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Original Article
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
  • 1,758 View
  • 190 Download
  • 1 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   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  
  • 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
Response
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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  • 58 Download
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Original Article
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
  • 6,440 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