Abstract
The measurement of glycaemic variation (GV) is conceived to be of clinical significance in determining diabetes outcomes. The debate as to the importance of GV has been complicated by studies using various meetrics of GV in qualitatively different datasets. The purpose of this review is to discuss the properties of 8 of the more commonly used metrics (M-value, MAGE, “J”-index, CONGA, BG rate of change, ADRR, Lability/HYPO score and GRADE). Comparable metrics that can be used to measure continuous glycaemic measurements (CGM) (SDBGL, “J”-index, MAGE, CONGA, GRADE) were then compared in assessing diabetic and non-diabetic datasets. In non-diabetic conditions there was very close correlation (correlation coefficients > 0.92) between SDBGL, MAGE and CONGA, however under diabetic conditions the correlation coefficients of the GV metrics diminished significantly. The varying GV metrics have varying inherent properties depending upon the purpose for which they were designed and should not be seen as being interchangeable. Investigators therefore need to be clear about the nature of their enquiry of GV and choose an appropriate metric.
Keywords: Glycaemic variation, Measurement, Comparison
Current Diabetes Reviews
Title: Measuring Glycaemic Variation
Volume: 6 Issue: 1
Author(s): Fergus J. Cameron, Susan M. Donath and Peter A. Baghurst
Affiliation:
Keywords: Glycaemic variation, Measurement, Comparison
Abstract: The measurement of glycaemic variation (GV) is conceived to be of clinical significance in determining diabetes outcomes. The debate as to the importance of GV has been complicated by studies using various meetrics of GV in qualitatively different datasets. The purpose of this review is to discuss the properties of 8 of the more commonly used metrics (M-value, MAGE, “J”-index, CONGA, BG rate of change, ADRR, Lability/HYPO score and GRADE). Comparable metrics that can be used to measure continuous glycaemic measurements (CGM) (SDBGL, “J”-index, MAGE, CONGA, GRADE) were then compared in assessing diabetic and non-diabetic datasets. In non-diabetic conditions there was very close correlation (correlation coefficients > 0.92) between SDBGL, MAGE and CONGA, however under diabetic conditions the correlation coefficients of the GV metrics diminished significantly. The varying GV metrics have varying inherent properties depending upon the purpose for which they were designed and should not be seen as being interchangeable. Investigators therefore need to be clear about the nature of their enquiry of GV and choose an appropriate metric.
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Cite this article as:
Cameron J. Fergus, Donath M. Susan and Baghurst A. Peter, Measuring Glycaemic Variation, Current Diabetes Reviews 2010; 6 (1) . https://dx.doi.org/10.2174/157339910790442592
DOI https://dx.doi.org/10.2174/157339910790442592 |
Print ISSN 1573-3998 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6417 |
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