Associations Between Depressive Symptoms, Fear of Hypoglycemia, Adherence to Management Behaviors and Metabolic Control in Children and Adolescents with Type 1 Diabetes.

We examined the relationship between two malleable risk factors, depressive symptoms and fear of hypoglycemia, in children and adolescents with Type 1 diabetes and their relationship to two important outcomes, adherence behaviors and metabolic control. To assess this relationship, we used a multidimensional measure of adherence, assessing frequency of both blood glucose monitoring and healthy behaviors including diet and exercise. We predicted that higher levels of depressive symptoms and higher levels of fear of hypoglycemia would be associated with worse metabolic control as mediated by poor adherence. Eighty-three children and adolescents ages 8 to 20 (M = 13.87, SD 3.21) were recruited from March 2014 to October 2014 at an outpatient diabetes clinic in a moderately sized Southeastern city within the USA. Nested models were evaluated using structural equation modeling. Adherence significantly mediated the relationship between depressive symptoms and metabolic control with more depressive symptoms leading to worse metabolic control. Adherence marginally mediated the relationship between fear of hypoglycemia and metabolic control; however, less fear of hypoglycemia was associated with worse metabolic control. In a combined model, adherence continued to significantly mediate the relationship between depressive symptoms and metabolic control, while also independently significantly mediating the relationship between fear of hypoglycemia and metabolic control. This finding was also contrary to the predicted relationship with less fear of hypoglycemia leading to worse metabolic control. The results indicate that youth with fewer depressive symptoms and more fear of hypoglycemia had better adherence to their treatment regimen, which was associated with better metabolic control. The results of this study highlight the importance of screening for depression and fear of hypoglycemia during routine clinic visits to optimize adherence and metabolic control.

Racial differences in neighborhood disadvantage, inflammation and metabolic control in black and white pediatric type 1 diabetes patients.

BACKGROUND: Racial variation in the relationship between blood glucose and hemoglobin A1c (HbA1c) complicates diabetes diagnosis and management in racially mixed populations. Understanding why HbA1c is persistently higher in blacks than whites could help reduce racial disparity in diabetes outcomes. OBJECTIVE: Test the hypothesis that neighborhood disadvantage is associated with inflammation and poor metabolic control in a racially mixed population of pediatric type 1 diabetes patients. METHODS: Patients (n = 86, 53 white, 33 black) were recruited from diabetes clinics. Self-monitored mean blood glucose (MBG) was downloaded from patient glucose meters. Blood was collected for analysis of HbA1c and C-reactive protein (CRP). Patient addresses and census data were used to calculate a concentrated disadvantage index (CDI). High CDI reflects characteristics of disadvantaged neighborhoods. RESULTS: HbA1c and MBG were higher (p < 0.0001) in blacks [10.4% (90.3 mmol/mol), 255 mg/dL] than whites [8.9% (73.9 mmol/mol), 198 mg/dL). CDI was higher in blacks (p < 0.0001) and positively correlated with HbA1c (r = 0.40, p = 0.0002) and MBG (r = 0.35, p = 0.0011) unless controlled for race. CDI was positively associated with CRP by linear regression within racial groups. CRP was not different between racial groups, and was not correlated with MBG, but was positively correlated with HbA1c when controlled for race (p = 0.04). CONCLUSIONS: Neighborhood disadvantage was associated with inflammation and poor metabolic control in pediatric type 1 diabetes patients. Marked racial differences in potential confounding factors precluded differentiation between genetic and environmental effects. Future studies should recruit patients matched for neighborhood characteristics and treatment regimen to more comprehensively assess racial variation in HbA1c.

Differences in Red Blood Cell Indices Do Not Explain Racial Disparity in Hemoglobin A1c in Children with Type 1 Diabetes.

We assessed the association of erythrocyte indices on mean blood glucose-independent racial disparity in hemoglobin A1c (HbA1c) in youth with type 1 diabetes. Blacks still had higher HbA1c after adjustment for mean blood glucose, red blood cell indices, age, and sex. Such differences need to be taken into account when interpreting HbA1c in Black patients.

Simultaneous analysis of reduced glutathione and glutathione disulfide by capillary zone electrophoresis.

This report describes modifications to a CZE method developed by Serru et al. (Clinical Chemistry 2001, 47, 1321-1324) for the simultaneous analysis of reduced glutathione (GSH) and glutathione disulfide (GSSG). Lowering the pH of the run buffer (75 mmol/L boric acid, 25 mmol/L bis-Tris) from pH 8.4 to 7.8 markedly improved GSH peak area reproducibility and allowed multiple samples to be analyzed without changing run buffers due to ion depletion. Sample preparation using red blood cells (RBC) instead of whole blood, combined with glutathione extraction at a lower concentration of metaphosphoric acid (5%), increased assay sensitivity and decreased interference. CZE assay results for clinical samples containing 1000 to 3200 µmol GSH/L RBC and 100 to 400 µmol GSSG/L RBC were highly correlated (r(2) ≥ 0.95) with results obtained using a commercial dithionitrobenze-based glutathione assay. The modified CZE assay has proven useful for the analysis of glutathione in both mouse and human RBC.

The glucosylamine oxidation pathway of vitamin C recycling.

The proposed glucosylamine oxidation pathway (GOP) is a two-step, intraerythrocyte, thermodynamically favorable nonenzymatic reaction that first binds glucose to the N-terminal valine of beta globin (ßVal1) to form a closed-chain glucosylamine that can spontaneously reduce oxidized vitamin C to its antioxidant form. This review summarizes analytical, biochemical and clinical research supporting the existence of the GOP and the surprising hypothesis that ßVal1 glucosylamine is a reducing agent that works cooperatively with reduced glutathione to dynamically regulate vitamin C recycling during naturally occurring periods of transiently or chronically elevated blood glucose and oxidant production. Rationale for the existence of the GOP is presented from the perspective of the hemoglobin glycation index, a clinically practical biomarker of risk for chronic vascular disease that we propose is mechanistically explained by person-to-person variation in GOP activity.

Advanced glycation endproducts in children with diabetes.

OBJECTIVES: To estimate skin content of advanced glycation endproducts (AGEs) by measurements of skin intrinsic fluorescence (SIF) from youth with diabetes in comparison with a population of youth and adults without diabetes. STUDY DESIGN: Using a specialized instrument, skin AGEs were estimated from skin auto-fluorescence induced at 420 nm and corrected for skin pigmentation (SIF420[kx0.5, km0.5]) in children with types 1 and 2 diabetes, as well as children and adults without diabetes. The effect of age, sex, ethnicity, and diabetes status on SIF420[kx0.5, km0.5] was analyzed. RESULTS: SIF420[kx0.5, km0.5] increased with chronologic age and was higher in children with diabetes compared with children without diabetes (P = .0001). SIF420[kx0.5, km0.5] from 43% of children with type 1 diabetes and 55% with type 2 diabetes overlapped the range of adults without diabetes. SIF420[kx0.5, km0.5] was higher in girls than boys in patients with diabetes patients. However, there was no effect of sex or race on SIF420[kx0.5, km0.5] in subjects without diabetes. CONCLUSIONS: After 4-6 years' exposure to diabetes, many children will have precociously high estimates of skin AGEs, comparable with levels that would naturally accumulate only after ∼25 years of chronologic aging. Potentially, this technology identifies children who are at increased risk for complications.

Two-dimensional analysis of glycated hemoglobin heterogeneity in pediatric type 1 diabetes patients.

Interindividual and ethnic variation in glycated hemoglobin levels, unrelated to blood glucose variation, complicates the clinical use of glycated hemoglobin assays for the diagnosis and management of diabetes. Assessing the types and amounts of glycated hemoglobins present in erythrocytes could provide insight into the mechanism. Blood samples and self-monitored mean blood glucose (MBG) levels were obtained from 85 pediatric type 1 diabetes patients. Glycated hemoglobin levels were measured using three primary assays (boronate-affinity chromatography, capillary isoelectric focusing (CIEF), and standardized DCA2000+ immunoassay) and a two-dimensional (2D) analytical system consisting of boronate-affinity chromatography followed by CIEF. The 2D system separated hemoglobin into five subfractions, four of which contained glycated hemoglobins. Glycated hemoglobin measurements were compared in patients with low, moderate, or high hemoglobin glycation index (HGI), a measure of glycated hemoglobin controlled for blood glucose variation. MBG was not significantly different between HGI groups. Glycated hemoglobin levels measured by all three primary assays and in all four glycated 2D subfractions were significantly different between HGI groups and highest in high HGI patients. These results show that interindividual variation in glycated hemoglobin levels was evident in diabetes patients with similar blood glucose levels regardless of which glycated hemoglobins were measured.

Effect of biological variation in HbA1c and blood glucose on the diagnosis of prediabetes.

INTRODUCTION: People with a low or high haemoglobin glycation index (HGI) have lower or higher HbA1c than other people with the same FPG. This study compared the prevalence of prediabetes based on FPG, 2hOGTT and HbA1c in people with low, moderate or high HGI. METHODS: Prediabetes was diagnosed based on ADA cutpoints in 10,488 NHANES participants without self-reported diabetes. HGI was calculated as the difference between a participant's observed HbA1c and a predicted HbA1c where predicted HbA1c = 0.024 FPG + 3.1. Participants were divided into low (HGI < -0.15%), moderate (HGI -0.15% to +0.15%) and high (HGI > +0.15%) HGI subgroups. RESULTS: The prevalence of prediabetes was 42.4% based on FPG, 27.2% based on HbA1c and 17.2% based on 2hOGTT. FPG and HbA1c thus overdiagnosed prediabetes by 25.2% and 10.0%, respectively, compared to the OGTT gold standard. Prevalence was (1) similar in low, moderate and high HGI participants based on 2hOGTT, (2) highest in low HGI participants based on FPG, and (3) highest in high HGI participants based on HbA1c. Among participants with mismatched FPG and HbA1c, OGTT was normal in (1) 79.5% of participants with normal FPG but prediabetic HbA1c (mean HGI = +0.53%), and (2) 75.2% of participants with normal HbA1c but prediabetic FPG (mean HGI = -0.30%). CONCLUSIONS: FPG overdiagnosed prediabetes in people with low HGI. HbA1c overdiagnosed prediabetes in people with high HGI. Clinical use of HGI could improve prediabetes diagnosis and help health care providers avoid inappropriate or delayed treatment of people with extremes of HGI.

Characterization of unstable hemoglobin A1c complexes by dynamic capillary isoelectric focusing.

Glucose spontaneously reacts with hemoglobin amino groups to produce unstable Schiff base complexes that can dissociate or rearrange to form stable Amadori products. We used dynamic capillary isoelectric focusing and boronate affinity chromatography to assess the formation and dissociation of unstable hemoglobin complexes in vitro. Formation was studied by incubating erythrocytes at 37°C for up to 24h in phosphate-buffered saline (PBS) supplemented with 0 to 55.6 mmol/L glucose. Dissociation was studied by incubating glucose-loaded erythrocytes in PBS without glucose. Dynamic capillary isoelectric focusing separated hemoglobin A1c into two subfractions identified as A1c1 and A1c2. The A1c1 subfraction contained both stable and unstable hemoglobin complexes. The A1c2 subfraction contained only unstable hemoglobin complexes. Both subfractions quantitatively increased in the presence of glucose and decreased in its absence. Rates of increase and decrease were faster and time to equilibrium was shorter for A1c2 (~4 h) compared with A1c1 (~20 h). Unstable hemoglobin complexes did not bind to boronate affinity columns but instead eluted intact in A1c1 and A1c2 subfractions from nonglycated affinity fractions. Cyanoborohydride reduction confirmed the presence of Schiff base complexes. Evidence of multiple unstable hemoglobin complexes with different rates of glycation suggests that new models are needed to describe nonenzymatic hemoglobin glycation.

Variation in the hemoglobin glycation index.

A high hemoglobin glycation index (HGI) has been repeatedly associated with greater risk for hypoglycemia in people with diabetes and greater risk for chronic vascular disease in people with or without diabetes. This review explores how different sources of analytical and biological variation in HbA1c and blood glucose individually and collectively affect the clinical information value of HGI. We conclude that HGI is a complex quantitative trait that is a clinically practical biomarker of risk for both hypoglycemia and chronic vascular disease.

Standardizing the haemoglobin glycation index.

AIMS: A high haemoglobin glycation index (HGI) is associated with greater risk for hypoglycaemia and chronic vascular disease. Standardizing how the HGI is calculated would normalize results between research studies and hospital laboratories and facilitate the clinical use of HGI for assessing risk. METHODS: The HGI is the difference between an observed HbA1c and a predicted HbA1c obtained by inserting fasting plasma glucose (FPG) into a regression equation describing the linear relationship between FPG and HbA1c in a reference population. We used data from the 2005-2016 U.S. National Health and Nutrition Examination Survey (NHANES) to identify a reference population of 18,675 diabetes treatment-naïve adults without self-reported diabetes. The reference population regression equation (predicted HbA1c = 0.024 FPG + 3.1) was then used to calculate the HGI and divide participants into low (<-0.150), moderate (-0.150 to <0.150) and high (≥0.150) HGI subgroups. Diabetes status was classified by OGTTs. RESULTS: As previously reported in multiple studies, a high HGI was associated with black race independent of diabetes status, and with older age, higher BMI and higher CRP in normal and prediabetic but not diabetic participants. The mean HGI was 0.6% higher in self-reported diabetic adults. The HGI was not associated with plasma insulin, HOMA-IR or 2 h OGTT in participants classified as normal, prediabetic or diabetic. CONCLUSIONS: The regression equation derived from this demographically diverse diabetes treatment-naïve adult NHANES reference population is suitable for standardizing how the HGI is calculated for both clinical use and in research to mechanistically explain population variation in the HGI and why a high HGI is associated with greater risk for chronic vascular disease.

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.

Implications of the Hemoglobin Glycation Index on the Diagnosis of Prediabetes and Diabetes.

OBJECTIVE: Fasting plasma glucose (FPG), 2-hour plasma glucose (2hPG) from a 75-g oral glucose tolerance test (OGTT) and glycated hemoglobin (HbA1c) can lead to different results when diagnosing prediabetes and diabetes. The Hemoglobin Glycation Index (HGI) quantifies the interindividual variation in glycation resulting in discrepancies between FPG and HbA1c. We used data from the Vitamin D and Type 2 Diabetes (D2d) study to calculate HGI, to identify HGI-associated variables, and to determine how HGI affects prediabetes and diabetes diagnosis. MEASUREMENTS: A linear regression equation [HbA1c (%) = 0.0164 × FPG (mg/dL) + 4.2] was derived using the screening cohort (n = 6829) and applied to calculate predicted HbA1c. This was subtracted from the observed HbA1c to determine HGI in the baseline cohort with 2hPG data (n = 3945). Baseline variables plus prediabetes and diabetes diagnosis by FPG, HbA1c, and 2hPG were compared among low, moderate, and high HGI subgroups. RESULTS: The proportion of women and Black/African American individuals increased from low to high HGI subgroups. Mean FPG decreased and mean HbA1c increased from low to high HGI subgroups, consistent with the HGI calculation; however, mean 2hPG was not significantly different among HGI subgroups. CONCLUSIONS: High HGI was associated with Black race and female sex as reported previously. The observation that 2hPG was not different across HGI subgroups suggests that variation in postprandial glucose is not a significant source of population variation in HGI. Exclusive use of HbA1c for diagnosis will classify more Black individuals and women as having prediabetes compared with using FPG or 2hPG.

Characterization of S-glutathionyl hemoglobin in homozygous sickle cell disease.

S-glutathionyl hemoglobin is a proposed biomarker of oxidative stress but has not been measured in sickle cell disease patients. Unlike the S-glutathionyl adduct of normal adult hemoglobin, S-glutathionyl sickle hemoglobin (HbSSG) cannot be directly measured by capillary isoelectric focusing, because it coelutes with fetal hemoglobin (HbF). This suggests that HbF, measured in sickle cell patients with or without hydroxyurea therapy, might contain endogenous HbSSG. As S-glutathionyl hemoglobin can form during sample storage, HbSSG could falsely elevate HbF levels in stored samples. We measured HbSSG based on the quantitative difference in the heterogeneous HbF/HbSSG peak before and after hemolysates were treated with dithiothreitol. Paired t tests showed that dithiothreitol reduced HbF/HbSSG in blood from pediatric sickle cell patients (n=25, mean decrease+/-SD=1.0%+/-0.6, P<0.05) but not in normal infants (n=25). Higher HbF levels in hydroxyurea-treated patients (n=5) were not attributable to HbSSG. HbSSG increased significantly within 1 day in samples stored at -20 degrees C but was unchanged in samples stored 60 days at-70 degrees C. We conclude that blood from sickle cell patients contained up to 2.2% HbSSG, and that endogenous HbSSG is a minor interferent in the measurement of HbF in fresh blood but a major interferent in improperly stored samples.

Genetic variation in mouse beta globin cysteine content modifies glutathione metabolism: implications for the use of mouse models.

Allelic variation in the mouse beta globin gene complex (Hbb) produces structurally different beta globins in different mouse strains. Like humans, mice with HbbS alleles produce a single beta globin with one reactive cysteine (beta Cys93). In contrast, mice with HbbD alleles produce two structurally different beta globins, each containing an additional cysteine (beta Cys13). beta Cys93 forms mixed disulfides with glutathione and plays a pivotal role in the activities of hemoglobin, glutathione, and nitric oxide. Similar roles for mouse beta Cys13 have not been described. We used capillary electrophoresis to compare reduced glutathione (GSH), glutathione disulfide (GSSG), and S-glutathionyl hemoglobin levels in erythrocytes from inbred C57BL/6J (homozygous HbbS/S) and 129S1/SvImJ (homozygous HbbD/D) mice and their homozygous and heterozygous B6129S/F2J hybrid offspring. S-glutathionyl hemoglobin was nearly undetectable in inbred or hybrid mice with only monocysteinyl beta globins (HbbS/S) but represented up to 10% of total hemoglobin in mice with polycysteinyl beta globins (HbbS/D or HbbD/D). The stepwise increase in beta globin sulfhydryl group concentration in HbbS/S, HbbS/D, and HbbD/D F2 mice was associated with increasing hemoglobin-bound glutathione and decreasing free glutathione (GSH + GSSG) concentrations. Total erythrocyte glutathione (GSH + GSSG + hemoglobin-bound) was not significantly different between groups. In vitro studies showed that beta Cys13 in mouse HbbD beta globins was more susceptible to disulfide exchange with GSSG than beta Cys93. We conclude that reactive beta globin sulfhydryl group concentration is genetically determined in mice, and that polycysteinyl beta globins markedly influence intraerythrocyte glutathione distribution between free and hemoglobin-bound compartments. Although Hbb heterozygosity and polycysteinyl beta globins are common in wild mouse populations, all common human beta globins contain only one reactive cysteine, and homozygosity is the norm. These fundamental differences in mouse and human beta globin genetics have important implications for the study of mouse biology and for the use of some mouse strains as models for humans.

Mean blood glucose and biological variation have greater influence on HbA1c levels than glucose instability: an analysis of data from the Diabetes Control and Complications Trial.

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 = 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 = -41,818.75, likelihood ratio chi2[7] = 7,218.71, P > chi2 = 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.

A comparison of the Glycosylation Gap and Hemoglobin Glycation Index in patients with diabetes.

PROBLEM: The Glycosylation Gap (GGAP) based on fructosamine (F) measurement and the Hemoglobin Glycation Index (HGI) based on mean blood glucose (MBG) are two indices of between-individual differences in glycated hemoglobin (HbA1c) adjusted for glycemia. We sought to simultaneously compare GGAP with HGI and other estimates of glycemia. METHODS: HbA1c, F, and MBG level were obtained at a clinic visit from 62 patients with Type 1 diabetes. GGAP and HGI were calculated from the data as previously described. The variables were compared by correlation analysis. The concordance of patient classification by GGAP and HGI was compared by weighted kappa test. RESULTS: The mean HbA1c=11.1+/-2.7%, F=372.0+/-136.6 mol/l, MBG=186.5+/-58.4 mg/dl, HGI=0.0+/-2.0, and GGAP=0.0+/-1.9. MBG, HbA1c, and F were all highly correlated with each other. The HGI and GGAP were highly correlated (r=.73, P<.0001) and similar in both magnitude and direction. There was good agreement between HGI and GGAP classifications of patients into high, moderate, and low glycation groups (P<.0075). CONCLUSIONS: GGAP and MBG give similar information regarding between-patient differences in HbA1c among patients with diabetes. Thus, biological variation in HbA1c is not an artifact of variability in glucose measurements comprising the MBG. Individual patient factors influence the intracellular glycation of HbA1c in addition to the effect of extracellular glycemia, which is manifested as a between-individual biological variation in HbA1c.

Biological variation in HbA1c predicts risk of retinopathy and nephropathy in type 1 diabetes.

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 = 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 (higher-than-predicted HbA(1c)) had three times greater risk of retinopathy (30 vs. 9%, P < 0.001) and six times greater risk of nephropathy (6 vs. 1%, P < 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.

The hemoglobin glycation index identifies subpopulations with harms or benefits from intensive treatment in the ACCORD trial.

OBJECTIVE: This 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 METHODS: ACCORD 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. RESULTS: Intensive 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. CONCLUSIONS: HGI 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.