the Digital Era
    Jee Hee Yoo, Jae Hyeon Kim
    The Journal of Korean Diabetes.2023; 24(4): 190.     CrossRef
  • 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
  • Hypoglycemic agents and glycemic variability in individuals with type 2 diabetes: A systematic review and network meta-analysis
    SuA Oh, Sujata Purja, Hocheol Shin, Minji Kim, Eunyoung Kim
    Diabetes and Vascular Disease Research.2022;[Epub]     CrossRef
  • Advanced Glycation End Products and Their Effect on Vascular Complications in Type 2 Diabetes Mellitus
    Jeongmin Lee, Jae-Seung Yun, Seung-Hyun Ko
    Nutrients.2022; 14(15): 3086.     CrossRef
  • Influence of dipeptidyl peptidase-4 inhibitors on glycemic variability in patients with type 2 diabetes: A meta-analysis of randomized controlled trials
    Shangyu Chai, Ruya Zhang, Ye Zhang, Richard David Carr, Yiman Zheng, Swapnil Rajpathak, Miao Yu
    Frontiers in Endocrinology.2022;[Epub]     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
  • Deterioration in glycemic control on schooldays among children and adolescents with type 1 diabetes: A continuous glucose monitoring-based study
    Yu Ding, Wenhao Zhang, Xiumei Wu, Tian Wei, Xulin Wang, Xueying Zheng, Sihui Luo
    Frontiers in Pediatrics.2022;[Epub]     CrossRef
  • Effect of repeated bolus and continuous glucose infusion on a panel of circulating biomarkers in healthy volunteers
    Roland Feldbauer, Matthias Wolfgang Heinzl, Carmen Klammer, Michael Resl, Johannes Pohlhammer, Klemens Rosenberger, Verena Almesberger, Florian Obendorf, Lukas Schinagl, Thomas Wagner, Margot Egger, Benjamin Dieplinger, Martin Clodi, Stephen L. Atkin
    PLOS ONE.2022; 17(12): e0279308.     CrossRef
  • Relationship between glycemic intraday variations evaluated in continuous glucose monitoring and HbA1c variability in type 2 diabetes: pilot study
    Akemi Tokutsu, Yosuke Okada, Keiichi Torimoto, Yoshiya Tanaka
    Diabetology & Metabolic Syndrome.2021;[Epub]     CrossRef
  • Time-in-range for monitoring glucose control: Is it time for a change?
    Virginia Bellido, Pedro José Pinés-Corrales, Rocío Villar-Taibo, Francisco Javier Ampudia-Blasco
    Diabetes Research and Clinical Practice.2021; 177: 108917.     CrossRef
  • Glucose Management Indicator for People with Type 1 Asian Diabetes Is Different from That of the Published Equation: Differences by Glycated Hemoglobin Distribution
    Jee Hee Yoo, Seung Hee Yang, Gyuri Kim, Jae Hyeon Kim
    Diabetes Technology & Therapeutics.2021;[Epub]     CrossRef
  • Health-Related Quality of Life, Family Conflicts and Fear of Injecting: Perception Differences between Preadolescents and Adolescents with Type 1 Diabetes and Their Mothers
    Marta Tremolada, Maria Cusinato, Sabrina Bonichini, Arianna Fabris, Claudia Gabrielli, Carlo Moretti
    Behavioral Sciences.2021; 11(7): 98.     CrossRef
  • Daytime Glycemic Variability and Frailty in Older Patients with Diabetes: a Pilot Study Using Continuous Glucose Monitoring
    Seung Min Chung, Yun Hee Lee, Chang Oh Kim, Ji Yeon Lee, Sang-Man Jin, Seung-Hyun Yoo, Jun Sung Moon, Kwang Joon 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
  • Recent Advances of Integrative Bio-Omics Technologies to Improve Type 1 Diabetes (T1D) Care
    Nisha Karwal, Megan Rodrigues, David D. Williams, Ryan J. McDonough, Diana Ferro
    Applied Sciences.2021; 11(24): 11602.     CrossRef
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
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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.

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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; : 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
  • 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 Article
Clinical Care/Education
Frequency of Self-Monitoring of Blood Glucose during the School Day Is Associated with the Optimal Glycemic Control among Korean Adolescents with Type 1 Diabetes
Eun Young Joo, Ji-Eun Lee, Hee Sook Kang, Shin Goo Park, Yong Hee Hong, Young-Lim Shin, Min Sohn
Diabetes Metab J. 2018;42(6):480-487.   Published online June 29, 2018
DOI: https://doi.org/10.4093/dmj.2018.0018
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AbstractAbstract PDFPubReader   
Background

This study aimed to evaluate the relationship between the frequency of self-monitoring of blood glucose (SMBG) and glycosylated hemoglobin (HbA1c) levels among Korean adolescents with type 1 diabetes mellitus (T1DM). Factors affecting the SMBG frequency were analyzed in order to improve their glycemic control.

Methods

Sixty-one adolescents aged 13 to 18 years with T1DM were included from one tertiary center. Clinical and biochemical variables were recorded. Factors associated with SMBG frequency were assessed using structured self-reported questionnaires.

Results

Average total daily SMBG frequency was 3.8±2.1 and frequency during the school day was 1.3±1.2. The mean HbA1c level was 8.6%±1.4%. As the daily SMBG frequency increased, HbA1c levels declined (P=0.001). The adjusted odds of achieving the target HbA1c in participants who performed daily SMBG ≥5 significantly increased 9.87 folds (95% confidence interval [CI], 1.58 to 61.70) compared with those performed SMBG four times a day. In the subjects whose SMBG frequency <1/day during the school day, an 80% reduction in the adjusted odds ratio 0.2 (95% CI, 0.05 to 0.86) showed compared to the group with performing two SMBG measurements in the school setting. The number of SMBG testing performed at school was significantly high for individuals assisted by their friends (P=0.031) and for those who did SMBG in the classrooms (P=0.039).

Conclusion

Higher SMBG frequency was significantly associated with lower HbA1c in Korean adolescents with T1DM. It would be necessary to establish the school environments that can facilitate adequate glycemic control, including frequent SMBG.

Citations

Citations to this article as recorded by  
  • Problems of blood glucose self-monitoring in patients with diabetes mellitus
    Yu. A. Kononova, V. B. Bregovskiy, A. Yu. Babenko
    Meditsinskiy sovet = Medical Council.2022; (21-1): 140.     CrossRef
  • Adherence as a Predictor of Glycemic Control Among Adolescents With Type 1 Diabetes: A Retrospective Study Using Real-world Evidence
    Sohayla A. Ibrahim, Maguy Saffouh El Hajj, Yaw B. Owusu, Maryam Al-Khaja, Amel Khalifa, Dalia Ahmed, Ahmed Awaisu
    Clinical Therapeutics.2022; 44(10): 1380.     CrossRef
  • Self-Care IoT Platform for Diabetic Mellitus
    Jai-Chang Park, Seongbeom Kim, Je-Hoon Lee
    Applied Sciences.2021; 11(5): 2006.     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
  • Habits and Routines during Transitions among Emerging Adults with Type 1 Diabetes
    Kathleen M. Hanna, Jed R. Hansen
    Western Journal of Nursing Research.2020; 42(6): 446.     CrossRef
  • How was the Diabetes Metabolism Journal added to MEDLINE?
    Hye Jin Yoo
    Science Editing.2020; 7(2): 201.     CrossRef
  • Differences of FreeStyle Libre Flash Glucose Monitoring System and Finger Pricks on Clinical Characteristics and Glucose Monitoring Satisfactions in Type 1 Diabetes Using Insulin Pump
    Ayman A Al Hayek, Asirvatham A Robert, Mohamed A Al Dawish
    Clinical Medicine Insights: Endocrinology and Diabetes.2019;[Epub]     CrossRef
Review
Clinical Diabetes & Therapeutics
Recent Updates on Type 1 Diabetes Mellitus Management for Clinicians
Ahmed Iqbal, Peter Novodvorsky, Simon R. Heller
Diabetes Metab J. 2018;42(1):3-18.   Published online February 23, 2018
DOI: https://doi.org/10.4093/dmj.2018.42.1.3
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AbstractAbstract PDFPubReader   

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune condition that requires life-long administration of insulin. Optimal management of T1DM entails a good knowledge and understanding of this condition both by the physician and the patient. Recent introduction of novel insulin preparations, technological advances in insulin delivery and glucose monitoring, such as continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring and improved understanding of the detrimental effects of hypoglycaemia and hyperglycaemia offer new opportunities and perspectives in T1DM management. Evidence from clinical trials suggests an important role of structured patient education. Our efforts should be aimed at improved metabolic control with concomitant reduction of hypoglycaemia. Despite recent advances, these goals are not easy to achieve and can put significant pressure on people with T1DM. The approach of physicians should therefore be maximally supportive. In this review, we provide an overview of the recent advances in T1DM management focusing on novel insulin preparations, ways of insulin administration and glucose monitoring and the role of metformin or sodium-glucose co-transporter 2 inhibitors in T1DM management. We then discuss our current understanding of the effects of hypoglycaemia on human body and strategies aimed at mitigating the risks associated with hypoglycaemia.

Citations

Citations to this article as recorded by  
  • Health-Related Quality of Life of Adolescents and Children With Type 1 Diabetes in the Jazan Region of Saudi Arabia
    Gassem A Gohal, Aqilah Majhali, Esaam Moafa, Sarah H Talebi, Bushra I Maashi, Amani Mutaen, Walaa J Alhamdan, Ibrahim M Dighriri
    Cureus.2024;[Epub]     CrossRef
  • An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems
    Sayyar Ahmad, Aleix Beneyto, Taiyu Zhu, Ivan Contreras, Pantelis Georgiou, Josep Vehi
    Scientific Reports.2024;[Epub]     CrossRef
  • Nose-to-brain delivery of insulin nanoparticles for diabetes management: A review
    Manoj Kumbhare, Ajaykumar Surana, Pravin Morankar
    Baghdad Journal of Biochemistry and Applied Biological Sciences.2023; 4(02): 39.     CrossRef
  • Clinical Effects of a Home Care Pilot Program for Patients with Type 1 Diabetes Mellitus: A Retrospective Cohort Study
    Sejeong Lee, KyungYi Kim, Ji Eun Kim, Yura Hyun, Minyoung Lee, Myung-Il Hahm, Sang Gyu Lee, Eun Seok Kang
    Diabetes & Metabolism Journal.2023; 47(5): 693.     CrossRef
  • Impact of an Acceptance and Commitment Therapy programme on HbA1c, self-management and psychosocial factors in adults with type 1 diabetes and elevated HbA1c levels: a randomised controlled trial
    Ingrid Wijk, Susanne Amsberg, Unn-Britt Johansson, Fredrik Livheim, Eva Toft, Therese Anderbro
    BMJ Open.2023; 13(12): e072061.     CrossRef
  • Role of sirtuin-1 (SIRT1) in hypoxic injury in pancreatic β-cells
    Ye-Jee Lee, Esder Lee, Young-Hye You, Yu-Bae Ahn, Ki-Ho Song, Ji-Won Kim, Seung-Hyun Ko
    Journal of Drug Targeting.2021; 29(1): 88.     CrossRef
  • Age at Diagnosis and the Risk of Diabetic Nephropathy in Young Patients with Type 1 Diabetes Mellitus
    Jong Ha Baek, Woo Je Lee, Byung-Wan Lee, Soo Kyoung Kim, Gyuri Kim, Sang-Man Jin, Jae Hyeon Kim
    Diabetes & Metabolism Journal.2021; 45(1): 46.     CrossRef
  • The impact of chemical engineering and technological advances on managing diabetes: present and future concepts
    Sabine Szunerits, Sorin Melinte, Alexandre Barras, Quentin Pagneux, Anna Voronova, Amar Abderrahmani, Rabah Boukherroub
    Chemical Society Reviews.2021; 50(3): 2102.     CrossRef
  • Surrogate markers and predictors of endogenous insulin secretion in children and adolescents with type 1 diabetes
    Jin-Na Yuan, Jian-Wei Zhang, Wayne S. Cutfield, Guan-Ping Dong, You-Jun Jiang, Wei Wu, Ke Huang, Xiao-Chun Chen, Yan Zheng, Bi-Hong Liu, José G. B. Derraik, Jun-Fen Fu
    World Journal of Pediatrics.2021; 17(1): 99.     CrossRef
  • Nano-based drug delivery systems used as vehicles to enhance polyphenols therapeutic effect for diabetes mellitus treatment
    Sónia Rocha, Mariana Lucas, Daniela Ribeiro, M. Luísa Corvo, Eduarda Fernandes, Marisa Freitas
    Pharmacological Research.2021; 169: 105604.     CrossRef
  • Dapagliflozin: an effective adjunctive treatment in type 1 diabetes
    Ghasem Yadegarfar, Mark Livingston, Gabriela Cortes, Ramadan Alshames, Kate Leivesley, Ann Metters, Linda Horne, Tom Steele, Adrian H. Heald
    Cardiovascular Endocrinology & Metabolism.2021; 10(2): 132.     CrossRef
  • Association between reduced serum levels of magnesium and the presence of poor glycemic control and complications in type 1 diabetes mellitus: A systematic review and meta-analysis
    Ana Kelen Rodrigues, Ana Elisa Melo, Caroline Pereira Domingueti
    Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2020; 14(2): 127.     CrossRef
  • Insulin-Producing Cell Transplantation Platform for Veterinary Practice
    Suryo Kuncorojakti, Sayamon Srisuwatanasagul, Krishaporn Kradangnga, Chenphop Sawangmake
    Frontiers in Veterinary Science.2020;[Epub]     CrossRef
  • Type 1 Diabetes Home Care Project and Educational Consultation
    Eun Chong Shin
    The Journal of Korean Diabetes.2020; 21(2): 88.     CrossRef
  • Decision-Making in Artificial Intelligence: Is It Always Correct?
    Hun-Sung Kim
    Journal of Korean Medical Science.2020;[Epub]     CrossRef
  • The FreeStyle Libre flash glucose monitoring system: how it has improved glycaemic control for people with type 1 diabetes in Eastern Cheshire, UK
    Ghasem Yadegarfar, Simon G. Anderson, Zohaib Khawaja, Gabriela Cortes, Kathryn Leivesley, Ann Metters, Linda Horne, Tom Steele, Adrian H. Heald
    Cardiovascular Endocrinology & Metabolism.2020; 9(4): 171.     CrossRef
  • Dose-dependent effects of necrostatin-1 supplementation to tissue culture media of young porcine islets
    Hien Lau, Nicole Corrales, Samuel Rodriguez, Colleen Luong, Mohammadreza Mohammadi, Veria Khosrawipour, Shiri Li, Michael Alexander, Paul de Vos, Jonathan R. T. Lakey, Zoltán Rakonczay
    PLOS ONE.2020; 15(12): e0243506.     CrossRef
  • New Insulin Pumps and Open Source Artificial Pancreas System in Korea
    Jae Hyeon Kim
    The Journal of Korean Diabetes.2020; 21(4): 197.     CrossRef
  • Perspective and general approach of diabetes in palliative care
    Díaz Rodríguez Juan Javier
    Hospice and Palliative Medicine International Journal.2018;[Epub]     CrossRef
  • The effects of safranal, a constitute of saffron, and metformin on spatial learning and memory impairments in type-1 diabetic rats: behavioral and hippocampal histopathological and biochemical evaluations
    Fatemeh Delkhosh-Kasmaie, Amir Abbas Farshid, Esmaeal Tamaddonfard, Mehdi Imani
    Biomedicine & Pharmacotherapy.2018; 107: 203.     CrossRef
Original Article
Clinical Care/Education
Improvement of Glycosylated Hemoglobin in Patients with Type 2 Diabetes Mellitus under Insulin Treatment by Reimbursement for Self-Monitoring of Blood Glucose
Young Shin Song, Bo Kyung Koo, Sang Wan Kim, Ka Hee Yi, Kichul Shin, Min Kyong Moon
Diabetes Metab J. 2018;42(1):28-42.   Published online September 28, 2017
DOI: https://doi.org/10.4093/dmj.2018.42.1.28
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AbstractAbstract PDFSupplementary MaterialPubReader   
Background

In Korea, the costs associated with self-monitoring of blood glucose (SMBG) for patients with type 2 diabetes mellitus (T2DM) under insulin treatment have been reimbursed since November 2015. We investigated whether this new reimbursement program for SMBG has improved the glycemic control in the beneficiaries of this policy.

Methods

Among all adult T2DM patients with ≥3 months of reimbursement (n=854), subjects without any changes in anti-hyperglycemic agents during the study period were selected. The improvement of glycosylated hemoglobin (HbA1c) was defined as an absolute reduction in HbA1c ≥0.6% or an HbA1c level at follow-up <7%.

Results

HbA1c levels significantly decreased from 8.5%±1.3% to 8.2%±1.2% during the follow-up (P<0.001) in all the study subjects (n=409). Among them, 35.5% (n=145) showed a significant improvement in HbA1c. Subjects covered under the Medical Aid system showed a higher prevalence of improvement in HbA1c than those with medical insurance (52.2% vs. 33.3%, respectively, P=0.012). In the improvement group, the baseline HbA1c (P<0.001), fasting C-peptide (P=0.016), and daily dose of insulin/body weight (P=0.024) showed significant negative correlations with the degree of HbA1c change. Multivariate analysis showed that subjects in the Medical Aid system were about 2.5-fold more likely to improve in HbA1c compared to those with medical insurance (odds ratio, 2.459; 95% confidence interval, 1.138 to 5.314; P=0.022).

Conclusion

The reimbursement for SMBG resulted in a significant improvement in HbA1c in T2DM subjects using insulin, which was more prominent in subjects with poor glucose control at baseline or covered under the Medical Aid system.

Citations

Citations to this article as recorded by  
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  • Self-Monitoring of Blood Glucose in Patients with Insulin-Treated Type 2 Diabetes Mellitus
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    Diabetes & Metabolism Journal.2018; 42(1): 26.     CrossRef

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