New and improved methods to characterize glycemic variability using continuous glucose monitoring.
BACKGROUND
Glycemic variability is a possible risk factor for development of complications from diabetes. Numerous methods have been used to characterize glycemic variability.


METHODS
We propose several new methods to characterize glycemic variability. We evaluated these methods empirically and theoretically and compared them with previous methods.


RESULTS
We describe (1) extension and generalization of the mean amplitude of glycemic excursion (MAGE), i.e., "within day variability," (2) extension and generalization of the mean of daily differences (MODD), i.e., the "between day-within time points variability," (3) "between daily means variability," (4) "between time points variability" of the glucose profile averaged over several days, (5) "within series variability" for a time segment of any arbitrary length, (6) new measures of the stability of the daily glycemic patterns, (7) new types of graphical displays, including within day variability, between day-within time points variability, and between daily means variability versus total variability, and between daily means variability versus within day variability, and (8) new methods to evaluate whether within series and between day-within time points variability fluctuate systematically by time of day. We examined the new measures in relation to previous measures of glycemic variability using correlation analysis on a clinical dataset for 85 subjects. MAGE, MODD, and continuous overall net glycemic action (CONGA(n)) are directly proportional to total standard deviation (SD). MAGE is highly correlated with both total SD and within day variability but weakly correlated with measures of between day variability. MODD is highly correlated with between day-within time points variability and total SD but weakly correlated with measures of within day variability.


CONCLUSIONS
We provide a systematic, logical framework to characterize multiple aspects of glycemic variability and have implemented a simple, practical computing format. This approach can help clinical researchers and clinicians identify the major sources of variability for any given patient and monitor responses to interventions.
Rodbard
David
D
Biomedical Informatics Consultants LLC, Potomac, Maryland 20854-4721, USA. drodbard@comcast.net
D000465:Algorithms; D000704:Analysis of Variance; D001786:Blood Glucose / Q000032:analysis*; D003627:Data Interpretation, Statistical; D048909:Diabetes Complications / Q000517:prevention & control*; D003920:Diabetes Mellitus / Q000097:blood; D006801:Humans; D018670:Monitoring, Ambulatory; D016011:Normal Distribution; D009716:Numerical Analysis, Computer-Assisted; D012307:Risk Factors; D013223:Statistics as Topic; D013997:Time Factors
D001786:Blood Glucose