Identifying Actionable Glucose Patterns in Type 1 and Type 2 Diabetes Using Continuous Glucose Monitoring and Algorithmic Pattern Recognition
Authors' 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.
Study context
- Population
- Individuals with T1D or T2D using continuous glucose monitoring (CGM) (Accu-Chek SmartGuide); 23,486 valid user-weeks23,486 user-weeks
- Participants
- 3,379 individuals
- Exposure
- T1D vs T2D
- T1Dn = 2,198
- T2Dn = 1,181
- Outcome
- Contribution of actionable patterns to total TAR and TBR
Methodological detailsreal-world CGM data, algorithmic pattern recognition, An algorithm identified 29 predefined glucose patterns weekly
- Real-world CGM data
- Algorithmic pattern recognition
- An algorithm identified 29 predefined glucose patterns weekly
Main finding
As reported in the abstract
Share of total TAR from actionable patterns, T1D
66.2 ± 14.7%
Share of total TAR from actionable patterns, T2D
58.0 ± 14.3%
Share of total TBR from actionable patterns, T1D
56.3 ± 2.6%
Share of total TBR from actionable patterns, T2D
42.2 ± 1.4%
Observational data: associations, not effects.
Further findings
Potential time in range (TIR) improvement with resolving day-time hyperglycemia pattern
- TIR improvement up to, T1D
- +10.72% (4.26, 16.9)
- TIR improvement up to, T2D
- +5.16% (0.0, 12.92)
- Events per week addressed, T1D
- 9.33 (8.0, 10.75)
- Events per week addressed, T2D
- 9.29 (8.0, 10.67)
Original publication
Composed by AI from the published abstract and checked against it. How Briefs are made →