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J Diabetes Sci Technol. 2009 July; 3(4): 635–643.
Published online 2009 July.
PMCID: PMC2769979
Clinical Advances in Hemoglobin A1c Measurement

Biologic Variability in Plasma Glucose, Hemoglobin A1c, and Advanced Glycation End Products Associated with Diabetes Complications

Abstract

Plasma glucose plays a key role in the complications of diabetes mellitus. Hemoglobin A1c (HbA1c) and circulating concentrations of advanced glycation end products (AGEs) are central to diabetes clinical care and pathophysiology. However, there is evidence for variation between individuals in the relationship of plasma glucose to both these measures and to specific complications. The glycation gap (GG) and hemoglobin glycation index represent tools for quantitating the variability in the relationship between plasma glucose and HbA1c useful for identification of underlying mechanisms. Recent evidence demonstrates the heritability of HbA1c, the GG, and AGEs, yet not of glycated serum proteins. There has been tremendous effort devoted to identifying the heritable basis of types 1 and 2 diabetes; however, studies on the heritable contributors to these mediators of glucose effect on complications are only beginning. New evidence for normal biologic variation in the distribution of glucose into the red blood cell (RBC) intracellular compartment and RBC lifespan in people with and without diabetes represent candidates for heritable mechanisms and contributors to the rise in HbA1c with age. Taken as a whole, genetic and mechanistic evidence suggests new potential targets for complications prevention and improvement in complications risk estimation. These observations could help tilt the risk–benefit balance in glycemic control toward a more beneficial outcome.

Keywords: HbA1c, glycated serum proteins, AGEs, genetics, glycation gap, estimaged average glucose

Introduction

Powerful homeostatic mechanisms ensure that the internal “milieu” is maintained within a range near a set point that maximizes the functional elements of that cell, tissue, or organism. To achieve these goals, complex regulatory genes, cells, and mediators are activated. All vertebrates have these components at a level of complexity unparalleled in other living things, perhaps because there is a relationship between degrees of regulation and increasing levels of multicellular complexity. It follows that those systems that sustain homeostasis are coded in the genome to maintain an evolutionary stable strategy and, therefore, that the given set point is itself genetically determined. Glucose, as one critical metabolic substrate for energy within cells, must be tightly regulated. As glucose shows biological diversity in blood concentrations (i.e., set points) for given individuals, that biological variation is, itself, probably genetically determined. In general, when disturbances of glucose are mild, the homeostatic response is correspondingly low-key. But when the change is severe or sustained, then the response can develop into a pathological process responsible for major human disease, including diabetes.

Blood Glucose Levels Are Strongly Inherited

Evidence from twin and family studies indicate that variation in glucose metabolism is genetically determined. Strikingly, genome-wide association studies identified several genetic loci associated with the risk of a major glucose metabolic disturbance, i.e., diabetes. The primary characteristics of type 2 diabetes mellitus (T2DM) include insulin resistance, relative insulin deficiency, and hyperglycemia.14 The disease develops due to genetic pre-disposition and environmental factors such as altered food habits, obesity, and sedentary lifestyle. The basis of the disease is altered homeostasis of glucose metabolism through inadequate insulin secretion. Common gene variants consistently associated with T2DM include genes involved in insulin secretion such as calpain 10,5,6 PPAR-γ coactivator 1,7 Glu23Lys potassium inward rectifying channel (KCNJ11),8,9 hepatocyte nuclear factor-4 alpha,1013 the glucose transporter (GLUT2),1416 and transcription factor 7-like 2 gene (TCF7L2).1719 Obesity, which is associated with insulin resistance, is also a major risk factor responsible for the development of T2DM and is potentially linked through disease risk genes such as Pro12Ala PPAR-γ,2022 FTO,23 and retinol binding protein.24 Estimates of high heritability of fasting blood glucose in some populations in some settings suggest that this as a suitable endpoint/marker for the identification of additional genes,25 subject to the constraints raised by Simonis-Bik and colleagues regarding the susceptibility of fasting glucose to environmental factors.26 The strongest association of fasting glucose has been with MTNR1B.27

Implications

Human genes were likely programmed in an energy-depleted environment, such as ancestral man might have encountered in the savannah (“thrifty genotype”)28 or that a fetus might encounter in the presence of placental insufficiency (“thrifty phenotype”).29 In either case, such a thrifty response would become maladaptive in an energy-replete environment. A genotype, after all, is only as good as the environment it finds itself in. One way whereby the environment influences genetically determined glucose homeostasis is through nongenetic events such as epigenetic factors. For example, epigenetic events influence adaptation of the cell to the intracellular energy status. A study revealed how transient hyperglycemia can induce long-lasting activating epigenetic changes (histone methylation) in the promoter of the nuclear factor B subunit p65 in endothelial cells in mice without diabetes, with increased p65 gene expression. Both the epigenetic changes and the gene expression changes persist for at least 6 days thereafter.30 These results highlight the dramatic and long-lasting effects that short-term hyperglycemic spikes can have on vascular cells and suggest that transient spikes of hyperglycemia may be a hemoglobin A1c (HbA1c)-independent risk factor for diabetes complications.

Blood Glucose is Associated with Glycated Hemoglobin

Early studies illustrated a relationship between blood glucose, over a 3-month period, and HbA1c levels. That relationship held, even when studying large number of patients over many years.3133 However, the correlation was never perfect, and the question arose as to why it was not.3437 Such an imperfect correlation might reflect either diversity in what each reflects or inaccuracy of each measurement or both. Potential resolution of the latter came with improved assays and the international standardization of HbA1c.3840 Reducing blood glucose sampling distribution bias using continuous glucose monitoring (CGM) suggested that biometric issues could be, in part, resolved. This supported the belief of some that, if HbA1c could be expressed as an estimated average glucose (eAG), then patients with diabetes could converse with clinicians in a common “currency.”41

The A1c-derived average glucose (ADAG) study was conducted to establish the best estimate of the relationship between mean blood glucose and HbA1c to permit use of eAG.42 The design of the ADAG study was to measure continuous glucose levels for 48 h every month for 4 months and at least a 7-point profile for 3 days per week using a handheld glucose meter. The acceptance criterion was that the mean glucose of ≥90% of subjects in the study be within 15% of the eAG derived from the regression line relating mean glucose to HbA1c. The variance (r2) accounted for by the “weighted” glucose, HbA1c was 0.84, and exactly 90% of patients were within 15%. Setting aside the issue of what a weighted glucose might mean and why normal subjects were included in the analysis, even this study illustrates the imperfect relationship between HbA1c and blood glucose.43 As also noted in another study from the Diabetes Research in Children Network (DirecNet) Study Group involving 47 children with type 1 diabetes mellitus (T1DM), utilizing CGM almost continuously for between 3 and 6 months, sampling once every minute, an r value of just 0.68 was found (variance r2 accounted for 0.46), leading the investigators to conclude that expressing HbA1c as mean glucose was “tenuous at best.”44 Some of the differences between the ADAG and DirecNet results could be methodological: (1) ADAG used laboratory HbA1c and calibrated CGM using HemoCue (which has higher precision), while DirecNet used point-of-care HbA1c determinations and calibration of CGM with routine self-glucose monitoring; and (2) ADAG selected for subjects at relatively stable glycemic control, while control was more variable in the DirecNet subjects. But since other studies have shown that HbA1c can vary with hemoglobinopathy, race, age, pregnancy, and renal dysfunction, it seems reasonable to conclude that there is a good but imperfect relationship between blood glucose and HbA1c.45

Implications

The relationship between blood glucose and HbA1c implies that HbA1c might be related to diabetic microvascular and macrovascular complications, and so it is.4651 But the relationship between blood glucose and complications is likely to be complicated, involving cell stress, reactive oxygen species, protein glycation, advanced protein glycation, and endothelial dysfunction.52,53 It follows that the relationship between HbA1c and diabetes complications is unlikely to be direct but instead serves as a surrogate marker of risk.

Glycated Hemoglobin is Strongly Inherited

A classic twin study of normal identical (monozygotic [MZ]) and nonidentical (dizygotic [DZ]) twin pairs demonstrated that genetic factors could influence HbA1c levels. Interclass correlations (r) were higher in MZ (r = 0.77) compared with DZ (r = 0.53) twin pairs, suggesting a substantial genetic effect with additive genetic effects (heritability), explaining 62% of population variance in HbA1c, the remainder being attributable to the influence of unique environment (23%) and age (14%).54

This is similar to but slightly lower than the heritability of 74% for HbA1c reported by Simonis-Bik and associates.26 Multivariate modeling indicated that genetic factors also have a substantial influence on fasting glucose levels (51%). However, HbA1c heritability could not be explained by genes in common with fasting glucose. In patients with T1DM, HbA1c levels were correlated in MZ twins concordant for diabetes (r = 0.68) but also in MZ twins discordant for the disease (r = 0.52). These significant correlations for HbA1c in both concordant and discordant pairs indicate a diabetes-independent familial effect. Thus HbA1c levels are substantially genetically determined and independent of the genes influencing fasting glucose. Even in T1DM, familial (i.e., diabetes-independent) factors influence protein glycation, implying that familial factors may explain, in part, the risk for microvascular complications, distinct from blood glucose levels. Age is one factor related to HbA1c levels. In the Framingham Offspring Study (FOS) and the National Health and Nutrition Examination Survey (NHANES) cohorts, HbA1c levels were positively associated with age in subjects without diabetes; in multivariate analysis, this was independent of fasting and 2 h plasma glucose,55 although that does not fully exclude any possible role of variation in glycemic control. Linear regression revealed comparable increases in HbA1c per year in the nondiabetes populations (0.014 and 0.010 U, respectively), using both cross-sectional and longitudinal data. The 97.5 percentiles for HbA1c were 6.0 and 5.6 for persons without diabetes aged less than 40 years in FOS and NHANES, respectively, compared with 6.6 and 6.2 for persons 70 years or older. In a genome-wide analysis, Pare and coworkers found a novel linkage of glycated hemoglobin to the hexokinase 1 locus (HK1), which could be mediated through hemoglobin glycation, glucose metabolism, and/or diabetes;56 also, linkage was found to sites previously associated with T2DM: GCK, SLC30A8, and G6PC2.

Implications

Hemoglobin A1c is subject to biological variation, including unidentified genetic factors. Implicit in current HbA1c interpretation is negligible variation in either red cell turnover in people who are hematologically normal or in the relationship between plasma glucose and red blood cell (RBC) glucose concentration to which hemoglobin is directly exposed. However, it would require only modest variation, a standard deviation of the order of 15% in either of these measures, to have a clinically important effect. Evidence presented later for such variation implies clinically relevant biological variation in HbA1c levels.

Glycated Hemoglobin is Associated with Glycation Gap

Persistent differences between HbA1c and blood glucose in subjects without diabetes have been ascribed to differences in “high glycator” and “low glycator” subsets.35,36 This observation led to efforts to fractionate the variance in HbA1c to determine whether components of it are more closely related to glycemic control, while others remain constant despite variations in glycemic control. One strategy has been to identify glycated serum proteins (GSPs) using the measure fructosamine (i.e., resulting in a measure identified as the glycation gap [GG], previously called the glycosylation gap57) and, in the other instance, by the mean of capillary blood glucose measured throughout the day (yielding a measure referred to as the hemoglobin glycation index [HGI]58,59). Cohen and colleagues reported that the GG is reproducible over time despite variation in glycemic control reflected in HbA1c and GSPs; GG correlated with the development of diabetic nephropathy in this retrospective study.57 In a prospective study, the higher the GG, the greater the subsequent frequency of diabetic retinopathy.60 McCarter and associates59 found that HGI, like GG, was reproducible over time and that retinopathy and nephropathy risk were predicted by the HGI determined on numerous extended capillary glucose profiles throughout the duration of the Diabetes Control and Complications Trial. Use of these analytic approaches is subject to a number of limitations, requiring careful consideration of possible interference from the dependence of serum fructosamine on serum protein metabolism, of mean capillary glucose on sampling frequency and distribution and monitoring technique, and of the imprecision of HbA1c techniques.

Implications

The association between HGI and complications and HbA1c and complications could be mutually dependent or independent. Separating the two would be difficult, as both are correlated and, indeed, the one is derived from the other. The Diabetes Control and Complications Trial presented statistical models suggesting that subjects with similar levels of HbA1c had a higher risk of retinopathy progression in the conventional treatment group than in the intensive treatment group.61 That analysis has been cited to support the hypothesis that specific patterns of glucose variation, in particular, postprandial hyperglycemia, contribute uniquely to an increased risk of microvascular complications above and beyond that explained by the level of HbA1c.62,63 Additional analyses by Lachin and coworkers showed that virtually all (96%) of the beneficial effect of intensive versus conventional therapy on progression of retinopathy is explained by the reductions in the mean levels of HbA1c, similarly for other outcomes.64 Further, subjects within the intensive and conventional treatment groups with similar levels of HbA1c over time have similar risks of retinopathy progression, especially after adjusting for factors on which they differ. It was concluded that HbA1c explained virtually all difference in risk of complications between the intensive and conventional groups and that a given level of HbA1c has similar effects within the two treatment groups. While the authors concluded that other components of hyperglycemia can only explain a small part of the differences in risk between intensive and conventional therapy over time, it is difficult to segregate the impact of hyperglycemia associated with HbA1c from that associated with HGI or GG since, in the analysis, HbA1c and HGI could be interchangeable.

Glycation Gap is Strongly Inherited

Evidence from both healthy twins and twins with diabetes indicates that HbA1c levels are genetically determined, which provides an independent line of evidence that HbA1c is, in part, determined by factors other than glycemic control. Given the two independent lines of evidence showing HbA1c variance and heritability,26,54 a logical question that arises is whether the heritable components of HbA1c are associated preferentially with the GG fraction or the GSP fraction of the HbA1c variance, as this would narrow the range of mechanisms involved and inform candidate gene studies. Researchers therefore conducted a classic twin study using healthy female MZ and DZ twins without diabetes.65 The predicted HbA1c was based on the regression line between HbA1c and GSP in a separate population spanning the pathophysiologic range. Glycation gap was more strongly correlated between MZ (r = 0.65) than DZ (r = 0.48) twins, adjusted for age and body mass index, i.e., 69% of population variance in GG is heritable, while the remaining 31% is due to unique environmental influences. In contrast, GSP was similarly correlated between MZ and DZ twins, hence not genetically determined. Glycation gap was strongly correlated to HbA1c (r = 0.48), attributable mostly to genetic factors. About one-third of the heritability of HbA1c is shared with GG; the remainder is specific to HbA1c.

Implications

There are two key implications of that study. First, it demonstrates a contrast between GSPs, which are not inherited, and HbA1c, which is inherited. This observation limits the range of candidate mechanisms that could account for the heritability of HbA1c. Second, it demonstrates that a measure, the GG, that captures the variation of HbA1c in a population beyond that which is attributable to blood glucose variation also captures a proportion of the heritability of HbA1c based on quantitative genetic modeling. This second observation could not have been predicted given that GG is derived from both HbA1c and GSPs and that the former is inherited while the latter is not. The present work illustrates how measures of HbA1c variance from blood glucose control, including the GG and Chalew's “hemoglobin glycosylation index,”66 are compatible with each other and internally consistent. They demonstrate that these measures in normal individuals capture different aspects of the same clinically important biological phenomenon, namely, that a HbA1c of a given value may not necessarily have the same clinical meaning in two different individuals with diabetes, even if a reduction of HbA1c by one percentage point does.

Hemoglobin A1c is a measure of glycemic control determined in the intra-RBC space, while glycation of serum proteins reflects a process in the extracellular compartment. By inference, the GG reflects the variance in HbA1c determined by processes in both the extracellular and intracellular compartments compared with those unique to the extracellular compartment. This study implicates genetically determined mechanisms active in the intracellular RBC compartment in the determination of HbA1c levels, potentially through the modification of glycation or deglycation of hemoglobin.

Potential Biologic Sources of the Glycation Gap

Demonstration of the GG led us to two investigative strategies: (1) establishment of a method to test the assumption by determining the distribution of a labeled glucose analog across the RBC membrane under controlled conditions in vitro and (2) application of state-of-the-art methods for RBC ex vivo labeling with biotin and re-infusion which permit more precise determination of RBC survival and quantitation of in vivo glycation rate. Using the labeled glucose analog technique, researchers found variation in glucose concentration inside compared to outside at steady state among individuals with and without diabetes. In a cross-sectional study, the ratio concentration inside/concentration outside increased with HbA1c and with the GG, but not with the corresponding serum fructosamine concentration.67 While this is strictly a correlation, it strongly implies that the higher the intra relative to extra-cell glucose concentration, the higher the HbA1c. While the mechanism remains elusive, if glucose entry into cells subject to diabetes complications is predominantly GLUT1-dependent, GG could potentially serve as a surrogate marker of risk for intracellular glucose-related complications.

Using the biotinylated RBC technique, researchers were able to show that the glycation of hemoglobin is indeed linear with time in people with diabetes at steady state glycemic control, a widely held assumption that had not previously been demonstrated in vivo.68 This property means that the clinically determined HbA1c will be linearly related to the RBC survival. High-precision RBC survival curves demonstrated curvilinear rather than linear RBC disappearance. This makes the traditional method for calculating red cell survival invalid and necessitated a new approach translating the curvilinearity into a “mean RBC age,” with values roughly half those of traditionally reported survivals.69 In subjects without and with diabetes, RBC mean cell age ranged from 38–59 days and 39–56 days, respectively. The linearity of glycation with time demonstrated in these experiments means that differences in RBC age (i.e., duration of hemoglobin exposure to circulating glucose) should result in corresponding differences in HbA1c at a given mean glucose concentration.

Implications

In other words, given three hematologically normal people with identical mean blood glucose differing in RBC survival across the range observed, if the one with the most typical RBC mean cell age has HbA1c 8.0%, having the shortest survival could lower the HbA1c to 6.4% and having the longest could raise it to 9.6%.68 The tight fit of the biotinylated RBC curves to the data and the similarity of mean cell age in a subset of the population suggest that this range of RBC survival is fairly commonly observed in the general population. However, intrasubject reproducibility of RBC survival, as outlined, needs to be confirmed.

Blood Glucose is Associated with Advanced Glycation End Products

The formation of advanced glycation end product (AGE) by glycation and oxidation alters the functional property of matrix proteins and mediates sustained cellular changes by binding to AGE receptors. Advanced glycation end product formation has been implicated in microvascular and macrovascular disease associated with diabetes.70,71 Heterogeneity of AGE implies that many products could be measured to estimate AGE formation. Of them, pentosidine and Nε carboxymethyl-lysine (CML) are the best characterized, CML can serve as a biomarker of oxidative stress resulting from sugar and lipid oxidation, and CML elevations are associated with diabetes and renal dysfunction.7275 It follows from these observations that AGEs are formed in the context of hyperglycemia but that the relationship between blood glucose levels and CML levels is not a simple one.

An Advanced Glycation End Product Can be Inherited

The formation of AGE is predominantly endogenous, despite also being derived from exogenous sources such as food,76 so levels may be genetically determined. Genetic factors do, indeed, determine serum CML levels, at least in normal subjects. In a study of normal MZ and DZ twins, it was found that additive genetic effects (heritability) explained 74% of the population variance, while the remainder was due to unique environment, with no contribution from shared environment.77 In these same normal twins, genetic factors influenced both fasting glucose levels (heritability, 51%) and HbA1c (heritability, 62%), but CML heritability could not be explained by genes in common with either. In striking contrast, a study of MZ twins discordant for T1DM found that twin serum CML levels were high, irrespective of diabetes status, with only limited heritability (unpublished personal observations).

Implications

The low heritability for serum CML levels in T1DM does not imply that these factors have no additive genetic variance. A low heritability means that, of all the observed variation, a small proportion is caused by variation in genotypes; it does not mean that additive genetic variance is small. Since we have previously shown that there is a strong heritability of serum CML levels in normal healthy subjects, it follows that a genetic effect on AGE levels, through AGE formation, AGE removal, or both, can be overridden in the context of disease or maladaption.

Summary

In summary, blood glucose is subject to powerful homeostatic forces. An evolutionary stable strategy has assured that homeostasis is maintained by genetic factors, ensuring that glucose metabolism is strongly inherited. It follows that surrogate markers of blood glucose levels are themselves genetically determined, though it is unclear whether this genetic effect is due to a direct effect of the glucose (e.g., protein glycation determining HbA1c or AGE levels) or an indirect effect, because glycation or deglycation rates are themselves genetically determined. Certainly, the genetic influence of HbA1c is not wholly dependent on genes responsible for fasting glucose. In either event, HbA1c is closely linked to vascular risk, especially microvascular risk. Components of this risk association will be glucose dependent and others glucose independent; the concept of GG aims to capture elements of this source of variation. HbA1c and glycation gap are genetically determined independent of fasting glucose. Reevaluation of some of the long-held assumptions about the relationship between HbA1c and plasma glucose raises new questions regarding potential new targets for intervention and new opportunities to refine complications risk assessment.

Abbreviations

ADAG
A1c-derived average glucose
AGE
advanced glycation end product
CGM
continuous glucose monitoring
CML
carboxymethyl-lysine
DirecNet
Diabetes Research in Children Network
DZ
dizygotic
eAG
estimated average glucose
FOS
Framingham Offspring Study
GG
glycation gap
GSP
glycated serum protein
HbA1c
hemoglobin A1c
HGI
hemoglobin glycation index
MZ
monozygotic
NHANES
National Health and Nutrition Examination Survey
RBC
red blood cell
T1DM
type 1 diabetes mellitus
T2DM
type 2 diabetes mellitus

References

1. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes. 1988;37(12):1595–1607. [PubMed]
2. Porte D., Jr Banting lecture 1990. Beta-cells in type II diabetes mellitus. Diabetes. 1991;40(2):166–180. [PubMed]
3. Polonsky KS. Lilly Lecture 1994. The beta-cell in diabetes: from molecular genetics to clinical research. Diabetes. 1995;44(6):705–717. [PubMed]
4. Defronzo RA. Banting Lecture. From the triumvirate to the ominous octet: a new paradigm for the treatment of type 2 diabetes mellitus. Diabetes. 2009;58(4):773–795. [PMC free article] [PubMed]
5. Horikawa Y, Oda N, Cox NJ, Li X, Orho-Melander M, Hara M, Hinokio Y, Lindner TH, Mashima H, Schwarz PE, del Bosque-Plata L, Horikawa Y, Oda Y, Yoshiuchi I, Colilla S, Polonsky KS, Wei S, Concannon P, Iwasaki N, Schulze J, Baier LJ, Bogardus C, Groop L, Boerwinkle E, Hanis CL, Bell GI. Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat Genet. 2000;26(2):163–175. [PubMed]
6. Evans JC, Frayling TM, Cassell PG, Saker PJ, Hitman GA, Walker M, Levy JC, O'Rahilly S, Rao PV, Bennett AJ, Jones EC, Menzel S, Prestwich P, Simecek N, Wishart M, Dhillon R, Fletcher C, Millward A, Demaine A, Wilkin T, Horikawa Y, Cox NJ, Bell GI, Ellard S, McCarthy MI, Hattersley AT. Studies of association between the gene for calpain-10 and type 2 diabetes mellitus in the United Kingdom. Am J Hum Genet. 2001;69(3):544–552. [PubMed]
7. Ek J, Andersen G, Urhammer SA, Gaede PH, Drivsholm T, Borch-Johnsen K, Hansen T, Pedersen O. Mutation analysis of peroxisome proliferator-activated receptor-gamma coactivator-1 (PGC-1) and relationships of identified amino acid polymorphisms to type II diabetes mellitus. Diabetologia. 2001;44(12):2220–2226. [PubMed]
8. Inoue H, Ferrer J, Welling CM, Elbein SC, Hoffman M, Mayorga R, Warren-Perry M, Zhang Y, Millns H, Turner R, Province M, Bryan J, Permutt MA, Aguilar-Bryan L. Sequence variants in the sulfonylurea receptor (SUR) gene are associated with NIDDM in Caucasians. Diabetes. 1996;45(6):825–831. [PubMed]
9. Gloyn AL, Weedon MN, Owen KR, Turner MJ, Knight BA, Hitman G, Walker M, Levy JC, Sampson M, Halford S, McCarthy MI, Hattersley AT, Frayling TM. Large-scale association studies of variants in genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes. 2003;52(2):568–572. [PubMed]
10. Hani EH, Suaud L, Boutin P, Chèvre JC, Durand E, Philippi A, Demenais F, Vionnet N, Furuta H, Velho G, Bell GI, Laine B, Froguel P. A missense mutation in hepatocyte nuclear factor-4 alpha, resulting in a reduced transactivation activity, in human late-onset non-insulin-dependent diabetes mellitus. J Clin Invest. 1998;101(3):521–526. [PMC free article] [PubMed]
11. Gragnoli C, Lindner T, Cockburn BN, Kaisaki PJ, Gragnoli F, Marozz G, Bell GI. Maturity-onset diabetes of the young due to a mutation in the hepatocyte nuclear factor-4 alpha binding site in the promoter of the hepatocyte nuclear factor-1 alpha gene. Diabetes. 1997;46(10):1648–1651. [PubMed]
12. Bulman MP, Dronsfield MJ, Frayling T, Appleton M, Bain SC, Ellard S, Hattersley AT. A missense mutation in the hepatocyte nuclear factor 4 alpha gene in a UK pedigree with maturity-onset diabetes of the young. Diabetologia. 1997;40(7):859–862. [PubMed]
13. Nakajima H, Yoshiuchi I, Hamaguchi T, Tomita K, Yamasaki T, Iizuka K, Okita K, Moriwaki M, Ono A, Oue T, Horikawa Y, Shingu R, Miyagawa J, Namba M, Hanafusa T, Matsuzawa Y. Hepatocyte nuclear factor-4 alpha gene mutations in Japanese non-insulin dependent diabetes mellitus (NIDDM) patients. Res Commun Mol Pathol Pharmacol. 1996;94(3):327–330. [PubMed]
14. Patel P, Bell GI, Cook JT, Turner RC, Wainscoat JS. Multiple restriction fragment length polymorphisms at the GLUT2 locus: GLUT2 haplotypes for genetic analysis of type 2 (non-insulin-dependent) diabetes mellitus. Diabetologia. 1991;34(11):817–821. [PubMed]
15. Baroni MG, Alcolado JC, Pozzilli P, Cavallo MG, Li SR, Galton DJ. Polymorphisms at the GLUT2 (beta-cell/liver) glucose transporter gene and non-insulin-dependent diabetes mellitus (NIDDM): analysis in affected pedigree members. Clin Genet. 1992;41(5):229–234. [PubMed]
16. Laukkanen O, Lindström J, Eriksson J, Valle TT, Hämäläinen H, Ilanne-Parikka P, Keinänen-Kiukaanniemi S, Tuomilehto J, Uusitupa M, Laakso M. Polymorphisms in the SLC2A2 (GLUT2) gene are associated with the conversion from impaired glucose tolerance to type 2 diabetes: the Finnish Diabetes Prevention Study. Diabetes. 2005;54(7):2256–2260. Finnish Diabetes Prevention Study. [PubMed]
17. Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J, Helgason A, Stefansson H, Emilsson V, Helgadottir A, Styrkarsdottir U, Magnusson KP, Walters GB, Palsdottir E, Jonsdottir T, Gudmundsdottir T, Gylfason A, Saemundsdottir J, Wilensky RL, Reilly MP, Rader DJ, Bagger Y, Christiansen C, Gudnason V, Sigurdsson G, Thorsteinsdottir U, Gulcher JR, Kong A, Stefansson K. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet. 2006;38(3):320–323. [PubMed]
18. Marzi C, Huth C, Kolz M, Grallert H, Meisinger C, Wichmann HE, Rathmann W, Herder C, Illig T. Variants of the transcription factor 7-like 2 gene (TCF7L2) are strongly associated with type 2 diabetes but not with the metabolic syndrome in the MONICA/KORA surveys. Horm Metab Res. 2007;39(1):46–52. [PubMed]
19. Horikoshi M, Hara K, Ito C, Nagai R, Froguel P, Kadowaki T. A genetic variation of the transcription factor 7-like 2 gene is associated with risk of type 2 diabetes in the Japanese population. Diabetologia. 2007;50(4):747–751. [PubMed]
20. Beamer BA, Yen CJ, Andersen RE, Muller D, Elahi D, Cheskin LJ, Andres R, Roth J, Shuldiner AR. Association of the Pro12Ala variant in the peroxisome proliferator-activated receptor-gamma2 gene with obesity in two Caucasian populations. Diabetes. 1998;47(11):1806–1808. [PubMed]
21. Clement K, Hercberg S, Passinge B, Galan P, Varroud-Vial M, Shuldiner AR, Beamer BA, Charpentier G, Guy-Grand B, Froguel P, Vaisse C. The Pro115Gln and Pro12Ala PPAR gamma gene mutations in obesity and type 2 diabetes. Int J Obes Relat Metab Disord. 2000;24(3):391–393. [PubMed]
22. Vaccaro O, Mancini FP, Ruffa G, Sabatino L, Colantuoni V, Riccardi G. Pro12Ala mutation in the peroxisome proliferator-activated receptor gamma2 (PPARgamma2) and severe obesity: a case-control study. Int J Obes Relat Metab Disord. 2000;24(9):1195–1199. [PubMed]
23. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, Perry JR, Elliott KS, Lango H, Rayner NW, Shields B, Harries LW, Barrett JC, Ellard S, Groves CJ, Knight B, Patch AM, Ness AR, Ebrahim S, Lawlor DA, Ring SM, Ben-Shlomo Y, Jarvelin MR, Sovio U, Bennett AJ, Melzer D, Ferrucci L, Loos RJ, Barroso I, Wareham NJ, Karpe F, Owen KR, Cardon LR, Walker M, Hitman GA, Palmer CN, Doney AS, Morris AD, Smith GD, Hattersley AT, McCarthy MI. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316(5826):889–894. [PMC free article] [PubMed]
24. Craig RL, Chu WS, Elbein SC. Retinol binding protein 4 as a candidate gene for type 2 diabetes and prediabetic intermediate traits. Mol Genet Metab. 2007;90(3):338–344. [PMC free article] [PubMed]
25. Mills GW, Avery PJ, McCarthy MI, Hattersley AT, Levy JC, Hitman GA, Sampson M, Walker M. Heritability estimates for beta cell function and features of the insulin resistance syndrome in UK families with an increased susceptibility to type 2 diabetes. Diabetologia. 2004;47(4):732–738. [PubMed]
26. Simonis-Bik AM, Eekhoff EM, Diamant M, Boomsma DI, Heine RJ, Dekker JM, Willemsen G, van Leeuwen M, de Geus EJ. The heritability of HbA1c and fasting blood glucose in different measurement settings. Twin Res Hum Genet. 2008;11(6):597–602. [PubMed]
27. Prokopenko I, Langenberg C, Florez JC, Saxena R, Soranzo N, Thorleifsson G, Loos RJ, Manning AK, Jackson AU, Aulchenko Y, Potter SC, Erdos MR, Sanna S, Hottenga JJ, Wheeler E, Kaakinen M, Lyssenko V, Chen WM, Ahmadi K, Beckmann JS, Bergman RN, Bochud M, Bonnycastle LL, Buchanan TA, Cao A, Cervino A, Coin L, Collins FS, Crisponi L, de Geus EJ, Dehghan A, Deloukas P, Doney AS, Elliott P, Freimer N, Gateva V, Herder C, Hofman A, Hughes TE, Hunt S, Illig T, Inouye M, Isomaa B, Johnson T, Kong A, Krestyaninova M, Kuusisto J, Laakso M, Lim N, Lindblad U, Lindgren CM, McCann OT, Mohlke KL, Morris AD, Naitza S, Orrù M, Palmer CN, Pouta A, Randall J, Rathmann W, Saramies J, Scheet P, Scott LJ, Scuteri A, Sharp S, Sijbrands E, Smit JH, Song K, Steinthorsdottir V, Stringham HM, Tuomi T, Tuomilehto J, Uitterlinden AG, Voight BF, Waterworth D, Wichmann HE, Willemsen G, Witteman JC, Yuan X, Zhao JH, Zeggini E, Schlessinger D, Sandhu M, Boomsma DI, Uda M, Spector TD, Penninx BW, Altshuler D, Vollenweider P, Jarvelin MR, Lakatta E, Waeber G, Fox CS, Peltonen L, Groop LC, Mooser V, Cupples LA, Thorsteinsdottir U, Boehnke M, Barroso I, Van Duijn C, Dupuis J, Watanabe RM, Stefansson K, McCarthy MI, Wareham NJ, Meigs JB, Abecasis GR. Variants in MTNR1B influence fasting glucose levels. Nat Genet. 2009;41(1):77–81. [PMC free article] [PubMed]
28. Neel JV. Diabetes mellitus: a “thrifty” genotype rendered detrimental by “progress”? Am J Hum Genet. 1962;14:353–362. [PubMed]
29. Hales CN, Barker DJ. Type 2 (non-insulin-dependent) diabetes mellitus: the thrifty phenotype hypothesis. Diabetologia. 1992;35(7):595–601. [PubMed]
30. El-Osta A, Brasacchio D, Yao D, Pocai A, Jones PL, Roeder RG, Cooper ME, Brownlee M. Transient high glucose causes persistent epigenetic changes and altered gene expression during subsequent normoglycemia. J Exp Med. 2008;205(10):2409–2417. [PMC free article] [PubMed]
31. Gonen B, Rubenstein A, Rochman H, Tanega SP, Horwitz DL. Haemoglobin A1: an indicator of the metabolic control of diabetic patients. Lancet. 1977;2(8041):734–737. [PubMed]
32. Nathan DM, Singer DE, Hurxthal K, Goodson JD. The clinical information value of the glycosylated hemoglobin assay. N Engl J Med. 1984;310(6):341–346. [PubMed]
33. Cohen MP. New York: Springer-Verlag; 1986. Diabetes and protein glycosylation: measurement and biologica relevance.
34. Kilpatrick ES. Problems in the assessment of glycaemic control in diabetes mellitus. Diabet Med. 1997;14(10):819–831. [PubMed]
35. Yudkin JS, Forrest RD, Jackson CA, Ryle AJ, Davie S, Gould BJ. Unexplained variability of glycated haemoglobin in non-diabetic subjects not related to glycaemia. Diabetologia. 1990;33(4):208–215. [PubMed]
36. Gould BJ, Davie SJ, Yudkin JS. Investigation of the mechanism underlying the variability of glycated haemoglobin in non-diabetic subjects not related to glycaemia. Clin Chim Acta. 1997;260(1):49–64. [PubMed]
37. Rohlfing C, Wiedmeyer HM, Little R, Grotz VL, Tennill A, England J, Madsen R, Goldstein D. Biological variation of glycohemoglobin. Clin Chem. 2002;48(7):1116–1118. [PubMed]
38. Hoelzel W, Weykamp C, Jeppsson JO, Miedema K, Barr JR, Goodall I, Hoshino T, John WG, Kobold U, Little R, Mosca A, Mauri P, Paroni R, Susanto F, Takei I, Thienpont L, Umemoto M, Wiedmeyer HM. IFCC Working Group on HbA1c Standardization. IFCC reference system for measurement of hemoglobin A1c in human blood and the national standardization schemes in the United States, Japan, and Sweden: a method-comparison study. Clin Chem. 2004;50(1):166–174. [PubMed]
39. Peterson KP, Pavlovich JG, Goldstein D, Little R, England J, Peterson CM. What is hemoglobin A1c? An analysis of glycated hemoglobins by electrospray ionization mass spectrometry. Clin Chem. 1998;44(9):1951–1958. [PubMed]
40. Mosca IF, Goodall I, Hoshino T, Jeppsson JO, John WG, Little RR, Miedema K, Myers GL, Reinauer H, Sacks DB, Weykamp CW. International Federation of Clinical Chemistry and Laboratory Medicine, IFCC Scientific Division. Global standardization of glycated hemoglobin measurement: the position of the IFCC Working Group. Clin Chem Lab Med. 2007;45(8):1077–1080. [PubMed]
41. Heine RJ. HbA1C—standardization and relationship to BG levels and complications. Diabetic Medicine. 2006;23(Suppl 4:17)
42. Nathan DM, Kuenen J, Borg R, Zheng H, Schoenfeld D, Heine RJ. A1c-Derived Average Glucose Study Group. Translating the A1C assay into estimated average glucose values. Diabetes Care. 2008;31(8):1473–1478. [PMC free article] [PubMed]
43. Kahn R, Fonseca V. Translating the A1C assay. Diabetes Care. 2008;31(8):1704–1707. [PMC free article] [PubMed]
44. Wilson DM, Kollman Diabetes Research in Children Network (DirecNet) Study Group. Relationship of A1C to glucose concentrations in children with type 1 diabetes: assessments by high-frequency glucose determinations by sensors. Diabetes Care. 2008;31(3):381–385. [PMC free article] [PubMed]
45. Barth JH, Marshall SM, Watson ID. Consensus meeting on reporting glycated haemoglobin (HbA1c) and estimated average glucose (eAG) in the UK: report to the National Director for Diabetes, Department of Health. Diabet Med. 2008;25(4):381–382. [PubMed]
46. Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993;329(14):977–986. [PubMed]
47. UK Prospective Diabetes Study Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33) Lancet. 1998;352(9131):837–853. [PubMed]
48. Ohkubo Y, Kishikawa H, Araki E, Miyata T, Isami S, Motoyoshi S, Kojima Y, Furuyoshi N, Shichiri M. Intensive insulin therapy prevents the progression of diabetic microvascular complications in Japanese patients with non-insulin-dependent diabetes mellitus: a randomized prospective 6-year study. Diabetes Res Clin Pract. 1995;28(2):103–117. [PubMed]
49. UK Prospective Diabetes Study (UKPDS) Group. Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34) Lancet. 1998;352(9131):854–865. [PubMed]
50. Nathan DM, Cleary PA, Backlund JY, Genuth SM, Lachin JM, Orchard TJ, Raskin P, Zinman B. Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study Research Group. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med. 2005;353(25):2643–2653. [PMC free article] [PubMed]
51. Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med. 2008;359(15):1577–1589. [PubMed]
52. Brownlee M. Biochemistry and molecular cell biology of diabetic complications. Nature. 2001;414(6865):813–820. [PubMed]
53. Brownlee M. The pathobiology of diabetic complications: a unifying mechanism. Diabetes. 2005;54(6):1615–1625. [PubMed]
54. Snieder H, Sawtell PA, Ross L, Walker J, Spector TD, Leslie RD. HbA(1c) levels are genetically determined even in type 1 diabetes: evidence from healthy and diabetic twins. Diabetes. 2001;50(12):2858–2863. [PubMed]
55. Pani LN, Korenda L, Meigs JB, Driver C, Chamany S, Fox CS, Sullivan L, D'Agostino RB, Nathan DM. Effect of aging on A1C levels in individuals without diabetes: evidence from the Framingham Offspring Study and the National Health and Nutrition Examination Survey 2001–2004. Diabetes Care. 2008;31(10):1991–1996. [PMC free article] [PubMed]
56. Paré G, Chasman DI, Parker AN, Nathan DM, Miletich JP, Zee RY, Ridker PM. Novel association of HK1 with glycated hemoglobin in a non-diabetic population: a genome-wide evaluation of 14,618 participants in the Women's Genome Health Study. PLoS Genet. 2008;4(12) e1000312. [PMC free article] [PubMed]
57. Cohen RM, Holmes YR, Chenier TC, Joiner CH. Discordance between HbA1c and fructosamine: evidence for a glycosylation gap and its relation to diabetic nephropathy. Diabetes Care. 2003;26(1):163–167. [PubMed]
58. Hempe JM, Gomez R, McCarter RJ, Jr, Chalew SA. High and low hemoglobin glycation phenotypes in type 1 diabetes: a challenge for interpretation of glycemic control. J Diabetes Complications. 2002;16(5):313–320. [PubMed]
59. McCarter RJ, Hempe JM, Gomez R, Chalew SA. Biological variation in HbA1c predicts risk of retinopathy and nephropathy in type 1 diabetes. Diabetes Care. 2004;27(6):1259–1264. [PubMed]
60. Cohen RM, LeCaire TJ, Lindsell CJ, Smith EP, D'Alessio DJ. Relationship of prospective GHb to glycated serum proteins in incident diabetic retinopathy: implications of the glycation gap for mechanism of risk prediction. Diabetes Care. 2008;31(1):151–153. [PubMed]
61. Diabetes Control and Complications Trial Research Group. The relationship of glycemic exposure (HbA1c) to the risk of development and progression of retinopathy in the Diabetes Control and Complications Trial. Diabetes. 1995;44(8):968–983. [PubMed]
62. Monnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP, Colette C. Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA. 2006;295(14):1681–1687. [PubMed]
63. Brownlee M, Hirsch IB. Glycemic variability: a hemoglobin A1c-independent risk factor for diabetic complications. JAMA. 2006;295(14):1707–1708. [PubMed]
64. Lachin JM, Genuth S, Nathan DM, Zinman B, Rutledge BN. DCCT/EDIC Research Group. Effect of glycemic exposure on the risk of microvascular complications in the diabetes control and complications trial—revisited. Diabetes. 2008;57(4):995–1001. [PubMed]
65. Cohen RM, Snieder H, Lindsell CJ, Beyan H, Hawa M, Blinko S, Edwards R, Spector TD, Leslie RD. Evidence for independent heritability of the glycation gap (glycosylation gap) fraction of HbA1c in nondiabetic twins. Diabetes Care. 2006;29(8):1739–1743. [PubMed]
66. McCarter RJ, Hempe JM, Gomez R, Chalew SA. Biological variation in HbA1c predicts risk of retinopathy and nephropathy in type 1 diabetes. Diabetes Care. 2004;27(6):1259–1264. [PubMed]
67. Khera PK, Joiner CH, Carruthers A, Lindsell CJ, Smith EP, Franco RS, Holmes YR, Cohen RM. Evidence for interindividual variation in the glucose gradient across the human red blood cell membrane and its relationship to hemoglobin glycation. Diabetes. 2008;57(9):2445–2452. [PMC free article] [PubMed]
68. Cohen RM, Franco RS, Khera PK, Smith EP, Lindsell CJ, Ciraolo PJ, Palascak MB, Joiner CH. Red cell life span heterogeneity in hematologically normal people is sufficient to alter HbA1c. Blood. 2008;112(10):4284–4291. [PubMed]
69. Lindsell CJ, Franco RS, Smith EP, Joiner CH, Cohen RM. A method for the continuous calculation of the age of labeled red blood cells. Am J Hematol. 2008;83(6):454–457. [PubMed]
70. Singh R, Barden A, Mori T, Beilin L. Advanced glycation end-products: a review. Diabetologia. 2001;44(2):129–146. [PubMed]
71. Nakamura Y, Horii Y, Nishino T, Shiiki H, Sakaguchi Y, Kagoshima T, Dohi K, Makita Z, Vlassara H, Bucala R. Immunohistochemical localization of advanced glycosylation end products in coronary atheroma and cardiac tissue in diabetes mellitus. Am J Pathol. 1993;143(6):1649–1656. [PubMed]
72. Ikeda K, Higashi T, Sano H, Jinnouchi Y, Yoshida M, Araki T, Ueda S, Horiuchi S. N (epsilon)-(carboxymethyl)lysine protein adduct is a major immunological epitope in proteins modified with advanced glycation end products of the Maillard reaction. Biochemistry. 1996;35(24):8075–8083. [PubMed]
73. Kislinger T, Fu C, Huber B, Qu W, Taguchi A, Du Yan S, Hofmann M, Yan SF, Pischetsrieder M, Stern D, Schmidt AM. N(epsilon)-(carboxymethyl)lysine adducts of proteins are ligands for receptor for advanced glycation end products that activate cell signaling pathways and modulate gene expression. J Biol Chem. 1999;274(44):31740–31749. [PubMed]
74. Reddy S, Bichler J, Wells-Knecht KJ, Thorpe SR, Baynes JW. N epsilon-(carboxymethyl)lysine is a dominant advanced glycation end product (AGE) antigen in tissue proteins. Biochemistry. 1995;34(34):10872–10878. [PubMed]
75. Wagner Z, Wittmann I, Mazák I, Schinzel R, Heidland A, Kientsch-Engel R, Nagy J. N(epsilon)-(carboxymethyl)lysine levels in patients with type 2 diabetes: role of renal function. Am J Kidney Dis. 2001;38(4):785–791. [PubMed]
76. Vlassara H, Cai W, Crandall J, Goldberg T, Oberstein R, Dardaine V, Peppa M, Rayfield EJ. Inflammatory mediators are induced by dietary glycotoxins, a major risk factor for diabetic angiopathy. Proc Natl Acad Sci U S A. 2002;99(24):15596–15601. [PubMed]
77. Leslie RD, Beyan H, Sawtell P, Boehm BO, Spector TD, Snieder H. Level of an advanced glycated end product is genetically determined: a study of normal twins. Diabetes. 2003;52(9):2441–2444. [PubMed]

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