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

Version of Record

Accepted for publication after peer review and revision.

Download
Cite
Share
CommentOpen annotations (there are currently 0 annotations on this page).

Version of Record updated: October 5, 2021 (This version)
Version of Record published: September 13, 2021 (Go to version)
Accepted: August 24, 2021
Received: April 15, 2021

1. Of interest
Remodeling of skeletal muscle myosin metabolic states in hibernating mammals

Christopher TA Lewis, Elise G Melhedegaard ... Julien Ochala

Research Article May 16, 2024
Further reading

Abstract
Introduction
Results
Discussion
Materials and methods
Appendix 1
Data availability
References
Article and author information
Metrics

Abstract

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.

Introduction

High glucose exposure in specific organs (particularly eye, kidney, and nerve) is a critical factor for the development of diabetes complications (Marcovecchio, 2017; Giacco and Brownlee, 2010). Laboratory HbA_1c is routinely used to assess glycaemic control, but studies report a disconnect between this glycaemic marker and diabetes complications in some individuals (Cohen et al., 2003; Bonora and Tuomilehto, 2011). The exact mechanisms for this are not always clear but, at least in some cases, likely related to inaccurate estimation of intracellular glucose exposure in the affected organs.

While raised intracellular glucose is responsible for diabetes complications (Giacco and Brownlee, 2010; Brownlee, 2005), extracellular hyperglycaemia selectively damages cells with limited ability to adjust cross-membrane glucose transport effectively (Brownlee, 2005). HbA_1c has been used as a biomarker for diabetes-related intracellular hyperglycaemia for two main reasons. First, the glycation reaction occurs within red blood cells (RBCs) and therefore HbA_1c is modulated by intracellular glucose level. Second, RBCs do not have the capacity to adjust glucose transporter GLUT1 levels and thus are unable to modify glucose uptake, behaving similarly to cells that are selectively damaged by extracellular hyperglycaemia (Brownlee, 2005). Therefore, under conditions of fixed RBC lifespan and glucose uptake, HbA_1c mirrors intracellular glucose exposure in organs affected by diabetes complications. However, given the inter-individual variability in both glucose uptake and RBC lifespan (Cohen et al., 2008; Khera et al., 2008), laboratory HbA_1c may not always reflect intracellular RBC glucose exposure. While variation in RBC glucose uptake is likely relevant to the risk of diabetes complications in susceptible organs, variation in red cell lifespan can affect haemoglobin glycation and HbA1c values, in turn compromising the accuracy of this glycaemic marker in predicting risk of complications. This explains the inability to clinically rely on laboratory HbA_1c in those with haematological disorders characterised by abnormal RBC turnover (American Diabetes Association, 2019) and represents a possible explanation for the apparent ‘disconnect’ between laboratory HbA_1c and development of complications in some individuals with diabetes (Figure 1).

Figure 1

Download asset Open asset

Individual red blood cell (RBC) lifespan can affect HbA_1c and diabetes treatment.

In some individuals, laboratory HbA_1c can be misleading and resulting in undertreatment, thus increasing the risk of complications, or overtreatment, predisposing to hypoglycaemia.

A kinetic model, which considers individual variations in both RBC turnover and glucose uptake, has been developed to explain the disconcordance of the glucose-HbA_1c relationship on individual level (Xu et al., 2021a). The current work aims to extend this model by providing a way to normalise against RBC lifespan variation when individual RBC lifespan becomes available. We propose a new clinical marker, which we term adjusted HbA_1c (aHbA_1c), by adjusting laboratory HbA_1c for a standard RBC lifespan of 106 days (English and Lenters-Westra, 2018) (equivalent to RBC turnover rate of 0.94% per day). The new glyacemic marker, aHbA_1c, is likely to be the most accurate marker of organ exposure to hyperglycaemia and risk of future diabetes-related complications.

Results

Of the 287 individuals in the original studies, 218 had predefined continuous glucose monitoring (CGM) coverage between at least two HbA_1c measurements. Of these, 131 individuals had adequate continuous glucose data to estimate RBC lifespan and glucose uptake rate. The subject characteristics of this sub-cohort are presented in Table 1.

Table 1

Main characteristics of the cohort studied.

N	131
Age [years; mean ± SD (range)]	53.5 ± 13.7 (18, 77)
Gender, male [number (percentage)]	86 (66%)
T1D [number (percentage)]T2D [number (percentage)]BMI [kg/m²; mean ± SD (range)]	51 (39%)80 (61%)29.8 ± 5.9 (18.8, 54.1)
Duration of diabetes (years)	17.7 ± 8.7 (2, 46)
Hypoglycaemic therapy	Multiple daily injections of insulin
Data are presented as mean ± SD (min, max) or n (%)

Mean (median, IQR) RBC lifespan was 94 (100, 86–102) days in those with T1D and 92 (100, 83–101) in those with T2D (Figure 2). In this cohort, the mean, median, IQR of the absolute difference between aHbA_1c and laboratory HbA_1c were 11.0, 3.9, 3.0–14.3 mmol/mol (1.0, 0.4, 0.3–1.3%) for T1D, and marginally higher at 15.1, 5.3, 4.1–22.5 mmol/mol (1.4, 0.5, 0.4–2.0%) for T2D subjects. As illustrated in the figure, those with the shorter RBC lifespan of 80 days showed around 22 mmol/mol (2%) lower laboratory HbA_1c than aHbA_1c. This may lead to underestimating intracellular glucose exposure in susceptible organs, in turn increasing the risk of complications. In contrast, those with RBC lifespan of 130 days demonstrated higher laboratory HbA_1c than aHbA_1c, which can give the impression of inadequate glycaemic control, leading to therapy escalation and predisposition to hypoglycaemia.

Figure 2

Download asset Open asset

Distribution of red blood cell (RBC) lifespan for type 1 (n = 51) and type 2 (n = 80) diabetes and adjustment to laboratory HbA_1c by RBC lifespan.

The number (percentage) of individuals having HbA_1c adjustments < 1 % (<11 mmol/mol), 1–2% (11–22 mmol/mol), 2–3% (22–33 mmol/mol), and >3% (>33 mmol/mol) were 90 (68%), 21 (16%), 12 (9%), and 8 (6%), respectively.

To further put these results into clinical context, two subjects with an identical laboratory HbA_1c of 63 mmol/mol (7.9%) but different RBC lifespans of 89 and 107 days, would have RBC-lifespan-adjusted aHbA_1c values of 78 mmol/mol (9.3%) and 62 mmol/mol (7.8%), respectively, indicating different future risk of diabetes complications. Another two individuals with different laboratory HbA_1c of 60 mmol/mol (7.6%) and 75 mmol/mol (9.0%), and corresponding RBC lifespans of 89 and 107 days, would have identical aHbA_1c value of 74 mmol/mol (8.9%). This would place them at similar risk of diabetes complications, despite the significantly different laboratory HbA_1c values. Generally, in individuals with RBC lifespan of approximately 93–123 days, aHbA_1c and laboratory HbA_1c showed relatively small differences (<11 mmol/mol or 1% when laboratory HbA_1c < 64 mmol/mol or 8%). In this cohort, 90 (69%) subjects were within RBC lifespan range of 93–123 days, while 39 (30%) subjects had RBC lifespan below 93 days and 2 (1.5%) subjects above 123 days.

Discussion

Variation in RBC lifespan and glucose uptake between individuals can lead to different laboratory HbA_1c despite similar hyperglycaemic exposure in the organs affected by diabetes complications. In order to individualise care and assess the personal risk of hyperglycaemic complications, laboratory HbA_1c levels should be adjusted to account for variability in RBC turnover through our proposed aHbA_1c. Without this adjustment, there is a risk of overestimating glucose levels that may cause hypoglycaemia through the unnecessary escalation of diabetes therapies, or alternatively, underestimation that may lead to undertreatment and subsequent high risk of complications. In addition, there are implications for the diagnosis of prediabetes and diabetes, as there may be misclassifications if the diagnosis is based solely on laboratory HbA_1c levels due to variable RBC lifespan across individuals.

RBC removal by senescence and erythrocyte apoptosis are complex processes, which can be affected by the presence of hyperglycaemia and known to vary both within and across individuals (Lang et al., 2012). In the meantime, potential differences in RBC glucose uptake (Khera et al., 2008) can also affect the relationship between blood glucose and HbA_1c. Several mathematical models (Malka et al., 2016; Fabris et al., 2020) have been developed to estimate laboratory HbA_1c from glucose levels or time in range, emphasising the importance of this area. Accurate estimation of ‘clinically relevant HbA1c’ will allow each person with diabetes to have an individualised glycaemic target that ensures adequate treatment, thus reducing the risk of complications while minimising hypoglycaemic risk.

A unique feature of our model (Xu et al., 2021a) is the inclusion of individual-specific RBC lifespan and glycation rate in the calculations. A weakness of this model, however, is the absence of a direct measure of RBC lifespan, which remains an estimate based on a mathematical calculation. However, the ability of the model to reflect laboratory HbA_1c, as we have previously shown, indicates a good level of accuracy at estimating RBC lifespan ( Xu et al., 2021c). In addition, the method is far simpler than complex methods for estimating RBC lifespan through labelling experiments that are not suited for routine clinical practice (Cohen et al., 2008). Future work may determine whether other measures, such as reticulocyte count or red cell distribution width (Brodksy, 2021; Kameyama et al., 2018; Kameyama et al., 2020), can further be added to the model to further improve the accuracy of estimating RBC lifespan and this remains an area for future research.

Since aHbA_1c reflects intracellular glucose exposure in RBCs, it is difficult to directly compare with extracellular glucose-derived glycaemic markers such as average glucose or time in range. As an intracellular marker, aHbA_1c should correlate with intracellular glucose levels, therefore providing a potentially accurate measure of glucose exposure of organs susceptible to diabetes complications. We summarise the advantages and drawbacks of different methods that measure average glucose control in Appendix 1—table 2.

Importantly, our study demonstrates that laboratory HbA_1c does not necessarily reflect intracellular glucose exposure of organs prone to diabetes complications. However, future work is required to show that adjusted A_1c is a better predictor of diabetes complications than laboratory HbA_1c. Moreover, it is unclear whether the use of aHbA_1c reduces the risk of hypoglycaemic complications as compared to reliance on laboratory HbA_1c, and these remain areas for future research.

In conclusion, quantitative aHbA_1c, derived from laboratory HbA_1c and CGM readings, has the potential to more accurately assess glycaemic exposure of different organs, providing a safer and more effective glycaemic guide for the management of individuals with diabetes. Future testing in larger populations and different ethnic groups is required to further increase confidence in the model. This to be followed by large prospective clinical studies to test the relationship between aHbA_1c and future microvascular/macrovascular diabetes complications as well as reducing the risk of hypoglycaemic exposure through avoidance of unnecessary therapy escalation.

Materials and methods

CGM and laboratory HbA_1c data from 139 type 1 (T1D) and 148 type 2 diabetes (T2D) patients, enrolled in two previous European clinical studies (Bolinder et al., 2016; Haak et al., 2017), were evaluated to calculate aHbA_1c as detailed below. These studies were designed to evaluate the benefits of CGM in those with T1D and those with T2D using multiple daily injections of insulin. Both studies were conducted after appropriate ethical approval and participants gave written informed consent. A total of 6 months’ CGM data were collected using the sensor-based flash glucose monitoring system (FreeStyle Libre; Abbott Diabetes Care, Witney, UK), while HbA_1c was measured by a central laboratory (ICON Laboratories, Dublin, Ireland) at 0, 3, and 6 months of the study. For T1D participants, the mean age was 44 years (range 18–70 years), 17 (33%) of whom were females. For T2D, the mean age was 59 years (range 33–77 years), 28 (35%) of whom were females.

Each subject had at least one data section consisting of two HbA_1c measurements connected by CGM data. Since the kinetic parameters are more sensitive to the data sections with larger between-day glucose changes, the parameters were successfully estimated for those individuals with sufficient day-to-day glucose variability, as evidenced by the model fit of RBC life converging between 50 and 180 days. These individual RBC lifespans or turnover rates were calculated according to previous model (Xu et al., 2021a) that considers both RBC turnover rate and glucose uptake. Briefly, the model aligns laboratory HbA_1c and the contemporaneous CGM-derived estimate of HbA_1c under optimal values for RBC turnover and glucose uptake of each individual. Since there is no simple clinical assay for RBC turnover and glucose uptake, these RBC parameters are estimated using a numerical method such that differences between laboratory HbA_1c and CGM-derived estimate are minimized. While the parameter identification method can be performed by repeated permutations across all reasonably possible values for RBC lifespan and uptake, our approach uses a far more efficient and reliable numerical method, as previously described (Xu et al., 2021a). Detailed model description and derivation are provided in Appendix 1. Deriving from the same model, we constructed aHbA_1c (Equation 1) that adjusts laboratory HbA_1c for individual RBC turnover variation for potential clinical use.

a H b A 1 c = \frac{H b A 1 c}{H b A 1 c + \frac{k_{a g e}^{r e f}}{k_{a g e}} (1 - H b A 1 c)}

In an approximation, $a H b A 1 c \frac{k_{a g e}}{k_{a g e}^{r e f}} H b A 1 c$ , where HbA_1c is laboratory HbA_1c, k_age is individual RBC turnover rate (%/day), $k_{a g e}^{r e f}$ is standard RBC turnover rate (0.94%/day). HbA_1c and aHbA_1c are in NGSP unit and decimal values should be used. For example, 8% HbA_1c should be applied as 0.08. Equation 1 for IFCC unit is available in Appendix 1.

Under the assumption of individually constant RBC life, the relationship between RBC turnover rate (k_age), RBC lifespan (L_RBC) and mean RBC age (MA_RBC) can be inter-converted using the simple formula: $2 * M A_{R B C} = L_{R B C} = \frac{1}{k_{a g e}}$ . Therefore, 0.94%/day standard RBC turnover rate is equivalent to 106 days of RBC life and 53 days of mean RBC age. Of note, the adjustment is not linear, decreasing RBC lifespan corresponds to more pronounced aHbA_1c adjustment than a seemingly comparable increase in RBC lifespan. All calculations in this study were done with Python/SciPy (Virtanen et al., 2020) software package.

Full derivation of the model is further provided in Appendix 1.

Appendix 1

Derivation of Equation (1): kinetic model review

The previously published kinetic model (Xu et al., 2021a) for glucose and HbA_1c relationship led to Equation (1), shown in the main text. We further describe here the derivation of the model for the convenience of the reader. We also cover how the model can be used to estimate personal kinetic parameters for RBC glucose uptake and RBC turnover rate.

Our model assumes: (1) first-order dependencies for concentrations of both haemoglobin in RBCs and intracellular glucose; (2) newly generated RBCs have a negligible amount of glycated haemoglobin; (3) RBCs have a fixed life, so that they are generated constantly and eliminated from circulation when they reach an age that is individual-specific.

The rate of change in glycated and non-glycated haemoglobin in RBCs can be modelled by differential equations:

d [H b G] / d t {= k}_{g} [G I] [H b] - r * α * A 1 c

d [H b] / d t = k_{g e n} / C - r * (1 - α * A 1 c) - k_{g} [G I] [H b]

[HbG] and [Hb] are the concentrations of glycated and non-glycated haemoglobin, respectively, while [GI] is intracellular glucose concentration. The k_g is the rate constant of haemoglobin glycation reaction in unit of (concentration*time)^–1, with a reported value of about 0.0019 dL/mg/day (Higgins and Bunn, 1981). C is the total haemoglobin concentration C = [Hb] + [HbG]. A_1c is the fraction of glycated haemoglobin [HbG]/C, r is RBC removal rate in unit of concentration/time, α is a coefficient used to scale HbA_1c to the fraction of glycated haemoglobin to be removed (has no units of measurement).

The glucose transporters on RBC membranes (GLUT1) follows Michaelis-Menten kinetic with a universal K_M approximately 26 mM (Ladyzynski et al., 2011). Intracellular glucose can be modelled with $d [G I] / d t = V_{m a x} * [G] / (K_{M} + [G]) - k_{c} * [G I]$ , where [G] is the extracellular glucose concentration and k_c is the rate of glucose consumption within RBCs. The maximum rate V_max should be proportional to the GLUT1 level on the membrane and we assume both k_c and V_max can vary between individuals. Since this process is fast, we use the equilibrium condition:

[G 1] = \frac{V_{m a x} * [G]}{K_{c} * (K_{M} + [G])} = \frac{V_{m a x}}{K_{M} * k_{c}} g = \frac{k_{g l y}}{K_{g}} g = \frac{k_{g l y}}{K_{g}} \frac{K_{M} * [G]}{K_{M} + [G]}

where $g = (K_{M} * [G]) / (K_{M} + [G])$ and $k_{g l y} = k_{g} * V_{m a x} / {(k}_{c} * K_{M})$ .

By definition, HbA_1c is the fraction of glycated haemoglobin found in RBCs:

A 1 c = [H b G] / C = (C - [H b]) / C .

In steady state, $d [H b] / d t = d [H b G] / d t = 0$ , Equation (a1) becomes

C * k_{g} / (α * r) = [H b G] / ([G I] [H b]) .

Combining with Equation (a3):

\frac{{C * k}_{g} * V_{m a x}}{α * r * {K_{M} * k}_{c}} = \frac{[H b G]}{g * [H b]}

By combining all parameters associated with cross-membrane glucose transport and glycation from the right-hand side of Equation (a4), we define the composite glycation rate constant

$k_{g l y} = k_{g} * V_{m a x} / {(k}_{c} * K_{M})$ , where k_g and K_M are universal constants for the non-enzymatic haemoglobin glycation reaction and glucose affinity to GLUT1, respectively. Therefore, k_gly can vary between individuals depending on k_c and V_max.

We attribute the rest of the parameters to RBC turnover k_age = α*r/C, which leads to the definition of the apparent glycation parameter K:

K = k_{g l y} / k_{a g e} = [H b G] / (g * [H b])

Under a hypothetical steady state of constant glucose level, HbA_1c should reach an equilibrium level, which is the ‘equilibrium HbA_1c’ or EA. Since C=[HbG]+[Hb], Equation (a5) can be re-written to $K = (C - [H b]) / (g * [H b])$ . Applying the definition HbA_1c = HbG/C = (C−[Hb])/C, we have:

E A = \frac{g}{K^{- 1} + g}

This relationship approximates the average glucose and HbA_1c for an individual with a stable day-to-day glucose profile.

From Equation (a3): $[G I] = \frac{V_{m a x}}{K_{M} * k_{c}} g = \frac{k_{g l y}}{k_{g}} g$ , and substituting into Equation (a6) gives:

[G I] = \frac{k_{a g e} * E A}{k_{g} * (1 - E A)}

Imaging two individuals who have identical intracellular glucose level ${[G I]}_{k_{a g e}} = {[G I]}_{k_{a g e}^{r e f}}$ , one with $k_{a g e}$ and the other with reference $k_{a g e}^{r e f}$ . The relationship of their equilibrium HbA_1c values is:

\frac{k_{a g e} * E A_{k_{a g e}}}{k_{g} * (1 - E A_{k_{a g e}})} = \frac{k_{a g e}^{r e f} * E A_{k_{a g e}^{r e f}}}{k_{g} * (1 - E A_{k_{a g e}^{r e f}})}

Again, k_g is the universal composite rate constant for glucose haemoglobin composite reaction. Simplifying this equation we have:

E A_{k_{a g e}^{r e f}} = \frac{E A_{k_{a g e}}}{E A_{k_{a g e}} + \frac{k_{a g e}^{r e f}}{k_{a g e}} (1 - E A_{k_{a g e}})}

In a steady state, $E A_{k_{a g e}}$ is the HbA_1c under RBC turnover rate of $k_{a g e}$ . The $E A_{k_{a g e}^{r e f}}$ is the HbA_1c under the same intracellular glucose level, when RBC turnover rate is a reference value of $k_{a g e}^{r e f}$ . Therefore, if we were to compare intracellular glucose exposure, steady-state HbA_1c should be adjusted to a reference RBC turnover rate, which lead to the aHbA_1c in Equation (a1) by replacing $E A_{k_{a g e}^{r e f}}$ with aHbA_1c and $E A_{k_{a g e}}$ with HbA_1c:

a H b A 1 C = \frac{H b A 1 C}{H b A 1 C + \frac{k_{a g e}^{r e f}}{k_{a g e}} (1 - H b A 1 C)} = \frac{1}{1 + \frac{k_{a g e}^{r e f}}{k_{a g e}} (\frac{1}{H b A 1 c} - 1)}

To simplify the above, an approximation to Equation (a1) is $a H b A 1 C \frac{k_{a g e}}{k_{a g e}^{r e f}} H b A 1 c$ . Note that $\frac{k_{a g e}}{k_{a g e}^{r e f}} = \frac{M_{R B C}^{r e f}}{M_{R B C}} = \frac{L_{R B C}^{r e f}}{L_{R B C}}$ , where M_RBC is the mean RBC age and L_RBC is the RBC lifespan.

The HbA_1c and aHbA_1c take NGSP unit in decimal form by default. For example, the decimal form of HbA_1c of 8% is 0.08. The unit for $k_{a g e}^{r e f}$ and $k_{a g e}$ should be %/day.

When IFCC unit (mmol/mol) is used for HbA_1c and aHbA_1c, Equation (a1) becomes:

a H b A 1 C = \frac{1092.9}{1 + \frac{k_{a g e}^{r e f}}{k_{a g e}} (\frac{100}{0.0915 * H b A 1 c + 2.15} - 1)} - 23.5

Estimations of k_gly and k_age from glucose and HbA_1c data and prospective validation: kinetic model review

Our previous publication (Xu et al., 2021a) gave the following relationship by solving the differential Equation (a1):

{A 1 c}_{t} = E A + ({A 1 c}_{0} - E A) {\cdot e}^{- (k_{g l y} * g + k_{a g e}) t}

Equation (a8) is suitable for a short time interval. For a longer time period, a recursive form is required:

{A 1 c}_{z} = {E A}_{z} (1 - D_{z}) + \sum_{i = 1}^{z - 1} {[E A}_{i} (1 - D_{i}) \prod_{j = i + 1}^{z} D_{j}] + {A 1 c}_{0} \prod_{j = 1}^{z} D_{j}

Equation (a9) describes HbA_1c change from time 0 to time z. A_1c0 and A_1cz are the starting and ending HbA_1c values. The time period is split into z time intervals with lengths of t_i (i = 1,2,3, …, z), where $D_{i} = e^{- (k_{g l y} * g_{i} + k_{a g e}) t_{i}}$ and $E A_{i} = \frac{g_{i}}{K^{- 1} + g_{i}}$ , and $g_{i}$ can be calculated from the average glucose (AG) in the time interval $g_{i} = (K_{M} * {A G}_{i}) / (K_{M} + {A G}_{i})$ .

The value A_1cz is equivalent to calculated HbA_1c (cHbA_1c) at the end of time interval t_z. While shorter time intervals – such as 4–6 hr – are expected to produce better results, we have shown that a time interval of 24 hr has produced acceptable performance (Xu et al., 2021a). Equation (a9) is central to our kinetic model. To estimate personal parameters k_gly and k_age, one or more data sections are needed, where a data section contains two HbA_1c measurements, one at the start of the time period and one at the end, with frequent (i.e. every 15 min) glucose levels in-between. The optimised individual k_gly and k_age pair should best align the HbA_1c and cHbA_1c, minimising the preferred error function, such as mean difference or sum-squared difference.

Once an individual’s k_gly and k_age pair are available, Equation (a9) is used to project future HbA_1c if provided frequent glucose measurements. Therefore, prospective model validation is possible when multiple data sections are available, such that one or more can be held out of the parameter estimation to be used for prospective evaluation. Appendix 1—table 1 summarises results (Xu et al., 2021a; Xu et al., 2021c; Xu et al., 2021b) when all but the last data section is used to determine the individual k_gly and k_age pairs, and the held-out final data section is used for evaluation. The agreement between the last HbA_1c and cHbA_1c is compared to the agreement between the last HbA_1c and the glucose management indicator (GMI) (Xu et al., 2021a; Xu et al., 2021c; Xu et al., 2021b). These studies demonstrated the superior accuracy of the kinetic model compared to the existing GMI method.

Appendix 1—table 1

Summary of kinetic model validation studies.

The mean absolute deviation differences between calculated HbA_1c (cHbA_1c) and glucose management indicator (GMI) are statically significant with p < 0.0001.

Study		T1D SAP ^[22]		DPV T1D ^[23]		Replace/mpact ^[9]
Country		Japan		Germany		Europe
Subject count (male)		51 (14)		352 (171)		120 (79) [TID 54 (37), T2D 66 (42)]
Age median (range)		42 (6–73)		12.5 (3–19)		52 (18–77)
HbA_1c test		Central lab		POC+ central lab		Central lab
CGM device		Medtronic		Abbott		Abbott
Method		cHbA_1c	GMI (14-day AG)	cHbA_1c	GMI (14-day AG)	cHbA_1c	GMI (14-day AG)
Abs. dev.% (mmol/mol)	Mean	0.11 (1.2)	0.47 (5.1)	0.32 (3.5)	0.57 (6.2)	0.31 (3.4)	0.66 (7.2)
	SD	0.06 (0.7)	0.46 (5.0)	0.28 (3.0)	0.55 (6.0)	0.22 (2.4)	0.46 (5.0)
	Median	0.10 (1.1)	0.36 (3.9)	0.26 (2.8)	0.46 (5.0)	0.27 (3.0)	0.5 (5.5)
Average bias% (mmol/mol)		0 (0)	–0.3 (–3.3)	0 (0)	0.4 (4.4)	0 (0)	–0.6 (–6.6)
R²		0.91	0.65	0.79	0.52	0.88	0.63

Glycaemic marker comparisons

Given the importance of intracellular glucose level in diabetes management. We provide following table to compare the intracellular aspects of some frequently used glycaemic markers.

Appendix 1—table 2

Main characteristics of markers assessing average glycaemic control.

		Intracellular (I) or extracellular (E) glucose	Affected by mean red blood cell (RBC) lifespan	Advantages	Disadvantages
CGM-derived	Average glucose	E	No	Can reflect both long- (weeks/months) and short-term (minutes, hours) glucose control	Extracellular measurements Lack of large-scale longitudinal studies demonstrating a direct association with long-term complications
CGM-derived	Time in range	E	No
Average fasting plasma glucose		E	No
Glycated albumin		E	No	Mid-term (weeks) average glucose
HbA_1c		I	Yes	Long-term (months) intracellular average glucose exposure Longitudinal studies demonstrating associations with long-term complications	Affected by variation in RBC lifespan Lacks accuracy in the presence of other conditions (anaemias, renal failure, haemoglobin variants, etc.)
Adjusted HbA_1c		I	No	Long-term intracellular average glucose Exposure normalised for RBC lifespan, providing a personalised glycaemic marker	Requires RBC lifespan determination Longitudinal studies linking with outcome are lacking
Intracellular glucose		I	No	Reflects both long- and short-term intracellular glucose control	No direct measurement available Requires RBC glucose uptake rate determination (Ladyzynski et al., 2011; Kameyama et al., 2021), which is not feasible in routine clinical practice

Data availability

Data file for figures have been provided.

References

1. American Diabetes Association
(2019) 6. Glycemic Targets: Standards of Medical Care in Diabetes-2019
Diabetes Care 42:S61–S70.
https://doi.org/10.2337/dc19-S006
- PubMed
- Google Scholar
(2016) Novel glucose-sensing technology and hypoglycaemia in Type 1 diabetes: A multicentre, non-masked, randomised controlled trial
Lancet 388:2254–2263.
https://doi.org/10.1016/S0140-6736(16)31535-5
- PubMed
- Google Scholar
1. Bonora E
2. Tuomilehto J
(2011) The pros and cons of diagnosing diabetes with A1C
Diabetes Care 34:S184–S190.
https://doi.org/10.2337/dc11-s216
- Google Scholar
Book
1. Brodksy RA
(2021)
Diagnosis of Hemolytic Anemia in Adults

UpToDate.
- Google Scholar
1. Brownlee M
(2005) The pathobiology of diabetic complications: A unifying mechanism
Diabetes 54:1615–1625.
https://doi.org/10.2337/diabetes.54.6.1615
- PubMed
- Google Scholar
(2003) Discordance between HbA1c _and fructosamine: Evidence for a glycosylation gap and its relation to diabetic nephropathy
Diabetes Care 26:163–167.
https://doi.org/10.2337/diacare.26.1.163
- PubMed
- Google Scholar
1. Cohen RM
2. Franco RS
3. Khera PK
4. Smith EP
5. Lindsell CJ
6. Ciraolo PJ
7. Palascak MB
8. Joiner CH
(2008) Red cell life span heterogeneity in hematologically normal people is sufficient to alter HBA1C
Blood 112:4284–4291.
https://doi.org/10.1182/blood-2008-04-154112
- PubMed
- Google Scholar
1. English E
2. Lenters-Westra E
(2018) HbA1c method performance: The great success story of global standardization
Critical Reviews in Clinical Laboratory Sciences 55:408–419.
https://doi.org/10.1080/10408363.2018.1480591
- PubMed
- Google Scholar
(2020) Estimation of Hemoglobin A1c from Continuous Glucose Monitoring Data in Individuals with Type 1 Diabetes: Is Time In Range All We Need?
Diabetes Technology & Therapeutics 22:501–508.
https://doi.org/10.1089/dia.2020.0236
- PubMed
- Google Scholar
1. Giacco F
2. Brownlee M
(2010) Oxidative stress and diabetic complications
Circulation Research 107:1058–1070.
https://doi.org/10.1161/CIRCRESAHA.110.223545
- PubMed
- Google Scholar
1. Haak T
2. Hanaire H
3. Ajjan RA
4. Hermanns N
5. Riveline JP
6. Rayman G
(2017) Flash glucose-sensing technology as a replacement for blood glucose monitoring for the management of insulin-treated type 2 diabetes: a multicenter, open-label randomized controlled trial
Diabetes Ther 8:55–73.
https://doi.org/10.1007/s13300-016-0223-6
- PubMed
- Google Scholar
1. Higgins PJ
2. Bunn HF
(1981) Kinetic analysis of the nonenzymatic glycosylation of hemoglobin
Journal of Biological Chemistry 256:5204–5208.
https://doi.org/10.1016/S0021-9258(19)69387-7
- Google Scholar
(2018) Steady-state relationship between average glucose, HbA1c and RBC lifespan
Journal of Theoretical Biology 447:111–117.
https://doi.org/10.1016/j.jtbi.2018.03.023
- PubMed
- Google Scholar
(2020) A novel method for calculating mean erythrocyte age using erythrocyte creatine
Aging 12:8702–8709.
https://doi.org/10.18632/aging.103193
- PubMed
- Google Scholar
1. Kameyama M
2. Okumiya T
3. Tokuhiro S
4. Matsumura Y
5. Matsui H
6. Ono Y
7. Iwasaka T
8. Hiratani K
9. Koga M
(2021) Estimation of the hemoglobin glycation rate constant
Scientific Reports 11:1–6.
https://doi.org/10.1038/s41598-020-80024-7
- Google Scholar
1. Khera PK
2. Joiner CH
3. Carruthers A
4. Lindsell CJ
5. Smith EP
6. Franco RS
7. Holmes YR
8. Cohen RM
(2008) Evidence for interindividual heterogeneity in the glucose gradient across the human red blood cell membrane and its relationship to hemoglobin glycation
Diabetes 57:2445–2452.
https://doi.org/10.2337/db07-1820
- PubMed
- Google Scholar
(2011) Hemoglobin glycation rate constant in non-diabetic individuals
Annals of Biomedical Engineering 39:2721–2734.
https://doi.org/10.1007/s10439-011-0366-6
- PubMed
- Google Scholar
1. Lang F
2. Lang E
3. Foller M
(2012) Physiology and pathophysiology of eryptosis
Transfusion Medicine and Hemotherapy 39:308–314.
https://doi.org/10.1159/000342534
- PubMed
- Google Scholar
(2016) Mechanistic modeling of hemoglobin glycation and red blood cell kinetics enables personalized diabetes monitoring
Sci Transl Med 8:359ra130.
https://doi.org/10.1126/scitranslmed.aaf9304
- Google Scholar
1. Marcovecchio ML
(2017) Complications of acute and chronic hyperglycemia
US Endocrinology 13:17–21.
https://doi.org/10.17925/USE.2017.13.01.17
- Google Scholar
(2020) SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Nature Methods 17:261–272.
https://doi.org/10.1038/s41592-019-0686-2
- PubMed
- Google Scholar
1. Xu Y
2. Dunn TC
3. Ajjan RA
(2021a) A kinetic model for glucose levels and hemoglobin A1C provides a novel tool for individualized diabetes management
Journal of Diabetes Science and Technology 15:294–302.
https://doi.org/10.1177/1932296819897613
- PubMed
- Google Scholar
(2021b) Personal Glycation Factors and Calculated Hemoglobin A1c for Diabetes Management: Real-World Data from the Diabetes Prospective Follow-up (DPV) Registry
Diabetes Technology & Therapeutics 23:452–459.
https://doi.org/10.1089/dia.2020.0553
- Google Scholar
1. Xu Y
2. Hirota Y
3. Ajjan RA
4. Yamamoto A
(2021c) Accurate prediction of HbA1c by continuous glucose monitoring using a kinetic model with patient-specific parameters for red blood cell lifespan and glucose uptake
Diabetes and Vascular Disease Research 12:1–6.
https://doi.org/10.1177/14791641211013734
- Google Scholar

Article and author information

Author details

Yongjin Xu

Abbott Diabetes Care, Alameda, United States

Contribution
Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review and editing

Competing interests
YX is an employee of Abbott Diabetes Care

"This ORCID iD identifies the author of this article:" 0000-0001-9446-8402
Richard M Bergenstal

International Diabetes Center, Park Nicollet, HealthPartners, Minneapolis, United States

Contribution
Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing - original draft, Writing - review and editing

Competing interests
RMB has received research support, has acted as a consultant, or has been on the scientific advisory board for Abbott Diabetes Care, Ascensia, DexCom, Eli Lilly, Hygieia, Johnson & Johnson, Medtronic, Merck, Novo Nordisk, Onduo, Roche, Sanofi and United Healthcare. RMB's employer, non-profit HealthPartners Institute, contracts for his services and no personal income goes to RMB.
Timothy C Dunn

Abbott Diabetes Care, Alameda, United States

Contribution
Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing - original draft, Writing - review and editing

Competing interests
TCD is an employee of Abbott Diabetes Care

"This ORCID iD identifies the author of this article:" 0000-0003-3487-2504
Ramzi A Ajjan

Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom

Contribution
Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing - original draft, Writing - review and editing

For correspondence
R.Ajjan@leeds.ac.uk

Competing interests
RAA received no payment for this work but has had research support and/or Honoraria from Abbott Diabetes Care, NovoNordisk, Eli Lilly, Johnson & Johnson, Boehringer Ingelheim, Bayer, Sanofi and AstraZeneca.

"This ORCID iD identifies the author of this article:" 0000-0002-1636-3725

Funding

Abbott Diabetes Care

Yongjin Xu
Timothy C Dunn

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was funded by Abbott Diabetes Care.

Ethics

Data were used from two previously published clinical studies in Europe (reference 10 and 11). Both studies were conducted after appropriate ethical approval and participants gave written informed consent.

Version history

Received: April 15, 2021
Accepted: August 24, 2021
Version of Record published: September 13, 2021 (version 1)
Version of Record updated: October 5, 2021 (version 2)

Copyright

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

1,292

views
155

downloads
13

citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Article PDF

Open citations (links to open the citations from this article in various online reference manager services)

Mendeley

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

Yongjin Xu
Richard M Bergenstal
Timothy C Dunn
Ramzi A Ajjan

(2021)

Addressing shortfalls of laboratory HbA_1c using a model that incorporates red cell lifespan

eLife 10:e69456.

https://doi.org/10.7554/eLife.69456

Categories and tags

Research organism

Human

Share this article

Cite this article

Individual red blood cell (RBC) lifespan can affect HbA1c and diabetes treatment.

Main characteristics of the cohort studied.

Distribution of red blood cell (RBC) lifespan for type 1 (n = 51) and type 2 (n = 80) diabetes and adjustment to laboratory HbA1c by RBC lifespan.

Summary of kinetic model validation studies.

Main characteristics of markers assessing average glycaemic control.

Author details

Yongjin Xu

Contribution

Competing interests

Richard M Bergenstal

Contribution

Competing interests

Timothy C Dunn

Contribution

Competing interests

Ramzi A Ajjan

Contribution

For correspondence

Competing interests

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

Categories and tags

Research organism

Further reading

Individual red blood cell (RBC) lifespan can affect HbA_1c and diabetes treatment.

Distribution of red blood cell (RBC) lifespan for type 1 (n = 51) and type 2 (n = 80) diabetes and adjustment to laboratory HbA_1c by RBC lifespan.