Table 1.
Summary of CGM metrics implemented in iglu.
Table 2.
Comparison of iglu functionality with existing R packages for CGM data.
Table 3.
Comparison of selected metrics across R packages using example dataset.
Fig 1.
Time series plots for five subjects.
Selected target range is [70, 180] mg/dL.
Table 4.
Summary of iglu visualization capabilities.
Fig 2.
(A) unsorted and (B) time-sorted lasagna plot for Subject 1; (C) unsorted customized multi-subject lasagna plot based on average values across days.
Fig 3.
Multi-subject lasagna plots in ‘red-orange’ color scheme.
(A) sorted within each subject and (B) sorted within each time point across subjects.
Fig 4.
Rate of change visualizations.
(A) time-series and (B) histogram plots of rate of change for two selected subjects from example dataset.
Fig 5.
Ambulatory Glucose Profile (AGP) for Subject 1 generated by iglu.
Fig 6.
Heatmap of all metrics calculated using iglu for 5 subjects with Type II diabetes.
Hierarchical clustering is performed on centered and scaled metric values using distance correlation and complete linkage. The cluster tree for metrics is cut at 6 groups, which can be interpreted as follows (from top to bottom): (1) in range metrics; (2) hypoglycemia metrics; (3) hyperglycemia metrics; (4) a mixture of variability and hyperglycemia metrics; (5) CVsd (standard deviation of CV, coefficient of variation, across days); (6) glucose variability metrics. The heatmap supports that Subject 2 has the worst hyperglycemia and Subject 5 has the highest glucose variability.
Fig 7.
(A) loading CGM data in .csv format; (B) calculating user-specified quantiles for each subject; (C) creating customized lasagna plot for the selected subject.