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

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.

Management of diabetes mellitus patients with sickle cell anemia: Challenges and therapeutic approaches

The coexistence of diabetes mellitus (DM) and sickle cell anemia (SCA) poses significant challenges in clinical management due to the complex interactions and overlapping complications associated with both conditions. Managing diabetes in individuals with SCA requires a comprehensive approach that addresses the unique physiological and pathological aspects of both diseases. This paper reviews the challenges encountered in the management of DM in patients with SCA and explores therapeutic strategies and approaches to optimize patient care. Challenges in the management of DM in individuals with SCA stem from several factors, including the impact of hemoglobin variants on glycemic control assessment, increased susceptibility to infections, altered immune response, and complications associated with both diseases. Moreover, the coexistence of SCA and DM heightens the susceptibility to infections due to compromised immune function, emphasizing the need for vigilant preventive measures, including vaccinations and close monitoring for infectious complications. Close collaboration among healthcare providers specializing in diabetes, hematology, and other relevant fields is crucial for developing comprehensive care plans. Individualized treatment strategies that balance glycemic control, pain management, and preventive care are essential to mitigate complications and optimize the overall health outcomes of patients with both DM and SCA. In conclusion, managing diabetes in the context of SCA necessitates a nuanced and patient-centered approach. By addressing the challenges and employing tailored therapeutic strategies, healthcare providers can improve the quality of life and health outcomes for individuals affected by both conditions.

Is It Time to Move Beyond TIR to TITR? Real-World Data from Over 20,000 Users of Continuous Glucose Monitoring in Patients with Type 1 and Type 2 Diabetes

The growing use of continuous glucose monitoring (CGM) has been supported by expert consensus and clinical guidelines on glycemic management in diabetes with time in range (TIR 70-180 mg/dL) representing a key CGM-derived glucose metric. Time in tight range (TITR) has also been proposed for clinical use, spanning largely normal glucose levels of 70-140 mg/dL. However, keeping such narrow glucose ranges can be challenging, and understanding the factors modulating TITR can help achieve these tight glycemic targets. Our real-life study aimed to evaluate the relationship between average glucose (AG) and TIR/TITR in a large cohort (n = 22,006) of CGM users, divided into four groups: self-identified as having type 1 diabetes (T1D) treated with insulin using multiple daily injections (MDI) or pumps; type 2 diabetes (T2D) on MDI or insulin pumps; T2D on basal insulin only; and T2D not on insulin treatment. The T2D groups, regardless of treatment type, displayed the highest TIR and TITR values, associated with lowest glycemic variability measured as glucose coefficient of variation (CV; 23-30%). The T1D group showed the lowest TIR and TITR, associated with the highest CVs (36-38%). Overall, higher CV was associated with lower TIR and TITR for AG values below 180 and 140 mg/dL, respectively, with the reverse holding true for AG values above these thresholds. The discordance between AG and TIR/TITR was less pronounced in T2D compared with T1D, attributed to lower CV in the former group. It was also observed that TITR has advantages over TIR for assessing glycemia status and progress toward more stringent A1C, particularly when approaching normal glucose levels. The data detail how CV affects the AG relationship with TIR/TITR, which has implications for CGM interpretation. In many instances TITR, rather than TIR, may be preferable to employ once AG falls below 140 mg/dL and near-normal glucose levels are required clinically.

Red blood cells as biomarkers and mediators in complications of diabetes mellitus: A review

Red blood cells (RBCs), traditionally recognized for their oxygen transport role, have garnered increasing attention for their significance as crucial contributors to the pathophysiology of diabetes mellitus. In this comprehensive review, we elucidate the multifaceted roles of RBCs as both biomarkers and mediators in diabetes mellitus. Amidst the intricate interplay of altered metabolic pathways and the diabetic milieu, RBCs manifest distinct alterations in their structure, function, and lifespan. The chronic exposure to hyperglycemia induces oxidative stress, leading to modifications in RBC physiology and membrane integrity. These modifications, including glycation of hemoglobin (HbA1c), establish RBCs as invaluable biomarkers for assessing glycemic control over extended periods. Moreover, RBCs serve as mediators in the progression of diabetic complications. Their involvement in vascular dysfunction, hemorheological changes, and inflammatory pathways contributes significantly to diabetic microangiopathy and associated complications. Exploring the therapeutic implications, this review addresses potential interventions targeting RBC abnormalities to ameliorate diabetic complications. In conclusion, comprehending the nuanced roles of RBCs as biomarkers and mediators in diabetes mellitus offers promising avenues for enhanced diagnostic precision, therapeutic interventions, and improved patient outcomes. This review consolidates the current understanding and emphasizes the imperative need for further research to harness the full potential of RBC-related insights in the realm of diabetes mellitus.

Clinical Risk Assessment and Comparison of Bias between Laboratory Methods for Estimation of HbA1c for Glycated Hemoglobin in Hyperglycemic Patients

INTRODUCTION

Hemoglobin A1c (HbA1c), also known as glycated hemoglobin, is a blood test used to evaluate and track a patient's blood sugar levels over the previous 2-3 months. We have compared the analytical performance of the D10 hemoglobin (HPLC) testing system to that of the immunoturbidimetric technique, which is a light-scattering immunoassay.

OBJECTIVES

To assess the clinical risk assessment between two methods (Compare the two Immunoturbidometric methods (AU680) vs. HPLC method (D10)) in hyperglycemic patients and assess the acceptability of the respective methods in the Clinical biochemistry laboratory.

METHODS

The charge of the globins in Hb was used as the basis for the HPLC method used to measure HbA1c. HPLC detects and quantifies even the tiniest Hb fractions and the full spectrum of Hb variants. HbA1c was measured using the immunoturbidimetric (AU 680 Beckmann coulter analyzer) and high-performance liquid chromatography (HPLC) techniques. Experiments also made use of immunoturbidimetric techniques (using an AU 680 Beckmann coulter analyzer equipment).

RESULTS

There is no statistically significant difference in HbA1c readings between male and female patients, as measured by either the Immunoturbidimetric or HPLC techniques.

CONCLUSION

The immunoturbidimetric and high-performance liquid chromatography techniques for estimating HbA1c yielded identical results. From the results of this study, we may deduce that both techniques are valid for estimating HbA1c. As a result, it may be suggested that both approaches can be used to estimate HbA1c in diabetic individuals.

Evaluation of HbA1c from CGM traces in an Indian population

Introduction

The development of continuous glucose monitoring (CGM) over the last decade has provided access to many consecutive glucose concentration measurements from patients. A standard method for estimating glycated hemoglobin (HbA1c), already established in the literature, is based on its relationship with the average blood glucose concentration (aBG). We showed that the estimates obtained using the standard method were not sufficiently reliable for an Indian population and suggested two new methods for estimating HbA1c.

Methods

Two datasets providing a total of 128 CGM and their corresponding HbA1c levels were received from two centers: Health Centre, Savitribai Phule Pune University, Pune and Joshi Hospital, Pune, from patients already diagnosed with diabetes, non-diabetes, and pre-diabetes. We filtered 112 data-sufficient CGM traces, of which 80 traces were used to construct two models using linear regression. The first model estimates HbA1c directly from the average interstitial fluid glucose concentration (aISF) of the CGM trace and the second model proceeds in two steps: first, aISF is scaled to aBG, and then aBG is converted to HbA1c via the Nathan model. Our models were tested on the remaining 32 data- sufficient traces. We also provided 95% confidence and prediction intervals for HbA1c estimates.

Results

The direct model (first model) for estimating HbA1c was HbA1cmmol/mol = 0.319 × aISFmg/dL + 16.73 and the adapted Nathan model (second model) for estimating HbA1c is HbA1cmmol/dL = 0.38 × (1.17 × ISFmg/dL) - 5.60.

Discussion

Our results show that the new equations are likely to provide better estimates of HbA1c levels than the standard model at the population level, which is especially suited for clinical epidemiology in Indian populations.

Racial and Ethnic Differences in the Association Between Mean Glucose and Hemoglobin A1c

Background: Studies have reported significantly higher hemoglobin A1c (A1C) in African American patients than in White patients with the same mean glucose, but less is known about other racial/ethnic groups. We evaluated racial/ethnic differences in the association between mean glucose, based on continuous glucose monitor (CGM) data, and A1C. Methods: Retrospective study among 1788 patients with diabetes from Kaiser Permanente Northern California (KPNC) who used CGM devices during 2016 to 2021. In this study population, there were 5264 A1C results; mean glucose was calculated from 124,388,901 CGM readings captured during the 90 days before each A1C result. Hierarchical mixed models were specified to estimate racial/ethnic differences in the association between mean glucose and A1C. Results: Mean A1C was 0.33 (95% confidence interval: 0.23-0.44; P < 0.0001) percentage points higher among African American patients relative to White patients for a given mean glucose. A1C results for Asians, Latinos, and multiethnic patients were not significantly different from those of White patients. The slope of the association between mean glucose and A1C did not differ significantly across racial/ethnic groups. Variance for the association between mean glucose and A1C was substantially greater within groups than between racial/ethnic groups (65% vs. 9%, respectively). Conclusions: For African American patients, A1C results may overestimate glycemia and could lead to premature diabetes diagnoses, overtreatment, or invalid assessments of health disparities. However, most of the variability in the mean glucose-A1C association was within racial/ethnic groups. Treatment decisions driven by guideline-based A1C targets should be individualized and supported by direct measurement of glycemia.

Low Blood Glucose Index Associated with Cardiovascular Events in Diabetic Hemodialysis Patients

INTRODUCTION

Blood glucose monitoring was vitally important in diabetic kidney disease (DKD) patients for preventing complications and improving survival rates. The associations between glycemic variability and blood biochemical indicators were underestimated in patients with DKD undergoing hemodialysis. Therefore, we primarily aimed to investigate the glycemic variability and 1-year risk of cardiovascular disease events in diabetic hemodialysis patients. And we secondarily aimed to explore the association between glycemic variability and blood biochemical indicators.

METHODS

In total, 27 patients were included in the final analysis. Continuous glucose monitoring (CGM) was used to evaluate glucose variability for 14 days. Patients were divided into two groups by the cutoff level of time in range (TIR; >70% or ≤70%). The three-point major adverse cardiovascular event (3P MACE) was recorded within 1 year.

RESULTS

After 1 year of follow-up, 4 patients in the high-TIR group and 3 patients in the low-TIR group had 3p MACE. Higher low blood glucose index (LBGI) level in diabetic hemodialysis patients increased the risk of 3p MACE outcomes (HR = 2.37, p = 0.018). And the level of albumin was positively associated with LBGI (β = 0.51, p = 0.036). The plasma levels of albumin, glycosylated hemoglobin, and hemoglobin were positively associated with other CGM parameters.

CONCLUSION

LBGI during 14 days was positively associated with the risk of cardiovascular events in diabetic hemodialysis patients.

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.

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).