Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control

Abstract

There are a large number of measures of glycemic variability, including standard deviation (SD), percentage coefficient of variation (%CV), interquartile range (IQR), mean amplitude of glucose excursion (MAGE), mean of daily differences (MODD), and continuous overlapping net glycemic action over an n-hour period (CONGA(n)). These are all highly correlated with the overall or "total" SD, SD(T). SD(T) is composed of several components corresponding to within-day variability, between-day variability (between daily means and between days-within specified time points), and the interaction of these sources of variability. We identify several subtypes of SD; each is highly correlated with SD(T). Variability may also depend on time of day. Numerous measures of quality of glycemic control have been proposed, including a weighted average of glucose values (M)(e.g., M(100) is M at 100 mg/dL), a measure of quality of glycemic control based on mean and SD (J), the Glycemic Risk Assessment Diabetes Equation (GRADE), the Index of Glycemic Control (IGC), the High Blood Glucose Index (HBGI), the Low Blood Glucose Index (LBGI), the Average Daily Risk Range (ADRR), and percentage of glucose values within specified ranges. These methods usually but not always give consistent results: they can differ widely in terms of their ability to detect responses to therapeutic interventions. Based on review of the advantages and limitations of these measures and on extensive experience in the application of these methods, we outline a systematic approach to the interpretation of continuous glucose monitoring data for use by clinical researchers and clinicians to evaluate the quality of glycemic control, glucose variability including within- and between-day variability, the day-to-day stability of glycemic patterns, and changes in response to therapy.