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Time in range-A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study
Marina Valenzano1, Ivan Cibrario Bertolotti2, Adriano Valenzano2
1Division of Endocrinology and Metabolic Diseases, Department of Medical Sciences, University of Turin, Torino, Piemonte, Italy.
Introduction:
The availability of easily accessible continuous glucose monitoring (CGM) metrics can improve glycemic control in diabetes, and they may even become a viable alternative to hemoglobin A1c (HbA1c) laboratory tests in the next years. The REALISM-T1D study (REAl-Life glucoSe Monitoring in Type 1 Diabetes) was aimed at contributing, with real-world data, to a deeper understanding of these metrics, including the time in range (TIR)-HbA1c relationship, to facilitate their adoption by diabetologists in everyday practice.
Research Design And Methods:
70 adults affected by type 1 diabetes were monitored for 1 year by means of either flash (FGM) or real-time (rtCGM) glucose monitoring devices. Follow-up visits were performed after 90, 180 and 365 days from baseline and percentage TIR70-180 evaluated for the 90-day time period preceding each visit. HbA1c tests were also carried out in the same occasions and measured values paired with the corresponding TIR data.
Results:
A monovariate linear regression analysis confirms a strong correlation between TIR and HbA1c as found in previous studies, but leveraging more homogeneous data (n=146) collected in real-life conditions. Differences were determined between FGM and rtCGM devices in Pearson's correlation (rFGM=0.703, rrtCGM=0.739), slope (β1,FGM=-11.77, β1,rtCGM=-10.74) and intercept (β0,FGM=141.19, β0,rtCGM=140.77) coefficients. Normality of residuals and homoscedasticity were successfully verified in both cases.
Conclusions:
Regression lines for two patient groups monitored through FGM and rtCGM devices, respectively, while confirming a linear relationship between TIR and A1c hemoglobin (A1C) in good accordance with previous studies, also show a statistically significant difference in the regression intercept, thus suggesting the need for different models tailored to device characteristics. The predictive power of A1C as a TIR estimator also deserves further investigations.
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