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.
Highlights
Epidemiological and clinical studies interested in circulating glucose as a risk factor or outcome typically measure levels in the blood (fasting, non-fasting and/or post-oral glucose) at a single or widely spaced time-points (e.g. every few years).[1,2,3,4] these are important health indicators, there has been an increasing appreciation that glucose levels and variability in free-living conditions during both the day and night, may provide important health measures in clinical (e.g. diabetic or obese) and ‘healthy’ populations.[5,6,7,8,9,10,11] Continuous glucose monitoring (CGM) systems measure interstitial glucose levels by VC The Author(s) 2020
We hypothesized that this is because a glucose trace with a larger overall variability will on average have a lower frequency resulting in a shorter ‘length of the line’ of the glucose trace after standardization
SGVP was less correlated with other GLU summary variables, compared with both median absolute deviation (MAD) and Glycaemic variability percentage (GVP)
Summary
Epidemiological and clinical studies interested in circulating glucose as a risk factor or outcome typically measure levels in the blood (fasting, non-fasting and/or post-oral glucose) at a single or widely spaced time-points (e.g. every few years).[1,2,3,4] these are important health indicators, there has been an increasing appreciation that glucose levels and variability in free-living conditions during both the day and night, may provide important health measures in clinical (e.g. diabetic or obese) and ‘healthy’ populations.[5,6,7,8,9,10,11] Continuous glucose monitoring (CGM) systems measure interstitial glucose levels by VC The Author(s) 2020. Overall glucose levels are characterized by the AUC, and GLU derives the mean AUC per minute so that these levels are comparable across time periods of different lengths (e.g. night-time vs day-time).[8] For each day, the AUC is calculated using the trapezoid method,[5] as the sum of the area of the trapezoids created using linear interpolation between sensor glucose values at adjacent time-points (as described above).
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