Three-day continuous glucose monitoring for rapid assessment of hypoglycemic events and glycemic variability in type 1 diabetic patients

Kim, Sang-Yong; Suh, Sunghwan; Kim, Mi Yeon; Chung, Haegeun; Hur, Kyu Yeon; Kim, Sun Wook; Chung, Jae Hoon; Lee, Myung-Shik; Min, Yong‐Ki; Kim, Kwang-Won; Kim, Jae Hyun

doi:10.1507/endocrj.k10e-378

Cited by 13 publications

(8 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As described in Table 3, SD has been reported to be strongly and linearly related to mean glucose [29]. Although SD is the most widely used method to define glycaemic variability, it is not the best marker of established specific outcomes of glycaemic variability such as hypoglycaemia [4,30,31] and intensive care unit mortality [5]. CV, which corrects for mean glucose by dividing SD by the mean, was a better predictor of hypoglycaemia than SD in several studies, including ours [4,19,30,31].…”

Section: Discussionmentioning

confidence: 97%

“…Although SD is the most widely used method to define glycaemic variability, it is not the best marker of established specific outcomes of glycaemic variability such as hypoglycaemia [4,30,31] and intensive care unit mortality [5]. CV, which corrects for mean glucose by dividing SD by the mean, was a better predictor of hypoglycaemia than SD in several studies, including ours [4,19,30,31]. Despite the independent association between SD and extent of albuminuria, the lack of a significant association between other indices of glycaemic variability and extent of albuminuria indicates that the association between SD and extent of albuminuria might be explained, at least in part, by the influence of mean glucose levels.…”

Section: Discussionmentioning

confidence: 99%

“…Glycaemic variability has been recognized as a predictor of hypoglycaemia [1][2][3][4] and an independent contributor to intensive care unit mortality [5]. A growing body of evidence indicates that postprandial glucose excursion [6][7][8][9] and/or glycaemic variability [2,[10][11][12] are important contributors to macrovascular complications and the atherosclerotic process in diabetes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Association between the extent of urinary albumin excretion and glycaemic variability indices measured by continuous glucose monitoring

Jin

Kim

et al. 2014

Diabet. Med.

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Association between the extent of urinary albumin excretion and glycaemic variability indices measured by continuous glucose monitoring

Jin

Kim

et al. 2014

Diabet. Med.

View full text Add to dashboard Cite

show abstract

“…That our simple model is unable to capture hypoglycemia is not too surprising, considering the complexity associated with understanding this phenomena in general (American Diabetes Association Workgroup on Hypoglycemia, 2005 ; Unger, 2012 ; Elliott et al, 2016 ). A substantial body of literature exists trying to explain it in different contexts such as in juvenile diabetes (Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group et al, 2011 ), or for type I diabetes patients (Kim et al, 2011 ), and in assessing its impact on productivity (Brod et al, 2011 ). Notably, Sampath et al ( 2016 ) have recently proposed a machine learning algorithm that combines different glycemic indices to successfully predict occurrences of nocturnal hypoglycemic incidents.…”

Section: Discussionmentioning

confidence: 99%

A Minimal Model Approach for Analyzing Continuous Glucose Monitoring in Type 2 Diabetes

et al. 2018

View full text Add to dashboard Cite

Continuous glucose monitoring (CGM), a technique that records blood glucose at a regular intervals. While CGM is more commonly used in type 1 diabetes, it is increasingly becoming attractive for treating type 2 diabetic patients. The time series obtained from a CGM provides a rich picture of the glycemic state of the subjects and may help have tighter control on blood sugar by revealing patterns in their physiological responses to food. However, despite its importance, the biophysical understanding of CGM is far from complete. CGM data series is complex not only because it depends on the composition of the food but also varies with individual physiology. All of these make a full modeling of CGM data a difficult task. Here we propose a simple model to explain CGM data in type 2 diabetes. The model combines a relatively simple glucose-insulin dynamics with a two-compartment food model. Using CGM data of a healthy and a diabetic individual we show that this model can capture liquid meals well. The model also allows us to estimate the parameters in a relatively straightforward manner. This opens up the possibility of personalizing the CGM data. The model also predicts insulin time series from the model, and the rate of appearance of glucose due to food. Our methodology thus paves the way for novel analyses of CGM which have not been possible before.

show abstract

“…They also showed that HbA1c is a poor predictor of hypoglycemic risk, whereas GV is a strong predictor of hypoglycemic episodes. Kim et al [ 56 ] found that Korean T1DM patients with hypoglycemic events had a significantly higher GV index, as calculated from the CGM data. Collectively, patients at risk for hypoglycemia (i.e., those receiving insulin or insulin secretagogues) constitute one category that requires GV monitoring.…”

Section: Glycemic Variability and Hypoglycemiamentioning

confidence: 99%

Glycemic Variability: How Do We Measure It and Why Is It Important?

2015

Self Cite

View full text Add to dashboard Cite

Chronic hyperglycemia is the primary risk factor for the development of complications in diabetes mellitus (DM); however, it is believed that frequent or large glucose fluctuations may independently contribute to diabetes-related complications. Postprandial spikes in blood glucose, as well as hypoglycemic events, are blamed for increased cardiovascular events in DM. Glycemic variability (GV) includes both of these events; hence, minimizing GV can prevent future cardiovascular events. Correcting GV emerges as a target to be pursued in clinical practice to safely reduce the mean blood glucose and to determine its direct effects on vascular complications in diabetes. Modern diabetes management modalities, including glucagon-related peptide-1-based therapy, newer insulins, modern insulin pumps and bariatric surgery, significantly reduce GV. However, defining GV remains a challenge primarily due to the difficulty of measuring it and the lack of consensus regarding the optimal approach for its management. The purpose of this manuscript was not only to review the most recent evidence on GV but also to help readers better understand the available measurement options and how the various definitions relate differently to the development of diabetic complications.

show abstract

Three-day continuous glucose monitoring for rapid assessment of hypoglycemic events and glycemic variability in type 1 diabetic patients

Cited by 13 publications

References 21 publications

Association between the extent of urinary albumin excretion and glycaemic variability indices measured by continuous glucose monitoring

Association between the extent of urinary albumin excretion and glycaemic variability indices measured by continuous glucose monitoring

A Minimal Model Approach for Analyzing Continuous Glucose Monitoring in Type 2 Diabetes

Glycemic Variability: How Do We Measure It and Why Is It Important?

Contact Info

Product

Resources

About