Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes

Abstract

Glucose control, glucose variability (GV), and risk for hypoglycemia are intimately related, and it is now evident that GV is important in both the physiology and pathophysiology of diabetes. However, its quantitative assessment is complex because blood glucose (BG) fluctuations are characterized by both amplitude and timing. Additional numerical complications arise from the asymmetry of the BG scale. In this Perspective, we focus on the acute manifestations of GV, particularly on hypoglycemia, and review measures assessing the amplitude of GV from routine self-monitored BG data, as well as its timing from continuous glucose monitoring (CGM) data. With availability of CGM, the latter is not only possible but also a requirement-we can now assess rapid glucose fluctuations in real time and relate their speed and magnitude to clinically relevant outcomes. Our primary message is that diabetes control is all about optimization and balance between two key markers-frequency of hypoglycemia and HbA1c reflecting average BG and primarily driven by the extent of hyperglycemia. GV is a primary barrier to this optimization, including to automated technologies such as the "artificial pancreas." Thus, it is time to standardize GV measurement and thereby streamline the assessment of its two most important components-amplitude and timing.

Figure 1

Fifteen-day glucose traces of two subjects who had identical HbA_1c of 8.0% but different degrees of GV. High GV in subject 1 was reflected by numerous episodes of both hypo- and hyperglycemia (A), whereas low GV in subject 2 resulted in no such episodes (B).

Figure 2

Principal components of GV. Glucose fluctuations are a process in time that has two dimensions—amplitude and time (A). Projected along its amplitude axis, this process is measured by metrics such as SD or MAGE (B). Projected along its time axis, this process is assessed by temporal characteristics, such as time within target range and time spent in hypo- or hyperglycemia (C).

Figure 3

VGA. A split of a population of 335 below the median ADRR (A) and above the median ADRR (B) illustrated by the VGA. Each data point has BG coordinates (minimum, maximum) for a subject during the observation period. Subjects in A are in good control with most of the data points (70.4%) in the A- and B-zones; subjects in B are in poor control with most of the data points (89.6%) in the C-, D-, and E-zones.

Figure 4

Poincaré plot of BG fluctuations depicting system dynamics. A: Plot of perfectly cyclic glucose fluctuations, an unrealistic but illustrative scenario. B: Plot of unstable BG fluctuations of a person in poor control. C: Plot of stable BG fluctuations of a person in good control.

Figure 5

Histograms of the BG rate of change before (A) and after (B) islet transplantation, illustrating increased system stability after the procedure.