Compositional Data Analysis of Glucose Profiles of Type 1 Diabetes Patients

Time spent in different glucose ranges indicate the occurrence of adverse events and measure the quality of glucose control in type one diabetes (T1D) patients. This work proposes a Compositional Data (CoDa) approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles limited to 6-h duration were analyzed at four different times of the day. These glucose profiles were distributed into time spent in five glucose ranges, which determine the composition. The log-ratio coordinates of the compositions were categorized through a clustering algorithm, which later made possible the obtainment of a linear model that should be used to predict the category of a 6-h period in different times of day. Leave-one-out cross-validation was performed, achieving an average above 90% of correct classification. A probabilistic model of transition between the category of the past 6-h of glucose to the category of the future 6-h period was obtained. Results show that the CoDa approach not only works as new analysis tool and is suitable for the categorization of glucose profiles, but also is a complementary tool for the prediction of different categories of glucose control. This prediction could assist patients to take correction measures in advance to adverse situations. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.