CGMS and Glycemic Variability, Relevance in Clinical Research to Evaluate Interventions in T2D, a Literature Review

Abstract

Glycemic variability (GV) appears today as an integral component of glucose homeostasis for the management of type 2 diabetes (T2D). This review aims at investigating the use and relevance of GV parameters in interventional and observational studies for glucose control management in T2D. It will first focus on the relationships between GV parameters measured by continuous glucose monitoring system (CGMS) and glycemic control and T2D-associated complications markers. The second part will be dedicated to the analysis of GV parameters from CGMS as outcomes in interventional studies (pharmacological, nutritional, physical activity) aimed at improving glycemic control in patients with T2D. From 243 articles first identified, 63 articles were included (27 for the first part and 38 for the second part). For both analyses, the majority of the identified studies were pharmacological. Lifestyle studies (including nutritional and physical activity-based studies, N-AP) were poorly represented. Concerning the relationships of GV parameters with those for glycemic control and T2D related-complications, the standard deviation (SD), the coefficient of variation (CV), the mean blood glucose (MBG), and the mean amplitude of the glycemic excursions (MAGEs) were the most studied, showing strong relationships, in particular with HbA1c. Regarding the use and relevance of GV as an outcome in interventional studies, in pharmacological ones, SD, MAGE, MBG, and time in range (TIR) were the GV parameters used as main criteria in most studies, showing significant improvement after intervention, in parallel or not with glycemic control parameters’ (HbA1c, FBG, and PPBG) improvement. In N-AP studies, the same results were observed for SD, MAGE, and TIR. Despite the small number of N-AP studies addressing both GV and glycemic control parameters compared to pharmacological ones, N-AP studies have shown promising results on GV parameters and would require more in-depth work. Evaluating CGMS-GV parameters as outcomes in interventional studies may provide a more integrative dimension of glucose control than the standard postprandial follow-up. GV appears to be a key component of T2D dysglycemia, and some parameters such as MAGE, SD, or TIR could be used routinely in addition to classical markers of glycemic control such as HbA1c, fasting, or postprandial glycemia.

Acute glycemic variability and risk of mortality in patients with sepsis: a meta-analysis

“…The definitions of parameters for acute GV were consistent with the criteria applied among the included studies. Specifically, the SDBG calculated as the square-root of the average of the squared differences between individual blood glucose values and the mean [ 30 ]. The CVBG was defined as the ratio of the standard deviation (SD) to the mean of blood glucose values during observational periods [ 30 ].…”

“…Specifically, the SDBG calculated as the square-root of the average of the squared differences between individual blood glucose values and the mean [ 30 ]. The CVBG was defined as the ratio of the standard deviation (SD) to the mean of blood glucose values during observational periods [ 30 ]. The MAGE was calculated by measuring the arithmetic mean of the differences between consecutive peaks and nadirs, provided that the differences are greater than one SD of the mean glucose value [ 30 ].…”

“…The CVBG was defined as the ratio of the standard deviation (SD) to the mean of blood glucose values during observational periods [ 30 ]. The MAGE was calculated by measuring the arithmetic mean of the differences between consecutive peaks and nadirs, provided that the differences are greater than one SD of the mean glucose value [ 30 ]. The GLI was calculated as the squared difference between consecutive glucose measures per unit of actual time between the samples [ 31 ].…”

Evaluation of a continuous glucose monitor-derived homeostasis metric for type 2 diabetes screening

“…This allows for an individual to closely monitor glucose concentrations and may completely eliminate the requirement for the multiple daily finger pricks required by traditional at-home glucose monitors [16]. Furthermore, CGMs provide information-rich time-series data that can be analyzed not only for glucose levels but for the dynamic properties of the glucose variability [17].…”

“…Prior attempts at creating analysis methods for CGM data involve constructing an index of glycemic variability (GV) using CGM features such as mean blood glucose (MBG), standard deviation of the CGM, coefficient of variation (CV) of the CGM, and the mean amplitude of glucose excursions (MAGE). Many studies have been conducted to relate the current GV metrics to diagnostics, diabetic outcome and other metrics of diabetic control, however no consensus has been reached on implementation [17].…”