65-LB: Real-World Continuous Glucose Monitoring Data on Time-in-Range from a U.S. Population, 2015–2019

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 65-LB
Author(s):  
RICHARD M. BERGENSTAL ◽  
ELISE HACHMANN-NIELSEN ◽  
JENS TARP ◽  
KAJSA KVIST ◽  
JOHN B. BUSE
Keyword(s):  
Continuous Glucose Monitoring ◽  
Real World ◽  
Glucose Monitoring ◽  
Monitoring Data ◽  
Time In Range

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

2021 ◽  
Vol 9 (1) ◽  
pp. e001045
Author(s):  
Marina Valenzano ◽  
Ivan Cibrario Bertolotti ◽  
Adriano Valenzano ◽  
Giorgio Grassi
Keyword(s):  
Type 1 Diabetes ◽  
Continuous Glucose Monitoring ◽  
Real World ◽  
Hemoglobin A1c ◽  
Linear Regression Analysis ◽  
Real Life ◽  
Glucose Monitoring ◽  
Significant Difference ◽  
Time In Range

IntroductionThe 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 methods70 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.ResultsA 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.ConclusionsRegression 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.

scholarly journals Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range

Diabetes Care ◽  
2019 ◽  
Vol 42 (8) ◽  
pp. 1593-1603 ◽  
Author(s):  
Tadej Battelino ◽  
Thomas Danne ◽  
Richard M. Bergenstal ◽  
Stephanie A. Amiel ◽  
Roy Beck ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Data Interpretation ◽  
Monitoring Data ◽  
International Consensus ◽  
Time In Range

Increased Time-in-Range with Sotagliflozin as Adjunct Therapy to Insulin in Adults with Type 1 Diabetes as Demonstrated by 24-Week Continuous Glucose Monitoring (inTandem1, inTandem2)

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1179-P ◽  
Author(s):  
THOMAS DANNE ◽  
BERTRAND CARIOU ◽  
JOHN B. BUSE ◽  
SATISH K. GARG ◽  
JULIO ROSENSTOCK ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Adjunct Therapy ◽  
Time In Range

948-P: Analysis of Key Metrics for Continuous Glucose Monitoring Data in Adults without Diabetes

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 948-P
Author(s):  
GUIDO FRECKMANN ◽  
STEFAN PLEUS ◽  
SINA ULBRICH ◽  
DELIA WALDENMAIER ◽  
ANNETTE BAUMSTARK ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Monitoring Data

860-P: Benefits of Real-Time Sharing and Following of Continuous Glucose Monitoring Data from Youth

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 860-P
Author(s):  
ANDREW PARKER ◽  
MARK DERDZINSKI ◽  
SARAH PUHR ◽  
JOHN WELSH ◽  
TOMAS C. WALKER ◽  
...  
Keyword(s):  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Monitoring Data ◽  
Time Sharing

280-OR: Machine Learning to Predict Hypoglycemic Events from Continuous Glucose Monitoring Data

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 280-OR
Author(s):  
PRATIK AGRAWAL ◽  
LINGTAO CAO ◽  
YUAN-CHI CHANG ◽  
GRETCHEN P. JACKSON ◽  
SARAH KEFAYATI ◽  
...  
Keyword(s):  
Machine Learning ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Monitoring Data
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