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Original Article
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
Received March 14, 2023  Accepted August 12, 2023  Published online March 4, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0083    [Epub ahead of print]
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
This study investigated the optimal coefficient of variance (%CV) for preventing hypoglycemia based on real-time continuous glucose monitoring (rt-CGM) data in people with type 1 diabetes mellitus (T1DM) already achieving their mean glucose (MG) target.
Methods
Data from 172 subjects who underwent rt-CGM for at least 90 days and for whom 439 90-day glycemic profiles were available were analyzed. Receiver operator characteristic analysis was conducted to determine the cut-off value of %CV to achieve time below range (%TBR)<54 mg/dL <1 and =0.
Results
Overall mean glycosylated hemoglobin was 6.8% and median %TBR<54 mg/dL was 0.2%. MG was significantly higher and %CV significantly lower in profiles achieving %TBR<54 mg/dL <1 compared to %TBR<54 mg/dL ≥1 (all P<0.001). The cut-off value of %CV for achieving %TBR<54 mg/dL <1 was 37.5%, 37.3%, and 31.0%, in the whole population, MG >135 mg/dL, and ≤135 mg/dL, respectively. The cut-off value for %TBR<54 mg/dL=0% was 29.2% in MG ≤135 mg/dL. In profiles with MG ≤135 mg/dL, 94.2% of profiles with a %CV <31 achieved the target of %TBR<54 mg/dL <1, and 97.3% with a %CV <29.2 achieved the target of %TBR<54 mg/ dL=0%. When MG was >135 mg/dL, 99.4% of profiles with a %CV <37.3 achieved %TBR<54 mg/dL <1.
Conclusion
In well-controlled T1DM with MG ≤135 mg/dL, we suggest a %CV <31% to achieve the %TBR<54 mg/dL <1 target. Furthermore, we suggest a %CV <29.2% to achieve the target of %TBR<54 mg/dL =0 for people at high risk of hypoglycemia.
Review
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
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  • 10 Web of Science
  • 15 Crossref
AbstractAbstract PDFPubReader   ePub   
Continuous glucose monitoring (CGM) technology has evolved over the past decade with the integration of various devices including insulin pumps, connected insulin pens (CIPs), automated insulin delivery (AID) systems, and virtual platforms. CGM has shown consistent benefits in glycemic outcomes in type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) treated with insulin. Moreover, the combined effect of CGM and education have been shown to improve glycemic outcomes more than CGM alone. Now a CIP is the expected future technology that does not need to be worn all day like insulin pumps and helps to calculate insulin doses with a built-in bolus calculator. Although only a few clinical trials have assessed the effectiveness of CIPs, they consistently show benefits in glycemic outcomes by reducing missed doses of insulin and improving problematic adherence. AID systems and virtual platforms made it possible to achieve target glycosylated hemoglobin in diabetes while minimizing hypoglycemia, which has always been challenging in T1DM. Now fully automatic AID systems and tools for diabetes decisions based on artificial intelligence are in development. These advances in technology could reduce the burden associated with insulin treatment for diabetes.

Citations

Citations to this article as recorded by  
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    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 Andrade, Boyang Chen, Jae Hyeon Kim
    Diabetes Technology & Therapeutics.2024;[Epub]     CrossRef
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    Wuyi Ming, Xudong Guo, Guojun Zhang, Yinxia Liu, Yongxin Wang, Hongmei Zhang, Haofang Liang, Yuan Yang
    Medical & Biological Engineering & Computing.2024;[Epub]     CrossRef
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    Dorothy Avoke, Abdallah Elshafeey, Robert Weinstein, Chang H. Kim, Seth S. Martin
    Endocrine Research.2024; : 1.     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
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    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
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    Jee Hee Yoo, Jae Hyeon Kim
    Endocrinology and Metabolism.2023; 38(5): 472.     CrossRef
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  • An Observational Pilot Study of a Tailored Environmental Monitoring and Alert System for Improved Management of Chronic Respiratory Diseases
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    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
Corrigendum
Time in Range from Continuous Glucose Monitoring: A Novel Metric for Glycemic Control
Jee Hee Yoo, Jae Hyeon Kim
Diabetes Metab J. 2021;45(5):795-795.   Published online September 30, 2021
DOI: https://doi.org/10.4093/dmj.2021.0256
Corrects: Diabetes Metab J 2020;44(6):828
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Original Article
Complications
Association of Urinary N-Acetyl-β-D-Glucosaminidase with Cardiovascular Autonomic Neuropathy in Type 1 Diabetes Mellitus without Nephropathy
Min Sun Choi, Ji Eun Jun, Sung Woon Park, Jee Hee Yoo, Jiyeon Ahn, Gyuri Kim, Sang-Man Jin, Kyu Yeon Hur, Moon-Kyu Lee, Jae Hyeon Kim
Diabetes Metab J. 2021;45(3):349-357.   Published online February 2, 2021
DOI: https://doi.org/10.4093/dmj.2019.0211
  • 5,595 View
  • 121 Download
  • 1 Web of Science
  • 2 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDFPubReader   ePub   
Background
Cardiovascular autonomic neuropathy (CAN) is a common microvascular complication of diabetes and related to albuminuria in diabetic nephropathy (DN). Urinary N-acetyl-β-D-glucosaminidase (uNAG) is a renal tubular injury marker which has been reported as an early marker of DN even in patients with normoalbuminuria. This study evaluated whether uNAG is associated with the presence and severity of CAN in patients with type 1 diabetes mellitus (T1DM) without nephropathy.
Methods
This cross-sectional study comprised 247 subjects with T1DM without chronic kidney disease and albuminuria who had results for both uNAG and autonomic function tests within 3 months. The presence of CAN was assessed by age-dependent reference values for four autonomic function tests. Total CAN score was assessed as the sum of the partial points of five cardiovascular reflex tests and was used to estimatethe severity of CAN. The correlations between uNAG and heart rate variability (HRV) parameters were analyzed.
Results
The association between log-uNAG and presence of CAN was significant in a multivariate logistic regression model (adjusted odds ratio, 2.39; 95% confidence interval [CI], 1.08 to 5.28; P=0.031). Total CAN score was positively associated with loguNAG (β=0.261, P=0.026) in the multivariate linear regression model. Log-uNAG was inversely correlated with frequency-domain and time-domain indices of HRV.
Conclusion
This study verified the association of uNAG with presence and severity of CAN and changes in HRV in T1DM patients without nephropathy. The potential role of uNAG should be further assessed for high-risk patients for CAN in T1DM patients without nephropathy.

Citations

Citations to this article as recorded by  
  • Determination of Diabetes-associated Cardiovascular Autonomic Neuropathy Risk Factors among Insulin and Non-insulin Dependent Diabetics
    Ibrahim Abdulsada, Zain Alabdeen Obaid, Farah Almerza, Mays Alwaeli, Anmar Al-Elayawi, Taha Al-Dayyeni, Harir Al-Tuhafy
    The Journal of Medical Research.2023; 9(6): 141.     CrossRef
  • Association between carotid atherosclerosis and presence of intracranial atherosclerosis using three-dimensional high-resolution vessel wall magnetic resonance imaging in asymptomatic patients with type 2 diabetes
    Ji Eun Jun, You-Cheol Hwang, Kyu Jeong Ahn, Ho Yeon Chung, Geon-Ho Jahng, Soonchan Park, In-Kyung Jeong, Chang-Woo Ryu
    Diabetes Research and Clinical Practice.2022; 191: 110067.     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
  • 9,731 View
  • 464 Download
  • 29 Web of Science
  • 34 Crossref
AbstractAbstract PDFPubReader   ePub   
Glycosylated hemoglobin (HbA1c) has been the sole surrogate marker for assessing diabetic complications. However, consistently reported limitations of HbA1c are that it lacks detailed information on short-term glycemic control and can be easily interfered with by various clinical conditions such as anemia, pregnancy, or liver disease. Thus, HbA1c alone may not represent the real glycemic status of a patient. The advancement of continuous glucose monitoring (CGM) has enabled both patients and healthcare providers to monitor glucose trends for a whole single day, which is not possible with HbA1c. This has allowed for the development of core metrics such as time spent in time in range (TIR), hyperglycemia, or hypoglycemia, and glycemic variability. Among the 10 core metrics, TIR is reported to represent overall glycemic control better than HbA1c alone. Moreover, various evidence supports TIR as a predictive marker of diabetes complications as well as HbA1c, as the inverse relationship between HbA1c and TIR reveals. However, there are more complex relationships between HbA1c, TIR, and other CGM metrics. This article provides information about 10 core metrics with particular focus on TIR and the relationships between the CGM metrics for comprehensive understanding of glycemic status using CGM.

Citations

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