The Challenges of Measuring Glycemic Variability

This commentary reviews several of the challenges encountered when attempting to quantify glycemic variability and correlate it with risk of diabetes complications. These challenges include (1) immaturity of the field, including problems of data accuracy, precision, reliability, cost, and availability; (2) larger relative error in the estimates of glycemic variability than in the estimates of the mean glucose; (3) high correlation between glycemic variability and mean glucose level; (4) multiplicity of measures; (5) correlation of the multiple measures; (6) duplication or reinvention of methods; (7) confusion of measures of glycemic variability with measures of quality of glycemic control; (8) the problem of multiple comparisons when assessing relationships among multiple measures of variability and multiple clinical end points; and (9) differing needs for routine clinical practice and clinical research applications.

[1]  William L Clarke,et al.  Comparison of the clinical information provided by the FreeStyle Navigator continuous interstitial glucose monitor versus traditional blood glucose readings. , 2010, Diabetes technology & therapeutics.

[2]  Cynthia R. Marling,et al.  Characterizing Blood Glucose Variability Using New Metrics with Continuous Glucose Monitoring Data , 2011, Journal of diabetes science and technology.

[3]  David Rodbard,et al.  Clinical Interpretation of Indices of Quality of Glycemic Control and Glycemic Variability , 2011, Postgraduate medicine.

[4]  David Rodbard,et al.  Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control. , 2009, Diabetes technology & therapeutics.

[5]  Howard Zisser,et al.  Improved quality of glycemic control and reduced glycemic variability with use of continuous glucose monitoring. , 2009, Diabetes technology & therapeutics.

[6]  Peter A Baghurst,et al.  Calculating the mean amplitude of glycemic excursion from continuous glucose monitoring data: an automated algorithm. , 2011, Diabetes technology & therapeutics.

[7]  E. Kilpatrick Arguments for and against the Role of Glucose Variability in the Development of Diabetes Complications , 2009, Journal of diabetes science and technology.

[8]  D. Rodbard,et al.  Assessing glycemic variation: why, when and how? , 2010, Pediatric endocrinology reviews : PER.

[9]  F. Service,et al.  Characteristics of Glycemic Stability , 1980, Diabetes Care.

[10]  Benyamin Grosman,et al.  Continuous Glucose Monitoring Considerations for the Development of a Closed-Loop Artificial Pancreas System , 2011, Journal of diabetes science and technology.

[11]  J. DeVries,et al.  A Decrease in Glucose Variability Does Not Reduce Cardiovascular Event Rates in Type 2 Diabetic Patients After Acute Myocardial Infarction , 2011, Diabetes Care.

[12]  Christian Weber,et al.  The assessment of glycemic variability and its impact on diabetes-related complications: an overview. , 2009, Diabetes technology & therapeutics.

[13]  J. Hans DeVries,et al.  Glucose variability is associated with intensive care unit mortality* , 2010, Critical care medicine.

[14]  K. Utsunomiya,et al.  Liraglutide narrows the range of circadian glycemic variations in Japanese type 2 diabetes patients and nearly flattens these variations in drug-naive type 2 diabetes patients: a continuous glucose monitoring-based study. , 2011, Diabetes technology & therapeutics.

[15]  M. Rewers,et al.  Glycaemic variability is associated with coronary artery calcium in men with Type 1 diabetes: the Coronary Artery Calcification in Type 1 Diabetes study , 2010, Diabetic medicine : a journal of the British Diabetic Association.

[16]  L. Quinn,et al.  Does glycemic variability impact mood and quality of life? , 2012, Diabetes technology & therapeutics.

[17]  Frits Holleman,et al.  Glucose variability; does it matter? , 2010, Endocrine reviews.

[18]  R. Rizza,et al.  Measurements of Glucose Control , 1987, Diabetes Care.

[19]  M. Hanefeld,et al.  The "glucose pentagon": assessing glycemic control of patients with diabetes mellitus by a model integrating different parameters from glucose profiles. , 2009, Diabetes technology & therapeutics.

[20]  David Rodbard,et al.  Glycemic variability: measurement and utility in clinical medicine and research--one viewpoint. , 2011, Diabetes technology & therapeutics.

[21]  David Rodbard,et al.  New and improved methods to characterize glycemic variability using continuous glucose monitoring. , 2009, Diabetes technology & therapeutics.

[22]  D R Matthews,et al.  Glycaemic risk assessment in children and young people with Type 1 diabetes mellitus , 2009, Diabetic medicine : a journal of the British Diabetic Association.

[23]  D. Rodbard,et al.  The Minimum Frequency of Glucose Measurements from Which Glycemic Variation Can Be Consistently Assessed , 2010, Journal of diabetes science and technology.

[24]  A. Ceriello,et al.  Glucose oscillations, more than constant high glucose, induce p53 activation and a metabolic memory in human endothelial cells , 2011, Diabetologia.

[25]  W. F. Taylor,et al.  Mean Amplitude of Glycemic Excursions, a Measure of Diabetic Instability , 1970, Diabetes.

[26]  B Wayne Bequette,et al.  Continuous Glucose Monitoring: Real-Time Algorithms for Calibration, Filtering, and Alarms , 2010, Journal of diabetes science and technology.

[27]  Boris Kovatchev,et al.  Statistical tools to analyze continuous glucose monitor data. , 2009, Diabetes technology & therapeutics.