Time in Specific Glucose Ranges, Glucose Management Indicator, and Glycemic Variability: Impact of Continuous Glucose Monitoring (CGM) System Model and Sensor on CGM Metrics

Discordance Between Glycated Hemoglobin A1c and the Glucose Management Indicator in People With Diabetes and Chronic Kidney Disease

INTRODUCTION

Assessment of glucose exposure via glycated hemoglobin A1c (HbA1c) has limitations for interpretation in individuals with diabetes and chronic kidney disease (CKD). The glucose management indicator (GMI) derived from continuous glucose monitoring (CGM) data could be an alternative. However, the concordance between HbA1c measured in laboratory and GMI (HbA1c-GMI) is uncertain in individuals with CKD. The purpose of this study is to analyze this discrepancy.

MATERIAL AND METHOD

We performed a multicentric, retrospective, observational study. A group of individuals with diabetes and CKD (n = 170) was compared with a group of individuals with diabetes without CKD (n = 185). All individuals used an intermittently scanned continuous glucose monitoring (isCGM). A comparison of 14-day and 90-day glucose data recorded by the isCGM was performed to calculate GMI and the discordance between lab HbA1c and GMI was analyzed by a Bland-Altman method and linear regression.

RESULTS

HbA1c-GMI discordance was significantly higher in the CKD group versus without CKD group (0.78 ± 0.57 [0.66-0.90] vs 0.59 ± 0.44 [0.50-0.66]%, P < .005). An absolute difference >0.5% was found in 68.2% of individuals with CKD versus 42.2% of individuals without CKD. We suggest a new specific formula to estimate HbA1c from the linear regression between HbA1c and mean glucose CGM, namely CKD-GMI = 0.0261 × 90-day mean glucose (mg/L) + 3.5579 (r2 = 0.59).

CONCLUSIONS

HbA1c-GMI discordance is frequent and usually in favor of an HbA1c level higher than the GMI value, which can lead to errors in changes in glucose-lowering therapy, especially for individuals with CKD. This latter population should benefit from the CGM to measure their glucose exposure more precisely.

The significance of coefficient of variation as a measure of hypoglycaemia risk and glycaemic control in real world users of the automated insulin delivery MiniMed 780G system

AIM

Use of the MiniMed 780G system (MM780G) can result in a reduction in mean and standard deviation (SD) of sensor glucose (SG) values. We assessed the significance of the coefficient of variation (CV) as a measure of hypoglycaemia risk and glycaemic control.

MATERIALS AND METHODS

Data from 10 404 MM780G users were analysed using multivariable logistic regression to assess the contribution of CV to (a) hypoglycaemia risk, measured as not reaching target <1% for time below range (TBR), and (b) achieving targets of time-in-range (TIR) >70% and glucose management indicator <7%. CV was compared with SD and low blood glucose index. To assess the relevance of CV <36% as a therapeutic threshold, we identified the CV cut-off point that optimally discriminated users at risk of hypoglycaemia.

RESULTS

The contribution of CV was the smallest in terms of risk of hypoglycaemia (vs. low blood glucose index and SD) and TIR and glucose management indicator targets (vs. SD). In all cases the models with SD showed the best fit. A CV <43.4% (95% CI: 42.9-43.9) was the optimal cut-off point with a correct classification rate of 87.2% (vs. 72.9% for CV <36%).

CONCLUSION

For MM780G users, CV is a poor marker for hypoglycaemia risk and glycaemic control. We recommend using, for the former, TBR and whether the TBR target is met (and not using CV <36% as a therapeutic threshold for hypoglycaemia); for the latter, TIR, time above range, whether targets are met and a discrete description of mean SG and SD of SG values.

Discrepancies in glycemic metrics derived from different continuous glucose monitoring systems in adult patients with type 1 diabetes mellitus

BACKGROUND

Continuous glucose monitoring systems have been widely used but discrepancies among various brands of devices are rarely discussed. This study aimed to explore differences in glycemic metrics between FreeStyle Libre (FSL) and iPro2 among adults with type 1 diabetes mellitus (T1DM).

METHODS

Participants with T1DM and glycosylated hemoglobin of 7%-10% were included and wore FSL and iPro2 for 2 weeks simultaneously. Datasets collected on the insertion and detachment day, and those with insufficient quantity (<90%) were excluded. Agreements of measurement accuracy and glycemic metrics were evaluated.

RESULTS

A total of 40 498 paired data were included. Compared with the values from FSL, significantly higher median value was observed in iPro2 (147.6 [106.2, 192.6] vs. 144.0 [100.8, 192.6] mg/dl, p < 0.001) and the largest discordance was observed in hypoglycemic range (median absolute relative difference with iPro2 as reference value: 25.8% [10.8%, 42.1%]). Furthermore, significant differences in glycemic metrics between iPro2 and FSL were also observed in time in range (TIR) 70-180 mg/dl (TIR, 62.8 ± 12.4% vs. 58.8 ± 12.3%, p = 0.004), time spent below 70 mg/dl (4.4 [1.8, 10.9]% vs. 7.2 [5.4, 13.3]%, p < 0.001), time spent below 54 mg/dl (0.9 [0.3, 4.0]% vs. 2.6 [1.3, 5.6]%, p = 0.011), and coefficient of variation (CV, 38.7 ± 8.5% vs. 40.9 ± 9.3%, p = 0.017).

CONCLUSIONS

During 14 days of use, FSL and iPro2 provided different estimations on TIR, CV, and hypoglycemia-related parameters, which needs to be considered when making clinical decisions and clinical trial designs.

Variation of Mean Absolute Relative Differences of Continuous Glucose Monitoring Systems Throughout the Day

BACKGROUND

There is an increasing use of continuous glucose monitoring (CGM) by people with diabetes. Measurement performance is often characterized by the mean absolute relative difference (MARD). However, MARD is influenced by a number of factors and little is known about whether MARD is stable throughout the day.

MATERIAL AND METHODS

A total of 24 participants with type 1 diabetes were enrolled in the study. The study was performed for seven in-patient days. Participants wore two CGM systems in parallel and performed additional frequent blood glucose (BG) measurements. On two days, glucose excursions were induced.MARD was calculated between pairs of CGM and BG values, with BG values serving as reference values. ARD values calculated from CGM-BG pairs were grouped by hour of the day. Results were analyzed separately for glucose excursion days and for regular days.

RESULTS

Total MARDs for the complete study duration were 12.5% ± 3.6% and 13.2% ± 2.4% (n = 24). Throughout the day marked variability of MARD was observed (8.0% ± 1.3%-16.3% ± 2.9% (G5); 9.1% ± 1.4%-16.3% ± 5.3% (FL), up to n = 157 each). Low(est) MARD values were observed before breakfast and dinner, when subjects were in or near a fasting state. Especially after breakfast and lunch, MARD values were higher than average.

CONCLUSIONS

Analytical performance of the two CGM systems, assessed by MARD, was found to vary markedly throughout the day. Activities of daily life likely triggered these variations. An increasing number of CGM users base therapeutic decisions on CGM values, and they should be aware of these variations of performance throughout the day.

HbA1c and Glucose Management Indicator Discordance Associated with Obesity and Type 2 Diabetes in Intermittent Scanning Glucose Monitoring System

Glucose management indicator (GMI) is frequently used as a substitute for HbA1c, especially when using telemedicine. Discordances between GMI and HbA1c were previously mostly reported in populations with type 1 diabetes (T1DM) using real-time CGM. Our aim was to investigate the accordance between GMI and HbA1c in patients with diabetes using intermittent scanning CGM (isCGM). In this retrospective cross-sectional study, patients with diabetes who used isCGM >70% of the time of the investigated time periods were included. GMI of four different time spans (between 14 and 30 days), covering a period of 3 months, reflected by the HbA1c, were investigated. The influence of clinical- and isCGM-derived parameters on the discordance was assessed. We included 278 patients (55% T1DM; 33% type 2 diabetes (T2DM)) with a mean HbA1c of 7.63%. The mean GMI of the four time periods was between 7.19% and 7.25%. On average, the absolute deviation between the four calculated GMIs and HbA1c ranged from 0.6% to 0.65%. The discordance was greater with increased BMI, a diagnosis of T2DM, and a greater difference between the most recent GMI and GMI assessed 8 to 10 weeks prior to HbA1c assessment. Our data shows that, especially in patients with increased BMI and T2DM, this difference is more pronounced and should therefore be considered when making therapeutic decisions.

Choice of Continuous Glucose Monitoring Systems May Affect Metrics: Clinically Relevant Differences in Times in Ranges

Background Continuous glucose monitoring-derived parameters are becoming increasingly important in the treatment of people with diabetes. The aim of this study was to assess whether these parameters, as calculated from different continuous glucose monitoring systems worn in parallel, are comparable. In addition, clinical relevance of differences was investigated.

Methods A total of 24 subjects wore a FreeStyle Libre (A) and a Dexcom G5 (B) sensor in parallel for 7 days. Mean glucose, coefficient of variation, glucose management indicator and time spent in different glucose ranges were calculated for each system. Pairwise differences between the two different continuous glucose monitoring systems were computed for these metrics.

Results On average, the two CGM systems indicated an identical time in range (67.9±10.2 vs. 67.9±11.5%) and a similar coefficient of variation; both categorized as unstable (38.1±5.9 vs. 36.0±4.8%). In contrast, the mean time spent below and above range, as well as the individual times spent below, in and above range differed substantially. System A indicated about twice the time spent below range than system B (7.7±7.2 vs. 3.8±2.7%, p=0.003). This could have led to different therapy recommendations in approximately half of the subjects.

Discussion The differences in metrics found between the two continuous glucose monitoring systems may result in different therapy recommendations. In order to make adequate clinical decisions, measurement performance of CGM systems should be standardized and all available information, including the HbA1c, should be utilized.