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Journal of Clinical Endocrinology & Metabolism , doi:10.1210/jc.2007-0790
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The Journal of Clinical Endocrinology & Metabolism Vol. 92, No. 9 3660-3666
Copyright © 2007 by The Endocrine Society

Genetic Determinants of Circulating Insulin-Like Growth Factor (IGF)-I, IGF Binding Protein (BP)-1, and IGFBP-3 Levels in a Multiethnic Population

Iona Cheng, Katherine DeLellis Henderson, Christopher A. Haiman, Laurence N. Kolonel, Brian E. Henderson, Matthew L. Freedman and Loïc Le Marchand

Department of Epidemiology and Biostatistics and Institute of Human Genetics (I.C.), University of California, San Francisco, San Francisco, California 94143-0794; Department of Preventive Medicine (K.D.H., C.A.H., B.E.H.), Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California 90089; Department of Medical Oncology (M.L.F.), Dana-Farber Cancer Institute, Boston, Massachusetts 02215; Program in Medical and Population Genetics (M.L.F.), Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts 02142; and Epidemiology Program (L.N.K., L.L.M.), Cancer Research Center of Hawaii, University of Hawaii, Honolulu, Hawaii 96813

Address all correspondence and requests for reprints to: Iona Cheng, Department of Epidemiology and Biostatistics, University of California, San Francisco, California 94143-0794. E-mail: chengi{at}humgen.ucsf.edu.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Context: Both circulating levels and genetic variation of IGFs have been associated with cancer risk, yet the relationship between the two is not well understood.

Objective: To investigate whether common genetic variation in IGF1, IGF binding protein 1 (IGFBP1), and IGFBP3 influences circulating levels of IGF-I, IGFBP-1, and IGFBP-3, we conducted a cross-sectional study of African-American, Native Hawaiian, Japanese-American, Latino, and white men and women in the Multiethnic Cohort.

Design: Plasma levels of IGF-I, IGFBP-1, and IGBFP-3 were measured by ELISA in a random sample of 837 Multiethnic Cohort participants. Previously identified tag single nucleotide polymorphisms (SNPs) for IGF1 (29 tag SNPs) and IGFBP1/IGFBP3 (23 tag SNPs) were genotyped among the 837 participants. Analysis of covariance was conducted to test for differences in mean IGF-I, IGFBP-1, and IGFBP-3 levels across respective IGF1, IGFBP1, and IGFBP3 genotypes, adjusting for previously identified dietary and lifestyle correlates.

Results: Five highly correlated IGFBP3 SNPs (rs3110697, rs2854747, rs2854746, rs2854744, and rs2132570) demonstrated strongly significant associations with IGFBP-3 levels when conservatively adjusted for multiple hypothesis testing (Bonferroni adjusted P trends = 7.75 x 10–8 to 1.44 x 10–5). Patterns of associations were consistent across the five racial/ethnic groups.

Conclusion: In summary, our study suggests that common genetic variation in IGFBP3 influences circulating levels of IGFBP-3 among African-Americans, Native Hawaiians, Japanese-Americans, Latinos, and whites.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
IGFs PLAY A KEY ROLE in regulating cellular growth. Circulating levels of IGF-I, IGF binding protein (IGFBP)-1, and IGFBP-3 have been associated with increased risk of breast, colorectal, and prostate cancers (1, 2, 3). More recently, genetic polymorphisms within IGF1, IGFBP1, and IGFBP3 have been investigated for their influence on cancer susceptibility (2, 4, 5, 6). However, the relationship between circulating levels of IGF-I, IGFBP-1, and IGFBP-3 and their genetic polymorphisms has yet to be clearly defined.

Twin studies have estimated that approximately 40–60% of the interindividual variation in circulating levels of IGF-I, IGFBP-1, and IGFBP-3 are attributed to heritable factors (7, 8). Previous studies have focused primarily on a small number of polymorphisms, such as the IGF1 (CA)n repeat polymorphism (9, 10, 11) and the IGFBP3 A-202C (rs2854744) polymorphism (4, 11, 12, 13, 14, 15, 16, 17, 18), whereas a recent study investigated IGF1, IGFBP1, and IGFBP3 tagging polymorphisms (4). From these studies, the most consistently observed association is the lower levels of circulating IGFBP-3 in the presence of the C allele of the IGFBP3 A-202C promoter polymorphism (4, 11, 12, 13, 14, 15, 16, 17, 18). The great majority of these previous studies have been limited to whites (4, 12, 13, 14, 15, 16, 18), and whether this association exists in other racial/ethnic groups is a question that has yet to be explored fully.

Within the Multiethnic Cohort (MEC), we have recently demonstrated that common genetic variation in IGF1 influences prostate cancer susceptibility (6). Furthermore, we have previously identified dietary and lifestyle regulators of circulating levels of IGF-I, IGFBP-1, and IGFBP-3 (19, 20, 21). These studies have identified racial/ethnic differences in circulating levels of IGF-I and IGFBP-3, as well as the interactive effects between race/ethnicity and obesity on IGF-I levels (19, 20). Building on this prior work, we investigated within the MEC whether inherited variation in IGF1, IGFBP1, and IGFBP3 influences circulating levels of IGF-I, IGFBP-1, and IGFBP-3. This is the first multiethnic study to assess comprehensively the genetic diversity of these loci in relation to their circulating levels.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
MEC

The MEC study is a large population-based cohort study of more than 215,000 men and women from Hawaii and Los Angeles. The cohort is comprised predominantly of five racial/ethnic groups: African-Americans, Native Hawaiians, Japanese-Americans, Latinos, and whites. Participants between the ages of 45 and 75 yr were recruited from 1993–1996, and completed a 26-page self-administered questionnaire that included information regarding height, weight, medical history, family history, diet, dietary supplements and medication use, and physical activity. All participants were between the ages of 47 and 82 yr at the time of blood draw. Further details are provided elsewhere (22).

The blood samples used in this study were collected on a subcohort of about 5000 randomly selected participants. The draw was completed in the morning, typically at the person’s home, after informed consent was obtained. The participation rate for providing a blood sample was 66%. This study was approved by the institutional review boards of the University of Hawaii and University of Southern California.

Plasma levels of IGF-I, IGFBP-1, and IGFBP-3 were measured among a random sample of 1000 of these MEC control participants [100 subjects in each sex and racial/ethnic group with equal representation of each 5-yr age group at blood draw (>45 yr for men and >55 yr for women)] (19, 20, 21). A total of 959 subjects had complete plasma measurements of IGFs, of whom 133 subjects were excluded for having prevalent breast, prostate, or colon cancer, or were premenopausal or taking estrogen replacement therapy at the time of blood draw, or had incomplete body mass index (BMI) information. A total of 826 subjects were included in this analysis.

Plasma measurement

To reduce interbatch variation, we blinded laboratory personnel to the sex and ethnicity of samples, and included an equal number of subjects from each sex/ethnic group for each assay batch. Plasma levels of IGF proteins were measured by ELISAs from Diagnostic System Laboratories (Webster, TX). IGF-I assays included an acid-ethanol precipitation of IGFBPs to minimize the interference of IGFBPs. The overall average intrabatch coefficient of variation was less than 10% for all IGF-related proteins (19, 20, 21). The overall average interbatch coefficients of variation were 13.9%, 14.6%, and 10.4% for IGF-I, IGFBP-1, and IGFBP-3, respectively (19, 20, 21).

Tag single nucleotide polymorphism (SNP) selection

For IGF1, we previously selected 29 tag SNPs to capture the common genetic variation of 64 SNPs (minor allele frequency ≥ 5%) that were genotyped in a multiethnic panel of 349 controls, spanning 156 kb at a density of one SNP every 2.4 kb (the results can be found in supplemental Table 1, which is published as supplemental data on The Endocrine Society’s Journals Online web site at http://jcem.endojournals.org) (6). The proportion of IGF1 SNPs that were captured at a pairwise r2 > 0.8 was 75% for African-Americans, 98% for Native Hawaiians, 96% for Japanese-Americans, 88% for Latinos, and 90% for whites. For IGFBP1 and IGFBP3, we previously identified 23 tag SNPs to capture the common genetic variation of 36 SNPs (minor allele frequency ≥ 5%) that were genotyped in a multiethnic panel, spanning the 71-kb locus at a density of one SNP every 2.0 kb (the results can be found in supplemental Table 2, which is published as supplemental data on The Endocrine Society’s Journals Online web site at http://jcem.endojournals.org) (5). The IGFBP3 missense polymorphism (rs2854746) and the A-202C polymorphism (rs2854744) were "forced" in to be selected as tags to ensure that these potentially relevant SNPs were examined. The proportion of IGFBP1 and IGFBP3 SNPs that were captured at a pairwise r2 > 0.8 was 76% for African-Americans, 84% for Native Hawaiians, 89% for Japanese-Americans, 86% for Latinos, and 91% for whites.

Genotyping

IGF1, IGFBP1, and IGFBP3 tag SNPs were genotyped using the TaqMan allelic discrimination assay (Applied Biosystems, Foster City, CA) by the University of Southern California/Norris Cancer Genomics Core Facility (5, 6). We tested for Hardy-Weinberg equilibrium for each SNP among controls of each racial/ethnic group. All SNPs were in Hardy-Weinberg equilibrium (at P > 0.01 level). For IGF1, the concordance for replicate samples was 99.7%, and the average successful genotyping was 97.9%. For IGFBP1/IGFBP3, the concordance for replicate samples was 99.8%, and the average successful genotyping was 97.4%.

Statistical analysis

We conducted an analysis of covariance to test for differences in mean IGF-I, IGFBP-1, and IGFBP-3 levels across respective IGF1, IGFBP1, and IGFBP3 genotypes/haplotypes. Haplotype frequencies were estimated by the expectation-maximization algorithm using the tagSNP software (23). Haplotype dosage (i.e. an estimate of the number of copies of haplotype h) for each individual and each haplotype, h, was computed using that individual’s genotype data and haplotype frequency estimates obtained from the E-M algorithm (24). Statistical analyses were performed on logarithmically transformed values of IGF-I, IGFBP-1, and IGFBP-3. Our multivariate regression analyses were based on the previously described models reported by DeLellis Henderson et al. (19, 20, 21). For IGF-I levels, our model included age, racial/ethnic group, sex, BMI, an interaction term for racial/ethnic group and BMI, and genotype/haplotype. For IGFBP-1 levels, our model included age, racial/ethnic group, sex, BMI, regular soda intake, an interaction term for age and BMI, and genotype/haplotype. For IGFBP-3 levels, our model included age, racial/ethnic group, sex, BMI, fat from meat intake, and genotype/haplotype. We calculated the partial correlation between genotype/haplotype and respective IGF levels, controlling for aforementioned covariates to determine the contribution of genotype/haplotype to the variance in IGF levels. We tested for heterogeneity of genotype effects across racial/ethnic groups by including an interaction term between genotype and racial/ethnic group in a multivariable model. We used the r2 selection method in conjunction with Mallow’s Cp to evaluate which combination of genotypes provided the best fit of our model of IGF levels. All analyses were performed in SAS version 9.0 (SAS Institute Inc., Cary, NC).


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Selected characteristics of the 826 MEC participants used in this study are presented in Table 1Go. Approximately 18–22% of the study subjects were from each of the five racial/ethnic groups. The mean age (~66 yr) was similar across the five groups. As previously reported, BMI varied markedly across the five racial/ethnic groups. African-Americans had the highest proportion of BMI more than 25 kg/m2, followed by Latinos, Native Hawaiians, whites, and Japanese-Americans.


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  		TABLE 1. Study characteristics of 826 MEC participants used for IGF analyses

 
For IGF-I, there were no significant associations between the 29 IGF1 tag SNPs and circulating levels of IGF-I (P trends >0.06; Fig. 1Go). For IGFBP-1 and IGFBP-3, seven of the 23 IGFBP1/IGFBP3 tag SNPs were nominally statistically significantly associated with IGFBP-1 and IGFBP-3 levels (P trends = 3.37 x 10–9 to 0.047; Fig. 2Go). In addition, four IGF1 SNPs [SNP11 (rs10735380), SNP25 (rs2139570), SNP27 (rs4764695), and SNP28 (rs1520219)] and one IGFBP3 SNP [SNP16 (rs2453839)] displayed evidence of heterogeneity across racial/ethnic groups on IGF levels. Results stratified by racial/ethnic groups for these five SNPs are presented in supplemental Table 3 (published as supplemental data on The Endocrine Society’s Journals Online web site at http://jcem.endojournals.org).


Figure 1
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  		FIG. 1. Association between 29 IGF1 tag SNPs and circulating IGF-I levels. Horizontal line indicates P = 0.05.

 

Figure 2
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  		FIG. 2. Association between 23 IGFBP1 and IGFBP3 tag SNPs and circulating IGFBP-1 and IGFBP-3 levels. White and black bars indicate IGFBP1 and IGFBP3 SNPs, respectively. Horizontal line indicates P = 0.05. Pvalues for SNPs 17, 19, 20, 21, 22, and 23 < 0.0001.

 
The most compelling associations were seen in IGFBP3, in which five SNPs were strongly associated with circulating IGFBP-3 levels: SNP17 (rs3110697), SNP19 (rs2854747), SNP20 (rs2854746), SNP21 (rs2854744), and SNP22 (rs2132570) (P trends = 3.37 x 10–9 to 6.24 x 10–7) (Table 2Go). We applied a Bonferroni correction of 23 IGFBP1/IGFBP3 SNP tests to the P values of these five SNPs, and the adjusted P values remained statistically significant (P trends = 7.75 x 10–8 to 1.44 x 10–5). Geometric mean circulating levels of IGFBP-3 decreased with additional copies of the minor allele for four IGFBP3 polymorphisms (SNP17, SNP19, SNP21, and SNP22), while IGFBP-3 levels increased with additional copies of the minor allele for SNP20. For all five racial/ethnic groups, these five IGFBP3 polymorphisms displayed relatively consistent patterns of associations on IGFBP-3 levels (Table 3Go).


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  		TABLE 2. Geometric means for plasma IGFBP-3 levels (ng/ml) by genotype for the five associated IGFBP3 SNPs

 

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  		TABLE 3. Geometric means for plasma IGFBP-3 levels (ng/ml) for the five associated IGFBP3 SNPs by racial/ethnic group

 
The five associated IGFBP3 polymorphisms (SNP17, SNP19, SNP20, SNP21, and SNP22) were reasonably highly correlated with each other across the five racial/ethnic groups (Table 4Go), with the exception of SNP22 (rs2132570) among African-Americans (r2 = 0.07–0.18). Of these five polymorphisms, SNP20 (rs2854746) and SNP22 (rs2132570) together provided the best fit for our model of IGFBP-3 levels using the r2 selection method in conjunction with Mallow’s Cp. SNP20 and SNP22 explained approximately 1% of the overall variance in IGFBP-3 levels, with our full model capturing 11.5% of variance in IGFBP-3 levels.


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  		TABLE 4. Pairwise correlation (D' and r2) between the IGFBP3 SNPs associated with plasma IGFBP-3 levels by racial/ethnic group

 
The IGFBP3 locus is located within a region of strong linkage disequilibrium from SNP17 to SNP23 (5). Two common haplotypes within this region, 3A (total frequency 44%) and 3B (total frequency 19%) (5), were significantly asso-ciated with circulating IGFBP-3 levels (P = 3.83 x 10–5 and P = 1.76 x 10–5, respectively; data not shown). Haplotype 3A was associated with higher levels of IGFBP-3 with increasing number of haplotypes. In contrast, haplotype 3B was associated with lower levels of IGFBP-3 with increasing number of haplotypes. These two haplotypes differed only at the alleles of the five IGFBP3-associated SNPs (SNP17, 19, 20, 21, and 22). Haplotype 3A harbored the alleles of these five polymorphisms that were associated with higher IGFBP-3 levels, whereas haplotype 3B harbored the alleles associated with lower IGFBP-3 levels. These IGFBP3 haplotypes explained approximately 1% and the full model 12% of the overall variance in IGFBP-3 levels.


   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
In this multiethnic study of African-Americans, Native Hawaiians, Japanese-Americans, Latinos, and whites, we comprehensively examined the genetic diversity in IGFI, IGFBP1, and IGFBP3, and tested whether common genetic variation at these loci influences circulating levels of IGF-I, IGFBP-1, and IGFBP-3. Our results indicate that inherited variation in IGFBP3 was associated with circulating levels of IGFBP-3. Specifically, we identified five IGFBP3 polymorphisms that were consistently associated with circulating IGFBP-3 levels across the five racial/ethnic groups. In addition, when we corrected for multiple hypotheses testing using a conservative Bonferroni approach, these IGFBP3 polymorphisms remained highly significant.

A previous study from the United Kingdom similarly examined IGF1 and IGFBP3 tagging polymorphisms in relation to circulating IGF-I and IGFBP-3 levels (4). Of nine IGF1 polymorphisms tested in that study, five and two polymorphisms were associated with circulating levels of IGF-I among females and males, respectively (4). We examined two of the IGF1 polymorphisms [rs1520220 (SNP18) and rs2946834 (SNP21)] that were associated with IGF-I levels in the United Kingdom study (P = 0.003 and P = 0.02, respectively) and found no association (P = 0.55 and P = 0.85, respectively). This discrepancy may be due to our reduced power for white-specific analysis, having 168 whites in contrast to 937 European subjects in the United Kingdom study. Of four IGFBP3 polymorphisms tested in the United Kingdom study, three and two polymorphisms were associated with circulating levels of IGFBP-3 among females and males, respectively (4). We examined the A-202C polymorphism that was the most strongly associated with IGFBP-3 levels in the United Kingdom study (P < 10–9 for females; P = 0.00004 for males) and found similar highly significant effects (P = 6.24 x 10–7). In total, nine studies, including our current study, have examined the IGFBP3 A-202C polymorphism in relation to IGFBP-3 levels and have consistently reported significant effects (4, 11, 12, 13, 14, 15, 16, 18). In a study from the European Prospective Investigation Cohort, seven IGFBP1 polymorphisms were tested for their association with circulating IGFBP-1 levels, and no significant associations were observed (18). The two nominally associated IGFBP1 polymorphisms (rs10228265 and rs1065781) in our study have not previously been examined.

Because of the strong regional correlation across the IGFBP3 locus (5), the predisposing allele responsible for influencing circulating levels of IGFBP-3 remains to be identified. The IGFBP3 A-202C polymorphism is a promising candidate because it was originally shown by Deal et al. (12) to influence promoter activity. The A allele has been shown in an in vitro assay to have a higher promoter activity, compared with the C allele. This is in line with the lower levels of IGFBP-3 observed in the presence of the C allele. In light of the strong biological support for this polymorphism, coupled with the consistent evidence of this association from prior reports and our multiethnic study, future work should examine the relationship between this polymorphism and tissue expression to assess further its functional role.

For four of the five IGFBP3 polymorphisms that were strongly associated with circulating levels of IGFBP-3, lower levels were observed in the presence of the minor allele. Because IGFBP-3 is the principal binding protein of circulating IGF-I, binding more than 90% of IGF-I in conjunction with the acid-labile subunit (25), lower levels of IGFBP-3 due to genetic variation may increase the bioavailability of IGF-I. This may ultimately influence the bioactivity of IGF-I in the circulation and tissues, leading to cellular growth and cancer susceptibility.

In previous nested case-control studies within the MEC, there was no association between common genetic variation in IGFBP3 and breast and prostate cancer risk (5). The significant effect of the five IGFBP3 polymorphisms on circulating IGFBP-3 levels in the absence of an effect on cancer highlights the complexity of the hormonal milieu of the IGF system. It is possible that other genetic variants and environmental factors may act individually or in concert to modulate the exposure of target tissues to IGFs and cancer susceptibility. As well, it is possible that IGFBP3 variants may influence hormonal levels, but not cancer risk. This has been seen for CYP19, in which genetic variation at this locus predicts estrogen levels, but not breast cancer risk (26).

Our study has several limitations. Although we were able to capture the majority of the common genetic variation across the IGF1, IGFBP1, and IGFBP3 genes, we have not exhaustively captured all of the common genetic diversity of these loci among the five racial/ethnic groups, especially among African-Americans (see Tag single nucleotide polymorphism (SNP) selection). In addition, our study cannot exclude the possibility that rare genetic variants may influence circulating levels.

In conclusion, our study of African-Americans, Native Hawaiians, Japanese-Americans, Latinos, and whites suggests that common genetic variation in IGFBP3 influences circulating IGFBP-3 levels, beyond the effects of previously reported dietary and lifestyle correlates. With replication in larger cohorts such as the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (http://epi.grants.cancer.gov/BPC3/), additional fine-mapping and mechanistic work will be needed to pinpoint the causal variant. Furthermore, it remains to be determined whether other genes in the GH (17) and IGF family impact circulating levels of IGFs because it may be the cumulative effect of several genes that drives cancer predisposition.


   Acknowledgments
 
We thank the participants of the Multiethnic Cohort Study. We also thank Sabina Rinaldi and Rudolf Kaaks for the plasma IGF hormone analyses, Annette Lum-Jones and Stephanie Riley for their genotyping efforts, and Jennifer Yamamoto for data analysis support.


   Footnotes
 
This work was supported by National Institutes of Health R25T training Grant CA 112355-01A1 (to I.C.), and National Cancer Institute Grants CA 63464 and CA 54281.

Disclosure Statement: I.C., C.A.H., L.N.K., B.E.H., M.L.F., and L.L.M. have nothing to declare. K.D.H. has equity interests in Molecular Endocrinology.

First Published Online June 12, 2007

Abbreviations: BMI, Body mass index; IGFBP, IGF binding protein; MEC, Multiethnic Cohort; SNP, single nucleotide polymorphism.

Received April 6, 2007.

Accepted June 4, 2007.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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