Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data

Abstract Background: High levels of glycemic variability are still observed in most patients with diabetes with severe insulin deficiency. Glycemic variability may be an important risk factor for acute and chronic complications. Despite its clinical importance, there is no consensus on the optimum method for characterizing glycemic variability. Method: We developed a simple new metric, the glycemic variability percentage (GVP), to assess glycemic variability by analyzing the length of the continuous glucose monitoring (CGM) temporal trace normalized to the duration under evaluation. The GVP is similar to other recently proposed glycemic variability metrics, the distance traveled, and the mean absolute glucose (MAG) change. We compared results from distance traveled, MAG, GVP, standard deviation (SD), and coefficient of variation (CV) applied to simulated CGM traces accentuating the difference between amplitude and frequency of oscillations. The GVP metric was also applied to data from clinical studies for the Dexcom G4 Platinum CGM in subjects without diabetes, with type 2 diabetes, and with type 1 diabetes (adults, adolescents, and children). Results: In contrast to other metrics, such as CV and SD, the distance traveled, MAG, and GVP all captured both the amplitude and frequency of glucose oscillations. The GVP metric was also able to differentiate between diabetic and nondiabetic subjects and between subjects with diabetes with low, moderate, and high glycemic variability based on interquartile analysis. Conclusion: A new metric for the assessment of glycemic variability has been shown to capture glycemic variability due to fluctuations in both the amplitude and frequency of glucose given by CGM data.

[1]  J. Hans DeVries,et al.  Glucose Variability: Where It Is Important and How to Measure It , 2013, Diabetes.

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

[3]  C. Cobelli,et al.  Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes , 2016, Diabetes Care.

[4]  J. Škrha,et al.  Glycemic variability is higher in type 1 diabetes patients with microvascular complications irrespective of glycemic control. , 2014, Diabetes technology & therapeutics.

[5]  G. Paolisso,et al.  Relationships Between Daily Acute Glucose Fluctuations and Cognitive Performance Among Aged Type 2 Diabetic Patients , 2010, Diabetes Care.

[6]  J. H. Kim,et al.  Glycemic Variability: How Do We Measure It and Why Is It Important? , 2015, Diabetes & metabolism journal.

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

[8]  David Rodbard,et al.  The Challenges of Measuring Glycemic Variability , 2012, Journal of diabetes science and technology.

[9]  H. Itoh,et al.  Relationships among different glycemic variability indices obtained by continuous glucose monitoring. , 2015, Primary care diabetes.

[10]  Eckhard Salzsieder,et al.  The use of a computer program to calculate the mean amplitude of glycemic excursions. , 2011, Diabetes technology & therapeutics.

[11]  Sang-Man Jin,et al.  Clinical factors associated with absolute and relative measures of glycemic variability determined by continuous glucose monitoring: an analysis of 480 subjects. , 2014, Diabetes research and clinical practice.

[12]  B. Buckingham,et al.  Application of Glycemic Variability Percentage: Implications for Continuous Glucose Monitor Utilization and Analysis of Artificial Pancreas Data. , 2017, Diabetes technology & therapeutics.

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

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

[15]  William L Clarke,et al.  Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application. , 2005, Diabetes technology & therapeutics.

[16]  O. Ilkayeva,et al.  Leptin therapy in insulin-deficient type I diabetes , 2010, Proceedings of the National Academy of Sciences.

[17]  B. Mandelbrot How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension , 1967, Science.

[18]  P. Choudhary,et al.  Evaluating rate of change as an index of glycemic variability, using continuous glucose monitoring data. , 2011, Diabetes technology & therapeutics.

[19]  L. Kessler,et al.  Glucose Variability , 2016, Journal of diabetes science and technology.

[20]  Attila J Szabó,et al.  Evaluation of an open access software for calculating glucose variability parameters of a continuous glucose monitoring system applied at pediatric intensive care unit , 2015, Biomedical engineering online.

[21]  Thomas Peyser,et al.  A new-generation continuous glucose monitoring system: improved accuracy and reliability compared with a previous-generation system. , 2013, Diabetes technology & therapeutics.

[22]  R. Vigersky Escaping the Hemoglobin A1c-Centric World in Evaluating Diabetes Mellitus Interventions , 2015, Journal of diabetes science and technology.

[23]  Bruce A Buckingham,et al.  A Simple Composite Metric for the Assessment of Glycemic Status from Continuous Glucose Monitoring Data: Implications for Clinical Practice and the Artificial Pancreas. , 2017, Diabetes technology & therapeutics.

[24]  H. Jung Clinical Implications of Glucose Variability: Chronic Complications of Diabetes , 2015, Endocrinology and metabolism.

[25]  Eckhard Salzsieder,et al.  Evaluation of the mean absolute glucose change as a measure of glycemic variability using continuous glucose monitoring data. , 2013, Diabetes technology & therapeutics.

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

[27]  Giovanni Sparacino,et al.  Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data , 2018, Journal of diabetes science and technology.

[28]  B HirschIrl,et al.  A Simple Composite Metric for the Assessment of Glycemic Status from Continuous Glucose Monitoring Data: Implications for Clinical Practice and the Artificial Pancreas. , 2017 .

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

[30]  P. Baghurst,et al.  Measuring glycaemic variation. , 2010, Current diabetes reviews.

[31]  I. Hirsch,et al.  Should minimal blood glucose variability become the gold standard of glycemic control? , 2005, Journal of diabetes and its complications.

[32]  J Hans Devries,et al.  Poor agreement of computerized calculators for mean amplitude of glycemic excursions. , 2014, Diabetes technology & therapeutics.

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

[34]  D. Owens,et al.  Toward Defining the Threshold Between Low and High Glucose Variability in Diabetes , 2016, Diabetes Care.

[35]  Daniel J Cox,et al.  Algorithmic evaluation of metabolic control and risk of severe hypoglycemia in type 1 and type 2 diabetes using self-monitoring blood glucose data. , 2003, Diabetes technology & therapeutics.

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

[37]  M. Orsini Federici,et al.  Assessment of the association between glycemic variability and diabetes-related complications in type 1 and type 2 diabetes. , 2014, Diabetes research and clinical practice.

[38]  Yongming Qu,et al.  Rate of hypoglycemia in insulin-treated patients with type 2 diabetes can be predicted from glycemic variability data. , 2012, Diabetes technology & therapeutics.

[39]  R. Voelker "Artificial Pancreas" Is Approved. , 2016, Journal of the American Medical Association (JAMA).

[40]  Howard Zisser,et al.  Improvement in glycemic excursions with a transcutaneous, real-time continuous glucose sensor: a randomized controlled trial. , 2006, Diabetes care.

[41]  P. Lapuerta,et al.  Sotagliflozin, a Dual SGLT1 and SGLT2 Inhibitor, as Adjunct Therapy to Insulin in Type 1 Diabetes , 2015, Diabetes Care.

[42]  G. Paolisso,et al.  Glucose variability: An emerging target for the treatment of diabetes mellitus. , 2013, Diabetes research and clinical practice.

[43]  Pratik Choudhary,et al.  Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups. , 2011, Diabetes technology & therapeutics.

[44]  Eckhard Salzsieder,et al.  Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies , 2015, BMC Endocrine Disorders.