Estimated average glucose and self-monitored mean blood glucose are discordant estimates of glycemic control.

OBJECTIVE: The A1C-Derived Average Glucose study recommended reporting A1C in estimated average glucose (eAG) equivalents. We compared eAG with self-monitored mean blood glucose (MBG) to determine whether eAG is systematically biased due to biological variation in the relationship between MBG and A1C. RESEARCH DESIGN AND METHODS: MBG and A1C were recorded from charts of 202 pediatric type 1 diabetic patients at 1,612 clinic visits. Patients were divided into groups with low, moderate, or high A1C bias based on a hemoglobin glycation index (HGI). RESULTS: The mean +/- SD values for MBG versus eAG were as follows: total population, 194 +/- 34 vs. 196 +/- 36 mg/dl; low-HGI group, 186 +/- 31 vs. 163 +/- 20 mg/dl; moderate-HGI group, 195 +/- 28 vs. 193 +/- 19 mg/dl; and high-HGI group, 199 +/- 42 vs. 230 +/- 31 mg/dl. CONCLUSIONS: eAG underestimated MBG in low HGI patients and overestimated MBG in high HGI patients. Disagreement between eAG and MBG downloaded from patient glucose meters will cause confusion if eAG is implemented for clinical use.

Hemoglobin glycation index: a robust measure of hemoglobin A1c bias in pediatric type 1 diabetes patients.

BACKGROUND: The hemoglobin glycation index (HGI) assesses biological variation in A1c after accounting for the effect of mean blood glucose (MBG). Previous studies minimized analytical variation that could mask biological variation and showed that HGI was consistent within individuals over time and positively associated with risk for microvascular complications. We tested the hypothesis that biological variation in A1c can be assessed by HGI calculated using routine MBG and A1c data obtained from a typical diabetes clinic. METHODS: Self-monitored MBG and A1c were collected from charts of 202 pediatric type 1 diabetes patients attending 1612 clinic visits over 6 yr. Predicted A1c was calculated from the linear regression equation of A1c on MBG in the study population. HGI was calculated by subtracting predicted A1c from observed A1c. Patients were divided into low, moderate, and high HGI tertile groups. RESULTS: Patients used 12 models of glucose meters. Download protocols varied with clinical practice over time. A1c was measured by multiple assays and laboratories. Despite this analytical heterogeneity, HGI was significantly different between individuals and correlated within individuals. MBG (mean ± SD, mg/dL) was similar in the low (186 ± 31), moderate (195 ± 28), and high (199 ± 42) HGI groups. A1c (%) was significantly different (p < 0.0001) in the low (7.6 ± 0.7), moderate (8.4 ± 0.7), and high (9.6 ± 1.1) HGI groups. CONCLUSION: Biological variation in A1c is a robust quantitative trait that can be assessed using HGI calculated from routine clinic data. This suggests that HGI could be used clinically for more personalized assessment of complications risk.