Genome-Wide Association Study of Serum Fructosamine and Glycated Albumin in Adults Without Diagnosed Diabetes: Results From the Atherosclerosis Risk in Communities Study

Abstract

Fructosamine and glycated albumin are potentially useful alternatives to hemoglobin A_1c (HbA_1c) as diabetes biomarkers. The genetic determinants of fructosamine and glycated albumin, however, are unknown. We performed genome-wide association studies of fructosamine and glycated albumin among 2,104 black and 7,647 white participants without diabetes in the Atherosclerosis Risk in Communities (ARIC) Study and replicated findings in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Among whites, rs34459162, a novel missense single nucleotide polymorphism (SNP) in RCN3, was associated with fructosamine (P = 5.3 × 10⁻⁹) and rs1260236, a known diabetes-related missense mutation in GCKR, was associated with percent glycated albumin (P = 5.9 × 10⁻⁹) and replicated in CARDIA. We also found two novel associations among blacks: an intergenic SNP, rs2438321, associated with fructosamine (P = 6.2 × 10⁻⁹), and an intronic variant in PRKCA, rs59443763, associated with percent glycated albumin (P = 4.1 × 10⁻⁹), but these results did not replicate. Few established fasting glucose or HbA_1c SNPs were also associated with fructosamine or glycated albumin. Overall, we found genetic variants associated with the glycemic information captured by fructosamine and glycated albumin as well as with their nonglycemic component. This highlights the importance of examining the genetics of hyperglycemia biomarkers to understand the information they capture, including potential glucose-independent factors.

Introduction

Diabetes is defined by elevated blood glucose levels (hyperglycemia). Hemoglobin A_1c (HbA_1c) is formed as glucose binds to hemoglobin molecules within erythrocytes and is the standard clinical measure of chronic hyperglycemia used to diagnose and monitor diabetes (1). However, factors related to the nonglycemic portion of HbA_1c such as erythrocyte turnover and hemoglobin characteristics can affect HbA_1c values (2).

There is growing interest in fructosamine and glycated albumin, additional biomarkers of hyperglycemia that demonstrate associations with diabetes risk and complications similar to HbA_1c (3–14). Fructosamine measures total serum protein bound to glucose. Glycated albumin is expressed as a percentage of serum albumin, the most abundant serum protein. Both biomarkers reflect glucose exposure over a shorter period of time (2–4 weeks) than HbA_1c (2–3 months) (15). Fructosamine may be used to monitor glycemic control in clinical situations where HbA_1c is problematic, such as in the setting of anemia or hemoglobinopathies (2). While glycated albumin is not frequently used in the U.S., it is widely used in Japan and other countries as a complement to HbA_1c to monitor short-term glycemic control (16).

The genetics of HbA_1c and glucose have been well studied; however, genetic factors that influence fructosamine and glycated albumin are uncharacterized. Of the known genome-wide association study (GWAS) variants associated with fasting glucose and HbA_1c in European ancestry cohorts, few loci are associated with both (17). Many HbA_1c variants are in genes related to hematological factors rather than glucose metabolism (18–20), while fasting glucose variants are in genes involved in glucose metabolism (although these variants are not all associated with diabetes) (21–23). This lack of overlap suggests that some underlying genetic variants are specific to particular biomarkers of hyperglycemia rather than to type 2 diabetes. Understanding the genetic determinants of fructosamine and glycated albumin should help in the interpretation of these tests and possibly extend our understanding of the pathophysiology of glucose metabolism. In particular, comparing the genetic overlap between different measures of glycemia may provide insight into the contributions of glycemic versus nonglycemic gene variants, i.e., to what extent genetic factors operate via pathways directly relevant to diabetes pathophysiology (“glycemic”) or operate via glycemic-independent pathways that do not influence glucose metabolism or diabetes risk (“nonglycemic,” such as the hematological variants associated with HbA_1c). If nonglycemic genetic variants strongly impact fructosamine and glycated albumin, this may need to be taken into account in the interpretation of these biomarkers as measures of hyperglycemia.

We conducted GWAS of fructosamine and glycated albumin in blacks and whites in the Atherosclerosis Risk in Communities (ARIC) Study. We also compared previously identified genetic determinants for HbA_1c and fasting glucose with fructosamine and glycated albumin to identify common genetic factors related to glucose metabolism and those that may be distinct to fructosamine and glycated albumin.

Research Design and Methods

Study Population

The ARIC Study is an ongoing prospective cohort of 15,792 participants initiated in 1987 (24). Participants were middle-aged adults recruited from four U.S. communities (Jackson, Mississippi; Forsyth County, North Carolina; Washington County, Maryland; and suburban Minneapolis, Minnesota). All study participants provided written informed consent, and study protocols were approved by the relevant institutional review boards.

In the current study, we included 9,751 participants (7,647 whites and 2,104 blacks) who attended visit 2 (1990–1992), consented for use of DNA, did not have diagnosed diabetes (self-reported diagnosis or use of diabetes medications), had valid data on fructosamine and glycated albumin, and had genotyping data meeting quality control criteria (Supplementary Fig. 1). Individuals with diagnosed diabetes were excluded to avoid potential bias caused by altered glucose levels as a result of diabetes treatment.

Genotyping

ARIC Study participants were genotyped using the Affymetrix 6.0 array and imputed separately by race using IMPUTE2 (25) with the 1000 Genomes Project phase 1 (March 2012) reference panel. Quality control excluded individuals based on single nucleotide polymorphism (SNP) mismatch, high discordance with previous TaqMan assay genotypes, genetic outlier status, and relatedness. SNPs with IMPUTE info score <0.8 or minor allele frequency (MAF) <0.05 were excluded. Only autosomal variants (on chromosomes 1–22) were considered. Principal components analysis was used to estimate population substructure with EIGENSTRAT (26).

Glycemic Markers

Fructosamine (Roche Diagnostics, Indianapolis, IN) and glycated albumin and serum albumin (GA-L; Asahi Kasei Pharma Corporation, Tokyo, Japan) were measured in 2012–2013 using a Roche Modular P800 system from serum collected at visit 2 and stored at −70°C (3). Percent glycated albumin was calculated per the manufacturer’s protocol: [(glycated albumin concentration in g/dL/serum albumin concentration in g/dL)*100/1.14] + 2.9. We also examined total glycated albumin (g/dL) as well as serum albumin (g/dL) to help distinguish genetic factors specific to serum protein concentration versus hyperglycemia.

Serum glucose was measured on the Roche Hitachi 911 analyzer using the hexokinase method (Roche Diagnostics), and HbA_1c was measured from whole blood stored at −70°C using high-performance liquid chromatography, standardized to the assay used in the Diabetes Control and Complications Trial (DCCT) (27).

Statistical Analysis

GWAS in blacks and whites were conducted using SNPTEST v2 (28) for all glycemic biomarkers using imputed allele dosage and controlling for age, sex, field center, and the first 10 principal components under an additive genetic model. Fructosamine and glycated albumin (both percent (%) and total [g/dL]) were transformed on the natural log scale; therefore, the effect sizes are the change in the natural log of the biomarker per each additional risk allele. Exponentiating the effect sizes thus corresponds to the percent higher or lower biomarker levels per additional risk allele. Fasting glucose and HbA_1c were not transformed. To identify additional independent SNPs associated with the traits, we performed conditional analyses for genome-wide significant findings. Using fructosamine, percent glycated albumin, and total glycated albumin as the dependent variable and the index SNP (SNP with the lowest P value in a region showing a genome-wide significant association) as a covariate, we evaluated the association between other SNPs with a MAF ≥1% within 250 kB of the index SNP or between recombination hotspots surrounding the index SNP. To estimate percentage of variance explained by each SNP, we used the equation R_i² = b_i² × var(SNP_i)/var(y) where b_i = the effect size of the association between the SNP_i and the phenotype y, var(SNP_i) is 2 × MAF_SNPi × (1-MAF_SNPi) and var(y) is the variance in the phenotype (29,30). We meta-analyzed across ancestries using a random-effects model by GWAMA (31).

In sensitivity analyses, we performed GWAS excluding case subjects with undiagnosed diabetes (participants with fasting glucose ≥126 mg/dL or nonfasting glucose >200 mg/dL). To further evaluate the genetic variants pertaining to serum protein levels rather than hyperglycemia, we evaluated the top fructosamine and glycated albumin SNPs for association with total glycated albumin and with serum albumin (transformed on the natural log scale). To determine the extent to which glycemic biomarkers shared genetics, we used the program LDSC, which calculated genetic correlations between the biomarkers from ARIC summary statistics by taking advantage of the LD structure (variants in LD with more SNPs are more likely to have larger effect sizes, which extends to the product of correlated traits). We used precomputed LD scores from 1000 Genomes European data (32). Genetic correlations are not bounded to [–1,1], and estimates outside of this range indicate strong genetic correlations.

Replication

Significant associations between genetic variants and fructosamine and percent glycated albumin in ARIC were evaluated for replication in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort, a prospective cohort study initiated in 1985 to evaluate risk factors for heart disease among unrelated young adults (33).

Serum specimens from 2005 to 2006 were stored at –70°C and used to analyze glycated albumin and fructosamine in 2014 using a Roche COBAS 6000 chemistry analyzer (Roche Diagnostics). Glycated albumin and fructosamine were measured using the same assays used in ARIC (glycated albumin by Lucica GA-L from Asahi Kasei Pharma Corporation and fructosamine via Roche Diagnostics).

Genotyping was performed using the Affymetrix 6.0 array. Standard quality control metrics were applied, and imputation to HapMap Phase II, Build 36, Release 22 was done using MACH (34). Genetic, covariate, fructosamine, and glycated albumin data were available on 1,304 whites and 608 blacks. Individuals with diabetes (current use of glucose-lowering medications or fasting glucose ≥126 mg/dL) were excluded from analysis. Linear regression analyses stratified by race were done for the association between significant ARIC SNPs and natural log–transformed fructosamine and percent glycated albumin adjusted for age, sex, field center, and the first three principal components.

Statistical analysis was done using SAS v9.4 (SAS Institute, Cary, NC) (data manipulation) and ProbABELv0.2 (35). If the ARIC SNP was not available in the CARDIA data set, we determined whether proxy SNPs in linkage disequilibrium (LD) with the ARIC SNP (r² > 0.7 from 1000 Genomes phase 3v5 European population, GRCh37 assembly using Single Nucleotide Polymorphisms Annotator (SNiPA) [36]) were available and, if so, analyzed the associations with the proxy SNP. We considered a Bonferroni-corrected one-sided P value threshold of <0.05 (0.1/2 SNPs per race) for replication significance. We meta-analyzed ARIC and CARDIA results using fixed-effects inverse variance weighted model by METAL (37).

Candidate SNP Analysis

We additionally evaluated previously identified fasting glucose (n = 41) and HbA_1c (n = 46) candidate SNPs from the National Human Genome Research Institute (NHGRI)-EBI Catalog of published GWAS using the search terms “fasting glucose” and “HbA1c” (http://www.ebi.ac.uk/gwas/ as of 14 December 2017) for association with fructosamine and percent glycated albumin in ARIC. SNPs were included if they were discovered in European ancestry cohorts, were genome-wide significant (P < 5 × 10⁻⁸), and were not in LD with each other (r² < 0.2, using SNiPA) (36). For the candidate SNP analyses, we used a study-wide significance threshold: two traits (fructosamine and glycated albumin), two races (black and white), and the number of candidate SNPs for each trait: P < 4.6 × 10⁻⁴ (0.05/(2*2*27)) for fasting glucose and P < 2.6 × 10⁻⁴ (0.05/(2*2*49)) for HbA_1c.

We additionally performed analyses controlling for known fasting glucose and HbA_1c SNPs with P < 0.05 in ARIC, compiled into scores calculated as the sum of the number of risk alleles, weighted by the effect size in ARIC among whites.

Comparison of Variance Explained

To compare the influence of glycemic and nonglycemic genetic variants on fructosamine and glycated albumin to that of HbA_1c, we calculated the percent phenotypic variance explained by the SNPs in our study to those from published results of HbA_1c. In addition, we calculated variance explained by known fasting glucose and albumin SNPs (identified by the same criteria used for fasting glucose and HbA_1c). Percent variance explained was calculated using the equation described above.

Results

Overall, 7,647 whites and 2,104 blacks from ARIC and 1,304 whites and 608 blacks from CARDIA were included in this study (Supplementary Table 1). Mean age in ARIC (56–57 years) was higher than in CARDIA (45–46 years). The cohorts had similar distribution of sex. CARDIA had a greater percentage of black participants and lower mean values for each measure of glycemia as compared with ARIC.

We identified four genome-wide significant loci in ARIC, two associated with fructosamine and two with percent glycated albumin. Three of these variants, rs34459162 intronic to RCN3, rs2438321 (intergenic), and rs59443763 intronic to PRKCA have not previously been reported to be associated with any glycemic traits in humans (Table 1). None of the analyses showed evidence for inflation (Supplementary Figs. 2–7).

Table 1.

Genome-wide significant loci for fructosamine and percent glycated albumin

SNP	Chr	Closest gene	A1/A2†	Outcome	Race	ARIC*					CARDIA*				ARIC+CARDIA*			ARIC White+Black*
						A1 freq	β (SE)‡	% diff§	P value	% variance explained	A1 freq	β (SE)‡	% diff§	P value	β (SE)‡	% diff§	P value	β (SE)‡	% diff§	P value
rs34459162||	19	RCN3	C/T	Fructosamine (μmol/L)	White	0.08	−0.02 (0.003)	−2%	5.3E-9	0.6%	0.09	−0.02 (0.014)	−2%	0.09	−0.02 (0.003)	−2%	4.9E-9	—	—	—
rs1260326	2	GCKR	T/C	Percent glycated albumin (%)	White	0.41	−0.01 (0.002)	−1%	5.9E-9	0.3%	0.43	−0.01 (0.004)	−1%	0.04	−0.01 (0.002)	−1%	2.3E-8	−0.007 (0.005)	−0.7%	0.14
rs2438321||	11	CNTN5	G/A	Fructosamine (μmol/L)	Black	0.11	0.03 (0.006)	3%	6.2E-9	1.8%	0.06	0.006 (0.011)	0.6%	0.57	0.03 (0.005)	3%	2.9E-8	0.02 (0.02)	2%	0.37
rs59443763	17	PRKCA	C/T	Percent glycated albumin (%)	Black	0.06	0.05 (0.009)	5%	4.1E-9	2.0%	—	—	—	—	—	—	—	—	—	—

Chr, chromosome; diff, difference; freq, frequency.

*ARIC: N = 7,647 whites, 2,104 blacks; CARDIA: N = 1,304 whites, 608 blacks; ARIC+CARDIA is a meta-analysis across the cohorts; ARIC White+Black is a meta-analysis across the ancestries in ARIC.

†A1 is the minor allele in whites.

‡Mean change in ln(outcome) for each additional A1 allele.

§Percent higher or lower levels of the outcome for each additional copy of the minor allele, calculated as e^β *100.

||rs34459162 and rs2438321 not available in CARDIA data set; evaluated proxy SNPs rs8105626 and rs35256014, respectively, in perfect LD (r² = 1 with ARIC SNP).

Among whites, rs34459162 (MAF = 0.08), a missense SNP in RCN3 on chromosome 19, was significantly associated with 1.8% lower fructosamine per minor allele (P = 5.3 × 10⁻⁹, variance explained = 0.6%) (Table 1 and Figs. 1 and 2). This SNP was also associated with total glycated albumin (P = 3.8 × 10⁻⁸) (Table 2). The association with percent glycated albumin approached genome-wide significance (P = 7.3 × 10⁻⁸), but this SNP was not associated with fasting glucose or HbA_1c (Table 2). A proxy for rs34459162 (rs8105626, in r² = 1 rs34459162) was not associated in CARDIA for association with fructosamine (P = 0.09), although the effect sizes were identical and meta-analysis across the cohorts was significant (P = 4.9 × 10⁻⁹) (Table 1). Conditional analysis in ARIC showed that the additional 63 significant SNPs in the region became nonsignificant after conditioning on rs34459162. In blacks, rs34459162 did not meet the info score >0.8 threshold and thus was not analyzed (Table 2).

Figure 1.

Manhattan plot for GWAS of fructosamine (log transformed) in whites (N = 7,647).

Figure 2.

Regional association plot for rs34459162 and fructosamine (log transformed) among whites (included SNPs with MAF ≥5% and imputation quality ≥0.8, insertions and deletions excluded).

Table 2.

Genome-wide significant loci for fructosamine and percent glycated albumin and their association with total glycated albumin, fasting glucose, and HbA_1c in ARIC

SNP	Closest gene	A1/A2‡	Race	1000 Genomes A1 freq§	A1 freq	Fructosamine (μmol/L)		Percent glycated albumin (%)		Total glycated albumin (g/dL)		Fasting glucose (mg/dL)		HbA1c (%)
						β (SE)	P value	β (SE)	P value	β (SE)	P value	β (SE)	P value	β (SE)	P value
rs34459162	RCN3	C/T	White	0.08	0.08	-0.02 (0.003)	5.3E-9	−0.02 (0.004)	7.3E-8	−0.03 (0.005)	3.8E-8	0.26 (0.56)	0.64	−0.02 (0.02)	0.25
			Black	—	—	—	—	—	—	—	—	—	—	—	—
rs1260326	GCKR	T/C	White	0.43	0.41	−0.003 (0.002)	0.02	−0.01 (0.002)	5.9E-9	−0.01 (0.003)	1.3E-5	−1.13 (0.28)	4.5E-5	−0.01 (0.008)	0.09
			Black	0.10	0.14	0.0005 (0.005)	0.93	−0.0003 (0.006)	0.96	0.001 (0.009)	0.9	−0.53 (1.16)	0.64	−0.01 (0.04)	0.76
rs2438321	CNTN5	G/A	White	0.30	0.24	−0.001 (0.002)	0.44	0.0005 (0.002)	0.82	−0.001 (0.003)	0.71	−0.21 (0.32)	0.52	0.001 (0.01)	0.95
			Black	0.11	0.11	0.03 (0.006)	6.2E-9	0.03 (0.007)	6.4E-5	0.05 (0.009)	2.0E-6	4.19 (1.24)	8.3E-4	0.14 (0.04)	6.8E-4
rs59443763	PRKCA	C/T	White	0.005	—	—	—	—	—	—	—	—	—	—	—
			Black	0.09	0.06	0.04 (0.008)	9.4E-7	0.05 (0.009)	4.1E-9	0.06 (0.01)	5.8E-7	7.2 (1.65)	9.6E-6	0.18 (0.05)	8.3E-4

freq, frequency. Genome-wide significant results are in boldface type. Fructosamine, glycated albumin percent, and total glycated albumin are log transformed.

‡A1 is the minor allele in whites.

§1000 Genomes populations: white, Utah residents (CEPH) with Northern and Western European ancestry (CEU); black, Americans of African ancestry in Southwest U.S. (ASW).

Among whites, rs1260326 (also known as rs343480), a known missense mutation in GCKR on chromosome 2 (MAF = 0.41), was significantly associated with 1.1% lower levels of percent glycated albumin per minor allele (P = 5.3 × 10⁻⁹, variance explained = 0.3%) (Supplementary Figs. 8 and 9) (Table 1). The association with percent glycated albumin was also significant in CARDIA (P = 0.04), with similar percent difference (0.8% lower per minor allele) and genome-wide significant meta-analysis results (2.3 × 10⁻⁸) (Table 1). The conditional analysis did not reveal additional independent signals in this region. This SNP was not associated with any biomarker among blacks (MAF = 0.14) (Table 2 and Supplementary Table 2), but power was limited, and the meta-analysis across ancestries was not significant (Table 1 and Supplementary Fig. 10).

Among blacks, rs2438321 on chromosome 11 (MAF = 0.11) was associated with 3.5% higher levels of fructosamine per minor allele at a genome-wide significant level (P = 6.2 × 10⁻⁹, variance explained = 1.8%) (Table 1 and Supplementary Figs. 11 and 12) and approached significance with percent glycated albumin (P = 6.4 × 10⁻⁵) and total glycated albumin (P = 2.0 × 10⁻⁶) (Table 2). rs2438321 was not associated with HbA_1c in blacks and was not associated with any of the markers of hyperglycemia in whites independently or in a meta-analysis across ancestries (Tables 1 and 2). This SNP was not available in the CARDIA data set; however, a proxy SNP in perfect LD (r ² = 1, rs35256014, MAF = 0.06) was present but did not replicate the association with fructosamine in blacks (P = 0.57), and meta-analysis results were also nonsignificant (Table 1 and Supplementary Fig. 13).

An intronic variant, rs59443763, in PRKCA on chromosome 17 (MAF = 0.06), was significantly associated with 5.4% higher percent glycated albumin per minor allele in blacks (P = 4.9 × 10⁻⁹, variance explained = 2%) (Table 1 and Supplementary Figs. 14 and 15). It was also associated with fructosamine (P = 9.4 × 10⁻⁷) and total glycated albumin (P = 5.8 × 10⁻⁷), although these associations did not meet genome-wide significance (Table 2). This SNP was not significant among whites or in trans-ancestry meta-analysis (Tables 1 and 2), but there was limited power to replicate (Supplementary Table 2). No proxy SNPs with r² > 0.7 were available in the CARDIA data set, and thus replication was not possible for this association.

Sensitivity Analyses

In analyses that excluded participants with undiagnosed diabetes, genome-wide significant results remained for the white sample (N = 7,229) but were no longer present among the reduced sample of black participants (N = 1,878) (Supplementary Table 3).

Of the SNPs significantly associated with fructosamine or glycated albumin, only rs2438321 in blacks (P = 0.002) was significantly associated with serum albumin with a Bonferroni corrected P value (0.05/(4 SNPs * 2 races) = 0.006) (Supplementary Table 4).

Controlling for fasting glucose or HbA_1c variants did not reveal any additional genome-wide significant variants, but for glycated albumin, controlling for fasting glucose score attenuated the P value for rs1260326 (P = 0.002) among whites.

Fructosamine, percent glycated albumin, and total glycated albumin had strong, statistically significant genetic correlations (0.92 to 1.17) indicating a large proportion of shared genetics (Supplementary Table 5). Correlations between fasting glucose and HbA_1c with the other biomarkers were moderate to substantial but were not significant.

Candidate SNP Analysis

We investigated SNPs previously identified in fasting glucose and HbA_1c GWAS for association with fructosamine and percent glycated albumin. Nineteen of the 41 fasting glucose SNPs were nominally (P < 0.05) associated with fasting glucose, and 13 of these associations were in the same direction in blacks and whites (Table 3 and Supplementary Table 6) and 33 had consistent direction with the discovery cohort in whites. Four variants (10%) were study-wide significantly associated with fructosamine and percent glycated albumin in whites, three of which were associated with percent glycated albumin and one of which was associated with fructosamine. No variants were study-wide significantly associated with fructosamine or percent glycated albumin in blacks.

Table 3.

Significance of associations between fasting glucose known genetic determinants and fructosamine and percent glycated albumin in ARIC

SNP	Chr:position	Closest gene	A1/A2	Whites				Blacks
				A1 freq	P value fructosamine	P value glycated albumin	P value fasting glucose	A1 freq	P value fructosamine	P value glycated albumin	P value fasting glucose
rs10747083	12:133041618	P2RX2	G/A	0.33	0.95	0.76	0.14	0.16	0.17	0.11	0.95
rs10830963	11:92708710	MTNR1B	G/C	0.28	0.001	0.003	3.3E-07	0.07	0.13	0.03	0.38
rs10885122	10:113042093	ADRA2A	T/G	0.12	0.82	0.75	0.76	0.68	0.24	0.72	0.21
rs11071657	15:62433962	C2CD4B	G/A	0.38	0.50	0.81	0.52	0.12	0.98	0.95	0.43
rs11558471	8:118185733	SLC30A8	G/A	0.32	0.005	8.2E-05	1.8E-05	0.09	0.31	0.18	0.65
rs11603334	11:72432985	ARAP1	A/G	0.16	0.01	0.08	0.47	0.06	0.79	0.95	0.76
rs11605924	11:45873091	CRY2	A/C	0.47	0.36	0.58	0.66	0.84	0.58	0.72	0.44
rs11708067	3:123065778	ADCY5	G/A	0.23	0.07	0.001	0.01	0.16	0.002	0.003	0.03
rs11715915	3:49455330	AMT	T/C	0.30	0.30	0.74	0.04	0.23	0.06	0.37	0.31
rs11920090	3:170717521	SLC2A2	A/T	0.13	0.004	0.002	0.34	0.35	0.47	0.30	0.28
rs13179048	5:95542726	PCSK1	A/C	0.31	0.11	0.13	0.51	0.08	0.18	0.52	0.78
rs1371614	2:27152874	DPYSL5	T/C	0.25	0.47	0.78	0.69	0.36	0.18	0.11	0.21
rs143399767	9:96182703	YRNA	C/A	0.01	0.65	0.24	0.55
rs1483121	11:48333360	OR4S1	A/G	0.13	0.41	0.36	0.74	0.04	0.46	0.42	0.94
rs16913693	9:111680359	IKBKAP	G/T	0.03	0.60	0.78	0.11	0.25	0.73	0.68	0.91
rs174550	11:61571478	FADS1	C/T	0.33	0.16	0.28	0.54	0.08	0.37	0.15	0.93
rs17762454	6:7213200	RREB1	T/C	0.26	0.13	0.32	0.001	0.16	0.16	0.76	0.75
rs2191349	7:15064309	TMEM195, DGKB	G/T	0.46	0.17	0.36	0.02	0.44	0.58	0.73	0.004
rs2293941	13:28491198	PDX1	A/G	0.22	0.02	0.005	0.03	0.17	0.38	0.60	0.77
rs2302593	19:46196634	GIPR	G/C	0.50	1.00	0.40	0.02	0.30	0.64	0.94	0.97
rs2657879	12:56865338	GLS2	G/A	0.18	0.93	0.94	0.31	0.06	0.05	0.23	0.04
rs2722425	8:40484239	ZMAT4	T/C	0.12	0.01	0.01	0.78	0.37	0.25	0.35	0.41
rs340874	1:214159256	PROX1	T/C	0.45	0.42	0.82	0.40	0.17	0.36	0.74	0.15
rs35767	12:102875569	IGF1	G/A	0.16	0.76	0.54	0.62	0.55	0.43	0.10	0.02
rs3736594	2:27995781	MRPL33	C/A	0.26	0.11	0.07	0.25	0.43	0.07	0.03	0.62
rs3783347	14:100839261	WARS	T/G	0.22	0.45	0.51	0.57	0.06	0.44	0.60	0.94
rs3829109	9:139256766	DNLZ	A/G	0.29	0.003	0.13	0.16	0.18	0.51	0.39	0.46
rs4506565	10:114756041	TCF7L2	T/A	0.31	3.9E-04	2.5E-05	2.7E-05	0.44	0.09	0.40	0.83
rs4607517	7:44235668	GCK	A/G	0.17	2.0E-04	0.003	5.0E-04	0.11	0.09	0.10	0.08
rs4841132	8:9183596	PPP1R3B	A/G	0.09	0.33	0.32	0.05	0.13	0.74	0.29	0.05
rs560887	2:169763148	G6PC2	T/C	0.30	0.08	0.003	7.3E-05	0.05	0.81	0.58	0.91
rs576674	13:33554302	KL	G/A	0.16	0.45	0.50	0.33	0.61	0.17	0.26	0.11
rs6048205	20:22559601	FOXA2	G/A	0.05	0.79	0.95	0.07	0.18	0.02	0.25	0.05
rs6072275	20:39743905	TOP1	A/G	0.16	0.57	0.30	0.01	0.08	0.13	0.12	0.08
rs6943153	7:50791579	GRB10	T/C	0.31	0.67	0.46	0.16	0.70	0.09	0.77	0.61
rs7034200	9:4289050	GLIS3	A/C	0.49	0.19	0.04	0.03	0.61	0.18	0.02	0.11
rs7651090	3:185513392	IGF2BP2	G/A	0.31	0.01	0.03	0.003	0.56	0.32	0.58	0.40
rs7708285	5:76425867	ZBED3	G/A	0.30	0.32	0.64	0.71	0.15	0.56	0.52	0.16
rs780094	2:27741237	GCKR	T/C	0.40	0.05	5.7E-05	1.0E-04	0.18	0.75	0.96	0.90
rs7944584	11:47336320	MADD	T/A	0.27	0.56	0.76	0.05	0.04	0.98	0.38	0.77
rs9368222	6:20686996	CDKAL1	A/C	0.27	0.19	0.09	0.27	0.20	0.96	0.80	0.68

freq, frequency. Candidate SNPs selected from NHGRI database based on previous genome-wide significant associations. P values that reach study-wide significance for fructosamine and glycated albumin, P < 3.0 × 10⁻⁴ (0.05/(2*2*41)), and P values that reach nominal significance (P < 0.05) for fasting glucose are in boldface type.

Thirty-one of 46 previously identified HbA_1c SNPs were nominally associated with HbA_1c, 15 of which were associated in the same direction in blacks and whites (Table 4 and Supplementary Table 7), and 43 had consistent direction with the discovery cohort in whites. Five SNPs (11%) demonstrated a study-wide significant association with fructosamine or glycated albumin in whites. All variants associated with multiple glycemic biomarkers had effects in the same direction.

Table 4.

Significance of associations between HbA_1c known genetic determinants and fructosamine and percent glycated albumin in ARIC

SNP	Chr:position	Closest gene	A1/A2	Whites				Blacks
				A1 freq	P value fructosamine	P value glycated albumin	P value HbA1c	A1 freq	P value fructosamine	P value glycated albumin	P value HbA1c
rs1046896	17:80685533	FN3KRP	T/C	0.31	0.034	0.048	0.052	0.24	0.661	0.455	0.585
rs10774625	12:111910219	ATXN2	G/A	0.48	0.571	0.032	0.008	0.09	0.221	0.142	0.503
rs10823343	10:71091013	HK1	G/A	0.26	0.452	0.065	1.1E-06	0.45	0.808	0.529	0.899
rs10830963	11:92708710	MTNR1B	G/C	0.28	0.001	0.003	0.006	0.07	0.134	0.035	0.222
rs11248914	16:293562	ITFG3	C/T	0.35	0.011	0.229	0.055	0.36	0.067	0.354	0.485
rs11558471	8:118185733	SLC30A8	G/A	0.32	0.005	8.2E-05	0.010	0.09	0.309	0.176	0.989
rs11603334	11:72432985	ARAP1	A/G	0.16	0.005	0.078	0.228	0.06	0.788	0.950	0.734
rs11708067	3:123065778	ADCY5	G/A	0.23	0.070	0.001	0.034	0.16	0.002	0.003	0.074
rs11954649	5:157055491	SOX30	G/C	0.00	NA	NA	NA	0.05	0.812	0.220	0.184
rs11964178	6:109562035	C6orf183	G/A	0.43	0.861	0.844	0.702	0.36	0.180	0.748	0.475
rs12621844	2:48414735	FOXN2	C/T	0.39	0.732	0.294	0.358	0.84	0.829	0.853	0.666
rs12819124	12:48409054	RP1	A/C	0.47	0.765	0.930	0.001	0.21	0.500	0.826	0.146
rs13134327	4:144659795	FREM3	A/G	0.32	0.800	0.662	0.031	0.30	0.220	0.854	0.772
rs1402837	2:169757354	G6PC2	T/C	0.22	0.016	9.3E-05	2.7E-07	0.31	0.471	0.728	0.656
rs1558902	16:53803574	FTO	A/T	0.41	0.545	0.017	0.131	0.11	0.065	0.176	0.514
rs17509001	2:24021231	ATAD2B	C/T	0.14	0.309	0.152	0.027	0.09	0.604	0.524	0.802
rs17533903	19:17256523	MYO9B	A/G	0.21	0.577	0.167	5.0E-04	0.24	0.403	0.232	0.689
rs17747324	10:114752503	TCF7L2	C/T	0.23	1.7E-04	3.2E-05	1.8E-04	0.07	0.396	0.755	0.890
rs1800562	6:26093141	HFE	A/G	0.06	0.337	0.128	0.001	0.01	0.846	0.670	0.146
rs198846	6:26107463	HFE	A/G	0.16	0.390	0.198	0.123	0.88	0.533	0.270	0.260
rs2110073	12:7075882	PHB2	T/C	0.10	0.321	0.938	0.002	0.42	0.177	0.349	0.160
rs2383208	9:22132076	MTAP	G/A	0.18	0.008	0.019	0.007	0.19	0.293	0.763	0.596
rs2408955	12:48499131	SENP1	G/T	0.48	0.567	0.879	0.002	0.63	0.813	0.824	0.632
rs267738	1:150940625	CERS2	G/T	0.21	0.444	0.434	0.511	0.04	0.853	0.745	0.297
rs2779116	1:158585415	SPTA1	T/C	0.27	0.714	0.792	9.9E-06	0.22	0.111	0.167	0.961
rs282587	13:113351662	ATP11A	G/A	0.12	0.586	0.248	1.0E-04	0.69	0.050	0.142	0.246
rs3824065	7:44247258	GCK	T/C	0.42	3.3E-04	0.002	0.125	0.24	0.086	0.134	0.910
rs4607517	7:44235668	GCK	A/G	0.17	2.0E-04	0.003	5.7E-05	0.11	0.088	0.097	0.244
rs4737009	8:41630405	ANK1	A/G	0.24	0.126	0.515	0.018	0.46	0.274	0.461	0.361
rs4745982	10:71089843	HK1	G/T	0.07	0.190	0.263	5.8E-06	0.10	0.889	0.921	0.602
rs4820268	22:37469591	TMPRSS6	G/A	0.46	0.264	0.350	0.010	0.72	0.073	0.029	0.181
rs560887	2:169763148	G6PC2	T/C	0.30	0.080	0.003	0.003	0.95	0.811	0.583	0.861
rs579459	9:136154168	ABO	C/T	0.23	0.268	0.340	0.001	0.13	0.210	0.487	0.600
rs592423	6:139840693	CITED2	A/C	0.46	0.514	0.535	0.063	0.61	0.323	0.605	0.333
rs6474359	8:41549194	ANK1	C/T	0.03	0.979	0.486	4.3E-04	0.27	0.077	0.047	0.879
rs6980507	8:42383084	SLC20A2	A/G	0.39	0.256	0.873	0.015	0.48	0.514	0.557	0.663
rs7040409	9:91503236	C9orf47	G/C	0.07	0.499	0.765	0.001	0.26	0.028	0.005	0.115
rs7616006	3:12267648	SYN2	G/A	0.43	0.705	0.385	0.181	0.36	0.974	0.959	0.836
rs761772	17:76122078	TMC6	C/T	0.13	0.077	0.109	0.009	0.13	0.480	0.361	0.861
rs7756992	6:20679709	CDKAL1	G/A	0.27	0.180	0.094	0.292	0.42	0.856	0.997	0.995
rs8192675	3:170724883	SLC2A2	C/T	0.30	5.4E-05	6.1E-05	0.010	0.29	0.232	0.239	0.024
rs837763	16:88853729	CDT1	C/T	0.44	0.591	0.804	0.046	0.56	0.492	0.554	0.533
rs857691	1:158626378	SPTA1	T/C	0.25	0.803	0.492	5.6E-06	0.30	0.092	0.226	0.894
rs9604573	13:114542858	GAS6	A/G	0.26	0.482	0.300	0.687	0.24	0.902	0.833	0.425
rs9818758	3:49382925	USP4	A/G	0.17	0.240	0.870	0.780	0.09	0.297	0.297	0.642
rs9914988	17:27183104	ERAL1	G/A	0.21	0.307	0.993	0.058	0.66	0.252	0.080	0.342

freq, frequency. Candidate SNPs selected from NHGRI database based on previous genome-wide significant associations. P values that reach study-wide significance for fructosamine and glycated albumin, P < 2.7 × 10⁻⁴ (0.05/(2*2*46)), and P values that reach nominal significance (P < 0.05) for HbA_1c are in boldface type.

Percent Variance Explained

SNPs associated with fasting glucose (N = 41) (Table 3) explained 1.4% of the variance in fructosamine, 3.2% of the variance in percent glycated albumin, and 1.9% of the variance in total glycated albumin among whites. Taking SNPs associated with serum albumin from the GWAS catalog explained 0.4% of the variance of fructosamine, 1.1% of the variance of percent glycated albumin, and 0.7% of the variance of total glycated albumin among the white sample.

Discussion

We identified four SNPs significantly associated with fructosamine and glycated albumin among either whites or blacks, one which replicated in a second cohort and three not previously associated with glycemic traits. Several known fasting glucose and HbA_1c SNPs were significantly associated with fructosamine or glycated albumin.

Among whites, rs1260326 was associated with percent glycated albumin. This variant reflects the same signal associated with type 2 diabetes and fasting glucose: it is in perfect LD with a known type 2 diabetes variant (r² = 1 among 1000 Genomes phase 3 Europeans with rs145819220, from a recent large type 2 diabetes GWAS [38]) and in strong LD with a known fasting glucose variant (r² = 0.91 with rs780094 [37,38]). rs1260326 is located in glucokinase (hexokinase 4) regulator (GCKR), which encodes a regulatory protein primarily active in the liver that inhibits glucokinase (GCK), the enzyme in the first step of glycolysis and involved in converting glucose to glycogen for storage. GCK is considered a glucose sensor that helps maintain glucose homeostasis. The GCKR protein product inhibits the activity of GCK, increasing serum glucose levels. GCKR is an established type 2 diabetes gene (39–41) and is associated with multiple other traits including kidney disease, triglyceride levels, and Crohn disease (42–44). Thus, this variant likely represents part of a glycemic pathway, but it is interesting that in our study it is only significantly associated with one measure of hyperglycemia, approached significance with fasting glucose (although controlling for fasting glucose score made this variant nonsignificant) and total glycated albumin, but is not associated with fructosamine or HbA_1c, given the moderate to strong correlations and genetic correlations among the biomarkers (Supplementary Tables 5 and 8). That GCKR is primarily expressed in the liver rather than the pancreas (45,46) aligns with the finding of association with fasting glucose, which measures hepatic glucose output. Albumin is also produced by the liver, while erythrocytes and hemoglobin are not likely affected by liver function, thus perhaps hepatic-specific genetic factors would be more likely to associate with percent glycated albumin levels than with HbA_1c. It is also possible that HbA_1c, affected by other glucose-altering factors, may mask the effect of rs1260326 on GCKR. Adjustment for serum albumin may explain the association with percent glycated albumin but not fructosamine.

We also identified several variants of potential interest that were significant in ARIC but lacked replication. Among whites, rs34459162, in RCN3, was associated with fructosamine and total glycated albumin. RCN3 encodes reticulocalbin 3, an EF-hand calcium binding domain (47). This SNP was not associated with serum albumin in our analysis, but a SNP in perfect LD with rs34459162, rs2280401, was associated with total protein in a Japanese population (48) and serum albumin in an East Asian population (49), indicating a possible impact on fructosamine and glycated albumin through nonglycemic pathways. Among blacks, we found two novel variants: rs2438321 (intergenic and closest to CNTN5, which encodes a glycosylphosphatidylinositol-anchored neuronal membrane protein, a member of the immunoglobulin superfamily and the contactin family) associated with fructosamine and rs59443763 (PRKCA, which encodes protein kinase C alpha, ubiquitous in cellular processes) associated with percent glycated albumin at a genome-wide level of significance. There is no prior literature on either of these as potential glycemic loci in diabetes. While we had sufficient power to replicate the results for whites in CARDIA (Supplementary Table 2), we had low power among blacks, which may be why these SNPs did not replicate. These variants became nonsignificant after excluding undiagnosed diabetes, which may be due to the greater number of individuals with undiagnosed diabetes among blacks than whites. Blacks had higher values of glycemic biomarkers, thus removing case subjects with undiagnosed diabetes could have had a greater impact on associations among blacks than whites. These variants should be evaluated in larger African ancestry data sets as they become available. rs34459162, rs2438321, and rs59443763 are of potential interest, but as these SNPs currently lack replication, we cannot rule out false positive results.

Results varied by ancestry for the SNPs available in both blacks and whites: neither SNP was significant in both blacks and whites, and meta-analyses results were nonsignificant. While this may partially be explained by differing allele frequencies (rs1260326: 0.41 in whites, 0.14 in blacks; rs2438321: 0.24 in whites, 0.11 in blacks), a differential effect by ancestry on fructosamine and glycated albumin is also possible. This may be particularly true for rs2438321, where the direction of effect differs across ancestries.

In addition to investigating fructosamine and glycated albumin individually, comparing to traditional glycemic markers (fasting glucose and HbA_1c) can help to clarify the biological pathways involved in diabetes. Fasting glucose–related SNPs explained almost twice the variance of percent glycated albumin than that of fructosamine. This may reflect the adjustment for serum albumin with percent glycated albumin and not with fructosamine, allowing percent glycated albumin levels to be influenced more by glucose levels and less by albumin levels. However, albumin SNPs also explained more variance of percent glycated albumin than that of fructosamine or total glycated albumin. Given the small percentages, it is difficult to draw first conclusions from these results.

Only five HbA_1c variants were significantly associated with fructosamine or glycated albumin. This is consistent with the findings that the majority of HbA_1c variants are related to erythrocyte and hemoglobin factors that we would not expect to be related to fructosamine or glycated albumin. Many associations of fructosamine or glycated albumin with HbA_1c SNPs or fasting glucose SNPs were present in whites but not blacks. This is not surprising given that the SNPs were originally detected in whites and that our sample size was larger for whites, with corresponding higher power to detect moderate associations. Not all of the previously discovered SNPs for fasting glucose and HbA_1c replicated for those outcomes in our sample, but this again may have to do with lack of power.

We found that both glycemic and nonglycemic genetic factors influenced fructosamine and glycated albumin levels. We identified a likely glycemic variant in a gene associated with type 2 diabetes (GCKR), supporting its role in diabetes biology, and a likely nonglycemic variant in a gene (RCN3) that may reflect the biology of a biomarker (i.e., influencing amount of serum protein available to be glycated) rather than the biology of type 2 diabetes. This contribution of glycemic and nonglycemic variants is similar to the pattern of genetic contribution to HbA_1c, for which the majority of genetic variants are nonglycemic (18,19). In our study, previously identified nonglycemic variants (18–20) explained 3.4% of the variance in HbA_1c and the glycemic variants explained 2.1% (Supplementary Table 9). Despite the previous studies having much larger sample sizes (and thus more power to detect associations with HbA_1c), the percent variance explained we found for fructosamine (0.6% by likely nonglycemic rs34459162) and glycated albumin (0.3% by likely glycemic rs1260326) was of a similar magnitude. Both Soranzo et al. (18) and Chen et al. (19) found that taking nonglycemic variants into account modestly impacted diabetes reclassification, and Wheeler et al. (20) found a more substantial effect. Given that that future larger studies on fructosamine and glycated albumin will likely reveal other significant variants, it will be important to determine whether the effect of nonglycemic variants is substantial enough to impact the clinical interpretation of fructosamine and glycated albumin.

A major limitation of this study was the limited sample size, particularly the smaller sample size in blacks. The differences in ancestries make replication of results difficult, particularly if allele frequencies differ, and warrant more studies focused on multiethnic populations. Also, the lack of an available SNP or proxy for rs59443763 in CARDIA, possibly due to the imputation reference panel (HapMap Phase II), impeded our ability to evaluate replication of this finding. In addition, the sample size for our replication cohort was much smaller than our discovery cohort, limiting our power to replicate the significant ARIC findings in blacks.

In summary, through GWAS in a community-based population of blacks and whites, we identified and replicated two significant variants associated with fructosamine and/or glycated albumin, one of which was novel. These variants map into a likely glycemic, known diabetes gene and a likely nonglycemic gene. This highlights the utility of examining genetics of diabetes biomarkers both for providing insight into the pathophysiology of diabetes and for better understanding glucose-independent influences on measures of hyperglycemia.