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OBJECTIVE -We hypothesized that biological variation in HbA 1c , distinct from variation attributable to mean blood glucose (MBG), would predict risk for microvascular complications in the Diabetes Control and Complications Trial (DCCT). RESEARCH DESIGN AND METHODS-A longitudinal multiple regression model was developed from MBG and HbA 1c measured in the 1,441 DCCT participants at quarterly visits. A hemoglobin glycation index (HGI ϭ observed HbA 1c -predicted HbA 1c ) was calculated for each visit to assess biological variation based on the directional deviation of observed HbA 1c from that predicted by MBG in the model. The population was subdivided by thirds into high-, moderate-, and low-HGI groups based on mean participant HGI during the study. Cox proportional hazard analysis compared risk for development or progression of retinopathy and nephropathy between HGI groups controlled for MBG, age, treatment group, strata, and duration of diabetes.RESULTS -Likelihood ratio and t tests on HGI rejected the assumption that HbA 1c levels were determined by MBG alone. At 7 years' follow-up, patients in the high-HGI group (higherthan-predicted HbA 1c ) had three times greater risk of retinopathy (30 vs. 9%, P Ͻ 0.001) and six times greater risk of nephropathy (6 vs. 1%, P Ͻ 0.001) compared with the low-HGI group.CONCLUSIONS -Between-individual biological variation in HbA 1c , which is distinct from that attributable to MBG, was evident among type 1 diabetic patients in the DCCT and was a strong predictor of risk for diabetes complications. Identification of the processes responsible for biological variation in HbA 1c could lead to novel therapies to augment treatments directed at lowering blood glucose levels and preventing diabetes complications.

OBJECTIVEThis study tested the hypothesis that intensive treatment in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial disproportionately produced adverse outcomes in patients with diabetes with a high hemoglobin glycation index (HGI = observed HbA1c − predicted HbA1c).RESEARCH DESIGN AND METHODSACCORD was a randomized controlled trial of 10,251 patients with type 2 diabetes assigned to standard or intensive treatment with HbA1c goals of 7.0% to 7.9% (53 to 63 mmol/mol) and less than 6% (42 mmol/mol), respectively. In this ancillary study, a linear regression equation (HbA1c = 0.009 × fasting plasma glucose [FPG] [mg/dL] + 6.8) was derived from 1,000 randomly extracted participants at baseline. Baseline FPG values were used to calculate predicted HbA1c and HGI for the remaining 9,125 participants. Kaplan-Meier and Cox regression were used to assess the effects of intensive treatment on outcomes in patients with a low, moderate, or high HGI.RESULTSIntensive treatment was associated with improved primary outcomes (composite of cardiovascular events) in the low (hazard ratio [HR] 0.75 [95% CI 0.59–0.95]) and moderate (HR 0.77 [95% CI 0.61–0.97]) HGI subgroups but not in the high HGI subgroup (HR 1.14 [95% CI 0.93–1.40]). Higher total mortality in intensively treated patients was confined to the high HGI subgroup (HR 1.41 [95% CI 1.10–1.80]). A high HGI was associated with a greater risk for hypoglycemia in the standard and intensive treatment groups.CONCLUSIONSHGI calculated at baseline identified subpopulations in ACCORD with harms or benefits from intensive glycemic control. HbA1c is not a one-size-fits-all indicator of blood glucose control, and taking this into account when making management decisions could improve diabetes care.

HGI reflects the effects of inflammation on HbA1c in a nondiabetic population of U.S. adults and may be a marker of risk associated with inflammation independent of FPG, race, and obesity.

OBJECTIVE -Mean blood glucose (MBG) over 2-3 months is a strong predictor of HbA 1c (A1C) levels. Glucose instability, the variability of blood glucose levels comprising the MBG, and biological variation in A1C (BV) have also been suggested as predictors of A1C independent of MBG. To assess the relative importance of MBG, BV, and glucose instability on A1C, we analyzed patient data from the Diabetes Control and Complications Trial (DCCT). RESEARCH DESIGN AND METHODS-A glucose profile set and sample for A1C were collected quarterly over the course of the DCCT from each participant (n ϭ 1,441). The glucose profile set consisted of seven samples, one each drawn before and 90 min after breakfast, lunch, and dinner and one before bedtime. MBG and glucose instability (SD of blood glucose [SDBG]) were calculated as the arithmetic mean and SD of glucose profile set samples for each visit, respectively. A statistical model was developed to predict A1C from MBG, SDBG, and BV, adjusted for diabetes duration, sex, treatment group, stratum, and race.RESULTS -Data from 32,977 visits were available. The overall model was highly statistically significant (log likelihood ϭ Ϫ41,818.75, likelihood ratio 2 [7] ϭ 7,218.71, P Ͼ 2 ϭ 0.0000). MBG and BV had large influences on A1C based on their standardized coefficients. SDBG had only 1/14 of the impact of MBG and 1/10 of the impact of BV.CONCLUSIONS -MBG and BV have a large influence on A1C, whereas SDBG is relatively unimportant. Consideration of BV as well as MBG in the interpretation of A1C may enhance our ability to monitor diabetes management and predict complications. Diabetes Care 29:352-355, 2006M aintenance of blood glucose levels as close as possible to the physiological range over time is an important goal in the current management of patients with type 1 diabetes. Assessment of a patient's diabetes management can be accomplished by directly analyzing the pattern of multiple blood glucose samples drawn over time (1). However, a high degree of cooperation is required on the part of the patient to collect a sufficient number of blood glucose samples that adequately represent typical diurnal glucose patterns. Once collected, statistical analysis is then necessary to assess the central tendency and variability of glucose levels. As an alternative, a patient's HbA 1c (A1C) level can be easily and conveniently determined from a single blood sample. A large number of studies have shown that A1C is strongly associated with the preceding mean blood glucose (MBG) level obtained from multiple blood glucose samples drawn over the preceding weeks and months (2-4). Based on the statistical relation of A1C and MBG, A1C is widely used as a clinical estimate of patient MBG (5). Monitoring MBG or A1C is an important guide in assessing diabetes management because poor glycemic control over time has been linked to the development and progression of microvascular diabetes complications (6).Over the last 2 decades, it has been shown that factors besides MBG may also influence A1C levels in diab...

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.

OBJECTIVEMean blood glucose (MBG) and MBG-independent factors both influence A1C levels. Race was related to A1C independent of MBG in adults. The goal of this study was to determine if racial disparity exists in A1C independent of MBG in children with diabetes.RESEARCH DESIGN AND METHODSParticipants included 276 children with type 1 diabetes. A1C and MBG were obtained from multiple clinic visits, and a hemoglobin glycation index (HGI) (an assessment of A1C levels independent of MBG) was calculated. A1C and HGI were analyzed controlling for age, diabetes duration, and MBG.RESULTSAfrican Americans had statistically significantly higher A1C (9.1 ± 0.1) and HGI (0.64 ± 0.11) than Caucasians (A1C 8.3 ± 0.1, HGI −0.15 ± 0.07) independent of covariates.CONCLUSIONSBecause of racial disparity in A1C, which is independent of MBG, we recommend that A1C and MBG be used together to make therapeutic decisions for children with diabetes.

OBJECTIVETo evaluate the relationship between skin advanced glycation end products (sAGEs) with mean blood glucose (MBG), hemoglobin A1c (HbA1c), and MBG-independent, between-patient differences in HbA1c among children with type 1 diabetes.RESEARCH DESIGN AND METHODSChildren aged 5 to 20 years with type 1 diabetes of at least 1 year duration participated. At a clinic visit, sAGE was estimated noninvasively by measurement of skin intrinsic fluorescence (SIF). SIF data were adjusted to correct for variation in skin pigmentation. MBG-independent, between-patient differences in HbA1c were examined by statistically controlling HbA1c for MBG or alternatively by use of a hemoglobin glycation index (HGI). Results were similar whether HbA1c, MBG, and HGI were analyzed as single values from the time of the SIF examination visit or as the mean values from all available visits of the patient.RESULTSHbA1c was correlated with MBG (r = 0.5; P < 0.001; n = 110). HbA1c and HGI, but not MBG, were statistically associated with SIF after adjustment for age, duration of diabetes, race, sex, and BMI z-score. SIF increased with age and duration of diabetes and was higher in girls than boys.CONCLUSIONSsAGE levels estimated by SIF increase with age, duration of diabetes, and female sex. sAGE is correlated with MBG-independent biological variation in HbA1c, but not with MBG itself. These results suggest that factors besides MBG that influence HbA1c levels also contribute to accumulation of sAGE.