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Carcinogenesis Advance Access originally published online on March 28, 2008
Carcinogenesis 2008 29(7):1360-1366; doi:10.1093/carcin/bgn083
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Polymorphisms of genes coding for ghrelin and its receptor in relation to anthropometry, circulating levels of IGF-I and IGFBP-3, and breast cancer risk: a case–control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC)

Laure Dossus, James D. McKay1, Federico Canzian, Stefan Wilkening, Sabina Rinaldi2, Carine Biessy2, Anja Olsen3, Anne Tjønneland3, Marianne U. Jakobsen4, Kim Overvad4, Françoise Clavel-Chapelon5, Marie-Christine Boutron-Ruault5, Agnes Fournier5, Jakob Linseisen, Annekatrin Lukanova, Heiner Boeing6, Eva Fisher6, Antonia Trichopoulou7, Christina Georgila7, Dimitrios Trichopoulos8,9, Domenico Palli10, Vittorio Krogh11, Rosario Tumino12, Paolo Vineis13, José Ramon Quirós14, Núria Sala15, Carmen Martínez-García16, Miren Dorronsoro17, Maria-Dolores Chirlaque18, Aurelio Barricarte19, Fränzel J.B. van Duijnhoven20, H.B. Bueno-de-Mesquita20, Carla H. van Gils21, Petra H.M. Peeters21, Göran Hallmans22, Per Lenner23, Sheila Bingham24,25, Kay Tee Khaw26, Tim J. Key27, Ruth C. Travis27, Pietro Ferrari2, Mazda Jenab2, Elio Riboli13 and Rudolf Kaaks*

Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
1 Genetic Epidemiology Group
2 Epidemiology Methods and Support Group, International Agency for Research on Cancer, Lyon 69372, France
3 Department of Diet, Cancer and Health, Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen 2100, Denmark
4 Department of Clinical Epidemiology, Aarhus University Hospital, Aalborg Aarhus 8000, Denmark
5 Department of Epidemiology and Public Health, Institut National de la Santé et de la Recherche Médicale, ERI 20, EA 4045 and Institut Gustave Roussy, Villejuif 94805, France
6 Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal 14558, Germany
7 Department of Hygiene and Epidemiology, University of Athens Medical School, Athens 11527, Greece
8 Department of Epidemiology, Harvard School of Public Health, Boston MA 02115, USA
9 Hellenic Health Foundation, Athens 11527, Greece
10 Molecular and Nutritional Epidemiology Unit, Center for Cancer Research and Prevention-Scientific Institute of Tuscany, Florence 50139, Italy
11 Nutritional Epidemiology Unit, National Cancer Institute, Milan 20133, Italy
12 Cancer Registry, Azienda Ospedaliera Civile M.P. Arezzo, Ragusa 97100, Italy
13 Department of Epidemiology and Public Health, Imperial College, London SW7, UK
14 Public Health and Health Planning Directorate, Asturias Oviedo 33001, Spain
15 Laboratori de Recerca Translacional, Department of Epidemiology, Catalan Institute of Oncology, Barcelona (Institut d'Investigatio Biomedica de Bellvitge), L'Hospitalet de Llobregat, Barcelona 08907, Spain
16 Andalusian School of Public Health, CIBER Epidemilogía y Salud Pública, Granada 18011, Spain
17 Department of Public Health of Guipuzkoa, San Sebastian, Donostia-San Sebastian 20013, Spain
18 Department of Epidemiology, Murcia Health Council, CIBER en Epidemiología y Salud Pública (CIBERESP), Murcia Granada 18011, Spain
19 Public Health Institute of Navarra, CIBERESP, Pamplona 31003, Spain
20 Center for Nutrition and Health, National Institute for Public Health and the Environment, Bilthoven 3720 BA, The Netherlands
21 Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht 3508 GA, The Netherlands
22 Department of Public Health and Clinical Medicine, Nutritional Research, Oncology, Umeå University, Umeå 90187, Sweden
23 Department of Radiation Sciences, Oncology, Umeå University, Umeå 90187, Sweden
24 MRC Dunn Human Nutrition Unit, Cambridge CB2 0XY, UK
25 MRC Centre for Nutritional Epidemiology in Cancer Prevention and Survival Department of Public Health and Primary Care, University of Cambridge CB1 8RN, UK
26 Department of Public Health and Primary Care, University of Cambridge CB1 8RN, UK
27 Cancer Research UK, Epidemiology Unit, University of Oxford, Oxford OX3 7XP, UK

* To whom correspondence should be addressed. Tel: +49 6221 422219; Fax: +49 6221 422203;Email: r.kaaks{at}dkfz.de


   Abstract
Top
 Abstract
Introduction
 Material and methods
 Results
 Discussion
 Funding
 References
 
Ghrelin, an endogenous ligand for the growth hormone secretagogue receptor, has two major functions: the stimulation of the growth hormone production and the stimulation of food intake. Accumulating evidence also suggests a role of ghrelin in cancer development. We conducted a case–control study on 1359 breast cancer cases and 2389 matched controls, nested within the European Prospective Investigation into Cancer and Nutrition, to examine the association of common genetic variants in the genes coding for ghrelin (GHRL) and its receptor (GHSR) with anthropometric measures, circulating insulin growth factor I (IGF-I) and insulin-like growth factor-binding protein 3 and breast cancer risk. Pair-wise tagging was used to select the 15 polymorphisms that represent the majority of common genetic variants across the GHRL and GHSR genes. A significant increase in breast cancer risk was observed in carriers of the GHRL rs171407-G allele (odds ratio: 1.2; 95% confidence interval: 1.0–1.4; P = 0.02). The GHRL single-nucleotide polymorphism rs375577 was associated with a 5% increase in IGF-I levels (P = 0.01). A number of GHRL and GHSR polymorphisms were associated with body mass index (BMI) and height (P between <0.01 and 0.04). The false-positive report probability (FPRP) approach suggests that these results are noteworthy (FPRP < 0.20). The results presented here add to a growing body of evidence that GHRL variations are associated with BMI. Furthermore, we have observed evidence for association of GHRL polymorphisms with circulating IGF-I levels and with breast cancer risk. These associations, however, might also be due to chance findings and further large studies are needed to confirm our results.

Abbreviations: BMI, body mass index; EPIC, European Prospective Investigation into Cancer and Nutrition; FPRP, false-positive report probability; GH, growth hormone; GHSR, growth hormone secretagogue receptor; IGFBP-3, insulin-like growth factor-binding protein 3; IGF-I, insulin growth factor I; SNP, single-nucleotide polymorphism


   Introduction
Top
 Abstract
 Introduction
Material and methods
 Results
 Discussion
 Funding
 References
 
Ghrelin, an endogenous ligand for the growth hormone secretagogue receptor (GHSR), is a 28-amino residue peptide predominantly produced by the stomach (1). In addition to the mature form of ghrelin, several posttranscriptional and posttranslational variants have been reported (2). Two molecular forms (ghrelin and des-acyl ghrelin), resulting from a different posttranscriptional modification of the protein, are observed in human plasma. An alternative processing of the prepropeptide generates also an aminated 23-aminoacid peptide, called obestatin, which binds to a different receptor, and has been shown in experimental animal models to oppose the effect of ghrelin (3). The ghrelin receptor has two known isoforms: one which is functional (GHSR-1a) and one spliced variant (GHSR-1b) with no known function (4). Only the acylated form of ghrelin can bind the GHSR-1a receptor (1).

Two main functions of ghrelin are documented: first, to stimulate growth hormone (GH) production through the activation of GHSR-1a in the hypothalamus (5); and second, to increase appetite and food intake (6,7) by mechanisms that could be independent of GHSR (8). Accumulating evidence also indicates a possible role of ghrelin in the control of cell proliferation, inhibition of apoptosis and cancer development (9–12).

Ghrelin production has been reported in both non-malignant breast tissues (13,14) and in breast carcinomas (15,16). The truncated isoform of the receptor, GHS-R1b, was reported to be highly expressed in breast cancer tissues compared with normal breast, and ghrelin, at physiological levels, has been shown to increase breast cancer cell proliferation (in vitro) (16). So far, only one epidemiological study examined the risk of breast cancer associated with polymorphisms of the genes coding for ghrelin (GHRL) or its receptor (GHSR) (17).

The role of the GH axis in breast cancer has been well documented (11,18). GH is the main endocrine stimulus of hepatic and tissue production of insulin growth factor I (IGF-I) and insulin-like growth factor-binding protein 3 (IGFBP-3). A number of epidemiological studies have shown an increased risk of breast cancer with elevated IGF-I levels (19,20). However, several studies observed a negative correlation between ghrelin and IGF-I levels in children and adolescents (21–25) or in adult subjects (26), indicating a possible inhibitory effect of ghrelin on the GH–IGF-I axis. Therefore, genetic variants in GHRL or GHSR that could alter GH-releasing activity might ultimately affect IGF-I levels in tissues and in the circulation. Only few studies, however, have examined the association between polymorphisms of these genes and IGF-I-circulating levels (27–29).

Weight gain and obesity are well-known risk factors for postmenopausal breast cancer (30). In addition to its GH-releasing activity, ghrelin is also known to induce a positive energy balance by stimulating appetite. Stimulation of food intake by ghrelin has been observed in both animals (7,31,32) and humans (6) and ghrelin administration has been shown to induce weight gain in rodents (33,34). The impact of ghrelin on obesity is less clear (9). Paradoxically, plasma ghrelin levels are lower in obese patients than in lean or anorexic individuals (21,35–38). On the other hand, a recent study showed an elevated expression of ghrelin in the hypothalamus of obese patients, indicating a more important role of central rather than peripheral ghrelin in obesity (39). The GHSR and GHRL genes are both located on chromosome 3, in regions which have been linked to obesity by several genome-wide linkage scans (40,41). A number of studies attempted to identify polymorphisms in the GHRL gene as determinants of obesity (27,29,42–46). One particular variant, Leu72Met (rs696217), was associated with early onset of obesity. Three studies investigated the relation between genetic polymorphisms in GHSR and obesity (28,47,48) with contradictory results.

We conducted a case–control study of 1359 breast cancer cases and 2389 controls, nested within the European Prospective Investigation into Cancer and Nutrition (EPIC), to examine the association of 15 common genetic variants in the GHSR and GHRL genes with anthropometric measures, circulating IGF-I and IGFBP-3 concentrations and breast cancer risk.


   Material and methods
Top
 Abstract
 Introduction
 Material and methods
Results
 Discussion
 Funding
 References
 
The EPIC study
The EPIC cohort consists of ~366 500 women and 153 500 men, aged 35–69 years, recruited between 1992 and 1998 in 23 research centers in 10 Western European countries. The vast majority (>97%) of subjects recruited in the EPIC cohort are of European Caucasian origin. All subjects provided extensive standardized questionnaire data on diet and non-dietary variables, as well as anthropometric measurements. About 80% of the participants also provided a blood sample.

Cases of cancer occurring after recruitment into the cohort are identified through local and national cancer registries in 7 of the 10 countries and in France, Germany and Greece by a combination of contacts with national health insurances and/or active follow-up through the study subjects or their next of kin. Follow-up on vital status, to monitor the population remaining at risk for cancer, is achieved through record linkage with mortality registries. A fully detailed description of the EPIC study has been published elsewhere (49,50).

Questionnaire data and anthropometry
Questionnaire data on non-dietary lifestyle and health factors included menstrual and reproductive history, current and previous use of oral contraceptives and postmenopausal hormone replacement therapy, history of previous illness and disorders or surgical operations, lifetime history of tobacco smoking and consumption of alcoholic beverages, physical activity, level of education and socioeconomic status and brief occupational history. In all countries included in the present analysis, except part of the cohort recruited through the Oxford research centre, height, weight and waist and hip circumferences were measured according to standardized protocols in light dressing. In part of the Oxford cohort, height, weight and body circumferences were self-reported.

Selection of cases and controls
Cases were selected among women who developed breast cancer after their recruitment into the EPIC study, before the end of the study period (for each study center defined by the latest end-date of follow-up) and who had no previous diagnosis of cancer (except non-melanoma skin cancer). For each case subject with breast cancer, two control subjects were chosen at random from cohort members alive and free of cancer (except non-melanoma skin cancer) at the time of diagnosis of the index case. Control subjects were matched to the cases by study center where the subject was enrolled in the cohort, as well as by menopausal status, age (±6 months) at enrollment, fasting status (<3, 3–6 and >6 h), time of the day of blood donation (±1 h), exogenous hormone use and phase of the menstrual cycle (51).

Approval for the study was given by the relevant ethical committees, both at the International Agency for Research on Cancer and in each of the EPIC recruitment centers.

DNA extraction
For 7 of the 10 countries participating in EPIC (UK, Germany, The Netherlands, Spain, Italy, France and Greece), buffy coat samples for the study subjects were retrieved from the EPIC biorepository and DNAs were extracted on an Autopure instrument (Gentra Systems, Minneapolis, MN) with Puregene chemistry (Gentra Systems). Because of small DNA quantity, whole-genome amplification was performed in Denmark and Sweden. Norway was not included in the study because blood samples have been collected only recently on a subsample of cohort participants, and so far only very few cases of breast cancer have been accumulated after blood collection.

Identification, selection and genotyping of single-nucleotide polymorphisms
We collected data on polymorphisms from publicly available databases, such as dbSNP (http://www.ncbi.nlm.nih.gov/SNP/), SNPper (http://snpper.chip.org/) and Frequency Finder (http://bluegenes.bsd.uchicago.edu/frequencyfinder/). We complemented databases searches with literature review and, for GHRL, with analysis of 95 subjects from the EPIC population by denaturating high-performance liquid chromatography. We included only polymorphisms whose existence in Caucasians is documented, either according to literature data or to our own experimental analysis by denaturating high-performance liquid chromatography. All new single-nucleotide polymorphisms (SNPs) identified in our laboratory by denaturating high-performance liquid chromatography searches have been deposited in dbSNP (http://www.ncbi.nlm.nih.gov/SNP). Among all polymorphisms thus identified, we retained only those with a minor allele frequency ≥5% in Caucasians. To this list, we then added the SNPs genotyped by the HapMap project (Phase II v20) in the GHRL and GHSR genomic regions. We used the R2 based Tagger (http://www.broad.mit.edu/mpg/tagger/), to choose a representative set of ‘tagging’ SNPs so that variations of all SNP alleles within the gene were captured with an R2 > 0.8 (52). We used R2 pair-wise tagging for all SNPs, with the exception of rs26311, in GHRL, which was represented by the ‘aggressive’ tagging method of Tagger, using two marker tagging between markers rs27498 and rs10490815.

Genotyping was performed by the 5' nuclease assay (TaqMan). TaqMan probes were synthesized by Applied Biosystems, Foster City, CA (with minor groove binder chemistry). One TaqMan assay for rs519384 could not be designed; however, this SNP was moderately tagged by rs572169 (R2 = 0.66). Positions and functions of the SNPs genotyped in this study are reported in Table I. Sequences of genotyping primers and probes are available upon request. Laboratory personnel were kept blinded to case–control status throughout the study. Repeated quality control genotypes (8% of the total) showed >99% concordance for all assays. Subjects with a completion rate <75% were excluded (N = 102). Genotyping call rates ranged between 93.5 and 97.5%. After genotyping of the samples, five SNPs were out of Hardy–Weinberg equilibrium in Denmark and therefore participants from this country were excluded from the present study. A total of 1359 cases and 2389 controls were included in the study.


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  		Table I. Details of the polymorphisms used in the present study

 
In this group of controls, the distributions of genotypes of all polymorphisms were in agreement with the Hardy–Weinberg equilibrium (P value > 0.01), with the exception of one polymorphism (rs35683, P value = 0.005). After exclusion of rs35683, all GHRL polymorphisms can still be captured by the remaining tagging SNPs with a R2 > 0.80. Two SNPs, rs35681 and rs35680, were then captured by the aggressive tagging method of Tagger, using two other markers (rs171407 and rs35684).

Hormone measurements
For a subgroup of 3276 women (including 1113 cases and 2163 controls) that were not using exogenous hormones at the time of blood collection (oral contraceptive or hormonal replacement therapy for menopause), measurements of circulating levels of IGF-I and IGFBP-3 were performed in the laboratory of the Hormones and Cancer Team, at International Agency for Research on Cancer, using enzyme-linked immunosorbent assays from Diagnostic System Laboratories (Webster, TX). For 127 subjects, measurements of IGFBP-3 were missing because of lack of serum for this particular study. The IGF-I assays included an acid–ethanol precipitation step to eliminate IGF-I-binding proteins, to avoid their interference with the IGF-I measurement. Measurements were performed on never-thawed serum sample aliquots. The mean intra- and interbatch coefficients of variation were 6.2 and 16.2%, respectively, for IGF-I and 7.2 and 9.7%, respectively, for IGFBP-3.

Statistical analyses
Relationships of polymorphic gene variants with serum levels of IGF-I and IGFBP-3, or with anthropometric variables, were estimated by standard normal regression models and percentage of difference in geometric means in each genotype category compared with the major homozygote category was calculated. All the analyses on IGF-I and IGFBP-3 were adjusted for age at blood donation, body mass index (BMI), laboratory batch and breast cancer case–control status. The analyses on anthropometric measures were adjusted for age at blood donation, country and case–control status. Subjects with BMI ≥30 were defined as obese and <25 as normal weight. Relationships of polymorphic variants with obesity [odds ratios (ORs) for obese versus normal weight] were estimated using unconditional logistic regression and adjusted for age at blood donation, country and breast cancer case–control status. The relationships with breast cancer risk were estimated using conditional logistic regression models, applied on the matched case–control sets. Analyses were performed under the recessive, dominant and codominant models.

We performed subgroup analyses on women with a breast cancer diagnosis either before or after 55 years (menopausal status at diagnosis is not available but 99% of the women enrolled in the EPIC cohort after age 55 declared themselves postmenopausal) and by categories of BMI (<25, 25–29 and 30+). Possible heterogeneity of effect between these groups and between EPIC countries was tested using {chi}2 tests, comparing the deviations of logistic β-coefficients observed in each subgroup relative to the overall β-coefficient.


   Results
Top
 Abstract
 Introduction
 Material and methods
 Results
Discussion
 Funding
 References
 
A total of 1359 breast cancer cases and 2389 matched controls were included in the present study. Baseline characteristics of breast cancer cases and controls are presented in Table II. The mean age at blood donation was 55 years (5th–95th percentiles: 40–68 years). The mean age at diagnosis of breast cancer was 57 (5th–95th percentiles: 43–71 years). At the time of diagnosis, 40% of the subjects were premenopausal and 60% postmenopausal. In all, 653 subjects (17% of the breast cancer cases and controls) had a BMI ≥30 and were classified as obese. After adjustment for laboratory batch, age at blood donation and BMI, circulating levels of IGF-I and IGFBP-3 were higher among breast cancer cases than among controls (geometric means: 229 ng/ml among cases versus 225 ng/ml among controls, for IGF-I; 3457 ng/ml among cases versus 3393 ng/ml among controls, for IGFBP-3) (Table II). This association was mostly driven by the effect observed in older women (>55 at diagnosis), as reported previously in a more detailed analysis of the relationship of IGF-I and IGFBP-3 levels with breast cancer risk in the EPIC study (20).


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  		Table II. Baseline characteristics of breast cancer cases and controls

 
The relationships between GHRL and GHSR polymorphisms and anthropometry are presented in Table III. Two GHRL polymorphisms showed a statistically significant association with reduced BMI for carriers of two copies of the minor allele, compared with non-carriers (–1.6 and –1.9% for rs171336 and rs171407, respectively). For rs171407, significance is reached for the dominant and for the codominant model. A statistically significant increased risk of obesity was observed for carriers of the minor allele of rs3755777 and rs10490815 [OR: 1.6; 95% confidence interval (CI): 1.1–2.4 and OR: 1.4; 95% CI: 1.0–2.1, respectively]. Heterozygotes of the GHSR SNP rs2922126 had 1.3% lower BMI and 19% reduced risk of being obese than homozygotes for the major allele. Carriers of two copies of the minor allele of three GHRL polymorphisms (rs171336, rs171407 and rs27647) were associated with a 0.4% greater height when compared with non-carriers while heterozygotes for rs3755777 and rs27498 showed a lower height (–0.4%). One GHSR polymorphism, rs572169, showed a statistically significant association with height (+0.3% for heterozygotes, Pcodominant = 0.02). When the analyses were stratified by breast cancer case–control status, the associations observed were in the same direction although not always statistically significant because of the smaller number of observation and loss of power.


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  		Table III. Association of GHRL and GHSR SNPs with anthropometry

 
The GHRL polymorphism rs3755777 showed a significant positive association with IGF-I levels (+5.5% for homozygotes of the minor allele, Precessive = 0.01). A borderline significant association with IGFBP-3 levels was observed for rs2075356 (–7.5% for homozygotes of the minor allele, Pcodominant = 0.05) (Table IV). No further significant association was observed after stratification by breast cancer case–control status or after exclusion of cases diagnosed in the first 2 years following blood donation.


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  		Table IV. Association of GHRL and GHSR SNPs with IGF-I and IGFBP-3 levels and breast cancer risk

 
The results of the association between breast cancer risk and GHRL and GHSR polymorphisms are presented in Table IV. In the GHRL gene, we observed a significant increase in breast cancer risk among carriers of the minor allele (G) of rs171407 (OR: 1.2; 95% CI: 1.0–1.4; Pdominant = 0.02). Homozygotes for the G allele of the GHSR SNP rs2948694 had also a significant increased risk of breast cancer (OR: 2.2; 95% CI: 1.1–4.3; Precessive = 0.03). Adjustment for BMI or IGF-I did not affect the risk estimates. No heterogeneity of the effect was observed after stratification by age at diagnosis of breast cancer (<55 and 55+ years), by categories of BMI (<25, 25–29 and 30+) or by EPIC country.


   Discussion
Top
 Abstract
 Introduction
 Material and methods
 Results
 Discussion
Funding
 References
 
To our knowledge, this is the first large-scale study to analyze simultaneously common variations in the GHRL and GHSR genes and association with breast cancer risk, IGF-I and IGFBP-3 levels and anthropometry.

In previous studies, no association was found between GHSR and GHRL polymorphisms and IGF-I levels among Caucasian normal weight subjects (27–29). Based on a much larger number of polymorphisms and subjects, our study showed that one GHRL polymorphism (rs3755777) was associated with 5% lower IGF-I-circulating levels.

So far, no study has been performed on GHRL and GHSR polymorphism and IGFBP-3-circulating levels. However, in some studies ghrelin-circulating levels have been positively associated with IGFBP-3-circulating levels (53,54). In the present study, one SNP of the GHRL gene (rs2075356) has been found to be associated with IGFBP-3-circulating levels although the trend was only borderline significant.

Among the 15 SNPs studied here, 2 showed a significant association with lower BMI and 5 showed an association with height. Two GHRL polymorphisms were observed more often in obese compared with normal weight subjects. Several studies have examined the possible implication of the GHRL gene polymorphisms in obesity. One particular SNP, Leu72Met (rs696217), has been associated with an early-onset obesity or with BMI (29,42,44,55), whereas no association was observed in other studies (43,45,46,56,57). In our study, this polymorphism is associated with a non-significant higher BMI. Another polymorphism (rs2075356) that was described to be marginally associated with BMI in young Japanese women (57) was also related to BMI in our study although the trend was not statistically significant.

Two studies have examined the association between obesity and GHSR gene variants among adults with conflicting results. Vartiainen et al. (28) found no association with BMI, whereas Baessler et al. (48) observed a 1.5-fold increased risk of obesity among carriers of five GHSR polymorphisms. Among those SNPs, one was also included in the present study (rs572169) and showed a significant greater height and a borderline significant 30% increase in risk of obesity.

For breast cancer, we observed a 20% increase in risk for carriers of the G allele of one GHRL SNP (rs171407) and a 2-fold increase for homozygotes of the GHSR polymorphism rs2948694. This later association, however, was based on a homozygote group of only 18 cases. These two polymorphisms are not located within coding regions and therefore do not lead to a change in amino acid sequence. One other study, including 405 breast cancer cases and 460 matched controls, investigated the association between genetic polymorphisms of GHRL and GHSR genes and breast cancer risk (17). This study showed an increased risk for the GHSR SNP rs495225 under a recessive model (OR = 2.5; 95% CI: 1.4–4.5), but the distributions of this particular SNP did not fall inside that expected by Hardy–Weinberg equilibrium. In our study, no effect of this SNP on breast cancer risk was observed.

Although >97% of the EPIC subjects are estimated to be of Caucasian origin, differences in allelic frequencies across Europe could in theory cause confounding by population stratification. However, we did not observe major variations in allele frequencies across countries for the SNP studied here. Moreover, cases and controls were systematically matched for EPIC recruitment center and regression analyses relating IGF-I, IGFBP-3 or anthropometry measurements to genetic variants were adjusted for the factor ‘country of recruitment’.

With the tagging approach used in our study (pair-wise tagging), we captured most of the haplotype information for the SNPs genotyped in HapMap. In fact, with the high r2 threshold used in our study (>0.8), our tagSNPs can resolve >80% of all existing haplotypes, as described by Carlson et al. (52). However, we cannot rule out the possibility that it might still exist untyped variants in these genes that the haplotype approach would capture better. We therefore performed also haplotype analyses [using the method described by Stram et al. (58)]. The results were very similar to the SNP results presented here and we thought that presenting both would have been overwhelming for the reader.

The large number of statistical tests performed in our study raises the question of potential false-positive results. To estimate the ‘noteworthiness’ of our results, we computed the false-positive report probability (FPRP) defined by Wacholder et al. (59). Using a prior probability of 0.1, noteworthy FPRPs <0.2 (the appropriate threshold for a study of this size) were observed for the association with breast cancer risk of rs171407 (FPRP = 0.15, with a power of 0.996 to detect an OR of 1.5), for the association with BMI levels of rs2922126 and rs171407 (FPRP = 0.08, with >90% power to detect a β-coefficient of –0.01) and for the association with IGF-I of rs3755777 (FPRP = 0.16, with a power of 0.98 to detect a β-coefficient of 0.05). FPRPs were also <0.20 for most of the associations with height (only the association between height and rs27647 was not noteworthy with an FPRP of 0.24). Higher FPRPs (>25%) were observed for the other associations with obesity and breast cancer, indicating that these results might be false positive. When using a prior probability of <0.01 (moderate), we obtained high FPRP values for all the associations tested here, all >0.47, and therefore, we cannot rule out the possibility that chance led to these associations.

Ghrelin has been implicated in cell proliferation (16), maintenance of the IGF-I–GH axis (5) and traits associated with obesity (9). Functionally, consequent genetic variation in the GHRL or GHSR genes would therefore be expected to manifest as associations across a number of these traits. It is then intriguing that the GHRL SNP rs171407 which we found associated with a lower BMI (–1.9%) and a greater height (+0.4%) was also associated with breast cancer risk (+20%). Similarly, the GHSR polymorphism rs572169 was associated with a 20% increase risk of breast cancer and greater height (+0.3%) but not with BMI. This might reflect an effect of these polymorphisms on breast cancer through the GH axis, independently of BMI or obesity and then independently of the action of ghrelin on appetite and food intake. It is also worth to note that, in addition to being associated with height and risk of obesity, the GHRL polymorphism rs3755777 was also associated with higher circulating IGF-I levels. These traits are interrelated, and therefore these associations may also reflect similar biological actions (i.e. altered height level might be influenced by changes in IGF-I); nevertheless, the associations we have observed would appear to be driven by differences in the biology rather than multiple testing.

The results presented here add to the accumulating body of evidence that genetic variation in GHRL and GHSR is associated with BMI. Furthermore, we have observed evidence for association of GHRL polymorphisms with circulating IGF-I levels and with breast cancer risk. Nevertheless, particularly as some of our findings involve recessive models with relatively few individuals, further large studies will be needed to confirm our results.


   Funding
Top
 Abstract
 Introduction
 Material and methods
 Results
 Discussion
 Funding
References
 
USA Department of Defense (DAMD17-01-1-0275).


   Acknowledgments
 
The EPIC study was funded by ‘Europe Against Cancer’ programme of the European Commission (SANCO); Ligue contre le Cancer (France); Société 3M (France); Mutuelle Générale de l'Education Nationale; Institut National de la Santé et de la Recherche Médicale; German Cancer Aid; German Cancer Research Centre; German Federal Ministry of Education and Research; Danish Cancer Society; Health Research Fund (FIS) of the Spanish Ministry of Health; the participating regional governments and institutions of Spain; Cancer Research UK; Medical Research Council, UK; the Stroke Association, UK; British Heart Foundation; Department of Health, UK; Food Standards Agency, UK; the Wellcome Trust, UK; Greek Ministry of Health and Social Solidarity; Hellenic Health Foundation; Italian Association for Research on Cancer; Italian National Research Council; Dutch Ministry of Public Health, Welfare and Sports; Dutch Ministry of Health; Dutch Prevention Funds; LK Research Funds; Dutch ZON (Zorg Onderzoek Nederland); World Cancer Research Fund; Swedish Cancer Society; Swedish Scientific Council; Regional Government of Skane, Sweden; Norwegian Cancer Society.

Conflict of Interest Statement: None declared.


   References
Top
 Abstract
 Introduction
 Material and methods
 Results
 Discussion
 Funding
 References
 

  1. Kojima M, et al. Ghrelin is a growth-hormone-releasing acylated peptide from stomach. Nature (1999) 402:656–660.[CrossRef][Web of Science][Medline]
  2. Gualillo O, et al. One ancestor, several peptides post-translational modifications of preproghrelin generate several peptides with antithetical effects. Mol. Cell. Endocrinol. (2006) 256:1–8.[CrossRef][Web of Science][Medline]
  3. Zhang JV, et al. Obestatin, a peptide encoded by the ghrelin gene, opposes ghrelin's effects on food intake. Science (2005) 310:996–999.[Abstract/Free Full Text]
  4. Howard AD, et al. A receptor in pituitary and hypothalamus that functions in growth hormone release. Science (1996) 273:974–977.[Abstract]
  5. van der Lely AJ, et al. Biological, physiological, pathophysiological, and pharmacological aspects of ghrelin. Endocr. Rev. (2004) 25:426–457.[Abstract/Free Full Text]
  6. Wren AM, et al. Ghrelin enhances appetite and increases food intake in humans. J. Clin. Endocrinol. Metab. (2001) 86:5992.[Abstract/Free Full Text]
  7. Nakazato M, et al. A role for ghrelin in the central regulation of feeding. Nature (2001) 409:194–198.[CrossRef][Web of Science][Medline]
  8. Toshinai K, et al. Des-acyl ghrelin induces food intake by a mechanism independent of the growth hormone secretagogue receptor. Endocrinology (2006) 147:2306–2314.
  9. Baldanzi G, et al. Ghrelin and des-acyl ghrelin inhibit cell death in cardiomyocytes and endothelial cells through ERK1/2 and PI 3-kinase/AKT. J. Cell Biol. (2002) 159:1029–1037.[Abstract/Free Full Text]
  10. Cassoni P, et al. Expression of ghrelin and biological activity of specific receptors for ghrelin and des-acyl ghrelin in human prostate neoplasms and related cell lines. Eur. J. Endocrinol. (2004) 150:173–184.[Abstract]
  11. Jeffery PL, et al. The potential autocrine/paracrine roles of ghrelin and its receptor in hormone-dependent cancer. Cytokine Growth Factor Rev. (2003) 14:113–122.[CrossRef][Web of Science][Medline]
  12. Yeh AH, et al. Ghrelin and a novel preproghrelin isoform are highly expressed in prostate cancer and ghrelin activates mitogen-activated protein kinase in prostate cancer. Clin. Cancer Res. (2005) 11:8295–8303.[Abstract/Free Full Text]
  13. Gnanapavan S, et al. The tissue distribution of the mRNA of ghrelin and subtypes of its receptor, GHS-R, in humans. J. Clin. Endocrinol. Metab. (2002) 87:2988.[Abstract/Free Full Text]
  14. Kierson JA, et al. Ghrelin and cholecystokinin in term and preterm human breast milk. Acta Paediatr. (2006) 95:991–995.[CrossRef][Web of Science][Medline]
  15. Cassoni P, et al. Identification, characterization, and biological activity of specific receptors for natural (ghrelin) and synthetic growth hormone secretagogues and analogs in human breast carcinomas and cell lines. J. Clin. Endocrinol. Metab. (2001) 86:1738–1745.[Abstract/Free Full Text]
  16. Jeffery PL, et al. Expression and function of the ghrelin axis, including a novel preproghrelin isoform, in human breast cancer tissues and cell lines. Endocr. Relat. Cancer (2005) 12:839–850.[Abstract/Free Full Text]
  17. Wagner K, et al. Polymorphisms in genes involved in GH1 release and their association with breast cancer risk. Carcinogenesis (2006) 27:1867–1875.[Abstract/Free Full Text]
  18. Laban C, et al. The GH-IGF-I axis and breast cancer. Trends Endocrinol. Metab. (2003) 14:28–34.[CrossRef][Web of Science][Medline]
  19. Fletcher O, et al. Polymorphisms and circulating levels in the insulin-like growth factor system and risk of breast cancer: a systematic review. Cancer Epidemiol. Biomarkers Prev. (2005) 14:2–19.[Abstract/Free Full Text]
  20. Rinaldi S, et al. IGF-I, IGFBP-3 and breast cancer risk in women: the European Prospective Investigation into Cancer and Nutrition (EPIC). Endocr. Relat. Cancer (2006) 13:593–605.[Abstract/Free Full Text]
  21. Whatmore AJ, et al. Ghrelin concentrations in healthy children and adolescents. Clin. Endocrinol. (Oxf) (2003) 59:649–654.[CrossRef][Medline]
  22. Bellone S, et al. Circulating ghrelin levels as function of gender, pubertal status and adiposity in childhood. J. Endocrinol. Invest. (2002) 25:RC13–RC15.[Web of Science][Medline]
  23. Bellone S, et al. Circulating ghrelin levels in newborns are not associated to gender, body weight and hormonal parameters but depend on the type of delivery. J. Endocrinol. Invest. (2003) 26:RC9–R11.[Web of Science][Medline]
  24. Kitamura S, et al. Ghrelin concentration in cord and neonatal blood: relation to fetal growth and energy balance. J. Clin. Endocrinol. Metab. (2003) 88:5473–5477.[Abstract/Free Full Text]
  25. Camurdan MO, et al. Serum ghrelin, IGF-I and IGFBP-3 levels in children with normal variant short stature. Endocr. J (2006) 53:479–484.[CrossRef][Web of Science][Medline]
  26. Poykko SM, et al. The negative association between plasma ghrelin and IGF-I is modified by obesity, insulin resistance and type 2 diabetes. Diabetologia (2005) 48:309–316.[CrossRef][Web of Science][Medline]
  27. Vivenza D, et al. Ghrelin gene polymorphisms and ghrelin, insulin, IGF-I, leptin and anthropometric data in children and adolescents. Eur. J. Endocrinol. (2004) 151:127–133.[Abstract]
  28. Vartiainen J, et al. Sequencing analysis of the ghrelin receptor (growth hormone secretagogue receptor type 1a) gene. Eur. J. Endocrinol. (2004) 150:457–463.[Abstract]
  29. Ukkola O, et al. Role of ghrelin polymorphisms in obesity based on three different studies. Obes. Res. (2002) 10:782–791.[Web of Science][Medline]
  30. Key TJ, et al. Epidemiology of breast cancer. Lancet Oncol. (2001) 2:133–140.[CrossRef][Medline]
  31. Wren AM, et al. The novel hypothalamic peptide ghrelin stimulates food intake and growth hormone secretion. Endocrinology (2000) 141:4325–4328.[Abstract/Free Full Text]
  32. Shintani M, et al. Ghrelin, an endogenous growth hormone secretagogue, is a novel orexigenic peptide that antagonizes leptin action through the activation of hypothalamic neuropeptide Y/Y1 receptor pathway. Diabetes (2001) 50:227–232.[Abstract/Free Full Text]
  33. Tschop M, et al. Ghrelin induces adiposity in rodents. Nature (2000) 407:908–913.[CrossRef][Web of Science][Medline]
  34. Wren AM, et al. Ghrelin causes hyperphagia and obesity in rats. Diabetes (2001) 50:2540–2547.[Abstract/Free Full Text]
  35. Tschop M, et al. Circulating ghrelin levels are decreased in human obesity. Diabetes (2001) 50:707–709.[Abstract/Free Full Text]
  36. Ravussin E, et al. Plasma ghrelin concentration and energy balance: overfeeding and negative energy balance studies in twins. J. Clin. Endocrinol. Metab. (2001) 86:4547–4551.[Abstract/Free Full Text]
  37. Shiiya T, et al. Plasma ghrelin levels in lean and obese humans and the effect of glucose on ghrelin secretion. J. Clin. Endocrinol. Metab. (2002) 87:240–244.[Abstract/Free Full Text]
  38. Rigamonti AE, et al. Plasma ghrelin concentrations in elderly subjects: comparison with anorexic and obese patients. J. Endocrinol. (2002) 175:R1–R5.[Abstract]
  39. Couce ME, et al. Potential role of hypothalamic ghrelin in the pathogenesis of human obesity. J. Endocrinol. Invest. (2006) 29:599–605.[Web of Science][Medline]
  40. Kissebah AH, et al. Quantitative trait loci on chromosomes 3 and 17 influence phenotypes of the metabolic syndrome. Proc. Natl Acad. Sci. USA (2000) 97:14478–14483.[Abstract/Free Full Text]
  41. Wu X, et al. A combined analysis of genomewide linkage scans for body mass index from the National Heart, Lung, and Blood Institute Family Blood Pressure Program. Am. J. Hum. Genet. (2002) 70:1247–1256.[CrossRef][Web of Science][Medline]
  42. Ukkola O, et al. Mutations in the preproghrelin/ghrelin gene associated with obesity in humans. J. Clin. Endocrinol. Metab. (2001) 86:3996–3999.[Abstract/Free Full Text]
  43. Hinney A, et al. Ghrelin gene: identification of missense variants and a frameshift mutation in extremely obese children and adolescents and healthy normal weight students. J. Clin. Endocrinol. Metab. (2002) 87:2716.[Abstract/Free Full Text]
  44. Miraglia del Giudice E, et al. Molecular screening of the ghrelin gene in Italian obese children: the Leu72Met variant is associated with an earlier onset of obesity. Int. J. Obes. Relat. Metab. Disord. (2004) 28:447–450.[CrossRef][Web of Science][Medline]
  45. Larsen LH, et al. Mutation analysis of the preproghrelin gene: no association with obesity and type 2 diabetes. Clin. Biochem. (2005) 38:420–424.[CrossRef][Web of Science][Medline]
  46. Bing C, et al. Large-scale studies of the Leu72Met polymorphism of the ghrelin gene in relation to the metabolic syndrome and associated quantitative traits. Diabet. Med. (2005) 22:1157–1160.[CrossRef][Web of Science][Medline]
  47. Wang HJ, et al. Ghrelin receptor gene: identification of several sequence variants in extremely obese children and adolescents, healthy normal-weight and underweight students, and children with short normal stature. J. Clin. Endocrinol. Metab. (2004) 89:157–162.[Abstract/Free Full Text]
  48. Baessler A, et al. Genetic linkage and association of the growth hormone secretagogue receptor (ghrelin receptor) gene in human obesity. Diabetes (2005) 54:259–267.[Abstract/Free Full Text]
  49. Riboli E, et al. European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr. (2002) 5:1113–1124.[CrossRef][Web of Science][Medline]
  50. Bingham S, et al. Diet and cancer—the European Prospective Investigation into Cancer and Nutrition. Nat. Rev. Cancer (2004) 4:206–215.[CrossRef][Web of Science][Medline]
  51. Kaaks R, et al. Serum sex steroids in premenopausal women and breast cancer risk within the European Prospective Investigation into Cancer and Nutrition (EPIC). J. Natl Cancer Inst. (2005) 97:755–765.[Abstract/Free Full Text]
  52. Carlson CS, et al. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am. J. Hum. Genet. (2004) 74:106–120.[CrossRef][Web of Science][Medline]
  53. Kraemer RR, et al. Rigorous running increases growth hormone and insulin-like growth factor-I without altering ghrelin. Exp. Biol. Med. (Maywood) (2004) 229:240–246.[Abstract/Free Full Text]
  54. Jarkovska Z, et al. Plasma levels of total and active ghrelin in acromegaly and growth hormone deficiency. Physiol. Res. (2006) 55:175–181.[Web of Science][Medline]
  55. Korbonits M, et al. A variation in the ghrelin gene increases weight and decreases insulin secretion in tall, obese children. J. Clin. Endocrinol. Metab. (2002) 87:4005–4008.[Abstract/Free Full Text]
  56. Jo DS, et al. Preproghrelin Leu72Met polymorphism in obese Korean children. J. Pediatr. Endocrinol. Metab. (2005) 18:1083–1086.[Web of Science][Medline]
  57. Ando T, et al. Variations in the preproghrelin gene correlate with higher body mass index, fat mass, and body dissatisfaction in young Japanese women. Am. J. Clin. Nutr. (2007) 86:25–32.[Abstract/Free Full Text]
  58. Stram DO, et al. Choosing haplotype-tagging SNPs based on unphased genotype data using a preliminary sample of unrelated subjects with an example from the Multiethnic Cohort Study. Hum. Hered. (2003) 55:27–36.[CrossRef][Web of Science][Medline]
  59. Wacholder S, et al. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J. Natl Cancer Inst. (2004) 96:434–442.[Abstract/Free Full Text]
Received January 9, 2008; revised March 3, 2008; accepted March 19, 2008.


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