Interpreting blood GLUcose data with R package iglu.

Steven Broll,David Buchanan,Elizabeth Chun,Irina Gaynanova,Naresh M Punjabi,Jacek Urbanek,John Muschelli,Laura Pyle

doi:10.1371/journal.pone.0248560

Steven Broll, David Buchanan + Show 6 more

Open Access

https://doi.org/10.1371/journal.pone.0248560

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Abstract

Continuous Glucose Monitoring (CGM) data play an increasing role in clinical practice as they provide detailed quantification of blood glucose levels during the entire 24-hour period. The R package iglu implements a wide range of CGM-derived metrics for measuring glucose control and glucose variability. The package also allows one to visualize CGM data using time-series and lasagna plots. A distinct advantage of iglu is that it comes with a point-and-click graphical user interface (GUI) which makes the package widely accessible to users regardless of their programming experience. Thus, the open-source and easy to use iglu package will help advance CGM research and CGM data analyses. R package iglu is publicly available on CRAN and at https://github.com/irinagain/iglu.

Highlights

Continuous Glucose Monitors (CGMs) are small wearable devices that record measurements of blood glucose levels at frequent time intervals
The visual inspection of the plots confirm the previous conclusions from comparison of Hyperglycemia index and metrics of glycemic variability across subjects: the majority of measurements for Subject 2 are above 180 mg/dL, the variability is larger for Subject 5
Several limitations exist when compared to existing CGM software

Summary

Introduction

Continuous Glucose Monitors (CGMs) are small wearable devices that record measurements of blood glucose levels at frequent time intervals. We are aware of two existing R packages for CGM data analyses: CGManalyzer [3] and cgmanalysis [4] These packages are primarily designed to read and organize CGM data, rather than provide an easy-to-use interface for a comprehensive evaluation of available CGM characteristics. While their analytical utility is undeniable, a substantial number of CGM metrics summarized in [2] is not available. EasyGV is another free CGM software in the form of a macro-enabled Excel workbook [5], and is more accessible compared to CGManalyzer and cgmanalysis It only allows calculation of 10 metrics. The package comes with two example datasets that follow this structure. example_data_5_subject contains Dexcom G4 CGM measurements from subjects with Type II diabetes

Subject 1 2015-06-06 17:05:27 137

Subject 2

Subject 5

Findings

Conclusion