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

Peyser, T. A.; Balo, Andrew; Buckingham, Bruce A.; Hirsch, Irl B.; García, Arturo Muñiz

doi:10.1089/dia.2017.0187

Cited by 74 publications

(64 citation statements)

References 43 publications

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“…The GVP function was based on the interquartile analysis to be published separately. 19 The mean glucose function is asymmetric and reaches a minimum in the tight euglycemic range and increases rapidly in value both below 80 mg/dL and above 140 mg/dL. The percent time in range function is a decreasing rectangular hyperbolic equation that asymptotes at a maximum value at 20% time in range and at a minimum value at 90% time in range.…”

Section: Methodsmentioning

confidence: 98%

A Simple Composite Metric for the Assessment of Glycemic Status from Continuous Glucose Monitoring Data: Implications for Clinical Practice and the Artificial Pancreas

Hirsch

Balo

Sayer

et al. 2017

Diabetes Technology & Therapeutics

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Background: The potential clinical benefits of continuous glucose monitoring (CGM) have been recognized for many years, but CGM is used by a small fraction of patients with diabetes. One obstacle to greater use of the technology is the lack of simplified tools for assessing glycemic control from CGM data without complicated visual displays of data. Methods: We developed a simple new metric, the personal glycemic state (PGS), to assess glycemic control solely from continuous glucose monitoring data. PGS is a composite index that assesses four domains of glycemic control: mean glucose, glycemic variability, time in range and frequency and severity of hypoglycemia. The metric was applied to data from six clinical studies for the G4 Platinum continuous glucose monitoring system (Dexcom, San Diego, CA). The PGS was also applied to data from a study of artificial pancreas comparing results from open loop and closed loop in adolescents and in adults. Results: The new metric for glycemic control, PGS, was able to characterize the quality of glycemic control in a wide range of study subjects with various mean glucose, minimal, moderate, and excessive glycemic variability and subjects on open loop versus closed loop control. Conclusion: A new composite metric for the assessment of glycemic control based on CGM data has been defined for use in assessing glycemic control in clinical practice and research settings. The new metric may help rapidly identify problems in glycemic control and may assist with optimizing diabetes therapy during timeconstrained physician office visits.

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Section: Methodsmentioning

confidence: 98%

A Simple Composite Metric for the Assessment of Glycemic Status from Continuous Glucose Monitoring Data: Implications for Clinical Practice and the Artificial Pancreas

Hirsch

Balo

Sayer

et al. 2017

Diabetes Technology & Therapeutics

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“…Dozens of metrics for variability have been proposed. [1][2][3][4][5][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] Table 1 displays some of the more important and frequently used metrics.…”

Section: Multiple Metrics For Glycemic Variabilitymentioning

confidence: 99%

“…Glycemic Variability Percentage. Peyser et al 19 proposed a metric that is closely related to MAG and DT, designated as glycemic variability percentage (GVP). This metric calculates the total length (L) of the line segments connecting successive glucose values on a graph, over and above the minimal length of a flat line segment of similar duration (L o ), expressed as a ratio to L o :…”

Section: Time Series Analysis Of Glucose Monitoring Datamentioning

confidence: 99%

Glucose Variability: A Review of Clinical Applications and Research Developments

Rodbard¹

2018

Diabetes Technology & Therapeutics

126

113

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Glycemic variability (GV) is a major consideration when evaluating quality of glycemic control. GV increases progressively from prediabetes through advanced T2D and is still higher in T1D. GV is correlated with risk of hypoglycemia. The most popular metrics for GV are the %Coefficient of Variation (%CV) and standard deviation (SD). The %CV is correlated with risk of hypoglycemia. Graphical display of glucose by date, time of day, and day of the week, and display of simplified glucose distributions showing % of time in several ranges, provide clinically useful indicators of GV. SD is highly correlated with most other measures of GV, including interquartile range, mean amplitude of glycemic excursion, mean of daily differences, and average daily risk range. Some metrics are sensitive to the frequency, periodicity, and complexity of glycemic fluctuations, including Fourier analysis, periodograms, frequency spectrum, multiscale entropy (MSE), and Glucose Variability Percentage (GVP). Fourier analysis indicates progressive changes from normal subjects to children and adults with T1D, and from prediabetes to T2D. The GVP identifies novel characteristics for children, adolescents, and adults with type 1 diabetes and for adults with type 2. GVP also demonstrated small rapid glycemic fluctuations in people with T1D when using a dual-hormone closed-loop control. MSE demonstrated systematic changes from normal subjects to people with T2D at various stages of duration, intensity of therapy, and quality of glycemic control. We describe new metrics to characterize postprandial excursions, day-to-day stability of glucose patterns, and systematic changes of patterns by day of the week. Metrics for GV should be interpreted in terms of percentiles and z-scores relative to identified reference populations. There is a need for large accessible databases for reference populations to provide a basis for automated interpretation of GV and other features of continuous glucose monitoring records.

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“…as if the peaks and troughs were stretched out into a line). This idea was recently suggested for CGM data (37) and previously proposed as a measure of complexity for time-series analyses in general (38). Intuitively, if you stretch out a glucose trace then the resultant straight line will tend to be longer when a trace has a larger overall variability (represented by MAD) and is more complex (a higher number of peaks, valleys and values (38)), see Supplementary figure 3 and (38) has a GVP of zero.…”

Section: Variability From One Moment To the Nextmentioning

confidence: 98%

Software application profile: GLU: A tool for analysing continuously measured glucose in epidemiology

Millard

Patel

Tilling

et al. 2018

Preprint

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MotivationContinuous glucose monitors (CGM) record interstitial glucose 'continuously', producing a sequence of measurements for each participant (e.g. the average glucose every 5 minutes over several days, both day and night). To analyze these data, researchers tend to derive summary variables such as the Area Under the Curve (AUC), to then use in subsequent analyses. To date, a lack of consistency and transparency of precise definitions used for these summary variables has hindered interpretation, replication and comparison of results across studies. We present GLU, an open-source software package for deriving a consistent set of summary variables from CGM data. General features GLU performs quality control of each CGM sample (e.g. addressing missing data), derives a diverse set of summary variables (e.g. AUC, and proportion of time spent in hypo-, normo-and hyper-glycaemic levels) covering six broad domains, and outputs these (with quality control information) to the user. Implementation GLU is implemented in R. AVAILABILITY GLU is available on GitHub at [https://github.com/MRCIEU/GLU]. Git tag v0.1 corresponds to the version presented here.

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

Cited by 74 publications

References 43 publications

A Simple Composite Metric for the Assessment of Glycemic Status from Continuous Glucose Monitoring Data: Implications for Clinical Practice and the Artificial Pancreas

A Simple Composite Metric for the Assessment of Glycemic Status from Continuous Glucose Monitoring Data: Implications for Clinical Practice and the Artificial Pancreas

Glucose Variability: A Review of Clinical Applications and Research Developments

Software application profile: GLU: A tool for analysing continuously measured glucose in epidemiology

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