Skip to main content
  • AACR Publications
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

  • Register
  • Log in
Advertisement

Main menu

  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
    • Reviewing
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • CEBP Focus Archive
    • Meeting Abstracts
  • For Authors
    • Call for Papers
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • OnlineFirst
    • Editors' Picks
    • Citation
    • Author/Keyword
  • News
    • Cancer Discovery News
  • AACR Publications
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

User menu

  • Register
  • Log in

Search

  • Advanced search
Cancer Epidemiology, Biomarkers & Prevention
Cancer Epidemiology, Biomarkers & Prevention

Advanced Search

  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
    • Reviewing
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • CEBP Focus Archive
    • Meeting Abstracts
  • For Authors
    • Call for Papers
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • OnlineFirst
    • Editors' Picks
    • Citation
    • Author/Keyword
  • News
    • Cancer Discovery News
Research Articles

The Association of Gastric Cancer Risk with Plasma Folate, Cobalamin, and Methylenetetrahydrofolate Reductase Polymorphisms in the European Prospective Investigation into Cancer and Nutrition

Stein Emil Vollset, Jannicke Igland, Mazda Jenab, Åse Fredriksen, Klaus Meyer, Simone Eussen, Håkon K. Gjessing, Per Magne Ueland, Guillem Pera, Núria Sala, Antonio Agudo, Gabriel Capella, Giuseppe Del Giudice, Domenico Palli, Heiner Boeing, Cornelia Weikert, H. Bas Bueno-de-Mesquita, Fátima Carneiro, Valeria Pala, Paolo Vineis, Rosario Tumino, Salvatore Panico, Göran Berglund, Jonas Manjer, Roger Stenling, Göran Hallmans, Carmen Martínez, Miren Dorronsoro, Aurelio Barricarte, Carmen Navarro, José R. Quirós, Naomi Allen, Timothy J. Key, Sheila Bingham, Jakob Linseisen, Rudolf Kaaks, Kim Overvad, Anne Tjønneland, Frederike L. Büchner, Petra H.M. Peeters, Mattijs E. Numans, Françoise Clavel-Chapelon, Marie-Christine Boutron-Ruault, Antonia Trichopoulou, Eiliv Lund, Nadia Slimani, Pietro Ferrari, Elio Riboli and Carlos A. González
Stein Emil Vollset
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jannicke Igland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mazda Jenab
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Åse Fredriksen
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Klaus Meyer
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Simone Eussen
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Håkon K. Gjessing
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Per Magne Ueland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Guillem Pera
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Núria Sala
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Antonio Agudo
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gabriel Capella
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Giuseppe Del Giudice
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Domenico Palli
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Heiner Boeing
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Cornelia Weikert
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
H. Bas Bueno-de-Mesquita
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Fátima Carneiro
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Valeria Pala
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Paolo Vineis
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rosario Tumino
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Salvatore Panico
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Göran Berglund
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jonas Manjer
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Roger Stenling
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Göran Hallmans
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Carmen Martínez
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Miren Dorronsoro
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Aurelio Barricarte
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Carmen Navarro
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
José R. Quirós
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Naomi Allen
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Timothy J. Key
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sheila Bingham
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jakob Linseisen
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rudolf Kaaks
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kim Overvad
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anne Tjønneland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Frederike L. Büchner
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Petra H.M. Peeters
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mattijs E. Numans
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Françoise Clavel-Chapelon
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Marie-Christine Boutron-Ruault
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Antonia Trichopoulou
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Eiliv Lund
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nadia Slimani
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Pietro Ferrari
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Elio Riboli
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Carlos A. González
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
DOI: 10.1158/1055-9965.EPI-07-0256 Published November 2007
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Abstract

Previous studies have shown inconsistent associations of folate intake and polymorphisms of the methylenetetrahydrofolate reductase (MTHFR) gene with gastric cancer risk. Our nested case-control study within the European Prospective Investigation into Cancer and Nutrition cohort is the first prospective study of blood folate levels and gastric cancer. Gastric cancer cases (n = 247) and controls (n = 631) were matched for study center, age, sex, and time of blood donation. Two common single nucleotide polymorphisms of the MTHFR gene were determined, as were plasma concentrations of folate, cobalamin (vitamin B12), total homocysteine, and methylmalonic acid (cobalamin deficiency marker) in prediagnostic plasma. Risk measures were calculated with conditional logistic regression. Although no relations were observed between plasma folate or total homocysteine concentrations and gastric cancer, we observed a trend toward lower risk of gastric cancer with increasing cobalamin concentrations (odds ratio, 0.79 per SD increase in cobalamin; P = 0.01). Further analyses showed that the inverse association between cobalamin and gastric cancer was confined to cancer cases with low pepsinogen A levels (marker of severe chronic atrophic gastritis) at the time of blood sampling. The 677 C→T MTHFR polymorphism was not associated with gastric cancer, but we observed an increased risk with the variant genotype of the 1298 A→C polymorphism (odds ratio, 1.47 for CC versus AA; P = 0.04). In conclusion, we found no evidence of a role of folate in gastric cancer etiology. However, we observed increased gastric cancer risk at low cobalamin levels that was most likely due to compromised cobalamin status in atrophic gastritis preceding gastric cancer. (Cancer Epidemiol Biomarkers Prev 2007;16(11):2416–24)

  • gastrointestinal cancers: stomach
  • diet and cancer
  • serum biomarkers of endogenous exposures (hormones, growth factors, etc.)
  • epidemiology
  • polymorphisms in genes that modify dietary exposures

Introduction

Gastric cancer is second only to lung cancer as a cause of worldwide cancer mortality (1). The estimated number of annual deaths worldwide is 850,000 (12% of all cancer deaths; ref. 1). The occurrence of gastric cancer has declined in industrialized countries over the past century but is still a major cause of cancer mortality in many developing countries (2). Although Helicobacter pylori (Hp) has been identified as a major cause of gastric cancer (3-5), diet and food preservation methods are still likely to modulate cancer occurrence (6, 7). The overall evidence from observational studies suggests a protective effect of, particularly, fruit, but also of vegetables (rich source of folate), on gastric cancer (6, 8). This evidence is weaker and not always statistically significant in recent meta-analyses of cohort studies (6, 8). Case-control studies with specific results on folate intake and gastric cancer risk suggest a protective role in some (9-13) but not all (14-16). A meta-analysis (17) showed a statistically nonsignificant weak protective effect of folate intake on gastric cancer risk among case-control studies, whereas two prospective studies did not observe any association (18, 19).

The evidence for a role of folate in cancer etiology has been strengthened by observations of a modulating role of the methylenetetrahydrofolate reductase (MTHFR) 677 C→T polymorphism in colorectal neoplasia (20, 21). The MTHFR enzyme activity, which is reduced in the TT genotype, affects folate distribution between separate folate species for nucleotide synthesis and DNA methylation (20, 22, 23). Notably, a series of case-control studies have shown that the variant TT genotype is a strong risk factor for gastric cancer in Chinese (24-28), Italian (29), and Mexican (30) populations but not in Korean (31), German (32), or Polish (33) populations. A meta-analysis recently summarized these studies and concluded that the relationship between MTHFR 677 C→T polymorphism and gastric cancer was present in East Asian but not in the other studied populations (34).

It is a weakness of previous studies on folate and gastric cancer that none were based on folate measurements in blood samples taken before the diagnosis of gastric cancer. Within the EurGast collaboration (35-39) of the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (40, 41), we had the opportunity to conduct the first prospective study on gastric cancer risk and folate levels measured in blood. Additionally, we included measurements of plasma cobalamin, total homocysteine (tHcy), methylmalonic acid (MMA), and the two common MTHFR single nucleotide polymorphisms (SNP; 677 C→T and 1298 A→C) to provide a more comprehensive assessment of the role of B vitamins in gastric cancer.

Materials and Methods

Study Population and Collection of Blood Samples

The design and methods of the EPIC study have been previously described in detail (40, 42). Briefly, the EPIC cohort consists of 23 centers in 10 European countries (Denmark, France, Greece, Germany, Italy, the Netherlands, Norway, Spain, Sweden, and United Kingdom). Between 1992 and 1998, country-specific dietary questionnaires, standardized lifestyle and personal history questionnaires, anthropometric data, and blood samples were collected from most participants.

The present study includes gastric cancer cases who were diagnosed after blood collection and matched control subjects from all EPIC countries except Norway. In each of the recruitment centers, both fasting and nonfasting blood samples of at least 30 mL were drawn from all participants and stored at 5°C to 10°C protected from light and transported to local laboratories for processing and aliquoting as previously described (40, 42). The only exception is the EPIC-Oxford center where blood samples were collected from a network of general practitioners and transported to a central laboratory in Oxford via mail. Although protected from light, the whole blood samples were exposed to ambient temperatures for up to 48 h. As tHcy and folate measurements are compromised by such handling, EPIC-Oxford samples were excluded from the present analyses (three cases and nine controls).

In all countries, except Denmark and Sweden, blood was separated into 0.5 mL fractions (serum, plasma, red cells, and buffy coat for DNA extraction). Each fraction was placed into straws, which were heat sealed and stored at ultralow temperatures (−196°C) in liquid nitrogen. One half of all aliquots were stored at the local study center and the other half in the central EPIC biorepository at the IARC (Lyon, France). In Denmark, blood fraction aliquots of 1.0 mL were stored locally in Nunc tubes at −150°C under nitrogen vapor. In Sweden, samples were stored in −70°C freezers.

Follow-up for Cancer Incidence

In EPIC, follow-up is based on population cancer registries (Denmark, Italy, the Netherlands, Norway, Spain, Sweden, and United Kingdom) and other methods, such as health insurance records, pathology registries, and active contact of study subjects or next of kin (France, Germany, and Greece). The follow-up period for the present study was for data reports received at IARC to the end of October 2002, representing complete follow-ups until either December 2000 or December 2001 for all centers using cancer registry data and until 2002 for France, Germany, and Greece. Cancers of the stomach included cancers coded as C16 (10th Revision of the International Statistical Classification of Diseases and Related Health Problems). The diagnosis, tumor site classification, and morphology (according to International Classification of Diseases for Oncology, second edition and Lauren classifications) of each identified cancer were confirmed and validated by an independent panel of pathologists with a representative from each EPIC country and a coordinator (43). The pathologist panel reviewed original histologic slides and/or recuts from the paraffin blocks and original histopathology reports that were provided by each EPIC center.

Nested Case-Control Study Design and Selection of Study Subjects

Incident gastric cancer cases were cohort members who developed cancer after recruitment into EPIC. The present study includes a total of 233 gastric adenocarcinomas and 14 adenocarcinomas of the gastroesophageal junction. These 247 cases are grouped together and referred to as gastric cancer. The cancer cases were divided into three groups by anatomic subsite: (a) tumors originating from the gastric cardia (n = 64 cases), combining tumors that reached the gastroesophageal junction, either crossing it or from below (all 14 gastroesophageal junction cancers) or not; (b) noncardial tumors (n = 112 cases), grouping cases from other sites in the stomach; and (c) tumors from unknown or mixed sites (n = 71 cases). When divided by histologic subtype, of the 247 gastric cancer cases, 85 were classified as diffuse and 85 as intestinal according to the Lauren classification. The remainder (n = 77 cases) were of unknown or mixed histologic types. All gastric lymphomas, gastric stump cancers, other gastric nonadenocarcinoma, esophageal nonadenocarcinomas, and otherwise unspecified malignant neoplasms of the stomach were excluded from this analysis. For each identified cancer case, control subjects with available blood samples were randomly selected from all cohort members who were alive and free of cancer (except nonmelanoma skin cancer) at the time of diagnosis of the case patient. A total of 631 controls were matched by gender, age group (±2.5 years), study center, and date of blood sample collection (±45 days). This study was approved by the Ethical Review Board of the IARC and those of all individual EPIC centers.

Laboratory Measurements

Plasma tHcy and MMA (marker for cobalamin status) were measured with a method based on methylchloroformate derivatization and gas chromatography-mass spectrometry (44). Plasma folate was determined by a Lactobacillus casei microbiological assay (45) and plasma vitamin B12 by a Lactobacillus leichmannii microbiological assay (46). Both the folate and vitamin B12 assays were adapted to a microtiter plate format and carried out by a robotic workstation (Microlab AT plus 2, Hamilton Bonaduz AG). The MTHFR 677 C→T (rs#1801133) and 1298 A→C (rs#1801131) polymorphisms were determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (47) in Bergen. In Barcelona, the same two polymorphisms were genotyped in a LightCycler 2.0 instrument (Roche Diagnostics) by real-time multiplex PCR and melting curve analysis of a fluorescently labeled sensor probe specific for each analyzed variant. Primers and probes (48) were synthesized by TibMolBiol. PCR mixtures and cycling conditions were essentially as described (49), with the exception that primers and probes for the two polymorphisms were mixed in the same reaction capillary (50).

When one of the Swedish centers was excluded because of small amount of DNA provided for matrix-assisted laser desorption/ionization time-of-flight analyses, the agreement between the Bergen and Barcelona laboratories were 99.7% to 99.9%. We gave preference to the genotyping results from the Barcelona laboratory in the three instances of discrepancies and for the Swedish center. Quantification of anti-Hp antibodies in stored plasma samples was done using an ELISA technique using incubation with lysates of the CCUG Hp strain (51). Pepsinogen A (PGA) was assayed in plasma by a commercial microplate-based quantitative ELISA kit (Biohit). Severe chronic atrophic gastritis was serologically defined as a PGA circulating level <22 μg/L (51).

We assessed correlations between estimated dietary folate and cobalamin intakes (mean levels were 268.3 and 7.3 μg/d) and the measured vitamin and marker levels among the controls. Spearman correlations (corrected for sex, age, energy intake, and country) between dietary folate and plasma folate and tHcy were 0.220 (P < 0.0001) and −0.124 (P = 0.003), respectively. Correlations between estimated intake of vitamin B12 and plasma cobalamin and MMA were 0.131 (P = 0.002) and −0.130 (P = 0.002), respectively.

Statistical Methods

Relative risks [odds ratios (OR)] and 95% confidence intervals (95% CI) for the gastric cancer cases and subgroups in relation to plasma concentrations of tHcy, folate, cobalamin, and MMA were calculated by conditional logistic regression using the SAS LOGISTIC procedure (SAS statistical software, version 9; SAS Institute), stratified by the case-control set. Risk estimates were computed with adjustments for total energy intake (in quartiles), Hp infection status, and smoking (variable categories: nonsmokers, ex-smokers, current smokers, and missing). These additional adjustments had only minor impact on the vitamin-gastric cancer associations. We also carried out analyses with additional adjustment for total carotenoids (38) and vitamin C (39).

The plasma analyte concentrations were examined by quartile categories with cut points based on the distribution of each specific analyte in all 631 controls combined. Models similar to the above were run with the logarithm of plasma measurements included in the model as continuous variables. In these models, the relative risk and 95% CI for each vitamin or marker were then calculated as the risk for a change in the plasma level by 1 SD. All the above models were run separately and tested for effect modification for each anatomic subsite (cardia, noncardia), histologic subtype (diffuse, intestinal), European region [northern-central, south (Italy, Greece, and Spain)], time from blood donation to cancer diagnosis (<2 years versus ≥2 years), age, sex, and Hp status.

The relationships between the two MTHFR polymorphisms and gastric cancer were studied with conditional logistic regression. The risk estimates were calculated with the wild-type as the reference category. A trend test with equally spaced integer weights for the genotypes was used to summarize the effect of each polymorphism. Additionally, we studied the two polymorphisms, which are in strong linkage disequilibrium (D′ = 0.999, r = −0.49), using a haplotype approach. Our haplotype notation gives the 677-allele first and then the 1298 allele (e.g., c-a). Overall, both polymorphisms were in Hardy-Weinberg equilibrium (P = 0.25 for the MTHFR 677 C→T and P = 0.61 for the 1298 A→C polymorphism). Haplotype frequencies were estimated by assuming Hardy-Weinberg equilibrium and using the expectation-maximization algorithm (52). The t-c haplotype was extremely rare and therefore excluded from the analysis. Ignoring the t-c haplotype, the three remaining haplotypes could be reconstructed with certainty in both cases and controls. The three haplotypes (single or double copies) were used as exposures in a conditional logistic regression model. Because all individuals carry two haplotypes, one might see different types of interactions between the two haplotypes (i.e., recessive or dominant effects of one haplotype relative to another). To give a comprehensive presentation of effects, we used a “reciprocal reference”; that is, we computed the effect of each specific haplotype relative to the remaining haplotypes, weighting according to the population haplotype frequencies (53). This means that, for any haplotype, the single-dose effect measures the relative increase (or decrease) in risk obtained by replacing any haplotype in an individual with this specific haplotype; the double-dose estimate is the effect of replacing both with this haplotype.

Effect modification of the folate-gastric cancer associations by the two MTHFR SNPs was studied with conditional logistic regression but stratifying on country instead of the matched sets and with age and sex as covariates.

Results

We studied 247 gastric cancer cases and 631 matched controls. Table 1 shows that the mean age at diagnosis was 62 years and that 40.5% of the cancer cases were women. Mean follow-up between blood donation and diagnosis of cancer was 3.3 years. Cancer cases had higher prevalence of positive Hp infection status and more smokers than their matched controls. Table 2 shows levels of folate, cobalamin, tHcy, and MMA among the 631 controls in categories by sex, age, smoking habits, European region, and the MTHFR polymorphisms. Whereas folate levels were lower in smokers (P = 0.0005), tHcy levels were higher in men (P = 0.002), in individuals >60 years of age (P < 0.0001), and in persons with the variant genotype (TT) of the 677 C→T polymorphism (P = 0.002). Cobalamin levels were higher in women (P = 0.02). MMA levels were lower in smokers (P = 0.002) and in controls <60 years of age (P = 0.005). Among the 247 cases, mean levels (SD) of the vitamins and markers were 13.9 (7.5) for folate, 10.7 (4.2) for tHcy, 284 (110) for cobalamin, and 0.203 (0.114) for MMA.

View this table:
  • View inline
  • View popup
Table 1.

Baseline characteristics and description of the study population of cases and controls

View this table:
  • View inline
  • View popup
Table 2.

B vitamin and marker levels [mean (SD)] in all control subjects (n = 631) by selected characteristics

B Vitamins and Gastric Cancer

We used quartiles as well as continuous representations of the B vitamin and marker levels to study associations with gastric cancer (Tables 3 and 4 ). For folate and tHcy, there was no evidence of associations with gastric cancer. For cobalamin, the conditional logistic regression analyses indicated a negative association with gastric cancer (OR, 0.79 per SD increase; P = 0.01). For MMA, the association with cancer was positive and weaker (OR, 1.17 per SD increase; P = 0.06). Further adjustment for total carotenoids or vitamin C levels did not change the results (data not shown).

View this table:
  • View inline
  • View popup
Table 3.

ORs (95% CI) for gastric cancer (n = 245) by quartiles and per SD (log scale) of B vitamins and markers

View this table:
  • View inline
  • View popup
Table 4.

ORs (95% CI) for gastric cancer for quartiles of B vitamins and markers by anatomic subsite, histologic subtype, and European region

We investigated the associations between cobalamin and MMA and gastric cancer further by analyzing separately cases with and without serologic evidence of severe atrophic gastritis (low levels of PGA). Table 3 shows that the associations between cobalamin and MMA and gastric cancer were confined to cases with low levels of pepsinogen. In this subgroup (44 cases), there were strong and highly significant dose-response relationships between gastric cancer and cobalamin (OR, 0.38 per SD increase; P = 0.001) and MMA (OR, 2.43 per SD increase; P < 0.001).

We further studied these cancer-metabolite associations in subgroups by anatomic subsite (cardia versus noncardia), geographic region, and histologic subtype (diffuse versus intestinal; Table 4). Folate showed opposite trends with respect to gastric cancer in cardia versus noncardia (Peffect modification = 0.04), but none of the subgroup effects were statistically significant. There was no effect modification of the cancer-metabolite associations by European region (northern-central versus southern Europe), sex, age (<60 or ≥60 years), or Hp infection status.

When we excluded the cancer cases with <2 years from blood sample to cancer diagnosis, the results were similar to the overall results given in Table 3.

MTHFR Polymorphisms and Gastric Cancer

Table 5 shows that the homozygote variant genotype of the 677 C→T SNP of the MTHFR gene was more prevalent in southern Europe compared with central/northern Europe (21% versus 10% among controls), whereas no important difference in prevalence was seen for the CC genotype of the 1298 A→ C polymorphism (8% versus 9%). Although we observed no association between the 677 C→T polymorphism and gastric cancer, there was an association of borderline significance (P for trend = 0.04) between the 1298 A→ C polymorphism and gastric cancer (OR, 1.47 comparing the CC genotype with the wild-type). Haplotype analysis confirmed that the risk increase was confined to the 1298 A→C polymorphism. Motivated by the higher prevalence of the 677 T allele in southern Europe, we did these analyses separately for European region, but we did not observe important differences in the cancer-SNP association.

View this table:
  • View inline
  • View popup
Table 5.

ORs (95% CI) for gastric cancer for the 677 C→T and 1298 A→C MTHFR SNPs and haplotypes by geographic grouping

Furthermore, we studied the association between plasma folate and gastric cancer in different genotypes of the two MTHFR SNPs. We found no evidence of effect modification of the folate-cancer association by the two SNPs (Table 6 ).

View this table:
  • View inline
  • View popup
Table 6.

ORs (95% CI) for gastric cancer (n = 245) by plasma folate quartiles (Q1-Q4) and per SD (log scale) difference in folate by MTHFR genotypes

Discussion

In this large population-based European nested case-control study of gastric cancer, we found no evidence of a relationship between plasma folate and cancer but an inverse association of cobalamin to cancer. MMA (which is elevated under cobalamin deficiency) showed a positive association with gastric cancer, whereas plasma tHcy was unrelated to cancer. Of the two common SNPs of the MTHFR gene, only the 1298 A→C SNP was associated with gastric cancer.

Strengths and Limitations

In contrast to previous prospective studies on folate and gastric cancer risk, we measured folate status in blood (plasma). A further strength of our study is the nested case-control design where cases are incident cases in a large multicountry cohort. Controls were matched to each case by age and sex and time and place of blood donation. In the EPIC study, extensive lifestyle and other relevant information and blood measurements have been collected on each individual. This facilitated comprehensive control of confounding and assessment of effect modification. All biochemical analyses were carried out in one laboratory and the presence of known relationships between tHcy and age, sex, smoking, and the 677 C→T MTHFR polymorphism (54) confirmed the data integrity of this multicenter study with local sample handling.

Gastric cancer is a complex disease with etiologic heterogeneity (55), and the number of cases in each anatomic subsite and histologic subtype was small and insufficient for precise estimation of risk measures in these subgroups. Nevertheless, with close to 250 cases, this is a large study. A short duration of follow-up, with a mean of ∼3 years, between blood donation to cancer diagnosis may have biased the observed results. Experimental data suggest that folate may both prevent carcinogenesis and promote cancer growth, with the possible implication that it is folate status in early and young adult life that protects against cancer (56). Thus, it remains a possibility that vitamin status at younger ages may be a better predictor of later cancer, as progression to cancer through severe chronic atrophic gastritis (57) may disturb intestinal vitamin [particularly cobalamin (58)] absorption in the last year(s) before diagnosis. A null finding in the present study may represent the sum of two such opposite effects of folate. However, to disentangle such complex and time-dependent relationships is beyond the scope of our study and would require much longer follow-up and serial measurements of vitamins and its markers.

Although nutrient blood measurements escape methodologic problems with diet questionnaires and food composition tables, a limitation of the former is that blood levels may imperfectly reflect intake or total body stores of a nutrient and that handling of blood samples may affect measured nutrient levels.

Interpretation

One motivation for studies of folate and gastric cancer has been the observed lower risk for such cancers in individuals with high intake of fruit and vegetables (6, 8). Folate is one of a multitude of potentially cancer chemopreventive compounds found in fruit and vegetables that could contribute to the protective effect. In a recent analysis of the EPIC-EurGast study (37), and also according to the results from other cohort studies (6, 8), no strong overall evidence was seen for a protective role of intake of fruit and vegetables in gastric cancer. The results from the EurGast study showed protective role of vegetable intake in the intestinal type of gastric cancer. Additionally, the EurGast analyses suggested a protective role of citrus fruit consumption in cardia gastric cancer (37). The lack of associations with prediagnostic plasma levels of folate and tHcy in the current nested case-control study is in line with these results and two prospective studies (17-19) on folate intake and gastric cancer risk that did not observe any association with dietary folate intake and gastric cancer.

We observed an inverse association between gastric cancer and cobalamin levels and a positive association with the cobalamin deficiency marker MMA. Taking the short follow-up into account, this finding could partly be due to impaired cobalamin status in atrophic gastritis (58) that often precedes gastric cancer (57). Our data support this hypothesis as the associations between cobalamin and its marker MMA and gastric cancer were confined to the cancer cases with serologic evidence of severe chronic atrophic gastritis.

The study of SNPs with metabolic effects has been used as arguments for a causal role of the affected metabolite in the disease under study (59). Both the 677 C→T and 1298 A→C SNPs of the MTHFR gene cause variant protein configurations and have metabolic effects on folate and tHcy levels, much stronger for the 677 C→T than for the 1298 A→C (despite the strong linkage disequilibrium close to 1; refs. 60, 61). The relationships of, particularly, the 677 C→T SNP with disease have been argued to imply causal roles for folate or homocysteine in disease etiology and progression (62, 63). Both the 677 C→T and the 1298 A→C SNPs have been extensively studied in case-control studies of pathologies as diverse as congenital malformations, cardiovascular disease, and many different cancers (20, 64). Generally, and also with respect to gastric cancer, most consistent associations with disease have been found for the 677 C→T SNP that also have more profound effects on plasma tHcy and folate levels (60). The number of controls in our study was too small to show the weaker relationships between the 1298 A→C polymorphism and folate and tHcy (60). A recent meta-analysis of the two MTHFR polymorphisms and gastric cancer showed that the T allele and TT genotype of the 677 SNP is a strong risk factor in East Asian (particularly Chinese) populations (34). No such relationship was found in European or other populations. The number of studies of gastric cancer and the 1298 A→C SNP was limited and allowed only to estimate its role in East Asian populations where it was observed to be a risk factor for gastric cancer. Thus, a null result for the 677 C→T SNP in our study of Europeans combined with a suggestion of an effect of the variant 1298 genotype on gastric cancer risk agrees well with the meta-analysis (34). Furthermore, we did not observe any evidence of effect modification by folate status on the MTHFR-cancer relationship as has been observed with colorectal cancer (63). Neither did additional analyses of cases originating from northern-central compared with southern Europe reveal any differential SNP-cancer relationships.

Conclusion

Although we found no evidence to support a role of folate in gastric cancer etiology, we observed increased gastric cancer risk at low cobalamin levels, most likely due to compromised cobalamin status in chronic atrophic gastritis that precedes gastric cancer. Of the two common SNPs of the MTHFR gene, the 1298 A→C variant genotype was associated with increased gastric cancer risk.

Acknowledgments

We thank Francisco Rico and Nadia García for their technical assistance in the genotyping analysis, the personnel of TibMolBiol (Berlin, Germany) for the design of the LightCycler PCR primers and hybridization probes, the members of the pathologist panel for their valuable work (Dr. Johan Offerhaus, Amsterdam, the Netherlands; Dr. Vicki Save, Cambridge, United Kingdom; Dr. Julio Torrado, San Sebastian, Spain; Dr. Gabriella Nesi, Firenze, Italy; Dr. U. Mahlke, Potsdam, Germany; Dr. Hendrik Bläker, Heildelberg; Germany; and Dr. Claus Fenger, Denmark), Dr. Dimitrious Roukos (Ioannina, Greece) for his contribution to the collection of pathologic material, and Catia Moutinho (Porto, Portugal) for her technical work in the preparation of pathologic material.

Footnotes

  • Grant support: European Commission FP5 project (QLG1-CT-2001-01049). 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 Center; German Federal Ministry of Education and Research; Danish Cancer Society; Health Research Fund (FIS) of the Spanish Ministry of Health (RCESP-C03/09); Red Temática de Investigación Cooperativa de Centros de Cáncer (C03/10); the participating regional governments and institutions of Spain; Cancer Research UK; Medical Research Council, United Kingdom; Stroke Association, United Kingdom; British Heart Foundation; Department of Health, United Kingdom; Food Standards Agency, United Kingdom; The Wellcome Trust, United Kingdom; Greek Ministry of Health; Greek Ministry of Education; Italian Association for Research on Cancer; Dutch Ministry of Public Health, Welfare and Sports; Dutch Ministry of Health; Dutch Prevention Funds; LK Research Funds; Dutch Zorg Onderzoek Nederland; World Cancer Research Fund; Swedish Cancer Society; Swedish Scientific Council; Regional Government of Skane, Sweden; Norwegian Cancer Society; and Foundation to Promote Research into Functional Vitamin B12 Deficiency, Norway. Some authors are partners of Environmental Cancer Risk, Nutrition and Individual Susceptibility, a network of excellence of the European Commission (6FP contract 513943).

  • The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

    • Accepted September 5, 2007.
    • Received March 21, 2007.
    • Revision received July 25, 2007.

References

  1. ↵
    World Health Organization. The World Health Report 2004. Geneva: WHO; 2004.
  2. ↵
    Verdecchia A, Mariotto A, Gatta G, Bustamante-Teixeira MT, Ajiki W. Comparison of stomach cancer incidence and survival in four continents. Eur J Cancer 2003;39:1603–9.
    OpenUrlCrossRefPubMed
  3. ↵
    Nomura A, Stemmermann GN, Chyou PH, Kato I, Perez-Perez GI, Blaser MJ. Helicobacter pylori infection and gastric carcinoma among Japanese Americans in Hawaii. N Engl J Med 1991;325:1132–6.
    OpenUrlPubMed
  4. Parsonnet J, Friedman GD, Vandersteen DP, et al. Helicobacter pylori infection and the risk of gastric carcinoma. N Engl J Med 1991;325:1127–31.
    OpenUrlPubMed
  5. ↵
    Helicobacter and Cancer Collaborative Group. Gastric cancer and Helicobacter pylori: a combined analysis of 12 case control studies nested within prospective cohorts. Gut 2001;49:347–53.
    OpenUrlAbstract/FREE Full Text
  6. ↵
    Riboli E, Norat T. Epidemiologic evidence of the protective effect of fruit and vegetables on cancer risk. Am J Clin Nutr 2003;78:559–69S.
    OpenUrl
  7. ↵
    Jakszyn P, Gonzalez CA. Nitrosamine and related food intake and gastric and oesophageal cancer risk: a systematic review of the epidemiological evidence. World J Gastroenterol 2006;12:4296–303.
    OpenUrlPubMed
  8. ↵
    International Agency for Research on Cancer. Fruit and vegetables. Lyon (France): IARC Press; 2003.
  9. ↵
    Gonzalez CA, Riboli E, Badosa J, et al. Nutritional factors and gastric cancer in Spain. Am J Epidemiol 1994;139:466–73.
    OpenUrlAbstract/FREE Full Text
  10. Mayne ST, Risch HA, Dubrow R, et al. Nutrient intake and risk of subtypes of esophageal and gastric cancer. Cancer Epidemiol Biomarkers Prev 2001;10:1055–62.
    OpenUrlAbstract/FREE Full Text
  11. Harrison LE, Zhang ZF, Karpeh MS, Sun M, Kurtz RC. The role of dietary factors in the intestinal and diffuse histologic subtypes of gastric adenocarcinoma: a case-control study in the U.S. Cancer 1997;80:1021–8.
    OpenUrlCrossRefPubMed
  12. Zhang ZF, Kurtz RC, Yu GP, et al. Adenocarcinomas of the esophagus and gastric cardia: the role of diet. Nutr Cancer 1997;27:298–309.
    OpenUrlPubMed
  13. ↵
    Nomura AM, Hankin JH, Kolonel LN, Wilkens LR, Goodman MT, Stemmermann GN. Case-control study of diet and other risk factors for gastric cancer in Hawaii (United States). Cancer Causes Control 2003;14:547–58.
    OpenUrlCrossRefPubMed
  14. ↵
    Lissowska J, Gail MH, Pee D, et al. Diet and stomach cancer risk in Warsaw, Poland. Nutr Cancer 2004;48:149–59.
    OpenUrlCrossRefPubMed
  15. Munoz N, Plummer M, Vivas J, et al. A case-control study of gastric cancer in Venezuela. Int J Cancer 2001;93:417–23.
    OpenUrlCrossRefPubMed
  16. ↵
    La Vecchia C, Ferraroni M, D'Avanzo B, Decarli A, Franceschi S. Selected micronutrient intake and the risk of gastric cancer. Cancer Epidemiol Biomarkers Prev 1994;3:393–8.
    OpenUrlAbstract
  17. ↵
    Larsson SC, Giovannucci E, Wolk A. Folate intake, MTHFR polymorphisms, and risk of esophageal, gastric, and pancreatic cancer: a meta-analysis. Gastroenterology 2006;131:1271–83.
    OpenUrlCrossRefPubMed
  18. ↵
    Botterweck AA, van den Brandt PA, Goldbohm RA. Vitamins, carotenoids, dietary fiber, and the risk of gastric carcinoma: results from a prospective study after 6.3 years of follow-up. Cancer 2000;88:737–48.
    OpenUrlCrossRefPubMed
  19. ↵
    Larsson SC, Giovannucci E, Wolk A. Folate intake and stomach cancer incidence in a prospective cohort of Swedish women. Cancer Epidemiol Biomarkers Prev 2006;15:1409–12.
    OpenUrlAbstract/FREE Full Text
  20. ↵
    Ueland PM, Hustad S, Schneede J, Refsum H, Vollset SE. Biological and clinical implications of the MTHFR C677T polymorphism. Trends Pharmacol Sci 2001;22:195–201.
    OpenUrlCrossRefPubMed
  21. ↵
    Sharp L, Little J. Polymorphisms in genes involved in folate metabolism and colorectal neoplasia: a HuGE review. Am J Epidemiol 2004;159:423–43.
    OpenUrlAbstract/FREE Full Text
  22. ↵
    Kim YI. Methylenetetrahydrofolate reductase polymorphisms, folate, and cancer risk: a paradigm of gene-nutrient interactions in carcinogenesis. Nutr Rev 2000;58:205–9.
    OpenUrlPubMed
  23. ↵
    Friso S, Choi SW, Girelli D, et al. A common mutation in the 5,10-methylenetetrahydrofolate reductase gene affects genomic DNA methylation through an interaction with folate status. Proc Natl Acad Sci U S A 2002;99:5606–11.
    OpenUrlAbstract/FREE Full Text
  24. ↵
    Miao X, Xing D, Tan W, Qi J, Lu W, Lin D. Susceptibility to gastric cardia adenocarcinoma and genetic polymorphisms in methylenetetrahydrofolate reductase in an at-risk Chinese population. Cancer Epidemiol Biomarkers Prev 2002;11:1454–8.
    OpenUrlAbstract/FREE Full Text
  25. Stolzenberg-Solomon RZ, Qiao YL, Abnet CC, et al. Esophageal and gastric cardia cancer risk and folate- and vitamin B(12)-related polymorphisms in Linxian, China. Cancer Epidemiol Biomarkers Prev 2003;12:1222–6.
    OpenUrlAbstract/FREE Full Text
  26. Shen H, Xu Y, Zheng Y, et al. Polymorphisms of 5,10-methylenetetrahydrofolate reductase and risk of gastric cancer in a Chinese population: a case-control study. Int J Cancer 2001;95:332–6.
    OpenUrlCrossRefPubMed
  27. Shen H, Newmann AS, Hu Z, et al. Methylenetetrahydrofolate reductase polymorphisms/haplotypes and risk of gastric cancer: a case-control analysis in China. Oncol Rep 2005;13:355–60.
    OpenUrlPubMed
  28. ↵
    Wang LD, Guo RF, Fan ZM, et al. Association of methylenetetrahydrofolate reductase and thymidylate synthase promoter polymorphisms with genetic susceptibility to esophageal and cardia cancer in a Chinese high-risk population. Dis Esophagus 2005;18:177–84.
    OpenUrlCrossRefPubMed
  29. ↵
    Graziano F, Kawakami K, Ruzzo A, et al. Methylenetetrahydrofolate reductase 677C/T gene polymorphism, gastric cancer susceptibility and genomic DNA hypomethylation in an at-risk Italian population. Int J Cancer 2006;118:628–32.
    OpenUrlCrossRefPubMed
  30. ↵
    Lacasana-Navarro M, Galvan-Portillo M, Chen J, Lopez-Cervantes M, Lopez-Carrillo L. Methylenetetrahydrofolate reductase 677C>T polymorphism and gastric cancer susceptibility in Mexico. Eur J Cancer 2006;42:528–33.
    OpenUrlCrossRefPubMed
  31. ↵
    Kim JK, Kim S, Han JH, et al. Polymorphisms of 5,10-methylenetetrahydrofolate reductase and risk of stomach cancer in a Korean population. Anticancer Res 2005;25:2249–52.
    OpenUrlAbstract/FREE Full Text
  32. ↵
    Sarbia M, Geddert H, Kiel S, et al. Methylenetetrahydrofolate reductase C677T polymorphism and risk of adenocarcinoma of the upper gastrointestinal tract. Scand J Gastroenterol 2005;40:109–11.
    OpenUrlCrossRefPubMed
  33. ↵
    Zhang FF, Terry MB, Hou L, et al. Genetic polymorphisms in folate metabolism and the risk of stomach cancer. Cancer Epidemiol Biomarkers Prev 2007;16:115–21.
    OpenUrlAbstract/FREE Full Text
  34. ↵
    Zintzaras E. Association of methylenetetrahydrofolate reductase (MTHFR) polymorphisms with genetic susceptibility to gastric cancer: a meta-analysis. J Hum Genet 2006;51:618–24.
    OpenUrlCrossRefPubMed
  35. ↵
    Gonzalez CA, Pera G, Agudo A, et al. Smoking and the risk of gastric cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC). Int J Cancer 2003;107:629–34.
    OpenUrlCrossRefPubMed
  36. Gonzalez CA, Jakszyn P, Pera G, et al. Meat intake and risk of stomach and esophageal adenocarcinoma within the European Prospective Investigation into Cancer and Nutrition (EPIC). J Natl Cancer Inst 2006;98:345–54.
    OpenUrlAbstract/FREE Full Text
  37. ↵
    Gonzalez CA, Pera G, Agudo A, et al. Fruit and vegetable intake and the risk of stomach and oesophagus adenocarcinoma in the European Prospective Investigation into Cancer and Nutrition (EPIC-EURGAST). Int J Cancer 2006;118:2559–66.
    OpenUrlCrossRefPubMed
  38. ↵
    Jenab M, Riboli E, Ferrari P, et al. Plasma and dietary carotenoid, retinol and tocopherol levels and the risk of gastric adenocarcinomas in the European prospective investigation into cancer and nutrition. Br J Cancer 2006;95:406–15.
    OpenUrlCrossRefPubMed
  39. ↵
    Jenab M, Riboli E, Ferrari P, et al. Plasma and dietary vitamin C levels and risk of gastric cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC-EURGAST). Carcinogenesis 2006;27:2250–7.
    OpenUrlAbstract/FREE Full Text
  40. ↵
    Riboli E, Hunt KJ, Slimani N, et al. European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr 2002;5:1113–24.
    OpenUrlCrossRefPubMed
  41. ↵
    Bingham S, Riboli E. Diet and cancer—the European Prospective Investigation into Cancer and Nutrition. Nat Rev Cancer 2004;4:206–15.
    OpenUrlCrossRefPubMed
  42. ↵
    Riboli E, Kaaks R. The EPIC Project: rationale and study design. European Prospective Investigation into Cancer and Nutrition. Int J Epidemiol 1997;26 Suppl 1:S6–14.
    OpenUrlAbstract/FREE Full Text
  43. ↵
    Carneiro F, Moutinho C, Pera G, et al. Pathology findings and validation of gastric and esophageal cancer cases in a European cohort (EPIC/EUR-GAST). Scand J Gastroenterol 2007;42:618–27.
    OpenUrlCrossRefPubMed
  44. ↵
    Windelberg A, Årseth O, Kvalheim G, Ueland PM. An automated assay for the determination of methylmalonic acid, total homocysteine and related amino acids in human serum or plasma using methylchloroformate derivatization and gas chromatography-mass spectrometry. Clin Chem 2005;51:2103–9.
    OpenUrlAbstract/FREE Full Text
  45. ↵
    Molloy AM, Scott JM. Microbiological assay for serum, plasma, and red cell folate using cryopreserved, microtiter plate method. Methods Enzymol 1997;281:43–53.
    OpenUrlCrossRefPubMed
  46. ↵
    Kelleher BP, Broin SD. Microbiological assay for vitamin B12 performed in 96-well microtitre plates. J Clin Pathol 1991;44:592–5.
    OpenUrlAbstract/FREE Full Text
  47. ↵
    Meyer K, Fredriksen A, Ueland PM. High-level multiplex genotyping of polymorphisms involved in folate or homocysteine metabolism by matrix-assisted laser desorption/ionization mass spectrometry. Clin Chem 2004;50:391–402.
    OpenUrlAbstract/FREE Full Text
  48. ↵
    von Ahsen N, Oellerich M, Schutz E. A method for homogeneous color-compensated genotyping of factor V (G1691A) and methylenetetrahydrofolate reductase (C677T) mutations using real-time multiplex fluorescence PCR. Clin Biochem 2000;33:535–9.
    OpenUrlCrossRefPubMed
  49. ↵
    Agudo A, Sala N, Pera G, et al. Polymorphisms in metabolic genes related to tobacco smoke and the risk of gastric cancer in the European prospective investigation into cancer and nutrition. Cancer Epidemiol Biomarkers Prev 2006;15:2427–34.
    OpenUrlAbstract/FREE Full Text
  50. ↵
    Nakamura S, Aoshima T, Ikeda M, Sekido Y, Shimokata K, Niwa T. Simultaneous detection of methylenetetrahydrofolate reductase gene polymorphisms, C677T and A1298C, by melting curve analysis with LightCycler. Anal Biochem 2002;306:340–3.
    OpenUrlCrossRefPubMed
  51. ↵
    Palli D, Masala G, Del Giudice G, et al. CagA+ Helicobacter pylori infection and gastric cancer risk in the EPIC-EURGAST study. Int J Cancer 2007;120:859–67.
    OpenUrlCrossRefPubMed
  52. ↵
    Ziegler A, König IR. A statistical approach to genetic epidemiology. Weinheim: Wiley-VCH Verlag; 2006.
  53. ↵
    Gjessing HK, Lie RT. Case-parent triads: estimating single- and double-dose effects of fetal and maternal disease gene haplotypes. Ann Hum Genet 2006;70:382–96.
    OpenUrlCrossRefPubMed
  54. ↵
    Refsum H, Ueland PM, Nygard O, Vollset SE. Homocysteine and cardiovascular disease. Annu Rev Med 1998;49:31–62.
    OpenUrlCrossRefPubMed
  55. ↵
    Correa P, Schneider BG. Etiology of gastric cancer: what is new? Cancer Epidemiol Biomarkers Prev 2005;14:1865–8.
    OpenUrlAbstract/FREE Full Text
  56. ↵
    Kim YI. Will mandatory folic acid fortification prevent or promote cancer? Am J Clin Nutr 2004;80:1123–8.
    OpenUrlAbstract/FREE Full Text
  57. ↵
    Weck MN, Brenner H. Prevalence of chronic atrophic gastritis in different parts of the world. Cancer Epidemiol Biomarkers Prev 2006;15:1083–94.
    OpenUrlAbstract/FREE Full Text
  58. ↵
    Carmel R. Cobalamin, the stomach, and aging. Am J Clin Nutr 1997;66:750–9.
    OpenUrlAbstract/FREE Full Text
  59. ↵
    Davey Smith G, Ebrahim S. What can mendelian randomisation tell us about modifiable behavioural and environmental exposures? BMJ 2005;330:1076–9.
    OpenUrlFREE Full Text
  60. ↵
    Ulvik A, Ueland PM, Fredriksen A, et al. Functional inference of the methylenetetrahydrofolate reductase 677 C >T and 1298A > C polymorphisms from a large-scale epidemiological study. Hum Genet 2007;121:57–64.
    OpenUrlCrossRefPubMed
  61. ↵
    Fredriksen A, Meyer K, Ueland PM, Vollset SE, Grotmol T, Schneede J. Large-scale population-based metabolic phenotyping of thirteen genetic polymorphisms related to one-carbon metabolism. Hum Mutat 2007;28:856–65.
    OpenUrlCrossRefPubMed
  62. ↵
    Davey Smith G, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 2003;32:1–22.
    OpenUrlAbstract/FREE Full Text
  63. ↵
    Kono S, Chen K. Genetic polymorphisms of methylenetetrahydrofolate reductase and colorectal cancer and adenoma. Cancer Sci 2005;96:535–42.
    OpenUrlCrossRefPubMed
  64. ↵
    Ueland PM, Rozen R, editors. MTHFR polymorphisms and disease. Georgetown (TX): Landes Bioscience; 2005.
View Abstract
PreviousNext
Back to top
Cancer Epidemiology Biomarkers & Prevention: 16 (11)
November 2007
Volume 16, Issue 11
  • Table of Contents
  • Table of Contents (PDF)
  • Index by Author

Sign up for alerts

View this article with LENS

Open full page PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for sharing this Cancer Epidemiology, Biomarkers & Prevention article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
The Association of Gastric Cancer Risk with Plasma Folate, Cobalamin, and Methylenetetrahydrofolate Reductase Polymorphisms in the European Prospective Investigation into Cancer and Nutrition
(Your Name) has forwarded a page to you from Cancer Epidemiology, Biomarkers & Prevention
(Your Name) thought you would be interested in this article in Cancer Epidemiology, Biomarkers & Prevention.
Citation Tools
The Association of Gastric Cancer Risk with Plasma Folate, Cobalamin, and Methylenetetrahydrofolate Reductase Polymorphisms in the European Prospective Investigation into Cancer and Nutrition
Stein Emil Vollset, Jannicke Igland, Mazda Jenab, Åse Fredriksen, Klaus Meyer, Simone Eussen, Håkon K. Gjessing, Per Magne Ueland, Guillem Pera, Núria Sala, Antonio Agudo, Gabriel Capella, Giuseppe Del Giudice, Domenico Palli, Heiner Boeing, Cornelia Weikert, H. Bas Bueno-de-Mesquita, Fátima Carneiro, Valeria Pala, Paolo Vineis, Rosario Tumino, Salvatore Panico, Göran Berglund, Jonas Manjer, Roger Stenling, Göran Hallmans, Carmen Martínez, Miren Dorronsoro, Aurelio Barricarte, Carmen Navarro, José R. Quirós, Naomi Allen, Timothy J. Key, Sheila Bingham, Jakob Linseisen, Rudolf Kaaks, Kim Overvad, Anne Tjønneland, Frederike L. Büchner, Petra H.M. Peeters, Mattijs E. Numans, Françoise Clavel-Chapelon, Marie-Christine Boutron-Ruault, Antonia Trichopoulou, Eiliv Lund, Nadia Slimani, Pietro Ferrari, Elio Riboli and Carlos A. González
Cancer Epidemiol Biomarkers Prev November 1 2007 (16) (11) 2416-2424; DOI: 10.1158/1055-9965.EPI-07-0256

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
The Association of Gastric Cancer Risk with Plasma Folate, Cobalamin, and Methylenetetrahydrofolate Reductase Polymorphisms in the European Prospective Investigation into Cancer and Nutrition
Stein Emil Vollset, Jannicke Igland, Mazda Jenab, Åse Fredriksen, Klaus Meyer, Simone Eussen, Håkon K. Gjessing, Per Magne Ueland, Guillem Pera, Núria Sala, Antonio Agudo, Gabriel Capella, Giuseppe Del Giudice, Domenico Palli, Heiner Boeing, Cornelia Weikert, H. Bas Bueno-de-Mesquita, Fátima Carneiro, Valeria Pala, Paolo Vineis, Rosario Tumino, Salvatore Panico, Göran Berglund, Jonas Manjer, Roger Stenling, Göran Hallmans, Carmen Martínez, Miren Dorronsoro, Aurelio Barricarte, Carmen Navarro, José R. Quirós, Naomi Allen, Timothy J. Key, Sheila Bingham, Jakob Linseisen, Rudolf Kaaks, Kim Overvad, Anne Tjønneland, Frederike L. Büchner, Petra H.M. Peeters, Mattijs E. Numans, Françoise Clavel-Chapelon, Marie-Christine Boutron-Ruault, Antonia Trichopoulou, Eiliv Lund, Nadia Slimani, Pietro Ferrari, Elio Riboli and Carlos A. González
Cancer Epidemiol Biomarkers Prev November 1 2007 (16) (11) 2416-2424; DOI: 10.1158/1055-9965.EPI-07-0256
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Acknowledgments
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF
Advertisement

Related Articles

Cited By...

More in this TOC Section

  • Screening Mammography, False Positive Results
  • A Potential miRNA Signature for RCC
  • ω-3 Polyunsaturated Fat and Survival After Colon Cancer
Show more Research Articles
  • Home
  • Alerts
  • Feedback
Facebook   Twitter   LinkedIn   YouTube   RSS

Articles

  • Online First
  • Current Issue
  • Past Issues

Info for

  • Authors
  • Subscribers
  • Advertisers
  • Librarians
  • Reviewers

About Cancer Epidemiology, Biomarkers & Prevention

  • About the Journal
  • Editorial Board
  • Permissions
  • Submit a Manuscript
AACR logo

Copyright © 2018 by the American Association for Cancer Research.

Cancer Epidemiology, Biomarkers & Prevention
eISSN: 1538-7755
ISSN: 1055-9965

Advertisement