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Red meat intake, doneness, polymorphisms in genes that encode

carcinogen-metabolizing enzymes and colorectal cancer risk

Michelle Cotterchio1,2, Beatrice A. Boucher1, Michael Manno1, Steven Gallinger3, Allan B.Okey4, and Patricia A. Harper5

1 Population Studies and Surveillance, Cancer Care Ontario, Toronto, Ontario

2 Department of Public Health Sciences, University of Toronto, Toronto, Ontario

3 Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario

4 Department of Pharmacology & Toxicology, University of Toronto, Toronto, Ontario

5 Program in Developmental and Stem Cell Biology, Research Institute, Hospital for Sick Children,

Toronto, Ontario

Abstract

Colorectal cancer literature regarding the interaction between polymorphisms in carcinogen-metabolizing enzymes and red meat intake/doneness is inconsistent. A case-control study wasconducted to evaluate the interaction between red meat consumption, doneness and polymorphismsin carcinogen-metabolizing enzymes. Colorectal cancer cases diagnosed 1997-2000, aged 20-74years, were identified through the population-based Ontario Cancer Registry and recruited by theOntario Family Colorectal Cancer Registry (OFCCR). Controls were sex- and age-group matchedrandom sample of Ontario population. Epidemiologic and food questionnaires were completed by1095 cases and 1890 controls; blood was provided by 842 and 1251, respectively. Multivariatelogistic regression was used to obtain adjusted odds ratio (OR) estimates. Increased red meat intakewas associated with increased colorectal cancer risk [OR (>5 servings/week vs. 2 servings/week)=1.67 (1.36, 2.05)]. Colorectal cancer risk also increased significantly with well-done meat intake[OR (>2 servings/week well-done vs. 2 servings/week rare-regular) = 1.57 (1.27, 1.93)]. Weevaluated interactions between genetic variants in 15 enzymes involved in the metabolism ofcarcinogens in overcooked meat (CYPs, GSTs, UGTs, SULT, NATs, mEH, AHR). CYP2C9 andNAT2 variants were associated with colorectal cancer risk. Red meat intake was associated withincreased colorectal cancer risk, regardless of genotypes; however, CYP1B1 combined variant andSULT1A1-638G>A variant significantly modified the association between red meat doneness intakeand colorectal cancer risk. In conclusion, well-done red meat intake was associated with an increasedrisk of colorectal cancer regardless of carcinogen-metabolizing genotype, although our data suggestspersons with CYP1B1 and SULT1A1 variants had the highest colorectal cancer risk.

Keywordsred meat intake; colorectal cancer risk; carcinogen-metabolizing polymorphisms; case-control study

Please address all correspondence and reprint requests to: Michelle Cotterchio, PhD Cancer Care Ontario, 620 University Avenue,Toronto, Ontario M5G 2L7 Canada Tel: (416) 971-9800 ext. 3205 Fax: (416) 971-6888 [email protected].

NIH Public AccessAuthor ManuscriptCancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC 2009 November 1.

Published in final edited form as:

Cancer Epidemiol Biomarkers Prev. 2008 November ; 17(11): 30983107. doi:

10.1158/1055-9965.EPI-08-0341.

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INTRODUCTION

Colorectal cancer is the third most common cancer in Canada and has a poor 5-year survivalrate of 60% (1). Both genetic and environmental factors are involved, with 20% of colorectalcancer patients exhibiting a familial component in which relatives have a doubling of risk(2). Since less than 5% of colorectal cancer is explained by identified genetic syndromes(3-4) common inherited polymorphisms of low penetrance are likely important.

Many case-control and prospective epidemiologic studies, as well as recent meta-analyses ofcohort studies, report that red meat consumption is associated with an increased risk ofcolorectal cancer and it has been suggested that this may be due to carcinogenic polycyclicaromatic hydrocarbons (PAH) and heterocyclic amines (HCA) produced when meat is cookedat high temperatures (e.g., 5-16). Furthermore, among the few studies to include assessmentof cooking methods, the association with colorectal cancer was found to be strongest for well-done red meat (7,13,17-19).

It is well documented that genes that encode enzymes involved in metabolism/activation ofcarcinogens such as PAH and HCA found in overcooked meat (e.g., CYP1A1/1A2/1B1,2E1,GSTs, UGTs, mEH) exhibit variation (e.g., 20-26). In general, bioactivation of pre-carcinogensis carried out by Phase I enzymes such as cytochromes P450 (CYP) (62-63) whereas Phase IIenzymes such as glutathione S-transferases (GST) and UDP-glucuronosyltransferases (UGT)usually detoxify reactive metabolites by conjugation and thus prevent metabolites frombinding to DNA (23-24). Genetic variants may alter enzyme function and carcinogenmetabolism and consequently modify the association between well-done meat intake andincreased colorectal cancer risk (27-31).

Colorectal cancer literature regarding the interaction between carcinogen-metabolizing genesand red meat intake/doneness is limited and inconsistent, although suggests certain geneticvariants may modify the association between red meat and colorectal cancer risk (18-19,29,32-38). To summarize the main studies to date, CYP2E1, GSTT1 and SULT1A1 were shownto significantly modify the association between red meat intake and colorectal cancer, whileinteraction findings for NAT2 vary between studies and CYP1A1, GSTM1, UGT1A7 andmEH did not modify risk (18,29,32-37,39). Two studies observed multiple-way interactions

with red meat intake, such as the combination of CYP1A2 and NAT2 (19) and the combinationof several variants in CYP genes (1A2, 2E1, 1B1, 2C9) (38). There is a paucity of data on theinteraction between many of the genetic variants in carcinogen-metabolizing enzymes and redmeat intake and doneness as regards colorectal cancer risk, and many previous studies had verylimited power to detect statistically significant gene-environment interactions.

We evaluated the association between red meat intake, doneness and genetic variants in keycarcinogen-metabolizing enzymes and colorectal cancer risk among over 2,000 cases andcontrols participating in the population-based Ontario Family Colorectal Cancer Registry(OFCCR). Understanding the interaction between modifiable risk factors and geneticsusceptibility may aid development of more tailored colorectal cancer primary preventionstrategies.

MATERIALS AND METHODSThe OFCCR is one of six international sites participating in the Cooperative Family Registryfor Colorectal Studies (CFR-Colon) established by the US National Cancer Institute (40). Themethods of the OFCCR have been described previously (41-43) and are reiterated below.Colorectal cancer cases and population controls participating in the OFCCR were used toconduct this study.

Cotterchio et al. Page 2

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Case and Control Recruitment (Subjects)

The population-based Ontario Cancer Registry (OCR) was used to identify and recruit into theOFCCR, living, incident colorectal cancer cases (pathology confirmed; InternationalClassification of Diseases 9th revision codes 153.0-153.9, 154.1-154.3, 154.8) (44) aged20-74 and diagnosed between July 1, 1997 and June 30, 2000. The OCR registers all cases ofcancer diagnosed among residents of Ontario using computerized probabilistic record linkageto resolve the four main sources of cancer information (pathology reports, hospital discharge

summaries, reports from Ontario's regional cancer centres, and death certificates).

Controls recruited into the OFCCR were a random sample of Ontario residents identified usingtwo methods. Population-based controls were randomly selected and frequency-matched,within sex and 5-year age groups, to the colorectal cancer cases. In 1999-2000, persons wereidentified using a list of residential telephone numbers in Ontario provided by Infodirect (BellCanada). Households were randomly selected from this list, and telephoned to obtain a censusof household members (age, sex). One eligible person within each household was randomlyselected and invited to participate. To increase the sample size and approach a 1:2 case:controlratio, additional population-based controls were recruited in 2001. An age- and sex-stratifiedrandom sample of persons was selected from a listing of all Ontario residents (homeownersand occupants) based on assessment rolls maintained by the provincial government. A re-abstraction study was able to link more than 95% of persons in the OCR to this population

database, suggesting that its' accuracy and completeness are high (45).

Data Collection

Physicians, identified from pathology reports, were asked to permit contact with their patient(s), and to provide the patient address, telephone number and vital status. Over 90% ofphysicians consented. Once a physician provided consent, patients were mailed a packagecontaining a letter, a family history questionnaire (FHQ), and a brochure describing the variousphases of the OFCCR (epidemiologic and food questionnaires, provide blood sample and enrolkin). A reminder post-card was sent several weeks after this mailing and non-responders werefollowed up with a telephone call several months after the initial mailing. Of the 6695 colorectalcancer cases mailed an FHQ, 3781 (57%) returned this questionnaire (participated). Themedian lag between diagnosis date (pathology report) and mailing of case invitation package

was approximately six months (for all cases including non-responders and refusals).

Following the completion and return of the FHQ (phase one), pedigrees were constructed. Eachcolorectal cancer case was then classified as 1) high familial risk (satisfying hereditarynonpolyposis colorectal cancer (HNPCC) Amsterdam criteria) (46), 2) intermediate familialrisk, or 3) low (sporadic) risk. Intermediate familial risk has a very broad definition and consistsof cases satisfying at least one of the following: a) two relatives with HNPCC cancers (thisincludes 14 cancer sites), and two (of three) are first degree kin, b) case and relative both withcolorectal cancer < 50 years of age, and c) any relative with colorectal cancer < 35 years ofage. All other cases not classified as high or intermediate familial risk were classified assporadic (with the exception of a few cases categorized as intermediate due to selectedpathology criteria such as multiple polyps (described in Cotterchio et al., (41)). All high andintermediate risk cases and a 25% random sample of the low risk cases were selected to

participate in phase two of the OFCCR. These participants were then asked to i) complete theself-administered epidemiologic questionnaire designed by the CFR-Colon and food frequencyquestionnaire (FFQ), ii) provide a blood sample, and iii) provide permission to contact theirrelatives.

Controls were mailed a cover letter along with the family history, epidemiologic, and foodfrequency questionnaires, and were also asked to provide a blood sample.



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Dietary and Epidemiologic Information

Dietary intake was determined using both the CFR-Colon epidemiologic questionnaire and anFFQ. The CFR-Colon epidemiology questionnaire contained several questions regardingdietary intake two years ago including questions about red meat intake and cooking methods.For example, how often a fruit/vegetable serving was eaten, how often a red meat serving(2-3 ounces) was eaten, how often a red meat serving cooked by broiling/grilling/barbequing/pan-frying was eaten and how well-done was red meat (inside and outside appearance). The

FFQ adopted by the CFR-Colon asked about foods eaten about two years ago and wasanalyzed using food composition databases that include values for macro- and micro-nutrients(47); this information was used to derive several potential confounding dietary variables.

Two variables were derived to describe red meat consumption: red meat servings per week andred meat doneness intake (reported in the epidemiologic questionnaire). Red meat servingsper week was defined as the reported number of servings of red meat consumed per week andincluded beef, pork, veal, lamb and venison. Red meat doneness consumption was definedby combining the number of servings of red meat per week that were cooked by broiling,grilling, barbecuing or pan-frying and the degree to which the meat was cooked. Rare wasdefined as red/pink inside and light/medium brown outside; well-done was defined as browninside or heavily browned/blackened outside. Four mutually exclusive red meat donenessconsumption categories were derived: 2 servings/week of rare-regular red meat, 2

servings/week of well-done red meat, >2 servings/week of rare-regular red meat, >2 servings/week of well-done red meat.

The 32-page CFR-Colon epidemiologic questionnaire also included many close-endedquestions about colorectal cancer screening, medical conditions, medication use, reproductivefactors, physical activity, alcohol consumption, smoking history, sociodemographic, andanthropometric measures.

Response Rates/Numbers

The 1536 incident colorectal cancer cases selected to participate in phase two of the OFCCRwere mailed epidemiologic and food questionnaires and asked to provide a blood sample. 1095(72%) cases completed the questionnaires (epidemiology questionnaire queried meat intake

and cooking methods) and are included in the meat intake data analysis. Eighty-three subjectswere excluded due to extreme caloric intakes (females: < 700 or > 4200 kcal and males: < 800or > 4900 kcal). The OFCCR classified cases based on their familial cancer history: 42 (4%)were high (HNPCC) risk, 483 (44%) were intermediate familial risk (defined above), and 570(52%) were low risk. Of these 1095 colorectal cancer cases, 842 (77%) provided blood (DNA)and thus were available for genotype analyses and evaluation of the possible interactionbetween meat intake and genetic variants.

Of the 4876 eligible controls identified and invited to participate, 2131 refused (43%), and of2745 mailed the questionnaire package, 1928 (70%) completed the FFQ, and 1944 (71%)completed the epidemiology questionnaire. 1890 controls are included in the red meat intakedata analysis (i.e., completed both questionnaires and had reasonable caloric intake). Of the1890 controls who completed both questionnaires, 1251 (66%) provided a blood (DNA)

sample, and these persons comprised the control dataset used for genotype analyses andevaluation of the possible interaction between meat intake and genetic variants.

Reasons for non-participation included language barrier, illness, too busy, and questionnairetoo long; however, the majority of cases and controls did not provide a reason. 95% ofparticipants in the OFCCR are Caucasian.



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DNA Preparation and Genotyping

The OFCCR obtained 40 ml of blood from participating cases and controls. DNA was extractedfrom lymphocytes using organic solvents or spin columns (Qiagen Inc.) and banked at 4C.

A priori, genetic variants were chosen for investigation based on an estimated minor allelefrequency of 5% with preference given to polymorphisms with a potential impact on function.Genotyping assays are standard assays from the literature. Assays to query the single nucleotide

polymorphism (SNP) of interest were performed using the TaqMan 5 nuclease allelediscrimination assay (Applied Biosystems). In general an allele-specific oligonucleotide probe,labeled with a fluorescent reporter and quencher dye, is cleaved during the amplificationprocess generating an increased intensity of fluorescence directly related to the accumulationof polymerase chain reaction (PCR) product. The reaction mix consisted of 5l TaqmanUniversal Master Mix no UNG (Applied Biosystems), combined primer and probe mix (permanufacturer's instructions), 20-50 ng of DNA template and water for a total reaction volumeof 10l. Cycling conditions for the reaction were 95 C for 10 minutes, followed by 40-45 cyclesof 94 C for 15 seconds and 60 C for 1 minute. Following PCR amplification, end-pointfluorescence was read using an ABI 7900HT Sequence Detection System and genotypes wereassigned using Allelic Discrimination Software (Applied Biosystems SDS Software v2.1).Appropriate controls representative of each genotype and multiple template controls wereincluded in each analysis. Microsatellite fragment analysis was used to genotype UGT1A1*28.

Briefly, PCR was performed on 50 ng of DNA in buffer [100 mM Tris-HCl (pH 8.0), 500 mMKCl, 1.5 mM MgCl2, 0.2 mM dNTP, 0.2 M of each primer and 1 unit of Taq Polymerase(Applied Biosystems)]. Cycling conditions were: initial denaturation at 95C for 2 minutesfollowed by 30 cycles of denaturation at 94C for 30 seconds, annealing at 55C for 30 secondsand extension at 72C for 45 seconds, with a 15 minute final extension at 72C. Microsatellitefragment analysis was performed using the ABI 3730 DNA Analyzer and Genemapper 3.5software (Applied Biosystems). A multiplex PCR for the simultaneous analysis of GSTM1-null and GSTT1-null was done using albumin as an internal positive control.

Approximately 5% of the samples were randomly selected for blinded duplicate analysis(verification), with an estimated genotyping error rate of 0.25%.

Statistical Data Analysis

Associations between red meat intake and red meat doneness variables and colorectal cancerrisk were examined by computing age-adjusted odds ratio (AOR) estimates and approximate95% confidence intervals (CI) (48). Multivariate logistic regression analyses were performedto obtain multivariate odds ratio (OR) estimates while simultaneously controlling for potentialconfounders (49). For both the red meat and meat doneness analyses, potential confoundingvariables were evaluated based on the 10% change-in-OR estimate methods (50). Potentialconfounders included: family history of colorectal cancer, diagnosis of inflammatory boweldisease, body mass index (BMI), colonic screening, non-steroidal anti-inflammatory drug use,smoking history, reproductive factors, lifetime physical activity, vegetable intake, fruit intake,folate, calcium, dietary fibre, saturated fat, total energy (calories), and alcohol consumption.No variables were identified as confounders in our dataset, and thus the final multivariatemodels were the most parsimonious and contained only sex and age group. All statistical

analysis was done using SAS v8.2 (SAS Institute, Cary, North Carolina, USA).

The possibility of interactions between selected genetic variants (CYP1A2 163C>A, rs762551;CYP2E1 1293G>C, rs3813867; CYP2E1 7632T>A, rs6413432; CYP2C9 430C>T,rs1799853; CYP2C9 1075A>C, rs1057910; CYP1A1 2455A>G, rs1048943; CYP1A13801T>C, rs4646903; CYP1B1 142C>G, rs10012; CYP1B1 4326C>G, rs1056836; CYP1B14390A>G, rs1800440; GSTM1-locus deletion; GSTT1-locus deletion; GSTM3delAGG,



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rs1799735; UGT1A7 W208R, rs11692021; UGT1A7 N129K, rs17868323; UGT1A1*28 A(TA)6TAA > A(TA)7TAA; mEH 17673T>C, rs1051740; SULT1A1 638G>A, rs9282861;NAT1 459G>A, rs4986990; NAT1 1088T>A, rs8190861; NAT2 341T>C, rs1801280; NAT2590G>A, rs1799930; NAT2 857G>A, rs1799931; ARH 1661G>A, rs2066853) and red meatintake/doneness was initially evaluated by stratified analyses (50). In addition, severalcomposite genotype variables were derived based on the known phenotype of a variety of SNPcombinations within the same gene (e.g., CYP1B1 wild type and increased activity) these

definitions are footnoted in Table 2. Interaction was formally assessed by the statisticalsignificance of the Likelihood Ratio Statistic (LRS P5 servings/week vs. < 2 servings/week)

=1.67 (1.36, 2.05]. Increased consumption of well-done red meat was associated with anincreased colorectal cancer risk [OR (>2 servings/week of well-done red meat vs. < 2 servings/week of rare-regular red meat) = 1.57 (1.27, 1.93)].

Table 2 shows the distribution of 29 genetic polymorphisms in 15 selected genes involved incarcinogen metabolism. For the majority of variants there was no association with colorectalcancer risk although a statistically significant association was observed for variants in CYP2C9430C>T, and NAT2 combined variants (fast acetylator). Also, CYP1A1 3801T>C wasassociated with colorectal cancer risk, though this was of borderline statistical significance(possibly due to very small number of carriers) and CYP1B1 4390A>G was also of borderlinesignificance.

The same 29 genetic variants encoding carcinogen-metabolizing enzymes were assessed for

interaction with red meat intake and red meat doneness (genotypes are listed in the Methodssection and Table 2). Increasing red meat intake was associated with increasing colorectalcancer risk, regardless of genotype stratification thus, no effect modification was observed(data not shown). However, as regards red meat doneness, several genetic variants modifiedthe association between intake and colorectal cancer risk, and these data are presented in Table3. Specifically, CYP1B1-combined variant and SULT1A1-638G>A were found to bestatistically significant effect modifiers of the association between red meat doneness intakeand colorectal cancer risk, and the interaction between CYP1B1-4326C>G and red meatdoneness was of borderline significance. Colorectal cancer risk increased to four-fold amongpersons in the highest red meat doneness category (> 2 servings of well-done red meat perweek) who also carried the combined CYP1B1 wildtype variants (i.e., not increased activity)(g*e interaction p value: 0.04). Carriers of the SULT1A1-638 GG genotype who consumed >2 well done meat servings/week had a greater than doubling of colorectal cancer risk and incomparison, a borderline statistically significant association of lower magnitude was observedwith this same meat intake level among persons with AA/GA genotypes (g*e interaction pvalue: 0.03). In addition, AHR-1661 was a borderline significant effect modifier (LRSinteraction P=0.07, data not shown). The majority of genetic variants are not presented in Table3 because no effect modification was observed by eyeballing the data and furthermore nostatistically significant interaction was apparent (i.e., increasing red meat doneness intake wasassociated with increased colorectal cancer risk regardless of genetic variant, and p-value >



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0.10 for interaction). As the sample size was limited, some gene-meat interactions may nothave been detected in our dataset and it is also possible that spurious associations were founddue to the many comparisons made.

The association between red meat intake/doneness and cancer risk was evaluated separatelyfor microsatellite instability MSI-high and MSI-low/stable colorectal cancer cases. We did notobserve a statistically significant difference; red meat intake was associated with a statistically

significant increased risk of colorectal cancer for both these types of tumours (as comparedwith controls) data not shown.

As a disproportionate number of cases in the OFCCR were from families classified as high/intermediate risk (vs. low risk), effect modification by familial risk was also assessed. Nodifferences across familial risk strata were identified with respect to the association betweenred meat intake/doneness and colorectal cancer risk (data not shown).

DISCUSSION

We report that increased consumption of both red meat and well-done red meat weresignificantly associated with increased colorectal cancer risk. Our findings are consistent withseveral meta-analyses (primarily of cohort studies) which concluded that red meat consumptionis associated with an increased risk of colorectal cancer (5-6,11), although to our knowledgewe are the first to evaluate this comprehensively among Canadians. We also evaluatedinteractions between red meat intake/doneness and polymorphic genes that encode enzymesinvolved in the metabolism of carcinogens found in well-done meat (CYPs, GSTs, UGTs,SULT, NATs, mEH, AHR). Our study assessed many genetic variants in 15 enzymes centralto the metabolism of PAHs and HCAs produced by overcooking red meat. Polymorphisms thatlead to a known change in function of these enzymes or that were previously shown to beassociated with risk of colon or other cancers were studied; however, rare variants wereexcluded a priori because statistical power was not sufficient. Of the many geneticpolymorphisms assessed, two were found to be significantly associated with colorectal cancerrisk - CYP2C9-430C>T and NAT2 fast/slow variant. Upon evaluation of possible effectmodification, red meat intake was found to be associated with colorectal cancer risk,regardless of genotype (no effect modification observed); however, two genetic variants

(CYP1B1-combined variant and SULT1A1-638G>A) significantly modified the associationbetween red meat doneness intake and colorectal cancer risk. Further investigation of thesepossible interactions is warranted.

Our findings suggest that CYP1B1 and SULT1A1 variants may modify the association betweenwell-done red meat intake and colorectal cancer risk. The positive association with colorectalcancer increased to four-fold among persons in the highest red meat doneness category (> 2servings of well-done red meat per week) who also carried the combined CYP1B1 wildtypevariants (i.e., not increased activity). We are the first study to specifically assess severalCYP1B1 variants, well-done red meat intake and colorectal cancer risk. Our finding is plausiblesince CYP1B1 bioactivates carcinogens such as PAHs found in burnt meat, and polymorphismsin the essential exon-3 heme-binding region alters this activity (52-54). Furthermore, CYP1B1is highly expressed in the colon and in colon cancers, and thus is available to interact with

carcinogens within the colon itself (55-56). We found that carriers of the SULT1A1-638 GGgenotype who consumed > 2 well done meat servings/week had a greater than doubling ofcolorectal cancer risk while no statistically significant association was observed with this samemeat intake level among persons with AA/GA genotypes. SULT1A1 is involved in Phase IImetabolism and is also expressed in numerous tissues including the colon (57). Somewhatsupporting our finding, a recent German study reported modification of the red meat - colorectalcancer risk association by SULT1A1 genotype although meat doneness was not assessed



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(37). Our findings could also be due to chance or bias, especially since many genetic variantswere assessed and the study response rate was less than optimal. Thus, replication by futurestudies is essential to further investigate the hypothesis that genetic variants in carcinogen-metabolizing enzymes may modify colorectal cancer risk associated with well-done red meatintake.

Consistent with our findings, many epidemiologic studies and several meta-analyses report

that red meat consumption is associated with an increased risk of colorectal cancer (e.g.,5-16). Few studies have evaluated red meat doneness and colorectal cancer risk, although,consistent with our findings most studies found the association with colorectal cancer wasstrongest for well-done red meat (7,13,17-18). To our knowledge only one Canadian study hasassessed red meat intake and colon cancer risk; however, the doneness of meat was notconsidered (16). Consistent with our findings, they observed an association between red meatand colon cancer, in particular proximal colon cancer risk (16). It is important to conductCanadian studies since nutrient values (such as fat and protein involved in PAH /HCAproduction) for Canadian and American beef differ because production methods are not thesame [Beef Information Centre: http://www.beefinfo.org/nutrient_data.cfm#faq11] (69).

Several studies have evaluated some genetic variants, red meat intake and colorectal cancerrisk, though data are sparse or non-existent for certain carcinogen-metabolizing genetic

variants. To summarize, CYP2E1, GSTT1 and SULT1A1 significantly modified theassociation between red meat intake and colorectal cancer risk, while CYP1A1, GSTM1,UGT1A7 and mEH did not modify risk and NAT2 findings varied between studies (18,29,32-37,39). A case-control colorectal cancer study conducted in Utah and California found nointeraction between red meat intake, well-done red meat consumption and CYP1A1 genotype,nor was the association between colorectal cancer risk and red meat consumption modified bythe combination of CYP1A1 and GSTM1 genotypes (32). A separate publication by theseauthors reported that the association between rectal cancer risk and red meat consumption/doneness was not significantly modified by NAT2 phenotype or GSTM1 genotype (18).Another American case-control study reported little to no association between many red meatintake variables and colon cancer risk, though the NAT2 variant slightly modified theseassociations while the GSTM1 variant had no impact (33). A British colorectal cancer case-control study reported some evidence of an interaction between GSTT1 and red meat intake;

however, no interaction was observed for mEH, CYP1A1 or GSTM1 (35). A case-control studyin Hawaii assessed well-done red meat intake, genetic variants and colorectal cancer risk andobserved that the largest statistically significant association was seen for the three-wayinteraction between well-done red meat, rapid CYP1A2 phenotype and rapid NAT2 genotype(19). A subsequent Hawaiian study reported that CYP2E1 (increased activity) may modify therectal cancer risk associated with red meat intake (29). The Nurses' Cohort Study reported aninteraction between NAT2 (fast/slow acetylator) and red meat intake as regards colorectalcancer risk (of borderline statistical significance) (34). A recent German colorectal cancer case-control study reported a moderate (though not statistically significant) interaction betweenNAT1/NAT2 combined genotype and red meat intake (36). A recent case-control studyconducted in France reported that the combination of several variants in CYP genes (1A2, 2E1,1B1, 2C9) modified (exacerbated) the association between red meat intake and colorectalcancer risk (38). Two small case-control studies recently evaluated SULT variants, meat intake

and colorectal cancer (37,58). Lilla et al (37) reported modification of the red meat-colorectalcancer risk by SULT1A1 genotype while the other study reported no effect modification(58), however the latter study was underpowered to detect an interaction with less than 300cases participating.

While most previous studies that assessed effect modification of the red meat colorectal cancerrisk association included only a limited number of genetic variants and were limited by small



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sample sizes, we evaluated 29 genetic polymorphisms in 15 selected genes known to be centralto the metabolism/bioactivation of carcinogens among nearly 900 cases and 1200 controls. Weare the first large study to investigate whether mEH modifies the association between red meatand colorectal cancer, with only one small cancer study previously published on this topic(35). Similar to our findings, this study reported that mEH does not modify the red meatcolorectal cancer association. We are the first to investigate red meat doneness, SULT variantsand colorectal cancer risk.

HCAs are formed during the pyrolysis of proteins in meat, and the quantity depends on cookingtemperature and duration, while PAHs are produced from the pyrolysis of fat (15,59). Mostchemical carcinogens require metabolic bioactivation in order to bind to DNA and form DNAadducts that exert a carcinogenic effect (60-61). Bioactivation of pre-carcinogens is usuallycarried out by Phase I enzymes such as CYPs (62-63) whereas Phase II enzymes such as GSTand UGT usually detoxify reactive metabolites by conjugation and thus prevent metabolitesfrom binding to DNA (23-24,64). Enzymes such as CYP1A2 and CYP1A1 are important inbioactivation of PAHs and HCAs involved in carcinogenesis (63,65-66). Factors that alter thelevel or activity of these enzymes may influence the body's response to carcinogens (30-31).For example, individuals with a rapid CYP1A2 phenotype who also excreted high levels ofPhIP (an HCA), had the lowest levels of PhIP DNA adducts in their colon (67).

Survival bias is a possible limitation of our study since fatal cases were excluded and thus caseswith better survival are over-represented. In addition, the lag between diagnosis andrecruitment into the OFCCR may have created a possible survivor bias since the survival ratefor colorectal cancer is moderate, though varies greatly by stage at diagnosis. However, it isreassuring that participation in the OFCCR was not statistically different for early versus late(metastatic) stage colorectal cancer cases (41). Although it has been reported that most coloncancer risk factors do not differ by stage of disease (68), survival bias may be a concern if redmeat intake affects survival. Although our response rate was not optimal, both cases andcontrols were selected from population-based sampling frames and many established riskfactors were found to be associated with colorectal cancer risk in our dataset suggesting thecases and controls are representative (42). Response bias is always a possible limitation whenresponse rates are not optimal; however, it is unlikely that non-response would be associatedwith inherited carcinogen-metabolizing genotypes. In an attempt to assess possible response

bias as regards sociodemographic factors, we previously published that the age and sexdistribution of colorectal cancer cases participating in the OFCCR did not differ from non-participating cases; however, colorectal cancer cases in rural areas were slightly more likelyto participate (41). Possible confounding by many colorectal cancer risk factors was evaluated,and adjusted for, in our analyses. Although potential confounders were evaluated, residual orunknown confounding always remains a possibility. Case-control studies are susceptible torecall bias because cases may report exposures differently than controls. Although we couldnot directly measure HCA and PAH, the CFR-Colon epidemiologic questionnaire asked notonly about red meat consumption but also the doneness of red meat eaten. As our sample sizewas moderate, it is possible that some gene-environment interactions were not detected. It isalso plausible that the observed interactions are spurious since many comparisons were made.Although multiple comparisons were made, this study was conducted with specific a priorihypotheses based on a candidate gene pathway approach that focused on enzymes involved in

carcinogen metabolism/activation and certain genetic variants likely to be functional. Lastly,the incomprehensive gene coverage due to the small number of variants selected per gene is alimitation of this study which used the candidate gene approach to investigate geneticinteractions.

This study adds to the growing body of evidence that suggests consumption of red meat(especially well-done meat) increases the risk of colorectal cancer, with this being the first



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Canadian study to evaluate well-done red meat intake and colorectal cancer risk. In general,the increased colorectal cancer risk among consumers of red meat was observed regardless ofcarcinogen-metabolizing genotype, although our data suggests consumers of well-done redmeat who carry CYP1B1 and SULT1A1 variants may exhibit higher colorectal cancer risk.Future studies are needed with greater power to simultaneously examine combinations ofrelevant genetic polymorphisms, red meat intake, doneness and colorectal cancer risk.

ACKNOWLEDGMENTSWe would like to thank the Ontario Family Colorectal Cancer Registry (OFCCR) staff, in particular Darshana Daftary(Cancer Care Ontario), the study co-ordinator, and Teresa Selander (Mount Sinai Hospital), the biospecimen manager,for their dedication to this study. We would also like to thank Zhanquin Liu (The Centre for Applied Genomics,Hospital for Sick Children) and Hui Zhang (Hospital for Sick Children) for their invaluable assistance in the laboratory.We thank Nancy Deming for her assistance with manuscript preparation. The content of this manuscript does notnecessarily reflect the views or policies of the NCI, nor does mention of trade names, commercial products, ororganizations imply endorsement by the US Government or the Consortium of Familial Registries.

Financial Support Supported by the National Cancer Institute of Canada (NCIC) with funds from the CanadianCancer Society (grant no. 013208) and the National Cancer Institute, National Institutes of Health, USA (RFA #CA-95-011, grant no. U01-CA74783).

REFERENCES

1. National Cancer Institute of Canada (NCIC). Canadian Cancer Statistics 2006 (Table 15). Toronto,Canada: 2006.

2. Carstensen B, Soll-Johanning H, Villadsen E, Sondergaard JO, Lynge E. Familial aggregation ofcolorectal cancer in the general population. Int J Cancer 1996;68:42835. [PubMed: 8945611]

3. de Leon MP, Pedroni M, Benatti P, et al. Hereditary colorectal cancer in the general population: fromcancer registration to molecular diagnosis. Gut 1999;45:328. [PubMed: 10369701]

4. Kinzler KW, Vogelstein B. Lessons from hereditary colorectal cancer. Cell 1996;87:15970. [PubMed:8861899]

5. Larsson S, Wolk A. Meat consumption and risk of colorectal cancer: a meta-analysis of prospectivestudies. Int J Cancer 2006;119(11):265764. [PubMed: 16991129]

6. Sandhu M, White I, McPherson K. Systematic review of the prospective cohort studies on meatconsumption and colorectal cancer risk: a meta-analytical approach. Cancer Epidemiol Biomarkers

Prev 2001;10:43946. [PubMed: 11352852]7. Gerhardsson de Verdier M, Hagman U, Peters R, Steineck G, vervik E. Meat, cooking methods and

colorectal cancer: a case-referent study in Stockholm. Int J Cancer 1991;49:5205. [PubMed:1917152]

8. Peters R, Pike M, Garabrant D, Mack T. Diet and colon cancer in Los Angeles, California. CancerCauses Control 1992;3:45773. [PubMed: 1525327]

9. Larsson S, Rafter J, Holmberg L, Bergkvist L, Wolk A. Red meat consumption and risk of cancers ofthe proximal colon, distal colon and rectum: the Swedish Mammography Cohort. Int J Cancer2005;113:82934. [PubMed: 15499619]

10. English D, MacInnis R, Hodge A, Hopper J, Haydon A, Giles G. Red meat, chicken, and fishconsumption and risk of colorectal cancer. Cancer Epidemiol Biomarkers Prev 2004;13:150914.[PubMed: 15342453]

11. Norat T, Lukanova A, Ferrari P, Riboli E. Meat consumption and colorectal cancer risk: dose-response

meta-analysis of epidemiological studies. Int J Cancer 2002;98:24156. [PubMed: 11857415]12. Norat T, Bingham S, Ferrari P, et al. Meat, fish, and colorectal cancer risk: the European Prospective

Investigation into cancer and nutrition. J Natl Cancer Inst 2005;97:90616. [PubMed: 15956652]

13. Butler L, Sinha R, Millikan R, et al. Heterocyclic amines, meat intake, and association with coloncancer in a population-based study. Am J Epidemiol 2003;157:43445. [PubMed: 12615608]

14. Chiu B, Ji B, Dai Q, et al. Dietary factors and risk of colon cancer in Shanghai, China. CancerEpidemiol Biomarkers Prev 2003;12:2018. [PubMed: 12646508]



NIH-PAA

uthorManuscript


NIH-PAAuthor

Manuscript


11/19

15. Schut H, Snyderwine E. DNA adducts of HCA food mutagens: implications for mutagenesis andcarcinogenesis (review). Carcinogenesis 1999;20:35368. [PubMed: 10190547]

16. Hu J, Morrison H, Mery L, et al. Diet and vitamin or mineral supplementation and risk of colon cancerby subsite in Canada. Eur J Cancer Prev 2007;16:27591. [PubMed: 17554200]

17. Nowell S, Coles B, Sinha R, et al. Analysis of total meat intake and exposure to individual heterocyclicamines in a case-control study of colorectal cancer: contribution of metabolic variation to risk. MutResearch 2002;506:17585.

18. Murtaugh M, Ma K, Sweeney C, Caan B, Slattery M. Meat consumption patterns and preparation,genetic variants of metabolic enzymes, and their association with rectal cancer in men and women.J Nutr 2004;134:77684. [PubMed: 15051825]

19. Le Marchand L, Hankin J, Pierce L, et al. Well-done red meat, metabolic phenotypes and colorectalcancer in Hawaii. Mutat Res 2002;506-507:20514. [PubMed: 12351160]

20. Sachse C, Smith G, Wilkie M, et al. A pharmacogenetic study to investigate the role of dietarycarcinogens in the etiology of colorectal cancer. Carcinogenesis 2002;23:183949. [PubMed:12419832]

21. Nebert D, Ingelman-Sundberg M, Daly A. Genetic epidemiology of environmental toxicity and cancersusceptibility: human allelic polymorphisms in drug-metabolizing enzyme genes, their functionalimportance, and nomenclature issues. Drug Metab Rev 1999;31:46787. [PubMed: 10335448]

22. Zheng W, Xie D, Jin F, et al. Genetic polymorphism of cytochrome P450-1B1 and risk of breastcancer. Cancer Epidemiol Biomarkers Prev 2000;9:14750. [PubMed: 10698474]

23. Burchell B, Soars M, Monaghan G, Cassidy A, Smith D, Ethell B. Drug-mediated toxicity caused bygenetic deficiency of UDP-glucuronosyltransferases. Toxicol Lett 2000;112/113:33340. [PubMed:10720749]

24. Strange R, Jones P, Fryer A. Gluathione S-transferase: genetics and role in toxicology. Toxicol Lett2000;112-113:35763. [PubMed: 10720752]

25. Thompson P, Ambrosone C. Molecular epidemiology of genetic polymorphisms in estrogenmetabolizing enzymes in human breast cancer. J Natl Cancer Inst Monographs 2000;27:12534.[PubMed: 10963624]

26. Dean B, Chang S, Doss GA, King C, Thomas PE. Glucuronidation, oxidative metabolism, andbioactivation of enterolactone in rhesus monkeys. Arch Biochem Biophys 2004;429:24451.[PubMed: 15313229]

27. Cross A, Sinha R. Meat-related mutagens/carcinogens in the etiology of colorectal cancer. EnvironMol Mutagen 2004;44:445. [PubMed: 15199546]

28. Chen J, Stampfer M, Hough H, et al. A prospective study of N-acetyltransferase genotype, red meatintake, and risk of colorectal cancer. Cancer Res 1998;58:330711. [PubMed: 9699660]

29. Le Marchand L, Donlon T, Seifried A, Wilkens L. Red meat intake, CYP2E1 genetic polymorphisms,and colorectal cancer risk. Cancer Epidemiol Biomarkers Prev 2002;11:101924. [PubMed:12376502]

30. Perera F, Weinstein I. Molecular epidemiology: recent advances and future directions. Carcinogenesis2000;21:51724. [PubMed: 10688872]

31. Williams JA. Single nucleotide polymorphisms, metabolic activation and environmentalcarcinogenesis: why molecular epidemiologists should think about enzyme expression.Carcinogenesis 2001;22:20914. [PubMed: 11181440]

32. Murtaugh M, Sweeney C, Ma K, Caan B, Slattery M. The CYP1A1 genotype may alter the associationof meat consumption patterns and preparation with the risk of colorectal cancer in men and women.J Nutr 2005;135:17986. [PubMed: 15671210]

33. Kampman E, Slattery M, Bigler J, et al. Meat consumption, genetic susceptibility, and colon cancerrisk: a United States multicenter case-control study. Cancer Epidemiol Biomarkers Prev 1999;8:1524. [PubMed: 9950235]

34. Chan A, Tranah G, Giovannucci E, Willett W, Hunter D, Fuchs C. Prospective study of N-acetyltransferase-2 genotypes, meat intake, smoking and risk of colorectal cancer. Int J Cancer2005;115:64852. [PubMed: 15700302]

35. Turner F, Smith G, Sachse C, et al. Vegetable, fruit and meat consumption and potential risk modifyinggenes in relation to colorectal cancer. Int J Cancer 2004;112:25964. [PubMed: 15352038]



NIH-PAA

uthorManuscript


NIH-PAAuthor

Manuscript


12/19

36. Lilla C, Verla-Tebit E, Risch A, et al. Effect of NAT1 and NAT2 genetic polymorphisms on colorectalcancer risk associated with exposure to tobacco smoke and red meat. Cancer Epidemiol BiomarkersPrev 2006;15:99107. [PubMed: 16434594]

37. Lilla C, Risch A, Verla-Tebit E, et al. SULT1A1 genotype and susceptibility to colorectal cancer. IntJ Cancer 2006;120:2016. [PubMed: 17013894]

38. Kry S, Buecher B, Robiou-du-Pont S, et al. Combinations of cytochrome P450 gene polymorphismsenhancing the risk for sporadic colorectal cancer related to red meat consumption. Cancer EpidemiolBiomarkers Prev 2007;16:14607. [PubMed: 17627011]

39. Butler L, Duguay Y, Millikan R, et al. Joint effects between UGT1A7 genotype and dietary carcinogenexposure on risk of colon cancer. Cancer Epidemiol Biomarkers Prev 2005;14:162632. [PubMed:16030093]

40. Newcomb P, Baron J, Cotterchio M, et al. for the Colon Cancer Family Registry. Colon cancer familyregistry: an international resource for studies of the genetic epidemiology of colon cancer. CancerEpidemiol Biomark Prev 2007;16:233143.

41. Cotterchio M, McKeown-Eyssen G, Sutherland H, et al. Ontario Familial Colon Cancer Registry:Methods and first year response rates. Chron Dis Can 2000;21:816.

42. Cotterchio M, Manno M, Klar N, McLaughlin J, Gallinger S. Colorectal screening is associated withreduced colorectal cancer risk: a case-control study within the population-based Ontario FamilialColorectal Cancer Registry. Cancer Causes Control 2005;16:86575. [PubMed: 16132797]

43. Cotterchio M, Boucher B, Manno M, Gallinger S, Okey A, Harper P. Dietary phytoestrogen intakeis associated with reduced colorectal cancer risk. J Nutrition 2006;136:304653. [PubMed:17116718]

44. International Classification of Diseases (ICD). Manual of the International Statistical Classificationof Disease, Injuries, and Causes of Death, 9th Revision Conference; World Health Organization;Geneva, Switzerland. 1977.

45. Holowaty, EJ.; Lee, G.; Dale, D.; Chong, N. A reabstraction study to estimate the accuracy andcompleteness of data elements in the Ontario Cancer Registry; American Association of CentralCancer Registries; Niagara-on-the-Lake, Ontario. 1994.

46. Vasen H, Mecklin J, Khan P, Lynch H. The international collaborative group on Hereditary Non-Polyposis Colorectal Cancer (ICG-HNPCC). Dis Colon Rectum 1991;34:4245. [PubMed: 2022152]

47. Stram DO, Hankin JH, Wilkens LR, et al. Calibration of the dietary questionnaire for a multiethniccohort in Hawaii and Los Angeles. Am J Epidemiol 2000;151:35870. [PubMed: 10695594]

48. Schlesselman, JJ. Case-control studies: Design, conduct, analysis. Oxford University Press; NewYork: 1982.

49. Hosmer, DW.; Lemeshow, S. Applied logistic regression. Wiley; New York: 2000.

50. Maldonado G, Greenland S. Simulation study by confounder-selection strategies. Am J Epidemiol1993;138:92336. [PubMed: 8256780]

51. Greenland S. Modeling and variable selection in epidemiologic analysis. Am J Public Health1989;79:3409. [PubMed: 2916724]

52. Shimada T, Watanabe J, Kawajiri K, et al. Catalytic properties of polymorphic human cytochromeP450 1B1 variants. Carcinogenesis 1999;20:160713. [PubMed: 10426814]

53. Bailey LR, Roodi N, Dupont WD, Parl FF. Association of cytochrome P450 1B1 (CYP1B1)polymorphism with steroid receptor status in breast cancer. Cancer Res 1998;58:503841. [PubMed:9823305]

54. Stoliov I, Akarsu AN, Alozie I, et al. Sequence analysis and homology modeling suggest that primarycongenital glaucoma on 2p21 results from mutations disrupting either the hinge region or the

conserved core structures of cytochrome P4501B1. Am J Human Genetics 1998;62:57384.[PubMed: 9497261]

55. Murray M. Drug-mediated inactivation of cytochrome P450. Clin Exp Pharmacol Physiol1997;24:46570. [PubMed: 9248661]

56. Fritsche E, Bruning T, Jonkmanns C, Ko Y, Bolt HM, Abel J. Detection of cytochrome P450 1B1Bfr I polymorphism: genotype distribution in healthy German individuals and in patients withcolorectal cancer. Pharmacogenetics 1999;9:4058. [PubMed: 10471075]



NIH-PAA

uthorManuscript


NIH-PAAuthor

Manuscript


13/19

57. Lindsay J, Wang L, Li Y, Zhou S. Structure, function and polymorphism of human cytosolicsulfertransferases. Current Drug Metab 2008;9:99105.

58. Moreno V, Glatt H, Guino E, et al. Polymorphisms in SULT1A1 and SULT1A2 are not related tocolorectal cancer (letter). Int J Cancer 2004;113:6836. [PubMed: 15455379]

59. Skog K, Johansson M, Jagerstad M. Carcinogenic HCA in model systems and cooked foods: a reviewon formation, occurrence and intake. Food Chem Toxicol 1998;36:87996. [PubMed: 9737435]

60. Gonzalez F, Gelboin H. Role of human cytochromes P450 in the metabolic activation of chemical

carcinogens and toxins. Drug Metab Rev 1994;26:16583. [PubMed: 8082563]61. Okey, AB.; Harper, PA.; Grant, DM.; Hill, RP. Chemical and Radiation Carcinogenesis. In: Tannock,IF.; Hill, RP.; Bristow, RG.; Harrington, L., editors. The Basic Science of Oncology. Vol. 4th edition.McGraw-Hill; Toronto: 2005. p. 25-48.

62. Guengerich FP, Kim DH, Iwasaki M. Role of human cytochrome P450 in the oxidation of many lowmolecular weight cancer suspects. Chem Res Toxicol 1991;4:16879. [PubMed: 1664256]

63. Guengerich FP. Metabolism of chemical carcinogens. Carcinogenesis 2000;21:34551. [PubMed:10688854]

64. Malfatti MA, Felton JS. N-glucuronidation of 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine(PhIP) and N-hydroxy-PhIP by specific human UDP-glucuronosyltransferases. Carcinogenesis2001;22:108793. [PubMed: 11408353]

65. Hammons G, Milton D, Stepps K, Guengerich F, Tukey R, Kadlubar F. Metabolism of carcinogenicheterocyclic and aromatic amines by recombinant human cytochrome P450 enzymes. Carcinogenesis

1997;18:8514. [PubMed: 9111224]66. Gonzalez FJ. The use of gene knockout mice to unravel the mechanisms of toxicity and chemical

carcinogenesis. Toxicol Lett 2001;120:199208. [PubMed: 11323178]

67. Malfatti MA, Dingley KH, Nowell-Kadlubar S, et al. The urinary metabolite profile of the dietarycarcinogen 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine is predictive of colon DNA adductsafter a low-dose exposure in humans. Cancer Res 2006;66:105417. [PubMed: 17079477]

68. Slattery ML, Edwards SL, Samowitz W. Stage of colon cancer at diagnosis: implications for riskfactor associations? Int J Epidemiol 1998;27:3827. [PubMed: 9698124]

69. Beef Information Centre. http://www.beefinfo.org/nutrient_data.cfm#faq11



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Table 1

Distribution of colorectal cancer cases, controls, and age-adjusted1 odds ratio (AOR) estimates for red meat intake andred meat doneness consumption

Variable

Cases (n=1095)

N2

(%)

Controls (n=1890)

N2

(%)AOR

(95% CI)

Red meat (servings/week)3

0 - 2 307 (29) 697 (37) 1.00 2.1 - 3 224 (21) 370 (20) 1.37 (1.10, 1.70) 3.1 - 5 265 (25) 417 (22) 1.45 (1.18, 1.78) > 5 276 (26) 378 (20) 1.67 (1.36, 2.05)Red meat doneness (servings/week) 3

2 rare/regular 234 (22) 511 (28) 1.00

2 well-done 278 (27) 487 (27) 1.23 (0.99, 1.53)

> 2 rare/regular 211 (20) 373 (21) 1.24 (0.98, 1.56)

> 2 well-done 321 (31) 446 (25) 1.57 (1.27, 1.93)

1Age at colorectal cancer diagnosis date for cases and referent date (June 30, 1999) for controls

2Numbers may not add to total due to missing values

3Intake 2 years ago



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Table 2

Distribution of colorectal cancer cases and controls, age-adjusted odds ratio (AOR) estimates and 95% confidenceintervals (CI) for several polymorphisms in genes encoding enzymes involved in the metabolism of carcinogens

Genetic Polymorphismsrs

Cases (N=842)N (%)

Controls (N=1251)N (%)

AOR (95% CI)

CYP1A2-163

AA 427 (51) 625 (50) 1.00 AC 334 (40) 501 (40) 0.98 (0.82, 1.18) CC 74 (9) 121 (10) 0.89 (0.65, 1.22)CYP1A1-2455

AA 755 (91) 1128 (91) 1.00 AG 68 (8) 110 (9) 0.92 (0.67, 1.87) GG 7 (1) 7 (1) 1.30 (0.43, 3.91)CYP1A1-3801

TT 647 (78) 971 (78) 1.00 TC 180 (22) 254 (20) 1.04 (0.84, 1.29) CC 6 (1) 22 (2) 0.40 (0.16, 1.00)CYP1A1 combined variants (derived)

Wildtype 1 649 (78) 976 (78) 1.00 Increased activity 2 179 (22) 268 (22) 0.99 (0.80, 1.22)CYP1B1-142

CC 424 (51) 624 (50) 1.00 CG 347 (42) 518 (42) 0.99 (0.83, 1.20) GG 61 (7) 107 (9) 0.81 (0.58, 1.14)CYP1B1-4326

CC 283 (34) 407 (33) 1.00 CG 382 (46) 604 (48) 0.91 (0.75, 1.12) GG 166 (20) 237 (19) 1.03 (0.80, 1.33)CYP1B1-4390

AA 549 (66) 867 (69) 1.00 AG 262 (32) 340 (27) 1.21 (1.00, 1.47) GG 21 (3) 42 (3) 0.82 (0.48, 1.40)CYP1B1 combined variants (derived)

Wildtype 3 159 (19) 245 (20) 1.00 Increased activity 4 668 (81) 1003 (80) 1.05 (0.84, 1.31)CYP2C9-430

CC 645 (77) 911 (73) 1.00 CT 172 (21) 321 (26) 0.76 (0.62, 0.94) TT 16 (2) 17 (1) 1.30 (0.65, 2.60)CYP2C9-1075

AA 719 (86) 1078 (86) 1.00 AC 114 (14) 162 (13) 1.06 (0.82, 1.37) CC 1 (0) 9 (1) 0.18 (0.02, 1.41)



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Cases (N=842)N (%)


AOR (95% CI)

CYP2E1-1293

GG 784 (94) 1162 (93) 1.00 CG 48 (6) 85 (7) 0.83 (0.58, 1.20) CC - - -CYP2E1-7362

TT 665 (80) 1008 (81) 1.00 AT 161 (19) 228 (18) 1.08 (0.86, 1.35) AA 8 (1) 12 (1) 1.02 (0.41, 2.51)NAT1-459

AG 42 (5) 57 (5) 1.00 GG 789 (95) 1187 (95) 0.93 (0.62, 1.41) AA - - -NAT1-1088

TT 509 (61) 764 (61) 1.00 AT 285 (34) 415 (33) 1.04 (0.86, 1.25)

AA 39 (5) 68 (6) 0.83 (0.55, 1.26)NAT1 combined variants (derived)

Slow acetylator (459GG + 1088TT) 473 (57) 714 (57) 1.00 Fast acetylator (all other combinations) 356 (43) 528 (43) 1.01 (0.85, 1.21)NAT2-341

CT 403 (48) 609 (49) 1.00 TT 287 (35) 383 (31) 1.13 (0.93, 1.38) CC 143 (17) 257 (21) 0.84 (0.66, 1.08)NAT2-590

GG 410 (49) 644 (52) 1.00 AG 365 (44) 505 (41) 1.12 (0.93, 1.34)

AA 60 (7) 98 (8) 0.95 (0.67, 1.34)NAT2-857

GG 788 (95) 1191 (95) 1.00 AG 44 (5) 57 (5) 1.13 (0.76, 1.70) AA - - -NAT2 combined variants(derived)

Slow acetylator 5 458 (55) 736 (59) 1.00 Fast acetylator 6 374 (45) 511 (41) 1.20 (1.01, 1.44)mEH-17673

TT 404 (49) 610 (49) 1.00 CT 354 (43) 526 (42) 1.01 (0.84, 1.22) CC 74 (9) 113 (9) 1.00 (0.73, 1.38)SULT1A1-638

GG 396 (48) 578 (46) 1.00



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Cases (N=842)N (%)


AOR (95% CI)

GA 353 (42) 523 (42) 0.97 (0.80, 1.17) AA 85 (10) 148 (12) 0.85 (0.63, 1.15)GSTM1 locus deletion

0 (no activity) 441 (53) 661 (53) 1.00 1 (wild-type) 395 (47) 588 (47) 0.98 (0.82, 1.17)GSTM3

AGG/AGG 599 (72) 893 (72) 1.00 AGG/deletion 203 (24) 327 (26) 0.92 (0.75, 1.13) deletion/deletion 31 (4) 28 (2) 1.64 (0.97, 2.76)GSTT1 locus deletion

0 (no activity) 157 (19) 219 (18) 1.00 1 (wild-type) 679 (81) 1029 (83) 0.89 (0.71, 1.12)UGT1A1 (*28)

HET 375 (46) 551 (44) 1.00 TA6 361 (44) 570 (46) 0.93 (0.77, 1.13)

TA7 88 (11) 126 (10) 1.03 (0.76, 1.40)UGT1A7-W208R

TT 295 (35) 465 (37) 1.00 CT 414 (50) 600 (48) 1.07 (0.88, 1.30) CC 125 (15) 184 (15) 1.05 (0.80, 1.38)UGT1A7-N129K

GG 338 (42) 492 (40) 1.00 GT 376 (46) 577 (47) 0.95 (0.79, 1.15) TT 98 (12) 169 (14) 0.85 (0.64, 1.14)UGT1A7 combined variants(derived)

Wildtype 287 (35) 462 (37) 1.00 Slightly reduced activity (387TG/GG +622TC)

407 (50) 592 (48) 1.09 (0.90, 1.32)

Very reduced activity (387GG + 622CC) 117 (15) 184 (15) 1.01 (0.76, 1.32)AHR-1661

GG 646 (76) 986 (79) 1.00 AG 168 (20) 244 (20) 1.04 (0.83, 1.29) AA 20 (2) 16 (1) 1.90 (0.97, 3.70)

AOR= age-adjusted odds ratio

rsThe reference sequence (rs) for each SNP is provided in the Methods section of this paper.

1CYP1A1: 2455AA/AG +3801TT or 2455GG +3801TC/CC

2CYP1A1: 2455AA/AG +3801CC/TC or 2455AA/AG +3801TC

3CYP1B1: 142CC +4326CC/CG +4390AA/AG/AA or 142CG +4326CC +4390AA or 142GG +4326CC +4390AG

4CYP1B1: all other combinations not listed in footnote 3



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5NAT2 (slow): 191GG + (341CC+590GG+857GG or 341TC+590GA+857GG or 341TC+590GG+857GA or 341TT+590GG+857GA/AA or 341TT

+590GA+857GA or 341TT+590AA+857GG)

6NAT2 (fast): all other combinations not listed in footnote 5



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Table 3

Odds ratio (OR) estimates and 95% confidence intervals (CI) for intake of red meat by doneness (cases vs. controls)stratified by selected genotypes that appear to possibly modify the red meat doneness and colorectal cancer riskassociation

----------------------------------------- Genotype --------------------------------

Red meatdoneness intake(servings/week)

OR1

(95% CI) OR1

(95% CI) OR1

(95% CI) p4

CYP1B1-4326

CCN=690 (33%)

GGN=403 (19%)

CGN=986 (47%)

2 rare/regular 1.00 1.00 1.00 0.06

2 well-done 1.14 (0.74, 1.77) 0.70 (0.39, 1.26) 1.65 (1.14, 2.38)

> 2 rare/regular 1.77 (1.11, 2.84) 0.64 (0.34, 1.20) 1.49 (1.00, 2.21)

> 2 well-done 2.14 (1.39, 3.31) 1.16 (0.65, 2.06) 1.86 (1.27, 2.70)

CYP1B1 combined variants (derived)

Wildtype2

N=404 (20%)Increased activity3

N=1671 (80%)-

2 rare/regular 1.00 1.00 0.04

2 well-done 2.32 (1.27, 4.25) 1.09 (0.82, 1.44)

> 2 rare/regular 2.48 (1.28, 4.80) 1.16 (0.86, 1.56)

> 2 well-done 4.09 (2.17, 7.71) 1.52 (1.15, 2.01)

SULT1A1-638

GGN=974 (47%)

AA/GAN=1109 (53%)

-

2 rare/regular 1.00 1.00 0.03

2 well-done 1.47 (1.02, 2.12) 1.06 (0.75, 1.50)

> 2 rare/regular 1.99 (1.34, 2.97) 0.93 (0.64, 1.34)

> 2 well-done 2.43 (1.66, 3.57) 1.39 (0.99, 1.95)

Note: data for the many other genotypes listed in Methods section are not presented because there was no evidence of effect modification

1OR: odds ratio, model is adjusted for age and sex; the control group is always the referent

2CYP1B1: 142CC +4326CC/CG +4390AA/AG/AA or 142CG +4326CC +4390AA or 142GG +4326CC +4390AG

3CYP1B1: all other combinations

4Likelihood Ratio Statistic p value after the addition of the product term (meat intake*genotype) to the model (< 0.05 is significant)


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