A New Analysis Tool for Continuous Glucose Monitor Data

Abstract

Background: With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools. Methods: In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual’s CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies. Results: In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability. Conclusions: We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.

Continuous glucose monitoring in patients with post‐bariatric hypoglycaemia reduces hypoglycaemia and glycaemic variability

“…Glucose metrics were calculated using the R package iglu, 38 while rGV was used for calculation of hypoglycaemia episodes. 39 All analyses were performed using R. indicate impaired awareness of hypoglycaemia. 35,36 The HFS-II survey includes a behaviour subsection (HFS-B), which addresses behaviours performed to avoid hypoglycaemia and yields scores from 0 to 60, and a worry subsection (HFS-W), which addresses concerns about hypoglycaemia and yields scores from 0 to 72.…”

“…Daytime was defined as 6:00 am to 11:59 pm and nighttime was defined as 12:00 am to 5:59 am . Glucose metrics were calculated using the R package iglu, 38 while rGV was used for calculation of hypoglycaemia episodes 39 . All analyses were performed using R.…”

Innovative Solution or Cause for Concern? The Use of Continuous Glucose Monitors in People Not Living with Diabetes: A Narrative Review.

“…32 As a result, alternative measures have been proposed to document glucose excursions, emphasising glycaemic variability. GV represents the intensity and frequency of glycaemic changes 34 and is pivotal in anticipating both micro-and macrovascular complications, aligning with elevated HbA1c, FPG, postprandial glycaemia, and insulin resistance. [35][36] Through GV measurement, CGMs were shown to delineate glucose dysregulation phases, identifying phenotypes like impaired glucose tolerance (IGT), impaired fasting glucose (IFG), T1DM, and T2DM.…”

“…[37][38] Each GV index distinctively records varying dimensions of glycaemic fluxes, including amplitude, frequency, duration, or pattern. 34 For instance, conventional glycaemic metrics, such as mean glucose (MG), standard deviation (SD), and coefficient of variation (CoV) fail to fully capture GV, often leaning towards hyperglycaemic overemphasis. 34 For PNLD, emphasising general glycaemic stability is typical; therefore, metrics evaluating glucose amplitude and frequency are relevant.…”

Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data

“…rGV rGV34 is a free open-source software package developed in R environment. It provides a GUI available at https://shiny.biostat.umn.edu/GV/.…”