Glycemic variability: the third component of the dysglycemia in diabetes. Is it important? How to measure it?

Abstract

THE dysglycemia of diabetes includes two components: (1) sustained chronic hyperglycemia that exerts its effects through both excessive protein glycation and activation of oxidative stress and (2) acute glucose fluctuations. Glycemic variability seems to have more deleterious effects than sustained hyperglycemia in the development of diabetic complications as both upward (postprandial glucose increments) and downward (interprandial glucose decrements) changes activate the oxidative stress. For instance, the urinary excretion rate of 8-iso-PGF2alpha, a reliable marker of oxidative stress, was found to be strongly, positively correlated (r = 0.86, p < .001) with glycemic variability assessed from the mean amplitude of glycemic excursions (MAGE) as estimated by continuous glucose monitoring systems (CGMS). These observations therefore raise the question of whether we have the appropriate tools for assessing glycemic variability in clinical practice. From a statistical point of view, the standard deviation (SD) around the mean glucose value appears as the "gold standard." By contrast, the MAGE index is probably more appropriate for selecting the major glucose swings that are calculated as the arithmetic mean of differences between consecutive peaks and nadirs, provided that the differences be greater than the SD around the mean values. Furthermore, calculating the MAGE index requires continuous glucose monitoring, which has the advantage to detect all isolated upward and downward acute glucose fluctuations. In conclusion, the increasing use of CGMSs will certainly promote better assessment and management of glycemic variability.

Keywords: glycemic assessment; glycemic importance; glycemic variability.

Figures

Figure 1.

Absolute contributions of postprandial glucose increments to HbA1c (percentage points, median, 95% confidence interval, 10th percentile, and 90th percentile) with worsening diabetes.

Figure 2.

Model suggested for illustrating the pathophysiological impacts of the excessive glycation of proteins and the activation of oxidative stress on the risk of diabetic complications (diagonal solid arrow). The contributions of the three components of dysglycemia, i.e., hyperglycemia at fasting (FPG), hyperglycemia during postprandial periods (PPG), and acute glucose fluctuations (MAGE), are indicated on the x, y, and z axes, respectively.

Figure 3.

Example of continuous glucose monitoring in one type 1 diabetes patient treated with a multiple-injection insulin regimen. The standard deviation around the mean glucose value and the MAGE were 65 mg/dl (3.6 mmol/liter) and 276 mg/dl (15.3 mmol/liter), respectively. The discrepancy between the two values was due to the fact that this patient exhibited a single large glucose swing inserted in modest glucose fluctuations over the remainder the day.

Figure 4.

Mean glucose concentration in 32 noninsulin-using type 2 diabetes patients exhibiting HbA1c levels between 7 and 7.9% (Reproduced from Reference 29, with permission from Diabetes Care.)