title: The use of a computer program to calculate the mean amplitude of glycemic excursions.
abstract: BACKGROUND
  The mean amplitude of glycemic excursions (MAGE), traditionally estimated with a graphical approach, is often used to characterize glycemic variability. Here, we tested a proposed software program for calculating MAGE.
  
  
  METHODS
  Development and testing of the software was based on retrospective analyses of 72-h continuous glucose monitoring profile data collected during two different clinical studies involving 474 outpatients (458 with type 2 and 16 with type 1 diabetes) in three cohorts (two type 2 diabetes and one type 1 diabetes), using the CGMS® Gold™ (Medtronic MiniMed, Northridge, CA). Correlation analyses and a Bland-Altman procedure were used to compare the results of MAGE calculations performed using the developed computer program (MAGE(C)) and the original method (MAGE(O)).
  
  
  RESULTS
  Close linear correlations between MAGE(C) and MAGE(O) were documented in the two type 2 and the type 1 diabetes cohorts (r = 0.954, 0.962, and 0.951, respectively; P < 0.00001 for all), as was the absence of any systematic error between the two calculation methods. Comparison of the two indices revealed no within-group differences but did show differences among the various antihyperglycemic treatments (P < 0.0001). In each of the study cohorts, MAGE(C) correlated strongly with the SD (r = 0.914-0.943), moderately with the mean of daily differences (r = 0.688-0.757), and weakly with glycosylated hemoglobin A1c and mean sensor glucose (r= 0.285 and r = 0.473, respectively).
  
  
  CONCLUSIONS
  The proposed computerized calculation of MAGE is a practicable method that may provide an efficient tool for assessing glycemic variability.
authors.affiliation: Diabetes Service Center, Karlsburg, Germany. kohnert@diabetes-karlsburg.de
authors.forename: Gert
authors.initials: G
authors.lastname: Fritzsche
authors.forename: Klaus-Dieter
authors.initials: KD
authors.lastname: Kohnert
authors.forename: Peter
authors.initials: P
authors.lastname: Heinke
authors.forename: Lutz
authors.initials: L
authors.lastname: Vogt
authors.forename: Eckhard
authors.initials: E
authors.lastname: Salzsieder
mesh_terms: D000368:Aged; D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism*; D015331:Cohort Studies; D003922:Diabetes Mellitus, Type 1 / Q000097:blood*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood*; D005260:Female; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D012189:Retrospective Studies
chemical_list: D001786:Blood Glucose; D006442:Glycated Hemoglobin