GLU: a software package for analysing continuously measured glucose levels in epidemiology

Abstract

Continuous glucose monitors (CGM) record interstitial glucose levels ‘continuously’, producing a sequence of measurements for each participant (e.g. the average glucose level every 5 min over several days, both day and night). To analyse 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. 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. GLU is implemented in R and is available on GitHub at https://github.com/MRCIEU/GLU. Git tag v0.2 corresponds to the version presented here.

Preliminary prospective study of real-time post-gastrectomy glycemic fluctuations during dumping symptoms using continuous glucose monitoring

“…Based on the glucose concentration values measured every 15 min by CGM and the details of the dumping symptoms described in the diary, dumping and control profiles were compared. All missing values in the data obtained employing CGM were replaced by linear interpolation as single imputation method[ 18 ]. All continuous variables were expressed as median values.…”

Postprandial Asymptomatic Glycemic Fluctuations after Gastrectomy for Gastric Cancer Using Continuous Glucose Monitoring Device

“…Furthermore, the asymptomatic glucose profiles were compared among the four operative procedures. All the missing values obtained by employing the CGM were adjusted by linear interpolation according to the single imputation method [ 16 ]. All continuous variables are presented as median values.…”

A New Analysis Tool for Continuous Glucose Monitor Data

“…Many metrics have been proposed in the diabetes literature [12][13][14][15][16][17][18][19][20][21][22][23][24] to summarize an individual's glucose profile, with specific emphasis on quantifying glycemic variability over one to two weeks. Glycemic variability, loosely defined as fluctuations in glucose levels within a day or across several days, 4 is related to many adverse events including poorer quality of life 5 and increased risk of cardiovascular events, 6 hypoglycemia, and mortality.…”

“…While previous work has shown that many of these metrics are highly correlated and thus provide overlapping information, 10 we present a large list of metrics so that researchers may choose which to focus on. While other software such as EasyGV, 11 GLU, 12 cgmanalyzer, 13 and cgmanalysis 14 can also calculate these metrics, they do not provide these metrics in an easy-to-use web-based tool that requires no prior coding knowledge. To our knowledge only one other web-based CGM analysis tool, iglu, 40 exists, but with different functionality than rGV.…”