Glycation gap: An additional tool for glycometabolic monitoring

Factors associated with hemoglobin glycation index in adults with type 1 diabetes mellitus: The FGM-Japan study

AIMS/INTRODUCTION

The discrepancy between HbA1c and glucose exposure may have significant clinical implications; however, the association between the hemoglobin glycation index (HGI) and clinical parameters in type 1 diabetes remains controversial. This study aimed to find the factors associated with HGI (laboratory HbA1c - predicted HbA1c derived from the continuous glucose monitoring [CGM]).

MATERIALS AND METHODS

We conducted a cross-sectional study of adults with type 1 diabetes (n = 211, age 50.9 ± 15.2 years old, female sex = 59.2%, duration of CGM use = 2.1 ± 1.0 years). All subjects wore the CGM for 90 days before HbA1c measurement. Data derived from the FreeStyle Libre sensor were used to calculate the glucose management indicator (GMI) and glycemic variability (GV) parameters. HGI was defined as the difference between the GMI and the laboratory HbA1c levels. The participants were divided into three groups according to the HGI tertile (low, moderate, and high). Multivariate regression analyses were performed.

RESULTS

The female sex ratio, HbA1c, and % coefficient of variation (%CV) significantly increased over the HGI tertile, while eGFR and Hb decreased over the HGI tertile. In multivariate analysis, the factors associated with HGI were %CV and eGFR, after adjusting for HbA1c level and sex (R²  = 0.44).

CONCLUSIONS

This study demonstrated that HGI is associated with female sex, eGFR, and some glycemic variability indices, independently of HbA1c. Minimizing glycemic fluctuations might reduce HGI. This information provides diabetic health professionals and patients with personalized diabetes management for adults with type 1 diabetes.

Glycated Serum Protein Genetics and Pleiotropy with Cardiometabolic Risk Factors

Measurements of fasting glucose (FG) or glycated hemoglobin A1c (HbA1c) are two clinically approved approaches commonly used to determine glycemia, both of which are influenced by genetic factors. Obtaining accurate measurements of FG or HbA1c is not without its challenges, though. Measuring glycated serum protein (GSP) offers an alternative approach for assessing glycemia. The aim of this study was to estimate the heritability of GSP and GSP expressed as a percentage of total serum albumin (%GA) using a variance component approach and localize genomic regions (QTLs) that harbor genes likely to influence GSP and %GA trait variation in a large extended multigenerational pedigree from Jiri, Nepal (n = 1,800). We also performed quantitative bivariate analyses to assess the relationship between GSP or %GA and several cardiometabolic traits. Additive genetic effects significantly influence variation in GSP and %GA levels (p values: 1.15 × 10^-5 and 3.39 × 10^-5, respectively). We localized a significant (LOD score = 3.18) and novel GSP QTL on chromosome 11q, which has been previously linked to type 2 diabetes. Two common (MAF > 0.4) SNPs within the chromosome 11 QTL were associated with GSP (adjusted pvalue < 5.87 × 10^-5): an intronic variant (rs10790184) in the DSCAML1 gene and a 3'UTR variant (rs8258) in the CEP164 gene. Significant positive correlations were observed between GSP or %GA and blood pressure, and lipid traits (p values: 0.0062 to 1.78 × 10^-9). A significant negative correlation was observed between %GA and HDL cholesterol (p = 1.12 × 10^-5). GSP is influenced by genetic factors and can be used to assess glycemia and diabetes risk. Thus, GSP measurements can facilitate glycemic studies when accurate FG and/or HbA1c measurements are difficult to obtain. GSP can also be measured from frozen blood (serum) samples, which allows the prospect of retrospective glycemic studies using archived samples.

Performance of glycated albumin for type 2 diabetes and prediabetes diagnosis in a South African population

OBJECTIVE

To assess the utility of glycated albumin (GA%) as a diagnostic marker of type 2 diabetes and prediabetes in an African population.

METHODS

GA% levels were determined in a sample of 1294 mixed ancestry adults (74.2% women) residing in Cape Town using an enzymatic method. The participants' glycemic status was based on oral glucose tolerance test (OGTT).

RESULTS

The mean age was 47.8 years with a mean body mass index (BMI) of 28.7 kg/m². Obesity was more pronounced in the screen-detected diabetes and prediabetes groups with mean BMI's of 32.5 kg/m² and 31.5 kg/m² respectively. The optimal thresholds of GA% to diagnose screen-detected diabetes and prediabetes, were 14.90% and 12.75% respectively. For screen-detected diabetes, the C-statistic was higher for HbA1c than GA% (p = .034) with values of 0.899 (95% CI 0.855-0.943) and 0.873 (0.782-0.892) respectively. The agreement between GA% and HbA1c at their optimal thresholds for diagnosing screen-detected diabetes, was kappa = 0.33 (95% CI 0.26-0.40) and was higher than the agreement for prediabetes, kappa = 0.16 (0.11-0.21). The performance of GA% to identify screen-detected diabetes at the optimal threshold of 14.90%, was 64.8% (95% CI 54.1%-74.6%) for sensitivity and 93.5% (92.0%-94.9%) for specificity. GA% was significantly less sensitive, but more specific than HbA1c (at the optimal threshold of 6.15%) for screen-detected diabetes diagnosis (both p ≤ .002 from McNemar tests for sensitivity and specificity comparisons).

CONCLUSIONS

GA% performed less well than HbA1c to identify participants with OGTT-diagnosed type 2 diabetes or prediabetes (HbA1c cut-off of 6.15% and 5.95% respectively) in this population.

Estimation of glycaemic control in the past month using ratio of glycated albumin to HbA1c

AIMS

To evaluate comprehensively the use of the glycated albumin to HbA_1c ratio for estimation of glycaemic control in the previous month.

METHODS

A total of 306 children with Type 1 diabetes mellitus underwent ≥10 simultaneous measurements of glycated albumin and HbA_1c . Correlation and concordance rates were examined between HbA_1c measurements taken 1 month apart (ΔHbA_1c ) and glycated albumin/HbA_1c ratio fluctuations were calculated as Z-scores from the cohort value at enrolment of this study cohort (method A) or the percent difference from the individual mean over time (method B).

RESULTS

Fluctuations in glycated albumin/HbA_1c ratio (using both methods) were weakly but significantly correlated with ΔHbA_1c , whereas concordance rates were significant for glycaemic deterioration but not for glycaemic improvement. Concordance rates were higher using method B than method A.

CONCLUSIONS

The glycated albumin/HbA_1c ratio was able to estimate glycaemic deterioration in the previous month, while estimation of glycaemic improvement in the preceding month was limited. Because method B provided a better estimate of recent glycaemic control than method A, the individual mean of several measurements of the glycated albumin/HbA_1c ratio over time may also identify individuals with high or low haemoglobin glycation phenotypes in a given population, such as Japanese children with Type 1 diabetes, thereby allowing more effective diabetes management.

Concordance the hemoglobin glycation index with glycation gap using glycated albumin in patients with type 2 diabetes

BACKGROUND

The hemoglobin glycation index (HGI) is an index of differences in the glycation of hemoglobin according to blood glucose level. The glycation gap (G-gap) is an empiric measure of the extent of disagreement between hemoglobin A1C (HbA1C) and glycated albumin (GA). The aim of this study was to investigate the extent of agreement between the HGI and G-gap with respect to GA level, and to elucidate factors related to a high HGI.

METHOD

Data were obtained from 105 patients with type 2 diabetes, and fasting blood glucose (FBG), HbA1c, and GA values were measured simultaneously. The G-gap was calculated as the difference between the measured and GA-based predicted HbA1c levels. HGI was calculated as the difference between measured and FBG-based predicted HbA1c levels.

RESULTS

The HGI and G-gap were highly correlated according GA (r=0.722, P<0.001). In general, the two indices were similar in terms of both direction and magnitude. The classification of patients as high, moderate, or low glycators based on HGI versus G-gap was consistent for the majority of the population and only 5% of patients were reclassified from high to low or low to high. Fasting C-peptide levels decreased linearly, and the percentage of patients using insulin increased linearly, between the lowest and highest HGI tertile (both P<0.05).

CONCLUSIONS

There was 95% agreement between the HGI and G-gap using GA among type 2 diabetes patients. Furthermore, a high HGI was associated with a higher prevalence of insulin use among type 2 diabetes patients.

Multicenter evaluation of an enzymatic method for glycated albumin

BACKGROUND

The use of glycated albumin (GA) has been proposed as an additional glycemic control marker particularly useful in intermediate-term monitoring and in situation when HbA_1c test is not reliable.

METHODS

We have performed the first multicenter evaluation of the analytical performance of the enzymatic method quantILab Glycated Albumin assay implemented on the most widely used clinical chemistry analyzers (i.e. Abbott Architect C8000, Beckman Coulter AU 480 and 680, Roche Cobas C6000, Siemens ADVIA 2400 and 2400 XPT).

RESULTS

The repeatability of the GA measurement (expressed as CV, %) implemented in the participating centers ranged between 0.9% and 1.2%. The within-laboratory CVs ranged between 1.2% and 1.6%. A good alignment between laboratories was found, with correlation coefficients from 0.996 to 0.998. Linearity was confirmed in the range from 7.6 to 84.7%.

CONCLUSION

The new enzymatic method for glycated albumin evaluated by our investigation is suitable for clinical use.