Does Glucose Variability Influence the Relationship Between Mean Plasma Glucose and HbA_1c Levels in Type 1 and Type 2 Diabetic Patients?

The ADAG study was conducted at 10 centers in the U.S., Europe, and Africa from 2006 to 2008 to define the relationship between HbA_1c and average glucose levels. Because a full description of this observational study has been published (14), we describe it here only briefly. A total of 268 individuals with type 1 diabetes and 159 individuals with type 2 diabetes (age 18–70 years) completed the study. Participants were selected based on stable glycemic control as evidenced by two HbA_1c values within one percentage point of each other in the 6 months prior to recruitment. Individuals with a wide range of HbA_1c levels were included.

Participants with conditions leading to major changes in glycemia (infectious disease, steroid therapy, and pregnancy) or conditions that might interfere with the measurement of HbA_1c or the relationship between HbA_1c and MPG (hemoglobinopathies [16], anemia, increased erythrocyte turnover, blood loss and/or transfusions, or chronic renal or liver disease) were excluded (14). The study was approved by the human studies committees at the participating institutions, and informed consent was obtained from all participants.

During the study period, CGM (Medtronic Minimed, Northridge, CA) was performed at home four times with 4-week intervals during the 16-week study period. Monitoring period lasted at least 48 h, during which time glucose levels were assessed every 5 min. CGM data were accepted for analysis if there were no gaps longer than 120 min and if the mean absolute difference with the Hemocue calibration results was <18%, as recommended by the manufacturer. For calibration purposes, participants performed SMBG with the Hemocue meter (Hemocue Glucose 201 plus; Hemocue, Ängelholm, Sweden) during the days of CGM.

For adequate calculation of MPG, subjects additionally performed a seven-point SMBG (OneTouch Ultra; Lifescan, Milipitas, CA) for at least 3 days per week during the weeks when CGM was not performed. All blood glucose values stated are plasma equivalents.

HbA_1c samples were analyzed with four highly intercorrelated DCCT-aligned assays: a high-performance liquid chromatography assay, two immunoassays, and an affinity assay (all approved by the National Glycohemoglobin Study Program). The mean HbA_1c value at the end of the 12 week study period was used (14).

Three indices of intraday glucose variability were calculated based on CGM: the SD of mean glucose concentrations, the mean amplitude of glycemic excursions (MAGE), and the continuous overlapping net glycemic action (CONGA). High SD, MAGE, and CONGA values indicate high intraday glucose variability. MAGE is the mean of the differences between consecutive peaks and nadirs, only including changes of >1 SD of glycemic values and thus capturing only major fluctuations (17). For the calculation of CONGA_n, the difference of the current value compared with the value n hours previously was calculated for each observation after the first n hours. The CONGA_n is the SD of these differences (18). In the analyses, we used CONGA at 4 h (CONGA₄). Calculations based on CGM data were calculated after exclusion of the initial 2 h of monitoring, which is considered to be an unstable calibration period.

First, we explored the correlations between MPG and HbA_1c and measures of glycemic variability as SD, MAGE, and/or CONGA₄ for the total diabetic population and the two diabetes types. Multiple linear regression was used to investigate confounding and effect-modifying influence of clinical parameters (glycemic variability) on the relation between the determinant (MPG) and outcome (HbA_1c) of interest. We then assessed which of the variability measures (SD, MAGE, or CONGA₄) had the strongest impact on the MPG-HbA_1c relationship by confounding or effect modification.

Effect modification was concluded when the slope of the interaction term of glycemic variability and determinant was significant. If no effect modification might be concluded, a parameter ΔB was computed as the relative difference of the slope of the determinant in the model without and with the clinical parameter. Confounding was concluded when the absolute value of ΔB exceeded the generally accepted threshold of 10%.

Multivariate confounding was investigated with a variant of stepwise regression, in which the stepping criterion was not a P value but the ΔB as long as it exceeded the threshold. For significance, a threshold of α = 0.05 was used.

Analyses were done for the total population and stratified for the type of diabetes. Finally, we illustrated the magnitude of the effect caused by the variability indices, by confounding or effect modification, on the MPG-HbA_1c relationship.

Of the 507 patients enrolled, 427 completed the study and had adequate glucose monitoring and HbA_1c samples for inclusion in the analyses. Two hundred and sixty-eight participants had type 1 diabetes, and 159 had type 2 diabetes. The CGM and the SMBG data during the 3-month period included approximately 2,400 and 300 measurements per subject, respectively. The relationship between the HbA_1c level at the end of the 3-month study period and MPG calculated over the preceding 3 months was expressed as the simple linear regressions. The formula for the total diabetic population was as follows: HbA_1c (%) = 0.028 × MPG (mg/dL) + 2.66 (R² = 0.80). The formula for type 1 diabetes was as follows: HbA_1c (%) = 0.028 × MPG (mg/dL) + 2.77 (R² = 0.77). The formula for type 2 diabetes was as follows: HbA_1c (%) = 0.028 × MPG (mg/dL) + 2.62 (R² = 0.82).

The clinical and glycemic characteristics are shown in Table 1. Mean HbA_1c (SD) for type 1 diabetic patients was 7.3% (1.1) and for type 2 diabetic patients was 6.8% (1.1).

All GV measures had significant influence on the MPG-HbA_1c relationship for the total population. The variability index SD showed the strongest influence on the MPG-HbA_1c relationship. None of the GV measures showed confounding for all diabetic patients pooled or for the type 1 or type 2 diabetic patients separately (Table 2).

In the type 1 diabetic patients, the effect modification of SD and CONGA₄ was significant (P < 0.01 and P = 0.02), and for the MAGE it was just not significant (P = 0.06) (Table 2). The MPG/HbA_1c linear regression formula with confounding for type 2 diabetes was as follows: HbA_1c (%) = 2.64 + 2.63 × MPG/100 + 0.58 × SD/100. The MPG-HbA_1c linear regression formula with effect modification for type 1 diabetes was as follows: HbA_1c (%) = 3.91 + 1.79 × MPG/100 − 1.37 × SD/100 + 1.25 × MPG/100 × SD/100. The impact of effect modification of low GV (SD = 30 mg/dL) versus high GV (SD = 100 mg/dL) for an MPG level of 160 mg/dL in type 1 diabetes on the HbA_1c level was 6.96 vs. 7.41%, respectively, as shown in Table 3. At an MPG level of 220 mg/dL (HbA_1c following the regression formula of 8.89%), a decline in the SD parameter from 100 to 30 mg/dL reduced HbA_1c from 9.23 to 8.26%.

For all patients pooled, there was no effect modification of the respective GV measures on the MPG-HbA_1c relationship. For type 2 diabetic patients, the impact of effect modification from the respective GV measures was far from significant (Table 2). The number of patients with a predefined SD is shown in Table 1 for all patients pooled and for the type 1 and type 2 diabetic patients separately.

This study demonstrated a significant effect of GV, as reflected by SD, on the MPG-HbA_1c relationship. High GV (SD) is associated with higher HbA_1c levels for a given MPG, and this effect was more pronounced at higher HbA_1c and MPG values. However, the magnitude of this effect of GV was small and only demonstrable in type 1 diabetic patients. Possibly, the type 2 diabetic patient group was too small (n = 159) and the variability in this group too low to find this interaction.

The ADAG study showed a tight correlation between HbA_1c and MPG, allowing the translation of HbA_1c into estimated average glucose (14,19). It has been suggested that GV could affect the MPG-HbA_1c relationship, but this has not previously been demonstrated (20–22). To our knowledge, the current study is the largest study reporting an influence of GV—as expressed by SD, MAGE, and CONGA₄ calculated from CGM—on the MPG-HbA_1c relationship. The discrepancies in the MPG-HbA_1c relationship are less likely caused by technical errors because this study included accurate and centralized measurements of HbA_1c values and intensively measured plasma glucose concentrations (∼2,700 values) in a large and diverse population. Also, individuals with conditions or treatment that might result in major changes in glycemia or interference with the HbA_1c assay, or the MPG-HbA_1c relationship, were excluded. These precautions allowed us to search for factors other than MPG that may contribute to HbA_1c.

In general, GV is higher in patients with poor glycemic control and in type 1 diabetic patients compared with type 2 diabetic patients, which can be attributed to insulin therapy and higher insulin sensitivity. High GV may affect glycation because of periodic exposure of the erythrocyte to high glucose levels and therefore to faster irreversible glycation.

Other factors like hyperglycemia-induced oxidative stress may affect the glycation process. In recent literature, it has been speculated that oxygen free radicals per se or with an associated decrease in antioxidants may modulate the formation of early glycated protein (10,11).

Brownlee (12) demonstrated that hyperglycemia stimulates oxidative stress. High GV and especially postprandial glucose excursions were also previously associated with oxidative stress in type 2 diabetes (13). The activation of oxidative stress, estimated from urinary excretion rates of isoprostanes, was highly correlated with MAGE calculated from CGM (13). However, Wentholt et al. (23) could not replicate these results in type 1 diabetes. Recently, Ceriello et al. (15) demonstrated that high intraday GV was more damaging to endothelial function than stable hyperglycemia and that oxidative stress plays a key role. Whether oxidative stress influences glycation needs to be determined.

On the other hand, it has been demonstrated that erythrocyte survival is shorter at chronic high glucose concentrations levels, which might falsely lower HbA_1c levels. Peterson et al. (24) showed that the life span of 51Cr-labeled erythrocytes increased in all seven subjects when their poorly controlled diabetes was adequately treated. Virtue et al. (25) concluded that there is a hyperglycemia-related decrease in erythrocyte survival as measured by carbon monoxide in the expired air, which results in an exponential underestimation of the severity of hyperglycemia at higher HbA_1c levels. Similarly, hyperglycemia-related osmotic stress may influence erythrocyte permeability and could cause damage to the erythrocyte and shorten its life span. These findings could lead to underestimation of HbA_1c at higher MPG levels, concealing a glycemic control worse than indicated by HbA_1c measurements. However, we found that type 1 diabetic patients with high GV display higher HbA_1c levels than suspected based on MPG. This effect was more pronounced at higher HbA_1c levels, indicating that focus on reducing GV, especially in patients with poor glycemic control, could help reduce HbA_1c levels.

Limitations of our study are that CGM has a limited range of reliable measurements between 2.2 mmol/L and 22.2 mmol/L. Therefore, theoretically, CGM performance could be less precise in patients with high glycemic variability. Furthermore, CGM has a lag time in glucose values compared with the venous measured values (the physiological gap), and this can result in underestimation of the influence of GV on the glycation of HbA_1c, and no measures of erythrocyte survival, oxidative stress, or clinical follow-up are available in this population.

In conclusion, at higher levels of GV the relationship between HbA_1c and MPG in patients with type 1 diabetes is altered, leading to a higher HbA_1c level for a given MPG. However, the impact (near the HbA_1c treatment target of 7%) is only modest.

The ADAG study is supported by research grants from the American Diabetes Association and the European Association for the Study of Diabetes.

Financial support was provided by Abbott Diabetes Care, Bayer Healthcare, GlaxoSmithKline, sanofi-aventis Netherlands, Merck & Company, LifeScan, and Medtronic Minimed. Supplies and equipment were provided by Medtronic Minimed, LifeScan, and Hemocue. R.J.H. is employed by and owns stocks of Eli Lilly and Company. No other potential conflicts of interest relevant to this article were reported.

J.C.K. researched data, contributed to discussion, and wrote the manuscript. R.B. researched data, contributed to discussion, and edited the manuscript. D.J.K., H.Z., and D.S. researched data. M.D. contributed to discussion and edited the manuscript. D.M.N. reviewed and edited the manuscript. R.J.H. contributed to discussion and reviewed and edited the manuscript.

Parts of this study were presented in abstract form at the 44th Annual Meeting of the European Association for the Study of Diabetes (Poster number 1065), 7–11 September 2008, Rome, Italy and at the 68th Scientific Sessions of the American Diabetes Association, San Francisco, California, 6–10 June 2008.