Discordance in the levels of hemoglobin A1C and glycated albumin: Calculation of the glycation gap based on glycated albumin level

The Correlation Between Glycation Gap and Renal Complications in Patients with Type 2 Diabetes Mellitus

Purpose

The aim of this study was to investigate the correlations between the glycation gap (GG) and renal complications such as urinary albumin-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) in type 2 diabetes mellitus patients.

Materials and Methods

A cross-sectional study was conducted on 104 individuals (52 males and 52 females), aged 36-93 years old. Fasting blood glucose (FBG), HbA1c, and serum fructosamine were measured simultaneously. GG was calculated as the difference between the measured and fructosamine-based predicted HbA1c levels (FHbA1c).

Results

There was a moderately positive correlation between HbA1c and fructosamine concentration (r = 0.488; p < 0.001). GG was positively correlated with UACR (r = 0.3275; p = 0.0007), negatively correlated with eGFR (r = -0.3400; p = 0.0004). HbA1c was positively correlated with UACR (r = 0.2437; p = 0.0127) but not correlated with eGFR (r = -0.444; p = 0.6542). Fructosamine has a positive correlation with eGFR (r = 0.2426; p = 0.0131) but not with UACR (r = -0.1021; p = 0.3025).

Conclusion

GG was positively correlated with UACR and inversely correlated with eGFR in type 2 Diabetes mellitus patients. This suggests that GG is a valuable index for predicting kidney complications due to diabetes.

Association between hemoglobin glycation index and diabetic kidney disease in type 2 diabetes mellitus in China: A cross- sectional inpatient study

Objective

To investigate the association between Hemoglobin Glycation Index (HGI) and Diabetic Kidney Disease (DKD) in Chinese type 2 diabetic individuals and to construct a risk score based on HGI to predict a person's risk of DKD.

Methods

We retrospectively analyzed 1622 patients with type 2 diabetes mellitus (T2DM). HGI was obtained by calculating the fasting plasma glucose (FPG) level into the formula, and they were grouped into low HGI group (L-HGI), medium HGI group (H-HGI) and high HGI group (H-HGI) according to tri-sectional quantile of HGI. The occurrence of DKD was analyzed in patients with different levels of HGI. Multivariate logistics regression analysis was used to analyze the risk factors of DKD in patients with T2DM.

Results

A total of 1622 patients with T2DM were enrolled in the study. Among them, 390 cases were DKD. The prevalence of DKD among the three groups was 16.6%, 24.2% and 31.3%. The difference was statistically significant (P = 0.000). There were significant differences in age (P=0.033), T2DM duration (P=0.005), systolic blood pressure (SBP) (P=0.003), glycosylated hemoglobin (HbA1c) (P=0.000), FPG (P=0.032), 2-hour postprandial plasma glucose (2h-PPG) (P=0.000), fasting C-peptide FCP (P=0.000), 2-hour postprandial C-peptide (2h-CP) (P=0.000), total cholesterol (TC) (P=0.003), low density lipoprotein cholesterol (LDL-C) (P=0.000), serum creatinine (sCr) (P=0.001), estimated glomerular filtration rate (eGFR) (P=0.000) among the three groups. Mantel-Haenszel chi-square test showed that there was a linear relationship between HGI and DKD (x2=177.469, p < 0.001). Pearson correlation analysis showed that with the increase of HGI level the prevalence of DKD was increasing (R= 0.445, P=0.000). It was indicated by univariate logistic regression analysis that individuals in H-HGI was more likely to develop DKD (OR: 2.283, 95% CI: 1.708~ 3.052) when compared with L-HGI. Adjusted to multiple factors, this trend still remained significant (OR: 2.660, 95% CI: 1.935~ 3.657). The combined DKD risk score based on HGI resulted in an area under the receiver operator characteristic curve (AUROC) of 0.702.

Conclusions

High HGI is associated with an increased risk of DKD. DKD risk score may be used as one of the risk predictors of DKD in type 2 diabetic population.

Association of hemoglobin glycation index and glycation gap with cardiovascular disease among US adults

AIMS

To investigate the association of hemoglobin glycation index (HGI) and glycation gap (GGap), reflecting mismatches between HbA1c and other measures of glycemia, with cardiovascular disease (CVD) in the general population.

METHODS

5966 US adult (age ≥ 20 years) participants were included from the National Health and Nutrition Examination Survey (NHANES) (1999-2004). In this cross-sectional study, predicted HbA1c was calculated based on fasting plasma glucose (FPG) and glycated albumin (GA), respectively. Multivariable binary logistic regression analysis was performed to explore the association of HGI and GGap with CVD prevalence.

RESULTS

Compared to the lowest tertile, the ORs with 95% CIs for CVD across the tertiles were 1.41 (1.01, 1.96) and 0.87 (0.58, 1.31) for HGI (P for trend = 0.535) and 1.06 (0.77, 1.47) and 1.60 (1.18, 2.17) for GGap (P for trend = 0.002) in the fully-adjusted model. Besides, the discordantly high GGap/low HbA1c group was associated with higher CVD prevalence compared with the low GGap/high HbA1c group (OR = 1.50, 95% CI, 1.04-2.16, P = 0.030).

CONCLUSIONS

GGap derived from GA is associated with CVD independent of traditional risk factors, even HbA1c, in US general adults. Considering the potential limitations of HbA1c, the introduction of GGap is warranted.

Consistency of the Glycation Gap with the Hemoglobin Glycation Index Derived from a Continuous Glucose Monitoring System

BACKGROUND

Discordances between glycated hemoglobin (HbA1c) levels and glycemic control are common in clinical practice. We aimed to investigate the consistency of the glycation gap with the hemoglobin glycation index (HGI).

METHODS

From 2016 to 2019, 36 patients with type 2 diabetes were enrolled. HbA1c, glycated albumin (GA), and fasting blood glucose levels were simultaneously measured and 72-hour continuous glucose monitoring (CGM) was performed on the same day. Repeated tests were performed at baseline and 1 month later, without changing patients' diabetes management. The HGI was calculated as the difference between the measured HbA1c and the predicted HbA1c that was derived from CGM. The glycation gap was calculated as the difference between the measured and GA-based predicted HbA1c levels.

RESULTS

Strong correlations were found between the mean blood glucose (MBG)-based HGI and the prebreakfast glucose-based HGI (r=0.867, P&lt;0.001) and between the glycation gap and the MBG-based HGI (r=0.810, P&lt;0.001). A close correlation was found between the MBG-based HGI at baseline and that after 1 month (r=0.729, P&lt;0.001), with a y-intercept of 0 and a positive slope.

CONCLUSION

The HGI and glycation gap were highly reproducible, and the magnitudes of repeated determinations were closely correlated. Patients with similar mean glucose levels may have significantly different HbA1c levels.

Potential Clinical Error Arising From Use of HbA1c in Diabetes: Effects of the Glycation Gap

The glycation gap (GGap) and the similar hemoglobin glycation index (HGI) define consistent differences between glycated hemoglobin and actual glycemia derived from fructosamine or mean blood glucose, respectively. Such a disparity may be found in a substantial proportion of people with diabetes, being >1 U of glycated HbA1c% or 7.2 mmol/mol in almost 40% of estimations. In this review we define these indices and explain how they can be calculated and that they are not spurious, being consistent in individuals over time. We evaluate the evidence that GGap and HGI are associated with variation in risk of complications and mortality and demonstrate the potential for clinical error in the unquestioning use of HbA1c. We explore the underlying etiology of the variation of HbA1c from mean glucose in blood plasma, including the potential role of enzymatic deglycation of hemoglobin by fructosamine-3-kinase. We conclude that measurement of GGap and HGI are important to diabetes clinicians and their patients in individualization of therapy and the avoidance of harm arising from consequent inappropriate assessment of glycemia and use of therapies.

Hemoglobin glycation index predicts cardiovascular disease in people with type 2 diabetes mellitus: A 10-year longitudinal cohort study

BACKGROUND AND AIMS

Previous studies have suggested that the hemoglobin glycation index (HGI) can be used as a predictor of diabetes-related complications. We examined the prognostic significance of a high HGI for cardiovascular disease (CVD) in an ongoing hospital-based cohort.

METHODS

From March 2003 to December 2004, 1302 consecutive patients with type 2 diabetes and without a prior history of CVD were enrolled. CVD was defined as the occurrence of coronary artery disease or ischemic stroke. The HGI was calculated as the measured glycated hemoglobin (HbA1c) minus predicted HbA1c. Predicted HbA1c were calculated for 1302 participants by inserting fasting blood glucose (FBG) into the equation, Predicted HbA1c level = 0.02106 × FBG [mg/dL] + 4.973. Cox proportional hazards models were used to identify the associations between the HGI and CVD after adjusting for confounding variables.

RESULTS

During 11.1 years of follow-up, 225 participants (17.2%) were newly diagnosed with CVD. The baseline HGI was significantly higher in subjects with incident CVD than in those without CVD, although the baseline FBG levels did not differ according to the occurrence of CVD. Compared with patients without CVD, those with CVD were older, had a longer duration of diabetes and hypertension, and used more insulin at baseline. A Cox hazard regression analysis revealed that the development of CVD was significantly associated with baseline HGI (hazard ratio [HR], 1.94; 95% confidence interval [CI], 1.31-2.87; p < 0.001, comparing the highest and lowest quartiles of HGI). This relationship was unchanged after additional adjustment for baseline HbA1c level (HR, 1.74; 95% CI, 1.08-2.81). The HRs of HbA1c in relation to outcomes were similar to or lower than those seen for HGI. After adjustment for HGI, the effect of the highest HbA1c on incident CVD disappeared.

CONCLUSIONS

High HGI was independently associated with incident CVD in patients with type 2 diabetes. Patients with high HGI at baseline had a higher inherent risk for CVD.

Rapid Non-Enzymatic Glycation of the Insulin Receptor under Hyperglycemic Conditions Inhibits Insulin Binding In Vitro: Implications for Insulin Resistance

The causes of insulin resistance are not well-understood in either type 1 or type 2 diabetes. Insulin (INS) is known to undergo rapid non-enzymatic covalent conjugation to glucose or other sugars (glycation). Because the insulin receptor (IR) has INS-like regions associated with both glucose and INS binding, we hypothesize that hyperglycemic conditions may rapidly glycate the IR, chronically interfering with INS binding. IR peptides were synthesized spanning IR- associated INS-binding regions. Glycation rates of peptides under hyperglycemic conditions were followed over six days using matrix assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry. INS conjugated to horse-radish peroxidase was used to determine INS binding to IR peptides in glycated and non-glycated forms. Several IR peptides were glycated up to 14% within days of exposure to 20-60 mM glucose. Rates of IR-peptide glycation were comparable to those of insulin. Glycation of four IR peptides significantly inhibits INS binding to them. Glycation of intact IR also decreases INS binding by about a third, although it was not possible to confirm the glycation sites on the intact IR. Glycation of the IR may therefore provide a mechanism by which INS resistance develops in diabetes. Demonstration of glycation of intact IR in vivo is needed.

Genome-wide meta-analysis in Japanese populations identifies novel variants at the TMC6-TMC8 and SIX3-SIX2 loci associated with HbA1c

Glycated haemoglobin (HbA1c) is widely used as a biomarker for the diagnosis of diabetes, for population-level screening, and for monitoring the glycaemic status during medical treatment. Although the heritability of HbA1c has been estimated at ~55-75%, a much smaller proportion of phenotypic variance is explained by the HbA1c-associated variants identified so far. To search for novel loci influencing the HbA1c levels, we conducted a genome-wide meta-analysis of 2 non-diabetic Japanese populations (n = 7,704 subjects in total). We identified 2 novel loci that achieved genome-wide significance: TMC6-TMC8 (P = 5.3 × 10-20) and SIX3-SIX2 (P = 8.6 × 10-9). Data from the largest-scale European GWAS conducted for HbA1c supported an association between the novel TMC6-TMC8 locus and HbA1c (P = 2.7 × 10-3). The association analysis with glycated albumin and glycation gap conducted using our Japanese population indicated that the TMC6-TMC8 and SIX3-SIX2 loci may influence the HbA1c level through non-glycaemic and glycaemic pathways, respectively. In addition, the pathway-based analysis suggested that the linoleic acid metabolic and 14-3-3-mediated signalling pathways were associated with HbA1c. These findings provide novel insights into the molecular mechanisms that modulate the HbA1c level in non-diabetic subjects.

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.

The impact of oxidized serum albumin on the oncotic pressure and hydration status of peritoneal dialysis patients

Objective

Hypoalbuminemia, fluid overload (FO), and oxidative stress (OS) may be related to cardiovascular morbidity and mortality in peritoneal dialysis (PD) patients. OS produces molecular modifications of serum albumin that interfere with its quantification by the commonly used bromocresol green assay. This study evaluated the impact of oxidized serum albumin (OSA) on oncotic pressure (OP) and hydration status.

Patients and methods

Twenty-four stable hypoalbuminemic PD patients were enrolled in the study. After performing physical examination, assessment of the hydration status using a whole-body bioimpedance spectroscopy technique was performed, and blood samples were drawn for determination of OP, serum albumin levels, and OSA.

Results

Extracellular to total body water (E/TBW) ratio was higher in patients with FO ≥1.5 L with or without edema than in patients with FO <1.5 L (P≤0.043). E/TBW ratio was higher in patients with FO ≥1.5 L and edema compared to those with FO ≥1.5 L but without edema (P=0.004). OP was significantly higher in patients with FO ≥1.5 L and without edema compared to those with FO ≥1.5 L and with edema (P<0.001). Albumin-detection index (ADI) in patients with FO ≥1.5 L and without edema was similar to ADI in patients with FO <1.5 L (P=0.520). ADI was significantly lower in patients with FO ≥1.5 L and without edema compared to those with FO ≥1.5 L and edema (P=0.034). E/TBW ratio correlated positively with the ADI (r=0.60, P=0.001) and inversely with the OP (r=−0.54, P=0.002).

Conclusion

Overhydration may be clinically undetectable in PD patients. Assessing the hydration status and measuring the total serum albumin levels, including the oxidized fraction, should be considered in evaluating hydration status in PD patients.