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Int J Mol Epidemiol Genet. 2011 August 30; 2(3): 261–285.
Published online 2011 August 3.
PMCID: PMC3166154

Prospective study of insulin-like growth factor-I, insulin-like growth factor-binding protein 3, genetic variants in the IGF1 and IGFBP3 genes and risk of coronary artery disease

Abstract

Although experimental studies have suggested that insulin-like growth factor I (IGF-I) and its binding protein IGFBP-3 might have a role in the aetiology of coronary artery disease (CAD), the relevance of circulating IGFs and their binding proteins in the development of CAD in human populations is unclear. We conducted a nested case-control study, with a mean follow-up of six years, within the EPIC-Norfolk cohort to assess the association between circulating levels of IGF-I and IGFBP-3 and risk of CAD in up to 1,013 cases and 2,055 controls matched for age, sex and study enrolment date. After adjustment for cardiovascular risk factors, we found no association between circulating levels of IGF-I or IGFBP-3 and risk of CAD (odds ratio: 0.98 (95% Cl 0.90-1.06) per 1 SD increase in circulating IGF-I; odds ratio: 1.02 (95% Cl 0.94-1.12) for IGFBP-3). We examined associations between tagging single nucleotide polymorphisms (tSNPs) at the IGF1 and IGFBP3 loci and circulating IGF-I and IGFBP-3 levels in up to 1,133 cases and 2,223 controls and identified three tSNPs (rs1520220, rs3730204, rs2132571) that showed independent association with either circulating IGF-I or IGFBP-3 levels. In an assessment of 31 SNPs spanning the IGF1 or IGFBP3 loci, none were associated with risk of CAD in a meta-analysis that included EPIC-Norfolk and eight additional studies comprising up to 9,319 cases and 19,964 controls. Our results indicate that IGF-I and IGFBP-3 are unlikely to be importantly involved in the aetiology of CAD in human populations.

Keywords: Epidemiology, Genetics of cardiovascular disease, Risk factors, IGF1, IGFBP3

Introduction

Insulin-like growth factor I (IGF-I) is a peptide hormone that shares sequence homology with insulin and has a fundamental role in somatic growth and cellular differentiation, metabolism and survival [1]. The majority of IGFs in the circulation are bound to IGF binding proteins, of which IGF binding protein 3 (IGFBP-3) is the most predominant, carrying > 80% of circulating IGF-I. But, although IGFBP-3 is an important regulator of the bioactivity of IGF-I, there is evidence to suggest that IGFBP-3 possesses functions independent of its role as an IGF-I carrier protein [2]. It has been suggested that circulating IGF-I is involved in the pathophysiological processes underlying coronary artery disease (CAD). Experimental data in animals have shown that overexpression or administration of IGF-I after myocardial infarction (MI) prevents cardiomyocyte death and improves cardiac function [3,4,5]. Evidence for improvement of cardiac function and increased ventricular mass has been shown in individuals with growth hormone deficiency after short-term IGF-I therapy [6]. Improved cardiac performance has also been demonstrated following administration of recombinant IGF-I to individuals with chronic heart failure [7].

Cross sectional observational studies have reported that circulating IGF-I concentrations are lower in individuals with CAD and MI [8,9,10], but the opposite has been reported as well [11]. Furthermore, increments in IGF-I levels are associated with reduced prevalence of atherosclerotic plaques, as measured by arterial ultrasound [12]. The prospective association between circulating levels of IGF-I and IGFBP-3 and subsequent risk of future CAD, however, remains uncertain [13,14,15,16,17,18,19,20]. Several studies have identified reproducible associations among genetic variants at the loci encoding IGF-I and IGFBP-3 and circulating levels of IGFs. To assess a possible link between circulating IGF-I and IGFBP-3 levels and CAD, there is a need for a comprehensive analysis of the relationship between circulating IGF-I and IGFBP-3 levels, genetic variation at the loci encoding these proteins, and risk of CAD. Therefore we sought to reproduce previously reported associations between tagging single nucleotide polymorphisms (tSNPs) spanning the IGF1 and IGFBP3 genes and circulating concentrations of IGF-I and IGFBP-3, and to use these SNPs as genetic tools to assess the possible relation between IGF-I and IGFBP-3 and risk of CAD in a prospective population-based study.

Materials and methods

Study design

We conducted a nested CAD case-control study among participants of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk cohort, a prospective population-based study of 25,663 men and women resident in Norfolk, United Kingdom, aged between 45 and 79 years [21]. Participants were recruited from age-sex registers of general practices. At the baseline survey between 1993 and 1997, participants completed a detailed health and lifestyle questionnaire, and additional data were collected by trained nurses at a clinic visit as previously described [21]. Participants were identified as incident CAD cases if they had a hospital admission and/or died with CAD as the underlying cause during follow-up. CAD was defined as ICD9 codes 410 to 414 or ICD10 codes 120-125 (including acute and old myocardial infarction, angina, aneurysm and dissection of the heart, and other ischaemic heart disease). We report results with follow-up to January 2003, an average of about 6 years. The study was approved by the Norwich District Health Authority Ethics Committee, and all participants gave written informed consent.

Participants

We selected study participants who did not report a history of heart attack or stroke at the baseline clinic visit. During follow-up, 1,138 apparently healthy individuals developed fatal or non-fatal CAD and were classified as cases. Controls were study participants who remained free of any cardiovascular disease during follow-up. Two controls were matched to each case by age (within 5 years), sex and date of inclusion visit (within a range of 3 months). Due to an insufficient number of available controls, because of this stringent matching, only 2,237 controls were identified, i.e. 39 cases were matched to one control and 1,099 cases were matched to two controls. For analyses of the associations among IGF1 and IGFBP3 tSNPs and risk of CAD, complete data were available for 1,133 cases and 2,223 controls. For analyses that also involved circulating IGF-I and IGFBP-3 levels, complete data were available for 1,013 cases and 2,055 controls.

Additional CAD case-control studies

We also combined data from the EPIC-Norfolk study with summary data from eight studies from existing collaborations [22] where genotypes were available in silico for 31 SNPs spanning the IGF1 and IGFBP3 genes (Table 1). These comprised 9,319 cases and 19,964 controls in a meta-analysis of IGF1/IGFBP3 SNPs and risk of CAD. In addition to EPIC-Norfolk, the studies included were the Ottawa Heart Studies-I and II [23,24], WTCCC CAD study [25,26], PENN CATH and MEDSTAR studies [27,28], and nested case-control studies derived from the CoLaus [29,30], GEMS [30,31,32] and Rotterdam [33,34] studies. Details of these studies and their genotyping and analytical approaches have been published previously and are summarised briefly in Table 1.

Table 1
Study characteristics and details of analysis metrics and methods for individual studies in CAD risk meta-analysis

Biochemical analyses

IGF-I and IGFBP-3 were measured in EPIC-Norfolk using non-fasting serum samples collected at baseline and stored frozen at -80°C for approximately 11 years before measurement. Serum levels of IGF-I were measured by radioim-munoassay using a monoclonal antibody (Blood Products, Elstree, Hertfordshire, UK) and recombinant peptide (Gropep, Adelaide, Australia) for standard and tracer (following iodination using the chloramine-T method). Samples were analysed following acid-acetone extraction to deplete IGF binding proteins (IGFBPs) with an excess of IGF-II added to the resulting extract in order to saturate any residual IGFBPs. Intra-assay coefficient of variation (CV) was 8% and inter-assay CV was 11.1%. Serum levels of IGFBP-3 were measured by radioimmunoassay using an in-house polyclonal antibody raised against recombinant IGFBP-3. The assay was calibrated against recombinant glycosylated IGFBP-3 (Dr C Maack, Celltrix, Santa Clara, CA, USA). Samples were analysed following a 1:100 dilution with antibody used at a final dilution of 1:20,000. Intra-assay coefficient of variation (CV) was 4% and inter-assay CV was 10.1%.

Tagging SNPs for IGF1 and IGFBP3 genes

We generated a set of tSNPs for the IGF1 and IGFBP3 genes using resequencing data from the University of Washington SeattleSNPs Programs for Genomic Applications (http://pga.gs.washington.edu/) and NIEHS resequencing program (http://egp.gs.washington.edu), respectively. From these databases, genotypes were available for SNPs spanning 2 kb upstream and downstream of each gene. The aim of our approach was to identify a set of tSNPs that capture all known common SNPs at these loci with a minor allele frequency (MAF) ≥ 0.05 in individuals of European ancestry. We identified 39 SNPs at the IGF1 locus and 26 SNPs at the IGFBP3 locus with a MAF ≥ 0.05 in Coriell individuals with European ethnicity (Figure 1A and B). We used the Tagger program embedded in Haploview [35,36] to generate a set of 13 tSNPs for the IGF1 gene, forcing in three SNPs (rs1520220, rs2946834, rs6220) that had previously been found to correlate with circulating IGF-I levels. [37] For IGFBP3, we generated a set of 15 tSNPs, forcing in three SNPs (rs2132571, rs2132572, rs2854744) previously found to correlate with circulating IGFBP-3 levels. [37] We subsequently discarded six tSNPs for which we were unable to design an assay due to the repetitive nature of the surrounding sequence (Figure 1A and B). These SNPs were not correlated with any other SNP and so alternative tSNPs could not be selected. This left a set of 22 tSNPs that captured 59 of the 65 common variants at a mean r2 of 0.97 (r represents the pairwise correlation coefficient between SNPs). A list of the tSNPs and details of the SNPs captured is given in Table 2.

Figure 1Figure 1
Feature maps of the IGF1 (A) and IGFBP3 (B) genes. Positions of the 39 SNPs at the IGF1 locus and the 26 SNPs at the IGFBP3 locus with MAF ≥0.05 in the resequenced CEPH samples are shown relative to the locus (purple) and chromosome (black bar). ...
Table 2
IGF1 and IGFBP3 tSNPs genotyped in EPIC-Norfolk

Genotyping

Genomic DNA for the EPIC-Norfolk case-control study was whole genome amplified (WGA) prior to genotyping using a GenomiPhi V2 DNA amplification kit (GE Healthcare, Amersham, UK) following the manufacturer's instructions. WGA DNA was normalised using a Quant-iT PicoGreen dsDNA kit (Invitrogen, Paisley, UK). We conducted concordance checks with genomic DNA using a panel of three SNPs in separate genomic locations and obtained > 95% concordance for these SNPs between WGA and genomic DNA. Nineteen tSNPs were genotyped by KBioscience (http://www.kbioscience.co.uk) using KASPar technology, and three (rs6220, rs35767, rs12821878) using a custom Taqman® assay (Applied Biosystems, Warrington, UK) at Strangeways Research Laboratory (University of Cambridge, Cambridge, UK) according to the manufacturer's instructions. Allele calling was done using the ABI PRISM® 7900HT Sequence Detection System (Applied Biosystems, Warrington, UK). Cases and controls were randomly allocated across DNA study plates with two duplicate samples and two water controls in each 96-well plate. Concordance between duplicate samples was > 97% for all SNPs using both methods.

Statistical analysis

Baseline characteristics were compared between cases and controls (Table 3). We natural log transformed triglycerides and C-reactive protein (CRP) before analysis because of their skewed distribution. IGF-I and IGFBP-3 levels were approximately normally distributed. To assess the association between circulating IGF-I and IGFBP-3 levels, cardiovascular risk factors and CAD risk in EPIC-Norfolk (Tables 4--7)7) we constructed quartiles of IGF-I and IGFBP-3 levels in control participants and applied these quartiles to cases. Selection of confounding risk factors was based on the proposed biological mechanisms linking IGFs to the development of CAD [38] and on previous reports examining the association between IGFs and risk of CAD [14,16,20] and their availability in EPIC-Norfolk. Because of the ambiguity of defining potential confounders and mediators for the association between IGFs and CAD risk, we added potential confounders (see footnote of Table 6) in sequential statistical models, as outlined below. We used linear regression to assess the association between IGF-I and IGFBP-3 quartiles and continuous cardiovascular risk factors in control participants only, with adjustment for age and sex. For categorical variables (sex, diabetes status and smoking status) we used logistic regression to test for association with IGF-I and IGFBP-3 quartiles, with adjustment for age and sex, where appropriate. To test for association between circulating levels of IGF-I and IGFBP-3 and risk of CAD, we used logistic regression to calculate odds ratios and 95% confidence intervals. We analysed these associations using IGF-I and IGFBP-3 levels defined as quartiles based on the distribution among controls and as continuous variables (for a 1 standard deviation (SD) increase). The lowest IGF-I and IGFBP-3 quartiles were used as reference categories and three models were used with increasing degrees of adjustment (Tables 6 and and7).7). For all three models, we adjusted for study enrolment date to account for differences in storage time of the blood samples. Log likelihood ratio tests were used to test for the presence of effect modification by sex between circulating IGF-I and IGFBP-3 levels and risk of CAD and for possible interaction between IGF-I and IGFBP-3 on risk of CAD.

Table 3
Baseline characteristics of CAD cases and controls, EPIC-Norfolk, 1993-2003
Table 4
Association between IGF-I levels and cardiovascular risk factors in controls at baseline, EPIC-Norfolk, 1993-2003
Table 6
Association between IGF-I levels and risk of future CAD, EPIC-Norfolk, 1993–2003
Table 7
Association between IGFBP-3 levels and risk of future CAD, EPIC-Norfolk, 1993—2003

Hardy-Weinberg equilibrium was assessed for each tSNP in control participants using the X2 statistic (1 df). In our primary tests for association, we used linear per allele regression to test for associations among tSNPs and circulating levels of IGF-I and IGFBP-3 in control participants, adjusted for age and sex. To test the linearity of associations between statistically associated tSNPs and circulating IGF-I or IGFBP-3 levels we used log likelihood ratio tests by comparing a general model with the linear per allele model. We also used log likelihood ratio tests to assess for the presence of sex interactions with statistically associated tSNPs and circulating IGF-I and IGFBP-3 levels, with the SNP defined as a linear variable.

To assess whether statistically associated tSNPs contributed independently to circulating IGF-I or IGFBP-3 levels we compared the log likelihood of a nested model (2 df) with that of the full model (3 df) by consecutively adding the other tSNPs (in a linear 1df form) to a model containing the strongest associated tSNP (general inheritance 2df form). We also conducted a reciprocal analysis, adding the strongest associated tSNP (1 df form) to a model with each of the other tSNPs (2 df form). We used pairwise correlation (r2) to assess the extent of linkage disequilibrium among tSNPs in control participants.

In a secondary analysis for all associated tSNPs, we combined summary estimates (regression coefficients and standard errors) from case and control participants from the above linear regression models using meta-analysis. We used a fixed effects model and inverse-variance weighted averages of regression coefficients to obtain a combined estimate of the overall association between tSNPs and circulating IGF-I and IGFBP-3 levels.

To test for associations among tSNPs and risk of CAD in EPIC-Norfolk we used logistic regression using general and linear models adjusted for age, sex and enrolment date. We combined summary estimates (log odds ratios and standard errors) for each of the eight additional CAD case-control studies using meta-analysis, as above, to obtain a combined estimate of the association between SNPs and risk of CAD for a linear model. Between-study heterogeneity was assessed using the X2 test.

To ascertain the presence of haplotype associations at the IGF1 and IGFBP3 genes with circulating IGF-I and IGFBP-3 levels and risk of CAD in EPIC-Norfolk, we constructed haplotypes for IGF1 and IGFBP3 tSNPs using the program TagSNPs, which uses an expectation-substitution approach to account for the uncertainty caused by unphased genotype data [39]. Individuals with > 50% missing genotype data were excluded. Haplotypes with a frequency of < 1% were considered rare, and were pooled for subsequent analyses. To test for global haplotype association with circulating levels and CAD risk, we used regression to compare a model with additive effects for each common haplotype (using the most common haplotype as referent) to a model with no haplotype effects. We used linear regression and logistic regression with no covariate adjustment to test the association between each haplotype and circulating IGF-I or IGFBP-3 levels and to calculate odds ratios and 95% confidence intervals for the association between each haplotype and risk of CAD, respectively, relative to the most common haplotype that was used as the reference.

All analyses were done using Stata 10.1 (Stata Corporation, College Station, TX, USA).

Results

Association analysis of circulating IGF-I and IGFBP-3 levels and risk of subsequent CAD

Table 3 shows the distribution of cardiovascular risk factors among cases and controls in the EPIC-Norfolk CAD nested case-control study. As expected and as previously reported [40], cases had more adverse cardiovascular risk profiles than control participants who remained free of disease during follow-up. There was no material difference between circulating IGF-I and IGFBP-3 levels among cases and controls (Table 3).

We assessed the association between quartiles of IGF-I and IGFBP-3 and baseline cardiovascular risk factors in control participants (Tables 4 and and5).5). Both IGF-I and IGFBP-3 levels were inversely associated with age, IGF-I levels were higher in men and IGFBP-3 levels higher in women, consistent with previous reports [14,16,17,19,20,41]. Circulating levels of IGF-I were positively associated with IGFBP-3 levels and both circulating IGF-I and IGFBP-3 levels showed positive associations with several cardiovascular risk factors (Tables 4 and and5).5). The latter findings are consistent with a previous report of a cross-sectional study demonstrating an association between higher levels of IGFBP-3 and the metabolic syndrome [42].

Table 5
Association between IGFBP-3 levels and cardiovascular risk factors in controls at baseline, EPIC-Norfolk, 1993–2003

We next examined the relationship between circulating IGF-I and IGFBP-3 levels and risk of CAD in EPIC-Norfolk. For IGF-I we found no evidence for association with CAD risk (Table 6). For IGFBP-3 we found some evidence for a weak unadjusted association between levels and risk of CAD but this attenuated after adjustment for confounding risk factors (Table 7). Previous studies demonstrating an association between circulating IGF-I and IGFBP-3 levels and risk of CAD have comprised a high proportion of cases with fatal CAD or myocardial infarction [14,15,16,20]. Therefore we conducted an additional subgroup analysis of fatal and non-fatal CAD cases and controls. We did not find any evidence for association for either IGF-I or IGFBP-3 with fatal CAD (data not shown). Furthermore there was no evidence for any interaction between sex and IGF-I or IGFBP-3 for risk of CAD or between IGF-I and IGFBP-3 for risk of CAD.

Association analysis of tagging SNPs and circulating IGF-I and IGFBP-3 levels

Of the 22 tSNPs genotyped in the EPIC-Norfolk CAD nested case-control study (Figure 1A and B and Table 2), three showed deviation from Hardy-Weinberg equilibrium (HWE) in control participants (footnote to Table 8) and so were discarded from further analysis. We assessed associations among the remaining 19 tSNPs and circulating levels of IGF-I and IGFBP-3 (Table 8). Three tSNPs exhibited a MAF < 0.05 in EPIC-Norfolk compared to that observed in resequenced Coriell individuals (Table 8 and Figure 1A and B). Eight tSNPs showed evidence for statistical association with either circulating IGF-I or IGFBP-3 levels at P ≥ 0.05 (Table 8). These include directionally consistent replication of associations between IGF1 tSNPs rs1520220 and rs6220 and circulating IGF-I levels, and between IGFBP3 tSNPs rs2132571 and rs2854744 and circulating IGFBP-3 levels, as shown in our previous report [37]. All eight tSNP associations remained statistically associated in a combined analysis of case and control participants (Table 8) and demonstrated linear associations with circulating IGF-I or IGFBP-3 levels (Table 8). By contrast to our previous report [37], there was no evidence for any interaction between sex and tSNPs with circulating IGF-I or IGFBP-3 levels.

Table 8
Statistical associations between IGF1 and IGFBP3 tSNPs, circulating IGF-I and IGFBP-3 levels and risk of CAD in EPIC-Norfolk participants

To contextualise the magnitudes of associations observed between tSNPs and circulating levels of IGF-I and IGFBP-3 for ease of interpretation, we present the proportion of a 1 SD change in circulating levels shown by each associated tSNP (Table 9). The five tSNPs showing association with IGF-I levels were associated with between 7-30% of a 1 SD change in circulating IGF -I concentrations per allele (Table 9). For IGFBP-3, the three statistically associated tSNPs (Table 8) were associated with between 7-11% of a 1 SD change in circulating IGFBP-3 concentrations per allele (Table 9). We assessed the independence of the individual tSNP associations with circulating IGF-I levels and found that tSNPs rs1520220 and rs3730204 at the IGF1 locus were sufficient to explain the associations for the other three tSNPs. Similarly at the IGFBP3 locus, tSNP rs2132571 was sufficient to explain the associations with circulating IGFBP-3 levels observed for the other two tSNPs.

Table 9
Summary statistics for IGFl and IGFBP3 tSNPs showing statistical association with circulating IGF-I and IGFBP-3 levels in up to 2,223 EPIC-Norfolk control participants

We conducted an analysis to assess for the presence of haplotype effects at the IGF1 and IGFBP3 loci that might reflect associations of untyped SNPs (Tables 10 and and11).11). There were no significant global haplotypic associations for IGF1 or IGFBP3 (P > 0.05). For IGF1, individual haplotypic associations were fully explained by the associations shown by individual SNPs (Table 9). However, for IGFBP3 a strong association with circulating IGFBP-3 levels was shown by a haplotype that contains the minor allele of both tSNPs rs2132571 and rs3793345 (Table 11). The magnitude of association of this haplotype with circulating levels is almost twice that shown for either SNP individually (18% of a 1SD change in IGFBP-3 levels compared with 11% for the two SNPs, Table 9), suggesting that tSNPs rs2132571 and rs3793345 might capture an untyped variant that has a strong effect on circulating IGFBP-3 levels.

Table 10
IGF1 haplotype analysis results for association with circulating IGF-I levels and risk of CAD in EPIC-Norfolk
Table 11
IGFBP3 haplotype analysis results for association with circulating IGFBP-3 levels and risk of CAD in EPIC-Norfolk

Association analysis of tagging SNPs and risk of CAD

It is possible that variation at the IGF1 and IGFBP3 genes could influence risk of CAD via pleiotropic effects on mechanisms independent from circulating levels; for example reflecting tissue-specific variation in IGF-I or IGFBP-3 action. We therefore assessed the association between IGF1 and IGFBP3 tSNPs and risk of CAD in the EPIC-Norfolk CAD nested case-control study. Table 8 shows the association between these 19 SNPs and risk of CAD in up to 1,133 cases and 2,223 controls. We did not find any robust evidence for association with CAD for any of the tSNPs in EPIC-Norfolk. Similarly, global or individual haplotype analyses did not reveal any robust associations with CAD risk for either IGF1 or IGFBP3 (Tables 10 and and11,11, global haplotypic P-values > 0.05). To increase statistical power for this analysis, we tested 31 SNPs spanning the IGF1 and IGFBP3 genes in eight CAD studies that are part of an established collaborative consortium. SNP genotypes were available in silico from genome-wide SNP chips or imputation analyses (Table 1). We combined our data from EPIC-Norfolk with these eight studies, in total comprising up to 9,319 cases and 19,964 controls. The results of this additional analysis are shown in Table 12 and show no evidence for association between common variants at the IGF1 and IGFBP3 genes and risk of CAD.

Table 12
Association between IGF1 and IGFBP3 SNPs and risk of CAD in a combined analysis of nine studies

Discussion

This is the first study to conduct a systematic assessment of the interrelationships among circulating levels of IGF-I and IGFBP-3, common genetic variants at these loci, and risk of CAD. By contrast with previous smaller studies that have assessed the relationship between circulating IGF-I and IGFBP-3 levels and risk of cardiovascular disease, our results-from the largest prospective study to date-suggest that circulating levels of IGF-I and IGFBP-3 and genetic variants at the IGF1 and IGFBP3 genes do not materially influence the risk of CAD.

Our findings suggesting a lack of association between circulating IGF-I and IGFBP-3 levels and risk of CAD differ from those in a population -based nested case-control study in Denmark that reported a positive association between circulating IGFBP-3 levels and ischemic heart disease risk [14] and in the Cardiovascular Health Study, where low circulating IGFBP-3 levels were associated with an increased risk of coronary events in elderly individuals [15]. The Danish study and other prospective studies have also reported inverse associations between circulating IGF-I levels and risk of ischemic heart disease [14,16,43] and heart failure [20], although a recent study has found a positive association between IGF-I levels and risk of heart failure, but no association with overall incidence of cardiovascular disease [13]. However, our results are consistent with three recent reports based on prospective cohort studies, two of which were conducted in women only [17,18,19]. The studies above are small and therefore subject to random variability, with no previous individual study exceeding 550 cases. Furthermore, previous studies have been conducted in relatively disparate populations (e.g. three were conducted in relatively elderly populations [13,16,20]). Such diversity highlights potential limitations in combining the findings of all available studies.

In addition to other known limitations of observational studies, such as incomplete control of confounders and bias, these apparently divergent findings may be due to several factors-heterogeneity in phenotype, age and sex of participants, heterogeneity among IGF-I and IGFBP-3 immunoassays, and pleiotropic effects of IGFs and their binding proteins that may have different actions in different contexts. We did not measure IGFBP-1 in EPIC-Norfolk, and it is possible that circulating levels of this binding protein may modify the relationships of other IGFs and clinical biomarkers on disease, which has been shown in the context of the metabolic syndrome [42,44]. Although we measured circulating IGFs in samples that were non-fasting and were measured only once at baseline, our study showed the expected associations between age and sex and circulating IGF-I and IGFBP-3 levels and consistent replication of several SNP associations with circulating levels, thus decreasing the likelihood of substantial random measurement error. Furthermore, intra-individual variation in levels of IGFs is low. Indeed, it has been reported in 249 participants with serial measurements (mean interval three years) that circulating levels of IGFs have high levels of within-person correlation (r > 0.8) over time [15].

We identified several SNPs at the IGF1 and IGFBP3 loci that are associated with circulating IGF-I and IGFBP-3 levels. At the IGF1 locus, five tSNPs were associated with circulating IGF-I levels. The associations for rs1520220, rs6220 and rs3730204 are consistent with previous reports [37,45,46,47,48,49]. In a multivariate analysis of the IGF1 tSNPs, we found that rs1520220 and rs3730204 were sufficient to account for the other three tSNP associations with circulating IGF-I levels. tSNP rs3730204 is located in the 3' untranslated region (UTR) of the IGF1 gene (Figure 1A, thus it is a good functional candidate as it could potentially influence post-transcriptional processing of IGF1 mRNA. However, this SNP is a low-frequency variant (MAF 0.02) and, with only two rare homozygotes in our dataset, it is likely that our observed association is inflated. As rs1520220 is located in intronic sequence it is more likely that this SNP is capturing an untyped functional variant elsewhere in the gene.

We found three tSNPs at the IGFBP3 gene that were associated with circulating IGFBP-3 levels. Again, associations for tSNPs rs2132571 and rs2854744 are consistent with previous reports [37,46,50,51,52,53]. In a multivariate analysis of the three associated IGFBP3 tSNPs we found that rs2132571 explained the other two tSNP associations with circulating IGFBP-3 levels at this locus in EPIC-Norfolk, although the results from our haplotype analysis suggest that rs2132571 and rs3793345 jointly capture an untyped functional variant with a substantial effect on circulating IGFBP-3 levels (Table 11). SNP rs2132571 lies 5' of the IGFBP3 locus and is moderately correlated with rs2854744 in EPIC-Norfolk (r2 0.61), thus it may be capturing some residual signal from this SNP that has been shown in cellular expression studies to directly influence promoter activity of the IGFBP3 gene [52].

Some of the disparities in the associations observed between specific SNPs and IGF-I and IGFBP-3 levels among studies are likely due to sampling variation and subtle differences in LD across these loci among populations. This reflects the fact that we are not directly assessing the causal variant(s) underlying these associations, perhaps with the exception of rs2854744 that shows very reproducible associations across multiple studies and ethnic groups and has evidence to support its functionality. Cellular expression studies will be required to assess the functionality of other associated tSNPs at both of these loci.

To our knowledge, our study is the first to assess the risk between common SNPs at the IGF1 and IGFBP3 loci with risk of CAD in a large prospective study. The strengths of our study are its size, and that we used a systematic tagging approach to capture all common SNPs (MAF ≥ 0.05) at the IGF1 and IGFBP3 loci. However, it is possible that there are rare variants at these loci that we have not captured in our current analysis and we may not have had sufficient statistical power to detect modest SNP associations with CAD risk. An assessment of the coverage between our SNPs and those in HapMap suggests that, at an r2 threshold of 0.8, we captured 45% and 66% of the variation above MAF 1% in IGF1 and IGFBP3 respectively spanning +/-10 kb of the loci, although this will be an underestimation because several of our tSNPs are not in HapMap. We may also have missed some additional associations between SNPs and circulating levels due to exclusion of three tSNPs that deviated from HWE in our samples and six SNPs which failed assay design. We did not genotype the microsatellite polymorphism in the promoter region of the IGF1 gene that has been reported to be associated with circulating IGF-I levels and risk of myocardial infarction, although these associations are inconsistent [53,54,55,56,57]. A recent paper by Dupuis et al has identified an association between SNP rs35767 and glycaemic traits, with the G allele associated with an adverse glycaemic profile [58]. Although the association between this SNP and circulating IGF-I levels was not statistically significant in our study, the corresponding allele (coded as C in our data) does show a trend with lower IGF-I levels (Table 8), consistent with these findings. Two recent reports by Palles et al and Schumacher et al have found a similar association between SNP rs35767 and IGF-I levels [59,60], so our lack of statistical association may have been simply due to insufficient sample size or variation among populations as discussed above. We did not find any evidence for association with SNP rs35767 and CAD risk in around 9,000 cases and 20,000 controls (Table 12), but given the small magnitude of the associations with glycaemic traits in Dupuis et al, it is possible that there might be a weak association with CAD that could be detected with a larger sample. Palles et al have also identified three SNPs that lie approximately 70 kb 5' to the IGF1 gene that are associated with circulating IGF-I levels [59]. These SNPs lie near to a putative transcription factor binding site that regulates IGF1 gene expression [59], thus there are likely to be additional variants both cis and trans that might affect the expression of IGFs that will need to be characterised by further resequencingand replication efforts. However, the comprehensive SNP tagging approach that we have used in our analysis is likely to have captured the majority of the common variation at the IGF1 and IGFBP3 loci that will be relevant to the general population.

In summary, our results suggest that circulating levels of insulin-like growth factor-I (IGF-I), its major binding protein IGF-binding protein 3 (IGFBP-3) and genetic variants at the loci encoding these proteins do not influence the risk of CAD.

Acknowledgments

We thank the participants, general practitioners and staff in the EPIC-Norfolk study. The EPIC-Norfolk study is supported by programme grants from the Medical Research Council UK and Cancer Research UK. This work was funded by British Heart Foundation project grant PG/05/047.

GEMS Investigators

Philip Barter, Y Antero Kesäniemi, Robert W Mahley, Scott M Grundy, Ruth McPherson, Gerard Waeber.

Conflicts of Interest

None

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