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

Association of hemoglobin glycation index with cardiovascular risk factors in non-diabetic adults: A cross-sectional study

BACKGROUND AND AIMS

The study aimed to explore the association of hemoglobin glycation index (HGI) with cardiovascular risk factors in non-diabetic adults.

METHODS

This cross-sectional study included 200 adults of 20-60 years of age. Predicted glycated hemoglobin (HbA1c) was calculated from linear regression equation. HGI was calculated using the formula HGI = measured HbA1c- predicted HbA1c. The study subjects were classified into three groups based on their HGI tertiles. Cardiovascular risk factors were compared between the groups and Pearson correlation test was done to correlate HGI with cardiovascular risk factors.

RESULTS

Serum total cholesterol, triglyceride, low density lipoprotein cholesterol (LDL-C) and very low density lipoprotein cholesterol (VLDL-C) showed significant increase with increase in HGI in non diabetic individuals. High HGI group had significantly high serum total cholesterol, triglyceride, LDL-C and VLDL-C compared to low HGI group. Serum total cholesterol, triglyceride, LDL-C and VLDL-C showed a statistically significant positive correlation with HGI.

CONCLUSION

We have found a statistically significant correlation of HGI with serum lipid profile, a significant cardiovascular risk factor in non-diabetic individuals. HGI, a simple derivative of HbA1c and fasting plasma glucose may be used to identify cardiovascular risk in non-diabetic individuals. Further prospective studies are required in larger sample size to confirm the clinical implications of HGI.

The Reproducibility and Usefulness of Estimated Average Glucose for Hyperglycemia Management during Health Checkups: A Retrospective Cross-Sectional Study

HbA1c reflects average glucose levels over 3 months, but it does not measure glycemic variability. This study aimed to determine the reproducibility and usefulness of HbA1c-derived estimated average glucose (eAG) and to analyze the factors associated with eAG during health checkups. This cross-sectional retrospective study consecutively selected subjects who had undergone health checkups at 16 health-promotion centers in 13 Korean cities in 2020. The subjects comprised 182,848 healthy subjects with normoglycemia, 109,555 with impaired fasting glucose (IFG), and 35,632 with diabetes. eAG was calculated using Nathan’s regression equation. In all subjects, fasting plasma glucose (FPG) was found to be fairly strongly correlated with eAG (r = 0.811). When the subjects were divided into FPG subgroups, the strength of the correlation decreased among those with normoglycemia and IFG (p < 0.001). Higher eAG levels were associated with older age, females, higher FPG, and lower HDL-C and triglycerides (p < 0.05). The proportion of subjects with a higher value of FPG than eAG was 46.3% in poorly controlled diabetic patients, compared with only 1.5% in normoglycemic subjects. This suggests eAG could help patients to understand their glycemic variability intuitively and healthcare providers to identify patients who might worsen in hyperglycemia control through measuring the difference between eAG and FPG.

Admission Random Blood Glucose, Fasting Blood Glucose, Stress Hyperglycemia Ratio, and Functional Outcomes in Patients With Acute Ischemic Stroke Treated With Intravenous Thrombolysis

Background

Stress hyperglycemia ratio (SHR), calculated as glucose/glycated hemoglobin, has recently been developed for assessing stress hyperglycemia and could provide prognostic information for various diseases. However, calculating SHR using random blood glucose (RBG) drawn on admission or fasting blood glucose (FBG) could lead to different results. This study intends to evaluate the association between SHR and functional outcomes in patients with acute ischemic stroke (AIS) with recombinant tissue plasminogen activator (r-tPA) intravenous thrombolysis.

Methods

Data from 230 patients with AIS following thrombolytic therapy with r-tPA in the Third Affiliated Hospital of Wenzhou Medical University from April 2016 to April 2019 were retrospectively reviewed. SHR1 was defined as [RBG (mmol/L)]/[HbA1c (%)] and SHR2 was defined as [FBG (mmol/L)]/[HbA1c (%)]. The outcomes included early neurological improvement (ENI), poor function defined as a modified Rankin Scale score (mRS) of 3–6, and all-cause death in 3 months. Multivariable logistic regression was performed to estimate the association between SHR and adverse outcomes.

Results

After adjustment for possible confounders, though patients with AIS with higher SHR1 tend to have a higher risk of poor outcome and death and unlikely to develop ENI, these did not reach the statistical significance. In contrast, SHR2 was independently associated with poor functional outcome (per 0.1-point increases: odds ratios (OR) = 1.383 95% CI [1.147–1.668]). Further adjusted for body mass index (BMI), triglyceride-glucose index (TyG), and diabetes slightly strengthen the association between SHR (both 1 and 2) and adverse outcomes. In subgroup analysis, elevated SHR1 is associated with poor functional outcomes (per 0.1-point increases: OR = 1.246 95% CI [1.041–1.492]) in non-diabetic individuals and the association between SHR2 and the poor outcomes was attenuated in non-cardioembolic AIS.

Conclusion

SHR is expected to replace random or fasting glucose concentration as a novel generation of prognostic indicator and a potential therapeutic target.

The Relationship Between Hemoglobin Glycation Variation Index and Vitamin D in Type 2 Diabetes Mellitus

OBJECTIVE

This study aimed to investigate the relationship between hemoglobin glycation variation index (HGI) and vitamin D in patients with type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

This is a cross-sectional study that recruited 347 patients with T2DM. The subjects were divided into no vitamin D deficiency group (25(OH)D ≥20 ng/mL) and vitamin D deficiency group (25(OH)D < 20 ng/mL). HGI was calculated as the difference between the measured and predicted values of HbA1c using the linear relationship between HbA1c level and fasting plasma glucose levels. All study participants were divided into high HGI and low HGI groups using the median of HGI as the boundary. At last, the subjects were divided into male group and female group, and these groups were further subdivided into vitamin D deficiency group and no vitamin D deficiency group.

RESULTS

The levels of HGI were significantly higher in the vitamin D deficiency group compared with the no vitamin D deficiency group for all patients. The same was true for female patients but not for male patients. The prevalence of vitamin D deficiency in the high HGI group was higher than that in the low HGI group. The high HGI group had lower vitamin D levels compared to the low HGI group. Compared to the male group, the female group had lower vitamin D levels but higher HGI levels. A negative correlation existed between 25(OH) D and HGI in all subjects, as well as in the female-only subgroups. In the male-only subgroups, there was no correlation between them, and this positive correlation still existed after adjusting for other factors in multilinear regression analysis.

CONCLUSION

Our study showed for the first time that HGI is inversely associated with vitamin D in all patients with T2DM, and the correlation was also found in female patients, but not in male patients.

Hemoglobin glycation index is associated with incident chronic kidney disease in subjects with impaired glucose metabolism: A 10-year longitudinal cohort study

AIM

We investigated the associations between hemoglobin glycation index (HGI) and incident chronic kidney disease (CKD) in treatment-naïve subjects with prediabetes or diabetes.

METHODS

We conducted a prospective cohort study comprising 2187 subjects with prediabetes or diabetes. HGI was calculated as the difference between the measured and predicted values of HbA1c using the linear relationship between HbA1c level and fasting plasma glucose levels. Incident CKD was considered if eGFR decreased to <60 mL/min/1.73 m² and by >25% from the baseline value during follow up. The hazard ratios (HRs) for incident CKD were calculated using Cox proportional hazards regression models.

RESULTS

The overall prevalence of CKD was 15.3% (n = 335) during the 10-year follow-up period. The prevalence of CKD increased significantly from the low to the high HGI groups. In the multivariate analysis, the highest HGI group showed the highest adjusted HR for incident CKD (HR, 1.57; 95% confidence interval, 1.06-2.34), and this remained significant even after adjusting for the HbA1c level.

CONCLUSIONS

High HGI was associated with an increased risk of incident CKD among treatment-naïve subjects with prediabetes or diabetes, suggesting that HGI may be used to predict CKD in these patients regardless of HbA1c levels.

Association between postprandial lipoprotein subclasses and Framingham cardiovascular disease risk stratification

OBJECTIVE

To determine the ability of postprandial lipoprotein subclass concentrations to stratify patients with respect to their risk for cardiovascular disease (CVD).

METHODS

Using the Framingham cardiovascular disease risk score (FRS) algorithm, a total of 112 consecutive patients referred for community health screening were stratified into two groups: (a) low-risk (FRS < 10%) and (b) intermediate/high-risk (FRS ≥ 10%). Serum lipoprotein subclass concentrations were determined by Vertical Auto Profile (VAP-II).

RESULTS

Fasting and postprandial levels of LDL4, HDL2, VLDL1 + 2, VLDL3, and RLP, as well as fasting levels of ApoB and postprandial levels of LDL3 and IDL1, were significantly different in the intermediate/high risk FRS group vs. the low-risk group (P < 0.05). Correlations between Framingham CVD risk and LDL3, LDL4, IDL1, VLDL1 + 2, VLDL3, RLP, and ApoB were positive while negative for HDL2 in both the fasting and postprandial states. Intermediate/high risk for CVD was shown to be significantly associated with both fasting and postprandial levels of VLDL1 + 2 and RLP, as well as with postprandial LDL4 and VLDL3, as determined using forward conditional logistic regression analysis. Postprandial levels of VLDL1 + 2 were better at identifying patients in the intermediate/high-risk FRS group than fasting levels, although the differences were not significant due to overlapping reference intervals. In addition, the association between RLP and VLDL subclasses relative to Framingham CVD risk increased significantly in the postprandial state (ΔR² = 0.023; ΔF = 7.178; ΔP = 0.025) but not in the fasting state.

CONCLUSIONS

The use of postprandial lipoprotein subclass concentrations is not inferior to the use of fasting levels in identifying intermediate/high-risk FRS individuals. In addition, changes in RLP and VLDL subclass concentrations in fasting vs. postprandial states may reveal lipid metabolic mechanisms associated with CVD.

Usefulness of estimated average glucose (eAG) in glycemic control and cardiovascular risk reduction

OBJECTIVE

One of the 8 regional health authority (RHA) zones in New Brunswick, Canada has implemented eAG since 2010. We sought to evaluate the clinical outcomes of glycemic control and cardiovascular risk levels before and after the eAG implementation in this zone; and to compare the overall outcomes of this zone with other 7 zones of the province.

METHODS

Data (838,407 HbA1c values and 612,314 LDL-c values) was extracted from all adult diabetic patients in the provincial Diabetes Registry from 2008 to 2014. The Kruskal-Wallis statistic was conducted to compare the medians and inter quartile ranges of HbA1c and LDL-c from different zones. The proportion of patients achieving therapeutic targets, the distribution of HbA1c and LDL-c values pre/post the eAG implementation in RHA Zone 1.1 were assessed by Chi-square analysis.

RESULTS

The proportion of patients achieving targets in Zone 1.1 were at an intermediate level among all 8 zones and the trends of Zone 1.1 were no different than other zones. There were statistically significant differences for Zone 1.1 in the distribution of HbA1c (Z = -12.5190, P < 0.001) and LDL-c (Z = 16.4410, P < 0.001) before and after the eAG reported. The proportion of patients with HbA1c < 53 mmol/mol (7.0%) of the RHA Zone 1.1 was significantly lower after eAG reported (49.85% vs. 47.24%, P < 0.001); while the proportion of patients with LDL-c < 2.6 mmol/L showed statistically significant increase (68.56% vs. 71.90%, P < 0.001).

CONCLUSION

The utilization of eAG has demonstrated no significant impact on glycemic control and cardiovascular risk reduction.

Racial disparity in HbA1c persists when fructosamine is used as a surrogate for mean blood glucose in youth with type 1 diabetes

BACKGROUND

Blacks have been reported to have higher hemoglobin A1c (HbA1c) than Whites even after adjustment for differences in blood glucose levels. Potentially glucose-independent racial disparity in HbA1c is an artifact of glucose ascertainment methods. In order to test this possibility, we examined the relationship of HbA1c with race after adjustment for concurrent fructosamine level as a surrogate for mean blood glucose (MBG).

METHODS

Youth with type 1 diabetes self-identified as either Black or White had blood drawn for HbA1c, fructosamine complete blood count, ferritin, and soluble transferrin receptor (sTfR) at a clinic visit. MBG was calculated as the average of self-monitored capillary glucoses over the preceding 30 days. The effect of race on HbA1c was evaluated in a general linear model adjusting for either MBG or fructosamine, along with other covariates.

RESULTS

Fructosamine was correlated with both HbA1c (r = 0.73, P < .0001), MBG (r = 0.46, P < .0001), red cell distribution width coefficient of variation (RDW-CV) (r = 0.31, P = .0045), Fe (r = 0.27, P = .017), and sTfR (r = 0.32, P = .0042). HbA1c was approximately 0.7% higher in Blacks than Whites after adjustment for fructosamine along with age, gender, RDW-CV, Fe, sTfR.

CONCLUSIONS

Blacks tend to have higher HbA1c than Whites even after statistical adjustment for fructosamine levels as a surrogate for MBG. Thus, HbA1c tends to overestimate corresponding MBG or fructosamine levels in Black patients. Racial differences should be taken into consideration when using HbA1c as a guide to diagnosis and therapy of diabetes in mixed-race populations.

Haemoglobin glycation index and risk for diabetes-related complications in the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) trial

AIMS/HYPOTHESIS

Previous studies have suggested that the haemoglobin glycation index (HGI) can be used as a predictor of diabetes-related complications in individuals with type 1 and type 2 diabetes. We investigated whether HGI was a predictor of adverse outcomes of intensive glucose lowering and of diabetes-related complications in general, using data from the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) trial.

METHODS

We studied participants in the ADVANCE trial with data available for baseline HbA_1c and fasting plasma glucose (FPG) (n = 11,083). HGI is the difference between observed HbA_1c and HbA_1c predicted from a simple linear regression of HbA_1c on FPG. Using Cox regression, we investigated the association between HGI, both categorised and continuous, and adverse outcomes, considering treatment allocation (intensive or standard glucose control) and compared prediction of HGI and HbA_1c.

RESULTS

Intensive glucose control lowered mortality risk in individuals with high HGI only (HR 0.74 [95% CI 0.61, 0.91]; p = 0.003), while there was no difference in the effect of intensive treatment on mortality in those with high HbA_1c. Irrespective of treatment allocation, every SD increase in HGI was associated with a significant risk increase of 14-17% for macrovascular and microvascular disease and mortality. However, when adjusted for identical covariates, HbA_1c was a stronger predictor of these outcomes than HGI.

CONCLUSIONS/INTERPRETATION

HGI predicts risk for complications in ADVANCE participants, irrespective of treatment allocation, but no better than HbA_1c. Individuals with high HGI have a lower risk for mortality when on intensive treatment. Given the discordant results and uncertain relevance beyond HbA_1c, clinical use of HGI in type 2 diabetes cannot currently be recommended.

10th Annual Symposium on Self-Monitoring of Blood Glucose, April 27-29, 2017, Warsaw, Poland

International experts in the field of diabetes and diabetes technology met in Warsaw, Poland, for the 10th Annual Symposium on Self-Monitoring of Blood Glucose. The goal of these meetings is to establish a global network of experts to facilitate new collaborations and research projects that can improve the lives of people with diabetes. The 2017 meeting comprised a comprehensive scientific program, parallel interactive workshops, and four keynote lectures.

Association between hemoglobin glycation index and hepatic steatosis in non-diabetic individuals

AIMS

Hemoglobin glycation index (HGI), which is the difference between the observed value of HbA1 and the predicted HbA1c based on plasma glucose levels, represents a measure of the degree of non-enzymatic glycation of hemoglobin and it has been found to be positively associated with diabetic complications. Herein we investigated whether HGI is associated with hepatic steatosis and related biomarkers in subjects without diabetes.

METHODS

1120 White individuals without diabetes were stratified in quartiles according to HGI levels. Hepatic steatosis was diagnosed by ultrasonography.

RESULTS

As compared with subjects in the lowest quartile of HGI those in the intermediate and high HGI groups displayed an unfavorable cardio-metabolic risk profile having significantly higher values of body mass index (BMI), waist circumference, % fat mass, total cholesterol, triglycerides, inflammatory markers such as high sensitivity C reactive protein, erythrocytes sedimentation rate, complement C3, platelets and white blood cell count, hepatic insulin resistance assessed by the liver IR index and lower concentrations of high-density lipoprotein. HGI was positively associated with the biomarker of liver damage alanine aminotransferase, and fatty liver index, an indicator of hepatic steatosis. In a logistic regression analysis adjusted for age, gender and BMI individuals in the highest quartile of HGI exhibited a 1.6-fold increased odd of having hepatic steatosis (95% CI: 1.03-2.41; p=0.03) as compared with subjects in the lowest quartile of HGI.

CONCLUSIONS

Higher levels of HGI may identify subjects without diabetes at increased risk of having hepatic steatosis.

Metrics for glycaemic control - from HbA1c to continuous glucose monitoring

As intensive treatment to lower levels of HbA_1c characteristically results in an increased risk of hypoglycaemia, patients with diabetes mellitus face a life-long optimization problem to reduce average levels of glycaemia and postprandial hyperglycaemia while simultaneously avoiding hypoglycaemia. This optimization can only be achieved in the context of lowering glucose variability. In this Review, I discuss topics that are related to the assessment, quantification and optimal control of glucose fluctuations in diabetes mellitus. I focus on markers of average glycaemia and the utility and/or shortcomings of HbA_1c as a 'gold-standard' metric of glycaemic control; the notion that glucose variability is characterized by two principal dimensions, amplitude and time; measures of glucose variability that are based on either self-monitoring of blood glucose data or continuous glucose monitoring (CGM); and the control of average glycaemia and glucose variability through the use of pharmacological agents or closed-loop control systems commonly referred to as the 'artificial pancreas'. I conclude that HbA_1c and the various available metrics of glucose variability reflect the management of diabetes mellitus on different timescales, ranging from months (for HbA_1c) to minutes (for CGM). Comprehensive assessment of the dynamics of glycaemic fluctuations is therefore crucial for providing accurate and complete information to the patient, physician, automated decision-support or artificial-pancreas system.

Elevated hemoglobin glycation index identify non-diabetic individuals at increased risk of kidney dysfunction

Hemoglobin glycation index (HGI), calculated as the difference between the observed value of HbA1 and the predicted HbA1c based on plasma glucose concentration, is a measure of the individual tendency toward non-enzymatic hemoglobin glycation which has been found to be positively associated with nephropathy in subjects with diabetes. In this cross-sectional study we aimed to evaluate whether higher HGI levels are associated with impaired kidney function also among nondiabetic individuals.

The study group comprised 1505 White nondiabetic individuals stratified in quartiles according to HGI levels. Estimated glomerular filtration rate (eGFR) was calculated by using the MDRD equation.

Individuals in the intermediate and high HGI groups exhibited a worse metabolic phenotype with increased levels of visceral obesity, total cholesterol, triglycerides, inflammatory biomarkers such as hsCRP and white blood cells count and lower values of HDL and insulin sensitivity assessed by Matsuda index in comparison to the lowest quartile of HGI. Subjects in the intermediate and high HGI groups displayed a graded decrease of eGFR levels in comparison with the lowest quartile of HGI. In a logistic regression analysis individuals in the highest quartile of HGI exhibited a significantly 3.6-fold increased risk of having chronic kidney disease (95% CI: 1.13–11.24, P = 0.03) and a significantly 1.6-fold increased risk of having a mildly reduced kidney function (95% CI: 1.19–2.28, P = 0.003) in comparison to individuals in the lowest HGI group.

In conclusion HGI may be a useful tool to identify nondiabetic individuals with an increased risk of having kidney dysfunction.

Association between hemoglobin glycation index with insulin resistance and carotid atherosclerosis in non-diabetic individuals

Hemoglobin glycation index (HGI), defined as the difference between the observed HbA1c value and the value of HbA1c predicted from plasma glucose levels, represents a measure of the degree of non-enzymatic glycation of hemoglobin and it has been found to be positively associated with micro- and macro-vascular complications in subjects with type 2 diabetes. To investigate the pathophysiological abnormalities responsible for the increased cardiovascular risk of patients with higher HGI, we evaluated the association of HGI with cardio-metabolic characteristics in nondiabetic offspring of type 2 diabetic individuals. Insulin sensitivity, measured by a hyperinsulinemic-euglycemic clamp, cardio-metabolic risk factors including lipid profile, uric acid and inflammatory factors, and ultrasound measurement of carotid intima-media thickness (IMT) were assessed in 387 nondiabetic individuals. Participants were stratified in tertiles according to HGI (high, moderate and low). As compared with subjects with low HGI, those with high HGI displayed an unfavorable cardio-metabolic risk profile having significantly higher values of BMI, waist circumference, triglycerides, uric acid, fasting insulin, inflammatory markers, such as high sensitivity C reactive protein, erythrocytes sedimentation rate, complement C3, fibrinogen, and white blood cell count, and carotid IMT, and lower HDL and insulin-stimulated glucose disposal. In a linear regression analysis model including several atherosclerotic risk factors such as gender, age, BMI, inflammatory factors, lipid profile, insulin-stimulated glucose disposal, fasting insulin, uric acid, and blood pressure, HGI was the major predictor of IMT (β = 0.35; P = 0.001). In a logistic regression analysis adjusted for confounders, individuals with high HGI showed a 2.7-fold increased risk of vascular atherosclerosis (OR 2.72, 95%CI 1.01-7.37) as compared with subjects with low HGI. The present findings support the notion that HGI may be a useful tool to identify a subset of nondiabetic individuals conceivably harboring a higher risk of cardiovascular disease.

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.

A Simple and Easy Process for the Determination of Estimated Plasma Glucose Level in Patients Presenting to Hospital: An Example of Multicentric Data Mining

The aim of the present study was to determine the relation between the simultaneous fasting plasma glucose level and HbA1c in a large population of patients presenting to the hospital, based on various measurement methods available for HbA1c. HbA1c levels of 162,210 patients presenting to various hospitals and laboratories were measured based on seven different systems, and at the same time, eAG levels were calculated based on HbA1c levels. The correlation coefficients (r) between serum plasma glucose and HbA1c levels were found to be 0.809, 0.774, 0.779, 0.817, 0.704, 0.796, and 0.747 in Bio-Rad Variant II, Tosoh G8, ADAMS A1c, Trinity Boronate Affinity, Chromsystems HPLC, Roche Tina-quant, and Abbott Architect, respectively. The concordance correlation coefficients between the eAG levels as calculated with the formulas provided in the text and the eAG levels as calculated according to NGSP directions (where eAG = (28.7*HbA1c) - 46.7) were found to be between 0.9339 and 0.9866. Despite the progress made for the standardization of HbA1c measurements, the relation between serum glucose and HbA1c still demonstrated certain discrepancies pertaining to the differences in measurement methodologies. As a conclusion, each laboratory could determine different eAG levels depending on the data originated by their individual analyzer.

Glycated Hemoglobin (HbA1c): Clinical Applications of a Mathematical Concept

Background and purpose:

Glycated hemoglobin (HbA1c) reflects the cumulative glucose exposure of erythrocytes over a preceding time frame proportional to erythrocyte survival. HbA1c is thus an areal function of the glucose-time curve, an educationally useful concept to aid teaching and clinical judgment.

Methods:

An ordinary differential equation is formulated as a parsimonious model of HbA1c. The integrated form yields HbA1c as an area-under-the-curve (AUC) of a glucose-time profile. The rate constant of the HbA1c model is then derived using the validated regression equation in the ADAG study that links mean blood glucose and HbA1c with a very high degree of goodness-of-fit.

Results:

This model has didactic utility to enable patients, biomedical students and clinicians to appreciate how HbA1c may be conceptually inferred from discrete blood glucose values using continuous glucose monitoring system (CGMS) or self-monitored blood glucose (SMBG) glucometer readings as shown in the examples. It can be appreciated how hypoglycemia can occur with rapid HbA1c decline despite poor glycemic control.

Conclusions:

Being independent of laboratory assay pitfalls, computed ‘virtual’ HbA1c serves as an invaluable internal consistency cross-check against laboratory-measured HbA1c discordant with SMBG readings suggestive of inaccurate/fraudulent glucometer records or hematologic disorders including thalassemia and hemoglobinopathy. This model could be implemented within portable glucometers, CGMS devices and even smartphone apps for deriving tentative ‘virtual’ HbA1c from serial glucose readings as an adjunct to measured HbA1c. Such predicted ‘virtual’ HbA1c readily accessible via glucometers may serve as feedback to modify behavior and empower diabetic patients to achieve better glycemic control.

Hemoglobin A1c and Self-Monitored Average Glucose: Validation of the Dynamical Tracking eA1c Algorithm in Type 1 Diabetes

BACKGROUND

Previously we have introduced the eA1c-a new approach to real-time tracking of average glycemia and estimation of HbA1c from infrequent self-monitoring (SMBG) data, which was developed and tested in type 2 diabetes. We now test eA1c in type 1 diabetes and assess its relationship to the hemoglobin glycation index (HGI)-an established predictor of complications and treatment effect.

METHODS

Reanalysis of previously published 12-month data from 120 patients with type 1 diabetes, age 39.15 (14.35) years, 51/69 males/females, baseline HbA1c = 7.99% (1.48), duration of diabetes 20.28 (12.92) years, number SMBG/day = 4.69 (1.84). Surrogate fasting BG and 7-point daily profiles were derived from these unstructured SMBG data and the previously reported eA1c method was applied without any changes. Following the literature, we calculated HGI = HbA1c - (0.009 × Fasting BG + 6.8).

RESULTS

The correlation of eA1c with reference HbA1c was r = .75, and its deviation from reference was MARD = 7.98%; 95% of all eA1c values fell within ±20% from reference. The HGI was well approximated by a linear combination of the eA1c calibration factors: HGI = 0.007552*θ1 + 0.007645*θ2 - 3.154 (P < .0001); 73% of low versus moderate-high HGIs were correctly classified by the same factors as well.

CONCLUSIONS

The eA1c procedure developed in type 2 diabetes to track in real-time changes in average glycemia and present the results in HbA1c-equivalent units has shown similar performance in type 1 diabetes. The eA1c calibration factors are highly predictive of the HGI, thereby explaining partially the biological variation causing discrepancies between HbA1c and its linear estimates from SMBG data.

Explaining the increased mortality in type 1 diabetes

Despite large improvements in the management of glucose levels and in the treatment of cardiovascular risk factors, the mortality rate in individuals with type 1 diabetes (T1D) is still high. Recently, Lind et al found that T1D individuals with glycated hemoglobin levels of 6.9% or lower had a risk of death from any cause or from cardiovascular causes that is twice as high as the risk for matched controls. T1D is a chronic disease with an early onset (e.g., pediatric age) and thus in order to establish a clear correlation between death rate and the glycometabolic control, the whole history of glycemic control should be considered; particularly in the early years of diabetes. The switch from a normo- to hyperglycemic milieu in an individual with T1D in the pediatric age, represents a stressful event that may impact outcomes and death rate many years later. In this paper we will discuss the aforementioned issues, and offer our view on these findings, paying a particular attention to the several alterations occurring in the earliest phases of T1D and to the many factors that may be associated with the chronic history of T1D. This may help us to better understand the recently published death rate data and to develop future innovative and effective preventive strategies.

Association between Inflammation and Biological Variation in Hemoglobin A1c in U.S. Nondiabetic Adults

CONTEXT

Inflammation is associated with higher glycated hemoglobin (HbA1c) levels. Whether the relationship is independent of blood glucose concentration remains unclear.

OBJECTIVE

The hemoglobin glycation index (HGI) was used to test the hypothesis that interindividual variation in HbA1c is associated with inflammation.

PARTICIPANTS

This study used nondiabetic adults from the National Health and Nutrition Examination Survey (1999-2008).

MAIN OUTCOME MEASURES

A subsample of participants was used to estimate the linear regression relationship between HbA1c and fasting plasma glucose (FPG). Predicted HbA1c were calculated for 7323 nondiabetic participants by inserting FPG into the equation, HbA1c = 0.017 × FPG (mg/dL) + 3.7. HGI was calculated as the difference between the observed and predicted HbA1c and the population was divided into low, moderate, and high HGI subgroups. Polymorphonuclear leukocytes (PMNL), monocytes, and C-reactive protein (CRP) were used as biomarkers of inflammation.

RESULTS

Mean HbA1c, CRP, monocyte, and PMNL levels, but not FPG, progressively increased in the low, moderate, and high HGI subgroups. There were disproportionately more Blacks than whites in the high HGI subgroup. CRP (ß, 0.009; 95% confidence interval [CI], 0.0001-0.017), PMNL (ß, 0.036; 95% CI, 0.010-0.062), and monocyte count (ß, 0.072; 95% CI, 0.041-0.104) were each independent predictors of HGI after adjustment for age, sex, race, triglycerides, hemoglobin level, mean corpuscular volume, red cell distribution width, and obesity status.

CONCLUSIONS

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.

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.

The relationship between estimated average glucose and fasting plasma glucose

BACKGROUND

Estimated average glucose (eAG) is a value calculated from hemoglobin A1c (HbA1c) that reflects average glycemic status over the preceding few months. A linear relationship between HbA1c and eAG was demonstrated by the International HbA1c-Derived Average Glucose (ADAG) Trial in 2008. We investigated the relationship between fasting plasma glucose (FPG) and eAG.

METHODS

This retrospective study was conducted by reviewing the medical records of 6443 subjects, including 5567 diabetic patients and 876 non-diabetic subjects. The levels of HbA1c and FPG were reviewed and eAG was calculated using the regression equation published by the ADAG trial: eAGmmol/L=1.59×HbA1c(NGSP, %)-2.59[eAGmg/dL= 28.7×HbA1c(NGSP, %)-46.7].

RESULTS

In all subjects, FPG showed a moderate correlation with eAG (r=0.672, p<0.001). When diabetic and non-diabetic subjects were divided into subgroups according to FPG level, the correlation between eAG and FPG decreased in both diabetic [FPG ≥10.0 mmol/L (180 mg/dL), r=0.425; FPG 7.2-9.9 mmol/L (130-179 mg/dL), r=0.373; FPG <7.2 mmol/L (130 mg/dL), r=0.202] and non-diabetic [FPG 5.6-6.9 mmol/L (100-125 mg/dL), r=0.363; FPG <5.6 mmol/L (100 mg/dL), r=0.186] subgroups as the FPG level decreased. The differences between eAG and FPG were statistically significant (p<0.001). Only 81% (4487/5567) of diabetic patients had a lower FPG level than eAG level.

CONCLUSIONS

Our results suggest that the relationship between eAG and FPG may depend on glycemic control, thereby enhancing our understanding of eAG.

Paediatric estimated average glucose in children with Type 1 diabetes

AIM

Estimated average glucose has been used to transform HbA1c into a glucose measure that might better inform patients of their glycaemic control. The data set used to obtain the estimated average glucose equation was derived in adults with Type 1 and Type 2 diabetes, along with normal healthy control subjects, and requires testing in children.

METHODS

This was a cross-sectional study of 234 children and young people (106 male) with Type 1 diabetes aged 4.0-23.5 years who underwent continuous glucose monitoring over a 5-day period along with a measure of HbA1c . Regression analysis was used to determine estimated average glucose and agreement was assessed with the average glucose estimated from the Nathan equation: Nathan average glucose equation = 1.59 (HbA1c% ) - 2.59.

RESULTS

Mean HbA1c was 76 mmol/mol (25.1) [9.1 (2.3)%] and mean continuous glucose monitoring tissue glucose was 10.4 (2.6) mmol/l. The relationship between continuous glucose monitoring tissue glucose and HbA1c was described by the paediatric equation: paediatric estimated average glucose = 0.49 (HbA1c %) + 5.95 (r = 0.45; P < 0.001). The mean paediatric estimated average glucose was 10.4 (1.1) mmol/l compared with that from the Nathan average glucose equation of 11.9 (3.7) mmol/l (P < 0.001). Overall, the paediatric estimated average glucose was 2.7 mmol/l lower than the Nathan estimated average glucose, with a 95% limit of agreement of ± 0.5 mmol/l. The agreement was very close with HbA1c values below 80 mmol/mol (9.5%).

CONCLUSION

These data suggest that the Nathan estimated average glucose could be used in children and young people with Type 1 diabetes. Caution should still be exercised in the estimates derived for average glucose as the data set is skewed in both Nathan and paediatric average glucose estimates in opposite directions because of the differences in average HbA1c .

Motherhood, apple pie, hemoglobin A(1C), and the DCCT

OBJECTIVE

To review recent literature on the limitations of hemoglobin A(1c) (HbA(1c)) as a marker of glycemic control.

METHODS

English-language literature published between 1985 and 2011 was reviewed specific to analyses of major trials relating glycemic control to complications of diabetes mellitus, as expressed through HbA(1c) as a marker of glycemic control.

RESULTS

HbA(1c) has been accepted as the most fundamental biomarker in diabetes, if not all of medicine, as it clearly predicts risk for diabetes-related complications. What is not generally appreciated is that HbA(1c) is a crude marker of glycemia with many limitations. It is now accepted that HbA(1c) does not reflect mean glucose for many people, and even for those it does, any level could represent a wide range of glycemia. While we have learned HbA(1c) is not a perfect biomarker, we also know that in the Diabetes Control and Complications Trial, HbA(1c) could only explain 11% of the variation in retinopathy risk between the conventional and intensive therapy groups. This important finding suggests that other glycemic and nonglycemic factors may be responsible for the pathogenesis of diabetes-related complications. One candidate is glycemic variability, which must be differentiated from postprandial hyperglycemia since hypoglycemia can also result in inflammatory activation. Importantly, although it is clear that in insulin-requiring patients glycemic variability is associated with hypoglycemia, we require a definitive prospective trial to confirm glycemic variability's association with one or more vascular complications.

CONCLUSIONS

What is abundantly clear is that the HbA(1c) message, as we know it, is too simplistic. While certain wholesome concepts such as motherhood and apple pie are accepted by all, the HbA(1c) message may be more complex than originally appreciated, and it may be time to reevaluate our most basic premise in diabetes.