Diabetes & Metabolism Journal

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; Received January 24, 2024 Accepted March 29, 2024 Published online August 28, 2024; DOI: https://doi.org/10.4093/dmj.2024.0039 [Epub ahead of print]

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

Original 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; Diabetes Metab J. 2024;48(3):429-439. Published online March 4, 2024; DOI: https://doi.org/10.4093/dmj.2023.0083

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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.

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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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.

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Microchemical Journal.2024; 201: 110713. CrossRef
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Anales de Pediatría.2024; 101(3): 183. CrossRef
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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

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Graphical Abstract Abstract PDF PubReader 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

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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
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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

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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.

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Endocrinology and Metabolism Clinics of North America.2024; 53(1): 123. CrossRef
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Xiaopeng Shao, Jingyi Lu, Rui Tao, Liang Wu, Yaxin Wang, Wei Lu, Hongru Li, Jian Zhou, Xia Yu
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Diabetologia.2024; 67(7): 1223. CrossRef
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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
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Jenna B. Shapiro, Anthony T. Vesco, Michael S. Carroll, Jill Weissberg-Benchell
Diabetes Technology & Therapeutics.2024; 26(11): 842. CrossRef
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Sanjay Kalra, Saptarshi Bhattacharya, A Dhingra, Sambit Das, Nitin Kapoor, Shehla Shaikh, Vivek Kolapkar, R V Lokesh Kumar, Kamlesh Patel, Rahul Kotwal
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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
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