Literature DB >> 20877258

Assessing glycemic variation: why, when and how?

Fergus J Cameron1, Peter A Baghurst, David Rodbard.   

Abstract

In the post-Diabetes Control and Complications Trial (DCCT) and Epidemiology of Diabetes Interventions and Complications (EDIC) era of type 1 diabetes mellitus (T1DM) care, glycosylated hemoglobin (A1C) has enjoyed primacy as the clinical outcome variable (1). Metabolic control as defined by A1C, however, only defines approximately 25% of the risk of subsequent microvascular pathology (2) and, hence, other glycemic outcome variables are also being canvassed as being of potential significance. Transcription-regulating actions of glucose and the phenomenon of "metabolic memory" have recently become recognized (3,4). Simultaneously, ambulant continuous glucose monitoring (CGM) technologies have become available. The convergence of these factors has increased the interest in the impacts of fluctuations in glycemia, otherwise known as glycemic variation (GV). Initially, this interest was focused upon the effects of post-prandial glycemic excursions (5), but more recently, associations of GV and oxidative stress, microvascular pathology (6), and GV prediction associated with closedloop insulin delivery (7) have evolved. Notwithstanding this emerging interest in GV, there still remains a lack of consensus as to the importance of GV, in what circumstances it can be measured, and what GV metrics are best suited for various purposes. The aim of this review is to discuss these 3 key areas: Why measure GV? When can GV be meaningfully assessed?; How to measure to GV?.

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Year:  2010        PMID: 20877258

Source DB:  PubMed          Journal:  Pediatr Endocrinol Rev        ISSN: 1565-4753


  8 in total

Review 1.  The challenges of measuring glycemic variability.

Authors:  David Rodbard
Journal:  J Diabetes Sci Technol       Date:  2012-05-01

2.  Test-retest reliability of a continuous glucose monitoring system in individuals with type 2 diabetes.

Authors:  Tasuku Terada; Sarah Loehr; Emmanuel Guigard; Linda J McCargar; Gordon J Bell; Peter Senior; Normand G Boulé
Journal:  Diabetes Technol Ther       Date:  2014-05-09       Impact factor: 6.118

3.  Evaluating quality of glycemic control: graphical displays of hypo- and hyperglycemia, time in target range, and mean glucose.

Authors:  David Rodbard
Journal:  J Diabetes Sci Technol       Date:  2014-10-14

4.  Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data.

Authors:  Thomas A Peyser; Andrew K Balo; Bruce A Buckingham; Irl B Hirsch; Arturo Garcia
Journal:  Diabetes Technol Ther       Date:  2017-12-11       Impact factor: 6.118

5.  Effects of sensor-augmented pump therapy on glycemic variability in well-controlled type 1 diabetes in the STAR 3 study.

Authors:  John B Buse; Yogish C Kudva; Tadej Battelino; Stephen N Davis; John Shin; John B Welsh
Journal:  Diabetes Technol Ther       Date:  2012-04-23       Impact factor: 6.118

6.  The Minimum Duration of Sensor Data From Which Glycemic Variability Can Be Consistently Assessed.

Authors:  Orla M Neylon; Peter A Baghurst; Fergus J Cameron
Journal:  J Diabetes Sci Technol       Date:  2014-02-09

7.  Effects of fluctuating glucose levels on neuronal cells in vitro.

Authors:  Vincenzo C Russo; Sandra Higgins; George A Werther; Fergus J Cameron
Journal:  Neurochem Res       Date:  2012-05-08       Impact factor: 3.996

8.  Fluctuation between fasting and 2-H postload glucose state is associated with chronic kidney disease in previously diagnosed type 2 diabetes patients with HbA1c ≥ 7%.

Authors:  Chuan Wang; Jun Song; Zeqiang Ma; Weifang Yang; Chengqiao Li; Xiuping Zhang; Xinguo Hou; Yu Sun; Peng Lin; Kai Liang; Lei Gong; Meijian Wang; Fuqiang Liu; Wenjuan Li; Fei Yan; Junpeng Yang; Lingshu Wang; Meng Tian; Jidong Liu; Ruxing Zhao; Li Chen
Journal:  PLoS One       Date:  2014-07-21       Impact factor: 3.240

  8 in total

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