Personal Glycation Factors and Calculated Hemoglobin A1c for Diabetes Management: Real-World Data from the Diabetes Prospective Follow-up (DPV) Registry

The Glucose Management Indicator: Time to Change Course?

Laboratory measurement of hemoglobin A1c (HbA1c) has, for decades, been the standard approach to monitoring glucose control in people with diabetes. Continuous glucose monitoring (CGM) is a revolutionary technology that can also aid in the monitoring of glucose control. However, there is uncertainty in how best to use CGM technology and its resulting data to improve control of glucose and prevent complications of diabetes. The glucose management indicator, or GMI, is an equation used to estimate HbA1c based on CGM mean glucose. GMI was originally proposed to simplify and aid in the interpretation of CGM data and is now provided on all standard summary reports (i.e., average glucose profiles) produced by different CGM manufacturers. This Perspective demonstrates that GMI performs poorly as an estimate of HbA1c and suggests that GMI is a concept that has outlived its usefulness, and it argues that it is preferable to use CGM mean glucose rather than converting glucose to GMI or an estimate of HbA1c. Leaving mean glucose in its raw form is simple and reinforces that glucose and HbA1c are distinct. To reduce patient and provider confusion and optimize glycemic management, mean CGM glucose, not GMI, should be used as a complement to laboratory HbA1c testing in patients using CGM systems.

Variation in the relationship between fasting glucose and HbA1c: implications for the diagnosis of diabetes in different age and ethnic groups

INTRODUCTION

Identify non-glycemic factors affecting the relationship between fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c), in order to refine diabetes diagnostic criteria.

RESEARCH DESIGN AND METHODS

Relationship between FPG-HbA1c was assessed in 12 531 individuals from 2001 to 2018 US National Health and Nutrition Examination Survey. Using a recently described method, FPG and HbA1c were used to calculate apparent glycation ratio (AGR) of red blood cells for different subgroups based on age, race, and gender.

RESULTS

At an FPG of 7 mmol/L, black individuals had a higher HbA1c (p<0.001, mean: 50.2 mmol/mol, 95% CI (49.8 to 50.4)) compared with white individuals (47.4 mmol/mol (47.2 to 47.5)). This corresponds to NGSP (National Glycohemoglobin Standardization Program) units of 6.7% and 6.5% for black versus white individuals, respectively. Similarly, individuals under 21 years had lower HbA1c (p<0.001, 47.9 mmol/mol (47.7 to 48.1), 6.5%) compared with those over 50 years (48.3 mmol/mol (48.2 to 48.5), 6.6%). Differences were also observed between women (p<0.001, 49.2 mmol/mol (49.1 to 49.3), 6.7%) and men (47.0 mmol/mol (46.8 to 47.1), 6.5%). Of note, the difference in HbA1c at FPG of 7 mmol/L in black females over 50 and white males under 21 years was 5 mmol/mol (0.46%). AGR differences according to race (p<0.001), age (p<0.001), and gender (p<0.001) explained altered glucose-HbA1c relationship in the analyzed groups.

CONCLUSIONS

FPG-HbA1c relationship is affected by non-glycemic factors leading to incorrect diagnosis of diabetes in some individuals and ethnic groups. Assessment of AGR helps understand individual-specific relationship between glucose levels and HbA1c, which has the potential to more accurately diagnose and manage diabetes.

Use of Continuous Glucose Monitoring in Pump Therapy Sensor Augmented Pump or Automated Insulin Delivery in Different Age Groups (0.5 to <26 Years) With Type 1 Diabetes From 2018 to 2021: Analysis of the German/Austrian/Swiss/Luxemburg DPV Registry

AIM

Insulin pump, continuous glucose monitoring (CGM), and sensor augmented pump (SAP) technology have evolved continuously leading to the development of automated insulin delivery (AID) systems. Evaluation of the use of diabetes technologies in people with T1D from January 2018 to December 2021.

METHODS

A patient registry (Diabetes Prospective Follow-up Database [DPV]) was analyzed for use of SAP (insulin pump + CGM ≥90 days, no automated dose adjustment) and AID (HCL or LGS/PLGS). In total 46,043 people with T1D aged 0.5 to <26 years treated in 416 diabetes centers (Germany, Austria, Luxemburg, and Switzerland) were included and stratified into 4 groups A-D according to age. Additionally, TiR and HbA1c were analyzed.

RESULTS

From 2018 to 2021, there was a significant increase from 28.7% to 32.9% (sensor augmented pump [SAP]) and 3.5% to 16.6% (AID) across all age groups, with the most frequent use in group A (<7 years, 38.8%-40.2% and 10.3%-28.5%). A similar increase in SAP and AID use was observed in groups B (7 to <11 years) and C (11 to <16 years): B: +15.8 PP, C: +15.9 PP. HbA1c improved significantly in groups C and D (16 to <26 years) (both P < .01). Time in range (TiR) increased in all groups (A: +3 PP; B: +5 PP; C: +5 PP; D: +5 PP; P < 0.01 for each group). Insulin pumps (61.0% versus 53.4% male) and SAP (33.5% versus 28.9% male) are used more frequently in females.

CONCLUSION

In recent years, we found an increasing use of new diabetes technologies and an improvement in metabolic control (TiR) across all age groups.

Evaluation of continuous glucose monitoring-derived person-specific HbA1c in the presence and absence of complications in type 1 diabetes

AIM

To evaluate the accuracy of a novel kinetic model at predicting HbA1c in a real-world setting and to understand and explore the role of diabetes complications in altering the glucose-HbA1c relationship and the mechanisms involved.

MATERIALS AND METHODS

Deidentified HbA1c and continuous glucose monitoring values were collected from 93 individuals with type 1 diabetes. Person-specific kinetic variables were used, including red blood cell (RBC) glucose uptake and lifespan, to characterize the relationship between glucose levels and HbA1c. The resulting calculated HbA1c (cHbA1c) was compared with glucose management indicator (GMI) for prospective agreement with laboratory HbA1c.

RESULTS

The cohort (42 men and 51 women) had a median age (IQR) of 61 (43, 72) years and a diabetes duration of 21 (10, 33) years. A total of 24 459 days of continuous glucose monitoring (CGM) data were available and 357 laboratory HbA1c were used to assess the average glucose-HbA1c relationship. cHbA1c had a superior correlation with laboratory HbA1c compared with GMI with a mean absolute deviation of 1.7 and 6.7 mmol/mol, r²  = 0.85 and 0.44, respectively. The fraction within 10% of absolute relative deviation from laboratory HbA1c was 93% for cHbA1c and 63% for GMI. Macrovascular disease had no effect on the model's accuracy, whereas microvascular complications resulted in a trend towards higher HbA1c, secondary to increased RBC glucose uptake.

CONCLUSIONS

cHbA1c, which takes into account RBC glucose uptake and lifespan, accurately reflects laboratory HbA1c in a real-world setting and can aid in the management of individuals with diabetes.

An Examination of the Glucose Management Indicator in Young Children with Type 1 Diabetes

BACKGROUND

Previous studies utilizing glucose data from continuous glucose monitors (CGM) to estimate the Glucose Management Indicator (GMI) have not included young children or determined appropriate GMI formulas for young children with type 1 diabetes (T1D).

METHODS

We extracted CGM data for 215 children with T1D (0-6 years) from a repository. We defined sampling periods ranging from the 3-27 days prior to an HbA1c measurement and compared a previously established GMI formula to a young child-specific GMI equation based on the sample's CGM data. We examined associations between HbA1c, GMI values, and other CGM metrics for each sampling period.

RESULTS

The young child-specific GMI formula and the published GMI formula did not evidence significant differences when using 21-27 days of CGM data. The young child-specific GMI formula demonstrated higher correlations to laboratory HbA1c when using 18 or fewer days of CGM data. Overall, the GMI estimate and HbA1c values demonstrate a strong relationship in young children with T1D.

CONCLUSIONS

Future research studies may consider utilizing the young child-specific GMI formula if the data collection period for CGM values is under 18 days. Further, researchers and clinicians may consider changing the default number of days of data used to calculate glycemic metrics in order to maximize validity of CGM-derived metrics.

Interindividual variability in average glucose-glycated haemoglobin relationship in type 1 diabetes and implications for clinical practice

AIM

Glycated haemoglobin (HbA1c) can fail to reflect average glucose levels, potentially compromising management decisions. We analysed variability in the relationship between mean glucose and HbA1c in individuals with diabetes.

MATERIALS AND METHODS

Three months of continuous glucose monitoring and HbA1c data were obtained from 216 individuals with type 1 diabetes. Universal red blood cell glucose transporter-1 Michaelis constant K_M and individualized apparent glycation ratio (AGR) were calculated and compared across age, racial and gender groups.

RESULTS

The mean age (range) was 30 years (8-72) with 94 younger than 19 years, 78 between 19 and 50 years, and 44 were >50 years. The group contained 120 women and 96 men with 106 white and 110 black individuals. The determined K_M value was 464 mg/dl and AGR was (mean ± SD) 72.1 ± 7 ml/g. AGR, which correlated with red blood cell lifespan marker, was highest in those aged >50 years at 75.4 ± 6.9 ml/g, decreasing to 73.2 ± 7.8 ml/g in 19-50 years, with a further drop to 71.0 ± 5.8 ml/g in the youngest group (p <0 .05). AGR differed between white and black groups (69.9 ± 5.8 and 74.2 ± 7.1 ml/g, respectively; p < .001). In contrast, AGR values were similar in men and women (71.5 ± 7.5 and 72.5 ± 6.6 ml/g, respectively; p = .27). Interestingly, interindividual AGR variation within each group was at least four-fold higher than average for between-group variation.

CONCLUSIONS

In this type 1 diabetes cohort, ethnicity and age, but not gender, alter the HbA1c-glucose relationship with even larger interindividual variations found within each group than between groups. Clinical application of personalized HbA1c-glucose relationships has the potential to optimize glycaemic care in the population with diabetes.

An Evaluation of Point-of-Care HbA1c, HbA1c Home Kits, and Glucose Management Indicator: Potential Solutions for Telehealth Glycemic Assessments

During the COVID-19 pandemic, fewer in-person clinic visits resulted in fewer point-of-care (POC) HbA1c measurements. In this sub-study, we assessed the performance of alternative glycemic measures that can be obtained remotely, such as HbA1c home kits and Glucose Management Indicator (GMI) values from Dexcom Clarity. Home kit HbA1c (n = 99), GMI, (n = 88), and POC HbA1c (n = 32) were collected from youth with T1D (age 9.7 ± 4.6 years). Bland-Altman analyses and Lin's concordance correlation coefficient (ρ_c) were used to characterize the agreement between paired HbA1c measures. Both the HbA1c home kit and GMI showed a slight positive bias (mean difference 0.18% and 0.34%, respectively) and strong concordance with POC HbA1c (ρ_c = 0.982 [0.965, 0.991] and 0.823 [0.686, 0.904], respectively). GMI showed a slight positive bias (mean difference 0.28%) and fair concordance (ρ_c = 0.750 [0.658, 0.820]) to the HbA1c home kit. In conclusion, the strong concordance of GMI and home kits to POC A1c measures suggest their utility in telehealth visits assessments. Although these are not candidates for replacement, these measures can facilitate telehealth visits, particularly in the context of other POC HbA1c measurements from an individual.

The relative contribution of diurnal and nocturnal glucose exposures to HbA1c in type 1 diabetes males: a pooled analysis

Purpose

The exact contribution of daily glucose exposure to HbA1c in people with type 1 diabetes (T1D) remains controversial. We examined the contribution of pre- and postprandial glycaemia, nocturnal and early-morning glycaemia, and glycaemic variability to HbA1c levels in T1D. In this analysis, we used clinical data, namely age, BMI and HbA1c, as well as glycaemic metrics (24-h glycaemia, postprandial, nocturnal, early-morning glycaemia, wake-up glucose, and glycaemic variability) obtained over a four-week period of continuous glucose monitoring (CGM) wear in thirty-two males with T1D.

Methods

The trapezoid method was used estimate the incremental area under the glucose curve (iAUC) for 24-h, postprandial (3-h period following breakfast, lunch, and dinner, respectively), nocturnal (between 24:00–04:00 AM), and early-morning (2-h period 2-h prior to wake-up) glycaemia. Linear regression analysis was employed whereby CGM-derived glycaemic metrics were explanatory variables and HbA1c was the outcome.

Results

Thirty-two T1D males (mean ± SD: age 29 ± 4 years; HbA1c 7.3 ± 0.9% [56 ± 13 mmol/mol]; BMI 25.80 ± 5.01 kg/m²) were included in this analysis. In linear models adjusted for age and BMI, HbA1c was associated with 24-h mean glucose (r² = 0.735, p < 0.001), SD (r² = 0.643, p = 0.039), and dinner iAUC (r² = 0.711, p = 0.001). CGM-derived metrics and non-glycaemic factors explained 77% of the variance in HbA1c, in which postprandial glucose accounted for 32% of the variance explained. The single greatest contributor to HbA1c was dinner iAUC resulting in 0.6%-point (~7 mmol/mol) increase in HbA1c per SD increase in dinner iAUC.

Conclusions

Using comprehensive CGM profiling, we show that postprandial glucose, specifically evening-time postprandial glucose, is the single largest contributing factor to HbA1c in T1D.

Trial registration number

NCT02204839 (July 30th 2014); NCT02595658 (November 3rd 2015).

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.

Teamwork, Targets, Technology, and Tight Control in Newly Diagnosed Type 1 Diabetes: the Pilot 4T Study

CONTEXT

Youth with type 1 diabetes (T1D) do not meet glycated hemoglobin A1c (HbA1c) targets.

OBJECTIVE

This work aimed to assess HbA1c outcomes in children with new-onset T1D enrolled in the Teamwork, Targets, Technology and Tight Control (4T) Study.

METHODS

HbA1c levels were compared between the 4T and historical cohorts. HbA1c differences between cohorts were estimated using locally estimated scatter plot smoothing (LOESS). The change from nadir HbA1c (month 4) to 12 months post diagnosis was estimated by cohort using a piecewise mixed-effects regression model accounting for age at diagnosis, sex, ethnicity, and insurance type. We recruited 135 youth with newly diagnosed T1D at Stanford Children's Health. Starting July 2018, all youth within the first month of T1D diagnosis were offered continuous glucose monitoring (CGM) initiation and remote CGM data review was added in March 2019. The main outcomes measure was HbA1c.

RESULTS

HbA1c at 6, 9, and 12 months post diagnosis was lower in the 4T cohort than in the historic cohort (-0.54% to -0.52%, and -0.58%, respectively). Within the 4T cohort, HbA1c at 6, 9, and 12 months post diagnosis was lower in those patients with remote monitoring than those without (-0.14%, -0.18% to -0.14%, respectively). Multivariable regression analysis showed that the 4T cohort experienced a significantly lower increase in HbA1c between months 4 and 12 (P < .001).

CONCLUSION

A technology-enabled, team-based approach to intensified new-onset education involving target setting, CGM initiation, and remote data review statistically significantly decreased HbA1c in youth with T1D 12 months post diagnosis.

Quality of Glycemic Control: Assessment Using Relationships Between Metrics for Safety and Efficacy

Numerous methods have been proposed as measures of quality of glycemic control resulting in confusion regarding the best choice of metric to use by clinicians and researchers. Some methods use a single metric such as HbA1c, Mean Glucose, %Time In Range (%TIR), or Coefficient of Variation (%CV). Others use a combination of up to seven metrics, for example, Q-Score, Comprehensive Glucose Pentagon (CGP), and Personal Glycemic State (PGS). A recently proposed Composite continuous Glucose monitoring index utilizes three metrics: %TIR, Time Below Range (%TBR), and standard deviation (SD) of glucose. This review proposes that only two metrics can be sufficient when monitoring an individual patient or when comparing two or more forms of management interventions. These two metrics comprise (1) a measure of efficacy such as Mean Glucose, HbA1c, %TIR, or %Time Above Range (%TAR) and (2) a measure of safety based on risk of hypoglycemia such as %TBR, Low Blood Glucose Index (LBGI), or frequency of specified types of hypoglycemic events per patient year. By analysis of the two-dimensional graphical and statistical relationships between metrics for safety and efficacy and by testing identity versus nonidentity of these relationships, one can improve sensitivity for detection of the effects of medications and of other therapeutic interventions, avoid the need for arbitrary scoring systems for glucose values falling within versus outside the target range, and offer the advantage of conceptual and practical simplicity.

Addressing shortfalls of laboratory HbA1c using a model that incorporates red cell lifespan

Laboratory HbA_1c does not always predict diabetes complications and our aim was to establish a glycaemic measure that better reflects intracellular glucose exposure in organs susceptible to complications. Six months of continuous glucose monitoring data and concurrent laboratory HbA_1c were evaluated from 51 type 1 diabetes (T1D) and 80 type 2 diabetes (T2D) patients. Red blood cell (RBC) lifespan was estimated using a kinetic model of glucose and HbA_1c, allowing the calculation of person-specific adjusted HbA_1c (aHbA_1c). Median (IQR) RBC lifespan was 100 (86–102) and 100 (83–101) days in T1D and T2D, respectively. The median (IQR) absolute difference between aHbA_1c and laboratory HbA_1c was 3.9 (3.0–14.3) mmol/mol [0.4 (0.3–1.3%)] in T1D and 5.3 (4.1–22.5) mmol/mol [0.5 (0.4–2.0%)] in T2D. aHbA_1c and laboratory HbA_1c showed clinically relevant differences. This suggests that the widely used measurement of HbA_1c can underestimate or overestimate diabetes complication risks, which may have future clinical implications.

American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus

OBJECTIVE

To provide evidence-based recommendations regarding the use of advanced technology in the management of persons with diabetes mellitus to clinicians, diabetes-care teams, health care professionals, and other stakeholders.

METHODS

The American Association of Clinical Endocrinology (AACE) conducted literature searches for relevant articles published from 2012 to 2021. A task force of medical experts developed evidence-based guideline recommendations based on a review of clinical evidence, expertise, and informal consensus, according to established AACE protocol for guideline development.

MAIN OUTCOME MEASURES

Primary outcomes of interest included hemoglobin A1C, rates and severity of hypoglycemia, time in range, time above range, and time below range.

RESULTS

This guideline includes 37 evidence-based clinical practice recommendations for advanced diabetes technology and contains 357 citations that inform the evidence base.

RECOMMENDATIONS

Evidence-based recommendations were developed regarding the efficacy and safety of devices for the management of persons with diabetes mellitus, metrics used to aide with the assessment of advanced diabetes technology, and standards for the implementation of this technology.

CONCLUSIONS

Advanced diabetes technology can assist persons with diabetes to safely and effectively achieve glycemic targets, improve quality of life, add greater convenience, potentially reduce burden of care, and offer a personalized approach to self-management. Furthermore, diabetes technology can improve the efficiency and effectiveness of clinical decision-making. Successful integration of these technologies into care requires knowledge about the functionality of devices in this rapidly changing field. This information will allow health care professionals to provide necessary education and training to persons accessing these treatments and have the required expertise to interpret data and make appropriate treatment adjustments.