Identifying Actionable Glucose Patterns in Type 1 and Type 2 Diabetes Using Continuous Glucose Monitoring and Algorithmic Pattern Recognition

Abstract

Background:

Continuous glucose monitoring (CGM) provides real-time glucose data, aiding diabetes management. Identifying glucose patterns is difficult for patients due to data overload, hindering self-management. This study aimed to systematically identify glucose patterns using Accu-Chek SmartGuide and quantify their impact on glucose management.

Methods:

This retrospective, observational analysis included real-world CGM data from 3379 individuals with type 1 diabetes (T1D; N = 2198) or type 2 diabetes (T2D; N = 1181), encompassing 23 486 valid user-weeks. An algorithm identified 29 predefined glucose patterns weekly. Pattern prevalence, demographic influence, persistence, their attribution to time above range/time below range (TAR/TBR) as well as their potential impact on time in range (TIR) in case of pattern resolution were analyzed.

Results:

Resolving glucose patterns, defined as repeatedly occurring glucose events, showed varying potential for glycemic improvement. Cumulatively, actionable patterns contributed significantly to total TAR (T1D: 66.2 ± 14.7%, T2D: 58.0 ± 14.3%) and TBR (T1D: 56.3 ± 2.6%, T2D: 42.2 ± 1.4%). For instance, resolving the day-time hyperglycemia pattern could improve TIR by up to +10.72% (4.26, 16.9) in T1D and +5.16% (0.0, 12.92) in T2D, addressing an average of 9.33 (8.0, 10.75) events per week in T1D and 9.29 (8.0, 10.67) in T2D.

Conclusion:

The majority of glucose excursions in T1D and T2D can be explained by recurring glucose patterns. Detecting these actionable patterns provides an opportunity to improve TIR. Targeting therapy and behavior change toward resolving these patterns is a critical step toward more personalized diabetes management.