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Sung Woon Park  (Park SW) 3 Articles
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
  • 5,751 View
  • 122 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
Response: An Electronic Health Record-Integrated Computerized Intravenous Insulin Infusion Protocol: Clinical Outcomes and in Silico Adjustment (Diabetes Metab J 2020;44:56–66)
Sung Woon Park, Seunghyun Lee, Won Chul Cha, Kyu Yeon Hur, Jae Hyeon Kim, Moon-Kyu Lee, Sung-Min Park, Sang-Man Jin
Diabetes Metab J. 2020;44(2):358-359.   Published online April 23, 2020
DOI: https://doi.org/10.4093/dmj.2020.0080
[Original]
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Drug/Regimen
An Electronic Health Record-Integrated Computerized Intravenous Insulin Infusion Protocol: Clinical Outcomes and in Silico Adjustment
Sung Woon Park, Seunghyun Lee, Won Chul Cha, Kyu Yeon Hur, Jae Hyeon Kim, Moon-Kyu Lee, Sung-Min Park, Sang-Man Jin
Diabetes Metab J. 2020;44(1):56-66.   Published online October 21, 2019
DOI: https://doi.org/10.4093/dmj.2018.0227
  • 6,634 View
  • 133 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   
Background

We aimed to describe the outcome of a computerized intravenous insulin infusion (CII) protocol integrated to the electronic health record (EHR) system and to improve the CII protocol in silico using the EHR-based predictors of the outcome.

Methods

Clinical outcomes of the patients who underwent the CII protocol between July 2016 and February 2017 and their matched controls were evaluated. In the CII protocol group (n=91), multivariable binary logistic regression analysis models were used to determine the independent associates with a delayed response (taking ≥6.0 hours for entering a glucose range of 70 to 180 mg/dL). The CII protocol was adjusted in silico according to the EHR-based parameters obtained in the first 3 hours of CII.

Results

Use of the CII protocol was associated with fewer subjects with hypoglycemia alert values (P=0.003), earlier (P=0.002), and more stable (P=0.017) achievement of a glucose range of 70 to 180 mg/dL. Initial glucose level (P=0.001), change in glucose during the first 2 hours (P=0.026), and change in insulin infusion rate during the first 3 hours (P=0.029) were independently associated with delayed responses. Increasing the insulin infusion rate temporarily according to these parameters in silico significantly reduced delayed responses (P<0.0001) without hypoglycemia, especially in refractory patients.

Conclusion

Our CII protocol enabled faster and more stable glycemic control than conventional care with minimized risk of hypoglycemia. An EHR-based adjustment was simulated to reduce delayed responses without increased incidence of hypoglycemia.

Citations

Citations to this article as recorded by  
  • Response: An Electronic Health Record-Integrated Computerized Intravenous Insulin Infusion Protocol: Clinical Outcomes and in Silico Adjustment (Diabetes Metab J 2020;44:56–66)
    Sung Woon Park, Seunghyun Lee, Won Chul Cha, Kyu Yeon Hur, Jae Hyeon Kim, Moon-Kyu Lee, Sung-Min Park, Sang-Man Jin
    Diabetes & Metabolism Journal.2020; 44(2): 358.     CrossRef
  • Letter: An Electronic Health Record-Integrated Computerized Intravenous Insulin Infusion Protocol: Clinical Outcomes and in Silico Adjustment (Diabetes Metab J 2020;44:56–66)
    Dongwon Yi
    Diabetes & Metabolism Journal.2020; 44(2): 354.     CrossRef

Diabetes Metab J : Diabetes & Metabolism Journal