Time in range–A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study. Issue 1 (29th January 2021) - Valenzano, Marina; Cibrario Bertolotti, Ivan; Valenzano, Adriano; Grassi, Giorgio
Abstract : 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 TIR 70–180 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.