Cargando…
Abstract 129: Continuous glucose monitoring in type 2 diabetes mellitus to assess glycemic control and glycemic variability
Background: Continuous glucose monitoring (CGM) can provide information beyond HbA1c and SMBG for glycemic control. Objectives: To assess glycemic variability (GV) and correlation of CGM metrics with HbA1c in type 2 diabetic (T2DM) individuals. Results: We enrolled 54 T2DM patients (age 53±7.8 years...
Formato: | Online Artículo Texto |
---|---|
Lenguaje: | English |
Publicado: |
Wolters Kluwer - Medknow
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9067792/ http://dx.doi.org/10.4103/2230-8210.342254 |
_version_ | 1784700085444018176 |
---|---|
collection | PubMed |
description | Background: Continuous glucose monitoring (CGM) can provide information beyond HbA1c and SMBG for glycemic control. Objectives: To assess glycemic variability (GV) and correlation of CGM metrics with HbA1c in type 2 diabetic (T2DM) individuals. Results: We enrolled 54 T2DM patients (age 53±7.8 years) on prior 3-month stable anti-diabetic medications for atleast 48-hours CGM (IPRO®2 Professional) with satisfactory agreement with glucometer cross-calibration. With 892.4±192.1 CGM readings, there was good correlation between CGM parameters and HbA1c (mean±SD- 9.45±2.57%) using Spearman's rho (π) analysis. There was positive correlation with HbA1c of glucose management indicator (GMI) (π=0.777, p<0.001), time-above-range (TAR) (π=0.739, p<0.001), area under curve (AUC) above limit (π=0.707, p<0.001), and negative correlation of Time-in-range (TIR) (π=-0.716, p<0.001). Looking into GV, there was no significant correlation of coefficient-of-variation (CV%) (π=-0.265, p=0.055) and weak positive correlation of standard deviation (SD) (π=0.274, p= 0.043) with HbA1c. More importantly, Time-below-range (TBR) ≥ 4% was seen in 8 (14.8%) patients, thus detecting unidentified asymptomatic hypoglycemias in 6 (11.1%) patients. Conclusions: Most CGM metrics correlated well with HbA1c, with additional advantage of identifying glycemic variability and asymptomatic hypoglycemias, These can be missed by infrequent home SMBG and HbA1c, and using CGM metrics can help tailor therapy to achieve a more optimal glycemic control, even in patients with relatively well-controlled HbA1c. |
format | Online Article Text |
id | pubmed-9067792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Wolters Kluwer - Medknow |
record_format | MEDLINE/PubMed |
spelling | pubmed-90677922022-05-05 Abstract 129: Continuous glucose monitoring in type 2 diabetes mellitus to assess glycemic control and glycemic variability Indian J Endocrinol Metab Abstracts … Esicon 2021 Background: Continuous glucose monitoring (CGM) can provide information beyond HbA1c and SMBG for glycemic control. Objectives: To assess glycemic variability (GV) and correlation of CGM metrics with HbA1c in type 2 diabetic (T2DM) individuals. Results: We enrolled 54 T2DM patients (age 53±7.8 years) on prior 3-month stable anti-diabetic medications for atleast 48-hours CGM (IPRO®2 Professional) with satisfactory agreement with glucometer cross-calibration. With 892.4±192.1 CGM readings, there was good correlation between CGM parameters and HbA1c (mean±SD- 9.45±2.57%) using Spearman's rho (π) analysis. There was positive correlation with HbA1c of glucose management indicator (GMI) (π=0.777, p<0.001), time-above-range (TAR) (π=0.739, p<0.001), area under curve (AUC) above limit (π=0.707, p<0.001), and negative correlation of Time-in-range (TIR) (π=-0.716, p<0.001). Looking into GV, there was no significant correlation of coefficient-of-variation (CV%) (π=-0.265, p=0.055) and weak positive correlation of standard deviation (SD) (π=0.274, p= 0.043) with HbA1c. More importantly, Time-below-range (TBR) ≥ 4% was seen in 8 (14.8%) patients, thus detecting unidentified asymptomatic hypoglycemias in 6 (11.1%) patients. Conclusions: Most CGM metrics correlated well with HbA1c, with additional advantage of identifying glycemic variability and asymptomatic hypoglycemias, These can be missed by infrequent home SMBG and HbA1c, and using CGM metrics can help tailor therapy to achieve a more optimal glycemic control, even in patients with relatively well-controlled HbA1c. Wolters Kluwer - Medknow 2022-03 /pmc/articles/PMC9067792/ http://dx.doi.org/10.4103/2230-8210.342254 Text en Copyright: © 2022 Indian Journal of Endocrinology and Metabolism https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. |
spellingShingle | Abstracts … Esicon 2021 Abstract 129: Continuous glucose monitoring in type 2 diabetes mellitus to assess glycemic control and glycemic variability |
title | Abstract 129: Continuous glucose monitoring in type 2 diabetes mellitus to assess glycemic control and glycemic variability |
title_full | Abstract 129: Continuous glucose monitoring in type 2 diabetes mellitus to assess glycemic control and glycemic variability |
title_fullStr | Abstract 129: Continuous glucose monitoring in type 2 diabetes mellitus to assess glycemic control and glycemic variability |
title_full_unstemmed | Abstract 129: Continuous glucose monitoring in type 2 diabetes mellitus to assess glycemic control and glycemic variability |
title_short | Abstract 129: Continuous glucose monitoring in type 2 diabetes mellitus to assess glycemic control and glycemic variability |
title_sort | abstract 129: continuous glucose monitoring in type 2 diabetes mellitus to assess glycemic control and glycemic variability |
topic | Abstracts … Esicon 2021 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9067792/ http://dx.doi.org/10.4103/2230-8210.342254 |