Steady-state relationship between average glucose, HbA1c and RBC lifespan

New Clues to Cardiovascular Disease: Erythrocyte Lifespan

Determination of erythrocyte lifespan is an important part of the diagnosis of hemolytic diseases. Recent studies have revealed alterations in erythrocyte lifespan among patients with various cardiovascular diseases, including atherosclerotic coronary heart disease, hypertension, and heart failure. This review summarizes the progress of research on erythrocyte lifespan in cardiovascular diseases.

Estimation of mean erythrocyte age using HbA1c or HbA1c/glycated albumin for evaluation of anemia severity

BACKGROUND

Hemoglobin A1c (HbA1c) levels are low in patients with hemolytic anemia, as HbA1c reflects mean erythrocyte age (MRBC ). Erythrocyte creatine (EC) is a hemolytic indicator that also reflects MRBC . We previously reported an equation for estimating MRBC using EC (EC-MRBC ).

AIMS

In this study, EC-MRBC was compared to the HbA1c level expressed in the International Federation of Clinical Chemistry and Laboratory Medicine units (iA1c) and to the iA1c/glycated albumin (GA) ratio to estimate MRBC .

METHODS

This study included 238 subjects, including patients with hemolytic anemia and/or type 2 diabetes mellitus (T2DM).

RESULTS

In non-diabetic individuals, both iA1c and iA1c/GA showed a strong positive correlation with EC-MRBC (p < 0.0001). The equations to estimate iA1c-MRBC and iA1c/GA-MRBC derived from the regression equations between EC-MRBC and iA1c, and EC-MRBC and iA1c/GA in nondiabetic individuals were 1.45 × iA1c and 20.0 × iA1c/GA, respectively. iA1c-MRBC and iA1c/GA-MRBC in non-diabetic individuals without hemolytic anemia were 57.6 ± 4.0 and 57.1 ± 6.4 days, respectively, and iA1c/GA-MRBC in T2DM patients without hemolytic anemia was 56.0 ± 8.8 days.; no significant difference was seen in the comparisons.

CONCLUSIONS

The MRBC can be estimated using iA1c or iA1c/GA in non-diabetic individuals, and iA1c/GA in T2DM patients.

The influence of shorter red blood cell lifespan on the rate of HbA1c target achieved in type 2 diabetes patients with a HbA1c detection value lower than 7

BACKGROUND

Variations in the red blood cell (RBC) lifespan can affect glycosylated hemoglobin (HbA1c) test values, but there is still a lack of evidence regarding how and to what degree the RBC lifespan influences HbA1c in the type 2 diabetes mellitus (T2DM) population owing to the restriction of traditional RBC lifespan-detection means. This study aimed to investigate the influence of RBC lifespan variation on HbA1c values in T2DM patients with a HbA1c detection value lower than 7%.

METHODS

Patients with HbA1c <7% were divided into two groups: RBC lifespan <90 days and RBC lifespan ≥90 days. We collected blood glucose levels at seven time points for three consecutive months, assessed the HbA1c and glycosylated albumin levels, and calculated the hemoglobin glycation index (HGI) for each patient.

RESULTS

There were no statistical differences in the HbA1c value between two groups, but the estimated glycosylated hemoglobin (eHbA1c) was significantly higher in patients with an RBC lifespan <90 days. The proportion of the eHbA1c ≥7% in the group with an RBC lifespan <90 days was significantly higher than the other group (33.87% vs. 12.50%, p < .01). Pearson analysis showed a significant negative correlation between RBC lifespan and the HGI in patients with T2DM (r = -0.348, p < .01).

CONCLUSION

A reduced RBC lifespan in T2DM patients caused a noticeable underestimate of the blood glucose levels as presented by HbA1c detection value.

Red Blood Cell Lifespan < 74 Days Can Clinically Reduce Hb1Ac Levels in Type 2 Diabetes

Variations in the red blood cell (RBC) lifespan can affect glycosylated hemoglobin (HbA1c) test values, but there is still a lack of evidence regarding how and to what degree the RBC lifespan influences HbA1c in the type 2 diabetes mellitus (T2DM) population owing to the restriction of traditional RBC lifespan detection means. In this study, we monitored 464 T2DM patients and 231 healthy control finger blood glucose levels at seven time points for three consecutive months. The HbA1c levels were assessed at the end of the third month as well as the RBC lifespan was measured through the CO breath test. T2DM patients were stratified into four quartile groups according to their RBC lifespans. There was no statistical significance in HbA1c among these four groups. However, the average blood glucose in the Q1 group was significantly higher than those in the other groups. Additionally, the contribution of RBC lifespan to HbA1c test value in the Q1 group was 14.07%, which was significantly higher than those in the other groups. Finally, we used multiple linear regression models to construct a mathematical formula to correct the HbA1c test value in the Q1 group, which would benefit the management of T2DM.

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.

Association of hemoglobin H (HbH) disease with hemoglobin A1c and glycated albumin in diabetic and non-diabetic patients

OBJECTIVES

Hemoglobin A1c (HbA1c) and glycated albumin (GA) are glycemic control status indicators in patients with diabetes mellitus. Hemoglobin H (HbH) disease is a moderately severe form of α-thalassemia. Here we examine the usefulness of HbA1c and GA in monitoring glycemic control in patients with HbH disease.

METHODS

HbA1c, GA, and an oral glucose tolerance test were performed in 85 patients with HbH disease and 130 healthy adults. HbA1c was measured using five methods, including two systems based on cation-exchange high-performance liquid chromatography (Variant II Turbo 2.0 and Bio-Rad D100), a capillary zone electrophoresis method (Capillarys 3 TERA), a boronate affinity HPLC method (Premier Hb9210), and an immunoassay (Cobas c501).

RESULTS

Significant lower levels of HbA1c were observed in patients with HbH disease than in healthy adults. In contrast, GA showed no statistically significant differences between participants with and without HbH disease. A considerable number of diabetic patients with HbH disease would be missed if using HbA1c as a diagnostic criterion for diabetes mellitus.

CONCLUSIONS

GA but not HbA1c is suitable for monitoring glycemic control in patients with HbH disease that can modify the discriminative ability of HbA1c for diagnosing diabetes.

Improved Estimation of Glycated Hemoglobin from Continuous Glucose Monitoring and Past Glycated Hemoglobin Data

Background: Accurate estimation of glycated hemoglobin (HbA1c) from continuous glucose monitoring (CGM) remains challenging in clinic. We propose two statistical models and validate them in real-life conditions against the current standard, glucose management indicator (GMI). Materials and Methods: Modeling utilized routinely collected data from patients with type 1 diabetes from central Poland (eligibility criteria: age >1 year, diabetes duration >3 months, and CGM use between 01/01/2015 and 12/31/2019). CGM records were extracted from dedicated Medtronic/Abbott databases and cross-referenced with HbA1c values; 28-day periods preceding HbA1c measurement with >75% of the sensor-active time were analyzed. We developed a mixed linear regression, including glycemic variability indices and patient's ID (glucose variability-based patient specific model, GV-PS) intended for closed-group use and linear regression using patient-specific error of GMI (proportional error-based patient agnostic model, PE-PA) for general use. Models were validated with either new HbA1cs from closed-group patients or separate patient-HbA1c pool. External validation was performed with data from clinical trials. Performance metrics included bias, its 95% confidence interval (95% CI), coefficient of determination (R²), and root mean square error (RMSE). Results: We included 723 HbA1c-CGM pairs from 174 patients (mean age 9.9 ± 4.4 years and diabetes duration 3.7 ± 3.6 years). GMI yielded R² = 0.58, with different bias between Medtronic and Abbott devices [0.120% vs. -0.152%, P < 0.0001], and overall 95% CI = -0.9% to +1%, RMSE = 0.47%. GV-PS successfully captured patient-specific variance (closed-group validation: R² = 0.83, bias = 0.026%, 95% CI = -0.562% to 0.591%, RMSE = 0.31%). PE-PA performed similarly on new patients (R² = 0.76, bias = -0.069%, 95% CI = -0.790% to 0.653%, RMSE = 0.37%). In external validation GMI, GV-PS, and PE-PA produced 73.8%, 87.5%, and 91.0% predictions within 0.5% (5.5 mmol/mol) from the true value. Conclusion: Constructed models performed better than GMI. PE-PA provided an accurate estimate of HbA1c with fast and straightforward implementation.

Estimation of the hemoglobin glycation rate constant

In a previous study, a method of obtaining mean erythrocyte age ([Formula: see text]) from HbA1c and average plasma glucose (AG) was proposed. However, the true value of the hemoglobin glycation constant ([Formula: see text] dL/mg/day), required for this model has yet to be well characterized. Another study also proposed a method of deriving [Formula: see text] from erythrocyte creatine (EC). Utilizing these formulae, this study aimed to determine a more accurate estimate of [Formula: see text]. One hundred and seven subjects including 31 patients with hemolytic anemia and 76 subjects without anemia were included in this study. EC and HbA1c data were analyzed, and [Formula: see text] using HbA1c, AG and the newly-derived constant, [Formula: see text] were compared to [Formula: see text] using traditional [Formula: see text] in three patients whose data were taken from previous case studies. A value of [Formula: see text] dL/mg/day was determined for [Formula: see text]. [Formula: see text] using HbA1c, AG and [Formula: see text] were found to no be significantly different (paired t-test, [Formula: see text]) to [Formula: see text] using traditional [Formula: see text]. [Formula: see text] enables the estimation of [Formula: see text] from HbA1c and AG.

A novel method for calculating mean erythrocyte age using erythrocyte creatine

Estimating the lifespan of erythrocytes is useful for the differential diagnosis of anemia. However, measuring the lifespan of erythrocytes was very difficult; therefore, it was seldom measured. Erythrocyte creatine (EC) decreases reflecting erythrocyte age. We developed a method to obtain mean erythrocyte age (MRBC) from EC.We reanalyzed the previously published data from 21 patients with hemolytic anemia, which included EC and the half-life of 51Cr.MRBC and loge EC showed excellent significant linearity (r = -0.9475, p < 0.001), proving that it could be treated as a mono-exponential relationship within the studied range (EC: 1.45 - 11.76 μmol/g Hb). We established an equation to obtain MRBC (days) from EC (μmol/g Hb): MRBC = -22.84loge EC + 65.83.This equation allowed calculation of MRBC based on EC which has practical applications such as the diagnosis of anemia.

A new non-invasive graphical method for quantification of cerebral blood flow with[[Formula: see text]] IMP

Objective

[\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{123}\hbox {I}$$\end{document}123I] N-isopropyl-p-iodoamphetamine (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{123}\hbox {I}$$\end{document}123I-IMP) is an ideal perfusion tracer for single photon emission computed tomography, which shows good linearity between cerebral blood flow (CBF) and accumulation. However, quantification of CBF using \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{123}\hbox {I}$$\end{document}123I-IMP without arterial blood sampling has been challenging, with previous methods requiring empirically obtained regression formulae to estimate CBF. Furthermore, the CBF value obtained via some of the previous methods would be affected by the clearance rate of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{123}\hbox {I}$$\end{document}123I-IMP from the lungs. This paper introduces a new non-invasive quantification method for CBF using \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{123}\hbox {I}$$\end{document}123I-IMP and dynamic planar images.

Methods

We have developed a theory based on Microsphere model. This method does not involve regression formulae for estimation and allows for direct measurement of CBF, considering the clearance rate of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{123}\hbox {I}$$\end{document}123I-IMP from the lungs. The study method is executed as easily as conventional Graph-Plot method. We compared the CBF values obtained by our study method and the established autoradiograph (ARG) method.

Results

CBF values obtained using the new method demonstrated significant correlation with values determined using ARG method.

Conclusions

The novel method described presents a reliable and more simple way of determining CBF when compared to current methods.