Cluster Analysis of Glucose Fluctuations in the Pre-Morning and Early Morning Hours in Patients with Type 1 Diabetes

2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB)(2023)

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摘要
Background and Aim: In patients with type 1 diabetes, glucose dynamic in the pre-morning and early morning hours may vary requiring a differentiated approach to glycemic management. In this study, we clustered data of continuous glucose monitoring from subjects with type 1 diabetes managed with basal-bolus insulin therapy in order to identify glucose fluctuation patterns. Materials and Methods: We used continuous glucose monitoring data obtained from 570 patients. Time intervals from 4:00 am to 8:00 am were analyzed. Clustering of time series was performed using a hierarchical clustering algorithm. Statistical significance of obtained cluster structure was evaluated using Monte Carlo method and the Silhouette Score. Results: We have identified 11 clusters, including 3 ones with at least one episode of hypoglycemia. Differences between the clusters included initial and final glucose levels, as well as the presence of uptrend or downtrend. Among clusters without hypoglycemia, two clusters showed normal or near normal glucose levels, two clusters were characterized by a slightly elevated glucose, other demonstrated moderate or severe hyperglycemia. In all clusters with hypoglycemia episode(s) the mean glucose level at the start of the time interval was close to the hypoglycemia threshold; patterns with an uptrend with a stable glucose level and a downtrend occurred in approximately equal proportions. Conclusion: Patients with type 1 diabetes demonstrate a variety of glucose patterns in the pre-morning and early morning hours. The data highlight the importance of differentiated approaches to therapy.
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关键词
continuous glucose monitoring,hypoglycemia,cluster analysis,hierarchical clustering,Silhouette Score,type 1 diabetes,insulin
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