  Continuous glucose monitoring (CGM) systems provide real-time, dynamic
glucose information by tracking interstitial glucose values throughout the day
(typically values are recorded every 5 minutes). CGMs are commonly used in
diabetes management by clinicians and patients and in research to understand
how factors of longitudinal glucose and glucose variability relate to disease
onset and severity and the efficacy of interventions. CGM data presents unique
bioinformatic challenges because the data is longitudinal, temporal, and there
are nearly infinite possible ways to summarize and use this data. There are
over 20 metrics of glucose variability, no standardization of metrics, and
little validation across studies. Here we present open source python and R
packages called cgmquantify, which contains over 20 functions with over 25
clinically validated metrics of glucose and glucose variability and functions
for visualizing longitudinal CGM data. This is expected to be useful for
researchers and may provide additional insights to patients and clinicians
about glucose patterns.
