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It has been firmly established that glycated haemoglobin (HbA1c) is an index of long-term glucose control and a predictor of complications in patients with diabetes mellitus . In addition, an association between HbA1c and cardiovascular disease risk has been shown in several studies in individuals without diabetes [2, 3]. An international expert committee recommended that HbA1c could be used as an indicator for the diagnosis of diabetes , but this is still strongly debated [5, 6]. Therefore, it is important to gain better insight into the factors that determine HbA1c in nondiabetic adults and their mechanisms of action.
Despite the fact that HbA1c is the most widely used and well-validated measure for determining average glycaemic control over time, discordance between HbA1c and other measures of glycaemic control is commonly observed. These differences between blood glucose data and HbA1c levels have generated controversy regarding the role of the sources of variation in HbA1c . Evidence suggests that HbA1c and glucose level partly reflect different processes, particularly in the nondiabetic range of glucose tolerance [8, 9].
Variation in HbA1c is thought to be subject to both genetic and nongenetic determinants . Nongenetic factors may explain about 40–60% of the HbA1c level in nondiabetic subjects [11, 12]. The two clinical variables that most clearly appear to influence HbA1c levels are red blood cell survival and blood glucose concentration. However, there are several other (glycaemia-independent) variables that can influence HbA1c, including race, smoking and age [13-15].
It has been estimated that the heritability of HbA1c is 40–60% [11, 12]. A total of 15 independent single-nucleotide polymorphisms (SNPs) that were significantly associated with HbA1c in nondiabetic adults were identified in two genome-wide association studies (GWASs) [16, 17]. The physiological mechanisms of regulation of HbA1c levels by these genetic loci remain unclear. It is considered that some SNPs modulate glycaemic physiology , whereas others may regulate nonglycaemic factors such as red blood cell (RBC) function .
The aim of this study was to investigate a wide range of determinants of HbA1c in nondiabetic Dutch adults participating in the population-based LifeLines Cohort Study . We selected genetic loci that have been established to be associated with HbA1c in previous GWASs [16, 17] and studied the individual and combined effects of these SNPs as well as the effects on HbA1c of clinical and lifestyle parameters and their interactions with genetic factors.
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The aim of this study was to investigate the genetic and nongenetic determinants of HbA1c in nondiabetic adults to evaluate the relative contribution of different factors (glycaemic vs. nonglycaemic and genetic vs. nongenetic) to HbA1c levels in individuals without diabetes. We found that age, gender, BMI, FPG, MCH, MCHC, current smoking and alcohol consumption were independently associated with HbA1c. A final predictive model with these eight nongenetic determinants explained 26.2% of the variance in HbA1c, of which FPG contributed less than half (i.e. 10.9%). In addition, we were able to confirm in our study population the previously identified association between three of 12 SNPs and HbA1c. The GRSs were also independently associated with HbA1c and explained 0.2% of the variance in HbA1c. There was a lower effect of the nonglycaemic GRS in women than men and an attenuation of the effect of the GRS of all 12 SNPs with increasing BMI. To our knowledge, this is the first study to identify these gene–environment interactions, therefore, they need to be viewed with some caution until independently confirmed in other population-based cohorts.
Because erythrocytes are freely permeable to glucose, the rate of formation of HbA1c is directly proportional to the ambient glucose concentration. However, a lack of agreement between HbA1c and other measures of glycaemic control is common. The reason for this remains unclear and the role of other sources of variation in HbA1c remains controversial. From the findings of a recent study investigating the relationships amongst HbA1c, FPG and 2-h postload plasma glucose, it was concluded that there is only a moderate correlation between glucose and HbA1c in the general population . About 20 years ago, Yudkin et al. suggested that the degree of glucose intolerance may explain only one third of the variance in HbA1c in a nondiabetic population . This suggestion is in line with our findings that the final predictive model with eight demographic, clinical and lifestyle parameters explained 26.2% of the variance in HbA1c, with only almost half due to FPG.
Glycation of haemoglobin A (HbA) begins during erythropoiesis and continues throughout the lifespan of the RBC in the circulation. Consequently, RBC lifespan determines the duration of exposure of haemoglobin to glucose and thereby HbA1c levels. Increased erythrocyte turnover, as observed in patients with haemolytic anaemia, results in lower HbA1c levels . By contrast, higher HbA1c levels were observed in patients with iron deficiency anaemia, probably because of a relatively high proportion of old erythrocytes [32, 33]. In line with our findings, Koga et al. demonstrated that MCH was negatively associated with HbA1c in premenopausal women; they suggested that this was explained by menstrual blood loss that causes iron deficiency and thereby relatively low levels of MCV and MCH . As our study population consisted of men and women (pre as well as postmenopausal), menstrual blood loss cannot be the only explanation of the observed relation between MCH and HbA1c. It is possible that a relative lack of iron caused by factors other than blood loss also leads to a relatively long lifespan of erythrocytes in individuals with lower MCH. Cohen et al. concluded that erythrocyte survival varies sufficiently amongst haematologically normal individuals to cause differences in HbA1c . If MCH is related to this variance in erythrocyte survival, this could explain the observed differences in HbA1c. More studies are needed to investigate the mechanisms by which erythrocyte indices influence HbA1c levels. However, the relatively high contribution of MCHC and MCH to the explained variance in HbA1c highlights that factors other than glycaemia determine HbA1c levels.
In line with other studies, we found a negative association between alcohol consumption and HbA1c [36, 37]. Facchini et al. found that low-to-moderate alcohol consumption in healthy men and women was associated with enhanced insulin-mediated glucose uptake and lower plasma glucose and insulin concentrations in response to oral glucose . The resulting lower levels of glycaemia could explain the lower levels of HbA1c related to alcohol consumption. Our finding of higher HbA1c levels in current smokers is also in line with results of earlier studies [14, 39, 40]. Even after adjusting for potential confounders such as abdominal obesity, there is a positive association between current smoking and HbA1c. Glycotoxins in cigarette smoke may induce the higher rate of glycation of HbA  or the relatively high degree of tissue hypoxia  may explain increased HbA1c levels in smokers .
In the current study we were able to replicate three of the previously reported significant SNPs associated with HbA1c, but the contribution of these SNPs to the explained variance in HbA1c is low. In contrast to other studies, we found a negative association between the T-allele of SNP rs7072268 and HbA1c, although this may have been a false-positive finding [17, 19]. Fluctuation of effects of individual SNPs is no longer a problem when the effects of all SNPs are added to calculate a GRS. This GRS can be used to determine the extent of the effect of all SNPs together on HbA1c. The GRS of all 12 SNPs was independently associated with HbA1c, but contributed only 0.2% to the explained variance in HbA1c. This small contribution of the genetic factors to the explained variance in HbA1c cannot be explained by the fact that it is introduced into the multivariate model after the clinical and lifestyle parameters, as the genetic factors also show only modest association with HbA1c in the univariate analyses (Table 2).
The effect of the ‘nonglycaemic GRS’ on HbA1c markedly attenuated when it was added to the final predictive model with the eight independent nongenetic risk factors. This attenuation could be due to the fact that MCHC and/or MCH act as mediator variables. If the action of these SNPs on HbA1c is at least in part through their effect on MCHC and/or MCH, the effect of the GRS will be reduced when MCHC and/or MCH is also included in the model. The effect of the glycaemic GRS on the other hand was largely unchanged.
The nonglycaemic GRS was positively associated with HbA1c in men but not in women, demonstrating a gender difference in the effect of these nonglycaemic SNPs on HbA1c. In addition, the effect of the GRS of all 12 SNPs was attenuated with increasing BMI. It is possible that the effect of being obese overrides the potential genetic effect of the SNPs.
An important strength of our study is that we were able to investigate the association between a wide range of nongenetic risk factors and HbA1c in a large population-based study. Consequently, we could adjust for potential confounding and investigate the independent associations between the risk factors and HbA1c. A limitation of this study is the lack of measures of glycaemia other than FPG, which does not capture variation in glycaemia throughout the day. Therefore, the relatively modest contribution of FPG to the explained variance in HbA1c was not unexpected. However, repeated measurements of glucose levels are not feasible in a cohort study with nondiabetic participants. More importantly, in spite of this lack of other measures of glycaemia the contribution of nonglycaemic factors to the explained variance in HbA1c is evident. Another potential limitation is that the oldest unrelated subject(s) from each family participating in the LifeLines Cohort Study were selected for inclusion in the GWAS. As a consequence, the sample in the current study is older and therefore not entirely representative of all participants in the LifeLines Cohort Study.
In conclusion, age, gender, BMI, FPG, MCH, MCHC, current smoking and alcohol consumption are independently associated with HbA1c. In addition, we were able to replicate the association between three previously identified SNPs and HbA1c in our study population. The added effect of the previously identified nonglycaemic SNPs is different in men and women, and the additional effect of all 12 SNPs was attenuated with increasing BMI. Our results suggest that a substantial part of the individual differences in HbA1c in the general population are determined by nonglycaemic factors. This should be taken into account when considering using HbA1c as a diagnostic test for diabetes.