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Time in range–A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7849891/ https://www.ncbi.nlm.nih.gov/pubmed/33514530 http://dx.doi.org/10.1136/bmjdrc-2019-001045 |
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author | Valenzano, Marina Cibrario Bertolotti, Ivan Valenzano, Adriano Grassi, Giorgio |
author_facet | Valenzano, Marina Cibrario Bertolotti, Ivan Valenzano, Adriano Grassi, Giorgio |
author_sort | Valenzano, Marina |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7849891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-78498912021-02-02 Time in range–A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study Valenzano, Marina Cibrario Bertolotti, Ivan Valenzano, Adriano Grassi, Giorgio BMJ Open Diabetes Res Care Emerging Technologies, Pharmacology and Therapeutics 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. BMJ Publishing Group 2021-01-29 /pmc/articles/PMC7849891/ /pubmed/33514530 http://dx.doi.org/10.1136/bmjdrc-2019-001045 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Emerging Technologies, Pharmacology and Therapeutics Valenzano, Marina Cibrario Bertolotti, Ivan Valenzano, Adriano Grassi, Giorgio Time in range–A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study |
title | Time in range–A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study |
title_full | Time in range–A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study |
title_fullStr | Time in range–A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study |
title_full_unstemmed | Time in range–A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study |
title_short | Time in range–A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study |
title_sort | time in range–a1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study |
topic | Emerging Technologies, Pharmacology and Therapeutics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7849891/ https://www.ncbi.nlm.nih.gov/pubmed/33514530 http://dx.doi.org/10.1136/bmjdrc-2019-001045 |
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