Diabetes & Metabolism Journal

Original Article

Genetics
Evaluation of Sex-Stratified Polygenic Risk Scores for Type 2 Diabetes Mellitus and Glycemic Traits in the Framingham Heart Study: Ningyuan Wang, Yixin Zhang, Philip Schroeder, Alicia Huerta-Chagoya, Ravi Mandla, James B. Meigs, Alisa K. Manning, Ching-Ti Liu, Josée Dupuis, Josep M. Mercader; Received June 25, 2025 Accepted October 14, 2025 Published online December 9, 2025; DOI: https://doi.org/10.4093/dmj.2025.0557 [Epub ahead of print]

401 View
21 Download

Abstract PDF Supplementary Material PubReader ePub: Background
Diabetes is a multifactorial disease with significant genetic predisposition. Polygenic risk scores (PRS) have been developed to estimate an individual’s genetic risk of a disease. Traditionally, PRS utilize sex-combined genome-wide association studies (GWAS) due to the limited availability of sex-stratified summary statistics. This study explores sex-dimorphic genetic effects and evaluates the potential benefits of incorporating sex-stratified effects in PRS for type 2 diabetes mellitus (T2DM) and glycemic traits by comparing PRS performance derived from sex-combined versus sex-stratified GWAS.
Methods
We performed a sex-heterogeneity test across sex-specific GWAS and identified nine signals with sex-dimorphic effects for T2DM. PRS[sex-combined] and PRS[sex-stratified] were developed using sex-combined and sex-stratified GWAS results for T2DM (41,444 cases and 354,539 controls), fasting glucose (n=120,595) and fasting insulin (n=98,210). We evaluated these PRS models in 8,379 participants (1,303 cases and 7,076 controls) from the Framingham Heart Study not included in the PRS derivation.
Results
Our findings suggest that sex-combined PRS currently offer better predictive performance for T2DM and glycemic traits.
Conclusion
These results highlight the need for larger sex-stratified studies and the optimization of sex-stratified risk models for clinical practice.

Brief Report

Technology/Device
Effectiveness of the Stage 4 Smart Insulin Pen DIA:CONN P8 for Glycemic Control in a Real-World Setting: So Yoon Kwon, Hyoseon Kwak, Jae Hyeon Kim; Received February 11, 2025 Accepted March 23, 2025 Published online September 3, 2025; DOI: https://doi.org/10.4093/dmj.2025.0112 [Epub ahead of print]

1,124 View
64 Download

Abstract PDF Supplementary Material PubReader ePub: This study evaluated whether a stage 4 smart insulin pen (SIP) provides superior glycemic control compared with a traditional insulin pen (TIP) in individuals with intensively insulin-treated diabetes. Forty-two adults with continuous glucose monitoring (CGM), multiple daily insulin injections, and no prior SIP use were included. After diabetes self-management education (DSME), the SIP group (n=21) initiated SIP, whereas the TIP group (n=21) continued their usual regimens. Glycemic metrics were assessed using CGM before and 2 weeks after DSME. Both groups demonstrated significant improvements in glycemic outcomes. However, SIP users exhibited superior improvements in the percentage of time in range, percentage of time below range (%TBR) <70 mg/dL, %TBR <54 mg/dL, and glycemic risk index compared with TIP users (between-group difference [BD] 11.0%, P=0.046; BD –2.6%, P=0.024; BD –0.9%, P=0.027; BD –18.2, P=0.022, respectively). These findings suggest that SIP, with its bolus calculation and CGM integration, is associated with improved glycemic outcomes in adults with intensively insulin-treated diabetes.

Original Articles

Basic and Translational Research
Effect of 4 Weeks Resonance Frequency Breathing on Glucose Metabolism and Autonomic Tone in Healthy Adults: Benedict Herhaus, Andreas Peter, Julia Hummel, Thomas Kubiak, Martin Heni, Katja Petrowski; Diabetes Metab J. 2025;49(6):1219-1228. Published online April 3, 2025; DOI: https://doi.org/10.4093/dmj.2024.0647

3,127 View
110 Download
1 Web of Science
1 Crossref

Abstract PDF Supplementary Material PubReader ePub

Background
The autonomic nervous system plays a crucial role in the brain’s communication with metabolically important peripheral organs, modulating insulin sensitivity and secretion. Increased sympathetic tone is a common feature in prediabetes and diabetes. The parasympathetic nervous system activity might be improvable through resonance frequency breathing (RFB) with heart rate variability biofeedback (HRV-BF) training.
Methods
We here investigated the effect of a 4-week mobile RFB-HRV-BF intervention on glucose metabolism and HRV of 30 healthy adults (17 females; mean age 25.77±3.64 years; mean body mass index 22.65±2.95 kg/m²). Before and after the intervention, glucose metabolism was assessed by 75 g oral glucose tolerance tests (with blood sampling every 30 minutes over 2 hours) and HRV was measured through electrocardiography.
Results
RFB-HRV-BF training did not influence glucose metabolism in healthy adults but reduced fasting as well as 2-hour-postload glucose in participants categorized as more insulin resistant before the intervention. In addition, RFB-HRV-BF training was associated with an increase in the time and frequency domain HRV parameters standard deviation of all NN-intervals, root mean square successive differences, HRV high-frequency and HRV low-frequency after 4 weeks of intervention.
Conclusion
Our findings introduce RFB-HRV-BF training as an effective tool to modulate the autonomic nervous system with a shift towards the parasympathetic tone. Along with the observed decrease in glycemia in those with lower insulin sensitivity, RFB-HRV-BF training emerges as a promising non-pharmacological approach to improve glucose metabolism which has to be further investigated in prediabetes and diabetes.

Citations

Citations to this article as recorded by

The autonomic nervous system in the regulation of glucose and lipid metabolism
Sabrina Wangler, Marc N. Jarczok, Matthew Ennis, Benedict Herhaus, Róbert Wagner, Ratika Sehgal, Martin Heni
Nature Reviews Endocrinology.2026;[Epub] CrossRef

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; Diabetes Metab J. 2025;49(4):883-892. Published online February 13, 2025; DOI: https://doi.org/10.4093/dmj.2024.0537

6,199 View
263 Download
6 Web of Science
9 Crossref

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.

Citations

Citations to this article as recorded by

GLDC attenuates liver ischemia-reperfusion injury by inhibiting macrophage recruitment and activation via PTBP1/P2RY6
Zhitao Li, Li Jin, Yuan Fang, Siming Qu, Bo Yuan, Kai Gan, Hanfei Huang
Cellular Signalling.2025; 135: 111976. CrossRef
The Physiological and Pathological Mechanisms of LIN2, LIN7, LIN10 and Their Tripartite Complex
Yangyang Shang, Xinyi Gan, Yue Dang, Jie Liu, Peijun Liu
Journal of Cellular and Molecular Medicine.2025;[Epub] CrossRef
Cinnamic Acid: A Shield Against High-Fat-Diet-Induced Liver Injury—Exploring Nrf2’s Protective Mechanisms
Asmahan Taher Alahdal, Laila Naif Al-Harbi, Ghedeir M. Alshammari, Ali Saleh, Mohammed Abdo Yahya
International Journal of Molecular Sciences.2025; 26(16): 7940. CrossRef
Sodium Butyrate Ameliorated Bile Acid Metabolism in Diabetes Mellitus by PI3K/AKT Signaling Pathway via the Gut–Liver Axis
Tingting Zhao, Xi Zhang, Qian Xiang, Yadi Liu, Xuling Li, Junling Gu, Wenqian Zhang, Zhe Wang, Yiran Li, Xiaoshan Lai, Yonghua Zhao, Youhua Xu
Current Issues in Molecular Biology.2025; 47(9): 732. CrossRef
Chronic Intermittent Low-Pressure Hypoxia Suppresses Inflammation and Regulates Glycolipids by Modulating Mitochondrial Respiration in db/db Mice
Xin Jiang, Keqing Yuan, Xiaofeng Ge, Lili Yu, Yufei Cui, Lianhai Jin, Ying Chang
Metabolites.2025; 15(11): 707. CrossRef
Expanding horizons: the role of robotic surgery in modern transplantation practices
Arya Afrooghe, Pedram Pirmoradian, Moein Ghasemi, Benyamin Mohammadi, Mahya Soleymani Mehranjani, Elham Ahmadi, Seyed Amir Miratashi Yazdi
Journal of Robotic Surgery.2025;[Epub] CrossRef
Elevated PEDF promotes the occurrence of diabetes mellitus via suppressing GSIS by downregulating the SNARE complex
Zhen Zhao, Yandan Tan, Jie Fang, Gan Xia, Junchen Li, Qilong Tang, Wanting Xie, Tianxiao Gao, Zhenzhen Fang, Ti Zhou, Xia Yang, Guoquan Gao, Weiwei Qi
Communications Biology.2025;[Epub] CrossRef
Distinct Circulating Biomarker Profiles Associated with Type 2 Diabetes in a Regional Cohort—A Cross-Sectional Study
Abdullah Alsrhani, Muhammad Atif, Aisha Farhana
Metabolites.2025; 15(12): 776. CrossRef
Application and Predictive Potential of Novel Insulin Resistance Assessment Indices in Metabolic Diseases
莹莹郑
Advances in Clinical Medicine.2025; 15(12): 2174. CrossRef

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

4,582 View
174 Download
2 Web of Science
2 Crossref

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.

Citations

Citations to this article as recorded by

Scoping review of subcutaneous glucose monitoring techniques
Eva Hrubá, Jan Kubíček, Martin Augustynek
Measurement.2026; 261: 119940. CrossRef
Current Status of Continuous Glucose Monitoring Use in South Korean Type 1 Diabetes Mellitus Population–Pronounced Age-Related Disparities: Nationwide Cohort Study
Ji Yoon Kim, Seohyun Kim, Jae Hyeon Kim
Diabetes & Metabolism Journal.2025; 49(5): 1040. CrossRef

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

9,240 View
305 Download
5 Web of Science
5 Crossref

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.

Citations

Citations to this article as recorded by

Glycemic variability before and during Ramadan fasting among adults with type 2 diabetes and hepatic cirrhosis: a prospective paired cohort using real‑time CGM
Sarah Firdausa, Irsan Hasan, Dicky L. Tahapary, Ignatius Bima Prasetya, Suharko Soebardi, Cleopas Martin Rumende, Hamzah Shatri, Cosphiadi Irawan, Wismandari Wisnu
Diabetes Research and Clinical Practice.2026; 231: 112955. CrossRef
Plasma C-Peptide Level and Continuous Glucose Monitoring-Derived Coefficient of Variation as a Predictable Risk Factor for Hypoglycemia in Koreans with Diabetes
Seung-Hyun Ko
Endocrinology and Metabolism.2025; 40(2): 198. CrossRef
Time in tight range and time in range for predicting the achievement of typical glucose management indicator and HbA1c targets
Seohyun Kim, Sang Ho Park, Jin A. Lee, So Hyun Cho, Rosa Oh, Ji Yoon Kim, You-Bin Lee, Gyuri Kim, Kyu Yeon Hur, Jae Hyeon Kim, Sang-Man Jin
Diabetologia.2025; 68(8): 1674. CrossRef
Associations of time in tight range, time in range, and glycated hemoglobin with albuminuria in type 1 diabetes: A cross-sectional study
Ji Yoon Kim, Seohyun Kim, Sang Ho Park, Jin A Lee, So Hyun Cho, Rosa Oh, Myunghwa Jang, You-Bin Lee, Gyuri Kim, Kyu Yeon Hur, Jae Hyeon Kim, Sang-Man Jin
Diabetes Research and Clinical Practice.2025; 226: 112325. CrossRef
When would I be surprised? Variability in predicted probability of survival for being “surprised” and “not surprised” to the surprise question
David Hui, Amy Ontai, Clark Andersen, John P. Maxwell, Yusuke Hiratsuka, Sang-Yeon Suh, Sun Hyun Kim, Eduardo Bruera
Supportive Care in Cancer.2025;[Epub] CrossRef

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

7,681 View
285 Download
3 Web of Science
4 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

Clinical, genetic, and proteomic characteristics of type 2 diabetes complicated with exogenous insulin antibody syndrome: a case-control study
Jinjing Wan, Leiluo Geng, Yiwen Fu, Qianting Zhang, Gaopeng Guan, Xue Jiang, Aimin Xu, Ping Jin
Diabetes Research and Clinical Practice.2025; 225: 112262. CrossRef
Expanding the Use of Continuous Glucose Monitoring in Type 2 Diabetes Mellitus: Impact on Glycemic Control and Metabolic Health
Mi-Joon Lee, Bum-Jeun Seo, Jae-Hyoung Cho
Life.2025; 15(10): 1543. CrossRef
Utility of continuous glucose monitoring in people with type 2 diabetes on insulin-based regimens in clinical practice: A case series and expert opinion
Soo Lim, Rimei Nishimura, Jothydev Kesavadev, Alice Pik Shan Kong, Margaret J McGill, Horng-Yih Ou, Chun-Kwan O, Chun-Chuan Lee, Xiaomei Zhang, Linong Ji, Chih-Yuan Wang
Diabetes Research and Clinical Practice.2025; : 113054. CrossRef
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

8,792 View
198 Download
6 Web of Science
6 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

The role of continuous glucose monitoring in improving glycemic control in adolescents with type 1 diabetes
Minkyu Seo, Kyu Hyun Park, Ji Won Park, EunJeong Kim, Do young Shin, Eungu Kang, Hyo-Kyoung Nam, Young-Jun Rhie, Kee-Hyoung Lee
Annals of Pediatric Endocrinology & Metabolism.2025; 30(5): 268. 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
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

24,655 View
780 Download
43 Web of Science
49 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

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.2026; 29(1): 48. CrossRef
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
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.2025; 42(12): 1540. CrossRef
Glycemia Risk Index is Associated With Risk of Albuminuria Among Individuals With Type 1 Diabetes
Ji Yoon Kim, Jee Hee Yoo, Nam Hoon Kim, Jae Hyeon Kim
Journal of Diabetes Science and Technology.2025;[Epub] CrossRef
Real-World Life Analysis of a Continuous Glucose Monitoring and Smart Insulin Pen System in Type 1 Diabetes: A Cohort Study
Paola Pantanetti, Giovanni Cangelosi, Sara Morales Palomares, Gaetano Ferrara, Federico Biondini, Stefano Mancin, Gabriele Caggianelli, Mauro Parozzi, Marco Sguanci, Fabio Petrelli
Diabetology.2025; 6(1): 7. CrossRef
Abuse-deterrent wearable device with potential for extended delivery of opioid drugs
Myoung Ju Kim, Jae Min Park, Jun Su Lee, Ji Yang Lee, Juhui Lee, Chang Hee Min, Min Ji Kim, Jae Hoon Han, Eun Jung Kwon, Young Bin Choy
Biomedical Engineering Letters.2025; 15(2): 427. CrossRef
Unlocking Real-Time Data Access in Diabetes Management: Toward an Interoperability Model
Pietro Randine, Miriam Kopperstad Wolff, Matthias Pocs, Ian R. O. Connell, Joseph A. Cafazzo, Eirik Årsand
Journal of Diabetes Science and Technology.2025;[Epub] CrossRef
Harnessing Machine Learning, a Subset of Artificial Intelligence, for Early Detection and Diagnosis of Type 1 Diabetes: A Systematic Review
Rahul Mittal, Matthew B. Weiss, Alexa Rendon, Shirin Shafazand, Joana R N Lemos, Khemraj Hirani
International Journal of Molecular Sciences.2025; 26(9): 3935. CrossRef
The usefulness of continuous glucose monitoring in the diagnostic approach to hypoglycemia after metabolic surgery
Diana Cristina Henao, Ana María Gómez, Sofía Robledo, Ricardo Rosero
Mini-invasive Surgery.2025;[Epub] CrossRef
Comparison of Real-Time and Intermittently-Scanned Continuous Glucose Monitoring for Glycemic Control in Type 1 Diabetes Mellitus: Nationwide Cohort Study
Ji Yoon Kim, Seohyun Kim, Jae Hyeon Kim
Diabetes & Metabolism Journal.2025; 49(3): 436. CrossRef
Associations of time in tight range, time in range, and glycated hemoglobin with albuminuria in type 1 diabetes: A cross-sectional study
Ji Yoon Kim, Seohyun Kim, Sang Ho Park, Jin A Lee, So Hyun Cho, Rosa Oh, Myunghwa Jang, You-Bin Lee, Gyuri Kim, Kyu Yeon Hur, Jae Hyeon Kim, Sang-Man Jin
Diabetes Research and Clinical Practice.2025; 226: 112325. CrossRef
Beyond insulin: advancing frontiers in cell-based and genetic therapies for type 1 diabetes management
Tamer A. Addissouky
The Egyptian Journal of Internal Medicine.2025;[Epub] CrossRef
A systematic review of continuous glucose monitoring sensors: principles, core technologies and performance evaluation
Xinying Wu, Xingyu Zhao, Weiliang Chen, Qinghua Chen, Linghai Kong, Peiyao Li
Sensors and Actuators Reports.2025; 10: 100361. CrossRef
Amperometric Determination of Glucose at Physiological pH by an Electrode Modified with a Composite Ni/Al-Layered Double Hydroxide and Electrochemically Reduced Graphene Oxide
Domenica Tonelli
Nanomaterials.2025; 15(15): 1172. CrossRef
Implantable electrochemical biosensors: Challenges, strategies, and applications
Sondes Ben-Aissa, Suryasnata Tripathy, Anthony Edward George Cass
Current Opinion in Electrochemistry.2025; 54: 101745. CrossRef
Current Status of Continuous Glucose Monitoring Use in South Korean Type 1 Diabetes Mellitus Population–Pronounced Age-Related Disparities: Nationwide Cohort Study
Ji Yoon Kim, Seohyun Kim, Jae Hyeon Kim
Diabetes & Metabolism Journal.2025; 49(5): 1040. CrossRef
Overcoming underutilization of continuous glucose monitoring devices with diabetes among disparate populations
Samar A. Nasser, Dragana Lovre, Farbod Zahedi Tajrishi, Keith C. Ferdinand
Endocrine Research.2025; : 1. CrossRef
Device Insertion Versus Material: Drivers of Inflammation in Diabetes Device Interfaces
Priscila Silva Cunegundes, Kenneth Wood, Jean Gabriel de Souza, Anjul Bhangu, Li Mao, Ulrike Klueh
Journal of Diabetes Science and Technology.2025;[Epub] CrossRef
Study of Antidiabetic Potential of Polyherbal Formulation
Varal Neeta. R., Miss Langhe Kiran.S
International Journal of Advanced Research in Science, Communication and Technology.2025; : 460. CrossRef
Noninvasive subterahertz glucose monitoring using a communication inspired eye diagram
Wonkyoung Lee, Eon Sang Kim, Sooyeon Kim, Heuk Park, Joon Ki Lee, Eunkyeong Baek, Chang Hee Jung, Seung-Hyun Cho
Scientific Reports.2025;[Epub] CrossRef
Impact of real-time continuous glucose monitoring and personalized digital health coaching on glycemic control and lifestyle in patients with type 2 diabetes and prediabetes
Yujin Park, Hye Rin Choi, Ji-eun Lee, Boram Choi, Chang-bo Noh, Jae-Heon Kang, Ye Seul Bae
Primary Care Diabetes.2025;[Epub] CrossRef
Efficacy and Safety of Stage 5 Connected Insulin Pens in Type 1 or Type 2 Diabetes: Randomized Controlled Trial Protocol
Ji Yoon Kim, Nam Hoon Kim, Soo Heon Kwak, Chang Hee Jung, Eun Seok Kang, Jun Sung Moon, Sun Joon Moon, So Yoon Kwon, Jee Hee Yoo, Younghoon Kim, Tae-min Lee, Chung-il Yang, Jae Hyeon Kim, Sang-Man Jin
Endocrinology and Metabolism.2025;[Epub] CrossRef
Recent advances in artificial intelligence-assisted endocrinology and diabetes
Ioannis T. Oikonomakos, Ranjit M. Anjana, Viswanathan Mohan, Charlotte Steenblock, Stefan R. Bornstein
Exploration of Endocrine and Metabolic Disease.2024; 1(1): 16. CrossRef
Accuracy and Safety of the 15-Day CareSens Air Continuous Glucose Monitoring System
Kyung-Soo Kim, Seung-Hwan Lee, Won Sang Yoo, Cheol-Young Park
Diabetes Technology & Therapeutics.2024; 26(4): 222. CrossRef
Real-World Continuous Glucose Monitoring Data from a Population with Type 1 Diabetes in South Korea: Nationwide Single-System Analysis
Ji Yoon Kim, Sang-Man Jin, Sarah B. Andrade, Boyang Chen, Jae Hyeon Kim
Diabetes Technology & Therapeutics.2024; 26(6): 394. CrossRef
Recent advances in the precision control strategy of artificial pancreas
Wuyi Ming, Xudong Guo, Guojun Zhang, Yinxia Liu, Yongxin Wang, Hongmei Zhang, Haofang Liang, Yuan Yang
Medical & Biological Engineering & Computing.2024; 62(6): 1615. CrossRef
Digital Health in Diabetes and Cardiovascular Disease
Dorothy Avoke, Abdallah Elshafeey, Robert Weinstein, Chang H. Kim, Seth S. Martin
Endocrine Research.2024; 49(3): 124. CrossRef
Continuous glucose monitoring with structured education in adults with type 2 diabetes managed by multiple daily insulin injections: a multicentre randomised controlled trial
Ji Yoon Kim, Sang-Man Jin, Kang Hee Sim, Bo-Yeon Kim, Jae Hyoung Cho, Jun Sung Moon, Soo Lim, Eun Seok Kang, Cheol-Young Park, Sin Gon Kim, Jae Hyeon Kim
Diabetologia.2024; 67(7): 1223. CrossRef
Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring
Anubhuti Juyal, Shradha Bisht, Mamta F. Singh
Blood Pressure Monitoring.2024; 29(5): 260. CrossRef
Emerging trends in functional molecularly imprinted polymers for electrochemical detection of biomarkers
Sanjida Yeasmin, Li-Jing Cheng
Biomicrofluidics.2024;[Epub] CrossRef
Continuous glucose monitoring in pregnant women with pregestational type 2 diabetes: a narrative review
Sylvia Ye, Ibrahim Shahid, Christopher J Yates, Dev Kevat, I-Lynn Lee
Obstetric Medicine.2024; 17(4): 194. CrossRef
Advancements in nanohybrid material-based acetone gas sensors relevant to diabetes diagnosis: A comprehensive review
Arpit Verma, Deepankar Yadav, Subramanian Natesan, Monu Gupta, Bal Chandra Yadav, Yogendra Kumar Mishra
Microchemical Journal.2024; 201: 110713. CrossRef
Current treatment options of diabetes mellitus type 1 in pediatric population
Petr Polák, Renata Pomahačová, Karel Fiklík, Petra Paterová, Josef Sýkora
Pediatrie pro praxi.2024; 25(3): 161. CrossRef
Efectividad de un sistema híbrido de circuito cerrado en pacientes con diabetes tipo 1 durante el ejercicio físico: un estudio descriptivo en la vida real
Ruben Martin-Payo, Maria del Mar Fernandez-Alvarez, Rebeca García-García, Ángela Pérez-Varela, Shelini Surendran, Isolina Riaño-Galán
Anales de Pediatría.2024; 101(3): 183. CrossRef
Effectiveness of a hybrid closed-loop system for children and adolescents with type 1 diabetes during physical exercise: A cross-sectional study in real life
Ruben Martin-Payo, Maria del Mar Fernandez-Alvarez, Rebeca García-García, Ángela Pérez-Varela, Shelini Surendran, Isolina Riaño-Galán
Anales de Pediatría (English Edition).2024; 101(3): 183. CrossRef
Real-time continuous glucose monitoring vs. self-monitoring of blood glucose: cost-utility in South Korean type 2 diabetes patients on intensive insulin
Ji Yoon Kim, Sabrina Ilham, Hamza Alshannaq, Richard F. Pollock, Waqas Ahmed, Gregory J. Norman, Sang-Man Jin, Jae Hyeon Kim
Journal of Medical Economics.2024; 27(1): 1245. CrossRef
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
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

10,364 View
304 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

9,696 View
312 Download
4 Web of Science
4 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; 31(2): 190. CrossRef
Capillary blood glucose testing by paramedics
Pete Gregory
Journal of Paramedic Practice.2025; 17(11): 446. 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

9,580 View
402 Download
12 Web of Science
13 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; 55(8): 1181. CrossRef
The usefulness of continuous glucose monitoring in the diagnostic approach to hypoglycemia after metabolic surgery
Diana Cristina Henao, Ana María Gómez, Sofía Robledo, Ricardo Rosero
Mini-invasive Surgery.2025;[Epub] CrossRef
Moving Beyond Glycated Hemoglobin to Glucose Patterns: Newer Indications for Continuous Glucose Monitor Use
Shubham Agarwal, Ron T. Varghese, Renato Savian, Cecilia C. Low Wang, Rodolfo J. Galindo
Endocrine Practice.2025; 31(8): 1046. CrossRef
Predicting hypoglycemia risk after gastrointestinal surgery in type 2 diabetes mellitus: a retrospective cohort study
Huilan Yao, Shijin Yuan, Hongying Pan, Sisi Hong, Chen Huang, Linfang Zhao, Hongdi Yuan, Lei Mei, Yinghong Zheng, Xiaolong Liu, Weina Lu
Frontiers in Endocrinology.2025;[Epub] CrossRef
Nutriomics and artificial intelligence nutrition obesity cohort (NAINOC): a design paper for a prospective cohort for nutrition and obesity research
Minyoung Lee, Sungha Park, Soo-Hyun Park, Ho-Young Park, Yu Ra Lee, Min-Sun Kim, Miso Nam, Jangho Lee, Hyein Seo, Yong-ho Lee, Chan Joo Lee, Jae-Ho Park, Hye Hyun Yoo, Hyun-Jin Kim, Kyong-Oh Shin, Yoshikazu Uchida, Kyungho Park
Clinical Hypertension.2025;[Epub] CrossRef
Impact of Carbohydrate Intake Fluctuations on Glucose Profiles: Insights from Continuous Glucose Monitoring-Based Patient Clustering
Hyun Ah Kim, Kyung Hee Kim, Young Lee, Yoon-Ju Song, Joon Ho Moon, Sung Hee Choi, Tae Jung Oh
Endocrinology and Metabolism.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

30,778 View
1,184 Download
69 Web of Science
68 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

Real-World Outcomes of Hybrid Closed-Loop System Use in Korean Youth With Childhood-Onset Type 1 Diabetes
Hyun Ah Woo, Su Jin Jeong, Yun Jeong Lee, Hwa Young Kim, Minjeong Gu, Ji Young Kim, Jaehyun Kim, Choong Ho Shin, Young Ah Lee
Journal of Korean Medical Science.2026;[Epub] CrossRef
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; 62(8): 1325. 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
Unlocking Real-Time Data Access in Diabetes Management: Toward an Interoperability Model
Pietro Randine, Miriam Kopperstad Wolff, Matthias Pocs, Ian R. O. Connell, Joseph A. Cafazzo, Eirik Årsand
Journal of Diabetes Science and Technology.2025;[Epub] CrossRef
Allogeneic Islet Transplantation: Chronicle of a Death Foretold?
Thierry Berney, Olivier Thaunat, Ekaterine Berishvili
Transplant International.2025;[Epub] CrossRef
Exogenous vs Endogenous Insulin Delivery: 2025
Ankita Patel, Rachel Todd, Nina L. Owen-Simon, Kasi McCune
Current Transplantation Reports.2025;[Epub] CrossRef
Long-term blood glucose prediction using deep learning-based noise reduction
Su-Jin Kim, Jun Sung Moon, Sung-Yoon Jung
ICT Express.2025; 11(4): 715. CrossRef
Trasplante de páncreas: evaluación radiológica de las técnicas quirúrgicas y potenciales complicaciones
A. Berasategui Criado, S. Cayón Somacarrera, R. Pellón Daben, F.J. González Sánchez, J.A. Echeverri Cifuentes, J. Crespo del Pozo
Radiología.2025; : 501696. CrossRef
Nano-based therapy for type 1 diabetes: from immuno-intervention to insulin delivery
Huiwen Pang, Zhuo Chen, Felicity Y. Han
Acta Biomaterialia.2025; 201: 101. CrossRef
Real-world evaluation of safety and efficacy of AHCL systems in young children with type 1 diabetes: a 1-year assessment
Daniele Franzone, Giordano Spacco, Andrea Piano, Giulia Siri, Giacomo Tantari, Giuseppe d’Annunzio, Maria Grazia Calevo, Mohamad Maghnie, Nicola Minuto, Marta Bassi
Frontiers in Endocrinology.2025;[Epub] CrossRef
Artificial Pancreas: The First 20 Years
Francis J. Doyle, Boris P. Kovatchev
Journal of Diabetes Science and Technology.2025; 19(4): 875. CrossRef
Artificial Pancreas: The First 20 Years*
Francis J. Doyle, Boris P. Kovatchev
Diabetes Technology & Therapeutics.2025; 27(S3): S1. CrossRef
Optimal model-based insulin bolus advisor for subjects with type 1 diabetes using continuous glucose monitoring
Martin Dodek, Eva Miklovičová
Computers in Biology and Medicine.2025; 196: 110508. CrossRef
Advancements of Glucose Monitoring Biosensor: Current State, Generations of Technological Progress and Innovation Dynamics
Arpita Dua, Abhijit Debnath, Kunal Kumar, Rupa Mazumder, Avijit Mazumder, Rajesh Kumar Singh, Saloni Mangal, Jahanvi Sanchitra, Fahad Khan, Soumya Tripathi, Sukriti Vishwas, Hema Chaudhary, Parul Sharma, Shikha Srivastava
Current Pharmaceutical Biotechnology.2025; 26(11): 1716. CrossRef
Dual hormone predictive control for a fully automated intraperitoneal artificial pancreas: Preclinical evaluation in pigs
Karim Davari Benam, Hasti Khoshamadi, Marte Kierulf Å m, Sverre Chr. Christiansen, Patrick Christian Bösch, Dag Roar Hjelme, Øyvind Stavdahl, Sven Magnus Carlsen, Sébastien Gros, Anders Lyngvi Fougner
Journal of Process Control.2025; 154: 103499. CrossRef
Artificial Intelligence in Patient Management
Nicholas Conley
Primary Care: Clinics in Office Practice.2025; 52(4): 733. CrossRef
Current Status of Continuous Glucose Monitoring Use in South Korean Type 1 Diabetes Mellitus Population–Pronounced Age-Related Disparities: Nationwide Cohort Study
Ji Yoon Kim, Seohyun Kim, Jae Hyeon Kim
Diabetes & Metabolism Journal.2025; 49(5): 1040. CrossRef
Multimodal large language models and mechanistic modeling for glucose forecasting in type 1 diabetes patients
J.C. Wolber, M. E. Samadi, J. Sellin, A. Schuppert
Journal of Biomedical Informatics.2025; 172: 104945. CrossRef
Effectiveness and safety of automated insulin delivery in type 2 diabetes: a meta-analysis
Baoqi Zeng, Hao Jia, Sihan Yang, Jiaming Zheng, Qingqing Yang, Lin Dou
Diabetology & Metabolic Syndrome.2025;[Epub] CrossRef
The differential impact of automated insulin delivery systems on body mass index in children with type 1 diabetes
John Pemberton, Catarina Leal, Anjanee Kohli, Lesley Drummond, Louise Collins, Zainaba Mohamed, Vrinda Saraff, Renuka P. Dias, Melanie Kershaw, Ruth Krone, Suma Uday
Journal of Pediatric Endocrinology and Metabolism.2025; 38(12): 1258. CrossRef
Dispositivi indossabili per la gestione del diabete
Filippo CARLUCCI, Antonella TABUCCHI, Marcello FIORINI, Lucrezia GALASSO, Alessandro TERRENI
Biochimica Clinica.2025;[Epub] 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

7,179 View
167 Download
1 Web of Science
1 Crossref

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.

Citations

Citations to this article as recorded by

Islet Tissue Macrophages in Immunity Homeostasis and Type 1 Diabetes
Yan Wang, Zhaoran Wang, Wenya Diao, Tong Shi, Jiahe Xu, Tiantian Deng, Chaoying Wen, Jienan Gu, Tingting Deng, Sixuan Wang, Cheng Xiao
Clinical Reviews in Allergy & Immunology.2025;[Epub] CrossRef

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

12,834 View
499 Download
7 Web of Science
8 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; 27(5): 2342. CrossRef
Analysis glycemic variability in pregnant women with various type of hyperglycemia
Xuexin Zhou, Ru Zhang, Shiwei Jiang, Decui Cheng, Hao Wu
BMC Pregnancy and Childbirth.2025;[Epub] CrossRef
Triple arm, prospective, real-world study comparing the efficacy of FDC teneligliptin + dapagliflozin to FDC sitagliptin + dapagliflozin, and FDC linagliptin + empagliflozin in Indian type 2 diabetes mellitus patients using CGM device: the Amplify-TIR stu
Suhas Erande, Mayur Agrawal, Sanjeev Gulati, Namdev Jagtap, N. S. Praveen Kumar, Vinod Kumar Kapoor, Sumit Bhushan, Rujuta Gadkari, Mayur Jadhav, Sanjay Choudhari, Saiprasad Patil, Hanmant Barkate
Cardiovascular Diabetology – Endocrinology Reports.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

14,361 View
510 Download
3 Web of Science
5 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

Comparison of Insulin Titration Strategies for Glycemic Control in Type 2 Diabetes: A Systematic Review and Network Meta-analysis
Kansak Boonpattharatthiti, Kirana Wechkunanukul, Noramon Mayang, E Lyn Lee, Anjana Fuangchan, Alice Y.Y. Cheng, Nathorn Chaiyakunapruk, Teerapon Dhippayom
Diabetes Care.2025; 48(5): 837. CrossRef
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

20,882 View
622 Download
58 Web of Science
60 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

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.2026; 20(1): 103. CrossRef
Empagliflozin versus metformin for glucose variability and metabolic outcomes in drug-naïve type 2 diabetes: The EMPA-FIT study
Soo Lim, Cheol Young Park, In Kyung Jeong, Ji Sung Yoon, Sang Yong Kim, Eun Seok Kang, Junghyun Noh, Kyu Yeon Hur, Sungrae Kim
Journal of Diabetes and its Complications.2026; 40(1): 109214. 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.2025; 42(09): 1119. CrossRef
Improved Glycemic Control in Insulin-Treated Individuals With Poorly Controlled Type 2 Diabetes Through Combined Structured Education With Real-Time Continuous Glucose Monitoring
Jee Hee Yoo, Ji Eun Jun, Soo Heon Kwak, Jae Hyeon Kim
Journal of Diabetes Science and Technology.2025;[Epub] CrossRef
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
How to take action beyond ambulatory glucose profile: Latin American expert recommendations on CGM data interpretation
Luis Eduardo Calliari, Álvaro Contreras Sepúlveda, Nicolás Coronel-Restrepo, Laura Kabakian, Rodrigo N. Lamounier, Emma Picasso, Adrian Proietti, Alex Ramírez-Rincón, Alicia E. Yépez-Rodriguez
Diabetology & Metabolic Syndrome.2025;[Epub] CrossRef
Comparison of Real-Time and Intermittently-Scanned Continuous Glucose Monitoring for Glycemic Control in Type 1 Diabetes Mellitus: Nationwide Cohort Study
Ji Yoon Kim, Seohyun Kim, Jae Hyeon Kim
Diabetes & Metabolism Journal.2025; 49(3): 436. CrossRef
Add-On Treatment with Gliclazide for Cancer Patients with Type 2 Diabetes Undergoing Cyclic Glucocorticoid-Containing Chemotherapy
Seung Eun Lee, Ju-Hyun Park, Dalyong Kim, Hyun-A Lee, Yun Seong Kang, Young Soon Yoon, Yun Jeong Jeong, Han Seok Choi, Kyoung-Ah Kim
Biomedicines.2025; 13(5): 1101. CrossRef
Relation of time in range to severity of coronary artery disease in patients with type 2 diabetes: A cross-sectional study
Weiguo Ma, Jinfeng Li, Xiaojuan Shao, Xiaohong Yin, Chuanqing Xie, Fenfen Wang, Ya Li
Open Medicine.2025;[Epub] CrossRef
Associations of time in tight range, time in range, and glycated hemoglobin with albuminuria in type 1 diabetes: A cross-sectional study
Ji Yoon Kim, Seohyun Kim, Sang Ho Park, Jin A Lee, So Hyun Cho, Rosa Oh, Myunghwa Jang, You-Bin Lee, Gyuri Kim, Kyu Yeon Hur, Jae Hyeon Kim, Sang-Man Jin
Diabetes Research and Clinical Practice.2025; 226: 112325. CrossRef
Effect of Ebenatide on glycemic metabolism and body fat in patients with type 2 diabetes mellitus
Cheng-lan Xu, Xiao-cen Kong, Xiao-mei Liu, Xiao-hua Xu, Bing-li Liu, Jian-hua Ma
Frontiers in Endocrinology.2025;[Epub] CrossRef
The effect of exercise with augmented reality glasses on metabolic parameters, anthropometric measurement values and quality of life in children aged 10–12 with type 1 diabetes: A randomized controlled trial
Harun Özbey, Meral Bayat
Journal of Pediatric Nursing.2025; 85: 239. CrossRef
Achievement of time in range goals among patients with diabetes using continuous glucose monitoring
Faezeh Azizi, Katherine Diehl, Lauren Enser, Samantha Heacock
Journal of the American Pharmacists Association.2025; 65(6): 102924. CrossRef
Impact of Carbohydrate Intake Fluctuations on Glucose Profiles: Insights from Continuous Glucose Monitoring-Based Patient Clustering
Hyun Ah Kim, Kyung Hee Kim, Young Lee, Yoon-Ju Song, Joon Ho Moon, Sung Hee Choi, Tae Jung Oh
Endocrinology and Metabolism.2025;[Epub] CrossRef
Comment on “Relationship Between Glycemic Indices and eGFR Values Among Type 2 Diabetes Mellitus Individuals With Chronic Kidney Disease Across Various Progression Stages”
Shyam Sundar Sah, Abhishek Kumbhalwar
Clinical Medicine Insights: Endocrinology and Diabetes.2025;[Epub] 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
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
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

9,243 View
147 Download
6 Web of Science
8 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

Pharmacomicrobiomics: Exploring the role of gut microbiota in type 2 diabetes and antidiabetic drug mechanisms
Dingsheng Wen, Xiangyue Wang, Yanping He, Xiaoqing Hao, Weihua Huang, Honghao Zhou, Wei Zhang, Xiong Li
Nutrition, Metabolism and Cardiovascular Diseases.2025; : 104379. CrossRef
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

Review

Others
Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications: Giacomo Cappon, Martina Vettoretti, Giovanni Sparacino, Andrea Facchinetti; Diabetes Metab J. 2019;43(4):383-397. Published online July 25, 2019; DOI: https://doi.org/10.4093/dmj.2019.0121

59,289 View
1,627 Download
298 Web of Science
312 Crossref

Abstract PDF PubReader ePub

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

Citations

Citations to this article as recorded by

Electrochemiluminescence-colorimetric dual-mode biosensor based on CeO2:Eu3+ nanozyme amplification for the detection of glucose
Mingxia Wang, Minggang Wei, Weiling Su, Liying Liu, Yanhong Li, Lijun Zhao, Feng Luan, Xuming Zhuang, Chunyuan Tian
Biosensors and Bioelectronics.2026; 291: 118027. CrossRef
Trend-weighted multi-resolution transformer for multi-parametric glucose prediction
Ömer Atılım Koca, Volkan Kılıç
Biomedical Signal Processing and Control.2026; 113: 108885. CrossRef
Empagliflozin versus metformin for glucose variability and metabolic outcomes in drug-naïve type 2 diabetes: The EMPA-FIT study
Soo Lim, Cheol Young Park, In Kyung Jeong, Ji Sung Yoon, Sang Yong Kim, Eun Seok Kang, Junghyun Noh, Kyu Yeon Hur, Sungrae Kim
Journal of Diabetes and its Complications.2026; 40(1): 109214. CrossRef
Applications of AI in the management of elderly diabetes patients
Tianci Xie, Ziyu Wang, Jieyu He, Jingjing Zhang
Ageing Research Reviews.2026; 114: 102960. CrossRef
The journey toward microbial photo-electrochemical biosensors: harnessing photosynthetic organisms for next-generation environmental sensing
Rosa Maria Matteucci, Elisabetta Fanizza, Matteo Grattieri
Bioelectrochemistry.2026; 169: 109205. CrossRef
Advancing Continuous Glucose Monitoring for Inpatient Clinical Decision Support: Individual Algorithmic Mean Absolute Relative Difference
Jill von Conta, Fin H. Bahnsen, Lutz Heinemann, Lukas van Baal, Jens Kleesiek, Dagmar Führer-Sakel, Susanne Tan
Diabetes Care.2026; 49(1): 99. CrossRef
Advances in saliva-based glucose sensors
Abhinav Sharma, Hendrik Faber, Nahoko Okimi, Thomas D. Anthopoulos, Taka-aki Yano
Biosensors and Bioelectronics: X.2026; 28: 100730. 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.2025; 19(6): 1671. 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.2025; 15(3): e295. CrossRef
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
Continuous Glucose Monitoring Among Patients With Type 1 Diabetes in Rwanda (CAPT1D) Phase I: Prospective Observational Feasibility Study
Jason Baker, Giacomo Cappon, Jean Claude Habineza, Corey H Basch, Steven Mey, Diana L Malkin-Washeim, Christian Schuetz, Niyonsenga Simon Pierre, Etienne Uwingabire, Alvera Mukamazimpaka, Paul Mbonyi, Sandhya Narayanan
JMIR Formative Research.2025; 9: e64585. CrossRef
Blood glucose level prediction in type 1 diabetes: A comparative analysis of interpretable artificial intelligence approaches
Ilaria Basile, Giovanna Sannino
Results in Engineering.2025; 25: 103681. CrossRef
Knowledge, Attitudes, and Practices of Chronic Type 2 Diabetes Patients in China Toward Continuous Glucose Monitoring: An Online Questionnaire Survey
Bingling Liu, Xueyi Wu, Xiao Zou, Fei Sun, Jie Yu
Diabetes, Metabolic Syndrome and Obesity.2025; Volume 18: 11. CrossRef
A case study on using a large language model to analyze continuous glucose monitoring data
Elizabeth Healey, Amelia Li Min Tan, Kristen L. Flint, Jessica L. Ruiz, Isaac Kohane
Scientific Reports.2025;[Epub] CrossRef
Recent advances in perioperative care of patients using new antihyperglycaemic drugs and devices dedicated to diabetes
Rami El Ojaimi, Gaëlle Cheisson, Emmanuel Cosson, Carole Ichai, Sophie Jacqueminet, Bogdan Nicolescu-Catargi, Alexandre Ouattara, Igor Tauveron, Paul Valensi, Dan Benhamou
Anaesthesia Critical Care & Pain Medicine.2025; 44(1): 101468. CrossRef
Polymer nanocomposites in diabetes management: advances in biosensors and drug delivery systems
Aniruddha Sen
Discover Materials.2025;[Epub] CrossRef
Unsupervised Retrospective Detection of Pressure Induced Failures in Continuous Glucose Monitoring Sensors for T1D Management
Elena Idi, Eleonora Manzoni, Andrea Facchinetti, Giovanni Sparacino, Simone Del Favero
IEEE Journal of Biomedical and Health Informatics.2025; 29(2): 1383. CrossRef
re Trends in Economic Modeling of Pediatric Diabetes Mellitus up to Date with the Clinical Practice Guidelines and the Latest Scientific Findings?
Roque Cardona-Hernandez, Alberto de la Cuadro-Grande, Julen Monje, María Echave, Itziar Oyaguez, Maria Alvarez, Isabel Leiva-Gea
Journal of Health Economics and Outcomes Research.2025; : 30. CrossRef
Innovations in Self-Powered glucose Biosensors: From energy harvesting to Continuous, Non-Invasive monitoring
Ali Esmaeilian, Fatemeh Yazdian, Alireza Saberi, Ali Hosseini, Abbas Rahdar, Sonia Fathi-karkan, Sadanand Pandey
Inorganic Chemistry Communications.2025; 174: 114076. CrossRef
A Prospective Pilot Study Demonstrating Noninvasive Calibration-Free Glucose Measurement
Martina Rothenbühler, Aritz Lizoain, Fabien Rebeaud, Adler Perotte, Marc Stoffel, J. Hans DeVries
Journal of Diabetes Science and Technology.2025;[Epub] CrossRef
Are Trends in Economic Modeling of Pediatric Diabetes Mellitus up to Date with the Clinical Practice Guidelines and the Latest Scientific Findings?
Roque Cardona-Hernandez, Alberto de la Cuadra-Grande, Julen Monje, María Echave, Itziar Oyagüez, María Álvarez, Isabel Leiva-Gea
Journal of Health Economics and Outcomes Research.2025;[Epub] CrossRef
Effectiveness of Digital Diabetes Management Technology on Blood Glucose in Patients With Type 2 Diabetes at Home: Systematic Review and Meta-Analysis
Yuping Xiao, Zhenzhen Wang, Lintao Zhang, Nina Xie, Fangyao Chen, Zihao Song, Sha Zhao
Journal of Medical Internet Research.2025; 27: e66441. CrossRef
An evaluation of YouTube videos on glucose sensor devices and type 1 diabetes mellitus: User perceptions, device features, and content reliability
Birsel MOLU, Gizem MOLU
Diabetes Research and Clinical Practice.2025; 222: 112069. CrossRef
Electrochemical biosensors for enhanced detection of diabetes mellitus
Yong Zhi, Shanshan Xie, Bowen Wei
Clinica Chimica Acta.2025; 571: 120221. CrossRef
Diabetes Technology Trends: A Review of the Latest Innovations
Erika L Lundgrin, Clare A Kelly, Natalie Bellini, Claudia Lewis, Ebne Rafi, Betul Hatipoglu
The Journal of Clinical Endocrinology & Metabolism.2025; 110(Supplement): S165. CrossRef
Simulation of a Surface Plasmon Resonance Biosensor Using a Combination of TiO2 and 2D Materials for Blood Glucose Detection
Mohammad Kouhi
Plasmonics.2025; 20(10): 8377. CrossRef
Machine Learning–Based Calibration of Commercial Continuous Glucose Monitoring Sensor in Nonserum Solutions: An In Vitro Validation Study
Megha Gautam, Aditya Choudhary, Deepak Agrawal
Indian Journal of Neurotrauma.2025; 22(02): 177. CrossRef
The Role of Neuroinflammation in the Comorbidity of Psychiatric Disorders and Internal Diseases
Grecu Ramona Cătălina, Victor Gheorman, Veronica Gheorman, Mircea-Cătălin Forțofoiu
Healthcare.2025; 13(7): 837. CrossRef
The Usefulness of the Glucose Management Indicator in Evaluating the Quality of Glycemic Control in Patients with Type 1 Diabetes Using Continuous Glucose Monitoring Sensors: A Cross-Sectional, Multicenter Study
Sandra Lazar, Ovidiu Potre, Ioana Ionita, Delia-Viola Reurean-Pintilei, Romulus Timar, Andreea Herascu, Vlad Florian Avram, Bogdan Timar
Biosensors.2025; 15(3): 190. CrossRef
Transformative Advances in Continuous Glucose Monitoring and the Impact of FDA Over‐the‐Counter Approval on Diabetes Care
Zain Afridi, Sameer Abdul Rauf, Syed Muhammad Nabil Ashraf, Md Ariful Haque
Health Science Reports.2025;[Epub] CrossRef
The future of wearable bioelectronics in healthcare
Annalisa Bonfiglio
Nature Reviews Electrical Engineering.2025; 2(6): 367. CrossRef
Use of an insulin titration protocol based on continuous glucose monitoring in postoperative cardiac surgery patients with type 2 diabetes and prediabetes: a randomized controlled trial
Sun-Joon Moon, Min-Su Kim, Yun Tae Kim, Ha-Eun Lee, Young-Woo Lee, Su-Ji Lee, Euy-Suk Chung, Cheol-Young Park
Cardiovascular Diabetology.2025;[Epub] CrossRef
A Review on Integrating the Circular Economy in Diabetic Appliances Manufacture and Management for Sustainable Healthcare
Shubham Singh Patel, Aarti Bains, Nemat Ali, Prince Chawla
Circular Economy and Sustainability.2025; 5(5): 3865. CrossRef
Measurement of lactate concentration using a minimally invasive needle with non-planer optical waveguides
Daigo Koiwa, Yoichi Haga, Noriko Tsuruoka
Biosensors and Bioelectronics.2025; 285: 117557. CrossRef
An Insulin-Sensitivity-Aware Meal-Bolus Decision Method Based on Event-Triggered Adaptive Dynamic Programming
Xiang Lu, Deheng Cai, Wei Liu, Linong Ji, Dawei Shi
IEEE Transactions on Automation Science and Engineering.2025; 22: 12816. CrossRef
Harnessing machine learning for improved diagnosis, drug discovery, and patient care
Jibon Kumar Paul, Mahir Azmal, Omar Faruk Talukder, ANM Shah Newaz Been Haque, Meghla Meem, Ajit Ghosh
Computational and Structural Biotechnology Reports.2025; 2: 100051. CrossRef
Glucose interpretation meaning and action (GIMA): Insights to blood glucose user interface interpretation in type 1 diabetes
Rujiravee Kongdee, Bijan Parsia, Hood Thabit, Simon Harper
DIGITAL HEALTH.2025;[Epub] CrossRef
Periodontal disease in adolescents with type 1 diabetes mellitus: A cross link between continuous glucose monitoring-derived metrics, caspase-3 levels, diabetic nephropathy and subclinical atherosclerosis
Nancy Samir Elbarbary, Doaa Adel Khattab, Basma Mohamed Sultan, Eman Abdel Rahman Ismail
Diabetes Research and Clinical Practice.2025; 224: 112234. CrossRef
Wireless Flexible Potentiometric Microsensors for Temperature-Compensated Sweat Electrolyte Monitoring
Jimin Lee, Leel Mazal Liberty, Ira Soltis, Kangkyu Kwon, David Chong, Youngjin Kwon, Woon-Hong Yeo
ACS Applied Materials & Interfaces.2025; 17(25): 36345. CrossRef
Comparison of Real-Time and Intermittently-Scanned Continuous Glucose Monitoring for Glycemic Control in Type 1 Diabetes Mellitus: Nationwide Cohort Study
Ji Yoon Kim, Seohyun Kim, Jae Hyeon Kim
Diabetes & Metabolism Journal.2025; 49(3): 436. CrossRef
Revolutionizing diabetes management: Artificial intelligence (AI) from assessment to advanced monitoring
Mobin Ghaderi, Kosar Chitzan-Zadeh, Milad Mokarami, Mobina Mokarami, Marzieh Shokoohi, Reza Golchin Vafa
Next Research.2025; 2(3): 100472. CrossRef
Recommendations for the Management of Diabetes During Ramadan Applying the Principles of the ADA/ EASD Consensus: Update 2025
Mahmoud Ibrahim, Ebtesam M. Ba‐Essa, Asma Ahmed, Ehtasham Ahmad, Firas A. Annabi, Hanene Chaabane, Dario Tuccinardi, Melanie J. Davies, Francesco De Domenico, Robert H. Eckel, Nancy Elbarbary, Pamela Houeiss, Silvia Manfrini, Shabeen Naz Masood, Omar Moba
Diabetes/Metabolism Research and Reviews.2025;[Epub] CrossRef
Parenteral nutrition weaning in pediatric intestinal failure patients enrolled in remote patient monitoring: A descriptive study
Stephanie B. Oliveira, Julia D. Thomas, Conrad Cole, Michael Helmrath, Samuel Kocoshis, Paul W. Wales
Journal of Parenteral and Enteral Nutrition.2025; 49(6): 768. CrossRef
The Role, Potential Benefits and Cost-effectiveness of Digital Tools for Diabetes Management
Anders L. Carlson, Sameer Badlani, Viral N. Shah
Clinical Therapeutics.2025; 47(8): 638. CrossRef
Optimizing wrist-worn wearable compliance with insights from two Parkinson’s disease cohort studies
Marjan J. Meinders, Laura Heathers, King Chung Ho, Laura Russell, Chris Li, Bastiaan R. Bloem, William J. Marks Jr, Ritu Kapur
npj Parkinson's Disease.2025;[Epub] CrossRef
Machine Learning and Deep Learning Approaches for Predicting Diabetes Progression: A Comparative Analysis
Oluwafisayo Babatope Ayoade, Seyed Shahrestani, Chun Ruan
Electronics.2025; 14(13): 2583. CrossRef
ChromaLipSense: Designing a lipstick-based biosensor for metabolic monitoring
Shuyi Sun, Yuan-Hao Ku, Jae Yong Suk, Ali K. Yetisen, Katia Vega
The Design Journal.2025; : 1. CrossRef
Clinical performance evaluation of the SiJoy GS1 continuous glucose monitor during oral glucose tolerance testing in healthy adults
Wanyi Zhao, Dongmei Zheng, Xianbao Shi, Feng Xu, Lin Chen, Xingmei Liu, Xiaolong Jin, Qingbo Guan, Chao Xu
Frontiers in Endocrinology.2025;[Epub] CrossRef
Considerations With Using a CGM in Older Individuals
Maria Tadros, Hayley Sewell, Katherine S. O’Neal
The Senior Care Pharmacist.2025; 40(7): 283. CrossRef
Recent advances in the management of type 2 diabetes mellitus
Drea N Agrawal, Madhukar Tikas, Kanchan Jangir, Vaishnavi H Mishra, Jagdish Makade, Yugeshwari R. Tiwade, Ankit K. Badge, Gulshan R. Bandre
Journal of Medical Society.2025; 39(1): 93. CrossRef
PPG Based Noninvasive Blood Glucose Monitoring Using Multi-View Attention and Cascaded BiLSTM Hierarchical Feature Fusion Approach
Mubashir Ali, Jingzhen Li, Bokun Fan, Zedong Nie
IEEE Journal of Biomedical and Health Informatics.2025; 29(7): 4692. CrossRef
Associations between Diabetes, Diabetes Duration, and Psychiatric Difficulties: A Nationwide Cross-sectional Study
Young-Chan Kim, Kyungdo Han, Suhyung Kim, Yoo Hyun Um, Tae-Won Kim, Ho Jun Seo, Seung-Chul Hong, Jong-Hyun Jeong
Clinical Psychopharmacology and Neuroscience.2025; 23(3): 476. CrossRef
IoT Enabled Non-Invasive Glucose Monitoring Through Breath Acetone
V. Mythily, G. T. Bhuvaneshwari, S. Divyashree, S. Madumitha
Journal of Computers, Mechanical and Management.2025; 4(1): 11. CrossRef
The Potential of Wearable Sensor Technologies in Enhancing Personalized Health Monitoring and Management
Durga Prasad Mishra, Prafulla Kumar Sahu
Biomedical Materials & Devices.2025;[Epub] CrossRef
Electrochemical and Nanomaterial‐Based Strategies for Nonenzymatic Glucose Detection: A Review
Reagan Aviha, Gymama Slaughter
ChemistryOpen.2025;[Epub] CrossRef
Application of Carbon Nanotube-Based Elastomeric Matrix for Capacitive Sensing in Diabetic Foot Orthotics
Monisha Elumalai, Andre Childs, Samantha Williams, Gabriel Arguello, Emily Martinez, Alaina Easterling, Dawn San Luis, Swaminathan Rajaraman, Charles M. Didier
Micromachines.2025; 16(7): 804. CrossRef
The Use of Continuous Glucose Monitoring in Comparison to Self-Monitoring of Blood Glucose in Gestational Diabetes: A Systematic Review
Bhavadharini Balaji, Wesley Hannah, Polina V. Popova, Uma Ram, Mohan Deepa, Janeline Lunghar, Kumaran Uthra, Haritha Sagili, Sadishkumar Kamalanathan, Ranjit Mohan Anjana, Viswanathan Mohan
Journal of Diabetes Science and Technology.2025;[Epub] CrossRef
Continuous glucose monitoring in type 2 diabetes: a systematic review of barriers and opportunities for care improvement
Maria Assunta Barchiesi, Armando Calabrese, Roberta Costa, Francesca Di Pillo, Antonio D’Uffizi, Luigi Tiburzi, Erum Zahid, Linda Graudins
International Journal for Quality in Health Care.2025;[Epub] CrossRef
Continuous Movement Monitoring at Home Through Wearable Devices: A Systematic Review
Gianmatteo Farabolini, Nicolò Baldini, Alessandro Pagano, Elisa Andrenelli, Lucia Pepa, Giovanni Morone, Maria Gabriella Ceravolo, Marianna Capecci
Sensors.2025; 25(16): 4889. CrossRef
The Development, Characteristics, and Challenges of Biosensors: The Example of Blood Glucose Meters
Hsuan-Yu Chen, Chiachung Chen
Chemosensors.2025; 13(8): 300. CrossRef
Pilot Study: Improving Glycemic Control Among Children and Adolescents With Obesity and Prediabetes With Real-Time Feedback Via Continuous Glucose Monitoring
Zachary Z. Mayer, Timothy P. Foster, Xiaofei Chi, Michael A. Tan, Matthew J. Gurka, Angelina V. Bernier
Diabetes Spectrum.2025; 38(3): 279. CrossRef
Real-time converter of intermittent-scanned glucose sensors to continuous glucose monitors with potential future applications for insulin delivery
Pablo S. Rivadeneira, Alejandro Mira
HardwareX.2025; 23: e00684. CrossRef
Global trends and hotspots in blood glucose management for elderly patients with diabetes: A bibliometric analysis (2000–2024)
Li Zhou, Lubi He
Medicine.2025; 104(33): e43919. CrossRef
Efficacy of intermittently scanned continuous glucose monitoring in patients with types 1 or 2 diabetes receiving insulin therapy: a systematic review and meta-analysis
Mayra Souza Botelho, Julia Simões Correa Galendi, Mariana Andrades Fiorini Monteiro Novo, Vania dos Santos Nunes-Nogueira
Diabetology & Metabolic Syndrome.2025;[Epub] CrossRef
Glucose interpretation meaning and action: enhancing type 1 diabetes decision-making with textual descriptions
Rujiravee Kongdee, Bijan Parsia, Hood Thabit, Simon Harper
Therapeutic Advances in Endocrinology and Metabolism.2025;[Epub] CrossRef
POC Sensor Systems and Artificial Intelligence—Where We Are Now and Where We Are Going?
Prashanthi Kovur, Krishna M. Kovur, Dorsa Yahya Rayat, David S. Wishart
Biosensors.2025; 15(9): 589. CrossRef
A Comprehensive Survey on Wearable Computing for Mental and Physical Health Monitoring
Tarek Elfouly, Ali Alouani
Electronics.2025; 14(17): 3443. CrossRef
Structurally Engineered All-Polymeric Microneedle Sensor for Highly Sensitive and Real-Time Monitoring of Glucose in Interstitial Fluids
Ju Hyeon Kim, Chuljin Hwang, Dae Yu Kim
ACS Sensors.2025; 10(9): 7025. CrossRef
Navigating Clinical Efficacy and Legal Boundaries: Implications of Nurse-Led Glycemic Management in Critical Care
Giuseppe Neri, Andrea Bruni, Eugenio Garofalo, Federico Longhini, Vincenzo Bosco
Healthcare.2025; 13(18): 2313. CrossRef
IoT-based approach for diabetes patient monitoring using machine learning
Sarra Ayouni, Muhammad Hamza Khan, Muhammad Ibrahim, Mohamed Maddeh, Nadeem Sarwar, Nazik Alturki
SLAS Technology.2025; 35: 100348. CrossRef
Designing the Future of Biosensing: Advances in Aptamer Discovery, Computational Modeling, and Diagnostic Applications
Robert G. Jesky, Louisa H. Y. Lo, Ryan H. P. Siu, Julian A. Tanner
Biosensors.2025; 15(10): 637. CrossRef
Glucose360: An Open-Source Python Platform with Event-Based Integration for Continuous Glucose Monitoring Data Analysis
Ben Ehlert, Dhruv Aron, Dalia Perelman, Yue Wu, Michael P. Snyder
Diabetes Technology & Therapeutics.2025;[Epub] CrossRef
Patient Experience with Continuous Glucose Monitoring During Dialysis in Type 2 Diabetes: A Qualitative Study
Miguel Angel Cuevas-Budhart, Dante Atzin Juncos Ríos, Maricruz Ponce Villavicencio, Marcela Ávila Diaz, María Begoña Ilabaca Avendaño, Maricela Beatriz Rocha-Carrillo, Ramón Paniagua
Journal of Clinical Medicine.2025; 14(19): 6943. CrossRef
Nanostructured Materials in Glucose Biosensing: From Fundamentals to Smart Healthcare Applications
Rajaram Rajamohan, Seho Sun
Biosensors.2025; 15(10): 658. CrossRef
Continuous glucose monitoring systems for adolescents and adults living with type 2 diabetes mellitus
Sachiko Y Kataoka, Takahiro Tsuge, Yuki Kataoka, Maria-Inti Metzendorf, Juan VA Franco, Norio Watanabe
Cochrane Database of Systematic Reviews.2025;[Epub] CrossRef
Conductometric Chemosensor for Saccharides Based on Thin Films of Poly(3-Thienylboronic) Acid: Measurements of Transversal Resistance
Berfinsu Kaya, Yulia Efremenko, Vladimir M. Mirsky
Biosensors.2025; 15(10): 679. CrossRef
The role of continuous glucose monitoring in the diagnosis and management of gestational diabetes mellitus
Hui Wei Leow, Karen Forbes, Eleanor Scott
Best Practice & Research Clinical Endocrinology & Metabolism.2025; 39(6): 102060. CrossRef
Use of Ambulatory Glucose Profile in Monitoring and Improved Control of Gestational Diabetes Mellitus When Compared to Self-monitoring of Blood Glucose
Mohammad Sabah Siddiqui, Superior Kawale, Rohini Rokkam, Sarita Agrawal, Amritava Ghosh
Journal of The Association of Physicians of India.2025; 73(8): 15. CrossRef
Wearable biomolecular sensing nanotechnologies in chronic disease management
Jiaobing Tu, Connor D. Flynn, Jeonghee Yeom, Zhenwei Wu, Shana O. Kelley, Wei Gao
Nature Nanotechnology.2025; 20(10): 1388. CrossRef
A Novel Glucose Electrochemical Sensor Based on Au‐Pt Bimetallic Hydrogel Operating in Neutral Media
Zhanhong Li, Longzhao Chen, Mengyi Jiang, Yue Sun, Wen Sun, Haotian Li, Zifeng Wang, Jean Louis Marty, Zhigang Zhu
ChemistrySelect.2025;[Epub] CrossRef
Integrating synthetic biology and biosensing: Current trends and future prospects
Karuna Korgaonkar, Nagarjuna Prakash Dalbanjan, Maruti J. Gurav, Manjunath P. Eelager, Vishwanath B. Chachadi, S.K. Praveen Kumar
Next Research.2025; 2(4): 100911. CrossRef
A machine learning model for hypoglycemia risk prediction in hospitalized patients with diabetes: development and validation
Liren Li, Jiayu Chen, Tongshu Guan, Ze Yu, Jinyuan Zhang, Ronghong Ji, Zike Li, Ming Lei, Fei Gao, Yilei Li, Ping Zheng
Diabetes Research and Clinical Practice.2025; 230: 112993. CrossRef
Continuous Glucose Monitoring under standardised conditions regarding diet, exercise and stress in Healthy Young People (CGM-HYPE study): An exploratory clinical trial
Florian Kinny, Stephanie Läer, Emina Obarcanin, Hisham Al-Obaidi
PLOS Digital Health.2025; 4(11): e0001087. CrossRef
Non-invasive biochemical sensing with AI-driven analytics: a comprehensive review of technologies, applications, and future directions
Umapathi Krishnamoorthy
Analytical Methods.2025; 17(48): 9736. CrossRef
Smart health monitoring: an interactive Raspberry Pi-based system for managing diabetes and hypertension in children
Ruppikha Sree Shankar, Tanuja Shailesh, Vijaya Arjunan R, Krishnaraj Chadaga, D. Cenitta
Journal of Cloud Computing.2025;[Epub] CrossRef
Cost‐effectiveness of continuous glucose monitoring in Chinese adults with uncontrolled type 2 diabetes: A modelling study stratified by baseline HbA1c
Guangxin Han, Xiaoning Zhang, Yubing Ye, Changsheng Wang, Li Hu, Hanbiao Wu, Zhikun Qiu, Fang Yuan, Jisheng Chen, Sha Lai
Diabetes, Obesity and Metabolism.2025;[Epub] CrossRef
Dispositivi indossabili per la gestione del diabete
Filippo CARLUCCI, Antonella TABUCCHI, Marcello FIORINI, Lucrezia GALASSO, Alessandro TERRENI
Biochimica Clinica.2025;[Epub] CrossRef
Effects of Continuous Glucose Monitoring on Glycemic Control, Mental Health and Self-Management in Adults with Type 1 Diabetes: A Randomized Controlled Trial
Rocío Romero-Castillo, Manuel Pabón-Carrasco, Shakira Kaknani-Uttumchandani, José Antonio Ponce-Blandón
Healthcare.2025; 13(24): 3197. CrossRef
The effect of education in obese type 2 diabetes adolescents and young adults based on continuous glucose monitoring and smartwatch-derived lifelog data
Hyun Ah Woo, Ja Hyang Cho, Kye Shik Shim, Do Hyun Kim, Hae Woon Jung, Mi Young Han
Annals of Pediatric Endocrinology & Metabolism.2025; 30(6): 305. CrossRef
Electrodeposited nanoflower platinum films for high-sensitivity glucose sensing and cellular activity monitoring
Yu-Lin Lee, Chih-Ching Tseng, Ren-Jei Chung, Ching-Yu Lin, Yu-Jui Fan, Po-Chun Chen
Surface and Coatings Technology.2025; : 133146. CrossRef
Risk factors for hypoglycaemia in adults with type 1 diabetes: a systematic review and meta-analysis
Qiang Zhang, Haojie Zhou, Ruonan Lin, Liping Hu, Xiaoli Zhu
BMC Endocrine Disorders.2025;[Epub] CrossRef
A Dual-Channel Wearable Ring With MODWT-Based Signal Processing for Non-Invasive Blood Glucose Estimation: A Pilot Study
Quoc Cuong Bui, Areum Durey, Juyoung Lee, Gyung-Su Byun
IEEE Access.2025; 13: 215786. CrossRef
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
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
Glycemic Outcomes and Nurse Perceptions of Continuous Glucose Monitoring for Hospitalized Patients
Alexandra Thullen, Rebecca Gerber, Alyson Keen
Journal of Nursing Care Quality.2024; 39(4): 310. CrossRef
Advances in Type 1 Diabetes Mellitus Management in Children
Mridu Bahal, Vineeta Pande, Jasleen Dua, Shailaja Mane
Cureus.2024;[Epub] CrossRef
Time‐in‐range derived from self‐measured blood glucose in people with type 2 diabetes advancing to iGlarLixi: A participant‐level pooled analysis of three phase 3 LixiLan randomized controlled trials
Martin Haluzík, Mohammed E. Al‐Sofiani, Alice Y. Y. Cheng, Felipe Lauand, Lydie Melas‐Melt, Julio Rosenstock
Diabetes, Obesity and Metabolism.2024; 26(11): 5046. CrossRef
Evaluating the Impact of Continuous Glucose Monitoring on Erectile Dysfunction in Type 1 Diabetes: A Focus on Reducing Glucose Variability and Inflammation
Nicola Tecce, Davide Menafra, Mattia Proganò, Mario Felice Tecce, Rosario Pivonello, Annamaria Colao
Healthcare.2024; 12(18): 1823. CrossRef
Indications, specifics and reimbursement of glucose sensors in type 2 diabetes patients
Kateřina Štechová
Vnitřní lékařství.2024; 70(6): 390. CrossRef
Enzyme free highly sensitive glucose biosensor based on Ag/Cu2O nanostructures deposited on TiO2 nanotube arrays
Bittu Kumar, Sudip Kumar Sinha
Monatshefte für Chemie - Chemical Monthly.2024; 155(11): 1095. CrossRef
The status of blood glucose monitoring and its influencing factors in Chinese patients with type 2 diabetes initiating premixed insulin: A prospective real-world study
Si Chen, Jingyi Lu, Danfeng Peng, Fengjing Liu, Wei Lu, Wei Zhu, Yuqian Bao, Jian Zhou, Weiping Jia
Diabetes Research and Clinical Practice.2024; 218: 111895. CrossRef
Ag@Au core–shell nanoparticles modified glassy carbon electrode synthesized by simple displacement reaction for non-enzymatic electrochemical glucose sensing
Huiling Huang, Tianyu Chen, Xinyu Qin, Bo Quan, Sun Ha Paek, Wang Zhang, Yuanzhe Piao
Journal of Electroanalytical Chemistry.2024; 975: 118726. CrossRef
Integrated Lab-on-a-Chip Optical Glucose Sensing System Using a Silicon Waveguide Bragg Grating Device
Hongqiang Li, Fanglin Xie, Ming Han, Lizhen Zhang, Lu Cao, Zhiyue Zhu, Enbang Li
IEEE Sensors Journal.2024; 24(20): 32034. CrossRef
Noninvasive Blood Glucose Monitoring Using Spatiotemporal ECG and PPG Feature Fusion and Weight-Based Choquet Integral Multimodel Approach
Jingzhen Li, Jingjing Ma, Olatunji Mumini Omisore, Yuhang Liu, Huajie Tang, Pengfei Ao, Yan Yan, Lei Wang, Zedong Nie
IEEE Transactions on Neural Networks and Learning Systems.2024; 35(10): 14491. CrossRef
Technology and Continuous Glucose Monitoring Access, Literacy, and Use Among Patients at the Diabetes Center of an Inner-City Safety-Net Hospital: Mixed Methods Study
Gaëlle Sabben, Courtney Telfort, Marissa Morales, Wenjia Stella Zhang, Juan C Espinoza, Francisco J Pasquel, Kate Winskell
JMIR Diabetes.2024; 9: e54223. CrossRef
Riemannian manifold-based geometric clustering of continuous glucose monitoring to improve personalized diabetes management
Jiafeng Song, Jocelyn McNeany, Yifei Wang, Tanicia Daley, Arlene Stecenko, Rishikesan Kamaleswaran
Computers in Biology and Medicine.2024; 183: 109255. CrossRef
Predicting Dysglycemia in Patients with Diabetes Using Electrocardiogram
Ho-Jung Song, Ju-Hyuck Han, Sung-Pil Cho, Sung-Il Im, Yong-Suk Kim, Jong-Uk Park
Diagnostics.2024; 14(22): 2489. CrossRef
Computational Model-Assisted Development of a Nonenzymatic Fluorescent Glucose-Sensing Assay
Lydia Colvin, Diana Al Husseini, Dandan Tu, Darin Dunlap, Tyler Lalonde, Muhammed Üçüncü, Alicia Megia-Fernandez, Mark Bradley, Wenshe Liu, Melissa A. Grunlan, Gerard L. Coté
ACS Sensors.2024; 9(11): 6218. CrossRef
당뇨병 치료의 진화: 관해를 향해가는 혁신적 약물치료와 첨단 관리기기의 결합
종한 최, 민경 문
Public Health Weekly Report.2024; 17(44): 1905. CrossRef
Continuous Glucose Monitoring: A Transformative Approach to the Detection of Prediabetes
Xueen Liu, Jiale Zhang
Journal of Multidisciplinary Healthcare.2024; Volume 17: 5513. CrossRef
Comprehensive Management of Pediatric Type 1 Diabetes Mellitus: A Review of Physician, Social Work, and Nursing Roles
Dana Aref Alenezi, Aljoharh Hassan Aljabar, Alrayyes, Sarah Fahad A, Mona Mohammad Fahad Alenazi, Saleh Khalaf Dahi Alsukibi, Nabil Abdulaziz Alharthi, Anood Abdi Jueran Alanazi, Nawal Abdi Jueran Alanazi, Tahany Theyab Awwad Alenazy, Mona Ali A
International Journal of Computational and Experimental Science and Engineering.2024;[Epub] CrossRef
Development of a Novel Insulin Sensor for Clinical Decision-Making
Eva Vargas, Eleonora M. Aiello, Jordan E. Pinsker, Hazhir Teymourian, Farshad Tehrani, Mei Mei Church, Lori M. Laffel, Francis J. Doyle, Mary-Elizabeth Patti, Eyal Dassau, Joseph Wang
Journal of Diabetes Science and Technology.2023; 17(4): 1029. CrossRef
Diabetes technology and sexual health: which role?
V. Zamponi, J. Haxhi, G. Pugliese, A. Faggiano, R. Mazzilli
Journal of Endocrinological Investigation.2023; 47(6): 1315. CrossRef
Discordance Between Glycated Hemoglobin A1c and the Glucose Management Indicator in People With Diabetes and Chronic Kidney Disease
Philippe Oriot, Claire Viry, Antoine Vandelaer, Sébastien Grigioni, Malanie Roy, Jean Christophe Philips, Gaëtan Prévost
Journal of Diabetes Science and Technology.2023; 17(6): 1553. CrossRef
Expertenaustausch zum Einsatz von kontinuierlichem Glukosemonitoring (CGM) im Diabetesmanagement: Eine aktuelle Bestandsaufnahme und Blick in die Zukunft
Andreas Thomas, Thomas Haak, Astrid Tombek, Bernhard Kulzer, Dominic Ehrmann, Olga Kordonouri, Jens Kroeger, Oliver Schubert-Olesen, Ralf Kolassa, Thorsten Siegmund, Nicola Haller, Lutz Heinemann
Diabetologie und Stoffwechsel.2023; 18(01): 57. CrossRef
Evaluation of the performance and usability of a novel continuous glucose monitoring system
Li Yan, Qiang Li, Qingbo Guan, Mingsong Han, Yu Zhao, Junfei Fang, Jiajun Zhao
International Journal of Diabetes in Developing Countries.2023; 43(4): 551. CrossRef
Efficacy of intermittent short‐term use of a real‐time continuous glucose monitoring system in non‐insulin–treated patients with type 2 diabetes: A randomized controlled trial
Sun Joon Moon, Kyung‐Soo Kim, Woo Je Lee, Mi Yeon Lee, Robert Vigersky, Cheol‐Young Park
Diabetes, Obesity and Metabolism.2023; 25(1): 110. CrossRef
Intermittent-scanned continuous glucose monitoring with low glucose alarms decreases hypoglycemia incidence in middle-aged adults with type 1 diabetes in real-life setting
Philippe Oriot, Michel P. Hermans
Journal of Diabetes and its Complications.2023; 37(2): 108385. CrossRef
Applications of Microwaves in Medicine
J.-C. Chiao, Changzhi Li, Jenshan Lin, Robert H. Caverly, James C. M. Hwang, Harel Rosen, Arye Rosen
IEEE Journal of Microwaves.2023; 3(1): 134. CrossRef
A Double-Needle Gold-Silver Electrodes Continuous Glucose Monitoring Device
C. Ben Ali Hassine, A. Tekin
IRBM.2023; 44(3): 100752. CrossRef
Accuracy of Flash Glucose Monitoring in Hemodialysis Patients With and Without Diabetes Mellitus
Michèle R. Weber, Matthias Diebold, Peter Wiesli, Andreas D. Kistler
Experimental and Clinical Endocrinology & Diabetes.2023; 131(03): 132. CrossRef
Minimally invasive electrochemical continuous glucose monitoring sensors: Recent progress and perspective
Yuanyuan Zou, Zhengkang Chu, Jiuchuan Guo, Shan Liu, Xing Ma, Jinhong Guo
Biosensors and Bioelectronics.2023; 225: 115103. CrossRef
Continuous Glucose Monitoring in Enterally Fed Children with Severe Central Nervous System Impairment
Marlena Górska, Joanna Kudzin, Anna Borkowska, Agnieszka Szlagatys-Sidorkiewicz, Agnieszka Szadkowska, Małgorzata Myśliwiec, Ewa Toporowska-Kowalska
Nutrients.2023; 15(3): 513. CrossRef
Prevalence of type 2 diabetes complications and its association with diet knowledge and skills and self‐care barriers in Tabriz, Iran: A cross‐sectional study
Habib Jalilian, Elnaz Javanshir, Leila Torkzadeh, Saeedeh Fehresti, Nazanin Mir, Majid Heidari‐Jamebozorgi, Somayeh Heydari
Health Science Reports.2023;[Epub] CrossRef
Status of continuous glucose monitoring use and management in tertiary hospitals of China: a cross-sectional study
Liping Chen, Xiaoqin Liu, Qin Lin, Hongmei Dai, Yong Zhao, Zumin Shi, Liping Wu
BMJ Open.2023; 13(2): e066801. CrossRef
Diboronic-Acid-Based Electrochemical Sensor for Enzyme-Free Selective and Sensitive Glucose Detection
Joong-Hyun Kim, Hongsik Choi, Chul-Soon Park, Heung-Seop Yim, Dongguk Kim, Sungmin Lee, Yeonkeong Lee
Biosensors.2023; 13(2): 248. CrossRef
Artificial intelligence biosensors for continuous glucose monitoring
Xiaofeng Jin, Andrew Cai, Tailin Xu, Xueji Zhang
Interdisciplinary Materials.2023; 2(2): 290. CrossRef
Continuous Glucose Monitoring in Dogs and Cats
Francesca Del Baldo, Federico Fracassi
Veterinary Clinics of North America: Small Animal Practice.2023; 53(3): 591. CrossRef
Accurate Post-Calibration Predictions for Noninvasive Glucose Measurements in People Using Confocal Raman Spectroscopy
Anders Pors, Kaspar G. Rasmussen, Rune Inglev, Nina Jendrike, Amalie Philipps, Ajenthen G. Ranjan, Vibe Vestergaard, Jan E. Henriksen, Kirsten Nørgaard, Guido Freckmann, Karl D. Hepp, Michael C. Gerstenberg, Anders Weber
ACS Sensors.2023; 8(3): 1272. CrossRef
Diabetes mellitus in der Akut- und Notfallmedizin
Leo Benning, Julian Krehl, Felix Patricius Hans
Notfallmedizin up2date.2023; 18(01): 45. CrossRef
Empowering People with Diabetes: Role of Continuous Glucose Monitor Systems
Sneha B Srivastava
American Journal of Lifestyle Medicine.2023; 17(3): 359. CrossRef
Diabétologie connectée : quelles sont les attentes des médecins et des patients ?
Nicolas Naïditch, Jean-Pierre Riveline
Médecine des Maladies Métaboliques.2023; 17(2): 2S3. CrossRef
Association of Vibrotactile Biofeedback With Reduced Ergonomic Risk for Surgeons During Tonsillectomy
Natalie A. Kelly, Abdulrahman Althubaiti, Aashika D. Katapadi, Adam G. Smith, Sarah C. Nyirjesy, Jane H. Yu, Amanda J. Onwuka, Tendy Chiang
JAMA Otolaryngology–Head & Neck Surgery.2023; 149(5): 397. CrossRef
The Evolution of Diabetes Technology – Options Toward Personalized Care
Maleeha Zahid, Samaneh Dowlatshahi, Abhishek H. Kansara, Archana R. Sadhu
Endocrine Practice.2023; 29(8): 653. CrossRef
A Personalized and Adaptive Insulin Bolus Calculator Based on Double Deep Q- Learning to Improve Type 1 Diabetes Management
Giulia Noaro, Taiyu Zhu, Giacomo Cappon, Andrea Facchinetti, Pantelis Georgiou
IEEE Journal of Biomedical and Health Informatics.2023; 27(5): 2536. CrossRef
Celebrating a Century of Insulin Discovery: A Critical Appraisal of the Emerging Alternative Insulin Delivery Systems
Ntethelelo Sibiya, Bonisiwe Mbatha, Phikelelani Ngubane, Andile Khathi
Current Drug Delivery.2023; 20(6): 656. CrossRef
Machine Learning–Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study
Nicholas Berin Chan, Weizi Li, Theingi Aung, Eghosa Bazuaye, Rosa M Montero
JMIR AI.2023; 2: e45450. CrossRef
Drug Delivery Systems for Personal Healthcare by Smart Wearable Patch System
Bikram Khadka, Byeongmoon Lee, Ki-Taek Kim
Biomolecules.2023; 13(6): 929. CrossRef
Wearable Electrochemical Glucose Sensors in Diabetes Management: A Comprehensive Review
Tamoghna Saha, Rafael Del Caño, Kuldeep Mahato, Ernesto De la Paz, Chuanrui Chen, Shichao Ding, Lu Yin, Joseph Wang
Chemical Reviews.2023; 123(12): 7854. CrossRef
Real-life 24-week changes in glycemic parameters among European users of flash glucose monitoring with type 1 and 2 diabetes and different levels of glycemic control
Annel Lameijer, Julia J. Bakker, Kalvin Kao, Yongjin Xu, Rijk O.B. Gans, Henk J.G. Bilo, Timothy C. Dunn, Peter R. van Dijk
Diabetes Research and Clinical Practice.2023; 201: 110735. CrossRef
Les médicaments anti-diabétiques : ce que le médecin anesthésiste réanimateur doit savoir
Michael Joubert
Anesthésie & Réanimation.2023; 9(3): 251. CrossRef
Glycemia control using remote technologies
L. A. Suplotova, O. O. Alieva
Meditsinskiy sovet = Medical Council.2023; 17(9): 81. CrossRef
Data-enabled learning and control algorithms for intelligent glucose management: The state of the art
Deheng Cai, Wenjing Wu, Marzia Cescon, Wei Liu, Linong Ji, Dawei Shi
Annual Reviews in Control.2023; 56: 100897. CrossRef
A Markov Model of Gap Occurrence in Continuous Glucose Monitoring Data for Realistic in Silico Clinical Trials
Martina Vettoretti, Martina Drecogna, Simone Del Favero, Andrea Facchinetti, Giovanni Sparacino
Computer Methods and Programs in Biomedicine.2023; 240: 107700. CrossRef
Volumetric Electron Transfer from Metabolites to Chemically Doped Polymer Electrodes
Siew Ting Melissa Tan, Gijun Lee, Kalee Rozylowicz, Adam Marks, Alberto Salleo
Advanced Functional Materials.2023;[Epub] CrossRef
Diabetes and hypertension MobileHealth systems: a review of general challenges and advancements
Bliss Utibe-Abasi Stephen, Benedicta C. Uzoewulu, Phillip Michael Asuquo, Simeon Ozuomba
Journal of Engineering and Applied Science.2023;[Epub] CrossRef
THE ASSESSMENT OF COMPENSATION OF CARBOHYDRATE METABOLISM IN PATIENTS WITH TYPE 2 DIABETES MELLITUS WITH METABOLIC SYNDROME BEYOND THE LIMITS OF GLYCATED HEMOGLOBIN
Taras V. Romaniv, Nadiya V. Skrypnyk, Ulyana V. Synko, Nataliia M. Voronych-Semchenko, Oleh V. Melnyk, Anna O. Hryb, Igor B. Boruchok
Wiadomości Lekarskie.2023; 76(6): 1385. CrossRef
Pros and cons of continous glucose monitoring
Marcin Ciechański, Edyta Witkowska, Agnieszka Ostańska, Adrianna Szafran, Klaudia Wiśniewska, Laura Piasek, Grzegorz Godek, Kacper Więcław, Katarzyna Stańko, Wiktor Terelak
Journal of Medical Science.2023;[Epub] CrossRef
Continuous Glucose Monitoring by Insulin-Treated Pilots Flying Commercial Aircraft Within the ARA.MED.330 Diabetes Protocol: A Preliminary Feasibility Study
Gillian L. Garden, Fariba Shojaee-Moradie, Ewan J. Hutchison, Brian M. Frier, Kenneth M. Shaw, Simon R. Heller, Gerd Koehler, Julia K. Mader, Declan Maher, Graham A. Roberts, David L. Russell-Jones
Diabetes Technology & Therapeutics.2023; 25(8): 543. CrossRef
Importance of continuous glucose monitoring in the treatment of diabetes mellitus
Sun Joon Moon, Won-Young Lee
Journal of the Korean Medical Association.2023; 66(7): 432. CrossRef
DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions
Temiloluwa Prioleau, Abigail Bartolome, Richard Comi, Catherine Stanger
Scientific Data.2023;[Epub] CrossRef
Testing the Real-World Accuracy of the Dexcom G6 Pro CGM During the Insulin-Only Bionic Pancreas Pivotal Trial
Martin Chase Marak, Peter Calhoun, Edward R. Damiano, Steven J. Russell, Katrina J. Ruedy, Roy W. Beck
Diabetes Technology & Therapeutics.2023; 25(11): 817. CrossRef
Use of continuous glucose monitoring in pediatric gastroenterology allows for personalized nutrition support care – Potential for collaboration between pediatric endocrinologists and gastroenterologists
Kathryn Hitchcock, Stephanie Oliveira
Journal of Pediatric Endocrinology and Diabetes.2023; 3: 34. CrossRef
Anti-biofouling strategies for implantable biosensors of continuous glucose monitoring systems
Yan Zheng, Dunyun Shi, Zheng Wang
Frontiers of Chemical Science and Engineering.2023; 17(12): 1866. CrossRef
A novel strategy for therapeutic drug monitoring: application of biosensors to quantify antimicrobials in biological matrices
Quanfang Wang, Sihan Li, Jiaojiao Chen, Luting Yang, Yulan Qiu, Qian Du, Chuhui Wang, Mengmeng Teng, Taotao Wang, Yalin Dong
Journal of Antimicrobial Chemotherapy.2023; 78(11): 2612. CrossRef
Hypoglycemic Effect of an Herbal Decoction (Modified Gangsimtang) in a Patient with Severe Type 2 Diabetes Mellitus Refusing Oral Anti-Diabetic Medication: A Case Report
Sungjun Joo, Hyonjun Chun, Jisu Lee, Seungmin Seo, Jungmin Lee, Jungtae Leem
Medicina.2023; 59(11): 1919. CrossRef
GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks
Taiyu Zhu, Kezhi Li, Pau Herrero, Pantelis Georgiou
IEEE Journal of Biomedical and Health Informatics.2023; 27(10): 5122. CrossRef
Millifluidic valves and pumps made of tape and plastic
Josue U. Amador-Hernandez, Pablo E. Guevara-Pantoja, Diana F. Cedillo-Alcantar, Gabriel A. Caballero-Robledo, Jose L. Garcia-Cordero
Lab on a Chip.2023; 23(20): 4579. CrossRef
Offline Deep Reinforcement Learning and Off-Policy Evaluation for Personalized Basal Insulin Control in Type 1 Diabetes
Taiyu Zhu, Kezhi Li, Pantelis Georgiou
IEEE Journal of Biomedical and Health Informatics.2023; 27(10): 5087. CrossRef
Flash Glucose Monitoring in Croatia: The Optimal Number of Scans per Day to Achieve Good Glycemic Control in Type 1 Diabetes
Silvija Canecki-Varzic, Ivana Prpic-Krizevac, Maja Cigrovski Berkovic, Dario Rahelic, Ema Schonberger, Marina Gradiser, Ines Bilic-Curcic
Medicina.2023; 59(11): 1893. CrossRef
The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP
Francesco Prendin, Jacopo Pavan, Giacomo Cappon, Simone Del Favero, Giovanni Sparacino, Andrea Facchinetti
Scientific Reports.2023;[Epub] CrossRef
SHMAD: A Smart Health Care System to Monitor Alzheimer’s Disease Patients
Shabana R. Ziyad, May Altulyan, Meshal Alharbi
Journal of Alzheimer's Disease.2023; 95(4): 1545. CrossRef
Séquelles fonctionnelles après résection pancréatique carcinologique. Un sujet d’actualité pour les patients et les praticiens
Andrea Mulliri, Michael Joubert, Marie-Astrid Piquet, Arnaud Alves, Benoît Dupont
Journal de Chirurgie Viscérale.2023; 160(6): 470. CrossRef
Functional sequelae after pancreatic resection for cancer
Andrea Mulliri, Michael Joubert, Marie-Astrid Piquet, Arnaud Alves, Benoît Dupont
Journal of Visceral Surgery.2023; 160(6): 427. CrossRef
Characteristics of glucose change in diabetes mellitus generalized through continuous wavelet transform processing: A preliminary study
Yoichi Nakamura, Shinya Furukawa
World Journal of Diabetes.2023; 14(10): 1562. CrossRef
Evaluating passive physiological data collection during Spravato treatment
Todd M. Solomon, Matus Hajduk, Martin Majernik, Jamileh Jemison, Alexander Deschamps, Jenna Scoggins, Adam Kolar, Miguel Amável Pinheiro, Peter Dubec, Ondrej Skala, Owen Muir, Amanda Tinkelman, Daniel R. Karlin, Robert Barrow
Frontiers in Digital Health.2023;[Epub] CrossRef
Fabrication of conductive Ag/AgCl/Ag nanorods ink on Laser-induced graphene electrodes on flexible substrates for non-enzymatic glucose detection
Rana Bagheri, Saeid Alikhani, Ebrahim Miri-Moghaddam
Scientific Reports.2023;[Epub] CrossRef
Co-design of Human-centered, Explainable AI for Clinical Decision Support
Cecilia Panigutti, Andrea Beretta, Daniele Fadda, Fosca Giannotti, Dino Pedreschi, Alan Perotti, Salvatore Rinzivillo
ACM Transactions on Interactive Intelligent Systems.2023; 13(4): 1. CrossRef
Analysis of blood glucose monitoring – a review on recent advancements and future prospects
Gayathri Priyadarshini R, Sathiya Narayanan
Multimedia Tools and Applications.2023; 83(20): 58375. CrossRef
Overview of modern sensors for continuous glucose monitoring
K. T. Momynaliev, M. V. Prokopiev, I. V. Ivanov
Diabetes mellitus.2023; 26(6): 575. CrossRef
A Prospective Multicenter Clinical Performance Evaluation of the C-CGM System
Mihailo Rebec, Kevin Cai, Ralph Dutt-Ballerstadt, Ellen Anderson
Journal of Diabetes Science and Technology.2022; 16(2): 390. CrossRef
Perceived Burdens and Benefits Associated With Continuous Glucose Monitor Use in Type 1 Diabetes Across the Lifespan
Vidita Divan, Margaret Greenfield, Christopher P. Morley, Ruth S. Weinstock
Journal of Diabetes Science and Technology.2022; 16(1): 88. CrossRef
Technologies for Diabetes Self-Monitoring: A Scoping Review and Assessment Using the REASSURED Criteria
Jessica Hanae Zafra-Tanaka, David Beran, Beatrice Vetter, Rangarajan Sampath, Antonio Bernabe-Ortiz
Journal of Diabetes Science and Technology.2022; 16(4): 962. CrossRef
Temporal Trends for Diabetes Management and Glycemic Control Between 2010 and 2019 in Korean Children and Adolescents with Type 1 Diabetes
Jaewon Choe, Seung Hyun Won, Yunsoo Choe, Sang Hee Park, Yun Jeong Lee, Jieun Lee, Young Ah Lee, Han Hyuk Lim, Jae-Ho Yoo, Seong Yong Lee, Eun Young Kim, Choong Ho Shin, Jae Hyun Kim
Diabetes Technology & Therapeutics.2022; 24(3): 201. CrossRef
International comparison of glycaemic control in people with type 1 diabetes: an update and extension
Regina Prigge, John A. McKnight, Sarah H. Wild, Aveni Haynes, Timothy W. Jones, Elizabeth A. Davis, Birgit Rami‐Merhar, Maria Fritsch, Christine Prchla, Astrid Lavens, Kris Doggen, Suchsia Chao, Ronnie Aronson, Ruth Brown, Else H. Ibfelt, Jannet Svensson,
Diabetic Medicine.2022;[Epub] CrossRef
Artificial intelligence perspective in the future of endocrine diseases
Mandana Hasanzad, Hamid Reza Aghaei Meybodi, Negar Sarhangi, Bagher Larijani
Journal of Diabetes & Metabolic Disorders.2022; 21(1): 971. CrossRef
Telehealth Technologies and Their Benefits to People With Diabetes
Chinenye O. Usoh, Kristine Kilen, Carolyn Keyes, Crystal Paige Johnson, Joseph A. Aloi
Diabetes Spectrum.2022; 35(1): 8. CrossRef
Acetylated Trifluoromethyl Diboronic Acid Anthracene with a Large Stokes Shift and Long Excitation Wavelength as a Glucose-Selective Probe
Hongsik Choi, Inhyeok Song, Chul Soon Park, Heung-seop Yim, Joong Hyun Kim
Applied Sciences.2022; 12(6): 2782. CrossRef
Suitability of the Unified Theory of Acceptance and Use of Technology 2 Model for Predicting mHealth Acceptance Using Diabetes as an Example: Qualitative Methods Triangulation Study
Patrik Schretzlmaier, Achim Hecker, Elske Ammenwerth
JMIR Human Factors.2022; 9(1): e34918. CrossRef
Continuous Glucose Monitoring System Based on Percutaneous Microneedle Array
Ming-Nan Chien, Yu-Jen Chen, Chin-Han Bai, Jung-Tung Huang
Micromachines.2022; 13(3): 478. CrossRef
Impact of COVID-19 Lockdown on the Metabolic Control Parameters in Patients with Diabetes Mellitus: A Systematic Review and Meta-Analysis
Ifan Ali Wafa, Nando Reza Pratama, Nurizzah Farahiyah Sofia, Elsha Stephanie Anastasia, Tiffany Konstantin, Maharani Ayuputeri Wijaya, M. Rifqi Wiyono, Lilik Djuari, Hermina Novida
Diabetes & Metabolism Journal.2022; 46(2): 260. CrossRef
Expert Roundtable on Continuous Glucose Monitoring
Cheryl Rosenfeld, Thomas Blevins, Grazia Aleppo, Gregory Forlenza, Diana Isaacs, Javier Morales, Jane Seley, Jeffrey Unger
Endocrine Practice.2022; 28(6): 622. CrossRef
Glucose variability and predicted cardiovascular risk after gastrectomy
Jun Shibamoto, Takeshi Kubota, Takuma Ohashi, Hirotaka Konishi, Atsushi Shiozaki, Hitoshi Fujiwara, Kazuma Okamoto, Eigo Otsuji
Surgery Today.2022; 52(11): 1634. CrossRef
Efficacy of once-weekly tirzepatide versus once-daily insulin degludec on glycaemic control measured by continuous glucose monitoring in adults with type 2 diabetes (SURPASS-3 CGM): a substudy of the randomised, open-label, parallel-group, phase 3 SURPASS
Tadej Battelino, Richard M Bergenstal, Angel Rodríguez, Laura Fernández Landó, Ross Bray, Zhentao Tong, Katelyn Brown
The Lancet Diabetes & Endocrinology.2022; 10(6): 407. CrossRef
Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes
Loïc Olçomendy, Louis Cassany, Antoine Pirog, Roberto Franco, Emilie Puginier, Manon Jaffredo, David Gucik-Derigny, Héctor Ríos, Alejandra Ferreira de Loza, Julien Gaitan, Matthieu Raoux, Yannick Bornat, Bogdan Catargi, Jochen Lang, David Henry, Sylvie Re
Frontiers in Endocrinology.2022;[Epub] CrossRef
Effect of divergent continuous glucose monitoring technologies on glycaemic control in type 1 diabetes mellitus: A systematic review and meta‐analysis of randomised controlled trials
Mona Elbalshy, Jillian Haszard, Hazel Smith, Sarahmarie Kuroko, Barbara Galland, Nick Oliver, Viral Shah, Martin I. de Bock, Benjamin J. Wheeler
Diabetic Medicine.2022;[Epub] CrossRef
Novel Glycemic Index Based on Continuous Glucose Monitoring to Predict Poor Clinical Outcomes in Critically Ill Patients: A Pilot Study
Eun Yeong Ha, Seung Min Chung, Il Rae Park, Yin Young Lee, Eun Young Choi, Jun Sung Moon
Frontiers in Endocrinology.2022;[Epub] CrossRef
Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms
Brian Bogue-Jimenez, Xiaolei Huang, Douglas Powell, Ana Doblas
Sensors.2022; 22(9): 3534. CrossRef
Generation of post-meal insulin correction boluses in type 1 diabetes simulation models for in-silico clinical trials: More realistic scenarios obtained using a decision tree approach
N. Camerlingo, M. Vettoretti, S. Del Favero, A. Facchinetti, P. Choudhary, G. Sparacino
Computer Methods and Programs in Biomedicine.2022; 221: 106862. CrossRef
A Miniaturized Optofluidic Glucose Monitoring System Based on Enzyme Colorimetry
Qingmei Xu, Chongwei Zou, Chengtao Sun, Xingguo Zhang, Haixia Yu, Dachao Li
IEEE Sensors Journal.2022; 22(10): 9246. CrossRef
Use and Trends of Diabetes Self-Management Technologies: A Correlation-Based Study
Jesús Fontecha, Iván González, Alfonso Barragán, Theodore Lim, Dario Pitocco
Journal of Diabetes Research.2022; 2022: 1. CrossRef
Nanotechnology in Diabetes Mellitus: Overview for Nurses
R Priya, Baba Vajrala
Pondicherry Journal of Nursing.2022; 15(1): 22. CrossRef
Effect of Different Glucose Monitoring Methods on Bold Glucose Control: A Systematic Review and Meta-Analysis
Yeling Wang, Congcong Zou, Han Na, Weixin Zeng, Xiaoyan Li, Xi Lou
Computational and Mathematical Methods in Medicine.2022; 2022: 1. CrossRef
Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology—A Review
Aminah Hina, Wala Saadeh
Sensors.2022; 22(13): 4855. CrossRef
Performance of islets of Langerhans conformally coated via an emulsion cross-linking method in diabetic rodents and nonhuman primates
Aaron A. Stock, Grisell C. Gonzalez, Sophia I. Pete, Teresa De Toni, Dora M. Berman, Alexander Rabassa, Waldo Diaz, James C. Geary, Melissa Willman, Joy M. Jackson, Noa H. DeHaseth, Noel M. Ziebarth, Anthony R. Hogan, Camillo Ricordi, Norma S. Kenyon, Ali
Science Advances.2022;[Epub] CrossRef
Review—Electrochemistry and Other Emerging Technologies for Continuous Glucose Monitoring Devices
Saroj Kumar Das, Kavya K. Nayak, P. R. Krishnaswamy, Vinay Kumar, Navakanta Bhat
ECS Sensors Plus.2022; 1(3): 031601. CrossRef
Design Strategies and Prospects in Developing Wearable Glucose Monitoring System Using Printable Organic Transistor and Microneedle: A Review
Fazliyatul Azwa Md Rezali, Norhayati Soin, Sharifah Fatmadiana Wan Muhamad Hatta, Mohamad Hazwan Mohd Daut, Muhammad Hafizuddin Al-Helmy Nouxman, Hanim Hussin
IEEE Sensors Journal.2022; 22(14): 13785. CrossRef
Review of Automated Insulin Delivery Systems for Type 1 Diabetes and Associated Time in Range Outcomes
Armaan Nallicheri, Katherine M Mahoney, Hanna A Gutow, Natalie Bellini, Diana Isaacs
Endocrinology.2022; 18(1): 27. CrossRef
Evaluation of Mesoporous TiO2 Layers as Glucose Optical Sensors
David Ortiz de Zárate, Sara Serna, Salvador Ponce-Alcántara, Jaime García-Rupérez
Sensors.2022; 22(14): 5398. CrossRef
A Prospective Study on Continuous Glucose Monitoring in Glycogen Storage Disease Type Ia: Toward Glycemic Targets
Alessandro Rossi, Annieke Venema, Petra Haarsma, Lude Feldbrugge, Rob Burghard, David Rodriguez-Buritica, Giancarlo Parenti, Maaike H Oosterveer, Terry G J Derks
The Journal of Clinical Endocrinology & Metabolism.2022; 107(9): e3612. CrossRef
Continuous glucose monitoring as a close to real life alternative to meal studies – a pilot study with a functional drink containing amino acids and chromium
Azat Samigullin, Per M. Humpert, Elin Östman
Frontiers in Medical Technology.2022;[Epub] CrossRef
An overview of recent advances in insulin delivery and wearable technology for effective management of diabetes
Sujeet Kumar Raj, M. Ravindra Babu, Sukriti Vishwas, M.V.N.L. Chaitanya, Vancha Harish, Gaurav Gupta, Dinesh Kumar Chellappan, Kamal Dua, Sachin Kumar Singh
Journal of Drug Delivery Science and Technology.2022; 75: 103728. CrossRef
Medical Certification of Pilots Through the Insulin-Treated Diabetes Mellitus Protocol at the FAA
Lynn K. Stanwyck, James R. DeVoll, Joyce Pastore, Zykevise Gamble, Anna Poe, Gabrielle V. Gui
Aerospace Medicine and Human Performance.2022; 93(8): 627. CrossRef
Rate of glycaemic control and associated factors in patients with type 2 diabetes mellitus treated with insulin-based therapy at selected hospitals in Northwest Ethiopia: a multicentre cross-sectional study
Ashenafi Kibret Sendekie, Eyayaw Ashete Belachew, Ephrem Mebratu Dagnew, Adeladlew Kassie Netere
BMJ Open.2022; 12(9): e065250. CrossRef
Glucose Profiles Assessed by Intermittently Scanned Continuous Glucose Monitoring System during the Perioperative Period of Metabolic Surgery
Kyuho Kim, Sung Hee Choi, Hak Chul Jang, Young Suk Park, Tae Jung Oh
Diabetes & Metabolism Journal.2022; 46(5): 713. CrossRef
Hypoglycemic events and glycemic control effects between NPH and premixed insulin in patients with type 2 diabetes mellitus: A real-world experience at a comprehensive specialized hospital in Ethiopia
Ashenafi Kibret Sendekie, Adeladlew Kassie Netere, Eyayaw Ashete Belachew, Rekha Samuel
PLOS ONE.2022; 17(9): e0275032. CrossRef
Continuous Glucose Monitoring for the Diagnosis of Gestational Diabetes Mellitus: A Pilot Study
Daria Di Filippo, Marrwah Ahmadzai, Melissa Han Yiin Chang, Ksana Horgan, Ru Min Ong, Justine Darling, Mahmood Akhtar, Amanda Henry, Alec Welsh, Daniela Foti
Journal of Diabetes Research.2022; 2022: 1. CrossRef
Caring for people with diabetes
Martha M. Funnell, Katherine A. Kloss, Robin B. Nwankwo
Nursing.2022; 52(11): 26. CrossRef
Tackling the challenges of developing microneedle-based electrochemical sensors
Hilmee Abdullah, Tonghathai Phairatana, Itthipon Jeerapan
Microchimica Acta.2022;[Epub] CrossRef
A Concise and Systematic Review on Non-Invasive Glucose Monitoring for Potential Diabetes Management
Soumyasanta Laha, Aditi Rajput, Suvra S. Laha, Rohan Jadhav
Biosensors.2022; 12(11): 965. CrossRef
Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions
Francesco Prendin, José-Luis Díez, Simone Del Favero, Giovanni Sparacino, Andrea Facchinetti, Jorge Bondia
Sensors.2022; 22(22): 8682. CrossRef
Wearable Sensor-Based Monitoring of Environmental Exposures and the Associated Health Effects: A Review
Xueer Lin, Jiaying Luo, Minyan Liao, Yalan Su, Mo Lv, Qing Li, Shenglan Xiao, Jianbang Xiang
Biosensors.2022; 12(12): 1131. CrossRef
Acceptability and feasibility of continuous glucose monitoring in people with diabetes: protocol for a mixed-methods systematic review of quantitative and qualitative evidence
Jennifer V. E. Brown, Ramzi Ajjan, Najma Siddiqi, Peter A. Coventry
Systematic Reviews.2022;[Epub] CrossRef
Utilization of Personalized Machine-Learning to Screen for Dysglycemia from Ambulatory ECG, toward Noninvasive Blood Glucose Monitoring
I-Min Chiu, Chi-Yung Cheng, Po-Kai Chang, Chao-Jui Li, Fu-Jen Cheng, Chun-Hung Richard Lin
Biosensors.2022; 13(1): 23. CrossRef
Effect of Hydroxychloroquine on Glycemic Variability in Type 2 Diabetes Patients Uncontrolled on Glimepiride and Metformin Therapy
Rajesh Rajput, Suyasha Saini, Siddhant Rajput, Parankush Upadhyay
Indian Journal of Endocrinology and Metabolism.2022; 26(6): 537. CrossRef
GESTATIONAL DIABETES MELLITUS: MODERN GLYCEMIA MONITORING SYSTEMS
YU.A. DUDAREVA, V.A. GURYEVA, G.V. NEMTSEVA
AVICENNA BULLETIN.2022; 24(1): 97. CrossRef
Extraction With Sweat-Sebum Emulsion as a New Test Method for Leachables in Patch-Based Medical Devices, Illustrated by Assessment of Isobornylacrylate (IBOA) in Diabetes Products
Herbert Fink, Nuno M. de Barros Fernandes, Jörg Weissmann, Manfred Frey
Journal of Diabetes Science and Technology.2021; 15(4): 792. CrossRef
Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator
Nunzio Camerlingo, Martina Vettoretti, Simone Del Favero, Andrea Facchinetti, Giovanni Sparacino
Journal of Diabetes Science and Technology.2021; 15(2): 346. CrossRef
Fit‐for‐Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience
Alan Godfrey, Benjamin Vandendriessche, Jessie P. Bakker, Cheryl Fitzer‐Attas, Ninad Gujar, Matthew Hobbs, Qi Liu, Carrie A. Northcott, Virginia Parks, William A. Wood, Vadim Zipunnikov, John A. Wagner, Elena S. Izmailova
Clinical and Translational Science.2021; 14(1): 62. CrossRef
Self-charging wearables for continuous health monitoring
Jiyong Kim, Salman Khan, Peng Wu, Sungjin Park, Hwanjoo Park, Choongho Yu, Woochul Kim
Nano Energy.2021; 79: 105419. CrossRef
Impact of Switching from Intermittently Scanned to Real-Time Continuous Glucose Monitoring Systems in a Type 1 Diabetes Patient French Cohort: An Observational Study of Clinical Practices
Yannis Préau, Martine Armand, Sébastien Galie, Pauline Schaepelynck, Denis Raccah
Diabetes Technology & Therapeutics.2021; 23(4): 259. CrossRef
Individualizing Time-in-Range Goals in Management of Diabetes Mellitus and Role of Insulin: Clinical Insights From a Multinational Panel
Sanjay Kalra, Shehla Shaikh, Gagan Priya, Manas P. Baruah, Abhyudaya Verma, Ashok K. Das, Mona Shah, Sambit Das, Deepak Khandelwal, Debmalya Sanyal, Sujoy Ghosh, Banshi Saboo, Ganapathi Bantwal, Usha Ayyagari, Daphne Gardner, Cecilia Jimeno, Nancy E. Barb
Diabetes Therapy.2021; 12(2): 465. CrossRef
Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy
Giulia Noaro, Giacomo Cappon, Martina Vettoretti, Giovanni Sparacino, Simone Del Favero, Andrea Facchinetti
IEEE Transactions on Biomedical Engineering.2021; 68(1): 247. CrossRef
Efficacy of telemedicine for persons with type 1 diabetes during Covid19 lockdown
Federico Boscari, Sara Ferretto, Ambra Uliana, Angelo Avogaro, Daniela Bruttomesso
Nutrition & Diabetes.2021;[Epub] CrossRef
Technological innovation of Continuous Glucose Monitoring (CGM) as a tool for commercial aviation pilots with insulin-treated diabetes and stakeholders/regulators: A new chance to improve the directives?
F. Strollo, A. Furia, P. Verde, A. Bellia, M. Grussu, A. Mambro, M.D. Petrelli, S. Gentile
Diabetes Research and Clinical Practice.2021; 172: 108638. CrossRef
Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges
Omer Mujahid, Ivan Contreras, Josep Vehi
Sensors.2021; 21(2): 546. CrossRef
Time in range–A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study
Marina Valenzano, Ivan Cibrario Bertolotti, Adriano Valenzano, Giorgio Grassi
BMJ Open Diabetes Research & Care.2021; 9(1): e001045. CrossRef
Machine learning for the diagnosis of early-stage diabetes using temporal glucose profiles
Woo Seok Lee, Junghyo Jo, Taegeun Song
Journal of the Korean Physical Society.2021; 78(5): 373. CrossRef
Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only
Francesco Prendin, Simone Del Favero, Martina Vettoretti, Giovanni Sparacino, Andrea Facchinetti
Sensors.2021; 21(5): 1647. CrossRef
A “Slide Rule” to Adjust Insulin Dose Using Trend Arrows in Adults with Type 1 Diabetes: Test in Silico and in Real Life
Daniela Bruttomesso, Federico Boscari, Giuseppe Lepore, Giulia Noaro, Giacomo Cappon, Angela Girelli, Lutgarda Bozzetto, Andrea Tumminia, Giorgio Grassi, Giovanni Sparacino, Luigi Laviola, Andrea Facchinetti
Diabetes Therapy.2021; 12(5): 1313. CrossRef
Glycemic variability and cardiovascular disease in patients with type 2 diabetes
Marcela Martinez, Jimena Santamarina, Adrian Pavesi, Carla Musso, Guillermo E Umpierrez
BMJ Open Diabetes Research & Care.2021; 9(1): e002032. CrossRef
Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments
Omar Diouri, Monika Cigler, Martina Vettoretti, Julia K. Mader, Pratik Choudhary, Eric Renard
Diabetes/Metabolism Research and Reviews.2021;[Epub] CrossRef
Smartphone-based colorimetric detection systems for glucose monitoring in the diagnosis and management of diabetes
Özlem Kap, Volkan Kılıç, John G. Hardy, Nesrin Horzum
The Analyst.2021; 146(9): 2784. CrossRef
The impact of hypoglycaemia on the quality of life of family members of adults with type 1 or type 2 diabetes: A qualitative systematic review
Mette Valdersdorf Jensen, Melanie Broadley, Jane Speight, Alison Scope, Louise Preston, Simon Heller, Bastiaan E. de Galan, Frans Pouwer, Christel Hendrieckx
Diabetic Medicine.2021;[Epub] CrossRef
A review of biosensor technology and algorithms for glucose monitoring
Yaguang Zhang, Jingxue Sun, Liansheng Liu, Hong Qiao
Journal of Diabetes and its Complications.2021; 35(8): 107929. CrossRef
Optical glucose biosensor built-in disposable strips and wearable electronic devices
Abdullah Reda, Sherif A. El-Safty, Mahmoud M. Selim, Mohamed A. Shenashen
Biosensors and Bioelectronics.2021; 185: 113237. CrossRef
Advances, Challenges, and Cost Associated with Continuous Glucose Monitor Use in Adolescents and Young Adults with Type 1 Diabetes
Karishma A. Datye, Daniel R. Tilden, Angelee M. Parmar, Eveline R. Goethals, Sarah S. Jaser
Current Diabetes Reports.2021;[Epub] CrossRef
Is HbA1c an ideal biomarker of well-controlled diabetes?
Georgia Kaiafa, Stavroula Veneti, George Polychronopoulos, Dimitrios Pilalas, Stylianos Daios, Ilias Kanellos, Triantafyllos Didangelos, Stamatina Pagoni, Christos Savopoulos
Postgraduate Medical Journal.2021; 97(1148): 380. CrossRef
Technology in the management of type 2 diabetes: Present status and future prospects
Aideen Daly, Roman Hovorka
Diabetes, Obesity and Metabolism.2021; 23(8): 1722. CrossRef
A Non-Invasive Flexible Glucose Monitoring Sensor Using a Broadband Reject Filter
Moussa Bteich, Jessica Hanna, Joseph Costantine, Rouwaida Kanj, Youssef Tawk, Ali H. Ramadan, Assaad A. Eid
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology.2021; 5(2): 139. CrossRef
Wearable patch delivery system for artificial pancreas health diagnostic-therapeutic application: A review
Nur Farrahain Nadia Ahmad, Nik Nazri Nik Ghazali, Yew Hoong Wong
Biosensors and Bioelectronics.2021; 189: 113384. CrossRef
Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies
Mary Katherine Ray, Alana McMichael, Maria Rivera-Santana, Jacob Noel, Tamara Hershey
JMIR Diabetes.2021; 6(2): e27027. CrossRef
Designing biomaterials for the modulation of allogeneic and autoimmune responses to cellular implants in Type 1 Diabetes
Magdalena M. Samojlik, Cherie L. Stabler
Acta Biomaterialia.2021; 133: 87. CrossRef
Evaluation of a continuous glucose monitoring system in neonatal foals
David Wong, Caitlin Malik, Katarzyna Dembek, Krista Estell, Megan Marchitello, Katie Wilson
Journal of Veterinary Internal Medicine.2021; 35(4): 1995. CrossRef
Flash Glucose Monitoring in the Netherlands: Increased monitoring frequency is associated with improvement of glycemic parameters
Annel Lameijer, Nicole Lommerde, Timothy C. Dunn, Marion J. Fokkert, Mireille A. Edens, Kalvin Kao, Yongjin Xu, R.O.B. Gans, Henk J.G. Bilo, Peter R. van Dijk
Diabetes Research and Clinical Practice.2021; 177: 108897. CrossRef
Utilisation, access and recommendations regarding technologies for people living with type 1 diabetes: consensus statement of the ADS/ADEA/APEG/ADIPS Working Group
Anthony J Pease, Sofianos Andrikopoulos, Mary B Abraham, Maria E Craig, Brett Fenton, Jane Overland, Sarah Price, David Simmons, Glynis P Ross
Medical Journal of Australia.2021; 215(10): 473. CrossRef
Catalytic effects of magnetic and conductive nanoparticles on immobilized glucose oxidase in skin sensors
Lilian C Alarcón-Segovia, Amay J Bandodkar, John A Rogers, Ignacio Rintoul
Nanotechnology.2021; 32(37): 375101. CrossRef
Optical Glucose Sensor Using Pressure Sensitive Paint
Jongwon Park
Sensors.2021; 21(13): 4474. CrossRef
Type 1 diabetes glycemic management: Insulin therapy, glucose monitoring, and automation
Bruce A. Perkins, Jennifer L. Sherr, Chantal Mathieu
Science.2021; 373(6554): 522. CrossRef
Clinical Utilities of Continuous Glucose Monitoring and Insulin Pumps in Pediatric Patients with Type 1 Diabetes
Jieun Lee, Jae Hyun Kim
The Ewha Medical Journal.2021; 44(3): 55. CrossRef
Personalized Postprandial Glucose Response–Targeting Diet Versus Mediterranean Diet for Glycemic Control in Prediabetes
Orly Ben-Yacov, Anastasia Godneva, Michal Rein, Smadar Shilo, Dmitry Kolobkov, Netta Koren, Noa Cohen Dolev, Tamara Travinsky Shmul, Bat Chen Wolf, Noa Kosower, Keren Sagiv, Maya Lotan-Pompan, Niv Zmora, Adina Weinberger, Eran Elinav, Eran Segal
Diabetes Care.2021; 44(9): 1980. CrossRef
Lack of Acceptance of Digital Healthcare in the Medical Market: Addressing Old Problems Raised by Various Clinical Professionals and Developing Possible Solutions
Jong Il Park, Hwa Young Lee, Hyunah Kim, Jisan Lee, Jiwon Shinn, Hun-Sung Kim
Journal of Korean Medical Science.2021;[Epub] CrossRef
Benefits of a Switch from Intermittently Scanned Continuous Glucose Monitoring (isCGM) to Real-Time (rt) CGM in Diabetes Type 1 Suboptimal Controlled Patients in Real-Life: A One-Year Prospective Study §
Yannis Préau, Sébastien Galie, Pauline Schaepelynck, Martine Armand, Denis Raccah
Sensors.2021; 21(18): 6131. CrossRef
A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems
Aleix Beneyto, Vicenç Puig, B. Wayne Bequette, Josep Vehi
Sensors.2021; 21(21): 7117. CrossRef
Editors’ Choice—Review—From Polarography to Electrochemical Biosensors: The 100-Year Quest for Selectivity and Sensitivity
William R. Heineman, Peter T. Kissinger, Kenneth R. Wehmeyer
Journal of The Electrochemical Society.2021; 168(11): 116504. CrossRef
Digital health and diabetes: experience from India
Jothydev Kesavadev, Gopika Krishnan, Viswanathan Mohan
Therapeutic Advances in Endocrinology and Metabolism.2021;[Epub] CrossRef
Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence
Sun Joon Moon, Inha Jung, Cheol-Young Park
Diabetes & Metabolism Journal.2021; 45(6): 813. CrossRef
Factors Associated with Adherence to Self-Monitoring of Blood Glucose Among Young People with Type 1 Diabetes in China: A Cross-Sectional Study
Wencong Lv, Jiaxin Luo, Qing Long, Jundi Yang, Xin Wang, Jia Guo
Patient Preference and Adherence.2021; Volume 15: 2809. CrossRef
Continuous glucose monitoring devices: A brief presentation (Review)
Doina Mihai, Diana Stefan, Daniela Stegaru, Georgiana Bernea, Ileana Vacaroiu, Toma Papacocea, Mircea Lupușoru, Adriana Nica, Ovidiu Stiru, Dorin Dragos, Octavian Olaru
Experimental and Therapeutic Medicine.2021;[Epub] CrossRef
Acute glycemic variability on admission predicts the prognosis in hospitalized patients with coronary artery disease: a meta-analysis
Zhaokun Pu, Lihong Lai, Xishan Yang, Yanyu Wang, Pingshuan Dong, Dan Wang, Yingli Xie, Zesen Han
Endocrine.2020; 67(3): 526. CrossRef
Glycemic profile of women with normoglycemia and gestational diabetes mellitus during early pregnancy using continuous glucose monitoring system
Charandeep Singh, Yashdeep Gupta, Alpesh Goyal, Mani Kalaivani, Vineeta Garg, Juhi Bharti, Seema Singhal, Garima Kachhawa, Vidushi Kulshrestha, Rajesh Kumari, Reeta Mahey, Jai B Sharma, Neerja Bhatla, Rajesh Khadgawat, Nandita Gupta, Nikhil Tandon
Diabetes Research and Clinical Practice.2020; 169: 108409. CrossRef
Efficacy of Intermittently Scanned Continuous Glucose Monitoring in the Prevention of Recurrent Severe Hypoglycemia
Timothy M.E. Davis, Penny Dwyer, Michelle England, P. Gerry Fegan, Wendy A. Davis
Diabetes Technology & Therapeutics.2020; 22(5): 367. CrossRef
How was the Diabetes Metabolism Journal added to MEDLINE?
Hye Jin Yoo
Science Editing.2020; 7(2): 201. CrossRef
Applying Nanomaterials to Modern Biomedical Electrochemical Detection of Metabolites, Electrolytes, and Pathogens
Itthipon Jeerapan, Thitaporn Sonsa-ard, Duangjai Nacapricha
Chemosensors.2020; 8(3): 71. CrossRef
Clinical Opportunities for Continuous Biosensing and Closed-Loop Therapies
Jason Li, Jia Y. Liang, Steven J. Laken, Robert Langer, Giovanni Traverso
Trends in Chemistry.2020; 2(4): 319. CrossRef
A single-blind, randomised, crossover study to reduce hypoglycaemia risk during postprandial exercise with closed-loop insulin delivery in adults with type 1 diabetes: announced (with or without bolus reduction) vs unannounced exercise strategies
Sémah Tagougui, Nadine Taleb, Laurent Legault, Corinne Suppère, Virginie Messier, Inès Boukabous, Azadeh Shohoudi, Martin Ladouceur, Rémi Rabasa-Lhoret
Diabetologia.2020; 63(11): 2282. CrossRef
Bimetallic PtAu alloy nanomaterials for nonenzymatic selective glucose sensing at low potential
Lingling Lin, Shaohuang Weng, Yanjie Zheng, Xiyao Liu, Shaoming Ying, Feng Chen, Donghong You
Journal of Electroanalytical Chemistry.2020; 865: 114147. CrossRef
Type 1 Diabetes in Youth and Technology-Based Advances in Management
Christopher Ferber, Catherine S. Mao, Jennifer K. Yee
Advances in Pediatrics.2020; 67: 73. CrossRef
Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors
Martina Vettoretti, Giacomo Cappon, Andrea Facchinetti, Giovanni Sparacino
Sensors.2020; 20(14): 3870. CrossRef
Efficacy and safety of evogliptin treatment in patients with type 2 diabetes: A multicentre, active‐controlled, randomized, double‐blind study with open‐label extension (the EVERGREEN study)
Gyuri Kim, Soo Lim, Hyuk‐Sang Kwon, Ie B. Park, Kyu J. Ahn, Cheol‐Young Park, Su K. Kwon, Hye S. Kim, Seok W. Park, Sin G. Kim, Min K. Moon, Eun S. Kim, Choon H. Chung, Kang S. Park, Mikyung Kim, Dong J. Chung, Chang B. Lee, Tae H. Kim, Moon‐Kyu Lee
Diabetes, Obesity and Metabolism.2020; 22(9): 1527. CrossRef
Association Between Continuous Glucose Monitoring-Derived Time in Range, Other Core Metrics, and Albuminuria in Type 2 Diabetes
Jee Hee Yoo, Min Sun Choi, Jiyeon Ahn, Sung Woon Park, Yejin Kim, Kyu Yeon Hur, Sang-Man Jin, Gyuri Kim, Jae Hyeon Kim
Diabetes Technology & Therapeutics.2020; 22(10): 768. CrossRef
A New Approach to Determining Liquid Concentration Using Multiband Annular Ring Microwave Sensor and Polarity Correlator
Waleed Sethi, Ahmed Ibrahim, Khaled Issa, Ali Albishi, Saleh Alshebeili
Electronics.2020; 9(10): 1616. CrossRef
Estrategia terapéutica en el paciente diabético (I). Empoderamiento del paciente y formación. Objetivos terapéuticos. Estilo de vida, alimentación, vacunación y consejos al paciente diabético
F.B. Rivas Sánchez, J. Sanz Cánovas, J. Martín Carmona, S. Jansen Chaparro
Medicine - Programa de Formación Médica Continuada Acreditado.2020; 13(17): 943. CrossRef
Current status of continuous glucose monitoring among Korean children and adolescents with type 1 diabetes mellitus
Jae Hyun Kim
Annals of Pediatric Endocrinology & Metabolism.2020; 25(3): 145. CrossRef
Towards sensor-based calving detection in the rangelands: a systematic review of credible behavioral and physiological indicators
Anita Z Chang, David L Swain, Mark G Trotter
Translational Animal Science.2020;[Epub] CrossRef
Electrochemical glucose sensors in diabetes management: an updated review (2010–2020)
Hazhir Teymourian, Abbas Barfidokht, Joseph Wang
Chemical Society Reviews.2020; 49(21): 7671. CrossRef
An analytical approach to determine the optimal duration of continuous glucose monitoring data required to reliably estimate time in hypoglycemia
Nunzio Camerlingo, Martina Vettoretti, Andrea Facchinetti, Giovanni Sparacino, Julia K. Mader, Pratik Choudhary, Simone Del Favero
Scientific Reports.2020;[Epub] CrossRef
Smartphone-Based Data Collection in Ophthalmology
Florian Philipp Raber, Rokas Gerbutavicius, Armin Wolf, Karsten Kortüm
Klinische Monatsblätter für Augenheilkunde.2020; 237(12): 1420. CrossRef
Glycemic Status Assessment by the Latest Glucose Monitoring Technologies
Ilaria Malandrucco, Benedetta Russo, Fabiana Picconi, Marika Menduni, Simona Frontoni
International Journal of Molecular Sciences.2020; 21(21): 8243. CrossRef
Medical Nutrition Therapy Using Continuous Glucose Monitoring System
Mee Ra Kweon
The Journal of Korean Diabetes.2020; 21(4): 216. CrossRef
Use of Flash Glucose Monitoring in Patients on Intensive Insulin Treatment
Jun Sung Moon
The Journal of Korean Diabetes.2020; 21(4): 184. CrossRef
Data Analysis and Accuracy Evaluation of a Continuous Glucose-Monitoring Device
Lijun Cai, Wancheng Ge, Zhigang Zhu, Xueling Zhao, Zhanhong Li
Journal of Sensors.2019; 2019: 1. CrossRef
Development of an Error Model for a Factory-Calibrated Continuous Glucose Monitoring Sensor with 10-Day Lifetime
Martina Vettoretti, Cristina Battocchio, Giovanni Sparacino, Andrea Facchinetti
Sensors.2019; 19(23): 5320. CrossRef

Short Communication

Epidemiology
Low-Normal Free Thyroxine Levels in Euthyroid Male Are Associated with Prediabetes: Sung Woo Kim, Jae-Han Jeon, Jun Sung Moon, Eon Ju Jeon, Mi-Kyung Kim, In-Kyu Lee, Jung Beom Seo, Keun-Gyu Park; Diabetes Metab J. 2019;43(5):718-726. Published online March 19, 2019; DOI: https://doi.org/10.4093/dmj.2018.0222

6,815 View
64 Download
5 Web of Science
2 Crossref

Abstract PDF Supplementary Material PubReader ePub

Abnormal thyroid function is associated with impaired glucose homeostasis. This study aimed to determine whether free thyroxine (FT4) influences the prevalence of prediabetes in euthyroid subjects using a cross-sectional survey derived from the Korea National Health and Nutrition Examination Survey, conducted between 2013 and 2015. We studied 2,399 male participants of >20 years of age who were euthyroid and non-diabetic. Prediabetic participants had lower FT4 concentrations than those without prediabetes, but their thyrotropin concentrations were similar. We stratified the population into tertiles according to FT4 concentration. After adjusting for multiple confounding factors, glycosylated hemoglobin (HbA1c) levels significantly decreased with increasing FT4 tertile, whereas fasting plasma glucose (FPG) levels were not associated with FT4 tertiles (HbA1c, P<0.01 in T3 vs. T1; FPG, P=0.489 in T3 vs. T1). The prevalence of prediabetes was significantly higher in T1 (odds ratio, 1.426; 95% confidence interval, 1.126 to 1.806; P<0.01) than in T3. In conclusion, subjects with low-normal serum FT4 had high HbA1c and were more likely to have prediabetes. These results suggest that low FT4 concentration is a risk factor for prediabetes in male, even when thyroid function is within the normal range.

Citations

Citations to this article as recorded by

Hypothyroidism: playing the cardiometabolic risk concerto
George J. Kahaly, Youshuo Liu, Luca Persani
Thyroid Research.2025;[Epub] CrossRef
Correlation between thyroid hormone values and anemia in elderly patients with diabetic nephropathy.
Jingjing Yu, Keke Tang, Yan Song
Investigación Clínica.2025; 66(2): 157. CrossRef

Original Articles

Clinical Diabetes & Therapeutics
Efficacy and Safety of Sodium-Glucose Cotransporter-2 Inhibitors in Korean Patients with Type 2 Diabetes Mellitus in Real-World Clinical Practice: A Ram Hong, Bo Kyung Koo, Sang Wan Kim, Ka Hee Yi, Min Kyong Moon; Diabetes Metab J. 2019;43(5):590-606. Published online February 28, 2019; DOI: https://doi.org/10.4093/dmj.2018.0134

11,009 View
151 Download
23 Web of Science
23 Crossref

Abstract PDF Supplementary Material PubReader ePub

Background

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

Methods

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

Results

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

Conclusion

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

Citations

Citations to this article as recorded by

Discrepancies in Dapagliflozin Response in Terms of Glycemic Control and Body Weight Reduction
Ji Eun Jun, Kyoung-Ah Kim, Nan-Hee Kim, Kwan-Woo Lee, In-Kyung Jeong
Endocrinology and Metabolism.2025; 40(2): 278. CrossRef
Effectiveness and Safety of Oral Quadruple Combination Therapy in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis
Jaehyun Bae, Min Heui Yu, Minyoung Lee, Bong-Soo Cha, Byung-Wan Lee
Endocrinology and Metabolism.2025; 40(2): 258. CrossRef
Evaluating Cardio-Protective Molecules by Efficacy Based on Weight Reduction and HbA1c Targets
Teodor Salmen, Valeria-Anca Pietrosel, Flaviana-Veronica Urzica, Diana-Elena Visan, Bianca-Margareta Salmen, Radu-Cristian Cimpeanu, Delia Reurean-Pintilei, Mihaela-Simona Popoviciu, Anca Pantea Stoian
Biomedicines.2025; 13(9): 2306. CrossRef
Effectiveness and Safety of Sodium-Glucose Cotransporter 2 Inhibitors Added to Dual or Triple Treatment in Patients with Type 2 Diabetes Mellitus
Yesol Hong, Yoomin Jeon, Yoona Choi, Tae Kyu Chung, Howard Lee
Diabetes Therapy.2024; 15(2): 487. CrossRef
Real-world assessment of effectiveness and safety profile of remogliflozin etabonate in management of type 2 diabetes mellitus
Bipin Sethi, Subhankar Chowdhury, Supratik Bhattacharya, Sagar Katare, Sachin Suryawanshi, Hanmant Barkate
International Journal of Diabetes in Developing Countries.2023; 43(2): 214. CrossRef
Effects of dapagliflozin compared with glimepiride on body composition in Asian patients with type 2 diabetes inadequately controlled with metformin: The BEYOND study
Hyeong Kyu Park, Kyoung‐Ah Kim, Kyung‐Wan Min, Tae‐Seo Sohn, In Kyung Jeong, Chul Woo Ahn, Nan‐Hee Kim, Ie Byung Park, Ho Chan Cho, Choon Hee Chung, Sung Hee Choi, Kang Seo Park, Seoung‐Oh Yang, Kwan Woo Lee
Diabetes, Obesity and Metabolism.2023; 25(9): 2743. CrossRef
Efficacy and Safety of Evogliptin Add-on Therapy to Dapagliflozin/Metformin Combinations in Patients with Poorly Controlled Type 2 Diabetes Mellitus: A 24-Week Multicenter Randomized Placebo-Controlled Parallel-Design Phase-3 Trial with a 28-Week Extensio
Jun Sung Moon, Il Rae Park, Hae Jin Kim, Choon Hee Chung, Kyu Chang Won, Kyung Ah Han, Cheol-Young Park, Jong Chul Won, Dong Jun Kim, Gwan Pyo Koh, Eun Sook Kim, Jae Myung Yu, Eun-Gyoung Hong, Chang Beom Lee, Kun-Ho Yoon
Diabetes & Metabolism Journal.2023; 47(6): 808. CrossRef
Real-world Data of Glycemic Control in a Suburban Population in Northern India during the COVID-19 Pandemic
Jaydip V. Revale, Preeti J. Revale
International Journal of Diabetes and Technology.2023; 2(2): 60. CrossRef
Sodium glucose co-transporter-2 inhibitor, Empagliflozin, is associated with significant reduction in weight, body mass index, fasting glucose, and A1c levels in Type 2 diabetic patients with established coronary heart disease: the SUPER GATE study
Satilmis Bilgin, Ozge Kurtkulagi, Tuba Taslamacioglu Duman, Burcin Meryem Atak Tel, Gizem Kahveci, Murat Kiran, Eray Erge, Gulali Aktas
Irish Journal of Medical Science (1971 -).2022; 191(4): 1647. CrossRef
Efficacy and Safety of Empagliflozin as Add-On Therapy in Patients of Type-2 Diabetes Mellitus
Nauman Wazir, Shafqat Ur Rehman
Journal of Gandhara Medical and Dental Science.2022; 9(1): 24. CrossRef
Five comparative cohorts to assess the risk of genital tract infections associated with sodium‐glucose cotransporter‐2 inhibitors initiation in type 2 diabetes mellitus
Wajd Alkabbani, Arsène Zongo, Jasjeet K. Minhas‐Sandhu, Dean T. Eurich, Baiju R. Shah, Mhd. Wasem Alsabbagh, John‐Michael Gamble
Diabetic Medicine.2022;[Epub] CrossRef
Effect of Dapagliflozin in Combination with Lobeglitazone and Metformin in Korean Patients with Type 2 Diabetes in Real-World Clinical Practice
Da Hea Seo, Young Ju Suh, Yongin Cho, Seong Hee Ahn, Seongha Seo, Seongbin Hong, Yong-ho Lee, Young Ju Choi, Eunjig Lee, So Hun Kim
Yonsei Medical Journal.2022; 63(9): 825. CrossRef
Using real-world data for supporting regulatory decision making: Comparison of cardiovascular and safety outcomes of an empagliflozin randomized clinical trial versus real-world data
Ha Young Jang, In-Wha Kim, Jung Mi Oh
Frontiers in Pharmacology.2022;[Epub] CrossRef
Study comparing the efficacy and renal safety for patients with diabetes switching from dapagliflozin to empagliflozin
Ai-Yu Yang, Hung-Chun Chen
International Journal of Clinical Pharmacy.2021; 43(4): 1015. CrossRef
Empagliflozin Regulates the AdipoR1/p-AMPK/p-ACC Pathway to Alleviate Lipid Deposition in Diabetic Nephropathy
Zhiqin Zhang, Lihua Ni, Lian Zhang, Dongqing Zha, Chun Hu, Lingli Zhang, Huiling Feng, Xiaobao Wei, Xiaoyan Wu
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2021; Volume 14: 227. CrossRef
Efficacy and Safety of Luseogliflozin in Patients with Type 2 Diabetes Complicated by Hepatic Dysfunction: A Single-Site, Single-Arm, Open-Label, Exploratory Trial
Hiroaki Seino
Diabetes Therapy.2021; 12(3): 863. CrossRef
Sodium–Glucose Cotransporter 2 Inhibitors and Risk of Retinal Vein Occlusion Among Patients With Type 2 Diabetes: A Propensity Score–Matched Cohort Study
Min-Kyung Lee, Bongsung Kim, Kyungdo Han, Jae-Hyuk Lee, Minhee Kim, Mee Kyoung Kim, Ki-Hyun Baek, Ki-Ho Song, Hyuk-Sang Kwon, Young-Jung Roh
Diabetes Care.2021; 44(10): 2419. CrossRef
Sodium-Glucose Cotransporter-2 Inhibitors Improve Cardiovascular Dysfunction in Type 2 Diabetic East Asians
Muhammad Afzal, Fahad Al-Abbasi, Muhammad Nadeem, Sultan Alshehri, Mohammed Ghoneim, Syed Imam, Waleed Almalki, Imran Kazmi
Metabolites.2021; 11(11): 794. CrossRef
Sodium-Glucose Cotransporter-2 Inhibitor for Renal Function Preservation in Patients with Type 2 Diabetes Mellitus: A Korean Diabetes Association and Korean Society of Nephrology Consensus Statement
Tae Jung Oh, Ju-Young Moon, Kyu Yeon Hur, Seung Hyun Ko, Hyun Jung Kim, Taehee Kim, Dong Won Lee, Min Kyong Moon
Diabetes & Metabolism Journal.2020; 44(4): 489. CrossRef
Sodium-glucose cotransporter-2 inhibitor for renal function preservation in patients with type 2 diabetes mellitus: A Korean Diabetes Association and Korean Society of Nephrology consensus statement
Tae Jung Oh, Ju-Young Moon, Kyu Yeon Hur, Seung Hyun Ko, Hyun Jung Kim, Taehee Kim, Dong Won Lee, Min Kyong Moon
Kidney Research and Clinical Practice.2020; 39(3): 269. CrossRef
Use and effectiveness of dapagliflozin in patients with type 2 diabetes mellitus: a multicenter retrospective study in Taiwan
Jung-Fu Chen, Yun-Shing Peng, Chung-Sen Chen, Chin-Hsiao Tseng, Pei-Chi Chen, Ting-I Lee, Yung-Chuan Lu, Yi-Sun Yang, Ching-Ling Lin, Yi-Jen Hung, Szu-Ta Chen, Chieh-Hsiang Lu, Chwen-Yi Yang, Ching-Chu Chen, Chun-Chuan Lee, Pi-Jung Hsiao, Ju-Ying Jiang, S
PeerJ.2020; 8: e9998. CrossRef
Long-Term Effectiveness and Safety of SGLT-2 Inhibitors in an Italian Cohort of Patients with Type 2 Diabetes Mellitus
Maria Mirabelli, Eusebio Chiefari, Patrizia Caroleo, Raffaella Vero, Francesco Saverio Brunetti, Domenica Maria Corigliano, Biagio Arcidiacono, Daniela Patrizia Foti, Luigi Puccio, Antonio Brunetti
Journal of Diabetes Research.2019; 2019: 1. CrossRef
An Age of Sodium-Glucose Cotransporter-2 Inhibitor Priority: Are We Ready?
Ji A Seo
Diabetes & Metabolism Journal.2019; 43(5): 578. CrossRef

Clinical Diabetes & Therapeutics
Predictors of the Therapeutic Efficacy and Consideration of the Best Combination Therapy of Sodium-Glucose Co-transporter 2 Inhibitors: Ji-Yeon Lee, Yongin Cho, Minyoung Lee, You Jin Kim, Yong-ho Lee, Byung-Wan Lee, Bong-Soo Cha, Eun Seok Kang; Diabetes Metab J. 2019;43(2):158-173. Published online January 25, 2019; DOI: https://doi.org/10.4093/dmj.2018.0057

9,739 View
200 Download
17 Web of Science
16 Crossref

Abstract PDF Supplementary Material PubReader ePub

Background

We investigated the predictive markers for the therapeutic efficacy and the best combination of sodium-glucose co-transporter 2 (SGLT2) inhibitors (empagliflozin, dapagliflozin, and ipragliflozin) therapy in patients with type 2 diabetes mellitus (T2DM).

Methods

A total of 804 patients with T2DM who had taken SGLT2 inhibitor as monotherapy or an add-on therapy were analyzed. Multivariate regression analyses were performed to identify the predictors of SGLT2 inhibitor response including the classes of baseline anti-diabetic medications.

Results

After adjusting for age, sex, baseline body mass index (BMI), diabetes duration, duration of SGLT2 inhibitor use, initial glycosylated hemoglobin (HbA1c) level, estimated glomerular filtration rate (eGFR), and other anti-diabetic agent usage, multivariate analysis revealed that shorter diabetes duration, higher initial HbA1c and eGFR were associated with better glycemic response. However, baseline BMI was inversely correlated with glycemic status; lean subjects with well-controlled diabetes and obese subjects with inadequately controlled diabetes received more benefit from SGLT2 inhibitor treatment. In addition, dipeptidyl peptidase 4 (DPP4) inhibitor use was related to a greater reduction in HbA1c in patients with higher baseline HbA1c ≥7%. Sulfonylurea users experienced a larger change from baseline HbA1c but the significance was lost after adjustment for covariates and metformin and thiazolidinedione use did not affect the glycemic outcome.

Conclusion

A better response to SGLT2 inhibitors is expected in Korean T2DM patients who have higher baseline HbA1c and eGFR with a shorter diabetes duration. Moreover, the add-on of an SGLT2 inhibitor to a DPP4 inhibitor is likely to show the greatest glycemic response.

Citations

Citations to this article as recorded by

Exploring the value of ChatGPT in selecting antidiabetic agents for type 2 diabetes
Seol A. Jang, Su Jin Kwon, Chul Sik Kim, Seok Won Park, Kyoung Min Kim
Diabetes, Obesity and Metabolism.2025; 27(10): 5761. CrossRef
Predictors of efficacy of Sodium‐GLucose Transporter‐2 inhibitors and Glucagon‐Like Peptide 1 receptor agonists: A retrospective cohort study
Daniele Scoccimarro, Giacomo Cipani, Ilaria Dicembrini, Edoardo Mannucci
Diabetes/Metabolism Research and Reviews.2024;[Epub] CrossRef
Short-term effectiveness of dapagliflozin versus DPP-4 inhibitors in elderly patients with type 2 diabetes: a multicentre retrospective study
M. L. Morieri, I. Raz, A. Consoli, M. Rigato, A. Lapolla, F. Broglio, E. Bonora, A. Avogaro, G. P. Fadini, Federica Ginestra, Gloria Formoso, Agostino Consoli, Francesco Andreozzi, Giorgio Sesti, Salvatore Turco, Luigi Lucibelli, Adriano Gatti, Raffaella
Journal of Endocrinological Investigation.2023; 46(7): 1429. CrossRef
Treatment effect heterogeneity following type 2 diabetes treatment with GLP1-receptor agonists and SGLT2-inhibitors: a systematic review
Katherine G. Young, Eram Haider McInnes, Robert J. Massey, Anna R. Kahkoska, Scott J. Pilla, Sridharan Raghavan, Maggie A. Stanislawski, Deirdre K. Tobias, Andrew P. McGovern, Adem Y. Dawed, Angus G. Jones, Ewan R. Pearson, John M. Dennis, Deirdre K. Tobi
Communications Medicine.2023;[Epub] CrossRef
Predictors of HbA1c treatment response to add-on medication following metformin monotherapy: a population-based cohort study
Wei Ying Tan, Wynne Hsu, Mong Li Lee, Ngiap Chuan Tan
Scientific Reports.2023;[Epub] CrossRef
Efficacy and Safety of Evogliptin Add-on Therapy to Dapagliflozin/Metformin Combinations in Patients with Poorly Controlled Type 2 Diabetes Mellitus: A 24-Week Multicenter Randomized Placebo-Controlled Parallel-Design Phase-3 Trial with a 28-Week Extensio
Jun Sung Moon, Il Rae Park, Hae Jin Kim, Choon Hee Chung, Kyu Chang Won, Kyung Ah Han, Cheol-Young Park, Jong Chul Won, Dong Jun Kim, Gwan Pyo Koh, Eun Sook Kim, Jae Myung Yu, Eun-Gyoung Hong, Chang Beom Lee, Kun-Ho Yoon
Diabetes & Metabolism Journal.2023; 47(6): 808. CrossRef
Effect of Dapagliflozin as an Add-on Therapy to Insulin on the Glycemic Variability in Subjects with Type 2 Diabetes Mellitus (DIVE): A Multicenter, Placebo-Controlled, Double-Blind, Randomized Study
Seung-Hwan Lee, Kyung-Wan Min, Byung-Wan Lee, In-Kyung Jeong, Soon-Jib Yoo, Hyuk-Sang Kwon, Yoon-Hee Choi, Kun-Ho Yoon
Diabetes & Metabolism Journal.2021; 45(3): 339. CrossRef
Angiotensin II up-regulates sodium-glucose co-transporter 2 expression and SGLT2 inhibitor attenuates Ang II-induced hypertensive renal injury in mice
Kana N. Miyata, Chao-Sheng Lo, Shuiling Zhao, Min-Chun Liao, Yuchao Pang, Shiao-Ying Chang, Junzheng Peng, Matthias Kretzler, Janos G. Filep, Julie R. Ingelfinger, Shao-Ling Zhang, John S.D. Chan
Clinical Science.2021; 135(7): 943. CrossRef
Sodium-Glucose Cotransporter-2 Inhibitor for Renal Function Preservation in Patients with Type 2 Diabetes Mellitus: A Korean Diabetes Association and Korean Society of Nephrology Consensus Statement
Tae Jung Oh, Ju-Young Moon, Kyu Yeon Hur, Seung Hyun Ko, Hyun Jung Kim, Taehee Kim, Dong Won Lee, Min Kyong Moon
Diabetes & Metabolism Journal.2020; 44(4): 489. CrossRef
Differential indication for SGLT-2 inhibitors versus GLP-1 receptor agonists in patients with established atherosclerotic heart disease or at risk for congestive heart failure
Francesco Giorgino, Irene Caruso, Julia Moellmann, Michael Lehrke
Metabolism.2020; 104: 154045. CrossRef
Clinical Predictors of the Hypoglycemic Effect of Sodium–Glucose Co-transporter-2 Inhibitors in Hyperuricemic Patients: A Retrospective Descriptive Observational Study
Toshinori Hirai, Yuya Kawagoe, Motoki Kei, Ryuichi Ogawa, Toshimasa Itoh
Biological and Pharmaceutical Bulletin.2020; 43(5): 782. CrossRef
Sodium-glucose cotransporter-2 inhibitor for renal function preservation in patients with type 2 diabetes mellitus: A Korean Diabetes Association and Korean Society of Nephrology consensus statement
Tae Jung Oh, Ju-Young Moon, Kyu Yeon Hur, Seung Hyun Ko, Hyun Jung Kim, Taehee Kim, Dong Won Lee, Min Kyong Moon
Kidney Research and Clinical Practice.2020; 39(3): 269. CrossRef
Efficacy of Once-Weekly Semaglutide vs Empagliflozin Added to Metformin in Type 2 Diabetes: Patient-Level Meta-analysis
Ildiko Lingvay, Matthew S Capehorn, Andrei-Mircea Catarig, Pierre Johansen, Jack Lawson, Anna Sandberg, Robert Shaw, Abby Paine
The Journal of Clinical Endocrinology & Metabolism.2020; 105(12): e4593. CrossRef
Letter: Predictors of the Therapeutic Efficacy and Consideration of the Best Combination Therapy of Sodium-Glucose Co-transporter 2 Inhibitors (Diabetes Metab J 2019;43:158–73)
Kyung-Soo Kim
Diabetes & Metabolism Journal.2019; 43(3): 377. CrossRef
Response: Predictors of the Therapeutic Efficacy and Consideration of the Best Combination Therapy of Sodium-Glucose Co-transporter 2 Inhibitors (Diabetes Metab J 2019;43:158–73)
Ji-Yeon Lee, Eun Seok Kang
Diabetes & Metabolism Journal.2019; 43(3): 379. CrossRef
An Age of Sodium-Glucose Cotransporter-2 Inhibitor Priority: Are We Ready?
Ji A Seo
Diabetes & Metabolism Journal.2019; 43(5): 578. CrossRef

Short Communication

Epidemiology
Associations between Breastfeeding and Type 2 Diabetes Mellitus and Glycemic Control in Parous Women: A Nationwide, Population-Based Study: Ga Eun Nam, Kyungdo Han, Do-Hoon Kim, Youn Huh, Byoungduck Han, Sung Jung Cho, Yong Gyu Park, Yong-Moon Park; Diabetes Metab J. 2019;43(2):236-241. Published online December 21, 2018; DOI: https://doi.org/10.4093/dmj.2018.0044

7,022 View
64 Download
4 Web of Science
5 Crossref

Abstract PDF PubReader ePub

We investigated associations between breastfeeding duration and number of children breastfed and type 2 diabetes mellitus (T2DM) and glycemic control among parous women. We performed a cross-sectional analysis of data for 9,960 parous women from the Korea National Health and Nutritional Examination Survey (2010 to 2013). Having ever breastfed was inversely associated with prevalent T2DM (adjusted odds ratio [OR], 0.60; 95% confidence interval [CI], 0.42 to 0.87). All ranges of total and average breastfeeding duration showed inverse associations with T2DM. Even short periods of breastfeeding were inversely associated with T2DM (adjusted OR, 0.61; 95% CI, 0.38 to 0.99 for a total breastfeeding duration ≤12 months; adjusted OR, 0.65; 95% CI, 0.42 to 0.99 for an average breastfeeding duration per child ≤6 months). A longer duration of breastfeeding was associated with better glycemic control in parous women with T2DM (P trend=0.004 for total breastfeeding duration; P trend <0.001 for average breastfeeding duration per child). Breastfeeding may be associated with a lower risk of T2DM and good glycemic control in parous women with T2DM. Breastfeeding may be a feasible method to prevent T2DM and improve glycemic control.

Citations

Citations to this article as recorded by

Lactation duration and the risk of type 2 diabetes mellitus in parous women: A perspective on socioeconomic status disparity
Qian Yi, Weidi Sun, Leying Hou, Jiajun Hao, He Bai, Shuting Li, Jing Wu, Changzheng Yuan, Xue Li, Sheyu Li, Peige Song
Journal of Advanced Research.2025;[Epub] CrossRef
Association of Childbirth and Breastfeeding with Diabetes Mellitus in Korean Women
Seulkee Kim, So Yeon Ryu
Journal of Health Informatics and Statistics.2024; 49(2): 109. CrossRef
Integration of nutrigenomics, melatonin, serotonin and inflammatory cytokines in the pathophysiology of pregnancy-specific urinary incontinence in women with gestational diabetes mellitus
Danielle Cristina Honorio França, Eduardo Luzía França, Luis Sobrevia, Angélica Mércia Pascon Barbosa, Adenilda Cristina Honorio-França, Marilza Vieira Cunha Rudge
Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease.2023; 1869(6): 166737. CrossRef
Association of lactation with maternal risk of type 2 diabetes: A systematic review and meta‐analysis of observational studies
Ana‐Catarina Pinho‐Gomes, Georgia Morelli, Alexandra Jones, Mark Woodward
Diabetes, Obesity and Metabolism.2021; 23(8): 1902. CrossRef
Updates in Gestational Diabetes Prevalence, Treatment, and Health Policy
Laura T. Dickens, Celeste C. Thomas
Current Diabetes Reports.2019;[Epub] CrossRef

Original Articles

Epidemiology
Discrepancies between Glycosylated Hemoglobin and Fasting Plasma Glucose for Diagnosing Impaired Fasting Glucose and Diabetes Mellitus in Korean Youth and Young Adults: Jieun Lee, Young Ah Lee, Jae Hyun Kim, Seong Yong Lee, Choong Ho Shin, Sei Won Yang; Diabetes Metab J. 2019;43(2):174-182. Published online November 2, 2018; DOI: https://doi.org/10.4093/dmj.2018.0046

10,546 View
94 Download
12 Web of Science
14 Crossref

Abstract PDF Supplementary Material PubReader ePub

Background

Glycosylated hemoglobin (HbA1c) has been recommended as a diagnostic test for prediabetes and diabetes. Here, we evaluated the level of agreement between diagnoses based on fasting plasma glucose (FPG) versus HbA1c levels and determined optimal HbA1c cutoff values for these diseases in youth and young adults.

Methods

The study included 7,332 subjects (n=4,129, aged 10 to 19 years in youth group; and n=3,203 aged 20 to 29 years in young adult group) from the 2011 to 2016 Korea National Health and Nutrition Examination Survey. Prediabetes and diabetes were defined as 100 to 125 mg/dL (impaired fasting glucose [IFG]) and ≥126 mg/dL for FPG (diabetes mellitus [DM] by FPG [DM_FPG]), and 5.7% to 6.4% and ≥6.5% for HbA1c, respectively.

Results

In the youth group, 32.5% with IFG had an HbA1c level of 5.7% to 6.4%, and 72.2% with DM_FPG had an HbA1c ≥6.5%. In the young adult group, 27.5% with IFG had an HbA1c level of 5.7% to 6.4%, and 66.6% with DM_FPG had an HbA1c ≥6.5%. Kappa coefficients for agreement between the FPG and HbA1c results were 0.12 for the youth group and 0.19 for the young adult group. In receiver operating characteristic curve analysis, the optimal HbA1c cutoff for IFG and DM_FPG were 5.6% and 5.9% in youths and 5.5% and 5.8% in young adults, respectively.

Conclusion

Usefulness of HbA1c for diagnosis of IFG and DM_FPG in Koreans aged <30 years remains to be determined due to discrepancies between the results of glucose- and HbA1c-based tests. Additional testing might be warranted at lower HbA1c levels to detect IFG and DM_FPG in this age group.

Citations

Citations to this article as recorded by

Clinical utility of glycated albumin and 1,5-anhydroglucitol in the screening and prediction of diabetes: A multi-center study
Kam-Ching Ku, Junda Zhong, Erfei Song, Carol Ho-Yi Fong, Karen Siu-Ling Lam, Aimin Xu, Chi-Ho Lee, Chloe Yu-Yan Cheung
World Journal of Diabetes.2025;[Epub] CrossRef
Artificial intelligence-based multiclass diabetes risk stratification for big data embedded with explainability: From machine learning to attention models
Ekta Tiwari, Siddharth Gupta, Anudeep Pavulla, Mustafa Al-Maini, Rajesh Singh, Esma R. Isenovic, Sumit Chaudhary, John L. Laird, Laura Mantella, Amer M. Johri, Luca Saba, Jasjit S. Suri
Biomedical Signal Processing and Control.2025; 106: 107672. CrossRef
Lower Dietary Magnesium Is Associated with a Higher Hemoglobin Glycation Index in the National Health and Nutrition Examination Survey
Juan Chen, Song Lin, Xingzhou Wang, Xiwei Wang, Pengxia Gao
Biological Trace Element Research.2024; 202(3): 878. CrossRef
Glycemic traits and colorectal cancer survival in a cohort of South Korean patients: A Mendelian randomization analysis
So Yon Jun, Sooyoung Cho, Min Jung Kim, Ji Won Park, Seung‐Bum Ryoo, Seung Yong Jeong, Kyu Joo Park, Aesun Shin
Cancer Medicine.2024;[Epub] CrossRef
Prevalence and clinical correlates of impaired fasting blood glucose in children and adolescents with depressive disorder and relationship with triglyceride and high-density lipoprotein ratio
Zhiwei Liu, Liang Sun, Feng Sun, Shu Cui, Yulong Zhang, Juan Wang, Zhaokun Zhang, Longlong Sun, Rongchun Yang, Gaofeng Yao, Yun Liu
Scientific Reports.2024;[Epub] CrossRef
Associations between HbA1c-derived estimated average glucose and fasting plasma glucose in patients with normal and abnormal hemoglobin patterns
Wilaiwan Sriwimol, Phattanapong Choosongsang, Pensiri Choosongsang, Warakorn Petkliang, Pittaya Treerut
Scandinavian Journal of Clinical and Laboratory Investigation.2022; 82(3): 192. CrossRef
Increasing prevalence of fasting hyperglycemia in adolescents aged 10–18 years and its relationship with metabolic indicators: the Korea National Health and Nutrition Examination Study (KNHANES), 2007–2018
Seung Eun Yoo, Ji Hyen Lee, Jung Won Lee, Hye Sook Park, Hye Ah Lee, Hae Soon Kim
Annals of Pediatric Endocrinology & Metabolism.2022; 27(1): 60. CrossRef
Differences in Clinical Indicators of Diabetes, Hypertension, and Dyslipidemia Among Workers Who Worked Long Hours and Shift Work
EunKyo Kang
Workplace Health & Safety.2021; 69(6): 268. CrossRef
Practice Patterns in the Acceptance of Medically Complex Living Kidney Donors with Obesity, Hypertension, Family History of Kidney Disease, or Donor-Recipient Age Discrepancy
Ziad Arabi, Muhammad Bukhari, Abdullah Hamad, Abdulrahman Altheaby, Saleh Kaysi
Avicenna Journal of Medicine.2021; 11(04): 172. CrossRef
Endocrine comorbidities of pediatric obesity
Jieun Lee, Jae Hyun Kim
Clinical and Experimental Pediatrics.2021; 64(12): 619. CrossRef
Association between handgrip strength and cardiovascular risk factors among Korean adolescents
Kyoung Kon Kim, Kyu Rae Lee, In Cheol Hwang
Journal of Pediatric Endocrinology and Metabolism.2020; 33(9): 1213. CrossRef
Hypertriglyceridemia is associated with long-term risk of cardiovascular events and specific comorbidity in very high-risk hypertensive patients
O. Ya. Korolyuk, O. M. Radchenko
The Ukrainian Biochemical Journal.2020; 92(2): 8. CrossRef
The Effect of Bariatric Surgery on Weight Loss and Metabolic Changes in Adults with Obesity
Stanisław Głuszek, Arkadiusz Bociek, Edyta Suliga, Jarosław Matykiewicz, Magdalena Kołomańska, Piotr Bryk, Przemysław Znamirowski, Łukasz Nawacki, Martyna Głuszek-Osuch, Iwona Wawrzycka, Dorota Kozieł
International Journal of Environmental Research and Public Health.2020; 17(15): 5342. CrossRef
Peculiarities of Clinical Presentations and Long–Term Complications in Patients with Coronary Artery Disease and Metabolic Syndrome, depending on their Serum Triglyceride Levels
O. Ya. Korolyuk
Ukraïnsʹkij žurnal medicini, bìologìï ta sportu.2020; 5(1): 125. CrossRef

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

8,417 View
90 Download
7 Web of Science
7 Crossref

Abstract PDF PubReader ePub

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

Epidemiology
Predictors of Incident Type 2 Diabetes Mellitus in Japanese Americans with Normal Fasting Glucose Level: You-Cheol Hwang, Wilfred Y. Fujimoto, Steven E. Kahn, Donna L. Leonetti, Edward J. Boyko; Diabetes Metab J. 2018;42(3):198-206. Published online April 25, 2018; DOI: https://doi.org/10.4093/dmj.2017.0100

5,988 View
39 Download
7 Web of Science
5 Crossref

Abstract PDF PubReader ePub

Background

Little is known about the natural course of normal fasting glucose (NFG) in Asians and the risk factors for future diabetes.

Methods

A total of 370 Japanese Americans (163 men, 207 women) with NFG levels and no history of diabetes, aged 34 to 75 years, were enrolled. Oral glucose tolerance tests were performed at baseline, 2.5, 5, and 10 years after enrollment.

Results

During 10 years of follow-up, 16.1% of participants met criteria for diabetes diagnosis, and 39.6% of subjects still had NFG levels at the time of diabetes diagnosis. During 5 years of follow-up, age (odds ratio [OR], 1.05; 95% confidence interval [CI], 1.01 to 1.10; P=0.026) and family history of diabetes (OR, 3.24; 95% CI, 1.42 to 7.40; P=0.005) were independently associated with future diabetes diagnosis; however, fasting glucose level was not an independent predictor. During 10 years of follow-up, family history of diabetes (OR, 2.76; 95% CI, 1.37 to 5.54; P=0.004), fasting insulin level (OR, 1.01; 95% CI, 1.00 to 1.02; P=0.037), and fasting glucose level (OR, 3.69; 95% CI, 1.13 to 12.01; P=0.030) were associated with diabetes diagnosis independent of conventional risk factors for diabetes.

Conclusion

A substantial number of subjects with NFG at baseline still remained in the NFG range at the time of diabetes diagnosis. A family history of diabetes and fasting insulin and glucose levels were associated with diabetes diagnosis during 10 years of follow-up; however, fasting glucose level was not associated with diabetes risk within the relatively short-term follow-up period of 5 years in subjects with NFG.

Citations

Citations to this article as recorded by

Associations of the Gap Between 2-Hour Post-Load Plasma Glucose and Fasting Blood Glucose With All-Cause or Cardiovascular Mortality in US Normoglycemic Adults
Weiwei Liu, Zhiming Liu, Chen Ding, Jie Li, Haifeng Jiang
Biological Research For Nursing.2025; 27(3): 391. CrossRef
Continuous Glucose Monitoring Metrics in Asians Without Diabetes: Differentiating Prediabetes From Normoglycemia
Suresh Rama Chandran, Gerald Gui Ren Sng, Celine Yu Hui Wong, Wei Mian Ang, Daphne Gardner
Journal of Diabetes Science and Technology.2025;[Epub] CrossRef
J-shape relationship between normal fasting plasma glucose and risk of type 2 diabetes in the general population: results from two cohort studies
Linfeng He, Wenbin Zheng, Zeyu Li, Lu Chen, Wen Kong, Tianshu Zeng
Journal of Translational Medicine.2023;[Epub] CrossRef
Fasting plasma glucose and risk of type 2 diabetes mellitus in a group of Chinese people with normoglycemia and without obesity
Ziqiong Wang, Zheng Liu, Sen He
Journal of Diabetes.2021; 13(7): 601. CrossRef
Hidden Risks behind Normal Fasting Glucose: Is It Significant?
Seung-Hwan Lee
Diabetes & Metabolism Journal.2018; 42(3): 196. 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

11,925 View
118 Download
22 Web of Science
22 Crossref

Abstract PDF PubReader ePub

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

A New Era in Islet Transplantation: Stem Cell-Derived and Gene-Edited Islet Therapies
Joonyub Lee, Seung-Hwan Lee
Diabetes & Metabolism Journal.2025; 49(6): 1201. CrossRef
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
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
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 Articles

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

7,735 View
55 Download
7 Web of Science
5 Crossref

Abstract PDF Supplementary Material PubReader ePub

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

Conundrum and chances of diabetes management in the Western Pacific Region: A narrative review
Yerin Hwang, Hyunmin Lee, Moon‐Kyu Lee
Journal of Diabetes Investigation.2025; 16(8): 1357. CrossRef
The status of blood glucose monitoring and its influencing factors in Chinese patients with type 2 diabetes initiating premixed insulin: A prospective real-world study
Si Chen, Jingyi Lu, Danfeng Peng, Fengjing Liu, Wei Lu, Wei Zhu, Yuqian Bao, Jian Zhou, Weiping Jia
Diabetes Research and Clinical Practice.2024; 218: 111895. CrossRef
Immunogenicity and Efficacy of Insulin Glargine Biosimilar Ezelin versus Originator Insulin Glargine in Patients with Type 2 Diabetes
Tri Juli Edi Tarigan, Adisti Dwijayanti, Susie Setyowati, Melva Louisa
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2021; Volume 14: 107. CrossRef
Insulin Glargine U100 Utilization in Patients with Type 2 Diabetes in an Italian Real-World Setting: A Retrospective Study
Luca Degli Esposti, Valentina Perrone, Stefania Saragoni, Valerio Blini, Stefano Buda, Rosella D’avella, Gina Gasperini, Fabio Lena, Francesca Fanelli, Luca Gazzi, Francesco Giorgino
Journal of Diabetes Research.2019; 2019: 1. CrossRef
Self-Monitoring of Blood Glucose in Patients with Insulin-Treated Type 2 Diabetes Mellitus
Kyung-Soo Kim
Diabetes & Metabolism Journal.2018; 42(1): 26. CrossRef

Clinical Care/Education
Comparison of Glucose Area Under the Curve Measured Using Minimally Invasive Interstitial Fluid Extraction Technology with Continuous Glucose Monitoring System in Diabetic Patients: Mei Uemura, Yutaka Yano, Toshinari Suzuki, Taro Yasuma, Toshiyuki Sato, Aya Morimoto, Samiko Hosoya, Chihiro Suminaka, Hiromu Nakajima, Esteban C. Gabazza, Yoshiyuki Takei; Diabetes Metab J. 2017;41(4):265-274. Published online July 31, 2017; DOI: https://doi.org/10.4093/dmj.2017.41.4.265

8,549 View
73 Download
6 Web of Science
4 Crossref

Abstract PDF PubReader ePub

Background

Continuous glucose monitoring (CGM) is reported to be a useful technique, but difficult or inconvenient for some patients and institutions. We are developing a glucose area under the curve (AUC) monitoring system without blood sampling using a minimally invasive interstitial fluid extraction technology (MIET). Here we evaluated the accuracy of interstitial fluid glucose (IG) AUC measured by MIET in patients with diabetes for an extended time interval and the potency of detecting hyperglycemia using CGM data as a reference.

Methods

Thirty-eight inpatients with diabetes undergoing CGM were enrolled. MIET comprised a pretreatment step using a plastic microneedle array and glucose accumulation step with a hydrogel patch, which was placed on two sites from 9:00 AM to 5:00 PM or from 10:00 PM to 6:00 AM. IG AUC was calculated by accumulated glucose extracted by hydrogel patches using sodium ion as standard.

Results

A significant correlation was observed between the predicted AUC by MIET and CGM in daytime (r=0.76) and nighttime (r=0.82). The optimal cutoff for the IG AUC value of MIET to predict hyperglycemia over 200 mg/dL measured by CGM for 8 hours was 1,067.3 mg·hr/dL with 88.2% sensitivity and 81.5% specificity.

Conclusion

We showed that 8-hour IG AUC levels using MIET were valuable in estimating the blood glucose AUC without blood sampling. The results also supported the concept of using this technique for evaluating glucose excursion and for screening hyperglycemia during 8 hours in patients with diabetes at any time of day.

Citations

Citations to this article as recorded by

LLM-Powered Prediction of Hyperglycemia and Discovery of Behavioral Treatment Pathways from Wearables and Diet
Abdullah Mamun, Asiful Arefeen, Susan B. Racette, Dorothy D. Sears, Corrie M. Whisner, Matthew P. Buman, Hassan Ghasemzadeh
Sensors.2025; 25(17): 5372. CrossRef
Perioperative Glucose Pragmatic (PROGRAM) Trial: Standardized Insulin Management in Surgical Patients
Matthew Zapf, C. Patrick Henson, Eunice Y. Huang, Jonathan P. Wanderer, Leslie C. Fowler, Karen Y. McCarthy, Robert E. Freundlich, Matthew S. Shotwell, Ronald Bell, Svetlana Eden, Miklos D. Kertai
Anesthesiology.2025; 143(4): 883. CrossRef
Efficacy of Postprandial Exercise in Mitigating Glycemic Responses in Overweight Individuals and Individuals with Obesity and Type 2 Diabetes—A Systematic Review and Meta-Analysis
Jie Kang, Brian M. Fardman, Nicholas A. Ratamess, Avery D. Faigenbaum, Jill A. Bush
Nutrients.2023; 15(20): 4489. CrossRef
Multifunctional Wearable System that Integrates Sweat‐Based Sensing and Vital‐Sign Monitoring to Estimate Pre‐/Post‐Exercise Glucose Levels
Yongseok Joseph Hong, Hyunjae Lee, Jaemin Kim, Minha Lee, Hyung Jin Choi, Taeghwan Hyeon, Dae‐Hyeong Kim
Advanced Functional Materials.2018;[Epub] CrossRef

Clinical Care/Education
Physician-Directed Diabetes Education without a Medication Change and Associated Patient Outcomes: Hun-Sung Kim, Hyunah Kim, Hae-Kyung Yang, Eun Young Lee, Yoo Jin Jeong, Tong Min Kim, So Jung Yang, Seo Yeon Baik, Seung-Hwan Lee, Jae Hyoung Cho, In Young Choi, Hyeon Woo Yim, Bong-Yun Cha; Diabetes Metab J. 2017;41(3):187-194. Published online May 12, 2017; DOI: https://doi.org/10.4093/dmj.2017.41.3.187

6,879 View
41 Download
1 Web of Science
3 Crossref

Abstract PDF PubReader ePub

Background

When patients with diabetes mellitus (DM) are first referred to a hospital from primary health care clinics, physicians have to decide whether to administer an oral hypoglycemic agent (OHA) immediately or postpone a medication change in favor of diabetes education regarding diet or exercise. The aim of this study was to determine the effect of diabetes education alone (without alterations in diabetes medication) on blood glucose levels.

Methods

The study was conducted between January 2009 and December 2013 and included patients with DM. The glycosylated hemoglobin (HbA1c) levels were evaluated at the first visit and after 3 months. During the first medical examination, a designated doctor also conducted a diabetes education session that mainly covered dietary management.

Results

Patients were divided into those who received no diabetic medications (n=66) and those who received an OHA (n=124). Education resulted in a marked decrease in HbA1c levels in the OHA group among patients who had DM for <1 year (from 7.0%±1.3% to 6.6%±0.9%, P=0.0092) and for 1 to 5 years (from 7.5%±1.8% to 6.9%±1.1%, P=0.0091). Those with DM >10 years showed a slightly lower HbA1c target achievement rate of <6.5% (odds ratio, 0.089; P=0.0024).

Conclusion

For patients who had DM for more than 5 years, higher doses or changes in medication were more effective than intensive active education. Therefore, individualized and customized education are needed for these patients. For patients with a shorter duration of DM, it may be more effective to provide initial intensive education for diabetes before prescribing medicines, such as OHAs.

Citations

Citations to this article as recorded by

Management Status of Patients with Type 2 Diabetes Mellitus at General Hospitals in Korea: A 5-Year Follow-Up Study
Jin Hee Jung, Jung Hwa Lee, Hyang Mi Jang, Young Na, Hee Sun Choi, Yeon Hee Lee, Yang Gyo Kang, Na Rae Kim, Jeong Rim Lee, Bok Rye Song, Kang Hee Sim
The Journal of Korean Diabetes.2022; 23(1): 64. CrossRef
Effect of Voluntary Participation on Mobile Health Care in Diabetes Management: Randomized Controlled Open-Label Trial
Da Young Lee, Seung-Hyun Yoo, Kyong Pil Min, Cheol-Young Park
JMIR mHealth and uHealth.2020; 8(9): e19153. CrossRef
Developing a multi-center clinical data mart of ACEI and ARB for real-world evidence (RWE)
Hun-Sung Kim, Sue Hyun Lee, Tong Min Kim, Ju Han Kim
Clinical Hypertension.2018;[Epub] CrossRef

The Effects of Propofol and Isoflurane on Blood Glucose during Abdominal Hysterectomy in Diabetic Patients: Shekoufeh Behdad, Abulghasem Mortazavizadeh, Vida Ayatollahi, Zahra Khadiv, Saidhossein Khalilzadeh; Diabetes Metab J. 2014;38(4):311-316. Published online August 20, 2014; DOI: https://doi.org/10.4093/dmj.2014.38.4.311

6,797 View
66 Download
28 Web of Science
30 Crossref

Abstract PDF PubReader ePub

Background

Acute hyperglycemia in the perioperative period is associated with significantly increased complications. In few human studies the effects of propofol and inhalational anesthetic on the glucose metabolism were compared. In this study we evaluated the effect of propofol and isoflurane on blood glucose during abdominal hysterectomy in diabetic patients.

Methods

After approval by the Ethical Committee and written informed consent, thirty 35 to 65 years old diabetic women underwent for elective abdominal hysterectomy under general anesthesia were studied in this randomized single blind clinical trial study. The plasma glucose was maintained at 100 to 180 mg/dL during the operation. Anesthesia protocol was similar in two groups except maintenance of anesthesia that was with infusion of propofol in the propofol group and with isoflurane in the isoflurane group. Blood glucose level and the rate of insulin intake during surgery compared between two groups.

Results

Mean blood glucose before induction of anesthesia did not have significant difference between two groups, but 60 and 90 minutes after starting the operation blood glucose in the propofol group was significantly lower than isoflurane group. Also with using Repeated Measure test, two groups was significantly different according to blood glucose (P=0.045). Mean of administration of insulin during the surgery did not have significant difference between two groups by using repeated measure test and P=0.271. Also mean of bispectral index in different times during the surgery between two groups didn't have significant difference (P=0.35 repeated measure test).

Conclusion

Blood glucose increased during maintenance of anesthesia with isoflurane compared to propofol during the surgery.

Citations

Citations to this article as recorded by

Effects of Intravenous or Inhalation Anesthesia on Blood Glucose in Patients with Type 2 Diabetes Mellitus: A PRISMA-Compliant Systematic Review and Meta-Analysis
Sang Min Yoon, Hyun Kang, Yoon Ji Choi, Sang Hun Kim, Seongtae Jeong, Sejong Jin
Medicina.2026; 62(1): 128. CrossRef
Bidirectional Relationship Between Anaesthetic Drugs and Metabolic Syndrome: Clinical Effects and Mechanisms
Yuying Huang, Qinghai Lan, Yijian Chen, Youchun Li, Baolin Zhong, Yuxin Zhan, Simin Deng
Journal of Cellular and Molecular Medicine.2025;[Epub] CrossRef
Stable intracranial imaging of dura mater-engrafted pancreatic islet cells in awake mice
Philip Tröster, Montse Visa, Ismael Valladolid-Acebes, Martin Köhler, Per-Olof Berggren
Nature Communications.2025;[Epub] CrossRef
Perioperative Glycemic Management in Cardiac Surgery: A Narrative Review
Yada Thongsuk, Nian Chih Hwang
Journal of Cardiothoracic and Vascular Anesthesia.2024; 38(1): 248. CrossRef
Ketone supplementation abolished isoflurane anesthesia-induced elevation in blood glucose level and increased recovery time from anesthesia in Wistar Albino Glaxo Rijswijk rats
Zsolt Kovács, Dominic P. D’Agostino, Csilla Ari
BMC Anesthesiology.2023;[Epub] CrossRef
Comparison of spinal versus general anesthesia on the perioperative blood glucose levels in patients undergoing lower abdominal and pelvic surgery: a prospective cohort study, Ethiopia
Hirbo Samuel, Betelihem Girma, Mestawet Negash, Esubalew Muluneh
Annals of Medicine & Surgery.2023; 85(4): 849. CrossRef
Impact of total intravenous anesthesia and total inhalation anesthesia as the anesthesia maintenance approaches on blood glucose level and postoperative complications in patients with type 2 diabetes mellitus: a double-blind, randomized controlled trial
Xinghui Xiong, Yong He, Cheng Zhou, Qin Zheng, Chan Chen, Peng Liang
BMC Anesthesiology.2023;[Epub] CrossRef
Epidural anesthesia as the component of postoperative analgesia in children
Elmira A. Satvaldieva, Otabek Ya. Fayziev, Anvar S. Yusupov, Shaira A. Agzamova, Yakupdjan N. Fayziev
Russian Pediatric Journal.2023; 26(3): 194. CrossRef
Influence of general anaesthesia on circulating biomarkers of glucose metabolism in pigs
Berit Ø Christoffersen, Cathrine J Bundgaard, Karina R Hjøllund, Johannes Josef Fels, Kirstine K Boll, Mille K Lyhne, Lisbeth H Olsen
Laboratory Animals.2023; 57(6): 650. CrossRef
A conformable microneedle sensor with photopatternable skin adhesive and gel electrolyte for continuous glucose monitoring
Joohyuk Kang, Kyung Yeun Kim, Seungwan Kim, Hyejun Hong, Byeong-Soo Bae, Seung-Kyun Kang, Wonryung Lee
Device.2023; 1(4): 100112. CrossRef
Intravital Ca2+ imaging of pancreatic β cell function after bariatric surgery
Philip Tröster, Per-Olof Berggren
Cell Calcium.2022; 104: 102566. CrossRef
Feasibility of Interstitial Fluid Ketone Monitoring with Microneedles
Robert M. Taylor, Justin T. Baca
Metabolites.2022; 12(5): 424. CrossRef
Use of a bio-electronic device comprising of targeted dual neuromodulation of the hepatic and celiac vagal branches demonstrated enhanced glycemic control in a type 2 diabetic rat model as well as in an Alloxan treated swine model
Jonathan J. Waataja, Raj K. Nihalani, Chris N. Honda, Charles J. Billington
Frontiers in Neuroscience.2022;[Epub] CrossRef
The influence of anesthesia type on perioperative maternal glycemic-stress response during elective cesarean section: A prospective cohort study
Diab A. Bani Hani, Omar F. Altal, Adel Bataineh, Mahmoud Al Athamneh, Mohammad Altarawneh, Moath Alshawaqfeh, Hajar Haddouchane, Sultan M. Al-Zyoud, Musa'b Hamarsheh, Ala”a A. Alhowary
Annals of Medicine and Surgery.2021; 64: 102209. CrossRef
Amelioration of Hippocampal Insulin Resistance Reduces Tau Hyperphosphorylation and Cognitive Decline Induced by Isoflurane in Mice
Liangyu Peng, Xin Fang, Fangxia Xu, Shuai Liu, Yue Qian, Xiangdan Gong, Xin Zhao, Zhengliang Ma, Tianjiao Xia, Xiaoping Gu
Frontiers in Aging Neuroscience.2021;[Epub] CrossRef
Propofol Improved Glucose Tolerance Associated with Increased FGF-21 and GLP-1 Production in Male Sprague-Dawley Rats
Chih-Cheng Wu, Chih-Jen Hung, Ya-Yu Wang, Shih-Yi Lin, Wen-Ying Chen, Yu-Hsiang Kuan, Su-Lan Liao, Ching-Ping Yang, Chun-Jung Chen
Molecules.2020; 25(14): 3229. CrossRef
Dexmedetomidine alleviates insulin resistance in hepatocytes by reducing endoplasmic reticulum stress
Fanfan Liu, Shaojun Zhu, Lifeng Ni, Ling’er Huang, Kuirong Wang, Yanfeng Zhou
Endocrine.2020; 67(1): 87. CrossRef
In vivo Ca2+ dynamics in single pancreatic β cells
Stefan Jacob, Martin Köhler, Philip Tröster, Montse Visa, Concha F. García‐Prieto, Tomas Alanentalo, Tilo Moede, Barbara Leibiger, Ingo B. Leibiger, Per‐Olof Berggren
The FASEB Journal.2020; 34(1): 945. CrossRef
Isoflurane and Carbon Dioxide Elicit Similar Behavioral Responses in Rats
Satyajit Kulkarni, Debra Hickman
Animals.2020; 10(8): 1431. CrossRef
Effect of Spinal Anesthesia versus General Anesthesia on Blood Glucose Concentration in Patients Undergoing Elective Cesarean Section Surgery: A Prospective Comparative Study
Khaled El-Radaideh, Ala’’a Alhowary, Mohammad Alsawalmeh, Ahmed Abokmael, Haitham Odat, Amer Sindiani
Anesthesiology Research and Practice.2019; 2019: 1. CrossRef
Alteration of FDG uptake by performing novel object recognition task in a rat model of Traumatic Brain Injury
Shalini Jaiswal, Nicole Hockenbury, Hongna Pan, Andrew Knutsen, Bernard J. Dardzinski, Kimberly R. Byrnes
NeuroImage.2019; 188: 419. CrossRef
Comparison of sevoflurane and propofol anesthesia on the incidence of hyperglycemia in patients with type 2 diabetes undergoing lung surgery
Hyuckgoo Kim, Jisoo Han, Sung Mee Jung, Sang-Jin Park, Nyeong Keon Kwon
Yeungnam University Journal of Medicine.2018; 35(1): 54. CrossRef
In vivo measurement of the rate constant of liver handling of glucose and glucose uptake by insulin-dependent tissues, using a mathematical model for glucose homeostasis in diabetic rats
Mercedes Lombarte, Maela Lupo, L. Fina Brenda, German Campetelli, A.R. Buzalaf Marilia, Marta Basualdo, Alfredo Rigalli
Journal of Theoretical Biology.2018; 439: 205. CrossRef
Biomimetic electrospun coatings increase the in vivo sensitivity of implantable glucose biosensors
Krishna Burugapalli, Shavini Wijesuriya, Ning Wang, Wenhui Song
Journal of Biomedical Materials Research Part A.2018; 106(4): 1072. CrossRef
Sevoflurane Impairs Insulin Secretion and Tissue‐Specific Glucose Uptake In Vivo
Kasper F. Høyer, Thomas S. Nielsen, Steve Risis, Jonas T. Treebak, Niels Jessen
Basic & Clinical Pharmacology & Toxicology.2018; 123(6): 732. CrossRef
Metabolic response to three different diets in lean cats and cats predisposed to overweight
Claudia Keller, Annette Liesegang, Diana Frey, Brigitta Wichert
BMC Veterinary Research.2017;[Epub] CrossRef
Anaesthesia and changes in parameters that reflect glucose metabolism in pigs – a pilot study
Elin Manell, Marianne Jensen-Waern, Patricia Hedenqvist
Laboratory Animals.2017; 51(5): 509. CrossRef
Isoflurane and Sevoflurane Induce Severe Hepatic Insulin Resistance in a Canine Model
Stella P. Kim, Josiane L. Broussard, Cathryn M. Kolka, Barbara Fam
PLOS ONE.2016; 11(11): e0163275. CrossRef
Impact of benzodiazepines on brain FDG-PET quantification after single-dose and chronic administration in rats
Jesús Silva-Rodríguez, Lara García-Varela, Esteban López-Arias, Inés Domínguez-Prado, Julia Cortés, Juan Pardo-Montero, Anxo Fernández-Ferreiro, Álvaro Ruibal, Tomás Sobrino, Pablo Aguiar
Nuclear Medicine and Biology.2016; 43(12): 827. CrossRef
Effects of Glucose Load on Catabolism during Propofol-Based Anesthesia with Remifentanil in Patients with Diabetes Mellitus: A Prospective Randomized Trial
Tomotsugu Yamada, Maiko Hasegawa-Moriyama, Mayumi Nakahara, Akira Matsunaga, Yuichi Kanmura
Open Journal of Anesthesiology.2015; 05(09): 197. CrossRef

Predicting Mortality of Critically Ill Patients by Blood Glucose Levels: Byung Sam Park, Ji Sung Yoon, Jun Sung Moon, Kyu Chang Won, Hyoung Woo Lee; Diabetes Metab J. 2013;37(5):385-390. Published online October 17, 2013; DOI: https://doi.org/10.4093/dmj.2013.37.5.385

6,111 View
43 Download
10 Crossref

Abstract PDF PubReader ePub

Background

The aim of this study is to observe the outcome of critically ill patients in relation to blood glucose level at admission and to determine the optimal range of blood glucose at admission predicting lower hospital mortality among critically ill patients.

Methods

We conducted a retrospective cohort study of a total 1,224 subjects (males, 798; females, 426) admitted to intensive care unit (ICU) from 1 January 2009 to 31 December 2010. Blood glucose levels at admission were categorized into four groups (group 1, <100 mg/dL; group 2, 100 to 199 mg/dL; group 3, 200 to 299 mg/dL; and group 4, ≥300 mg/dL).

Results

Among 1,224 patients, 319 patients were already known diabetics, and 296 patients died in ICU. Five hundred fifty-seven subjects received insulin therapy, and 118 received oral hypoglycemic agents. The overall mortality rate was 24.2% (296 patients). The causes of death and mortality rates of diabetic patients were not different from nondiabetic subjects. The mortality curve showed J shape, and there were significant differences in mortality between the groups of blood glucose levels at admission. Group 2 had the lowest mortality rate (P<0.05).

Conclusion

These results suggest that serum glucose levels upon admission into ICU is associated with clinical outcomes in ICU patients. Blood glucose level between 100 and 199 mg/dL at the time of ICU admission could predict lower hospital mortality among critically ill patients.

Citations

Citations to this article as recorded by

HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction
Youru Li, Zhenfeng Zhu, Xiaobo Guo, Shaoshuai Li, Yuchen Yang, Yao Zhao
ACM Transactions on Knowledge Discovery from Data.2024; 18(1): 1. CrossRef
Feature extraction from unequal length heterogeneous EHR time series via dynamic time warping and tensor decomposition
Chi Zhang, Hadi Fanaee-T, Magne Thoresen
Data Mining and Knowledge Discovery.2021; 35(4): 1760. CrossRef
Serum electrolytes levels in patients with type 2 diabetes mellitus: a cross-sectional study
Shridhar Reshma, Sushith Sushith, Mangalore Balakrishna Prathima, D'Sa Janice, Gopal R Madan, Pragathi Gowda, Kiran PK Kumar, Mohandas Rai, Bhuvanesh Sukhlal Kalal
Diabetes mellitus.2020; 23(3): 223. CrossRef
Systemic Corticosteroids and Mortality in Severe and Critical COVID-19 Patients in Wuhan, China
Jianfeng Wu, Jianqiang Huang, Guochao Zhu, Yihao Liu, Han Xiao, Qian Zhou, Xiang Si, Hui Yi, Cuiping Wang, Daya Yang, Shuling Chen, Xin Liu, Zelong Liu, Qiongya Wang, Qingquan Lv, Ying Huang, Yang Yu, Xiangdong Guan, Yanbing Li, Krishnarajah Nirantharakum
The Journal of Clinical Endocrinology & Metabolism.2020; 105(12): e4230. CrossRef
Derivation and validation of a new nutritional index for predicting 90 days mortality after ICU admission in a Korean population
Da-Hye Son, Kyung-Sub Kim, Hye-Sun Lee, Ji-Won Lee, Cheung-Soo Shin
Journal of the Formosan Medical Association.2020; 119(8): 1283. CrossRef
Acute pancreatic beta cell apoptosis by IL-1β is responsible for postburn hyperglycemia: Evidence from humans and mice
Jun Li, Jie Xu, Xinghua Qin, Hongyan Yang, Juntao Han, Yanhui Jia, Huayu Zhu, Liang Zhu, Jia Li, Wenjun Xie, Dahai Hu, Xing Zhang, Feng Gao
Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease.2019; 1865(2): 275. CrossRef
Determinants of mortality in patients with type 2 diabetes: a review
Jana Engelmann, Ulf Manuwald, Constanze Rubach, Joachim Kugler, Andreas L. Birkenfeld, Markolf Hanefeld, Ulrike Rothe
Reviews in Endocrine and Metabolic Disorders.2016; 17(1): 129. CrossRef
The Impact of Measurement Frequency on the Domains of Glycemic Control in the Critically Ill-A Monte Carlo Simulation
James S. Krinsley, David E. Bruns, James C. Boyd
Journal of Diabetes Science and Technology.2015; 9(2): 237. CrossRef
Response: Predicting Mortality of Critically Ill Patients by Blood Glucose Levels (Diabetes Metab J2013;37:385-90)
Byung Sam Park, Ji Sung Yoon
Diabetes & Metabolism Journal.2014; 38(1): 81. CrossRef
Predicting Mortality of Critically Ill Patients by Blood Glucose Levels (Diabetes Metab J2013;37:385-90)
Hyeong Kyu Park
Diabetes & Metabolism Journal.2013; 37(6): 484. CrossRef

Influence of the Duration of Diabetes on the Outcome of a Diabetes Self-Management Education Program: Seung-Hyun Ko, Sin-Ae Park, Jae-Hyoung Cho, Sun-Hye Ko, Kyung-Mi Shin, Seung-Hwan Lee, Ki-Ho Song, Yong-Moon Park, Yu-Bae Ahn; Diabetes Metab J. 2012;36(3):222-229. Published online June 14, 2012; DOI: https://doi.org/10.4093/dmj.2012.36.3.222

7,670 View
51 Download
42 Crossref

Abstract PDF PubReader ePub

Background

Diabetes education and lifestyle modification are critical components in controlling blood glucose levels of people with type 2 diabetes. Until now, available data on the effectiveness of education with respect to the duration of diabetes are limited. We investigated whether adherence to lifestyle behavior modification prompted by diabetes education was influenced by the duration of diabetes.

Methods

Two hundred and twenty-five people with type 2 diabetes were recruited for an intensive, collaborative, group-based diabetes education program with annual reinforcement. We divided the patients into two groups based on the duration of their diabetes prior to the education program (≤1 year [≤1Y] vs. ≥3 years [≥3Y]). Dietary habits, physical activity, and the frequency of blood glucose self-monitoring were evaluated with a questionnaire prior to education and at the follow-up endpoint.

Results

The mean follow-up period was 32.2 months. The mean hemoglobin A1c (A1C) value was significantly lower in the ≤1Y group. Self-care behaviors, measured by scores for dietary habits (P=0.004) and physical activity (P<0.001), were higher at the endpoint in the ≤1Y group than in the ≥3Y group. Logistic regression analysis revealed that a longer diabetes duration before education was significantly associated with mean A1C levels greater than or equal to 7.0% (53 mmol/mol).

Conclusion

Diabetes duration influenced the effectiveness of diabetes education on lifestyle behavior modification and glycemic control. More-intense, regular, and sustained reinforcement with encouragement may be required for individuals with longstanding type 2 diabetes.

Citations

Citations to this article as recorded by

Risk of Retinal Vein Occlusion between Glucagon-Like Peptide-1 Receptor Agonists and Dipeptidyl Peptidase-4 Inhibitors in Type 2 Diabetes
Ssu-Yu Pan, Chien-Hsiang Weng, Shang-Feng Tsai, Yi-Jing Sheen, Hui-Ju Lin, Peng-Tai Tien, Jun-Fu Lin, Ching-Heng Lin, I-Jong Wang, Chien-Chih Chou
Ophthalmology Science.2025; 5(4): 100734. CrossRef
Determinants of adherence to the Mediterranean diet among individuals with type 2 diabetes mellitus living in Mediterranean countries: a systematic review
Janot J. Ayoub, Suzan A. Haidar, Ellen E. Blaak, Nanne K. De Vries
Frontiers in Nutrition.2025;[Epub] CrossRef
Green diabetes project: A comprehensive characterization of sustainable diabetes practices
Valentim Lopes, Diana Carvalho, Adriana De Sousa Lages
Diabetes Research and Clinical Practice.2025; 224: 112208. CrossRef
Knowledge–Behavior Relationships and Technology Adoption Among Patients With Diabetes: A Mixed‐Methods Analysis of Smart Foot Care Technology
Ting‐Ting Yeh, Jawl‐Shan Huang, Yun‐Chieh Chou
Journal of Foot and Ankle Research.2025;[Epub] CrossRef
Predictors of psychosocial adaptation in haemodialysis patients according to haemodialysis vintage: a quantitative study
Ae Kyung Chang, Ah Young Kim
Contemporary Nurse.2025; 61(4): 377. CrossRef
Dietary Knowledge and Eating Patterns Among Patients Living With Diabetes in Lebowakgomo, Limpopo: A Cross-Sectional Study
MH Mphasha, MA Mahlatjie, K. Mphahlele, PF Mogofe, C. Mokobodi
SN Comprehensive Clinical Medicine.2025;[Epub] CrossRef
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
Effect of aerobic training on baroreflex sensitivity, heart rate recovery, and heart rate variability in type 2 diabetes: a systematic review
Sarah Parveen, Muhammad Azharuddin, Majumi M. Noohu
International Journal of Diabetes in Developing Countries.2024; 44(4): 652. CrossRef
The impact of focused education on diabetic foot care knowledge among adult type 2 diabetic patients: A hospital-based comparative study
Christiana Chika Agunanne, Chijioke Stanley Anyigor-ogah, Ikenna Alphonsus Ozioko, Nnaemeka Christopher Oguejiofor, Okechukwu Anyigor-ogah, Benjamin Chukwuemeka Agunanne, Chibueze Ogbodo Nweke, Idika Mba Idika
Indian Journal of Medical Sciences.2024; 77: 76. CrossRef
Income disparity in completion of diabetes self-management education: Results from the 2023 Community Health Survey
Eunji Nam, Changyong Choi, Jeongmyeong Lee
Korean Journal of Health Education and Promotion.2024; 41(5): 125. 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
Continuing the quality improvement of an electronic personal health record and interactive website for people with diabetes in Scotland (My Diabetes My Way)
Cathy Shields, Nicholas T. Conway, Brian Allardice, Deborah J. Wake, Scott G. Cunningham
Diabetic Medicine.2023;[Epub] CrossRef
The use of nomogram for detecting mild cognitive impairment in patients with type 2 diabetes mellitus
Rehanguli Maimaitituerxun, Wenhang Chen, Jingsha Xiang, Yu Xie, Atipatsa C. Kaminga, Xin Yin Wu, Letao Chen, Jianzhou Yang, Aizhong Liu, Wenjie Dai
Journal of Diabetes.2023; 15(5): 448. CrossRef
DENTAL IMPLANT TREATMENT IN DIABETES MELLITUS (DM) PATIENTS: REVIEW
Hakob Khachatryan
BULLETIN OF STOMATOLOGY AND MAXILLOFACIAL SURGERY.2023; : 121. CrossRef
An association between socioeconomic status and preventive screening for diabetic eye and kidney complications among individuals with type 2 diabetes
Changwoo Shon, Jongnam Hwang
Korean Journal of Health Education and Promotion.2023; 40(5): 27. CrossRef
Association of quality of life with medication adherence and glycemic control in patients with type 1 diabetes
Farimah Fayyaz, Parham Mardi, Sahar Sobhani, Leily Sokoty, Fatemeh Aghamahdi, Mostafa Qorbani
Journal of Diabetes & Metabolic Disorders.2023; 23(1): 783. CrossRef
Managing type 2 diabetes and depression with Mindfulness-integrated Cognitive Behavior Therapy (MiCBT)
Fateh Sohrabi, Ahmad Sohrabi, Narges Shams-Alizadeh, Bruno A. Cayoun
Discover Psychology.2022;[Epub] CrossRef
Perception of benefits and barriers associated with dementia prevention behaviors among people with diabetes
Noppamas Pipatpiboon, Nut Koonrungsesomboon, Wachira Suriyawong, Jirapas Sripetchwandee, Sue Turale
Nursing & Health Sciences.2022; 24(1): 274. CrossRef
An exploratory study on the knowledge, attitude and practice of sharp disposal among type 2 diabetes mellitus patients in Northern Peninsular Malaysia
Jin Yi Choo, Yen Ping Ng, Ahmad Kamal Ariffin Abdul Jamil, Wei Kai Heng, Yee Mei Ng, Jing Ng, Cheng Hoon Yap
Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2022; 16(4): 102479. CrossRef
Efficacy of Personalized Diabetes Self-care Using an Electronic Medical Record–Integrated Mobile App in Patients With Type 2 Diabetes: 6-Month Randomized Controlled Trial
Eun Young Lee, Seon-Ah Cha, Jae-Seung Yun, Sun-Young Lim, Jin-Hee Lee, Yu-Bae Ahn, Kun-Ho Yoon, Min Kyung Hyun, Seung-Hyun Ko
Journal of Medical Internet Research.2022; 24(7): e37430. CrossRef
Real-world Benefits of Diabetes Management App Use and Self-monitoring of Blood Glucose on Glycemic Control: Retrospective Analyses
Ya-Ting Chang, Yu-Zhen Tu, Hung-Yi Chiou, Ken Lai, Neng Chun Yu
JMIR mHealth and uHealth.2022; 10(6): e31764. CrossRef
Glycemic control and its influencing factors in type 2 diabetes patients in Anhui, China
Xiu-Ya Xing, Xin-Yi Wang, Xi Fang, Jing-Qiao Xu, Ye-Ji Chen, Wei Xu, Hua-Dong Wang, Zhi-Rong Liu, Sha-Sha Tao
Frontiers in Public Health.2022;[Epub] CrossRef
Adherence to Mediterranean diet and advanced glycation endproducts in patients with diabetes
Marko Grahovac, Marko Kumric, Marino Vilovic, Dinko Martinovic, Ante Kreso, Tina Ticinovic Kurir, Josip Vrdoljak, Karlo Prizmic, Joško Božić
World Journal of Diabetes.2021; 12(11): 1942. CrossRef
Impact of Diabetes and Disease Duration on Work Status Among U.S. Older Adults
Miriam Mutambudzi, Cesar Gonzalez Gonzalez, Rebecca Wong
Journal of Aging and Health.2020; 32(5-6): 432. CrossRef
The association of illness perceptions and God locus of health control with self-care behaviours in patients with type 2 diabetes in Saudi Arabia
Mohsen Alyami, Anna Serlachius, Ibrahim Mokhtar, Elizabeth Broadbent
Health Psychology and Behavioral Medicine.2020; 8(1): 329. CrossRef
The Impact of a Community-Based Food Education Program on Nutrition-Related Knowledge in Middle-Aged and Older Patients with Type 2 Diabetes: Results of a Pilot Randomized Controlled Trial
Carlos Vasconcelos, António Almeida, Maria Cabral, Elisabete Ramos, Romeu Mendes
International Journal of Environmental Research and Public Health.2019; 16(13): 2403. CrossRef
Diabetes-Related Health Care Utilization and Dietary Intake Among Food Pantry Clients
Eric M. Bomberg, Sophie Rosenmoss, Morgan Smith, Elaine Waxman, Hilary K. Seligman
Health Equity.2019; 3(1): 644. CrossRef
Factors Contributing to Sharp Waste Disposal at Health Care Facility Among Diabetic Patients in North-East Peninsular Malaysia
Ummu Atiyyah Hasan, Suhaily Mohd Hairon, Najib Majdi Yaacob, Aziah Daud, Anees Abdul Hamid, Norzaihan Hassan, Mohd Faiz Ariffin, Lau Yi Vun
International Journal of Environmental Research and Public Health.2019; 16(13): 2251. CrossRef
Effectiveness of Diabetes Community Sharp Disposal Education Module in Primary Care: An Experimental Study in North-East Peninsular Malaysia
Ummu Atiyyah Hasan, Suhaily Mohd Hairon, Najib Majdi Yaacob, Aziah Daud, Anees Abdul Hamid, Norzaihan Hassan, Mohd Faiz Ariffin, Lau Yi Vun
International Journal of Environmental Research and Public Health.2019; 16(18): 3356. CrossRef
Analysis of the Relationship between the Referral and Evolution of Patients with Type 2 Diabetes Mellitus
Camilo Alvarez, Cecilia Saint-Pierre, Valeria Herskovic, Marcos Sepúlveda
International Journal of Environmental Research and Public Health.2018; 15(7): 1534. CrossRef
Clinical characteristics of Chinese patients with duration of type 2 diabetes >40 years
Tingting Song, Yingying Luo, Xuhui Wang, Jing Li, Qian Han, Hong Zhu, Wei Zhao, Weidong Li, Zhong Sun, Xilin Yang
Journal of Diabetes.2017; 9(1): 45. CrossRef
Impacto de intervenções educativas na redução das complicações diabéticas: revisão sistemática
Marcela Machado Menezes, Camila Takáo Lopes, Lilia de Souza Nogueira
Revista Brasileira de Enfermagem.2016; 69(4): 773. CrossRef
The Well-Being and Treatment Satisfaction of Diabetic Patients in an Outpatient Setting at a General Hospital in Korea
Hwa-Sun Park, Sin Na Lee, Min Young Baek, Seung Hee Yu, Young Sil Eom, Sihoon Lee, Ki Young Lee, Yeun Sun Kim, Byung Joon Kim, Kwang Won Kim, Ie Byung Park
The Journal of Korean Diabetes.2016; 17(2): 123. CrossRef
Poor glycemic control in type 2 diabetes in the South of the Sahara: The issue of limited access to an HbA1c test
Alioune Camara, Naby M. Baldé, Joelle Sobngwi-Tambekou, André P. Kengne, Mansour M. Diallo, Alain P.K. Tchatchoua, Amadou Kaké, Ngamani Sylvie, Beverley Balkau, Fabrice Bonnet, Eugène Sobngwi
Diabetes Research and Clinical Practice.2015; 108(1): 187. CrossRef
Effect of Group Training on Glycemic Control in Patients With Diabetes Type-I
Marjan Naseri, Shahnaz Rostami, Bahman Dashtebozorgi
Jundishapur Journal of Chronic Disease Care.2015;[Epub] CrossRef
Clinical audit of patients using DPP4 inhibitors in longstanding type 2 diabetes
K.V.S. Hari Kumar, A.K. Gupta
Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2015; 9(4): 277. CrossRef
Moderate diet-induced weight loss is associated with improved insulin sensitivity in middle-aged healthy obese Korean women
Hye-Ok Lee, Jung-Eun Yim, Young-Seol Kim, Ryowon Choue
Nutrition Research and Practice.2014; 8(4): 469. CrossRef
Inverse Association between Glycated Albumin and Insulin Secretory Function May Explain Higher Levels of Glycated Albumin in Subjects with Longer Duration of Diabetes
Yong-ho Lee, Mi Hyang Kown, Kwang Joon Kim, Eun Young Lee, Daham Kim, Byung-Wan Lee, Eun Seok Kang, Bong Soo Cha, Hyun Chul Lee, Marià Alemany
PLoS ONE.2014; 9(9): e108772. CrossRef
Systematic review of the psychometric properties and theoretical grounding of instruments evaluating self‐care in people with type 2 Diabetes Mellitus
Jorge Caro‐Bautista, Francisco Javier Martín‐Santos, Jose Miguel Morales‐Asencio
Journal of Advanced Nursing.2014; 70(6): 1209. CrossRef
It's Still Not Too Late to Make a Change: Current Status of Glycemic Control in Korea
Sang Yong Kim
Diabetes & Metabolism Journal.2014; 38(3): 194. CrossRef
Efficacy of the Smartphone-Based Glucose Management Application Stratified by User Satisfaction
Hun-Sung Kim, Wona Choi, Eun Kyoung Baek, Yun A Kim, So Jung Yang, In Young Choi, Kun-Ho Yoon, Jae-Hyoung Cho
Diabetes & Metabolism Journal.2014; 38(3): 204. CrossRef
Education as Prescription for Patients with Type 2 Diabetes Mellitus: Compliance and Efficacy in Clinical Practice
Mi Yeon Kim, Sunghwan Suh, Sang-Man Jin, Se Won Kim, Ji Cheol Bae, Kyu Yeon Hur, Sung Hye Kim, Mi Yong Rha, Young Yun Cho, Myung-Shik Lee, Moon Kyu Lee, Kwang-Won Kim, Jae Hyeon Kim
Diabetes & Metabolism Journal.2012; 36(6): 452. CrossRef

A Survey on Ubiquitous Healthcare Service Demand among Diabetic Patients: Soo Lim, So-Youn Kim, Jung Im Kim, Min Kyung Kwon, Sei Jin Min, Soo Young Yoo, Seon Mee Kang, Hong Il Kim, Hye Seung Jung, Kyong Soo Park, Jun Oh Ryu, Hayley Shin, Hak Chul Jang; Diabetes Metab J. 2011;35(1):50-57. Published online February 28, 2011; DOI: https://doi.org/10.4093/dmj.2011.35.1.50

9,795 View
31 Download
25 Crossref

Abstract PDF PubReader ePub

Background

Advanced information technology can be used when developing diagnostic and treatment strategies to provide better care for diabetic patients. However, the levels of need and demand for the use of technological advances have not been investigated in diabetic patients. We proposed and developed an individualized, ubiquitous (U)-healthcare service using advanced information technology for more effective glucose control. Prior to our service initiation, we surveyed patient needs and other pertinent information.

Methods

During August 2009, we conducted a 34-item questionnaire survey among patients with diabetes who were older than 40 years in two certain hospitals in Korea.

Results

The mean age of the 228 participants was 61.2±9 years, and males made up 49.1% of the sample. Seventy-one percent replied that they wanted individualized healthcare service, and they also wanted their health information to be delivered through mobile devices such as a cellular phone or a personal digital assistant (40.4%). Most patients had never heard of U-healthcare services (81.1%); however, after explaining the concept, 71.1% of participants responded that they would use the service if it was provided. Despite their willingness, participants were concerned about technical difficulty in using the service (26.3%) as well as the cost of the service (29.8%).

Conclusion

The current study suggests that more than 70% of diabetic patients are interested in using U-healthcare services. To encourage widespread use, the application program or device of U-healthcare services should be simple, easy to use and affordable while also including a policy for the protection of private information.

Citations

Citations to this article as recorded by

Pseudonym revocation system for IoT-based medical applications
Nabila Bermad, Salah Zemmoudj, Louiza Bouallouche-Medjkoune, Lyes Khoukhi
Computer Networks.2025; 257: 110983. CrossRef
Steering AI-driven Personalization of Scientific Text for General Audiences
Taewook Kim, Dhruv Agarwal, Jordan Ackerman, Manaswi Saha
Proceedings of the ACM on Human-Computer Interaction.2025; 9(7): 1. CrossRef
Willingness of diabetes mellitus patients to use mHealth applications and its associated factors for self-care management in a low-income country: an input for digital health implementation
Agmasie Damtew Walle, Tigist Andargie Ferede, Adamu Ambachew Shibabaw, Sisay Maru Wubante, Habtamu Alganeh Guadie, Chalachew Msganaw Yehula, Addisalem Workie Demsash
BMJ Health & Care Informatics.2023; 30(1): e100761. CrossRef
Development and Operation of a Video Teleconsultation System Using Integrated Medical Equipment Gateway: a National Project for Workers in Underserved Areas
Hyun Sang Park, Kwang il Kim, Jae Young Soh, Young Ho Hyun, Bang Eun Lee, Jong Hwa Lee, Jung Gwon Jo, Han Chae Lee, Hwa Sun Kim
Journal of Medical Systems.2020;[Epub] CrossRef
Patient experience with an educational mobile health application: A pilot study on usability and feasibility in a Saudi population
Sireen Abdul Rahim Shilbayeh, Sahar Abd El Rahman Ismail, Meihua Qian
Cogent Psychology.2020;[Epub] CrossRef
Smart Care Based on Telemonitoring and Telemedicine for Type 2 Diabetes Care: Multi-Center Randomized Controlled Trial
Ji Yun Jeong, Jae-Han Jeon, Kwi-Hyun Bae, Yeon-Kyung Choi, Keun-Gyu Park, Jung-Guk Kim, Kyu Chang Won, Bong Soo Cha, Chul Woo Ahn, Dong Won Kim, Chang Hee Lee, In-Kyu Lee
Telemedicine and e-Health.2018; 24(8): 604. CrossRef
Satisfaction Survey on Information Technology-Based Glucose Monitoring System Targeting Diabetes Mellitus in Private Local Clinics in Korea
Hun-Sung Kim, So Jung Yang, Yoo Jin Jeong, Young-Eun Kim, Seok-Won Hong, Jae Hyoung Cho
Diabetes & Metabolism Journal.2017; 41(3): 213. CrossRef
Willingness of patients with diabetes to use an ICT-based self-management tool: a cross-sectional study
Tomomi Shibuta, Kayo Waki, Nobuko Tomizawa, Ayumi Igarashi, Noriko Yamamoto-Mitani, Satoko Yamaguchi, Hideo Fujita, Shigeko Kimura, Katsuhito Fujiu, Hironori Waki, Yoshihiko Izumida, Takayoshi Sasako, Masatoshi Kobayashi, Ryo Suzuki, Toshimasa Yamauchi, T
BMJ Open Diabetes Research & Care.2017; 5(1): e000322. CrossRef
Evaluating the Effect of Web-Based Iranian Diabetic Personal Health Record App on Self-Care Status and Clinical Indicators: Randomized Controlled Trial
Amirabbas Azizi, Robab Aboutorabi, Zahra Mazloum-Khorasani, Monavar Afzal-Aghaea, Hamed Tabesh, Mahmood Tara
JMIR Medical Informatics.2016; 4(4): e32. CrossRef
Development, Validation, and Evaluation of Web-Based Iranian Diabetic Personal Health Record: Rationale for and Protocol of a Randomized Controlled Trial
Amirabbas Azizi, Robab Aboutorabi, Zahra Mazloum-Khorasani, Monavar Afzal-Aghaea, Mahmood Tara
JMIR Research Protocols.2016; 5(1): e39. CrossRef
Randomized, Open-Label, Parallel Group Study to Evaluate the Effect of Internet-Based Glucose Management System on Subjects with Diabetes in China
Hun-Sung Kim, Chenglin Sun, So Jung Yang, Lin Sun, Fei Li, In Young Choi, Jae-Hyoung Cho, Guixia Wang, Kun-Ho Yoon
Telemedicine and e-Health.2016; 22(8): 666. CrossRef
Environment and Its Influence on Health and Demographics in South Korea
Ramiro Bravo Santisteban, Young Kim, Umar Farooq, Tae-Seong Kim, Sekyoung Youm, Seung-Hun Park
International Journal of Environmental Research and Public Health.2016; 13(2): 183. CrossRef
U-Healthcare Center Service in Busan City, South Korea: An Empirical Analysis and the Results of 1 Year of Service
Ramiro D. Bravo Santisteban, Sekyoung Youm, Seung-Hun Park
Telemedicine and e-Health.2015; 21(10): 774. CrossRef
Users’ perception on telemedicine service: a comparative study of public healthcare and private healthcare
Mi Jung Rho, Kun Ho Yoon, Hun-Sung Kim, In Young Choi
Multimedia Tools and Applications.2015; 74(7): 2483. CrossRef
An Activity Recognition Model Using Inertial Sensor Nodes in a Wireless Sensor Network for Frozen Shoulder Rehabilitation Exercises
Hsueh-Chun Lin, Shu-Yin Chiang, Kai Lee, Yao-Chiang Kan
Sensors.2015; 15(1): 2181. CrossRef
Current Clinical Status of Telehealth in Korea: Categories, Scientific Basis, and Obstacles
Hun-Sung Kim, Hyunah Kim, Suehyun Lee, Kye Hwa Lee, Ju Han Kim
Healthcare Informatics Research.2015; 21(4): 244. CrossRef
New Directions in Chronic Disease Management
Hun-Sung Kim, Jae-Hyoung Cho, Kun-Ho Yoon
Endocrinology and Metabolism.2015; 30(2): 159. CrossRef
Using mobile phones in healthcare management for the elderly
Hun-Sung Kim, Kye-Hwa Lee, Hyunah Kim, Ju Han Kim
Maturitas.2014; 79(4): 381. CrossRef
Future Prospects of Health Management Systems Using Cellular Phones
Hun-Sung Kim, Yunji Hwang, Jae-Ho Lee, Hye Young Oh, Yi-Jun Kim, Hyeon Yoon Kwon, Hyoseung Kang, Hyunah Kim, Rae Woong Park, Ju Han Kim
Telemedicine and e-Health.2014; 20(6): 544. CrossRef
Exploring the Relationship Among User Satisfaction, Compliance, and Clinical Outcomes of Telemedicine Services for Glucose Control
Mi Jung Rho, Si Ra Kim, Hun-Sung Kim, Jae-Hyoung Cho, Kun-Ho Yoon, Seong K. Mun, In Young Choi
Telemedicine and e-Health.2014; 20(8): 712. CrossRef
Impact of Information Technology on the Therapy of Type-1 Diabetes: A Case Study of Children and Adolescents in Germany
Rolf-Dietrich Berndt, Claude Takenga, Petra Preik, Sebastian Kuehn, Luise Berndt, Herbert Mayer, Alexander Kaps, Ralf Schiel
Journal of Personalized Medicine.2014; 4(2): 200. CrossRef
Perception of Influencing Factors on Acceptance of Mobile Health Monitoring Service: A Comparison between Users and Non-users
Jaebeom Lee, Mi Jung Rho
Healthcare Informatics Research.2013; 19(3): 167. CrossRef
Telerehabilitation Needs: A Bidirectional Survey of Health Professionals and Individuals with Spinal Cord Injury in South Korea
Jongbae Kim, Shinyoung Lim, Jayeon Yun, Da-hye Kim
Telemedicine and e-Health.2012; 18(9): 713. CrossRef
Development of a smartphone-based diabetes self-care management system
Sang Youl Rhee, Jeong-taek Woo, Young Seol Kim, Young Kil Choi
Personalized Medicine Universe.2012; 1(1): 85. CrossRef
Features of Mobile Diabetes Applications: Review of the Literature and Analysis of Current Applications Compared Against Evidence-Based Guidelines
Taridzo Chomutare, Luis Fernandez-Luque, Eirik Årsand, Gunnar Hartvigsen
Journal of Medical Internet Research.2011; 13(3): e65. CrossRef

The Changes in Early Phase Insulin Secretion in Newly Diagnosed, Drug Naive Korean Prediabetes Subjects: Sang Youl Rhee, Joo Young Kim, Suk Chon, You Cheol Hwang, In Kyung Jeong, Seungjoon Oh, Kyu Jeung Ahn, Ho Yeon Chung, Jeong-taek Woo, Sung Woon Kim, Jin-Woo Kim, Young Seol Kim; Korean Diabetes J. 2010;34(3):157-165. Published online June 30, 2010; DOI: https://doi.org/10.4093/kdj.2010.34.3.157

7,330 View
36 Download
15 Crossref

Abstract PDF PubReader ePub

Background

There have been no systematic observations regarding changes in early phase insulin secretion among Korean prediabetes and early stage type 2 diabetes mellitus (T2DM) patients.

Methods

We conducted 75-g oral glucose tolerance tests (OGTT) in 873 subjects with suspected abnormal glucose tolerance. All subjects were diagnosed as having normal glucose tolerance (NGT), prediabetes (preDM), or T2DM according to the OGTT results and the insulin secretory and insulin resistance indices of each subject were calculated. Additionally, we analyzed the changes in early phase insulin secretion according to changes in fasting (Glc₀), post-prandial (Glc₁₂₀) glucose and HbA1c (A1c) levels.

Results

As compared to subjects with NGT, the insulin secretory indices of the preDM and T2DM subjects progressively declined, and the insulin resistance indices were progressively aggravated. Early phase insulin secretion decreased rapidly according to the increments of Glc₀, Glc₁₂₀ and A1c, and these changes were most prominent in the NGT stage. Compared to the control group, the early phase insulin secretion levels of the preDM or T2DM subjects were less than 50% when Glc₀ was over 100 mg/dL, Glc₁₂₀ was over 145 mg/dL, and A1c was over 5.8%.

Conclusion

This study suggests that progressive beta cell dysfunction in Koreans may be initiated and rapidly aggravated during the period generally designated as 'normal.'

Citations

Citations to this article as recorded by

Hospital-Based Korean Diabetes Prevention Study: A Prospective, Multi-Center, Randomized, Open-Label Controlled Study
Sang Youl Rhee, Suk Chon, Kyu Jeung Ahn, Jeong-Taek Woo
Diabetes & Metabolism Journal.2019; 43(1): 49. CrossRef
Differential role of insulin resistance and β-cell function in the development of prediabetes and diabetes in middle-aged and elderly Chinese population
Xueli Cai, Lili Xia, Yuesong Pan, Dian He, Huiping Zhu, Tiemin Wei, Yan He
Diabetology & Metabolic Syndrome.2019;[Epub] CrossRef
Effects of Primary Aldosteronism and Different Therapeutic Modalities on Glucose Metabolism
Mi Kyung Kwak, Jee Yang Lee, Beom-Jun Kim, Seung Hun Lee, Jung-Min Koh
Journal of Clinical Medicine.2019; 8(12): 2194. CrossRef
Insulin resistance increases the risk of incident type 2 diabetes mellitus in patients with non‐alcoholic fatty liver disease
Yuya Seko, Yoshio Sumida, Saiyu Tanaka, Kojiroh Mori, Hiroyoshi Taketani, Hiroshi Ishiba, Tasuku Hara, Akira Okajima, Atsushi Umemura, Taichiro Nishikawa, Kanji Yamaguchi, Michihisa Moriguchi, Kazuyuki Kanemasa, Kohichiroh Yasui, Shunsuke Imai, Keiji Shim
Hepatology Research.2018;[Epub] CrossRef
Longitudinal Changes in Insulin Resistance, Beta-Cell Function and Glucose Regulation Status in Prediabetes
Chul-Hee Kim, Hong-Kyu Kim, Eun-Hee Kim, Sung-Jin Bae, Jaewon Choe, Joong-Yeol Park
The American Journal of the Medical Sciences.2018; 355(1): 54. CrossRef
Prenatal Dexamethasone Exposure Programs the Development of the Pancreas and the Secretion of Insulin in Rats
Yu-Chieh Chen, Ying-Hua Huang, Jiunn-Ming Sheen, You-Lin Tain, Hong-Ren Yu, Chih-Cheng Chen, Miao-Meng Tiao, Ho-Chang Kuo, Li-Tung Huang
Pediatrics & Neonatology.2017; 58(2): 135. CrossRef
Insulin Secretory Capacity and Insulin Resistance in Korean Type 2 Diabetes Mellitus Patients
Jong-Dai Kim, Won-Young Lee
Endocrinology and Metabolism.2016; 31(3): 354. CrossRef
The effect of glargine versus glimepiride on pancreatic β-cell function in patients with type 2 diabetes uncontrolled on metformin monotherapy: open-label, randomized, controlled study
Jun Sung Moon, Kyoung Soo Ha, Ji Sung Yoon, Hyoung Woo Lee, Hyun Chul Lee, Kyu Chang Won
Acta Diabetologica.2014; 51(2): 277. CrossRef
Association of Obstructive Sleep Apnea and Glucose Metabolism in Subjects With or Without Obesity
Nan Hee Kim, Nam H. Cho, Chang-Ho Yun, Seung Ku Lee, Dae Wui Yoon, Hyun Joo Cho, Jae Hee Ahn, Ji A. Seo, Sin Gon Kim, Kyung Mook Choi, Sei Hyun Baik, Dong Seop Choi, Chol Shin
Diabetes Care.2013; 36(12): 3909. CrossRef
Relative contributions of insulin resistance and β‐cell dysfunction to the development of Type 2 diabetes in Koreans
C.‐H. Kim, H.‐K. Kim, E. H. Kim, S. J. Bae, J.‐Y. Park
Diabetic Medicine.2013; 30(9): 1075. CrossRef
Associations among Body Mass Index, Insulin Resistance, and Pancreatic β-Cell Function in Korean Patients with New-Onset Type 2 Diabetes
Jin Ook Chung, Dong Hyeok Cho, Dong Jin Chung, Min Young Chung
The Korean Journal of Internal Medicine.2012; 27(1): 66. CrossRef
High normal HbA1c levels were associated with impaired insulin secretion without escalating insulin resistance in Japanese individuals: the Toranomon Hospital Health Management Center Study 8 (TOPICS 8)
Y. Heianza, Y. Arase, K. Fujihara, H. Tsuji, K. Saito, S. D. Hsieh, S. Kodama, H. Shimano, N. Yamada, S. Hara, H. Sone
Diabetic Medicine.2012; 29(10): 1285. CrossRef
The Prediabetic Period: Review of Clinical Aspects
Sang Youl Rhee, Jeong-Taek Woo
Diabetes & Metabolism Journal.2011; 35(2): 107. CrossRef
Predictive characteristics of patients achieving glycaemic control with insulin after sulfonylurea failure
Y.-H. Lee, B.-W. Lee, S. W. Chun, B. S. Cha, H. C. Lee
International Journal of Clinical Practice.2011; 65(10): 1076. CrossRef
Early Insulin Secretory Dysfunction in Korean Prediabetic Subjects: Should We Change the Criteria for "Prediabetes?"
Chul-Hee Kim
Korean Diabetes Journal.2010; 34(3): 154. CrossRef

The Combination of Fasting Plasma Glucose and Glycosylated Hemoglobin as a Predictor for Type 2 Diabetes in Korean Adults.: Chan Hee Lee, Woo Jin Chang, Hyun Hee Chung, Hyun Jung Kim, Sang Hyun Park, Jun Sung Moon, Ji Eun Lee, Ji Sung Yoon, Kyung Ah Chun, Kyu Chang Won, Ihn Ho Cho, Hyoung Woo Lee; Korean Diabetes J. 2009;33(4):306-314. Published online August 1, 2009; DOI: https://doi.org/10.4093/kdj.2009.33.4.306

4,379 View
30 Download
8 Crossref

Abstract PDF

BACKGROUND
The oral glucose tolerance test (OGTT) for detection of diabetes is difficult to perform in clinical settings. The aim of this study is to evaluate the performance of a more practical detection test, combined fasting plasma glucose (FPG) and glycosylated hemoglobin (HbA1c), as a predictor of diabetes mellitus (DM) in a Korean sample. METHODS: We examined 2,045 (M = 1,276, mean age = 47.8 +/- 9.0 yrs) medical check-up program participants between January 2002 to December 2003. FPG, HbA1c and a number of other biochemical tests were performed at baseline and four after years after initial screening. Patients who originally presented with diabetes were excluded. The characteristics of newly-diagnosed DM patients and non-diabetic patients were compared. RESULTS: The incidence of newly diagnosed diabetes was 1.6% (32/2,045) after four years of follow up. The subjects in the DM group were older, had higher levels of SBP, DBP, FPG, HbA1c, triglyceride, HDL cholesterol, GGT and LDH (P < 0.05). In multivariate logistic regression analysis, FPG (odds ratio [OR] 1.124) and HbA1c (OR 4.794) were significantly correlated with onset of diabetes (P < 0.05). The interaction parameter between FPG and HbA1c was more than 1.0, indicating that the two effects are synergistic. The predictive cut-off values of HbA1c and FPG were 5.35% (area under curve [AUC] = 0.944) and 102.5 mg/dL (AUC = 0.930), respectively. CONCLUSION: The combination of HbA1c above 5.35% and FPG above 102.5 mg/dL predicted the onset of diabetes in a Korean sample. These results suggest that the combination of FPG and HbA1c may be useful for predicting progression to type 2 diabetes in east Asians.

Citations

Citations to this article as recorded by

The Distribution and Characteristics of Abnormal Findings Regarding Fasting Plasma Glucose and HbA1c - Based on Adults Except for Known Diabetes
Seyoung Kwon, Youngak Na
The Korean Journal of Clinical Laboratory Science.2017; 49(3): 239. CrossRef
Factors Affecting Diabetic Screening Behavior of Korean Adults: A Multilevel Analysis
Hyeongsu Kim, Minjung Lee, Haejoon Kim, Kunsei Lee, Sounghoon Chang, Vitna Kim, Jun Pyo Myong, Soyoun Jeon
Asian Nursing Research.2013; 7(2): 67. CrossRef
Impact of HbA1c Criterion on the Detection of Subjects with Increased Risk for Diabetes among Health Check-Up Recipients in Korea
Hong-Kyu Kim, Sung-Jin Bae, Jaeone Choe
Diabetes & Metabolism Journal.2012; 36(2): 151. CrossRef
The Utility of HbA1c as a Diagnostic Criterion of Diabetes
Hee-Jung Kim, Eun Young Choi, Eal Whan Park, Yoo Seock Cheong, Hong-Yoen Lee, Ji Hyun Kim
Korean Journal of Family Medicine.2011; 32(7): 383. CrossRef
Predictive Clinical Parameters for the Therapeutic Efficacy of Sitagliptin in Korean Type 2 Diabetes Mellitus
Soon Ae Kim, Woo Ho Shim, Eun Hae Lee, Young Mi Lee, Sun Hee Beom, Eun Sook Kim, Jeong Seon Yoo, Ji Sun Nam, Min Ho Cho, Jong Suk Park, Chul Woo Ahn, Kyung Rae Kim
Diabetes & Metabolism Journal.2011; 35(2): 159. CrossRef
Optimal range of HbA1c for the prediction of future diabetes: A 4-year longitudinal study
Ji Cheol Bae, Eun Jung Rhee, Won Young Lee, Se Eun Park, Cheol Young Park, Ki Won Oh, Sung Woo Park, Sun Woo Kim
Diabetes Research and Clinical Practice.2011; 93(2): 255. CrossRef
The Combination of Fasting Plasma Glucose and Glycosylated Hemoglobin as a Predictor for Type 2 Diabetes in Korean Adults (Korean Diabetes J 33(4):306-314, 2009)
Soo Lim
Korean Diabetes Journal.2009; 33(5): 448. CrossRef
The Combination of Fasting Plasma Glucose and Glycosylated Hemoglobin as a Predictor for Type 2 Diabetes in Korean Adults (Korean Diabetes J 33(4):306-314, 2009)
Chan Hee Lee, Hyoung Woo Lee
Korean Diabetes Journal.2009; 33(5): 451. CrossRef

Effects of 'Ubiquitous Healthcare' on the Ability of Self-Management in Elderly Diabetic Patients.: Sung Hoon Yu, Sun Hee Kim, So Yeon Kim, Sung Hee Choi, Soo Lim, Yoon Seok Chang, Hak Jong Lee, Young Joo Park, Hak Chul Jang; Korean Diabetes J. 2009;33(1):58-64. Published online February 1, 2009; DOI: https://doi.org/10.4093/kdj.2009.33.1.58

3,581 View
39 Download
5 Crossref

Abstract PDF

BACKGROUND
The need for a new healthcare system is growing due to the paradigm shift from health supervision to health maintenance. Previously, we performed a pilot study that examined the effectiveness of a ubiquitous healthcare (U-healthcare) diabetes management program which consists of self-monitoring of blood glucose (SMBG) and mobile phone services for elderly patients with type 2 diabetes mellitus. In this study, we investigated the effect of a diabetes management program using U-healthcare based on the self-care skills of elderly patients with diabetes mellitus. METHODS: From July to October 2005, 17 patients were recruited and provided with a blood glucometer with the ZigBee module and a mobile phone. In addition, the patients' understanding of diabetes self-care skills was examined at the beginning and end of the study. At the end of the study, we determined the level of patient satisfaction regarding U-healthcare services. RESULTS: The patients' test scores on their understanding of diabetes mellitus improved from 57.2 +/- 20.7 to 72.7 +/- 13.4%. Specifically, patient knowledge of the basic principles for a proper diabetic diet (52.9% vs. 82.4%, P = 0.046), foods that influence blood sugar level (41.2% vs. 76.5%, P = 0.007) and the influence of beverage choice (41.2% vs. 64.7%, P = 0.007), all increased. In addition, a significant increase in knowledge of living standards regarding diabetes mellitus was observed (64.7% vs. 88.2%, P = 0.0032). CONCLUSION: We conclude that the U-healthcare incorporating SMBG could be promising, as it improves self-management skills of diabetes mellitus patients, as well as their understanding of the disease.

Citations

Citations to this article as recorded by

Self-management of Chronic Conditions Using mHealth Interventions in Korea: A Systematic Review
Jae Yoon Yi, Yujin Kim, Yoon-Min Cho, Hongsoo Kim
Healthcare Informatics Research.2018; 24(3): 187. CrossRef
Effectiveness of the Smart Care Service for Diabetes Management
Young-Soon Chung, Yongsuk Kim, Chang Hee Lee
Healthcare Informatics Research.2014; 20(4): 288. CrossRef
Ubiquitous Healthcare Service Has the Persistent Benefit on Glycemic Control and Body Weight in Older Adults With Diabetes
Seon Mee Kang, Min Joo Kim, Hwa Young Ahn, Ji Won Yoon, Min Kyong Moon, Hye Seung Jung, Sung Hee Choi, Soo Lim, Kyong Soo Park, Hak C. Jang
Diabetes Care.2012; 35(3): e19. CrossRef
Improved Glycemic Control Without Hypoglycemia in Elderly Diabetic Patients Using the Ubiquitous Healthcare Service, a New Medical Information System
Soo Lim, Seon Mee Kang, Hayley Shin, Hak Jong Lee, Ji Won Yoon, Sung Hoon Yu, So-Youn Kim, Soo Young Yoo, Hye Seung Jung, Kyong Soo Park, Jun Oh Ryu, Hak C. Jang
Diabetes Care.2011; 34(2): 308. CrossRef
A Survey on Ubiquitous Healthcare Service Demand among Diabetic Patients
Soo Lim, So-Youn Kim, Jung Im Kim, Min Kyung Kwon, Sei Jin Min, Soo Young Yoo, Seon Mee Kang, Hong Il Kim, Hye Seung Jung, Kyong Soo Park, Jun Oh Ryu, Hayley Shin, Hak Chul Jang
Diabetes & Metabolism Journal.2011; 35(1): 50. CrossRef

Average Daily Risk Range-Index of Glycemic Variability-Related Factor in Type 2 Diabetic Inpatients.: Shin Ae Park, Seung Hyun Ko, Seung Hwan Lee, Jae Hyung Cho, Sung Dae Moon, Sang A Jang, Ki Ho Song, Hyun Shik Son, Kun Ho Yoon, Bong Yun Cha, Ho Young Son, Yu Bae Ahn; Korean Diabetes J. 2009;33(1):31-39. Published online February 1, 2009; DOI: https://doi.org/10.4093/kdj.2009.33.1.31

3,557 View
43 Download
2 Crossref

Abstract PDF

BACKGROUND
It is known that chronic sustained hyperglycemia and its consequent oxidative stress causes diabetic complication in type 2 diabetes. It has been further proven that glycemic variability causes oxidative stress. The aim of this study is to measure the average daily risk range (ADDR)-index of glycemic variability, and to evaluate relevant variables. METHODS: We measured the blood glucose level of type 2 diabetic patients who were treated with multiple daily injections from January to July, 2008. The blood glucose levels were checked four times a day for 14 days and were conversed according to the ADRR formula. The degree of glycemic variability was categorized into non-fluctuation and fluctuation groups. We collected patient data on age, sex, duration of diabetes, body mass index, HOMA(IR), HOMA(betacell) and HbA1c. RESULTS: A total of 97 patients were enrolled in this study. The mean age, duration of diabetes, HbA1c and mean ADRR were 57.6 +/- 13.4, 11.5 +/- 8.5 years, 10.7 +/- 2.5%, and 26.6 +/- 9.8, respectively. We classified 18.5% of the patients to the non-fluctuation group, and 81.5% to the fluctuation group. ADRR was significantly correlated with duration of diabetes, fasting and postprandial glucose, fructosamine, HbA1c and BMI and HOMAbetacell. In addition, this study confirmed that BMI, HOMAbetacell and HbA1c were ADRR-related independent variables. CONCLUSION: ADRR can be used as an index for blood glucose fluctuation in type 2 diabetic patients. Measuring ADRR in patients with low BMI and a long duration of diabetes is helpful to improve the effectiveness of their care.

Citations

Citations to this article as recorded by

Relationships between Thigh and Waist Circumference, Hemoglobin Glycation Index, and Carotid Plaque in Patients with Type 2 Diabetes
Myung Ki Yoon, Jun Goo Kang, Seong Jin Lee, Sung-Hee Ihm, Kap Bum Huh, Chul Sik Kim
Endocrinology and Metabolism.2020; 35(2): 319. CrossRef
Reversal of Hypoglycemia Unawareness with a Single-donor, Marginal Dose Allogeneic Islet Transplantation in Korea: A Case Report
Hae Kyung Yang, Dong-Sik Ham, Heon-Seok Park, Marie Rhee, Young Hye You, Min Jung Kim, Ji-Won Kim, Seung-Hwan Lee, Tae Ho Hong, Byung Gil Choi, Jae Hyoung Cho, Kun-Ho Yoon
Journal of Korean Medical Science.2015; 30(7): 991. CrossRef

An Association between 609 C --> T Polymorphism in NAD(P)H: Quinone Oxidoreductase 1 (NQO1) Gene and Blood Glucose Levels in Korean Population.: Dohee Kim; Korean Diabetes J. 2009;33(1):24-30. Published online February 1, 2009; DOI: https://doi.org/10.4093/kdj.2009.33.1.24

3,293 View
27 Download
1 Crossref

Abstract PDF

BACKGROUND
NAD(P)H: quinone oxidoreductase 1 (NQO1), which is an obligate two-electron reductase that utilizes NAD(P)H as an electron donor and is involved in the protection against oxidative stress, is likely involved in beta-cell destruction. We evaluated the frequency of the NQO1 polymorphism and its association with blood glucose levels. METHODS: Genotypes were determined using a polymerase chain reaction restriction fragment length polymorphism-based assay in 56 patients and 48 healthy subjects. Fasting blood glucose, insulin, and lipid profiles were measured and homeostasis model assessment (HOMA)-insulin resistance (IR) was calculated from fasting glucose and insulin levels in the healthy subjects. RESULTS: The genotype frequencies of NQO1 polymorphism were C/C (56.7%), C/T (42.3%), and T/T (1.0%). There were no associations between the NQO1 polymorphism and body mass index, blood pressure, lipid profile, HbA1c, postprandial glucose, and HOMA-IR. However, NQO1 mutants (C/T and T/T) showed weak but significantly higher fasting blood glucose levels compared with wild type (C/C). CONCLUSION: Our data suggest that NQO1 609 C --> T polymorphism may be associated with glucose metabolism.

Citations

Citations to this article as recorded by

Association of Nuclear Factor‐Erythroid 2‐Related Factor 2, Thioredoxin Interacting Protein, and Heme Oxygenase‐1 Gene Polymorphisms with Diabetes and Obesity in Mexican Patients
Angélica Saraí Jiménez-Osorio, Susana González-Reyes, Wylly Ramsés García-Niño, Hortensia Moreno-Macías, Martha Eunice Rodríguez-Arellano, Gilberto Vargas-Alarcón, Joaquín Zúñiga, Rodrigo Barquera, José Pedraza-Chaverri, Silvana Hrelia
Oxidative Medicine and Cellular Longevity.2016;[Epub] CrossRef

Randomized Controlled Trial

Short-term Therapeutic Efficacy of Different Oral Hypoglycemic Agents Combined with Once Daily Insulin Glargine in Type 2 Diabetic Subjects with Failure of Sulfonylurea and Metformin Combination.: Seong Il Hong, Hyeong Jin Kim, Jong Myon Bae, Sun Ok Song, Kyung Suk Park, Byung Soo Jeon, Seun Duk Hwang, Jin Yi Choi, Jeong Hun Kim, Hyuk Jin Kwon, Ja Sung Choi, Myoung Lyeol Woo, Ji Hoon Cho, Young Jun Won; Korean Diabetes J. 2007;31(4):336-342. Published online July 1, 2007; DOI: https://doi.org/10.4093/jkda.2007.31.4.336

3,073 View
25 Download

Abstract PDF: Backgroud: Although the extended duration of action of insulin glargine supports a convenient once daily injection, the combination with other short acting insulins or oral hypoglycemic agents is required to control postprandial hyperglycemia in type 2 diabetes. The present study was designed to compare the short-term therapeutic efficacy of oral hypoglycemic agents with once daily insulin glargine, switching from a multiple daily injection regimen. METHODS: After control with the intensive regimen (daily lispro insulin and glargine) during 5~7 days, 80 in-patients with type 2 diabetes were randomized and treated with four oral hypoglycemic agents (glimepiride 4 mg qd, metformin 500 mg bid, nateglinide 90 mg tid, or acarbose 100 mg tid) plus once daily insulin glargine during 5 days. Blood glucose concentration was recorded by seven daily estimations (before each meal, 2 hours after each meal, and bedtime). Blood glucose concentrations and area under the curves (AUCs) of blood glucose were compared among four groups. RESULTS: The area under the curve of blood glucose of metformin, glimepiride, nateglinide, and acarbose groups were 165.5 +/- 46.0, 178.5 +/- 36.5, 209.9 +/- 55.1, and 224.9 +/- 55.8 mmol/L/hr respectively. Blood glucose concentrations and area under the curves of blood glucose of glimepiride and metformin groups were significantly lower than those of acarbose group. Also, those of metformin group were significantly lower than those of nateglinide group. Conclusions: Metformin or glimepiride are more effective oral hypoglycemic agent than nateglinide or acarbose in the combination with insulin glargine in type 2 diabetic subjects with failure of sulfonylurea and metformin combination.

Original Articles

Glycemic Control and Health Behaviors through Diabetes Mellitus Education in a Clinic.: Kyeong Soon Chang, Kwan Lee, Hyun Sul Lim; Korean Diabetes J. 2006;30(1):73-81. Published online January 1, 2006; DOI: https://doi.org/10.4093/jkda.2006.30.1.73

2,561 View
33 Download
4 Crossref

Abstract PDF

BACKGROUND
This study was carried out to examine the changes in the health behaviors and glycemic control before and after administering a Diabetes Mellitus (DM) education program in a clinic. METHODS: The author conducted a questionnaire and analyzed the blood chemistry with the fasting plasma blood sugar (FBS) and hemoglobin A1c (HbA1c) level of 80 patients in a clinic for 6 months from February to July 2004. The study group was divided into a poorly controlled (PC) group and well-controlled (WC) group according to the FBS or HbA1c level. The author then educated the subjects about general knowledges for DM over a 6-month period. The changes in the results before and after the DM education were measured as the changes in the health behaviors along with the changes in the FBS, and HbA1c levels. RESULTS: The study subjects contained 20 males and 20 females in each groups, and the major age group was the fifth decade (22 cases, 27.5%). The mean values for the total health behavior scores after the DM education program in the PC and WC group were 16.2 +/- 1.9, and 16.2 +/- 1.7 respectively, and were significantly higher than that before the education program (11.4 +/- 2.1, 15.3 +/- 1.9, P < 0.05). The mean FBS levels after the DM education program in the PC and WC groups were 130.2 +/- 22.8 mg/dL, and 116.2 +/- 16.6 mg/dL respectively, and was significantly lower than that before the education program (186.3 +/- 33.5 mg/dL, 135.3 +/- 16.3 mg/dL, P < 0.05). The mean HbA1c levels after the DM education program in the PC and WC groups were 7.0 +/- 0.8%, and 6.2 +/- 0.4% respectively, which were significantly lower than that before the education program (9.2 +/- 1.4%, 6.5 +/- 0.4%, P < 0.05). CONCLUSION: This study suggests that a DM education program in a clinic is effective in improving the health behaviors and laboratory findings in DM patients.

Citations

Citations to this article as recorded by

Economic Evaluation of Diabetes Education
Jin-Won Noh, Young Dae Kwon, Jin-Hee Jung, Kang Hee Sim, Hee-Sook Kim, Minjae Choi, Jumin Park
The Journal of Korean Diabetes.2015; 16(4): 293. CrossRef
Group Classification on Management Behavior of Diabetic Mellitus
Sung-Hong Kang, Soon-Ho Choi
Journal of the Korea Academia-Industrial cooperation Society.2011; 12(2): 765. CrossRef
Effect on Glycemic, Blood Pressure, and Lipid Control according to Education Types
Mi-Ju Choi, Seung-Hyun Yoo, Kum-Rae Kim, Yoo-Mi Bae, Sun-Hee Ahn, Seong-Shin Kim, Seong-Ah Min, Jin-Sun Choi, Seung-Eun Lee, Yeo-Jin Moon, Eun Jung Rhee, Cheol-Young Park, Won Young Lee, Ki Won Oh, Sung Woo Park, Sun Woo Kim
Diabetes & Metabolism Journal.2011; 35(6): 580. CrossRef
The Effects of Regular Walking Exercise on Metabolic Syndrome, Cardiovascular Risk Factors, and Depressive Symptoms in the Elderly with Diabetic Mellitus
Ki-Wol Sung, Ji-Hyun Lee
Journal of Korean Academy of Community Health Nursing.2010; 21(4): 409. CrossRef

Clinical Experience of the Reverse Iontopheresis Based Glucose Measuring System: GlucallTM.: Sang Youl Rhee, Suk Chon, Gwanpyo Koh, Seungjoon Oh, Jeong Taek Woo, Sung Woon Kim, Jin Woo Kim, Young Seol Kim; Korean Diabetes J. 2005;29(2):167-172. Published online March 1, 2005

1,424 View
20 Download

Abstract PDF: BACKGROUND
Finger pricking is currently the common method of blood glucose measurement in patients with diabetes mellitus. However, diabetes patients have proven reluctant to regularly check their glucose profiles because of the small amount of blood that is required. Recently, a non-invasive and continuous glucose monitoring device that is based on reverse iontophoresis(GlucallTM) has been developed. In this study we wanted to evaluate the accuracy and the clinical acceptability of this new device. METHODS: The study was conducted during the period from November 2003 to January 2004 on 19 in-patients who had been admitted to Kyung Hee University Hospital. Glucose measurements using GlucallTM were performed between 10am and 4pm. The concurrent plasma glucose levels were checked hourly and they were subsequently compared with the GlucallTM data. RESULTS: The mean error(ME) of the GlucallTM measurements was -3.45+/-52.99mg/dL with a mean absolute relative error(MARE) of 20+/-15.16%. Measurements obtained by GlucallTM had a correlation coefficient of 0.784(P<0.05) with the plasma glucose levels, as was determined by linear regression analysis. This correlation was consistent regardless of the time of data collection. However, after excluding such confounding variables as age and gender, the correlation coefficient exhibited a tendency to increase. 98.9% of the results were clinically acceptable according to Clarke error grid analysis. CONCLUSION: GlucallTM does not yet have the reliability and accuracy to wholly replace the conventional methods. However, further technical advancements to reduce its shortcomings will make this device useful for the management of diabetes patients

The Long-term Effect of a Structured Diabetes Education Program for Uncontrolled Type 2 Diabetes Mellitus Patients-a 4-Year Follow-up.: Min Sun Song, Ki Ho Song, Seung Hyun Ko, Yu Bai Ahn, Joon Sung Kim, Jin Hee Shin, Yang Kyung Cho, Kun Ho Yoon, Bong Youn Cha, Ho Young Son, Dong Han Lee; Korean Diabetes J. 2005;29(2):140-150. Published online March 1, 2005

2,046 View
75 Download

Abstract PDF: BACKGROUND
Diabetes mellitus is a chronic illness with many metabolic complications. The prevalence of diabetes mellitus has markedly increased. Until now, however, little data have been presented for the long-term evaluation of a structured diabetes education program (SDEP) for patients with type 2 diabetes mellitus. The aim of this study was to examine the effects of the SDEP on glycemic control, lipid profiles, and self-care behavior over a four-year follow-up period. METHODS: A total of 248 diabetic patients completed the SDEP from December 1999 to September 2000. Ninety-eight patients were followed-up for more than four years and 75 of them were selected for the study, after those subjects having a baseline glycated hemoglobin(HbA1c) levels below 7.9% were excluded. The laboratory data included the glycemic control status(fasting blood sugar and HbA1c), serum creatinine, and lipid profiles. Compliance with their diet, self monitoring of blood glucose, and their exercise frequency were monitored with a questionnaire that was completed by the patients when they visited the hospital. The data were analyzed by using repeated ANOVA measures and chi2 testing for detecting trends. RESULTS: There were no significant decreases in the fasting blood glucose, creatinine, total cholesterol, triglycerides or low density lipoprotein cholesterol for the SDEP group compared with the control group. The self-care behavior of the SDEP group was much better than that of the control group and it was well maintained. Although the self-care behavior tended to deteriorate with time in the SDEP group, the exercise frequency did not change. The HbA1c level was much improved in the SDEP group(HbA1c: SDEP, 7.9+/-1.2% vs. 8.9+/-1.6% for the control; P =0.009). High density lipoprotein(HDL) cholesterol was also relatively improved in the SDEP group(HDL cholesterol: SDEP, 1.1+/-0.2 mmol/L vs. 1.0+/-0.3mmol/L for the control; P=0.006). CONCLUSIONS: The glycemic control status of diabetic patients who undertook the SDEP was satisfactory for one year after the program, although all the habitual compliance measures decreased gradually with time over the total four years. These results demonstrate that the SDEP for patients with diabetes is useful in improving their long-term glycemic control and self-care behavior. Regular and sustained reinforcement with encouragement will be required for the diabetic patients to maintain their self-care

Effect of Various Snacks and Meal woth Different Kinds of Staples on Bloos Glucose, Insulin, and C- Peptide Levels in Healthy and Type 2 Diabetic Patients.: Youn Sang Oh, Hye Ok Lee, Ryo Won Cho; Korean Diabetes J. 1999;23(4):601-612. Published online January 1, 2001

1,490 View
23 Download

Abstract PDF: BACKGROUND
There are large differences in the glycemic response among different foods. These differences complicate the glycemic response of foods in diabetic patients. Studies have found that the diet with low glycemic index foods had positive effects on the clinical management of diabetic and hyperlipidemia patients. The concept of glycemic index of foods and the values of glycemic index of meals were measured in this study to suggest the dietary recommendation for type 2 diabetic patients. METHODS: The blood levels of glucose, insulin, C-peptide were measured for healthy subjects and Type 2 diabetic patients after taking various foods and meals. The isocaloric glycemic index of each food and meal was calculated in comparison with standard foods and meals. RESULTS: For the normal subjects, serum glucose, insulin, and C-peptide reached peaks 30 minutes after the meals. The glycemic indices of red bean bread (122.2%),orange juice (106.8%), ice cream (106.3%), and yoghurt (101.6%) were the highest among the respective groups. For the Type 2 diabetic patients, serum glucose reached peaks 60 minutes after the ingestion of meals. The isocaloric glycemic indices were red bean (93.7%), barley (94.6%), mixed cereal (97.9%), Brown rice (98.8%), rice (100%), and milled foxtail (106.9%) in increasing order. CONCLUSION: It is expected that isocaloric glycemic index of Korean foods and meals that are most frequent]y and preferentially eaten by diabetic patients can be used as a guide for menu selection and also to educate the dietary guideline for diabetic patients.

Fasting Serum Insulin Levels in Relation to Age and Body Mass Index and Serum Glucose Level in Healthy Subjects in Korea.: Sang Ah Chang, Ho Young Son, Bong Yun Cha, Sung Dae Moon, Ki Ho Song, Soon Jib Yoo, Kun Ho Yoon, Moo Il Kang, Kwang Woo Lee, Sung Ku Kang; Korean Diabetes J. 1997;21(4):433-443. Published online January 1, 2001

3,100 View
64 Download

Abstract PDF: BACKGROUND
Ethnic variability in the relationship between glucose tolerance and insulin secretion has been reported. Clinical characteristics of Korean diabetic patients are different from that of diabetic patients in Western countries. It is generally assumed that typical IDDM or obese diabetic patients are relatively rare among Korean subjects. This study attempted to define the characteristics of fasting serum insulin levels of healthy Korean adult subjects. Futhermore, we tried to evaluate the relationship between fasting serum insulin level and age, body mass index, serum glucose. METHODS: We examined 1917 Korean subjects who had fasting blood glucose within normal range (3.6~6.4mmol/L). The fasting insulin levels, total choiesterol, triglyceride concentrations and anthropometric characteristics(body weight, height and body mass index(BMI)) of these subjects were measured. RESULTS: 1) Mean fasting insulin levels were 33.9+0.5pmol/ L, the fasting insulin levels in men and women were 34.9+0.6 and 31.8+0.6pmol/L, respectively. 2) The fasting insulin levels of obese(BMI>25) subjects were significantly higher than those of non-obese subjects(43.2+ 1.2 pmol/L vs. 30.6+0.6 pmol/L, p<0.001). 3) There were significant differences in the basal insulin levels among the age groups, and fasting blood glucose levels were increased with aging. 4) In a multiple stepwise regression analysis, insulin levels were positively correlated with serum triglycerides, fasting blood glucose, body mass index and negatively correlated with age. Conclusion : The fasting insulin levels of healthy subjects in Korea were relatively lower than the previously measured value of Caucasians. The insulin levels were decreased with aging and increased with the elevation of BMI, fasting blood glucose and triglyceride.

First
Prev
Page of 1
Next
Last

Search