Time in range-A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study

型糖尿病持续血糖监测中血糖达标时间与糖化血红蛋白 A1c 的关系:一项真实世界研究

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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 (r(FGM)=0.703, r(rtCGM)=0.739), slope (β(1,FGM)=-11.77, β(1,rtCGM)=-10.74) and intercept (β(0,FGM)=141.19, β(0,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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