scholarly journals The Changing Landscape of Glycemic Targets: Focus on Continuous Glucose Monitoring

2020 ◽  
Author(s):  
Pamela R. Kushner ◽  
Davida F. Kruger
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Treatment Decisions ◽  
Diabetes Treatment ◽  
Good Glycemic Control ◽  
Patients With Diabetes ◽  
Time In Range ◽  
Glycemic Targets

Continuous glucose monitoring (CGM) provides comprehensive assessment of daily glucose measurements for patients with diabetes and can reveal high and low blood glucose values that may occur even when a patient’s A1C is adequately controlled. Among the measures captured by CGM, the percentage of time in the target glycemic range, or “time in range,” (typically 70–180 mg/dL) has emerged as one of the strongest indicators of good glycemic control. This review examines the shift to using CGM to assess glycemic control and guide diabetes treatment decisions, with a focus on time in range as the key metric of glycemic control.

scholarly journals The Changing Landscape of Glycemic Targets: Focus on Continuous Glucose Monitoring

2020 ◽  
Author(s):  
Pamela R. Kushner ◽  
Davida F. Kruger
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Treatment Decisions ◽  
Diabetes Treatment ◽  
Good Glycemic Control ◽  
Patients With Diabetes ◽  
Time In Range ◽  
Glycemic Targets

Continuous glucose monitoring (CGM) provides comprehensive assessment of daily glucose measurements for patients with diabetes and can reveal high and low blood glucose values that may occur even when a patient’s A1C is adequately controlled. Among the measures captured by CGM, the percentage of time in the target glycemic range, or “time in range,” (typically 70–180 mg/dL) has emerged as one of the strongest indicators of good glycemic control. This review examines the shift to using CGM to assess glycemic control and guide diabetes treatment decisions, with a focus on time in range as the key metric of glycemic control.

scholarly journals Correlation between short- and mid-term hemoglobin A1c and glycemic control determined by continuous glucose monitoring

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jen-Hung Huang ◽  
Yung-Kuo Lin ◽  
Ting-Wei Lee ◽  
Han-Wen Liu ◽  
Yu-Mei Chien ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Hemoglobin A1c ◽  
Glucose Monitoring ◽  
Mean Amplitude ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Before And After ◽  
Patients With Diabetes

Abstract Background Glucose monitoring is vital for glycemic control in patients with diabetes mellitus (DM). Continuous glucose monitoring (CGM) measures whole-day glucose levels. Hemoglobin A1c (HbA1c) is a vital outcome predictor in patients with DM. Methods This study investigated the relationship between HbA1c and CGM, which remained unclear hitherto. Data of patients with DM (n = 91) who received CGM and HbA1c testing (1–3 months before and after CGM) were retrospectively analyzed. Diurnal and nocturnal glucose, highest CGM data (10%, 25%, and 50%), mean amplitude of glycemic excursions (MAGE), percent coefficient of variation (%CV), and continuous overlapping net glycemic action were compared with HbA1c values before and after CGM. Results The CGM results were significantly correlated with HbA1c values measured 1 (r = 0.69) and 2 (r = 0.39) months after CGM and 1 month (r = 0.35) before CGM. However, glucose levels recorded in CGM did not correlate with the HbA1c values 3 months after and 2–3 months before CGM. MAGE and %CV were strongly correlated with HbA1c values 1 and 2 months after CGM, respectively. Diurnal blood glucose levels were significantly correlated with HbA1c values 1–2 months before and 1 month after CGM. The nocturnal blood glucose levels were significantly correlated with HbA1c values 1–3 months before and 1–2 months after CGM. Conclusions CGM can predict HbA1c values within 1 month after CGM in patients with DM.

scholarly journals Correlation Between Short- and Mid-Term Hemoglobin A1c and Glycemic Control Determined by Continuous Glucose Monitoring

2021 ◽  
Author(s):  
Jen-Hung Huang ◽  
Yung-Kuo Lin ◽  
Ting-Wei Lee ◽  
Han-Wen Liu ◽  
Yu-Mei Chien ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Hemoglobin A1c ◽  
Glucose Monitoring ◽  
Mean Amplitude ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Before And After ◽  
Patients With Diabetes

Abstract Background: Glucose monitoring is vital for glycemic control in patients with diabetes mellitus (DM). Continuous glucose monitoring (CGM) measures whole-day glucose levels. Hemoglobin A1c (HbA1c) is a vital outcome predictor in patients with DM. Methods: This study investigated the relationship between HbA1c and CGM, which remained unclear hitherto. Data of patients with DM (n = 91) who received CGM and HbA1c testing (1-3 months before and after CGM) were retrospectively analyzed. Diurnal and nocturnal glucose, highest CGM data (10%, 25%, and 50%), mean amplitude of glycemic excursions (MAGE), percent coefficient of variation (%CV), and continuous overlapping net glycemic action were compared with HbA1c values before and after CGM. Results: The CGM results were significantly correlated with HbA1c values measured 1 (r = 0.69) and 2 (r = 0.39) months after CGM and 1 month (r = 0.35) before CGM. However, glucose levels recorded in CGM did not correlate with the HbA1c values 3 months after and 2-3 months before CGM. MAGE and %CV were strongly correlated with HbA1c values 1 and 2 months after CGM, respectively. Diurnal blood glucose levels were significantly correlated with HbA1c values 1-2 months before and 1 month after CGM. The nocturnal blood glucose levels were significantly correlated with HbA1c values 1-3 months before and 1-2 months after CGM.Conclusions: CGM can predict HbA1c values within 1 month after CGM in patients with DM.

scholarly journals Analysis of time in range depending on the method of assessing the glucose level

2021 ◽  
pp. 46-55
Author(s):  
L. A. Suplotova ◽  
A. S. Sudnitsyna ◽  
N. V. Romanova ◽  
K. A. Sidorenko ◽  
L. U. Radionova ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Glucose Level ◽  
Glucose Monitoring ◽  
Diabetes Type ◽  
Diabetes Type 1 ◽  
Self Monitoring ◽  
Patients With Diabetes ◽  
Time In Range

Introduction. In recent years, there has been an increase in the prevalence and incidence diabetes type 1. The high-quality glycemic control is critical in reducing the risk of developing and progression of vascular complications and adverse outcomes of diabetes. Self-monitoring blood glucose (SMBG) and professional continuous glucose monitoring (PCGM) provide the data set which must be interpreted using multiple indicators of glycemic control. A number of researchers have demonstrated the relationship between the time in range (TIR) and the risk of developing both micro- and macrovascular complications of diabetes. Considering the insufficient amount of data on TIR differences depending on the glucose level assessment method and the significant potential of using this indicator for the stratification of the risk of both micro- and macrovascular complications of diabetes, the study of TIR differences based on the data of PCGM and SMBG is relevant at present.Aims. To estimate the time range according to professional continuous glucose monitoring and self-monitoring of blood glucose levels in the patients with diabetes type 1 among the adult population to improve the control of the disease course.Materials and methods. An interventional open-label multicenter study in the patients with diabetes type 1 was conducted. The patients with diabetes type 1 aged 18 and older, with the disease duration of more than 1 year receiving the therapy with analog insulin was enrolled into the study. The calculation of the indicators of the time spent in the ranges of glycemia was carried out on the basis of the data of PCGM and SMBG.Results and discussion. We examined 218 patients who met the inclusion criteria and did not have exclusion criteria. The presented differences in the indicators of time in ranges indicate the comparability of the SMBG and PCGM methods.Conclusions. When assessing the indicators of time in the ranges of glycemia obtained on the basis of the data of PCGM and SMBG, clear correlations and linear dependence were demonstrated, which indicates the comparability of these parameters regardless of the measurement method.

scholarly journals The Use of HbA1c, Glycated Albumin and Continuous Glucose Monitoring to Assess Glucose Control in the Chronic Kidney Disease Population Including Dialysis

Nephron ◽  
2020 ◽  
Vol 145 (1) ◽  
pp. 14-19
Author(s):  
Tobias Bomholt ◽  
Therese Adrian ◽  
Kirsten Nørgaard ◽  
Ajenthen G. Ranjan ◽  
Thomas Almdal ◽  
...  
Keyword(s):  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Glycated Albumin ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Glycated Haemoglobin ◽  
Time Data ◽  
Precise Estimation ◽  
Patients With Diabetes ◽  
Mean Glucose

<b><i>Background:</i></b> Glycated haemoglobin A<sub>1c</sub> (HbA<sub>1c</sub>) has limitations as a glycemic marker for patients with diabetes and CKD and for those receiving dialysis. Glycated albumin is an alternative glycemic marker, and some studies have found that glycated albumin more accurately reflects glycemic control than HbA<sub>1c</sub> in these groups. However, several factors are known to influence the value of glycated albumin including proteinuria. Continuous glucose monitoring (CGM) is another alternative to HbA<sub>1c</sub>. CGM allows one to assess mean glucose, glucose variability, and the time spent in hypo-, normo-, and hyperglycemia. Currently, several different CGM models are approved for use in patients receiving dialysis; CKD (not on dialysis) is not a contraindication in any of these models. Some devices are for blind recording, while others provide real-time data to patients. Small studies suggest that CGM could improve glycemic control in hemodialysis patients, but this has not been studied for individual CKD stages. <b><i>Summary:</i></b> Glycated albumin and CGM avoid the pitfalls of HbA<sub>1c</sub> in CKD and dialysis populations. However, the value of glycated albumin may be affected by several factors. CGM provides a precise estimation of the mean glucose. Here, we discuss the strengths and limitations for using HbA1c, glycated albumin, or CGM in CKD and dialysis population. <b><i>Key Messages:</i></b> Glycated albumin is an alternative glycemic marker but is affected by proteinuria. CGM provides a precise estimation of mean glucose and glucose variability. It remains unclear if CGM improves glycemic control in the CKD and dialysis populations.

scholarly journals Measures of Accuracy for Continuous Glucose Monitoring and Blood Glucose Monitoring Devices

2018 ◽  
Vol 13 (3) ◽  
pp. 575-583 ◽  
Author(s):  
Guido Freckmann ◽  
Stefan Pleus ◽  
Mike Grady ◽  
Steven Setford ◽  
Brian Levy
Keyword(s):  
Blood Glucose ◽  
Continuous Glucose Monitoring ◽  
Measurement Accuracy ◽  
Glucose Monitoring ◽  
Blood Glucose Monitoring ◽  
Glucose Measurement ◽  
Bias Analysis ◽  
Patients With Diabetes ◽  
Monitoring Devices ◽  
Error Grid

Currently, patients with diabetes may choose between two major types of system for glucose measurement: blood glucose monitoring (BGM) systems measuring glucose within capillary blood and continuous glucose monitoring (CGM) systems measuring glucose within interstitial fluid. Although BGM and CGM systems offer different functionality, both types of system are intended to help users achieve improved glucose control. Another area in which BGM and CGM systems differ is measurement accuracy. In the literature, BGM system accuracy is assessed mainly according to ISO 15197:2013 accuracy requirements, whereas CGM accuracy has hitherto mainly been assessed by MARD, although often results from additional analyses such as bias analysis or error grid analysis are provided. The intention of this review is to provide a comparison of different approaches used to determine the accuracy of BGM and CGM systems and factors that should be considered when using these different measures of accuracy to make comparisons between the analytical performance (ie, accuracy) of BGM and CGM systems. In addition, real-world implications of accuracy and its relevance are discussed.

scholarly journals Self-measurement of Blood Glucose and Continuous Glucose Monitoring – Is There Only One Future?

2018 ◽  
Vol 14 (2) ◽  
pp. 24 ◽  
Author(s):  
Lutz Heinemann ◽  
Andreas Stuhr
Keyword(s):  
Blood Glucose ◽  
Continuous Glucose Monitoring ◽  
Subcutaneous Tissue ◽  
Glucose Monitoring ◽  
Relative Difference ◽  
Management Options ◽  
Measurement Quality ◽  
Glucose Levels ◽  
Wide Range ◽  
Patients With Diabetes

Monitoring glycaemic control in patients with diabetes has evolved dramatically over the past decades. The introduction of easy-to-use systems for self-monitoring of blood glucose (SMBG) utilising capillary blood samples has resulted in the availability of a wide range of systems, providing different measurement quality. Systems for continuous glucose monitoring (CGM) – used mainly in patients with type 1 diabetes (T1D) – were made possible by the development of glucose sensors that measure glucose levels in the interstitial fluid (ISF) in the subcutaneous tissue of the skin. CGM readings might not correspond exactly to SMBG measurement results taken at the same time, especially during rapid changes in either blood glucose or ISF glucose levels. The mean absolute relative difference is the most popular method used for characterising the measurement performance of CGM systems. Unlike the International Organization for Standardization 15197:2013 criteria for SMBG systems, no accuracy standards for CGM systems exist. Measurement quality of CGM systems can vary based on several factors, limiting their safety and effective use in managing diabetes. Patients have to be trained adequately to make safe and efficient use of CGM systems (like with SMBG systems). Also, systems for CGM must be evaluated in terms of patient safety and the ability to provide accurate measurements regardless of the fluctuation of glucose levels. As new technological advancements in glucose monitoring are essential for improved management options of diabetes, such as automated insulin dosing systems, there is a need for a critical view of all such developments. It is likely that both, SMBG and CGM systems, will play important future roles in the treatment of diabetes.

scholarly journals Time in range is a tool for assessing the quality of glycemic control in diabetes

2021 ◽  
Vol 24 (3) ◽  
pp. 282-290
Author(s):  
L. A. Suplotova ◽  
A. S. Sudnitsyna ◽  
N. V. Romanova ◽  
M. V. Shestakova
Keyword(s):  
Glycemic Control ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Macrovascular Complications ◽  
International Consensus ◽  
Glucose Levels ◽  
Target Values ◽  
Patients With Diabetes ◽  
Time In Range ◽  
Diagnostic Capabilities

The presence of continuous glucose monitoring (CGM) systems has expanded diagnostic capabilities. The implementation of this technology into clinical practice allowed to determine the patterns and tendencies of excursions in glucose levels, to obtain reliable data concerning short-term glycemic control. Taking into consideration the large amount of obtained information using CGM systems, more than 30 different indicators characterizing glycemic variability were proposed. However, it is very difficult for a practitioner to interpret the data obtained due to the variety of indicators and the lack of their target values. The first step in the standardization of indices was the creation of the International Guidelines for CGM in 2017, where the Time in Range (TIR) (3,9–10,0 mmol/l, less often 3,9–7,8 mmol/l) was significant. To complement the agreed parameters and simplify the interpretation of obtained data using CGM, in 2019 the recommendations were prepared for the International Consensus on Time in Range, where TIR was validated as an additional component of the assessment of glycemic control along with HbA1c. In the literature review the issues of the association of TIR with the development of micro- and macrovascular complications in type 1 and 2 diabetes are considered. The relationship with other indicators of the glycemic control assessment was also analyzed and the dependence of insulin therapy on TIR was shown. TIR is a simple and convenient indicator, it has a proven link with micro- and macrovascular complications of diabetes and can be recommended as a new tool for assessing the glycemic control. The main disadvantage of TIR usage is the insufficient apply of CGM technology by the majority of patients with diabetes.

Optimal Data Collection Period for Continuous Glucose Monitoring to Assess Long-Term Glycemic Control: Revisited

2022 ◽  
pp. 193229682110691
Author(s):  
Simon Lebech Cichosz ◽  
Morten Hasselstrøm Jensen ◽  
Ole Hejlesen
Keyword(s):  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Sampling Time ◽  
Short Term ◽  
Worst Case ◽  
Glycemic Targets ◽  
Glycemic Target

Background and Objective: It is not clear how the short-term continuous glucose monitoring (CGM) sampling time could influence the bias in estimating long-term glycemic control. A large bias could, in the worst case, lead to incorrect classification of patients achieving glycemic targets, nonoptimal treatment, and false conclusions about the effect of new treatments. This study sought to investigate the relation between sampling time and bias in the estimates. Methods: We included a total of 329 type 1 patients (age 14-86 years) with long-term CGM (90 days) data from three studies. The analysis calculated the bias from estimating long-term glycemic control based on short-term sampling. Time in range (TIR), time above range (TAR), time below range (TBR), correlation, and glycemic target classification accuracy were assessed. Results: A sampling time of ten days is associated with a high bias of 10% to 47%, which can be reduced to 4.9% to 26.4% if a sampling time of 30 days is used ( P < .001). Correct classification of patients archiving glycemic targets can also be improved from 81.5% to 91.9 to 90% to 95.2%. Conclusions: Our results suggest that the proposed 10-14 day CGM sampling time may be associated with a high correlation with three-month CGM. However, these estimates are subject to large intersubject bias, which is clinically relevant. Clinicians and researchers should consider using assessments of longer durations of CGM data if possible, especially when assessing time in hypoglycemia or while testing a new treatment.

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