Primary care diabetes assessment when HbA1c and other measures of glycemia disagree

AIMS

Although diabetes management decisions in primary care are typically based largely on HbA1c, mismatches between HbA1c and other measures of glycemia that are increasingly more available present challenges to optimal management. This study aimed to assess a systematic approach to identify the frequency of mismatches of potential clinical significance amongst various measures of glycemia in a primary care setting.

METHODS

Following screening to exclude conditions known to affect HbA1c interpretation, HbA1c, and fructosamine were obtained and repeated after ∼90 days on 53 adults with prediabetes or type 2 diabetes. A subset of 13 participants with repeat labs wore continuous glucose monitoring (CGM) for 10 days.

RESULTS

As expected, HbA1c and fructosamine only modestly correlated (initial R2 = 0.768/repeat R2 = 0.655). The HbA1c/fructosamine mismatch frequency of ± 0.5% (using the following regression HbA1c = 0.015 *fructosamine + 2.994 calculated from the initial sample) was 27.0%. Of the 13 participants with CGM data, HbA1c and CGM-based Glucose Management Indicator correlated at R2 = 0.786 with a mismatch frequency of ± 0.5% at 46.2% compared to a HbA1c/fructosamine mismatch frequency of ± 0.5% at 30.8%.

CONCLUSIONS

HbA1c is frequently mismatched with fructosamine and CGM data. As each of the measures has strengths and weaknesses, the utilization of multiple different measures of glycemia may be informative for diabetes assessment in the clinical setting.

Effectiveness of Blood Glucose Time in Range to Reduce Risk of Blood Glucose Extrusion and Improve Blood Glucose Metrics in Type 1 Diabetic Patients

BACKGROUND

With evolving diabetes technology, continuous glucose monitoring (CGM) and time in range have been advanced as critical measurements to assess complications. They have shown improvement in A1C levels and decreased episodes of blood glucose extrusion.

AIMS

This study aimed to assess the awareness and utilization of blood glucose time in range and its effectiveness in reducing the risk of blood glucose extrusion and improving blood glucose metrics among patients with type 1 diabetes mellitus.

METHODS

A retrospective study included 342 patients who met the inclusion criteria and were using the CGM, aiming for a TIR of 70% daily. Glycemic control was followed using TIR data, blood glucose extrusion frequency (including hyperglycemia and hypoglycemia events), active sensor time, average blood glucose, and glucose management indicator (GMI) levels.

RESULTS

A total of 342 individuals participated in this study, the majority of whom were below 18 years of age (62.3%). The hypoglycemic frequency was significantly increased compared to the baseline, and most participants experienced hypoglycemia events (p = 0.0001). The incidences increased over time, with 90.9% and 93% having hypoglycemia at 60 and 90 days (p = 0.0001), respectively. The active scan and sensor time were not followed, which led to the blood glucose target not being achieved, with no improvement throughout the study. Consequently, no improvement occurred in glycemic control.

CONCLUSION

CGM technology has been promising and proven effective in improving glycemic. However, our study did not show these benefits as expected, which could be explained by the underutilization and improper use of the CGM.

MiniMed 780G™ advanced hybrid closed-loop system performance in Egyptian patients with type 1 diabetes across different age groups: evidence from real-world users

BACKGROUND

Advanced hybrid closed loop (AHCL) system provides both automated basal rate and correction boluses to keep glycemic values in a target range.

OBJECTIVES

To evaluate the real-world performance of the MiniMed™ 780G system among different age groups of Egyptian patients with type 1diabetes.

METHODS

One-hundred seven AHCL system users aged from 3 to 71 years were enrolled. Data uploaded by patients were aggregated and analyzed. The mean glucose management indicator (GMI), percentage of time spent within glycemic ranges (TIR), time below range (TBR) and time above range (TAR) were determined.

RESULTS

Six months after initiating Auto Mode, patients spent a mean of 85.31 ± 22.04% of the time in Auto Mode (SmartGuard) and achieved a mean GMI of 6.95 ± 0.58% compared with 7.9 ± 2.1% before AHCL initiation (p < 0.001). TIR 70-180 mg/dL was increased post-AHCL initiation from 63.48 ± 10.14% to 81.54 ± 8.43% (p < 0.001) while TAR 180-250 mg/dL, TAR > 250 mg/dL, TBR < 70 mg/dL and TBR < 54 mg/dL were significantly decreased (p < 0.001). After initiating AHCL, TIR was greater in children and adults compared with adolescents (82.29 ± 7.22% and 83.86 ± 9.24% versus 78.4 ± 7.34%, respectively; p < 0.05). The total daily dose of insulin was increased in all age groups primarily due to increased system-initiated insulin delivery including auto correction boluses and basal insulin.

CONCLUSIONS

MiniMed™ 780G system users across different age groups achieved international consensus-recommended glycemic control with no serious adverse effects even in challenging age group as children and adolescents.

Real-World User and Clinician Perspective and Experience with MiniMed™ 780G Advanced Hybrid Closed Loop System

INTRODUCTION

The advanced hybrid closed loop (AHCL) MiniMed™ 780G system changes basal insulin delivery every 5 min and auto bolus in response to sensor glucose values. We assessed the performance of the AHCL system in real-world settings for individuals with type 1 diabetes (T1DM) as well as user and clinician perspectives and satisfaction.

METHODS

We held two peer group discussions: one having adults with T1DM/parents of children and adolescents with T1DM to understand their experiences with the AHCL system and another with healthcare providers (HCPs). Responses from the discussions were analyzed and categorized into themes by two independent researchers, with any inconsistencies resolved by consensus. We also analyzed data from the system uploaded to CareLink personal software. Glycemic outcomes, including time in range (TIR), time below range (TBR), time above range (TAR), mean sensor glucose (SG) levels, glucose management indicator (GMI), sensor use, and percentage of time spent in AHCL, were determined.

RESULTS

The peer group discussions revealed numerous key themes and issues for each group, such as the significance of setting reasonable expectations, carbohydrate counting and bolus dosing, technical difficulties, and overall user experience. The users (n = 25; T1DM; 17 female; age 13.8 ± 7.49 years; A1C 6.54 ± 0.45%; duration of diabetes 6 ± 6.78 years) were very satisfied with the system. Most users experienced consistent blood glucose values with very few hypoglycemic episodes. However, there were a few limitations reported, such as hyperglycemic episodes caused by inaccuracies in carb counting, issues with sensor connectivity, and cannula blockages or kinking for those using insulin Fiasp. Users achieved a mean GMI of 6.4 ± 0.26%, TIR of 83.0 ± 8.12%, TBR (54-70 mg/dL) of 2.0 ± 0.81%, TBR* (< 54 mg/dL) of 0%. All of the users achieved a TIR of > 70%.

CONCLUSION

The use of the AHCL system in T1DM resulted in robust glycemic control, minimizing hypoglycemia. Providing training to both users and HCPs can help them use the system effectively.

Personalized Glycated Hemoglobin in Diabetes Management: Closing the Gap with Glucose Management Indicator

Glycated hemoglobin (HbA1c) has played a central role in the management of diabetes since the end of the landmark Diabetes Control and Complications Trial 30 years ago. However, it is known to be subject to distortions related to altered red blood cell (RBC) properties, including changes in cellular lifespan. On occasion, the distortion of HbA1c is associated with a clinical pathological condition affecting RBCs, however, the more frequent scenario is related to interindividual RBC variations that alter HbA1c-average glucose relationship. Clinically, these variations can potentially lead to over- or underestimating glucose exposure of the individual to the extent that may put the person at excess risk of over- or undertreatment. Furthermore, the variable association between HbA1c and glucose levels across different groups of people may become an unintentional driver of inequitable health care delivery, outcomes, and incentives. The subclinical effects within the normal expected physiological range of RBCs can be large enough to alter clinical interpretation of HbA1c and addressing this will help with individualized care and decision making. This review describes a new glycemic measure, personalized HbA1c (pA1c), that may address the clinical inaccuracies of HbA1c by taking into account interindividual variability in RBC glucose uptake and lifespan. Therefore, pA1c represents a more sophisticated understanding of glucose-HbA1c relationship at an individual level. Future use of pA1c, after adequate clinical validation, has the potential to refine glycemic management and the diagnostic criteria in diabetes.

Discordance Between Glucose Management Indicator and Glycated Hemoglobin in People Without Diabetes

Background: In recent years, continuous glucose monitor (CGM) use is increasing in people without diabetes to promote healthy lifestyle. CGM metrics such as glucose management indicator (GMI), a statistical formula to estimate glycated hemoglobin (HbA1c) from sensor glucose, is commonly used to approximate HbA1c. This study was aimed to evaluate discordance between GMI and HbA1c in people without diabetes. Methods: Children and nonpregnant adults (age ≥6 years) without diabetes (laboratory HbA1c <5.7% and negative islet antibodies) were invited to participate in a multicenter prospective study aimed to evaluate glycemic profiles in nondiabetic individuals. Each participant wore a blinded Dexcom G6 for up to 10 days. GMI was calculated from mean sensor glucose and discordance between GMI and HbA1c was analyzed. Results: Of 201 screened participants, 153 participants (mean age 31.2 ± 21.0 years, 66.0% female, HbA1c 5.1% ± 0.3%) were included in the analysis. Mean GMI was 0.59% higher than laboratory HbA1c in participants without diabetes. The discordance between GMI and HbA1c of 0.4% or greater was 71% in participants without diabetes compared with 39% in the original GMI development cohort. Conclusion: GMI does not accurately estimate HbA1c in healthy people without diabetes. Clinical trial registration number is: NCT00717977.

Performance of the Glucose Management Indicator (GMI) in Type 2 Diabetes

BACKGROUND

The glucose management indicator (GMI) is an estimated measure of hemoglobin A1c (HbA1c) recommended for the management of persons with diabetes using continuous glucose monitoring (CGM). However, GMI was derived primarily in young adults with type 1 diabetes, and its performance in patients with type 2 diabetes is poorly characterized.

METHODS

We conducted a prospective cohort study in 144 adults with obstructive sleep apnea and type 2 diabetes not using insulin (mean age: 59.4 years; 45.1% female). HbA1c was measured at the study screening visit. Participants simultaneously wore 2 CGM sensors (Dexcom G4 and Abbott Libre Pro) for up to 4 weeks (2 weeks at baseline and 2 weeks at the 3-month follow-up visit). GMI was calculated using all available CGM data for each sensor.

RESULTS

Median wear time was 27 days (IQR: 23-29) for the Dexcom G4 and 28 days (IQR: 24-29) for the Libre Pro. The mean difference between HbA1c and GMI was small (0.12-0.14 percentage points) (approximately 2 mmol/mol). However, the 2 measures were only moderately correlated (r = 0.68-0.71), and there was substantial variability in GMI at any given value of HbA1c (root mean squared error: 0.66-0.69 percentage points [7 to 8 mmol/mol]). Between 36% and 43% of participants had an absolute difference between HbA1c and GMI ≥0.5 percentage points (≥5 mmol/mol), and 9% to 18% had an absolute difference >1 percentage points (>11 mmol/mol). Discordance was higher in the Libre Pro than the Dexcom G4.

CONCLUSIONS

GMI may be an unreliable measure of glycemic control for patients with type 2 diabetes and should be interpreted cautiously in clinical practice.Clinicaltrials.gov Registration Number: NCT02454153.

Assessment of the Glucose Management Indicator Using Different Sampling Durations

We compared the glucose management indicator (GMI) calculated using 14 days of continuous glucose monitor (CGM) data with GMI calculated using <14 days. Analysis included 581 individuals with type 1 diabetes or type 2 diabetes from five clinical trials. The correlation between the 14- and 7-day GMI was 0.95 and the correlation between 14 days versus 10, 5, and 3 days GMI was 0.98, 0.91, and 0.86, respectively. The percentages of GMI values within 0.3% of the 14-day GMI were 98% with 10-day GMI, 87% with 7-day GMI, 77% with 5-day GMI, and 60% with 3-day GMI. Minimal differences were observed between GMI computed using 14 days of data compared with GMI computed with 7 days. Although 10-14 days of CGM data are preferred for computing GMI, for most patients a satisfactory estimate of HbA1c can be obtained with 7 days of data.

Relationship Between Time-in-Range, HbA1c, and the Glucose Management Indicator in Pregnancies Complicated by Type 1 Diabetes

Objective: We aimed to evaluate relationships between time-in-range (TIR 63-140 mg/dL), glycated hemoglobin A1c (HbA1c) level, and the glucose management indicator (GMI) in pregnant women with type 1 diabetes. Research Design and Methods: Continuous glucose monitoring (CGM) data from 27 women with type 1 diabetes were collected prospectively throughout pregnancy. Up to 90-days of CGM data were correlated with point-of-care HbA1c levels measured in the clinic at each trimester. GMI levels were calculated using a published regression formula. Liner models were used to compare TIR, HbA1c, and GMI by each trimester. Results: There was a significant negative correlation between TIR and HbA1c; each 10% increase in TIR was associated with a 0.3% reduction in HbA1c. The correlation between TIR and HbA1c was stronger (r = -0.8) during the second and third trimesters than during the first trimester (r = -0.4). There was good correlation between TIR and GMI during each trimester (r = 0.9 for each trimester). The relationship between GMI and HbA1c especially during second (r = 0.8) and third trimesters (r = 0.8) was strong. Conclusion: In the first trimester, the correlation between HbA1c level and TIR was relatively small, while that of TIR and GMI was very strong, thus GMI may better reflect glycemic control than HbA1c in early pregnancy. Each 10% increase in TIR was associated with a 0.3% reduction in HbA1c throughout pregnancy, which was lower than other published studies in nonpregnant populations reporting a 0.5%-0.8% reduction in HbA1c. Further studies are needed to understand the relationship between TIR and GMI and how GMI may affect maternal and fetal complications. Clinical Trial Registration number: NCT02556554.

Glucose Management Indicator for People with Type 1 Asian Diabetes Is Different from That of the Published Equation: Differences by Glycated Hemoglobin Distribution

Background: We aimed to determine whether there are racial differences in glucose management indicator (GMI) equation for Asians and propose an adjusted GMI equation specific to Asians. Methods: This was a 24-week, prospective, observational study. A total of 106 Korean subjects with type 1 diabetes was included in the analyses. Continuous glucose monitoring (CGM: Dexcom G5) data and glycated hemoglobin (HbA1c) were obtained at the end of 3 months (n = 106) and 6 months (n = 70) of use of a CGM device. Full 3-month CGM data were collected from 176 patients. Results: Linear regression analysis between HbA1c and CGM-derived mean glucose (GMI [%] = 2.814 + 0.026 × mean glucose [mg/dL], R² = 0.739, P < 0.001) showed significant correlation. An increase corresponding to each 25 mg/dL increase of mean glucose was higher with the Asian-Dexcom-specific GMI (0.7%) than with the published GMI (0.6%). The mean Asian-Dexcom-specific GMI was significantly lower than the published GMI (P = 0.022), especially in patients with HbA1c <7.0% (<6.0%: P = 0.003, 6.0%-6.9%: P = 0.001). Conclusions: The GMI equation specific for Asian Type 1 diabetes was different from the published GMI equation. For a given CGM-derived mean glucose, GMI calculated with the published equation could overestimate HbA1c in Asian subjects with HbA1c <7.0%. Although race partially explains the differences in GMI equation between published and Asian data, future research with larger databases is needed to develop a specific formula for Asian populations.

HbA1c and Glucose Management Indicator Discordance: A Real-World Analysis

Background: There can be marked discordance between laboratory and estimated (using the glucose management indicator [GMI]) glycated hemoglobin (HbA1c) from continuous glucose monitoring (CGM). This may cause errors in diabetes management. This study evaluates discordance between laboratory and CGM-estimated HbA1c (eA1C). Methods: We performed a retrospective review of patients with diabetes who use CGM. The patients were seen at the University of Washington (UW) Diabetes Care Center from 2012 to 2019. We used UW's Institute of Translational Health Sciences to extract eligible encounters from the electronic medical record. We required that patients use CGM and that HbA1c and sensor data be obtained fewer than 4 weeks apart. There were no exclusion criteria. We calculated HbA1c-GMI discordance for each subject and assessed for any impact of comorbidities. We defined HbA1c-GMI discordance as absolute difference between laboratory and eA1C. Results: This study included 641 separate office encounters. Ninety-one percent of patients had type 1 diabetes. Most patients had diabetes for greater than 20 years. The mean duration of CGM wear was 24.5 ± 8 days. Only 11% of patients had HbA1c-GMI discordance <0.1%, but 50% and 22% had differences ≥0.5% and ≥1%. There was increased discordance with advanced chronic kidney disease (estimated glomerular filtration rate <60). Discussion: We found substantial discordance between laboratory and eA1C in a real-world setting. Clinicians need be aware that HbA1c may not as accurately reflect mean glucose as previously appreciated.

The Calculation of the Glucose Management Indicator Is Influenced by the Continuous Glucose Monitoring System and Patient Race

Objective: To determine whether the glucose management indicator (GMI), an estimate of hemoglobin A1c (HbA1c) derived from continuous glucose monitoring (CGM) mean glycemia, differs by CGM system and patient race. Methods: One hundred three patients with prediabetes or stable diabetes and a minimum of 10 days of CGM data collected with the FreeStyle Libre CGM system immediately before measurement of HbA1c were included in this clinic-based observational study that used data from electronic health records in an academic endocrinology clinic. HbA1c and Libre CGM-measured mean glucose were plotted to derive a race-agnostic and race-specific regression equations to calculate a Libre-specific GMI (GMI_Li). The mean GMI derived from the published formula (GMI_P) was compared with GMI_Li. Results: Mean ± SD (standard deviation) age of patients was 61.9 ± 13.3 years; 50% were of nonwhite race and 77% had type 2 diabetes; mean HbA1c was 62 mmol/mol (7.8%). The mean (range) number of days with available CGM data was 26 (10-90). The mean ± SD GMI_Li was higher than the GMI_P in the entire cohort (7.9% ± 1.0% vs. 7.5% ± 1.0%, respectively; P = 0.01) and among Asian patients (7.9% ± 0.9% vs. 7.2% ± 1.0%, respectively; P = 0.03). Conclusions: In a cohort with prediabetes or stable diabetes, the regression equation to calculate GMI varied by CGM system and patient race. The development of device- and race-specific regression equations for GMI may be warranted.

HbA1c: The Glucose Management Indicator, Time in Range, and Standardization of Continuous Glucose Monitoring Reports in Clinical Practice

Continuous glucose monitoring (CGM) use is growing rapidly among people with diabetes and beginning to be standard of care for managing glucose levels in insulin therapy. With this increased use, there is a need to standardize CGM data. CGM standardization has been set forth by expert panels. The Glucose Management Indicator is a concept using the CGM-derived mean glucose to provide a value that can be understood similarly to hemoglobin A_1c. The times an individual spends in various glucose ranges is emerging as an important set of metrics. Metrics derived from patient CGM data are changing the way diabetes is managed.

A View Beyond HbA1c: Role of Continuous Glucose Monitoring

Hemoglobin A1C (HbA1c) is used as an index of average blood glucose measurement over a period of months and is a mainstay of blood glucose monitoring. This metric is easy to measure and relatively inexpensive to obtain, and it predicts diabetes-related microvascular complications. However, HbA1c provides only an approximate measure of glucose control; it does not address short-term glycemic variability (GV) or hypoglycemic events. Continuous glucose monitoring (CGM) is a tool which helps clinicians and people with diabetes to overcome the limitations of HbA1c in diabetes management. Time spent in the glycemic target range and time spent in hypoglycemia are the main CGM metrics that provide a more personalized approach to diabetes management. Moreover, the glucose management indicator (GMI), which calculates an approximate HbA1c level based on the average CGM-driven glucose level, facilitates individual decision-making when the laboratory-measured HbA1c and estimated HbA1c are discordant. GV, on the other hand, is a measure of swings in blood glucose levels over hours or days and may contribute to diabetes-related complications. In addition, addressing GV is a major challenge during the optimization of glycemia. The degree of GV is associated with the frequency, duration, and severity of the hypoglycemic events. Many factors affect GV in a patient, including lifestyle, diet, the presence of comorbidities, and diabetes therapy. Recent evidence supports the use of some glucose-lowering agents to improve GV, such as the new ultra-long acting insulin analogs, as these agents have a smoother pharmacodynamic profile and improve glycemic control with fewer fluctuations and fewer nocturnal hypoglycemic events. These newer glucose-lowering agents (such as incretin hormones or sodium-glucose cotransporter 2 inhibitors) can also reduce the degree of GV. However, randomized trials are needed to evaluate the effect of GV on important diabetes outcomes. In this review, we discuss the role of HbA1c as a measure of glycemic control and its limitations. We also explore additional glycemic metrics, with a focus on time (duration) in glucose target range, time (duration) in hypoglycemia, GV, GMI, and their correlation with clinical outcomes.