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Background:  Glycemic variability (GV) is an important component of glycemic control for patients with type 1 diabetes (T1D). The inadequacy of existing measurements lies in the fact that they view the variability from different aspects, so that no consensus has been reached among physicians as to which metrics to use in practice. Moreover, although GV, from 1 day to another, can show very different patterns, few metrics have been dedicated to daily evaluations.  Materials and Methods:  A reference (stable glycemia) statistical model is built based on a combination of daily computed canonical glycemic control metrics including variability. The metrics are computed for subjects from the TRIMECO islet transplantation trial, selected when their β -score (composite score for grading success) is ≥6 after a transplantation. Then, for any new daily glycemia recording, its likelihood with respect to this reference model provides a multimetric score of daily GV severity. In addition, determining the likelihood value that best separates the daily glycemia with β -score = 0 from that with β -score ≥6, we propose an objective decision rule to classify daily glycemia into "stable" or "unstable."  Results:  The proposed characterization framework integrates multiple standard metrics and provides a comprehensive daily GV index, based on which, long-term variability evaluations and investigations on the implicit link between variability and β -score can be carried out. Evaluation, in a daily GV classification task, shows that the proposed method is highly concordant to the experience of diabetologists.  Conclusion:  A multivariate statistical model is proposed to characterize the daily GV of subjects with T1D. The model has the advantage to provide a single variability score that gathers the information power of a number of canonical scores, too partial to be used individually. A reliable decision rule to classify daily variability measurements into stable or unstable is also provided.

Characterization of Daily Glycemic Variability in Subjects with Type 1 Diabetes Using a Mixture of Metrics