Diabetes & Metabolism Journal

Review

Clinical Diabetes & Therapeutics
Recent Updates on Type 1 Diabetes Mellitus Management for Clinicians: Ahmed Iqbal, Peter Novodvorsky, Simon R. Heller; Diabetes Metab J. 2018;42(1):3-18. Published online February 23, 2018; DOI: https://doi.org/10.4093/dmj.2018.42.1.3

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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.

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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
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
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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
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Sabine Szunerits, Sorin Melinte, Alexandre Barras, Quentin Pagneux, Anna Voronova, Amar Abderrahmani, Rabah Boukherroub
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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
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Ghasem Yadegarfar, Mark Livingston, Gabriela Cortes, Ramadan Alshames, Kate Leivesley, Ann Metters, Linda Horne, Tom Steele, Adrian H. Heald
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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
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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
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Original Articles

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

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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

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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

Others
Evaluation of a Novel Glucose Area Under the Curve (AUC) Monitoring System: Comparison with the AUC by Continuous Glucose Monitoring: Satoshi Ugi, Hiroshi Maegawa, Katsutaro Morino, Yoshihiko Nishio, Toshiyuki Sato, Seiki Okada, Yasuo Kikkawa, Toshihiro Watanabe, Hiromu Nakajima, Atsunori Kashiwagi; Diabetes Metab J. 2016;40(4):326-333. Published online July 26, 2016; DOI: https://doi.org/10.4093/dmj.2016.40.4.326

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Background

Management of postprandial hyperglycemia is a key aspect in diabetes treatment. We developed a novel system to measure glucose area under the curve (AUC) using minimally invasive interstitial fluid extraction technology (MIET) for simple monitoring of postprandial glucose excursions. In this study, we evaluated the relationship between our system and continuous glucose monitoring (CGM) by comparing glucose AUC obtained using MIET with that obtained using CGM for a long duration.

Methods

Twenty diabetic inpatients wearing a CGM system were enrolled. For MIET measurement, a plastic microneedle array was applied to the skin as pretreatment, and hydrogels were placed on the pretreated area to collect interstitial fluid. Hydrogels were replaced every 2 or 4 hours and AUC was predicted on the basis of glucose and sodium ion levels.

Results

AUC predicted by MIET correlated well with that measured by CGM (r=0.93). Good performances of both consecutive 2- and 4-hour measurements were observed (measurement error: 11.7%±10.2% for 2 hours and 11.1%±7.9% for 4 hours), indicating the possibility of repetitive measurements up to 8 hours. The influence of neither glucose fluctuation nor average glucose level over the measurement accuracy was observed through 8 hours.

Conclusion

Our system showed good relationship with AUC values from CGM up to 8 hours, indicating that single pretreatment can cover a large portion of glucose excursion in a day. These results indicated possibility of our system to contribute to convenient monitoring of glucose excursions for a long duration.

Citations

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Continuous glucose monitoring metrics and pregnancy outcomes in insulin‐treated diabetes: A post‐hoc analysis of the GlucoMOMS trial
Doortje Rademaker, Anne W. T. van der Wel, Rik van Eekelen, Daphne N. Voormolen, Harold W. de Valk, Inge M. Evers, Ben Willem Mol, Arie Franx, Sarah E. Siegelaar, Bas B. van Rijn, J. Hans DeVries, Rebecca C. Painter
Diabetes, Obesity and Metabolism.2023; 25(12): 3798. CrossRef
Regimen comprising GLP-1 receptor agonist and basal insulin can decrease the effect of food on glycemic variability compared to a pre-mixed insulin regimen
Yi-Hsuan Lin, Chia-Hung Lin, Yu-Yao Huang, Hsin-Yun Chen, An-Shun Tai, Shih-Chen Fu, Sheng-Hwu Hsieh, Jui-Hung Sun, Szu-Tah Chen, Sheng-Hsuan Lin
European Journal of Medical Research.2022;[Epub] CrossRef
Advantages of Applying Artificial Intelligent System to Medical Neurology (Preprint)
Zhenqiang Fu, Jingtao Wang, Jingtao Wang
JMIR Medical Informatics.2020;[Epub] CrossRef
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 & Metabolism Journal.2017; 41(4): 265. CrossRef

1,5-Anhydroglucitol as a Useful Marker for Assessing Short-Term Glycemic Excursions in Type 1 Diabetes: Hannah Seok, Ji Hye Huh, Hyun Min Kim, Byung-Wan Lee, Eun Seok Kang, Hyun Chul Lee, Bong Soo Cha; Diabetes Metab J. 2015;39(2):164-170. Published online March 9, 2015; DOI: https://doi.org/10.4093/dmj.2015.39.2.164

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Background

Type 1 diabetes is associated with more severe glycemic variability and more frequent hypoglycemia than type 2 diabetes. Glycemic variability is associated with poor glycemic control and diabetic complications. In this study, we demonstrate the clinical usefulness of serum 1,5-anhydroglucitol (1,5-AG) for assessing changes in glycemic excursion in type 1 diabetes.

Methods

Seventeen patients with type 1 diabetes were enrolled in this study. A continuous glucose monitoring system (CGMS) was applied twice at a 2-week interval to evaluate changes in glycemic variability. The changes in serum glycemic assays, including 1,5-AG, glycated albumin and hemoglobin A1c (HbA1c), were also evaluated.

Results

Most subjects showed severe glycemic excursions, including hypoglycemia and hyperglycemia. The change in 1,5-AG level was significantly correlated with changes in the glycemic excursion indices of the standard deviation (SD), mean amplitude of glucose excursion (MAGE), lability index, mean postmeal maximum glucose, and area under the curve for glucose above 180 mg/dL (r=-0.576, -0.613, -0.600, -0.630, and -0.500, respectively; all P<0.05). Changes in glycated albumin were correlated with changes in SD and MAGE (r=0.495 and 0.517, respectively; all P<0.05). However, changes in HbA1c were not correlated with any changes in the CGMS variables.

Conclusion

1,5-AG may be a useful marker for the assessment of short-term changes in glycemic variability. Furthermore, 1,5-AG may have clinical implications for the evaluation and treatment of glycemic excursions in type 1 diabetes.

Citations

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Glycemic dispersion: a new index for screening high glycemic variability
Rui Shi, Lei Feng, Yan-Mei Liu, Wen-Bo Xu, Bei-Bei Luo, Ling-Tong Tang, Qian-Ye Bi, Hui-Ying Cao
Diabetology & Metabolic Syndrome.2023;[Epub] CrossRef
Digital Behavior Change Interventions to Reduce Sedentary Behavior and Promote Physical Activity in Adults with Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Xiaoyan Zhang, Xue Qiao, Ke Peng, Shan Gao, Yufang Hao
International Journal of Behavioral Medicine.2023;[Epub] CrossRef
DBS are suitable for 1,5-anhydroglucitol monitoring in GSD1b and G6PC3-deficient patients taking SGLT2 inhibitors to treat neutropenia
Joseph P. Dewulf, Nathalie Chevalier, Sandrine Marie, Maria Veiga-da-Cunha
Molecular Genetics and Metabolism.2023; 140(3): 107712. CrossRef
The correlation between serum 1, 5-anhydroglucitol and β-cell function in Chinese adults with different glucose metabolism statuses
Yuexing Yuan, Yuanyuan Tan, Yao Wang, Shanhu Qiu, Jiao Yang, Cheng Chen
International Journal of Diabetes in Developing Countries.2023;[Epub] CrossRef
HbA1c combined with glycated albumin or 1,5‐anhydroglucitol improves the efficiency of diabetes screening in a Chinese population
Junyi Qian, Cheng Chen, Xiaohang Wang, Yuanyuan Tan, Jiao Yang, Yuexing Yuan, Juan Chen, Haijian Guo, Bei Wang, Zilin Sun, Yao Wang
Diabetic Medicine.2022;[Epub] CrossRef
Assessment of glycemia in chronic kidney disease
Mohamed Hassanein, Tariq Shafi
BMC Medicine.2022;[Epub] CrossRef
Continuous subcutaneous insulin infusion alters microRNA expression and glycaemic variability in children with type 1 diabetes
Emma S. Scott, Andrzej S. Januszewski, Luke M. Carroll, Gregory R. Fulcher, Mugdha V. Joglekar, Anandwardhan A. Hardikar, Timothy W. Jones, Elizabeth A. Davis, Alicia J. Jenkins
Scientific Reports.2021;[Epub] CrossRef
Red rice koji extract alleviates hyperglycemia by increasing glucose uptake and glucose transporter type 4 levels in skeletal muscle in two diabetic mouse models
Takakazu Yagi, Koji Ataka, Kai-Chun Cheng, Hajime Suzuki, Keizaburo Ogata, Yumiko Yoshizaki, Kazunori Takamine, Ikuo Kato, Shouichi Miyawaki, Akio Inui, Akihiro Asakawa
Food & Nutrition Research.2020;[Epub] CrossRef
How tightly controlled do fluctuations in blood glucose levels need to be to reduce the risk of developing complications in people with Type 1 diabetes?
R. Livingstone, J. G. Boyle, J. R. Petrie
Diabetic Medicine.2020; 37(4): 513. CrossRef
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Mikhail B. Antsiferov, Gagik R. Galstyan, Alexey V. Zilov, Alexander Y. Mayorov, Tatyana N. Markova, Nikolay A. Demidov, Olga M. Koteshkova, Dmitry N. Laptev, Alisa V. Vitebskaya
Diabetes mellitus.2019; 22(3): 281. CrossRef
Hyperglycemia and Carotenoid Intake Are Associated with Serum Carotenoids in Youth with Type 1 Diabetes
Namrata Sanjeevi, Leah M. Lipsky, Tonja R. Nansel
Journal of the Academy of Nutrition and Dietetics.2019; 119(8): 1340. CrossRef
Correlation of Serum 1,5-AG with Uric Acid in Type 2 Diabetes Mellitus with Different Renal Functions
Kai Zhang, Bizhen Xue, Yuexing Yuan, Yao Wang
International Journal of Endocrinology.2019; 2019: 1. CrossRef
Glycaemic control and glycaemic variability in older people with diabetes
Hermes J Florez
The Lancet Diabetes & Endocrinology.2018; 6(6): 433. CrossRef
Alternate glycemic markers reflect glycemic variability in continuous glucose monitoring in youth with prediabetes and type 2 diabetes
Christine L. Chan, Laura Pyle, Megan M. Kelsey, Lindsey Newnes, Amy Baumgartner, Philip S. Zeitler, Kristen J. Nadeau
Pediatric Diabetes.2017; 18(7): 629. CrossRef
1,5-anidroglucitolo: un marcatore non tradizionale di iperglicemia
Gabriella Lavalle, Roberto Testa, Maria Elisabetta Onori, Raffaella Vero, Anna Vero
La Rivista Italiana della Medicina di Laboratorio - Italian Journal of Laboratory Medicine.2017; 13(3-4): 139. CrossRef
Glycemic control and variability in association with body mass index and body composition over 18months in youth with type 1 diabetes
Leah M. Lipsky, Benjamin Gee, Aiyi Liu, Tonja R. Nansel
Diabetes Research and Clinical Practice.2016; 120: 97. CrossRef
How Can We Easily Measure Glycemic Variability in Diabetes Mellitus?
Suk Chon
Diabetes & Metabolism Journal.2015; 39(2): 114. CrossRef
Alternative biomarkers for assessing glycemic control in diabetes: fructosamine, glycated albumin, and 1,5-anhydroglucitol
Ji-Eun Lee
Annals of Pediatric Endocrinology & Metabolism.2015; 20(2): 74. CrossRef
Glycemic Variability: How Do We Measure It and Why Is It Important?
Sunghwan Suh, Jae Hyeon Kim
Diabetes & Metabolism Journal.2015; 39(4): 273. CrossRef

Effectiveness of 3-Day Continuous Glucose Monitoring for Improving Glucose Control in Type 2 Diabetic Patients in Clinical Practice: Soo Kyoung Kim, Hye Jeong Kim, Taehun Kim, Kyu Yeon Hur, Sun Wook Kim, Moon-Kyu Lee, Yong-Ki Min, Kwang-Won Kim, Jae Hoon Chung, Jae Hyeon Kim; Diabetes Metab J. 2014;38(6):449-455. Published online December 15, 2014; DOI: https://doi.org/10.4093/dmj.2014.38.6.449

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Background

The aim of this study was to investigate whether adjusting diabetic treatment regimens according to the information obtained from a continuous glucose monitoring system (CGMS) might lead to improved glycemic control in patients with type 2 diabetes.

Methods

We reviewed the medical charts of 172 patients who used the CGMS for 1 year starting in December 2008 and the records of 1,500 patients who visited their regular outpatient clinics during December 2008. Of these patients, a total of 65 CGMS patients and 301 regular outpatients (control group) were enrolled in the study after propensity score matching. There were no differences in baseline glycated hemoglobin (HbA1c), age, and duration of diabetes between the CGMS and the control groups after propensity score matching. The changes in the HbA1c levels from baseline to 6 months were calculated.

Results

The CGMS group showed a significant improvement in the HbA1c level compared to the control group at 3 months (7.9%±1.6% vs. 7.4%±1.2%, P=0.001) and at 6 months (7.4%±1.2% vs. 7.9%±1.6%, P=0.010). There were significant differences in the treatment modality changes between the CGMS group and the control group.

Conclusion

Using a 3-day CGMS was advantageous for improving glucose control in patients with type 2 diabetes and may help these patients to optimize glycemic control in clinical practice.

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Zhao Liu, Hiromitsu Hayashi, Kazuki Matsumura, Norio Uemura, Yuta Shiraishi, Hiroki Sato, Hideo Baba
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Professional continuous glucose monitoring in patients with diabetes mellitus: A systematic review and meta‐analysis
Sergio Di Molfetta, Irene Caruso, Angelo Cignarelli, Annalisa Natalicchio, Sebastio Perrini, Luigi Laviola, Francesco Giorgino
Diabetes, Obesity and Metabolism.2023; 25(5): 1301. CrossRef
American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus
George Grunberger, Jennifer Sherr, Myriam Allende, Thomas Blevins, Bruce Bode, Yehuda Handelsman, Richard Hellman, Rosemarie Lajara, Victor Lawrence Roberts, David Rodbard, Carla Stec, Jeff Unger
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Jong Il Park, Hwa Young Lee, Hyunah Kim, Jisan Lee, Jiwon Shinn, Hun-Sung Kim
Journal of Korean Medical Science.2021;[Epub] CrossRef
A head‐to‐head comparison of personal and professional continuous glucose monitoring systems in people with type 1 diabetes: Hypoglycaemia remains the weak spot
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Clinical and economic benefits of professional CGM among people with type 2 diabetes in the United States: analysis of claims and lab data
Joseph A. Sierra, Mona Shah, Max S. Gill, Zachery Flores, Hiten Chawla, Francine R. Kaufman, Robert Vigersky
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Jothydev Kesavadev, Robert Vigersky, John Shin, Pradeep Babu Sadasivan Pillai, Arun Shankar, Geethu Sanal, Gopika Krishnan, Sunitha Jothydev
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Gyuri Kim, Sewon Oh, Sang-Man Jin, Kyu Yeon Hur, Jae Hyeon Kim, Moon-Kyu Lee
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So Ra Kim, Yong-ho Lee, Sang-Guk Lee, Sun Hee Lee, Eun Seok Kang, Bong-Soo Cha, Hyun Chul Lee, Jeong-Ho Kim, Byung-Wan Lee
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Ian Blumer
Journal of Diabetes Science and Technology.2016; 10(3): 790. CrossRef
Glycemic Variability: How Do We Measure It and Why Is It Important?
Sunghwan Suh, Jae Hyeon Kim
Diabetes & Metabolism Journal.2015; 39(4): 273. CrossRef

The Correlation and Accuracy of Glucose Levels between Interstitial Fluid and Venous Plasma by Continuous Glucose Monitoring System: Young Ha Baek, Heung Yong Jin, Kyung Ae Lee, Seon Mee Kang, Woong Ji Kim, Min Gul Kim, Ji Hyun Park, Soo Wan Chae, Hong Sun Baek, Tae Sun Park; Korean Diabetes J. 2010;34(6):350-358. Published online December 31, 2010; DOI: https://doi.org/10.4093/kdj.2010.34.6.350

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Background

Clinical experience with the continuous glucose monitoring systems (CGMS) is limited in Korea. The objective of this study is to evaluate the accuracy of the CGMS and the correlation between interstitial fluid and venous plasma glucose level in Korean healthy male subjects.

Methods

Thirty-two subjects were served with glucose solution contained same amount of test food's carbohydrate and test foods after separate overnight fasts. CGMS was performed over 3 days during hopitalization for each subjects. Venous plasma glucose measurements were carried out during 4 hours (0, 0.25, 0.5, 0.75, 1, 2, 4 hours) just before and after glucose solution and test food load. The performance of the CGMS was evaluated by comparing its readings to those obtained at the same time by the hexokinase method using the auto biochemistry machine (Hitachi 7600-110). Also, correlations between glucose recorded with CGMS and venous plasma glucose value were examined.

Results

CGMS slightly underestimated the glucose value as compared with the venous plasma glucose level (16.3 ± 22.2 mg/dL). Correlation between CGMS and venous plasma glucose values throughout sensor lifetime is 0.73 (regression analysis: slope = 1.08, intercept = 8.38 mg/dL). Sensor sensitivity can deteriorate over time, with correlations between venous blood glucose and CGMS values dropping from 0.77 during 1st day to 0.65 during 2nd and 3rd day.

Conclusion

The accuracy of data provided by CGMS may be less than expected. CGMS sensor sensitivity is decreased with the passage of time. But, from this study, CGMS can be used for glucose variability tendency monitoring conveniently to the Korean.

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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
Correlation between short- and mid-term hemoglobin A1c and glycemic control determined by continuous glucose monitoring
Jen-Hung Huang, Yung-Kuo Lin, Ting-Wei Lee, Han-Wen Liu, Yu-Mei Chien, Yu-Chun Hsueh, Ting-I Lee, Yi-Jen Chen
Diabetology & Metabolic Syndrome.2021;[Epub] CrossRef
Accuracy of Flash Glucose Monitoring During Postprandial Rest and Different Walking Conditions in Overweight or Obese Young Adults
Xiaoyuan Zhang, Fenghua Sun, Waris Wongpipit, Wendy Y. J. Huang, Stephen H. S. Wong
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