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

Association between the haemoglobin glycation index (HGI) and clinical outcomes in patients with acute decompensated heart failure

BACKGROUND

A growing number of studies show that people with similar blood glucose levels have different levels of glycosylated haemoglobin (HbA1c), and relying only on HbA1c may lead to clinical decision-making errors. The haemoglobin glycation index (HGI) quantifies the difference in HbA1c among individuals and is strongly linked to the risk of cardiovascular disease. However, the connection between this phenomenon and the poor outcomes of patients with acute decompensated heart failure (ADHF) is currently unknown.

PATIENTS AND METHODS

This retrospective, single-centre-based cohort study included 1531 hospitalized patients with ADHF from September 2010 to January 2020. The HGI is calculated from the difference between the observed and predicted HbA1c values [predicted HbA1c = 0.024 × fasting plasma glucose (FPG) (mg/dL)+3.1]. The endpoints examined in the study included all-cause death, cardiovascular (CV) death, and major adverse cardiac events (MACE). We fitted multivariable-adjusted Cox proportional hazard models to investigate the association between the HGI and clinical outcomes.

RESULTS

During the five-year follow-up, 427 (27.9%) patients died from all causes, 232 (15.6%) from CV death, and 848 (55.4%) from MACE. The restricted cubic spline analysis also showed that the cumulative risk of all-cause and CV deaths decreased linearly with increasing HGI. According to multivariate Cox proportional hazard models, the highest tertile of the HGI was associated with a lower incidence of all-cause and cardiovascular deaths [all-cause death, adjusted hazard ratio (HR): 0.720, 95% confidence interval (CI): 0.563-0.921, p = 0.009; CV death, adjusted HR: 0.619, 95% CI: 0.445-0.861, p = 0.004]. A 1% increase in the HGI was associated with a 12.5% reduction in the risk of all-cause death and a 20.8% reduction in the risk of CV death.

CONCLUSIONS

A high HGI was directly associated with a reduction in all-cause and CV deaths but was not associated with MACE. These findings may be helpful in the management of patients with ADHF.

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.

[Alternative strategies to the use of glycosylated hemoglobin in monitoring the glycemic status of diabetic patients with end-stage renal disease]

BACKGROUND

Diabetes mellitus (DM) is one of the leading causes of end-stage renal disease. Glycosylated hemoglobin (HbA1c) is the recommended glycemic marker to achieve an optimal glycemic control that is essential to prevent comorbidities associated with the disease. However, in patients on haemodialysis (HD) this marker has important limitations, this reason has led us to search alternative markers such as glycosylated albumin (AG), labile fraction of glycosylated hemoglobin (LHbA1c) or glycation indices.

PATIENTS AND METHODS

We enrolled 47 patients in HD, 23 with DM, obtaining samples for the determination of de AG, HbA1c y LHbA1c. Glycation indices, which allow estimated the HbA1c using glucose, AG or LHbA1c, were calculated including a control group composed of 75 diabetic patients without kidney disease.

RESULTS

Diabetic patients in HD had significantly higher mean values than patients without DM for glucose [160 (44) vs 96 (12)mg/dL], HbA1c [6,4 (1,0) vs 4,9 (0,3)%], AG [16,0 (5,1) vs 12,9 (1,6)%] and LHbA1c [2,0 (0,3) vs 1,7 (0,2)%]. HbA1c calculated using glycation indices was significantly higher than measured in all HD patients, regardless of the marker used for the estimation.

CONCLUSIONS

The glycemic markers evaluated (glucose, AG and LHbA1c), could reflect a possible underestimation of the real glycemic state by HbA1c because of the limitations of this marker in HD patients. The use of alternative markers, knowing their limitations, could improve the monitoring of patients on HD and, therefore, reduce the risk of developing DM2 complications.

Trends in Quantification of HbA1c Using Electrochemical and Point-of-Care Analyzers

Glycated hemoglobin (HbA1c), one of the many variants of hemoglobin (Hb), serves as a standard biomarker of diabetes, as it assesses the long-term glycemic status of the individual for the previous 90-120 days. HbA1c levels in blood are stable and do not fluctuate when compared to the random blood glucose levels. The normal level of HbA1c is 4-6.0%, while concentrations > 6.5% denote diabetes. Conventionally, HbA1c is measured using techniques such as chromatography, spectroscopy, immunoassays, capillary electrophoresis, fluorometry, etc., that are time-consuming, expensive, and involve complex procedures and skilled personnel. These limitations have spurred development of sensors incorporating nanostructured materials that can aid in specific and accurate quantification of HbA1c. Various chemical and biological sensing elements with and without nanoparticle interfaces have been explored for HbA1c detection. Attempts are underway to improve the detection speed, increase accuracy, and reduce sample volumes and detection costs through different combinations of nanomaterials, interfaces, capture elements, and measurement techniques. This review elaborates on the recent advances in the realm of electrochemical detection for HbA1c detection. It also discusses the emerging trends and challenges in the fabrication of effective, accurate, and cost-effective point-of-care (PoC) devices for HbA1c and the potential way forward.

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.

Heterogeneous treatment effects of intensive glycemic control on major adverse cardiovascular events in the ACCORD and VADT trials: a machine-learning analysis

BACKGROUND

Evidence to guide type 2 diabetes treatment individualization is limited. We evaluated heterogeneous treatment effects (HTE) of intensive glycemic control in type 2 diabetes patients on major adverse cardiovascular events (MACE) in the Action to Control Cardiovascular Risk in Diabetes Study (ACCORD) and the Veterans Affairs Diabetes Trial (VADT).

METHODS

Causal forests machine learning analysis was performed using pooled individual data from two randomized trials (n = 12,042) to identify HTE of intensive versus standard glycemic control on MACE in patients with type 2 diabetes. We used variable prioritization from causal forests to build a summary decision tree and examined the risk difference of MACE between treatment arms in the resulting subgroups.

RESULTS

A summary decision tree used five variables (hemoglobin glycation index, estimated glomerular filtration rate, fasting glucose, age, and body mass index) to define eight subgroups in which risk differences of MACE ranged from - 5.1% (95% CI - 8.7, - 1.5) to 3.1% (95% CI 0.2, 6.0) (negative values represent lower MACE associated with intensive glycemic control). Intensive glycemic control was associated with lower MACE in pooled study data in subgroups with low (- 4.2% [95% CI - 8.1, - 1.0]), intermediate (- 5.1% [95% CI - 8.7, - 1.5]), and high (- 4.3% [95% CI - 7.7, - 1.0]) MACE rates with consistent directions of effect in ACCORD and VADT alone.

CONCLUSIONS

This data-driven analysis provides evidence supporting the diabetes treatment guideline recommendation of intensive glucose lowering in diabetes patients with low cardiovascular risk and additionally suggests potential benefits of intensive glycemic control in some individuals at higher cardiovascular risk.

Association between hemoglobin glycation index and subclinical myocardial injury in the general population free from cardiovascular disease

BACKGROUND AND AIMS

The relationship between hemoglobin glycation index (HGI) and the diagnosis and prognosis of cardiovascular disease (CVD) has been verified by previous studies. However, it remains unknown whether HGI has a predictive effect on subclinical myocardial injury (SC-MI). The purpose of the present study was to explore the relationship between HGI and SC-MI in the general population free from CVD.

METHODS AND RESULTS

The present study included 6009 participants free of CVD from the third National Health and Nutrition Examination Survey. Binary Logistic regression analysis was used to tested the association between HGI and SC-MI. As results, the HGI was significantly higher in participants with SC-MI compared with those without, and the HGI was positively correlated with SC-MI and other metabolic disorder parameters. Each 1-unit increase of HGI and glycated hemoglobin A1c (HbA1c) was independently associated with higher risk of SC-MI (P < 0.05), while fasting plasma glucose (FPG) was no longer a predictive indicator of SC-MI with the increase of confounding factors [OR (95% CI): 1.001 (0.999-1.003), P = 0.305]. And in the subgroup analysis, HGI, only in participants without diabetes, was independently associated with higher risk of SC-MI, while HbA1c and FPG had no independent predictive role in both diabetic and non-diabetic participants.

CONCLUSIONS

HGI was a significant predictor of SC-MI in the general population free from CVD.

Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control

Purpose Machine learning is an attractive tool for identifying heterogeneous treatment effects (HTE) of interventions but generalizability of machine learning derived HTE remains unclear. We examined generalizability of HTE detected using causal forests in two similarly designed randomized trials in type II diabetes patients. Methods We evaluated published HTE of intensive versus standard glycemic control on all-cause mortality from the Action to Control Cardiovascular Risk in Diabetes study (ACCORD) in a second trial, the Veterans Affairs Diabetes Trial (VADT). We then applied causal forests to VADT, ACCORD, and pooled data from both studies and compared variable importance and subgroup effects across samples. Results HTE in ACCORD did not replicate in similar subgroups in VADT, but variable importance was correlated between VADT and ACCORD (Kendall's tau-b 0.75). Applying causal forests to pooled individual-level data yielded seven subgroups with similar HTE across both studies, ranging from risk difference of all-cause mortality of -3.9% (95% CI -7.0, -0.8) to 4.7% (95% CI 1.8, 7.5). Conclusions Machine learning detection of HTE subgroups from randomized trials may not generalize across study samples even when variable importance is correlated. Pooling individual-level data may overcome differences in study populations and/or differences in interventions that limit HTE generalizability.

Effects of the hemoglobin glycation index on hyperglycemia diagnosis: Results from the REACTION study

AIMS

This study aimed to assess the effects of the hemoglobin glycation index (HGI) on hyperglycemia diagnosis and summarize the general characteristics of patients with a high-HGI phenotype.

METHODS

The fasting plasma glucose and glycated hemoglobin (HbA1c) levels of participants (n = 47,648) were used to estimate a linear regression equation and determine the baseline HGI. Overall, 42,317 participants without a history of diabetes were included in the final analysis. The participants were divided into three groups according to the tertiles (low, moderate, and high) of baseline HGI. Proportions and variables were compared among the three HGI groups. A multivariate ordered logistic regression model was used to explore associations between related variables and the high-HGI phenotype.

RESULTS

Regression analysis indicated that the high-HGI phenotype was positively associated with female sex, advanced age, obesity, increased low-density lipoprotein and triglyceride levels, decreased high-density lipoprotein cholesterol, and postprandial glycemic excursion levels (all P < 0.05). The prevalence of hyperglycemia increased from the low- to the high-HGI groups when using HbA1c for diagnosis.

CONCLUSIONS

Individuals with high HGI have similar clinical characteristics. Measuring HbA1c alone for diagnosis could lead to inappropriate diabetes management decisions in people with low or high HGI.

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.

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.

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.

Effects of Propolis Extract and Propolis-Derived Compounds on Obesity and Diabetes: Knowledge from Cellular and Animal Models

Propolis is a natural product resulting from the mixing of bee secretions with botanical exudates. Since propolis is rich in flavonoids and cinnamic acid derivatives, the application of propolis extracts has been tried in therapies against cancer, inflammation, and metabolic diseases. As metabolic diseases develop relatively slowly in patients, the therapeutic effects of propolis in humans should be evaluated over long periods of time. Moreover, several factors such as medical history, genetic inheritance, and living environment should be taken into consideration in human studies. Animal models, especially mice and rats, have some advantages, as genetic and microbiological variables can be controlled. On the other hand, cellular models allow the investigation of detailed molecular events evoked by propolis and derivative compounds. Taking advantage of animal and cellular models, accumulating evidence suggests that propolis extracts have therapeutic effects on obesity by controlling adipogenesis, adipokine secretion, food intake, and energy expenditure. Studies in animal and cellular models have also indicated that propolis modulates oxidative stress, the accumulation of advanced glycation end products (AGEs), and adipose tissue inflammation, all of which contribute to insulin resistance or defects in insulin secretion. Consequently, propolis treatment may mitigate diabetic complications such as nephropathy, retinopathy, foot ulcers, and non-alcoholic fatty liver disease. This review describes the beneficial effects of propolis on metabolic disorders.

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.

Limited benefit of haemoglobin glycation index as risk factor for cardiovascular disease in type 2 diabetes patients

BACKGROUND

The haemoglobin glycation index (HGI) has been proposed as a marker of interindividual differences in haemoglobin glycosylation. Previous studies have shown a relationship between high HGI and risk of cardiovascular disease (CVD) in patients with diabetes. However, no studies have investigated the role of previous CVD in this association.

METHODS

The study cohort comprised patients with type 2 diabetes mellitus (T2DM; n=1910) included in the Second Manifestations of Arterial Disease (SMART) study. The relationship between either HGI or HbA_1c and a composite of cardiovascular events as the primary outcome, and mortality, cardiovascular mortality, myocardial infarction and stroke as secondary outcomes, was investigated using Cox proportional-hazards models. Similar analyses were performed after stratification according to previous CVD.

RESULTS

A 1-unit higher HGI was associated with a 29% greater risk of a composite of cardiovascular events (HR: 1.29, 95% CI: 1.06-1.57) in patients without previous CVD, whereas no such relationship was seen in patients with previous CVD (HR: 0.96, 95% CI: 0.86-1.08). The direction and magnitude of the hazard ratios (HRs) of HGI and HbA_1c in relation to outcomes were similar. Additional adjustment for HbA_1c in the association between HGI and outcomes lowered the HRs.

CONCLUSION

Similar to HbA_1c, higher HGI is related to higher risk of cardiovascular events in patients with T2DM without CVD. As HbA_1c has proved to be a comparable risk factor, and obtaining and interpreting the HGI is complicated, any additional benefit of applying the HGI in clinical settings is likely to be limited.

Evidence That Differences in Fructosamine-3-Kinase Activity May Be Associated With the Glycation Gap in Human Diabetes

The phenomenon of a discrepancy between glycated hemoglobin levels and other indicators of average glycemia may be due to many factors but can be measured as the glycation gap (GGap). This GGap is associated with differences in complications in patients with diabetes and may possibly be explained by dissimilarities in deglycation in turn leading to altered production of advanced glycation end products (AGEs). We hypothesized that variations in the level of the deglycating enzyme fructosamine-3-kinase (FN3K) might be associated with the GGap. We measured erythrocyte FN3K concentrations and enzyme activity in a population dichotomized for a large positive or negative GGap. FN3K protein was higher and we found a striking threefold greater activity (323%) at any given FN3K protein level in the erythrocytes of the negative-GGap group compared with the positive-GGap group. This was associated with lower AGE levels in the negative-GGap group (79%), lower proinflammatory adipokines (leptin-to-adiponectin ratio) (73%), and much lower prothrombotic PAI-1 levels (19%). We conclude that FN3K may play a key role in the GGap and thus diabetes complications such that FN3K may be a potential predictor of the risk of diabetes complications. Pharmacological modifications of its activity may provide a novel approach to their prevention.

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.