The GMI/HbA1c ratio does not independently predict diabetic retinopathy in adults with Type 1 Diabetes

Real-World Diabetes Technology: Overcoming Barriers and Disparities.

Continuous glucose monitoring: Musing on our progress <i>in memory of Dr Andrew Jay Drexler</i>

Background. An integral part of diabetes mellitus (DM) management is its timely diagnosis. The reference method to study the quality of glycemic control is evaluating the level of glycated hemoglobin (HbA1c) as one of the most accessible and informative tools. However, the introduction of novel technologies, namely the use of continuous glucose monitoring (CGM), has given patients with DM, their relatives and healthcare professionals an access to new indicators of glycemic control such as time in range (TIR), time above range and time below range. These indicators are criteria for compensation of carbohydrate metabolism according to the 2023 American Diabetes Association guidelines. The purpose of the study was to compare the effectiveness of using glucometers and the FreeStyle Libre system for flash glucose monitoring in patients with DM. Materials and methods. An examination of 60 patients with type 2 DM who received insulin therapy was conducted. They were aged 45–65 years, with DM duration of 5 ± 2 years. All participants were divided into two equal groups of 30 people each. The first group consisted of patients with CGM devices, the second group used glucometers for daily glycemic control. Additional CGM parameters were used: glucose coefficient of variation, glucose management indicator, which shows the probable level of HbA1c. Results. When evaluating the data obtained from patients who used CGM, it is important to note their high adherence to the use of flash glucose monitoring. The use of CGM made it possible to achieve compensation of carbohydrate metabolism faster compared to patients who used glucometers to correct glycemia. Discontinuation of CGM resulted in poorer glycemic control. The obtained results prove that the compensation of patients depends primarily on their motivation, willingness to follow medical prescriptions, careful glycemic control, and compliance with medical recommendations. Conclusions. For better control of the DM course, patients should use CGM. In order to assess diabetes compensation, it is not enough to consider only TIR. Time below range indicating hypoglycemia, time above range indicating hyperglycemia, glucose management indicator should also be taken into account. Discontinuation of CGM resulted in a loss of approximately half of the initial TIR gain achieved while using CGM. CGM is more favored among patients than a 7-point self-monitoring of blood glucose.

Background: Hyperglycemia is a known risk factor for diabetic retinopathy (DR) but the association between glycemic variability and DR is unclear. We aim to evaluate the glycemic variability in DR through retrospective continuous glucose monitoring (CGM) and assess its effect on the clinical profile of participants with or without DR. Material and Methods: Retrospective observational hospital-based case-control study. We collected anthropometric and clinical data of 74 people with type 2 diabetes from our ophthalmology database whose retrospective CGM data were available. Among them, 37 had DR (cases) and 37 did not have DR (controls). The data were analyzed using SPSS version 27. Results: Duration of diabetes and glycosylated hemoglobin (HbA1c) was significantly higher and the estimated glomerular filtration rate (eGFR) was significantly lower in the cases compared to the controls. CGM markers, like time-above-range, average glucose, glucose management indicator, were higher while time-in-range was lower in the cases compared to the controls (P = ns). Time-below-range targets in people &gt;65 years were met in a lower proportion (p &lt; 0.05) of people in the cases (50%) compared to the controls (92%). Conclusion: Duration of diabetes, low eGFR, and high HbA1c showed significant association with retinopathy in type 2 diabetes. Although markers of glycemic variability did not show a statistically significant difference in cases compared to controls, all indices of glycemic variability were numerically higher in people with DR. Hypoglycemia in elderly participants with DR and its implications on achieving targets requires more research.

Background: Continuous glucose monitoring (CGM) has shown its benefits in pregnant women with diabetes. Flash glucose monitoring (FGM), as one of the CGM types, has not been well assessed in this patient group. The interpretation of a big volume of information on glycaemia obtained with various CGM devices is possible with statistical analysis according to the algorithms proposed by manufacturers. While these algorithms cannot be comprehensive, evaluation of alternative approaches to the CGM data statistical analysis and comparison of the results obtained with different devices seem reasonable. No unified algorithm for modification of antidiabetic treatment according to the CGM results has been yet developed. This study was performed in a pregnant patient with type 1 diabetes mellitus (T1DM) to demonstrate the methods to individualized analysis of the data from various devices (CGM, FGM, glucometer) that could be used in routine clinical practice.Aim: To evaluate the individual advantages and disadvantages of the simultaneous use of FGM, CGM and SMBG in a pregnant woman with type 1 diabetes.Materials and methods: This was an observational case study with a retrospective assessment of the patient's data obtained with FGM, CGM and a glucometer in a 31-year female patient with T1DM of 6-year duration and 9 weeks of gestation, who had been on pump insulin therapy for one year and had an HbA1c level of 5.4%. During the study the patient continued her pump therapy and performed blood glucose self-monitoring (BGSM) and simultaneously used FGM and CGM. The following FGM data were compared with CGM and glucometer results: measurement numbers, time in range, mean daily glucose, mean absolute difference (MAD), and mean absolute relative difference (MARD).Results: The FGM-derived mean daily glucose was lower than that measured with the glucometer: 5.1±1.9 mmol/L vs 6.4±2.2 mmol/L (p&lt;0.001). The number of measurements with FGM was 32.0±12.9 times daily and with a glucometer 15.1±5.5 times daily (p&lt;0.001). MAD values were minimal in the hypoglycemic range (0.5±0.3 mmol/L) and maximal in the hyperglycemic range (1.6±1.2 mmol/L, р&lt;0.001). The MARD values were significantly smaller in the hyperglycemic than in the normoglycemic (16.6±12.6% vs 21.3±14.0%, р=0.035). The highest MAD and MARD were observed on the Day 1 of the sensor installation. The comparison of FGM and the glucometer readings with the Clarke consensus error grid showed that 82% of the FGM readings were in zone A or B. The FGM accuracy was higher from Day 2 to Day 9 (72.5% of the FGM readings in zone A). MAD between FGM and CGM readings was not different from that between FGM and the glucometer: 1.3±1.0 mmol/L and 1.2±0.9 mmol/L, respectively (p=0.09). MARD for the FGM and CGM comparison was higher than that for FGM and glucometer comparison: 24.4±23.0% and 18.8±13.5%, respectively (р&lt;0.001). The Pearson's correlation coefficient FGM and CGM seemed lower than that between FGM and the glucometer (0.837 and 0.889, respectively). FGM has identified more hypoglycemic events compared to CGM: time below range was 29.4% and 8.8%, respectively, p&lt;0.001).Conclusion: The FGM readings highly correlate with the glucometer. The FGM difference with the glucometer was lower in the hypo- and hyperglycemic ranges. FGM shows higher values for time below range than CGM. It is necessary to continue the study of the clinical acceptability of FGM in pregnant women and determination of its optimal regimen for the treatment of this patient category, as well as to develop an algorithm for treatment modification based on the results of FGM.

How can we reach the target of glucose control in type 1 diabetes?

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

IDF21-0450 Effect of COVID-19 lockdown and Ramadan fasting on glucose control in patients with diabetes: a glucose monitoring study

Objective: Disparities were compared between flash glucose monitoring (FGM) sensor glucose (SG) and blood glucose (BG) values vs. between continuous glucose monitoring (CGM) SG and BG values to establish the accuracy of FGM SG values in patients with type 1 diabetes. Methods: FGM (FreeStyle Libre Pro; Abbott Japan) and a sensor-augmented pump (SAP) with built-in CGM (MiniMed 620G; Medtronic Minimed) were simultaneously put in place in 8 outpatients with type 1 diabetes receiving SAP therapy (Age, 50.9 ± 6.3 years; BMI, 20.4±2.6 kg/m2; HbA1c, 7.4±0.3%; urinary C-peptide excretion, 1.5±0.7 µg/day; duration of SAP therapy, 11.9±3.7 months; insulin dose, 34.2±14.5 U/day). SMBG BG values at 385 points registered on Minimed 620G for calibration purposes and CGM/FGM SG values at the same time points, were selected to examine the accuracy of FGM SG values. Both these devices were evaluated for accuracy in terms of 1) simple correlation coefficients between CGM/FGM SG values and BG values; and 2) mean absolute relative differences (MARD) in SG values of CGM/FGM relative to BG values as reference. Results: 1) A strong positive correlations were found between the CGM/FGM SG values and BG values (r = 0.984, p &amp;lt; 0.001 / r = 0.852, p&amp;lt; 0.001). 2) The MARD in CGM/FGM SG values were 5.81 ± 6.66% (0-39.4%) / 17.5 ± 24.8% (0-213.6%), and the frequency of MARD showing 10% or less accounted for 83% of all CGM SG values, and 48.3% of all FGM SG values. The relationship between the MARD of FGM SG values and the durations of FGM put in place varied depending on the number of days FGM put in place. Additionally, the disparity between the FGM SG values and the BG values varied greatly depending on the individual patient. Conclusions: While FGM represents an innovative device for glucose monitoring, there is a wide disparity between the FGM SG and BG values in some patients with type 1 diabetes, suggesting that FGM may be appropriately positioned as an adjunct, but not an alternative, to SMBG. Disclosure M. Tanizawa: None. H. Takahashi: None. Y. Mori: None. K. Utsunomiya: None.

BackgroundNew technologies for the management of children with type 1 diabete (T1D) are constantly and rapidly evolving. However, few real-life studies have been conducted, and rarely in the youngest patients (<6 years). AimTo study parental satisfaction with continuous and flash glucose monitoring devices in young children with T1D. MethodsA questionnaire was completed by the parents of 114 children under the age of 6 years with T1D treated with an insulin pump followed-up in one of the hospitals of the French ADIM network between January and July 2020. ResultsOne hundred and nine patients (96 %) were equipped with a glucose monitor and 95 % (104/109) of parents stated that they were satisfied or very satisfied with their child's monitoring device, with no significant difference in satisfaction rates between flash and continuous glucose monitoring. The parameter most strongly associated with satisfaction was confidence in the reliability of the device (p = 0.008). Parents who struggled to apply the device were significantly less satisfied (p = 0.024). In real-life use, 83 % of parents (90/109) used additional adhesives, 28 % reported mild skin reactions (30/108) and 39 % severe skin reactions (42/108), 50 % stated that applying the device was not painful, and 95 % found the device easy to apply. The most commonly reported unexpected difficulties were device malfunction (by 16 respondents), the device being too large and causing scarring (6 respondents), and lengthy calibration (6 respondents). ConclusionThe vast majority of parents in this group of young children with T1D were satisfied with continuous or flash glucose monitoring. Satisfaction was strongly associated with confidence in the reliability of the device. Reported adverse effects such as skin reaction and difficulties attaching the device highlight the importance of data on real-life use.

Objective: Continuous glucose monitoring (CGM) has emerged as a key component of diabetes care but there are issues with data synthesis and application. A published regression equation converts CGM-derived mean glycemia into an estimate of laboratory-measured HbA1c, termed glucose management indicator (GMI). How the GMI formula defers by CGM device is not known. We compared the GMI derived from published formula vs. GMI derived from FreeStyle Libre specific regression equation. Research Design and Methods: We conducted an observational study using EHR data from a single academic endocrinology practice (Tufts Medical Center, Boston, MA). We included data from patients with diabetes and a minimum of 10-days of sensor data collected with FreeStyle Libre Pro or Personal, immediately prior to measurement of HbA1c. We plotted HbA1c and Libre average glucose and derived a Libre specific regression equation. We compared the GMI derived from the published formula (GMIp) with the Libre-specific GMI (GMIL). Results: Data were available from 59 patients (age 62 [range 22-90], 36% women, BMI 29 Kg/m2; 80% had type 2 diabetes; 70% had diabetes ≥10 years disease; HbA1c 8.1% (range 4.8-13.7). The mean number of days with CGM data was 28 (range 10-90). The Libre specific regression equation formula was GMI (%) = 5.04 + 0.0173*(mean glucose in mg/dL)]. Mean GMIp was 8.1% (SD 0.9) and mean GMIL was 7.6% (SD 1.3)(p=0.004). Conclusion: The GMI derived from a device-specific regression equation differs from the published GMI. The development of a device specific GMI may be warranted. Disclosure E. Angellotti: None. S. Muppavarapu: None. R. Siegel: None. A.G. Pittas: None.

Early randomized controlled trials (RCTs) showed a strong inverse relationship between A1C and severe hypoglycemia (SH) risk, which could potentially limit therapy intensification and resulted in some guidelines suggesting increased A1C goals in at-risk patients. Subsequently, RCTs demonstrated that real-time continuous glucose monitoring (CGM) alters this relationship and attenuates SH risk associated with low A1C values ≥6.5%. The glucose management indicator (GMI) reflects average CGM glucose and correlates with A1C. This analysis uses real-world evidence to explore the relationship between GMI and hypoglycemia exposure. Data were from an anonymized convenience sample of 151,315 U.S.-based Dexcom G6 CGM app users with good device adherence evidenced by having uploaded ≥80% of possible CGM values in 3Q 2019. Hypoglycemia exposure was reported as the percentage of glucose values &amp;lt;70 or &amp;lt;54 mg/dL (level 1 or level 2 hypoglycemia, respectively). The Figure shows hypoglycemia and the number of users in six GMI categories. Although there are small, incremental increases in hypoglycemia until GMI is &amp;lt;6.5%, time spent in level 1 and level 2 hypoglycemia remains below the consensus goals of &amp;lt;4% and &amp;lt;1% for users with GMI values ≥6.5%. On average, CGM users with GMI values ≥6.5% have acceptably low rates of hypoglycemia, obviating the need to relax GMI goals as a strategy to reduce the risk of SH. Disclosure T.C. Walker: Employee; Self; Dexcom, Inc. J. Welsh: Employee; Self; CSL Behring. G.J. Norman: Employee; Self; Dexcom, Inc. A. Parker: Employee; Self; Dexcom, Inc. Stock/Shareholder; Self; Dexcom, Inc. D.A. Price: Employee; Self; Dexcom, Inc.

Diabetic retinopathy (DR) is a complication of diabetes that can lead to vision loss. Outcomes of continuous glucose monitoring (CGM) and insulin pump use in DR are not well understood. To assess the use of CGM, insulin pump, or both, and DR and proliferative diabetic retinopathy (PDR) in adults with type 1 diabetes (T1D). A retrospective cohort study of adults with T1D in a tertiary diabetes center and ophthalmology center was conducted from 2013 to 2021, with data analysis performed from June 2022 to April 2023. Use of diabetes technologies, including insulin pump, CGM, and both CGM and insulin pump. The primary outcome was development of DR or PDR. A secondary outcome was the progression of DR for patients in the longitudinal cohort. Multivariable logistic regression models assessed for development of DR and PDR and association with CGM and insulin pump use. A total of 550 adults with T1D were included (median age, 40 [IQR, 28-54] years; 54.4% female; 24.5% Black or African American; and 68.4% White), with a median duration of diabetes of 20 (IQR, 10-30) years, and median hemoglobin A1c (HbA1c) of 7.8% (IQR, 7.0%-8.9%). Overall, 62.7% patients used CGM, 58.2% used an insulin pump, and 47.5% used both; 44% (244 of 550) of the participants had DR at any point during the study. On univariate analysis, CGM use was associated with lower odds of DR and PDR, and CGM with pump was associated with lower odds of PDR (all P < .05), compared with no CGM use. Multivariable logistic regression adjusting for age, sex, race and ethnicity, diabetes duration, microvascular and macrovascular complications, insurance type, and mean HbA1c, showed that CGM was associated with lower odds of DR (odds ratio [OR], 0.52; 95% CI, 0.32-0.84; P = .008) and PDR (OR, 0.42; 95% CI, 0.23-0.75; P = .004), compared with no CGM use. In the longitudinal analysis of participants without baseline PDR, 79 of 363 patients (21.8%) had progression of DR during the study. In this cohort study of adults with T1D, CGM use was associated with lower odds of developing DR and PDR, even after adjusting for HbA1c. These findings suggest that CGM may be useful for diabetes management to mitigate risk for DR and PDR.

Glucose management indicator (GMI) was developed using a linear regression formula to estimate A1C from mean glucose. The study population that was used to develop and validate GMI had a mean A1C of 7.3 ± 0.8%. Therefore, we hypothesize that GMI may overestimate A1C in those with A1C &amp;lt;6.5%. Non-pregnant adults (≥18 years) with T1D for ≥2 years and using Dexcom G6 for ≥6 months from Barbara Davis Center for Diabetes (n=93, 40.6 ± 12.6 years, female 58.4%, A1C 5.9 ± 0.4%) and subjects from “Glucose Sensor Profile in Healthy Non-diabetic Subjects” study (N=153, 31.2± 21.0 years, female 66.0%, A1C 5.1 ± 0.3%) were included. All participants used Dexcom G6 continuous glucose monitor (CGM) . Up to days (healthy participants) and 15 days (T1D patients) of CGM data was compared with laboratory A1C. Discordance in GMI and A1C is presented by A1C &amp;lt;5.7% (healthy participants) and A1C between 5.7-6.4% (T1D patients) . The degree of discordance between GMI and A1C was higher in those with A1C&amp;lt;6.5% compared to original GMI development cohort [Figure 1A]. The differences between A1C and GMI were largely negative in both A1C categories (A1C &amp;lt;5.7% and A1C of 5.7-6.4%) . On average, GMI was 0.59% higher in the A1C &amp;lt; 5.7% group and 0.49% higher in the A1C 5.7 - 6.4% group (both p &amp;lt; 0.0001) [Figure 1B and 1C]. The current GMI formula may overestimate A1C and may need to be re-assessed for those with A1C &amp;lt;6.5%. Disclosure V.Shah: Advisory Panel; Medscape, Sanofi, Consultant; Dexcom, Inc., Research Support; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Novo Nordisk. T.B.Vigers: None. L.Pyle: None. S.Dubose: None. P.Calhoun: None. R.M.Bergenstal: Advisory Panel; Hygieia, Medtronic, Roche Diabetes Care, Zealand Pharma A/S, Consultant; Abbott Diabetes, Ascensia Diabetes Care, Bigfoot Biomedical, Inc., CeQur SA, Dexcom, Inc., Eli Lilly and Company, Novo Nordisk, Onduo LLC, Sanofi, United HealthCare Services, Inc., Research Support; Abbott Diabetes, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk, Sanofi.

While A1C is well established as an important risk marker for diabetes complications, with the increasing use of continuous glucose monitoring (CGM) to help facilitate safe and effective diabetes management, it is important to understand how CGM metrics, such as mean glucose, and A1C correlate. Estimated A1C (eA1C) is a measure converting the mean glucose from CGM or self-monitored blood glucose readings, using a formula derived from glucose readings from a population of individuals, into an estimate of a simultaneously measured laboratory A1C. Many patients and clinicians find the eA1C to be a helpful educational tool, but others are often confused or even frustrated if the eA1C and laboratory-measured A1C do not agree. In the U.S., the Food and Drug Administration determined that the nomenclature of eA1C needed to change. This led the authors to work toward a multipart solution to facilitate the retention of such a metric, which includes renaming the eA1C the glucose management indicator (GMI) and generating a new formula for converting CGM-derived mean glucose to GMI based on recent clinical trials using the most accurate CGM systems available. The final aspect of ensuring a smooth transition from the old eA1C to the new GMI is providing new CGM analyses and explanations to further understand how to interpret GMI and use it most effectively in clinical practice. This Perspective will address why a new name for eA1C was needed, why GMI was selected as the new name, how GMI is calculated, and how to understand and explain GMI if one chooses to use GMI as a tool in diabetes education or management.