Diabetes & Metabolism Journal

Original Article

Others
Contributions of Hepatic Insulin Resistance and Islet β-Cell Dysfunction to the Blood Glucose Spectrum in Newly Diagnosed Type 2 Diabetes Mellitus: Mengge Yang, Ying Wei, Jia Liu, Ying Wang, Guang Wang; Received September 5, 2024 Accepted December 12, 2024 Published online February 13, 2025; DOI: https://doi.org/10.4093/dmj.2024.0537 [Epub ahead of print]

656 View
70 Download

Abstract PDF Supplementary Material PubReader ePub: Background
Our previous studies have investigated the role of hepatic insulin resistance (hepatic IR) and islet β-cell function in the pathogenesis of diabetes. This study aimed to explore the contributions of hepatic IR and islet β-cell dysfunction to the blood glucose spectrum in patients with newly diagnosed type 2 diabetes mellitus.
Methods
Hepatic IR was assessed by the hepatic insulin resistance index (HIRI). Islet β-cell function was assessed by insulin secretion- sensitivity index-2 (ISSI2). The associations between blood glucose spectrum and hepatic IR and ISSI2 were analyzed.
Results
A total of 707 patients with new-onset diabetes were included. The fasting blood glucose (FBG) and 30 minutes postload blood glucose elevated with rising HIRI (both P for trend <0.001). The FBG, 30 minutes, 2 hours, and 3 hours post-load blood glucose elevated with decreasing ISSI2 quartiles (all P for trend <0.001). There was a negative correlation between ISSI2 and HIRI after adjusting blood glucose levels (r=–0.199, P<0.001).
Conclusion
Hepatic IR mainly contributed to FBG and early-phase postprandial plasma glucose, whereas β-cell dysfunction contributed to fasting and postprandial plasma glucose at each phase.

Brief Report

Technology/Device
Effectiveness of Predicted Low-Glucose Suspend Pump Technology in the Prevention of Hypoglycemia in People with Type 1 Diabetes Mellitus: Real-World Data Using DIA:CONN G8: Jee Hee Yoo, Ji Yoon Kim, Jae Hyeon Kim; Diabetes Metab J. 2025;49(1):144-149. Published online August 28, 2024; DOI: https://doi.org/10.4093/dmj.2024.0039

1,577 View
130 Download

Abstract PDF Supplementary Material PubReader ePub: We evaluated the effectiveness of the predictive low-glucose suspend (PLGS) algorithm in the DIA:CONN G8. Forty people with type 1 diabetes mellitus (T1DM) who used a DIA:CONN G8 for at least 2 months with prior experience using pumps without and with PLGS were retrospectively analyzed. The objective was to assess the changes in time spent in hypoglycemia (percent of time below range [%TBR]) before and after using PLGS. The mean age, sensor glucose levels, glucose threshold for suspension, and suspension time were 31.1±22.8 years, 159.7±23.2 mg/dL, 81.1±9.1 mg/dL, and 111.9±79.8 min/day, respectively. Overnight %TBR <70 mg/dL was significantly reduced after using the algorithm (differences=0.3%, from 1.4%±1.5% to 1.1%±1.2%, P=0.045). The glycemia risk index (GRI) improved significantly by 4.2 (from 38.8±20.9 to 34.6±19.0, P=0.002). Using the PLGS did not result in a change in the hyperglycemia metric (all P>0.05). Our findings support the PLGS in DIA:CONN G8 as an effective algorithm to improve night-time hypoglycemia and GRI in people with T1DM.

Original Articles

Type 1 Diabetes
Optimal Coefficient of Variance Threshold to Minimize Hypoglycemia Risk in Individuals with Well-Controlled Type 1 Diabetes Mellitus: Jee Hee Yoo, Seung Hee Yang, Sang-Man Jin, Jae Hyeon Kim; Diabetes Metab J. 2024;48(3):429-439. Published online March 4, 2024; DOI: https://doi.org/10.4093/dmj.2023.0083

3,318 View
256 Download

Abstract PDF Supplementary Material PubReader 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
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

4,515 View
252 Download
1 Web of Science
1 Crossref

Abstract PDF Supplementary Material PubReader 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
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

5,326 View
161 Download
4 Web of Science
5 Crossref

Abstract PDF Supplementary Material PubReader 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
Disparities in Diabetes Technology Uptake in Youth and Young Adults With Type 1 Diabetes: A Global Perspective
Rebecca Baqiyyah Conway, Janet Snell-Bergeon, Kyoko Honda-Kohmo, Anil Kumar Peddi, Salbiah Binti Isa, Shakira Sulong, Laurien Sibomana, Andrea Gerard Gonzalez, Jooyoun Song, Kate Elizabeth Lomax, Ching-Nien Lo, Wondong Kim, Aveni Haynes, Martin de Bock, M
Journal of the Endocrine Society.2024;[Epub] 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

Reviews

Technology/Device
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

13,816 View
575 Download
26 Web of Science
33 Crossref

Abstract PDF PubReader ePub

Continuous glucose monitoring (CGM) technology has evolved over the past decade with the integration of various devices including insulin pumps, connected insulin pens (CIPs), automated insulin delivery (AID) systems, and virtual platforms. CGM has shown consistent benefits in glycemic outcomes in type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) treated with insulin. Moreover, the combined effect of CGM and education have been shown to improve glycemic outcomes more than CGM alone. Now a CIP is the expected future technology that does not need to be worn all day like insulin pumps and helps to calculate insulin doses with a built-in bolus calculator. Although only a few clinical trials have assessed the effectiveness of CIPs, they consistently show benefits in glycemic outcomes by reducing missed doses of insulin and improving problematic adherence. AID systems and virtual platforms made it possible to achieve target glycosylated hemoglobin in diabetes while minimizing hypoglycemia, which has always been challenging in T1DM. Now fully automatic AID systems and tools for diabetes decisions based on artificial intelligence are in development. These advances in technology could reduce the burden associated with insulin treatment for diabetes.

Citations

Citations to this article as recorded by

Inter-temporal dynamic joint learning model considering intra- and inter-day mutable correlations for blood glucose level prediction
Shuang Wen, Hongru Li, Yinghua Yang
Biomedical Signal Processing and Control.2025; 101: 107204. CrossRef
Glycemia Risk Index is Associated With Risk of Albuminuria Among Individuals With Type 1 Diabetes
Ji Yoon Kim, Jee Hee Yoo, Nam Hoon Kim, Jae Hyeon Kim
Journal of Diabetes Science and Technology.2025;[Epub] CrossRef
Real-World Life Analysis of a Continuous Glucose Monitoring and Smart Insulin Pen System in Type 1 Diabetes: A Cohort Study
Paola Pantanetti, Giovanni Cangelosi, Sara Morales Palomares, Gaetano Ferrara, Federico Biondini, Stefano Mancin, Gabriele Caggianelli, Mauro Parozzi, Marco Sguanci, Fabio Petrelli
Diabetology.2025; 6(1): 7. CrossRef
Abuse-deterrent wearable device with potential for extended delivery of opioid drugs
Myoung Ju Kim, Jae Min Park, Jun Su Lee, Ji Yang Lee, Juhui Lee, Chang Hee Min, Min Ji Kim, Jae Hoon Han, Eun Jung Kwon, Young Bin Choy
Biomedical Engineering Letters.2025; 15(2): 427. CrossRef
Recent advances in artificial intelligence-assisted endocrinology and diabetes
Ioannis T. Oikonomakos, Ranjit M. Anjana, Viswanathan Mohan, Charlotte Steenblock, Stefan R. Bornstein
Exploration of Endocrine and Metabolic Disease.2024; 1(1): 16. CrossRef
Accuracy and Safety of the 15-Day CareSens Air Continuous Glucose Monitoring System
Kyung-Soo Kim, Seung-Hwan Lee, Won Sang Yoo, Cheol-Young Park
Diabetes Technology & Therapeutics.2024; 26(4): 222. CrossRef
Real-World Continuous Glucose Monitoring Data from a Population with Type 1 Diabetes in South Korea: Nationwide Single-System Analysis
Ji Yoon Kim, Sang-Man Jin, Sarah B. Andrade, Boyang Chen, Jae Hyeon Kim
Diabetes Technology & Therapeutics.2024; 26(6): 394. CrossRef
Recent advances in the precision control strategy of artificial pancreas
Wuyi Ming, Xudong Guo, Guojun Zhang, Yinxia Liu, Yongxin Wang, Hongmei Zhang, Haofang Liang, Yuan Yang
Medical & Biological Engineering & Computing.2024; 62(6): 1615. CrossRef
Digital Health in Diabetes and Cardiovascular Disease
Dorothy Avoke, Abdallah Elshafeey, Robert Weinstein, Chang H. Kim, Seth S. Martin
Endocrine Research.2024; 49(3): 124. CrossRef
Continuous glucose monitoring with structured education in adults with type 2 diabetes managed by multiple daily insulin injections: a multicentre randomised controlled trial
Ji Yoon Kim, Sang-Man Jin, Kang Hee Sim, Bo-Yeon Kim, Jae Hyoung Cho, Jun Sung Moon, Soo Lim, Eun Seok Kang, Cheol-Young Park, Sin Gon Kim, Jae Hyeon Kim
Diabetologia.2024; 67(7): 1223. CrossRef
Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring
Anubhuti Juyal, Shradha Bisht, Mamta F. Singh
Blood Pressure Monitoring.2024; 29(5): 260. CrossRef
Emerging trends in functional molecularly imprinted polymers for electrochemical detection of biomarkers
Sanjida Yeasmin, Li-Jing Cheng
Biomicrofluidics.2024;[Epub] CrossRef
Continuous glucose monitoring in pregnant women with pregestational type 2 diabetes: a narrative review
Sylvia Ye, Ibrahim Shahid, Christopher J Yates, Dev Kevat, I-Lynn Lee
Obstetric Medicine.2024; 17(4): 194. CrossRef
Advancements in nanohybrid material-based acetone gas sensors relevant to diabetes diagnosis: A comprehensive review
Arpit Verma, Deepankar Yadav, Subramanian Natesan, Monu Gupta, Bal Chandra Yadav, Yogendra Kumar Mishra
Microchemical Journal.2024; 201: 110713. CrossRef
Current treatment options of diabetes mellitus type 1 in pediatric population
Petr Polák, Renata Pomahačová, Karel Fiklík, Petra Paterová, Josef Sýkora
Pediatrie pro praxi.2024; 25(3): 161. CrossRef
Efectividad de un sistema híbrido de circuito cerrado en pacientes con diabetes tipo 1 durante el ejercicio físico: un estudio descriptivo en la vida real
Ruben Martin-Payo, Maria del Mar Fernandez-Alvarez, Rebeca García-García, Ángela Pérez-Varela, Shelini Surendran, Isolina Riaño-Galán
Anales de Pediatría.2024; 101(3): 183. CrossRef
Effectiveness of a hybrid closed-loop system for children and adolescents with type 1 diabetes during physical exercise: A cross-sectional study in real life
Ruben Martin-Payo, Maria del Mar Fernandez-Alvarez, Rebeca García-García, Ángela Pérez-Varela, Shelini Surendran, Isolina Riaño-Galán
Anales de Pediatría (English Edition).2024; 101(3): 183. CrossRef
Real-time continuous glucose monitoring vs. self-monitoring of blood glucose: cost-utility in South Korean type 2 diabetes patients on intensive insulin
Ji Yoon Kim, Sabrina Ilham, Hamza Alshannaq, Richard F. Pollock, Waqas Ahmed, Gregory J. Norman, Sang-Man Jin, Jae Hyeon Kim
Journal of Medical Economics.2024; 27(1): 1245. CrossRef
Impact of missed insulin doses on glycaemic parameters in people with diabetes using smart insulin pens
Malavika Varma, David J T Campbell
Evidence Based Nursing.2024; : ebnurs-2024-104109. CrossRef
Clinical Trial Protocol for Porcine Islet Xenotransplantation in South Korea
Byung-Joon Kim, Jun-Seop Shin, Byoung-Hoon Min, Jong-Min Kim, Chung-Gyu Park, Hee-Jung Kang, Eung Soo Hwang, Won-Woo Lee, Jung-Sik Kim, Hyun Je Kim, Iov Kwon, Jae Sung Kim, Geun Soo Kim, Joonho Moon, Du Yeon Shin, Bumrae Cho, Heung-Mo Yang, Sung Joo Kim,
Diabetes & Metabolism Journal.2024; 48(6): 1160. CrossRef
Long-Term Benefits of Continuous Glucose Monitoring Combined with Insulin Pump Therapy
Rukhsana Zulfiqar, Komal Abbas, Saeeda Khan, Kanwal Fatima, Adnan Manzoor, Muhammad Awais
Indus Journal of Bioscience Research.2024; 2(2): 785. CrossRef
Continuous Glucose Monitoring—New Diagnostic Tool in Complex Pathophysiological Disorder of Glucose Metabolism in Children and Adolescents with Obesity
Marko Simunovic, Marko Kumric, Doris Rusic, Martina Paradzik Simunovic, Josko Bozic
Diagnostics.2024; 14(24): 2801. CrossRef
Innovations in Diabetes Management for Pregnant Women: Artificial Intelligence and the Internet of Medical Things
Ellen M. Murrin, Antonio F. Saad, Scott Sullivan, Yuri Millo, Menachem Miodovnik
American Journal of Perinatology.2024;[Epub] CrossRef
Glycemic Outcomes During Early Use of the MiniMed™ 780G Advanced Hybrid Closed-Loop System with Guardian™ 4 Sensor
Toni L. Cordero, Zheng Dai, Arcelia Arrieta, Fang Niu, Melissa Vella, John Shin, Andrew S. Rhinehart, Jennifer McVean, Scott W. Lee, Robert H. Slover, Gregory P. Forlenza, Dorothy I. Shulman, Rodica Pop-Busui, James R. Thrasher, Mark S. Kipnes, Mark P. Ch
Diabetes Technology & Therapeutics.2023; 25(9): 652. CrossRef
Navigating the Seas of Glycemic Control: The Role of Continuous Glucose Monitoring in Type 1 Diabetes Mellitus
Jun Sung Moon
Diabetes & Metabolism Journal.2023; 47(3): 345. CrossRef
APSec1.0: Innovative Security Protocol Design with Formal Security Analysis for the Artificial Pancreas System
Jiyoon Kim, Jongmin Oh, Daehyeon Son, Hoseok Kwon, Philip Virgil Astillo, Ilsun You
Sensors.2023; 23(12): 5501. CrossRef
Advances and Development of Electronic Neural Interfaces
Xue Jiaxiang, Liu Zhixin
Journal of Computing and Natural Science.2023; : 147. CrossRef
Continuous Glucose Monitoring (CGM) and Metabolic Control in a Cohort of Patients with Type 1 Diabetes and Coeliac Disease
Flavia Amaro, Maria Alessandra Saltarelli, Marina Primavera, Marina Cerruto, Stefano Tumini
Endocrines.2023; 4(3): 595. CrossRef
Comparison of Glycemia Risk Index with Time in Range for Assessing Glycemic Quality
Ji Yoon Kim, Jee Hee Yoo, Jae Hyeon Kim
Diabetes Technology & Therapeutics.2023; 25(12): 883. CrossRef
The Benefits Of Continuous Glucose Monitoring In Pregnancy
Jee Hee Yoo, Jae Hyeon Kim
Endocrinology and Metabolism.2023; 38(5): 472. CrossRef
The Growing Challenge of Diabetes Management in an Aging Society
Seung-Hwan Lee
Diabetes & Metabolism Journal.2023; 47(5): 630. CrossRef
An Observational Pilot Study of a Tailored Environmental Monitoring and Alert System for Improved Management of Chronic Respiratory Diseases
Mohammed Alotaibi, Fady Alnajjar, Badr A Alsayed, Tareq Alhmiedat, Ashraf M Marei, Anas Bushnag, Luqman Ali
Journal of Multidisciplinary Healthcare.2023; Volume 16: 3799. CrossRef
Smart Insulin Pen: Managing Insulin Therapy for People with Diabetes in the Digital Era
Jee Hee Yoo, Jae Hyeon Kim
The Journal of Korean Diabetes.2023; 24(4): 190. CrossRef

Guideline/Fact Sheet
Comprehensive Understanding for Application in Korean Patients with Type 2 Diabetes Mellitus of the Consensus Statement on Carbohydrate-Restricted Diets by Korean Diabetes Association, Korean Society for the Study of Obesity, and Korean Society of Hypertension: Jong Han Choi, Jee-Hyun Kang, Suk Chon; Diabetes Metab J. 2022;46(3):377-390. Published online May 25, 2022; DOI: https://doi.org/10.4093/dmj.2022.0051

7,138 View
283 Download
4 Web of Science
4 Crossref

Abstract PDF PubReader ePub

The Joint Committee of the Korean Diabetes Association, the Korean Society for the Study of Obesity, and the Korean Society of Hypertension announced a consensus statement on carbohydrate-restricted diets and intermittent fasting, representing an emerging and popular dietary pattern. In this statement, we recommend moderately-low-carbohydrate or low-carbohydrate diets, not a very-low-carbohydrate diet, for patients with type 2 diabetes mellitus. These diets can be considered a dietary regimen to improve glycemic control and reduce body weight in adults with type 2 diabetes mellitus. This review provides the detailed results of a meta-analysis and systematic literature review on the potential harms and benefits of carbohydrate-restricted diets in patients with diabetes. We expect that this review will help experts and patients by fostering an in-depth understanding and appropriate application of carbohydrate-restricted diets in the comprehensive management of diabetes.

Citations

Citations to this article as recorded by

The effects of low-carbohydrate diet on glucose and lipid metabolism in overweight or obese patients with T2DM: a meta-analysis of randomized controlled trials
Wende Tian, Shuyu Cao, Yongxin Guan, Zihao Zhang, Qiyu Liu, Jianqing Ju, Ruixi Xi, Ruina Bai
Frontiers in Nutrition.2025;[Epub] CrossRef
Efficacy of convenience meal-type foods designed for diabetes in the management of metabolic syndrome based on a 3-week trial
Do Gyeong Lee, In Gyeong Kang, Tae Seok Kim, Yun Ahn, Sang Yun Lee, Hye Jin Ahn, Yoo Kyoung Park
Nutrition.2024; 118: 112287. CrossRef
Long-Term Results of a Digital Diabetes Self-Management and Education Support Program Among Adults With Type 2 Diabetes: A Retrospective Cohort Study
Ashley Berthoumieux, Sarah Linke, Melinda Merry, Alison Megliola, Jessie Juusola, Jenna Napoleone
The Science of Diabetes Self-Management and Care.2024; 50(1): 19. CrossRef
Medical nutrition therapy for diabetes mellitus
Suk Chon
Journal of the Korean Medical Association.2023; 66(7): 421. 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

7,160 View
292 Download
4 Web of Science
3 Crossref

Abstract PDF PubReader 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

Citations to this article as recorded by

Pain-Related Responses in Preterm Babies Using Automated and Laser Heel-Lancing Devices
Hea Jin Lee, Myoung Soo Kim, Mi Lim Chung
Creative Nursing.2025;[Epub] CrossRef
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
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

7,072 View
363 Download
8 Web of Science
8 Crossref

Abstract PDF Supplementary Material PubReader 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

Citations to this article as recorded by

Continuous peri-operative glucose monitoring in noncardiac surgery
Alessandro Putzu, Elliot Grange, Raoul Schorer, Eduardo Schiffer, Karim Gariani
European Journal of Anaesthesiology.2025; 42(2): 162. CrossRef
Reality of post-gastrectomy stress hyperglycemia revealed by continuous glucose monitoring: a prospective study
Keiji Nishibeppu, Takeshi Kubota, Yudai Nakabayashi, Hiroyuki Inoue, Kazuya Takabatake, Takuma Ohashi, Hirotaka Konishi, Atsushi Shiozaki, Hitoshi Fujiwara, Eigo Otsuji
Surgery Today.2025;[Epub] CrossRef
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
Consensus Considerations and Good Practice Points for Use of Continuous Glucose Monitoring Systems in Hospital Settings
Julie L.V. Shaw, Raveendhara R. Bannuru, Lori Beach, Nuha A. ElSayed, Guido Freckmann, Anna K. Füzéry, Angela W.S. Fung, Jeremy Gilbert, Yun Huang, Nichole Korpi-Steiner, Samantha Logan, Rebecca Longo, Dylan MacKay, Lisa Maks, Stefan Pleus, Kendall Rogers
Diabetes Care.2024; 47(12): 2062. 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
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

20,663 View
974 Download
52 Web of Science
49 Crossref

Graphical Abstract Abstract PDF Supplementary Material PubReader ePub

Since Banting and Best isolated insulin in the 1920s, dramatic progress has been made in the treatment of type 1 diabetes mellitus (T1DM). However, dose titration and timely injection to maintain optimal glycemic control are often challenging for T1DM patients and their families because they require frequent blood glucose checks. In recent years, technological advances in insulin pumps and continuous glucose monitoring systems have created paradigm shifts in T1DM care that are being extended to develop artificial pancreas systems (APSs). Numerous studies that demonstrate the superiority of glycemic control offered by APSs over those offered by conventional treatment are still being published, and rapid commercialization and use in actual practice have already begun. Given this rapid development, keeping up with the latest knowledge in an organized way is confusing for both patients and medical staff. Herein, we explore the history, clinical evidence, and current state of APSs, focusing on various development groups and the commercialization status. We also discuss APS development in groups outside the usual T1DM patients and the administration of adjunct agents, such as amylin analogues, in APSs.

Citations

Citations to this article as recorded by

Implantable Fluorogenic DNA Biosensor for Stress Detection
Irina Drachuk, Namrata Ramani, Svetlana Harbaugh, Chad A. Mirkin, Jorge L. Chávez
ACS Applied Materials & Interfaces.2025; 17(1): 130. CrossRef
CHoKI-based MPC for blood glucose regulation in Artificial Pancreas
Beatrice Sonzogni, José María Manzano, Marco Polver, Fabio Previdi, Antonio Ferramosca
IFAC Journal of Systems and Control.2025; 31: 100294. CrossRef
Automated insulin delivery in pregnant women with type 1 diabetes: a systematic review and meta-analysis
Qin Yang, Jiayi Hao, Huijing Cui, Qingqing Yang, Feng Sun, Baoqi Zeng
Acta Diabetologica.2025;[Epub] CrossRef
Designing and developing a prescription digital therapeutic for at-home heart rate variability biofeedback to support and enhance patient outcomes in post-traumatic stress disorder treatment
Rebecca Macy, Flavio Somanji, Oleksandr Sverdlov
Frontiers in Digital Health.2025;[Epub] CrossRef
Generalized reinforcement learning control algorithm for fully automated insulin delivery system
Vega Pradana Rachim, Junyoung Yoo, Jaeyeon Lee, Yein Lee, Sung-Min Park
Expert Systems with Applications.2025; 274: 126909. CrossRef
Integration of a Safety Module to Prevent Rebound Hypoglycemia in Closed-Loop Artificial Pancreas Systems
María F. Villa-Tamayo, Patricio Colmegna, Marc D. Breton
Journal of Diabetes Science and Technology.2024; 18(2): 318. CrossRef
The effects of acute hyperglycaemia on sports and exercise performance in type 1 diabetes: A systematic review and meta-analysis
Bonar McGuire, Hashim Dadah, Dominic Oliver
Journal of Science and Medicine in Sport.2024; 27(2): 78. CrossRef
A new approach to stabilize diabetes systems with time-varying delays and disturbance rejection
S. Syafiie, Fahd Alharbi, Abdullah Ali Alshehri, Bassam Hasanain
Journal of the Franklin Institute.2024; 361(1): 543. CrossRef
Effects of Low-Dose Glucagon on Subcutaneous Insulin Absorption in Pigs
Ingrid Anna Teigen, Marte Kierulf Åm, Misbah Riaz, Sverre Christian Christiansen, Sven Magnus Carlsen
Current Therapeutic Research.2024; 100: 100736. CrossRef
Enhancing equity in access to automated insulin delivery systems in an ethnically and socioeconomically diverse group of children with type 1 diabetes
John Pemberton, Louise Collins, Lesley Drummond, Renuka P Dias, Ruth Krone, Melanie Kershaw, Suma Uday
BMJ Open Diabetes Research & Care.2024; 12(3): e004045. CrossRef
Robust Online Correlation Method for Identification of a Nonparametric Model of Type 1 Diabetes
Martin Dodek, Eva Miklovičová
IEEE Access.2024; 12: 35899. CrossRef
Comparison between a tubeless, on-body automated insulin delivery system and a tubeless, on-body sensor-augmented pump in type 1 diabetes: a multicentre randomised controlled trial
Ji Yoon Kim, Sang-Man Jin, Eun Seok Kang, Soo Heon Kwak, Yeoree Yang, Jee Hee Yoo, Jae Hyun Bae, Jun Sung Moon, Chang Hee Jung, Ji Cheol Bae, Sunghwan Suh, Sun Joon Moon, Sun Ok Song, Suk Chon, Jae Hyeon Kim
Diabetologia.2024; 67(7): 1235. CrossRef
Prevention and treatment of type 1 diabetes: in search of the ideal combination therapy targeting multiple immunometabolic pathways
Marcelo Maia Pinheiro, Felipe Moura Maia Pinheiro, Maria Luisa Garo, Donatella Pastore, Francesca Pacifici, Camillo Ricordi, David Della-Morte, Marco Infante
Metabolism and Target Organ Damage.2024;[Epub] CrossRef
Mitigating diabetes associated with reactive oxygen species (ROS) and protein aggregation through pharmacological interventions
Giulia Bennici, Hanan Almahasheer, Mawadda Alghrably, Daniela Valensin, Arian Kola, Chrysoula Kokotidou, Joanna Lachowicz, Mariusz Jaremko
RSC Advances.2024; 14(25): 17448. CrossRef
Real‐world glycaemic outcomes of automated insulin delivery in type 1 diabetes: A meta‐analysis
Qin Yang, Baoqi Zeng, Jiayi Hao, Qingqing Yang, Feng Sun
Diabetes, Obesity and Metabolism.2024; 26(9): 3753. CrossRef
Efficacy of advanced hybrid closed loop systems in cystic fibrosis related diabetes: a pilot study
Marta Bassi, Daniele Franzone, Francesca Dufour, Giordano Spacco, Federico Cresta, Giuseppe d’Annunzio, Giacomo Tantari, Maria Grazia Calevo, Carlo Castellani, Nicola Minuto, Rosaria Casciaro
Frontiers in Endocrinology.2024;[Epub] CrossRef
Emerging Technologies in Endocrine Drug Delivery: Innovations for Improved Patient Care
Mahvish Renzu, Carly Hubers, Kendall Conway, Viktoriya Gibatova, Vidhi Mehta, Wael Taha
Cureus.2024;[Epub] CrossRef
A stochastic model-based control methodology for glycemic management in the intensive care unit
Melike Sirlanci, George Hripcsak, Cecilia C. Low Wang, J. N. Stroh, Yanran Wang, Tellen D. Bennett, Andrew M. Stuart, David J. Albers
Frontiers in Medical Engineering.2024;[Epub] CrossRef
Advances in Type 1 Diabetes Mellitus Management in Children
Mridu Bahal, Vineeta Pande, Jasleen Dua, Shailaja Mane
Cureus.2024;[Epub] CrossRef
Expansion of the Pancreas Transplant Recipient Pool: Appropriate for Most or Are There Limits?
Jonathan A. Fridell, Robert J. Stratta
Current Transplantation Reports.2024; 11(4): 276. CrossRef
Reinforcement Learning: A Paradigm Shift in Personalized Blood Glucose Management for Diabetes
Lehel Dénes-Fazakas, László Szilágyi, Levente Kovács, Andrea De Gaetano, György Eigner
Biomedicines.2024; 12(9): 2143. CrossRef
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; 40(12): 2095. CrossRef
A novel pulse-modulated closed-loop artificial pancreas based on intravenous administration of insulin and glucagon
Simon L. Goede, Victor H. Snels, Willem-Jan W. J. H. Berghuis, Jan P. C. Bernards, Urs Wyder
Scientific Reports.2024;[Epub] CrossRef
100 Years of insulin: A chemical engineering perspective
B. Wayne Bequette
Korean Journal of Chemical Engineering.2023; 40(1): 1. CrossRef
Efficacy of intermittent short‐term use of a real‐time continuous glucose monitoring system in non‐insulin–treated patients with type 2 diabetes: A randomized controlled trial
Sun Joon Moon, Kyung‐Soo Kim, Woo Je Lee, Mi Yeon Lee, Robert Vigersky, Cheol‐Young Park
Diabetes, Obesity and Metabolism.2023; 25(1): 110. CrossRef
Identifiable prediction animal model for the bi-hormonal intraperitoneal artificial pancreas
Karim Davari Benam, Hasti Khoshamadi, Marte Kierulf Åm, Øyvind Stavdahl, Sebastien Gros, Anders Lyngvi Fougner
Journal of Process Control.2023; 121: 13. CrossRef
Advances in Continuous Glucose Monitoring and Integrated Devices for Management of Diabetes with Insulin-Based Therapy: Improvement in Glycemic Control
Jee Hee Yoo, Jae Hyeon Kim
Diabetes & Metabolism Journal.2023; 47(1): 27. CrossRef
CGM accuracy: Contrasting CE marking with the governmental controls of the USA (FDA) and Australia (TGA): A narrative review
John S Pemberton, Emma G Wilmot, Katharine Barnard‐Kelly, Lalantha Leelarathna, Nick Oliver, Tabitha Randell, Craig E Taplin, Pratik Choudhary, Peter Adolfsson
Diabetes, Obesity and Metabolism.2023; 25(4): 916. CrossRef
Evaluation of awareness and attitude of paediatric nursing students, nurses, and adolescents regarding type one diabetes advanced devices and virtual nursing
Howaida Moawad Ahmed Ali
Kontakt.2023; 25(2): 100. CrossRef
Predicting the output error of the suboptimal state estimator to improve the performance of the MPC-based artificial pancreas
Martin Dodek, Eva Miklovičová
Control Theory and Technology.2023; 21(4): 541. CrossRef
A Markov Model of Gap Occurrence in Continuous Glucose Monitoring Data for Realistic in Silico Clinical Trials
Martina Vettoretti, Martina Drecogna, Simone Del Favero, Andrea Facchinetti, Giovanni Sparacino
Computer Methods and Programs in Biomedicine.2023; 240: 107700. CrossRef
Drug delivery breakthrough technologies – A perspective on clinical and societal impact
Beate Bittner, Manuel Sánchez-Félix, Dennis Lee, Athanas Koynov, Joshua Horvath, Felix Schumacher, Simon Matoori
Journal of Controlled Release.2023; 360: 335. CrossRef
Importance of continuous glucose monitoring in the treatment of diabetes mellitus
Sun Joon Moon, Won-Young Lee
Journal of the Korean Medical Association.2023; 66(7): 432. CrossRef
Constrained Versus Unconstrained Model Predictive Control for Artificial Pancreas
Chiara Toffanin, Lalo Magni
IEEE Transactions on Control Systems Technology.2023; 31(5): 2288. CrossRef
Intelligent Insulin vs. Artificial Intelligence for Type 1 Diabetes: Will the Real Winner Please Stand Up?
Valentina Maria Cambuli, Marco Giorgio Baroni
International Journal of Molecular Sciences.2023; 24(17): 13139. CrossRef
Artificial Intelligence in Efficient Diabetes Care
Gopal Bhagwan Khodve, Sugato Banerjee
Current Diabetes Reviews.2023;[Epub] CrossRef
The artificial pancreas: two alternative approaches to achieve a fully closed-loop system with optimal glucose control
M. K. Åm, I. A. Teigen, M. Riaz, A. L. Fougner, S. C. Christiansen, S. M. Carlsen
Journal of Endocrinological Investigation.2023; 47(3): 513. CrossRef
Multivariable Automated Insulin Delivery System for Handling Planned and Spontaneous Physical Activities
Mohammad Reza Askari, Mohammad Ahmadasas, Andrew Shahidehpour, Mudassir Rashid, Laurie Quinn, Minsun Park, Ali Cinar
Journal of Diabetes Science and Technology.2023; 17(6): 1456. CrossRef
Advanced Technology (Continuous Glucose Monitoring and Advanced Hybrid Closed-Loop Systems) in Diabetes from the Perspective of Gender Differences
Maria Grazia Nuzzo, Marciano Schettino
Diabetology.2023; 4(4): 519. CrossRef
Artificial Pancreas under a Zone Model Predictive Control based on Gaussian Process models: toward the personalization of the closed loop
Marco Polver, Beatrice Sonzogni, Mirko Mazzoleni, Fabio Previdi, Antonio Ferramosca
IFAC-PapersOnLine.2023; 56(2): 9642. CrossRef
Personalized Constrained MPC for glucose regulation
Chiara Toffanin, Lalo Magni
IFAC-PapersOnLine.2023; 56(2): 9648. CrossRef
Automated Insulin Delivery Systems in Children and Adolescents With Type 1 Diabetes: A Systematic Review and Meta-analysis of Outpatient Randomized Controlled Trials
Baoqi Zeng, Le Gao, Qingqing Yang, Hao Jia, Feng Sun
Diabetes Care.2023; 46(12): 2300. CrossRef
Novel Glycemic Index Based on Continuous Glucose Monitoring to Predict Poor Clinical Outcomes in Critically Ill Patients: A Pilot Study
Eun Yeong Ha, Seung Min Chung, Il Rae Park, Yin Young Lee, Eun Young Choi, Jun Sung Moon
Frontiers in Endocrinology.2022;[Epub] CrossRef
Dual‐hormone artificial pancreas for glucose control in type 1 diabetes: A meta‐analysis
Baoqi Zeng, Hao Jia, Le Gao, Qingqing Yang, Kai Yu, Feng Sun
Diabetes, Obesity and Metabolism.2022; 24(10): 1967. CrossRef
Dual-Hormone Insulin-and-Pramlintide Artificial Pancreas for Type 1 Diabetes: A Systematic Review
Alezandra Torres-Castaño, Amado Rivero-Santana, Lilisbeth Perestelo-Pérez, Andrea Duarte-Díaz, Analia Abt-Sacks, Vanesa Ramos-García, Yolanda Álvarez-Pérez, Ana M. Wäagner, Mercedes Rigla, Pedro Serrano-Aguilar
Applied Sciences.2022; 12(20): 10262. CrossRef
History of insulin treatment of pediatric patients with diabetes in Korea
Jae Hyun Kim, Choong Ho Shin, Sei Won Yang
Annals of Pediatric Endocrinology & Metabolism.2021; 26(4): 237. CrossRef

Short Communication

Type 1 Diabetes
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

5,555 View
160 Download

Abstract PDF PubReader 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
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

9,908 View
474 Download
6 Web of Science
6 Crossref

Graphical Abstract Abstract PDF Supplementary Material PubReader 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

Citations to this article as recorded by

Time in range—A new gold standard in type 2 diabetes research?
Ashni Goshrani, Rose Lin, David O'Neal, Elif I. Ekinci
Diabetes, Obesity and Metabolism.2025;[Epub] CrossRef
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
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

11,502 View
484 Download
2 Web of Science
4 Crossref

Graphical Abstract Abstract PDF Supplementary Material PubReader ePub

Background
To compare the efficacy and safety of two insulin self-titration algorithms, Implementing New Strategies with Insulin Glargine for Hyperglycemia Treatment (INSIGHT) and EDITION, for insulin glargine 300 units/mL (Gla-300) in Korean individuals with uncontrolled type 2 diabetes mellitus (T2DM).
Methods
In a 12-week, randomized, open-label trial, individuals with uncontrolled T2DM requiring basal insulin were randomized to either the INSIGHT (adjusted by 1 unit/day) or EDITION (adjusted by 3 units/week) algorithm to achieve a fasting self-monitoring of blood glucose (SMBG) in the range of 4.4 to 5.6 mmol/L. The primary outcome was the proportion of individuals achieving a fasting SMBG ≤5.6 mmol/L without noct urnal hypoglycemia at week 12.
Results
Of 129 individuals (age, 64.1±9.5 years; 66 [51.2%] women), 65 and 64 were randomized to the INSIGHT and EDITION algorithms, respectively. The primary outcome of achievement was comparable between the two groups (24.6% vs. 23.4%, P=0.876). Compared with the EDITION group, the INSIGHT group had a greater reduction in 7-point SMBG but a similar decrease in fasting plasma glucose and glycosylated hemoglobin. The increment of total daily insulin dose was significantly higher in the INSIGHT group than in the EDITION group (between-group difference: 5.8±2.7 units/day, P=0.033). However, body weight was significantly increased only in the EDITION group (0.6±2.4 kg, P=0.038). There was no difference in the occurrence of hypoglycemia between the two groups. Patient satisfaction was significantly increased in the INSIGHT group (P=0.014).
Conclusion
The self-titration of Gla-300 using the INSIGHT algorithm was effective and safe compared with that using the EDITION algorithm in Korean individuals with uncontrolled T2DM (ClinicalTrials.gov number: NCT03406663).

Citations

Citations to this article as recorded by

Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? A Systematic Review of Basal Insulin Dose Guidance
Camilla Heisel Nyholm Thomsen, Stine Hangaard, Thomas Kronborg, Peter Vestergaard, Ole Hejlesen, Morten Hasselstrøm Jensen
Journal of Diabetes Science and Technology.2024; 18(5): 1185. CrossRef
Comparative efficacy and safety of weekly tirzepatide versus weekly insulin in type 2 diabetes: A network meta‐analysis of randomized clinical trials
Hazem Ayesh, Sajida Suhail, Suhail Ayesh, Kevin Niswender
Diabetes, Obesity and Metabolism.2024; 26(9): 3801. CrossRef
Basal insulin titration algorithms in patients with type 2 diabetes: the simplest is the best (?)
V.I. Katerenchuk
INTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine).2023; 19(1): 72. CrossRef
Issues of insulin therapy for type 2 diabetes and ways to solve them
V.I. Katerenchuk, A.V. Katerenchuk
INTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine).2023; 19(3): 240. CrossRef

Review

Type 1 Diabetes
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

13,081 View
533 Download
47 Web of Science
49 Crossref

Abstract PDF PubReader 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.

Citations

Citations to this article as recorded by

Improved Glycemic Control in Insulin-Treated Individuals With Poorly Controlled Type 2 Diabetes Through Combined Structured Education With Real-Time Continuous Glucose Monitoring
Jee Hee Yoo, Ji Eun Jun, Soo Heon Kwak, Jae Hyeon Kim
Journal of Diabetes Science and Technology.2025;[Epub] CrossRef
Quantification of the relation between continuous glucose monitoring observation period and the estimation error in assessing long-term glucose regulation
Stennie Zoet, Thomas Urgert, Anouk Veldhuis, Bert-Jan van Beijnum, Gozewijn D Laverman
BMJ Open Diabetes Research & Care.2025; 13(1): e004768. CrossRef
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; 18(1): 67. 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; 26(12): 960. 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

Original Article

Basic Research
Role of Intestinal Microbiota in Metabolism of Voglibose In Vitro and In Vivo: Mahesh Raj Nepal, Mi Jeong Kang, Geon Ho Kim, Dong Ho Cha, Ju-Hyun Kim, Tae Cheon Jeong; Diabetes Metab J. 2020;44(6):908-918. Published online April 6, 2020; DOI: https://doi.org/10.4093/dmj.2019.0147

7,084 View
136 Download
6 Web of Science
7 Crossref

Abstract PDF Supplementary Material PubReader ePub

Background

Voglibose, an α-glucosidase inhibitor, inhibits breakdown of complex carbohydrates into simple sugar units in intestine. Studies showed that voglibose metabolism in the liver might be negligible due to its poor intestinal absorption. Numerous microorganisms live in intestine and have several roles in metabolism and detoxification of various xenobiotics. Due to the limited information, the possible metabolism of voglibose by intestinal microbiota was investigated in vitro and in vivo.

Methods

For the in vitro study, different concentrations of voglibose were incubated with intestinal contents, prepared from both vehicle- and antibiotics-treated mice, to determine the decreased amount of voglibose over time by using liquid chromatography-mass spectrometry. Similarly, in vivo pharmacodynamic effect of voglibose was determined following the administration of voglibose and starch in vehicle- and antibiotic-pretreated non-diabetic and diabetic mice, by measuring the modulatory effects of voglibose on blood glucose levels.

Results

The in vitro results indicated that the remaining voglibose could be significantly decreased when incubated with the intestinal contents from normal mice compared to those from antibiotic-treated mice, which had less enzyme activities. The in vivo results showed that the antibiotic pretreatment resulted in reduced metabolism of voglibose. This significantly lowered blood glucose levels in antibiotic-pretreated mice compared to the control animals.

Conclusion

The present results indicate that voglibose would be metabolized by the intestinal microbiota, and that this metabolism might be pharmacodynamically critical in lowering blood glucose levels in mice.

Citations

Citations to this article as recorded by

A Novel Stability Indicating Analytical Development and Validation of an RP-HPLC Assay Method for the Quantification of Voglibose in Bulk and its Formulation
Md. Nazmul Sardar, Ekhlass Uddin, Md. Shahin Reza, Shajahan Talukdar Sunny, Md. Faysal Khan Shuvo, Md. Mamun Hossain, Md. Faruk Hossen, Md. Ali Asraf, Md. Kudrat-E-Zahan
Asian Journal of Research in Chemistry.2024; : 344. CrossRef
Pharmacomicrobiomics and type 2 diabetes mellitus: A novel perspective towards possible treatment
Liyang Jia, Shiqiong Huang, Boyu Sun, Yongguang Shang, Chunsheng Zhu
Frontiers in Endocrinology.2023;[Epub] CrossRef
Phenolics from endophytic fungi as natural α-glucosidase inhibitors: A comprehensive review
Muhammad Imran Tousif, Saba Tauseef, Sadeer Nabeelah, Jugreet Sharmeen, Gokhan Zengin, Lesetja Legoabe, Muhammad Imran, Mohamad Fawzi Mahomoodally
Journal of Molecular Structure.2023; 1291: 135852. CrossRef
Ligand-targeted fishing of α-glucosidase inhibitors from Tribulus terrestris L. based on chitosan-functionalized multi-walled carbon nanotubes with immobilized α-glucosidase
Xin Meng, Hou Zong, Zhong Zheng, Junpeng Xing, Zhiqiang Liu, Fengrui Song, Shu Liu
Analytical and Bioanalytical Chemistry.2023; 415(14): 2677. CrossRef
Isolation, structure elucidation, and biological activities of sesquiterpenes and phthalides from two edible mushrooms Pleurotus species
Jewel C De Padua, Emi Fukushima-Sakuno, Kotomi Ueno, Thomas Edison E dela Cruz, Atsushi Ishihara
Bioscience, Biotechnology, and Biochemistry.2023; 87(12): 1429. CrossRef
Effects of Oral Glucose-Lowering Agents on Gut Microbiota and Microbial Metabolites
Dongmei Wang, Jieying Liu, Liyuan Zhou, Qian Zhang, Ming Li, Xinhua Xiao
Frontiers in Endocrinology.2022;[Epub] CrossRef
18:0 Lyso PC, a natural product with potential PPAR-γ agonistic activity, plays hypoglycemic effect with lower liver toxicity and cardiotoxicity in db/db mice
Yiming Ma, Xinyi Du, Dandan Zhao, Kegong Tang, Xiaona Wang, Shaoting Guo, Xiaobei Li, Song Mei, Na Sun, Jiaqi Liu, Chengyu Jiang
Biochemical and Biophysical Research Communications.2021; 579: 168. CrossRef

First
Prev
Page of 3
Next
Last

Search