Lack of Association Between Hemoglobin A1c and Continuous Glucose Monitor Metrics Among Individuals with Prediabetes and Normoglycemia.

Relationships between HbA1c and continuous glucose monitoring metrics of glycaemic control and glucose variability in a large cohort of children and adolescents with type 1 diabetes

Multicentre, randomised, controlled study of the impact of continuous sub-cutaneous glucose monitoring (GlucoDay ®) on glycaemic control in type 1 and type 2 diabetes patients

Abstracts from ATTD 2016 9th International Conference on Advanced Technologies & Treatments for Diabetes Milan, Italy-February 3-6, 2016.

Continuous glucose monitoring versus blood glucose monitoring for risk of severe hypoglycaemia and diabetic ketoacidosis in children, adolescents, and young adults with type 1 diabetes: a population-based study

Background: Rebound hyperglycemia (RHyper), rebound hypoglycemia (RHypo), extended hyperglycemia (EHyper), and extended hypoglycemia (EHypo) are newly defined continuous glucose monitoring (CGM) metrics. Here, we investigated the characteristics of these new metrics and the relationship between new CGM metrics and standard metrics. Materials and Methods: In this retrospective cohort study, 30,000 CGM users with at least 90 days of CGM data were randomly selected from Dexcom Clarity database. Standard and new CGM metrics were calculated for each user. Four different cutoffs were used to define RHyper and RHypo, and two cutoffs were used to define EHyper and EHypo events. The number of RHyper, RHypo, EHyper, and EHypo events per week, mean duration of events, and mean area under the curve of events were calculated. For rebound events, the rate of change (ROC) was calculated. Pearson correlation and simple linear regression were used to analyze the data. Results: Mean time in 70-180 mg/dL was 61.8 ± 20.7%, mean glucose was 173 ± 37.1 mg/dL, and coefficient of variation (CV) was 32.1 ± 7.2%. RHyper, RHypo, and EHyper were more frequent during daytime and increased throughout the day. EHypo mostly occurred during nighttime. CV correlated strongly with RHyper (70-180 mg/dL) events/week (r = 0.67) and RHypo (180 to 70 mg/dL) events/week (r = 0.64). Time in range had the strongest correlation with EHyper events/week (r = -0.88) among new metrics. RHyper events and RHypo events were strongly correlated with each other (r = 0.92). RHyper and RHypo ROC have a stronger correlation with CV than the correlation between CV and time below range (TBR) metrics. Conclusions: For rebound and extended metrics, the most important metric was the number of events/week. RHyper and RHypo had a stronger correlation with CV and hypoglycemia metrics (TBR) than the correlation between CV and TBR. Thus, rebound events have the potential to detect hypoglycemia events caused by glycemic variability. [Figure: see text].

To investigate the association between continuous glucose monitoring (CGM) metrics and perinatal outcomes in insulin-treated diabetes mellitus in pregnancy. In a post-hoc analysis of the GlucoMOMS randomized controlled trial, we investigated the association between the metrics of an offline, intermittent CGM, glycated haemoglobin (HbA1c) and perinatal outcomes per trimester in different types of diabetes (type 1, 2 or insulin-treated gestational diabetes mellitus [GDM]). Data were analysed using multivariable binary logistic regression. Outcomes of interest were neonatal hypoglycaemia, pre-eclampsia, preterm birth, large for gestational age (LGA) and Neonatal Intensive Care Unit (NICU) admission. The glucose target range was defined as 3.5-7.8 mmol/L (63-140 mg/dL). Of the 147 participants (N = 50 type 1 diabetes, N = 94 type 2 diabetes/insulin-treated GDM) randomized to the CGM group of the GlucoMOMS trial, 115 participants had CGM metrics available and were included in the current study. We found that, in pregnancies with type 1 diabetes, a higher second trimester mean glucose was associated with LGA (odds ratio 2.6 [95% confidence interval 1.1-6.2]). In type 2 and insulin-treated gestational diabetes, an increased area under the curve above limit was associated with LGA (odds ratio 10.0 [95% confidence interval 1.4-72.8]). None of the CGM metrics were associated with neonatal hypoglycaemia, pre-eclampsia, shoulder dystocia, preterm birth and NICU admission rates for pregnancies complicated by any type of diabetes. In this study, in type 2 diabetes or insulin-treated GDM, the glucose increased area under the curve above limit was associated with increased LGA. In type 1 diabetes, the mean glucose was the major determinant of LGA. Our study found no evidence that other CGM metrics determined adverse pregnancy outcomes.

Background: There is a need for accurate glycemic control metrics in patients with diabetes and end-stage kidney disease (ESKD). Hence, we assessed the relationship of continuous glucose monitoring (CGM) metrics and laboratory-measured hemoglobin A1c (HbA1c) in patients with type 2 diabetes (T2D) treated by hemodialysis. Methods: This prospective observational study included adults (age 18-80 years) with T2D (HbA1c 5%-12%), treated by hemodialysis (for at least 90 days). Participants used a Dexcom G6 Pro CGM for 10 days. Correlation analyses between CGM metrics [mean glucose, glucose management indicator (GMI), and time-in-range (TIR 70-180 mg/dL)] and HbA1c were performed. Results: Among 59 participants (mean age was 57.7 ± 9.3 years, 58% were female, 86% were non-Hispanic blacks), the CGM mean glucose level was 188.9 ± 45 mg/dL (95% CI: 177.2, 200.7), the mean HbA1c and GMI were 7.1%±1.3% and 7.8%±1.1%, respectively (difference 0.74% ± 0.95). GMI had a strong negative correlation with TIR 70-180 mg/dL (r = -0.96). The correlation between GMI and HbA1c (r = 0.68) was moderate. Up to 29% of participants had a discordance between HbA1c and GMI of <0.5%, with 22% having a discordance between 0.5% and 1%, and 49% having a discordance of >1%. Conclusions: In patients with diabetes and ESKD treated by hemodialysis, the GMI has a strong correlation with TIR, while HbA1c underestimated the average glucose and GMI. Given the limitations of HbA1c in this population, GMI or mean glucose and TIR may be considered as more appropriate glucose control markers.

Continuous Glucose Monitoring for Diagnosis and Treatment of Neonatal Hypoglycemia

The accuracy of the GlucoWatch G2 Biographer (GW2B; Cygnus, Inc., Redwood City, CA) was assessed in children and adolescents with type 1 diabetes mellitus (T1DM). During a 24-h clinical research center stay, 89 children and adolescents with T1DM (3.5-17.7 years old) wore 174 GW2Bs and had frequent serum glucose determinations during the day and night and during insulin-induced hypoglycemia and meal-induced hyperglycemia, resulting in 3672 GW2B-reference glucose pairs. The median relative absolute difference between the GW2B and reference glucose values was 16% (25th, 75th percentiles = 7%, 29%). The proposed International Organisation for Standardisation criteria were met for 60% of sensor values. Accuracy was better at higher serum glucose levels than low glucose levels. Accuracy degraded slightly as the sensor aged. Time of day, subject age, gender, or body mass index did not impact GW2B accuracy. There were no cases of serious skin reactions. Although the accuracy of this generation of sensor does not approach that of current home glucose meters, the majority of sensor glucose values are within 20% of the serum glucose. This level of accuracy may be sufficient for detecting trends and modifying diabetes management. Further longitudinal outpatient studies are needed to assess the utility of the GW2B as a management tool to improve glycemic control and decrease the incidence of severe hypoglycemia in children with diabetes.

In Brief OBJECTIVE To compare the daily glycemic profile reflected by continuous and intermittent blood glucose monitoring in pregnant women with type 1 diabetes and to compare the treatment protocols based on the two monitoring methods. METHODS The study sample consisted of 34 gravid patients at gestational weeks 16–32, with type 1 diabetes being treated by multiple insulin injections. Data derived from the continuous glucose monitoring system for 72 hours were compared with fingerstick glucose measurements performed 6–8 times per day. During the study period, patients documented the time of food intake, insulin injections, and hypoglycemic events. Data on demographics, gravidity, parity, body mass index, hemoglobin A1c, and fructosamine levels were collected for each patient. RESULTS An average (± standard deviation) of 780 ± 54 glucose measurements was recorded for each patient with continuous glucose monitoring. The mean total time of hyperglycemia (glucose level greater than 140 mg/dL) undetected by the fingerstick method was 192 ± 28 minutes per day. Nocturnal hypoglycemic events (glucose level less than 50 mg/dL) were recorded in 26 patients; in all cases, there was an interval of 1–4 hours before clinical manifestations appeared or the event was revealed by random blood glucose examination. Based on the additional information obtained by continuous monitoring, the insulin therapeutic regimen was adjusted in 24 patients (70%). CONCLUSION Continuous glucose monitoring can diagnose high postprandial blood glucose levels and nocturnal hypoglycemic events that are unrecognized by intermittent blood glucose monitoring and may serve as a basis for determining treatment regimens. A large, prospective study on maternal and neonatal outcome is needed to evaluate the clinical implications of this new monitoring technique. Continuous glucose monitoring may be a method for adjusting treatment in gravid women with type 1 diabetes mellitus.

The Official Journal of ATTD Advanced Technologies &amp; Treatments for Diabetes Conference 22‐25 February 2023 I Berlin &amp; Online

Unreliable oral glucose tolerance test and haemoglobin A1C in beta thalassaemia major – a case for continuous glucose monitoring?

Continuous glucose monitoring (CGM) is a new technology that allows patients to measure glucose levels continuously over several days. It has several advantages over traditional glucose meters in that it does not involve repeated finger sticks and can measure trends and track changes in glucose levels over time. The Clinical and Laboratory Standards Institute, working with the Diabetes Technology Society, published Performance Metrics for Continuous Interstitial Glucose Monitoring; Approved Guideline, which provides recommendations for methods for determining analytical and clinical metrics of CGMs. The document provides guidance on how CGM data should be presented, compared between devices, and compared between measurement technologies. The document serves as a roadmap for the testing of CGM devices and will ultimately advance the potential of this exciting technology. Performance Metrics for Continuous Interstitial Glucose Monitoring; Approved Guideline represents the consensus view on preparing and presenting CGM data.

Introduction: There are no studies in older adults with type 2 diabetes that examine the link between day-to-day glucose patterns and cognitive dysfunction. Continuous glucose monitoring (CGM) is a minimally invasive technology that can assess glucose every 1-15 minutes and characterize nuanced patterns in glucose. Hypothesis: CGM can provide a comprehensive picture of glycemic variability in people with cognitive dysfunction. Methods: We conducted a cross-sectional study of participants with diabetes in the Atherosclerosis Risk in Communities (ARIC) Study (Visit 9, 2021-2022). ARIC participants wore a CGM (Abbott Libre Pro) and underwent cognitive testing in three cognitive domains (memory, executive functioning, and language). Scores were standardized and averaged to give a global cognitive z-score. We compared HbA1c and clinically used CGM parameters (mean glucose, coefficient of variation, percent time in glucose range of 70-180 mg/dL, and percent time above 180 mg/dL) across quartiles of cognitive z-scores, adjusting for age and sex. We calculated p-values for trends from linear or quantile regression treating the median of cognitive Z-score in each quartile as a continuous variable. Results: We included 383 participants with diabetes (mean age, 83 years). Across categories of cognitive functioning, participants in the lowest cognitive functioning categories had significantly higher HbA1c, glucose variability, and less percent time spent with glucose in range (70-180 mg/dl) (Figure) . There was no clear trend in CGM mean glucose (p=0.15) (Figure, Panel B) . Conclusions: Cognitive dysfunction was associated with an increased burden of glycemic dysregulation assessed through CGM and HbA1c. Our results suggest CGM may be a useful complement to HbA1c for monitoring glycemic control in this population. Figure: Hemoglobin A1c (HbA1c) and continuous glucose monitoring (CGM) metrics across quartiles of cognitive Z-scores in older participants with diabetes in the ARIC Study (2021-2022)

The association of continuous glucose monitoring (CGM) frequency with glycemic control among people with type 2 diabetes has not been well-studied. To evaluate the association of CGM frequency with glycemic status over 12 months vs no CGM use. This retrospective, propensity score-matched, cross-sectional study used Optum deidentified Market Clarity Data (claims and electronic medical record data) obtained between January 1, 2019, and December 31, 2023, including data from 6 months prior to each participant's index date with 12 months follow-up. Participants with type 2 diabetes, aged 18 years or older, and with hemoglobin A1C (HbA1C) levels between 7.0% to 15.0% at baseline were included. Number of days using CGM during the 12-month postindex period (frequency 1, ≥1 to ≤90 days; frequency 2, >90 to ≤180 days; frequency 3, 180 to ≤270 days; frequency 4, >270 days; control, no CGM use). The primary outcome was change in HbA1C relative to frequency of CGM use vs no CGM. A mixed-model analysis was used to determine HbA1C changes across groups. The analysis included 9258 patients (4207 female [45.4%]; mean [SD] age, 55.9 [10.6] years), with 4629 patients in the control group, 1081 in frequency 1, 523 in frequency 2, 540 in frequency 3, and 2485 in frequency 4. High CGM use (frequency 4) was associated with greater reductions in HbA1C at 12 months (-1.52 percentage points; 95% CI, -1.73 to -1.32 percentage points) vs no CGM use (-0.63 percentage points; 95% CI, -0.80 to -0.45 percentage points). CGM users experienced the greatest reductions at approximately 3 months (frequency 1: -0.59 percentage points; 95% CI, -0.96 to -0.21 percentage points; frequency 2: -0.57 percentage points; 95% CI, -1.10 to -0.05 percentage points; frequency 3: -0.79 percentage points; 95% CI, -1.25 to -0.34 percentage points; frequency 4: -0.91 percentage points; 95% CI, -1.12 to -0.70 percentage points) compared with control patients (-0.28 percentage points; 95% CI, -0.47 to -0.09 percentage points). No further glycemic improvement was observed in frequency 2 and frequency 3 groups after 6 months. Improvements in patients in frequency 1 and frequency 4 groups were sustained for the duration of the postindex period. The addition of a glucagon-like peptide-1 receptor agonist in the frequency 4 group was associated with an HbA1C treatment difference of -1.13 percentage points (95% CI, -1.46 to -0.80 percentage points) vs controls at approximately 12 months. This cross-sectional study found that frequent use of CGM (>75% sensor wear) was associated with improved glycemic control compared with infrequent or no use of CGM. These findings suggest that clinicians should monitor CGM use at 6 months, identify potential therapeutic obstacles, and encourage continuous use of CGM.