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

Abstract

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.

Keywords: Diabetes complications; Diabetes mellitus; Glycemic variability.

Fig. 1. Glycemic variability in three hypothetical patients who have the same mean blood glucose concentration. Patient B has relatively small variations during the day and on different days; this patient should have little difficulty in lowering daily mean blood glucose concentrations without inducing hypoglycemia. In comparison, patient A has marked blood glucose variations on the same day and patient C has marked blood glucose variations on different days.

Fig. 2. Calculation of mean amplitude of glucose excursion (MAGE). In the first step, all the local maximum/minimum values are determined. The next step is an assessment of maximum/minimum pairs against the standard deviation (SD). If the difference from minimum to maximum is greater than the SD, this variation from mean measure is retained. If the local maximum/minimum is less than 1 SD it is excluded from further calculations. These troughs are retained and summed to achieve the MAGE.