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The American Journal of Clinical Nutrition logoLink to The American Journal of Clinical Nutrition
. 2012 Sep 26;96(5):1119–1128. doi: 10.3945/ajcn.112.040345

Using gene-environment interaction analyses to clarify the role of well-done meat and heterocyclic amine exposure in the etiology of colorectal polyps123

Zhenming Fu, Martha J Shrubsole, Guoliang Li, Walter E Smalley, David W Hein, Zhi Chen, Yu Shyr, Qiuyin Cai, Reid M Ness, Wei Zheng
PMCID: PMC3471199  PMID: 23015320

Abstract

Background: The role of well-done meat intake and meat-derived mutagen heterocyclic amine (HCA) exposure in the risk of colorectal neoplasm has been suggested but not yet established.

Objective: With the use of gene-environment interaction analyses, we sought to clarify the association of HCA exposure with colorectal polyp risk.

Design: In a case-control study including 2057 colorectal polyp patients and 3329 controls, we evaluated 16 functional genetic variants to construct an HCA-metabolizing score. To derive dietary HCA-exposure amount, data were collected regarding dietary intake of meat by cooking method and degree of doneness.

Results: A 2-fold elevated risk associated with high red meat intake was found for colorectal polyps or adenomas in subjects with a high HCA-metabolizing risk score, whereas the risk was 1.3- to 1.4-fold among those with a low risk score (P-interaction ≤ 0.05). The interaction was stronger for the risk of advanced or multiple adenomas, in which an OR of 2.8 (95% CI: 1.8, 4.6) was observed for those with both a high HCA-risk score and high red meat intake (P-interaction = 0.01). No statistically significant interaction was found in analyses that used specific HCA exposure derived from dietary data.

Conclusion: High red meat intake is associated with an elevated risk of colorectal polyps, and this association may be synergistically modified by genetic factors involved in HCA metabolism.

INTRODUCTION

High meat intake has been shown to be associated with an increased risk of colorectal cancer (1, 2). Heterocyclic amines (HCAs)4 are mutagens found in meats cooked at high temperatures (35), which may explain part of the positive association between meat intake and colorectal cancer risk. Colorectal adenomatous polyps (adenomas) are well-established precursors of colorectal cancer (6, 7). Recent studies also suggest that some hyperplastic polyps may progress to cancer (8). Several previous observational studies have provided support for a possible association between the risk of colorectal polyps and a high intake of red meat (917). It is unclear, however, whether the meat/polyp association is mediated through high exposure to meat-derived mutagens or other constituents in meat. Observational studies, however, have multiple inherent limitations, including potential selection bias and confounding effects. Additional research, such as that using Mendelian randomization analyses, is needed to reduce bias associated with observational studies.

Meat-derived HCAs are procarcinogens, which must be activated by metabolizing enzymes to exert their carcinogenic effects (1820). Some phase I enzymes [including cytochrome P450 (CYP) 1A2 and CYP1B1] and phase II enzymes [such as sulfotransferases (SULTs) and N-acetyltransferases (NATs)] are involved in the bioactivation of HCAs. On the other hand, some phase II enzymes, including UDP-glucuronosyltransferases (UGTs) and glutathione S-transferases (GSTs), are responsible for the detoxification of HCAs (20). The aryl hydrocarbon receptor (AhR) is an important xenobiotic signaling mediator that enhances the expression of both phase I and II enzymes (21), which affect HCA metabolism. Therefore, the internal dose of HCA exposure, and the resulting biological effects, is influenced by the balance of enzymes that activate and detoxify HCAs. The amount and function for key HCA-metabolizing enzymes are determined, in part, by genetic polymorphisms of genes encoding these enzymes. Therefore, the categorization of HCA-metabolizing enzyme genotypes provides a tool to further classify participants into groups with different internal HCA-exposure levels among those with the same external (dietary) HCA-exposure level. In other words, if HCA exposure is causally associated with colorectal polyp risk, we would expect that this association may be modified by genetic polymorphisms of enzymes involved in HCA metabolism, and subjects with a high internal exposure to biologically active HCA, as predicted by both external exposure assessment and HCA-metabolizing pattern, may be at a particularly elevated risk of polyps. We used data and DNA samples collected in the Tennessee Colorectal Polyp Study to test this hypothesis. Because HCA-metabolizing genotypes are established through the random assortment process during gamete formation, they should be independent of external HCA exposure and are unlikely to be related to confounding factors. Therefore, our study is consistent with the Mendelian randomization analysis in studying gene-environment interactions in the risk of diseases, which helps to reduce or even eliminate potential bias associated with observation studies (2224).

SUBJECTS AND METHODS

Recruitment of study participants

The Tennessee Colorectal Polyp Study is an ongoing colonoscopy-based case-control study conducted in Nashville, Tennessee. Detailed methods used in this study were described elsewhere (15, 25). Eligible participants aged 40–75 y were identified from patients scheduled for colonoscopy at an academic medical center (Vanderbilt University Medical Center) and a Veterans Affairs (VA) medical center (Tennessee Valley Health System, Nashville, TN) between 1 February 2003 and 26 March 2010. The study was approved by the Institutional Review Boards of all participating institutions.

Excluded from our study were participants who had genetic colorectal cancer syndromes or a history of inflammatory bowel disease, adenomatous polyps, or any cancer other than nonmelanoma skin cancer. Of 10,074 eligible participants, 7330 (72.8%) provided written informed consent, of whom 6331 (86.4% of responders) completed a telephone interview. On the basis of the colonoscopy and pathologic findings, polyp cases were categorized as follows: cases with hyperplastic polyps only and cases with any adenomas (including patients with adenoma only or synchronous adenoma and hyperplastic polyp). Eligible controls were participants who had received a complete colonoscopy reaching the cecum and were found to be polyp-free. Twenty-four cases were excluded because of missing data. Of those with completed interviews, 5386 participants were genotyped. The current analyses included 1527 cases with any adenomas, 530 cases with hyperplastic polyps only, and 3329 polyp-free controls. On the basis of the endoscopic report, advanced adenomas were defined as adenomas with a diameter ≥1 cm, high-grade dysplasia, or tubulovillous or villous morphology.

Assessment of dietary and other lifestyle factors

In the telephone interview, participants were asked about medication use, demographics, medical history, and selected lifestyle factors; the interview included the use of a meat-specific questionnaire regarding 11 food items as described in detail previously (15, 26). This meat-specific questionnaire was developed to assess carcinogen exposure from intake of cooked meat (27). The questionnaire has been used in many previous studies (11, 14, 16, 28, 29), including our previous study of breast cancer (30). Data regarding meat-intake frequency, usual portion size, and degree of doneness were obtained for 11 meat items by cooking method [oven-broiled or oven-baked, grilled or barbecued, pan fried, deep fried (for chicken and fish), and all other ways]. Participants were asked to report their usual preference of meat doneness by using a series of color photographs. Information about cigarette smoking, alcohol drinking, and nonsteroidal antiinflammatory drug (NSAID) use was also collected in the questionnaires. Regular cigarette smoking was defined as smoking ≥1 cigarette/d for ≥3 mo continuously. Regular alcohol drinking was defined as consumption of ≥5 drinks/wk for 12 mo continuously. Regular NSAIDs users were defined as those who used NSAIDs ≥3 times/wk for ≥12 mo continuously. All cutoffs for dietary intake were based on quartile distributions in control participants.

Genetic variant selection and genotyping assays

For this study, we selected 10 key enzymes involved in HCA metabolism. These enzymes are categorized into HCA activation and detoxification (Table 1). Through an extensive literature search, we identified 24 known functional genetic variants in genes involved in the HCA-metabolism pathway (Table 1). Of these, 22 were successfully genotyped. Variants that failed in the genotyping were C1095A (3′UTR, rs15561) and T1088A (3′UTR, rs1057126) in the NAT1 gene. Four NAT1 single nucleotide polymorphisms (SNPs), ie, C97T (R33Stop), C190T (R64W), C620T (T207I), and A752T (D251V) were monomorphic in our population and thus were excluded. As a result, 2 NAT1 SNPs [ie, NAT1*14 (G560A, rs4986782) and NAT1*15 (C559T, rs5030839)] were left, which could not be used to impute NAT1 phenotype; thus, they were excluded from the analysis. The 16 genetic variants included in the analysis were AhR (G1661A, rs2066853), CYP1A2*1K (C163A, rs762551), CYP1B1*3 (G4329C, rs1056836), CYP1B1*4 (A4393G, rs1800440), SULT1A1*2 (G638A, rs9282861), EPHX1 (T337C, rs1051740), UGT1A7*9 (G343A, rs61261057), GSTM1 homozygous deletion, GSTT1 homozygous deletion, and 7 NAT2 SNPs [(C282T, rs1041983), (A803G, rs1208), (C481T, rs1799929), (G590A, rs1799930), (G857A, rs1799931), (G191A, rs1801279), and (T341C, rs1801280)].

TABLE 1.

Polymorphisms in selected heterocyclic amine metabolizing genes and their effect on enzyme function

Gene Variant (alleles)12 Risk allele frequency3 Amino acid change Effect of risk allele on metabolizing enzymatic activity
Activation enzymes
AHR rs2066853(G/A) 0.183 R554K Higher inducibility (21)
CYP1A2 rs762551 (C/A) 0.716 NA Increasing enzymatic activity (31)
CYP1B1 rs1056836 (G/C) 0.532 V432L Increasing mRNA expression (32)
rs1800440 (A/G) 0.827 N453S Increasing enzymatic activity (33)
NAT24 rs1041983 (C/T) 0.335 Y94Y The combined effect of all 7 SNPs was categorized as slow (reference), intermediate, and rapid acetylation phenotypes (increasing enzymatic activity) (34, 35)
rs1208 (A/G) 0.426 R268K
rs1799929 (C/T) 0.409 L161L
rs1799930 (G/A) 0.711 R197Q
rs1799931 (G/A) 0.048 G286E
rs1801279 (G/A) 0.008 R64Q
rs1801280 (C/T) 0.570 I114T
SULT1A1 rs9282861 (G/A) 0.264 R213H Increasing enzymatic activity (36, 37)
Detoxification enzymes
SULT1A1 rs1051740 (T/C) 0.290 Y113H Decreasing enzymatic activity (38)
UGT1A7 rs61261057 (G/A) 0.003 G115S Decreasing enzymatic activity (39)
GSTM1 Homozygous deletion 0.486 Null Depleted enzymatic activity (40, 41)
GSTT1 Homozygous deletion 0.186 Null Depleted enzymatic activity (41)
1

Database of single nucleotide polymorphisms (http://www.ncbi.nlm.nih.gov/SNP/).

2

Risk allele is highlighted in bold.

3

Frequency of the risk allele among controls in the Tennessee Colorectal Polyp Study.

4

Database of single nucleotide polymorphisms (http://louisville.edu/medschool/pharmacology/nat/). See Supplemental Table 3 under “Supplemental data” in the online issue for details on phenotype imputation.

Genotyping assays were conducted by using genomic DNA extracted from blood or buccal cells (42). All allelic gene polymorphisms were assessed by TaqMan OpenArray system. The TaqMan OpenArray Assay-on-Demand reagents were available from Applied Biosystems (ABI) for all SNPs except NAT2 G191A (rs1801279). The primers for NAT2 G191A (rs1801279) polymorphisms are self-designed and synthesized by ABI (primers were GGAGTTGGGCTTAGAGGCTATTTT and CAGAAGTTGATTGACCTGGAGACA; probes were VIC-CCACCCCGGTTTC and FAM-CCCACCCTGGTTTC). The primers and probes for these SNPs were preloaded by ABI. DNA sample (2.5 μL) and TaqMan Universal PCR Master Mix (2.5 μL) were mixed in a 384-well plate. Polymerase chain reaction (PCR) was performed, consisting of an initial denaturation step at 93°C for 10 min and 50 cycles at 95°C for 45 s, 94°C for 13 s, and 53°C for 134 s and a post-PCR hold at 25°C for 2 min. The fluorescence imaging of 3 genotyping plates could be performed together with the ABI OpenArray NT Imager. Allele frequencies were determined by ABI OpenArray software (AutoCaller). Laboratory staff members were blinded to the case-control status of the samples. Quality control protocols for genotyping assays were used as described previously (42). Briefly, each 384-well plate contained 4 water blanks, 8 Centre d'Etude du Polymorphisme Humain 1347-02 DNA, and 16 blinded quality-control samples. The blinded quality-control samples were taken from the second tube of study samples included in the study. Quality-control samples were distributed across the 384-well plates. Concordance rate for the blinded quality-control samples was 100% for all of these SNPs. In addition, the DNA of 45 white samples from the HapMap and/or Perlegen projects was purchased from Coriell Cell Repositories (http://locus.umdnj.edu/ccr/) and genotyped for all 20 SNPs. The average consistency rate of the 20 SNPs was 99.3% compared with data from HapMap (http://www.hapmap.org) and Perlegen (http://genome.perlegen.com). Call rates for all SNPs were >95% (mean call rate = 97.0%), and all genotypes were consistent with Hardy-Weinberg equilibrium with P > 0.05 in controls.

Statistical analysis

Meat intake and meat-derived mutagen exposure were calculated as previously described (26). Briefly, meats were classified by type (total meat and red meat) for statistical analysis. The software CHARRED (http://www.charred.cancer.gov/), developed by the US National Cancer Institute (27), was used to estimate exposure levels to meat-derived mutagens, including 2-amino-3,8-dimethylimidazo[4,5-f]-quinoxaline (MeIQx), 2-amino-3,4,8-trimethylimidazo-[4,5-f]-quinoxaline (DiMeIQx), 2-amino-I-methyl-6-phenylimidazo-[4,5-b]-pyridine (PhIP), benzo[α]pyrene, and the overall mutageneity index, which measures overall mutageneity by revertant colonies per gram of daily meat intake through a standard Ames test with Salmonella typhimurium strain TA98 (43).

Each polymorphism was tested in controls to ensure fitting with Hardy-Weinberg equilibrium. A risk allele was defined as an allele that either increases the activity or amount of an HCA-activation enzyme or reduces the activity or amount of an HCA-detoxification enzyme. Predicted NAT2 phenotype was derived based on a combination of several SNPs provided elsewhere (see Supplemental Table S1 under “Supplemental data” in the online issue) and was classified as slow, intermediate, or fast, with a score of 0, 1, or 2, respectively. For 9 additional genetic variants, each participant received a score of 0, 1, or 2 for carrying 0, 1, or 2 risk alleles, respectively (see Supplemental Table S2 under “Supplemental data” in the online issue). The HCA-metabolizing score was derived by summing the individual risk score across all enzymes involved in HCA activation (AhR, CYP1A2, CYP1B1, NAT2, and SULT1A1) and detoxification [EPHX1 (epoxide hydrolase), UGT1A7, GSTM1, and GSTT1) with a total possible score of 17. The median overall HCA-metabolizing score is 10 in control subjects, and this score was used to classify subjects into low-risk and high-risk HCA-metabolizing groups. By using a cubic spline regression model, we found that among those in the top quartile group of total meat intake, this score was positively associated with polyp risk (P-trend = 0.090). No association of this score with polyp risk was observed among those in the bottom group, the lower 75% intake of total meat. Because there is no threshold effect for this score, it was reasonable to use the median as the cutoff in our analyses.

General linear models and Mantel-Haenszel chi-square tests were used to compare the distribution of demographic characteristics and known or hypothesized colorectal cancer risk factors between HCA-metabolizing risk groups and between case and control groups, with additional adjustment for age and sex when appropriate. Unconditional logistic regression models were used to estimate ORs and their 95% CIs for the association between genetic and lifestyle risk factors and polyp risk. ORs were adjusted for known or hypothesized colorectal cancer risk factors and selected confounders for colorectal adenoma, which showed significantly different distributions between cases and controls. Variables selected for multivariate analysis included age (40–49, 50–59, 60–64, or ≥65 y), sex, study site (academic medical center or VA medical center), educational attainment (high school or less, some college, college graduate, or graduate or professional education), cigarette smoking status (never, former, current, or pack-years), regular alcohol consumption (never, former, or current), BMI (continuous), regular exercise (yes or no), regular NSAID use (never, former, or current), year of recruitment, recruitment before or after colonoscopy, and total energy intake level. Energy intake level for those who did not provide FFQ information (n = 21) were input with age- (40–49, 50–59, 60–64, or ≥65 y) and sex-specific mean values. P values for the linear trend tests were derived by treating the quartile variable as a continuous variable in the models by assigning 0, 1, 2, and 3 to quartiles 1 through 4, respectively (44). Likelihood ratio tests were used to evaluate multiplicative interactions by comparing the models with and without interaction terms (44). Interaction tests were based on the analysis by using quartile distributions of dietary intake and a dichotomized genetic summary score. P values ≤0.05 (2-sided probability) were considered statistically significant. All analyses were conducted by using SAS statistical software (version 9.2; SAS Institute).

RESULTS

The function of HCA-metabolizing enzymes and functional genetic variants of these enzymes included in this analysis are summarized in Table 1. Highlighted in bold are the risk alleles, which are predicted to increase internal HCA-carcinogen exposure, based on results from in vitro functional studies.

Distributions of selected demographic characteristics and major risk factors for colorectal cancer are presented in Table 2 for the 2 polyp case groups and the polyp-free controls. The proportion of polyp-free controls was higher in the academic medical center than in the VA medical center. Compared with controls, polyp cases were more likely to be male, smokers, and regular alcohol consumers and less likely to use NSAIDs regularly. Cases also had a higher BMI, lower educational attainment, and higher daily total energy intake than did controls. Cases with any adenoma were older than controls, whereas hyperplastic polyp–only cases were similar in age to controls. Case-control distributions of race and indication for colonoscopy were comparable. Compared with controls, cases consumed a significantly higher amount of total meat and red meat, had a higher exposure to HCAs (MeIQx, PhIP, and DiMeIQx), and had a higher overall meat mutageneity.

TABLE 2.

Selected demographic characteristics and major known risk factors for colorectal cancer by study group: the Tennessee Colorectal Polyp Study, 2003–20101

Polyp cases
Characteristic Controls(n = 3329) Any adenoma2(n = 1527) P value3 HPP only(n = 530) P value3
Study site (%) <0.001 <0.001
 Vanderbilt University 72.8 57.5 54.0
 Veterans Affairs 26.2 42.5 46.0
Age (y) 56.8 ± 7.64 58.8 ± 7.2 <0.001 56.7 ± 7.9 0.823
Sex, female (%) 43.7 24.8 <0.001 30.0 <0.001
Indications for colonoscopy (%)5 0.158 0.576
 Screening 58.6 57.2 56.1
 Other 41.4 42.8 43.9
Educational attainment (%)5 <0.001
 High school or less 24.5 31.0 <0.001 33.0
 Some college 28.7 29.8 31.3
 College graduate 20.3 20.0 18.6
 Graduate or professional education 26.5 19.3 17.1
Race, white (%) 87.4 87.4 0.977 90.4 0.752
Colorectal cancer, family history (%)5 8.4 9.9 0.258 8.1 0.371
Regular cigarette smoking (%)5 49.1 61.3 <0.001 72.6 <0.001
Regular alcohol consumption (%)5 43.1 47.0 <0.001 50.4 <0.001
BMI (kg/m2)6 28.1 28.8 0.009 28.9 <0.001
Regular exercise (%)5 57.5 52.2 <0.001 53.6 <0.001
Regular NSAID use (%)5 52.9 50.8 0.398 46.8 0.015
Total energy intake (kcal/d)56 2031 2101 <0.001 2093 <0.001
Total meat intake (g/d)6 110.3 127.3 <0.001 131.2 <0.001
Red meat intake (g/d)6 57.5 76.3 <0.001 79.9 <0.001
MeIQx (ng/d)6 57.2 75.3 <0.001 78.6 <0.001
PhIP (ng/d)6 262.9 300.7 <0.001 297.5 0.018
DiMeIQx (ng/d)6 4.8 5.9 <0.001 6.2 <0.001
Overall meat mutageneity index 10,577 12,106 <0.001 13,198 0.002
1

DiMeIQx, 2-amino-3,4,8-trimethylimidazo-[4,5-f]-quinoxaline; HPP, hyperplastic polyp; MeIQx, 2-amino-3,8-dimethylimidazo[4,5-f]-quinoxaline; NSAID, nonsteroidal antiinflammatory drug; PhIP, 2-amino-I-methyl-6-phenylimidazo-[4,5-b]-pyridine.

2

Defined as adenoma only or synchronous adenoma and hyperplastic polyp.

3

Derived from t test for continuous variables and chi-square test for categorical variables. P values reflect the comparison of controls with each of the case groups, and an adjusted P value of 0.025 (0.05/2) was considered statistically significant.

4

Mean ± SD (all such values).

5

Standardized by age (40–49, 50–59, 60–64, and ≥65 y) and sex distribution of all study participants.

6

All values are means.

No apparent significant association was observed between the summary HCA-metabolizing risk score and the risk of any polyp groups, including patients with all polyps, any adenoma, or hyperplastic polyp only (Table 3). Data regarding the association of polyp risk with each of the SNPs in HCA-metabolizing enzymes are presented elsewhere (see in Supplemental Table S2 under “Supplemental data” in the online issue). Again, no statistically significant association was found with any of these SNPs.

TABLE 3.

Association between HCA-metabolizing scores and colorectal polyp risk: the Tennessee Colorectal Polyp Study, 2003–20101

Polyp cases
All polyps (n = 2057)
Any adenoma3 (n = 1527)
HPP only (n = 530)
HCA-metabolizing score by quartile (score)2 Controls (n = 3329) n OR (95% CI)4 n OR (95% CI)4 n OR (95% CI)4
Q1 (5, 9) 1672 1041 1.0 (reference) 776 1.0 (reference) 265 1.0 (reference)
Q2 (10) 751 439 0.9 (0.8, 1.1) 316 0.9 (0.7, 1.0) 123 1.1 (0.8, 1.4)
Q3 (11) 518 325 1.0 (0.9, 1.2) 242 1.0 (0.8, 1.2) 83 1.0 (0.8, 1.4)
Q4 (12, 17) 388 252 1.1 (0.9, 1.3) 193 1.1 (0.9, 1.4) 59 1.0 (0.8, 1.4)
P-trend 0.505 0.465 0.710
1

HCA, heterocyclic amine; HPP, hyperplastic polyp; Q, quartile.

2

Defined based on the quartile of overall risk score distribution of controls.

3

Defined as adenoma only or synchronous adenoma and hyperplastic polyp.

4

Adjusted for age, sex, study site, educational attainment, red meat intake, smoking, alcohol consumption, BMI, physical activity, regular use of nonsteroidal antiinflammatory drugs, total energy intake, year of recruitment, and recruitment before or after colonoscopy.

Estimates of the risk of polyps stratified by HCA-metabolizing risk category and dietary exposure variables are shown in Table 4. With the exception of white meat, dietary exposure variables were, in general, more strongly associated with the risk of all polyps combined in those with a high HCA-metabolizing risk (overall score >10) than in those with a low score, and tests for interactions were statistically significant for red meat intake (P = 0.037). A similar pattern of association was observed in the analysis of any-adenoma group (middle panel), in which the modifying effect of HCA-metabolizing risk score was statistically significant for total meat and red meat intake exposure (P-interaction ≤ 0.05 for both). More apparent associations with red meat and HCA-related variables were found in the analysis of more clinically significant adenomas (advanced or multiple adenomas, right column in Table 4) than with the previous 2 case groups. The strongest association was observed for those with both a high HCA-metabolizing score and a high red meat intake (OR: 2.8; 95% CI: 1.8, 4.6; P-interaction = 0.01). No statistically significant finding was observed in analyses of interactions between polyp risk and specific HCA exposure or overall mutageneity index derived by using dietary variants. Analyses conducted among patients with hyperplastic polyps did not show any statistically significant interactions, perhaps because of the small number of cases in that group (data not shown).

TABLE 4.

Joint association of dietary intake with HCA-metabolizing risk category (low or high) with polyp risk: the Tennessee Colorectal Polyp Study, 2003–20101

All polyp cases (n = 2057)
Any adenoma cases3 (n = 1527)
Advanced or multiple adenoma cases (n = 659)
Low risk
High risk
Low risk
High risk
Low risk
High risk
Intake level2 n4 OR (95% CI)5 n4 OR (95% CI)5 n4 OR (95% CI)5 n4 OR (95% CI)5 n4 OR (95% CI)5 n4 OR (95% CI)5
Total meat
 Q1 (low) 264/496 1.0 (Ref) 174/359 1.0 (Ref) 205/496 1.0 (Ref) 139/359 1.0 (Ref) 80/496 1.0 (Ref) 61/359 1.0 (Ref)
 Q2 244/473 1.0 (0.8, 1.3) 155/352 0.9 (0.7, 1.2) 185/473 1.0 (0.8, 1.3) 100/352 0.8 (0.6, 1.0) 71/473 1.0 (0.7, 1.5) 47/352 0.9 (0.6, 1.4)
 Q3 304/489 1.1 (0.8, 1.3) 237/338 1.3 (1.0, 1.7) 219/489 1.0 (0.8, 1.3) 172/338 1.2 (0.9, 1.7) 84/489 0.9 (0.7, 1.3) 80/338 1.4 (0.9, 2.1)
 Q4 390/491 1.1 (0.9, 1.4) 289/331 1.4 (1.1, 1.8) 284/491 1.1 (0.8, 1.4) 223/331 1.3 (1.1, 1.8) 123/491 1.2 (0.8, 1.6) 113/331 1.6 (1.1, 2.3)
 P-trend 0.217 0.002 0.555 0.005 0.391 0.004
 P-interaction 0.061 0.034 0.044
Red meat
 Q1 (low) 219/495 1.0 (Ref) 118/370 1.0 (Ref) 165/495 1.0 (Ref) 90/370 1.0 (Ref) 64/495 1.0 (Ref) 35/370 1.0 (Ref)
 Q2 241/463 1.1 (0.9, 1.4) 200/358 1.5 (1.2, 2.1) 182/463 1.1 (0.9, 1.5) 145/358 1.5 (1.1, 2.1) 68/463 1.1 (0.7, 1.6) 62/358 1.7 (1.1, 2.8)
 Q3 297/489 1.2 (0.9, 1.5) 216/333 1.6 (1.2, 2.1) 219/489 1.1 (0.9, 1.5) 156/333 1.5 (1.1, 2.1) 82/489 1.1 (0.7, 1.5) 76/333 2.0 (1.3, 3.3)
 Q4 445/502 1.4 (1.1, 1.7) 321/319 2.0 (1.5, 2.7) 327/502 1.3 (1.0, 1.7) 243/319 2.0 (1.5, 2.8) 144/502 1.4 (1.0, 2.0) 128/319 2.8 (1.8, 4.6)
 P-trend 0.012 <0.001 0.037 <0.001 0.055 0.001
 P-interaction 0.037 0.035 0.010
White meat
 Q1 (low) 326/517 1.0 (Ref) 263/366 1.0 (Ref) 250/517 1.0 (Ref) 200/366 1.0 (Ref) 103/517 1.0 (Ref) 104/366 1.0 (Ref)
 Q2 354/470 1.2 (1.0, 1.5) 194/358 0.8 (0.7, 1.1) 261/470 1.2 (1.0, 1.6) 137/358 0.8 (0.6, 1.1) 114/470 1.4 (1.0, 1.9) 59/358 0.7 (0.5, 1.0)
 Q3 266/476 1.0 (0.8, 1.3) 175/327 0.9 (0.7, 1.2) 196/476 1.0 (0.8, 1.3) 129/327 0.9 (0.7, 1.3) 72/476 0.9 (0.7, 1.3) 61/327 0.9 (0.6, 1.3)
 Q4 256/486 0.9 (0.7, 1.2) 223/329 1.1 (0.9, 1.5) 186/486 0.9 (0.7, 1.1) 168/329 1.2 (0.9, 1.5) 69/486 0.8 (0.6, 1.2) 77/329 1.1 (0.7, 1.6)
 P-trend 0.238 0.246 0.178 0.257 0.114 0.528
 P-interaction 0.113 0.093 0.133
MeIQx
 Q1 (low) 250/524 1.0 (Ref) 146/397 1.0 (Ref) 187/524 1.0 (Ref) 107/397 1.0 (Ref) 73/524 1.0 (Ref) 46/397 1.0 (Ref)
 Q2 239/469 1.0 (0.8, 1.3) 186/335 1.4 (1.0, 1.8) 183/469 1.1 (0.8, 1.4) 136/335 1.4 (1.0, 1.9) 72/469 1.0 (0.7, 1.5) 67/335 1.5 (1.0, 2.4)
 Q3 314/482 1.2 (1.0, 1.5) 229/319 1.5 (1.2, 2.0) 228/482 1.2 (0.9, 1.6) 169/319 1.6 (1.2, 2.1) 89/482 1.2 (0.8, 1.6) 71/319 1.6 (1.0, 2.5)
 Q4 399/474 1.2 (1.0, 1.5) 294/329 1.6 (1.2, 2.1) 295/474 1.2 (0.9, 1.5) 222/329 1.6 (1.2, 2.2) 124/474 1.2 (0.8, 1.6) 117/329 1.9 (1.3, 2.9)
 P-trend 0.033 0.001 0.088 0.002 0.277 0.003
 P-interaction 0.164 0.105 0.060
PhIP
 Q1 (low) 279/526 1.0 (Ref) 184/395 1.0 (Ref) 206/526 1.0 (Ref) 142/395 1.0 (Ref) 81/526 1.0 (Ref) 72/395 1.0 (Ref)
 Q2 245/474 1.0 (0.8, 1.2) 205/329 1.3 (1.0, 1.6) 184/474 1.0 (0.8, 1.3) 148/329 1.2 (0.9, 1.7) 73/474 1.0 (0.7, 1.5) 68/329 1.2 (0.8, 1.8)
 Q3 331/457 1.3 (1.1, 1.6) 217/345 1.2 (1.0, 1.6) 249/457 1.4 (1.1, 1.8) 154/345 1.2 (0.9, 1.6) 101/457 1.5 (1.0, 2.1) 70/345 1.2 (0.8, 1.7)
 Q4 347/492 1.2 (0.9, 1.5) 249/311 1.5 (1.1, 1.9) 254/492 1.2 (0.9, 1.5) 190/311 1.5 (1.1, 2.0) 103/492 1.2 (0.9, 1.7) 91/311 1.5 (1.0, 2.2)
 P-trend 0.028 0.008 0.026 0.013 0.073 0.049
 P-interaction 0.397 0.489 0.621
DiMeIQx
 Q1 (low) 270/530 1.0 (Ref) 193/391 1.0 (Ref) 206/530 1.0 (Ref) 145/391 1.0 (Ref) 77/530 1.0 (Ref) 70/391 1.0 (Ref)
 Q2 269/459 1.2 (0.9, 1.5) 192/347 1.0 (0.8, 1.3) 196/459 1.2 (0.9, 1.5) 144/347 1.1 (0.8, 1.4) 84/459 1.3 (0.9, 1.9) 57/347 0.9 (0.6, 1.3)
 Q3 284/475 1.1 (0.9, 1.4) 183/324 1.0 (0.8, 1.3) 212/475 1.1 (0.9, 1.5) 132/324 1.0 (0.7, 1.3) 85/475 1.2 (0.8, 1.7) 70/324 1.0 (0.7, 1.6)
 Q4 379/485 1.3 (1.0, 1.6) 287/318 1.4 (1.1, 1.8) 279/485 1.3 (1.0, 1.6) 213/318 1.4 (1.1, 1.9) 112/485 1.2 (0.9, 1.7) 104/318 1.4 (0.9, 2.0)
 P-trend 0.041 0.011 0.080 0.028 0.307 0.047
 P-interaction 0.418 0.438 0.284
Overall mutageneity index
 Q1 (low) 258/526 1.0 (Ref) 158/396 1.0 (Ref) 198/526 1.0 (Ref) 121/396 1.0 (Ref) 75/526 1.0 (Ref) 58/396 1.0 (Ref)
 Q2 265/464 1.2 (0.9, 1.5) 217/338 1.6 (1.2, 2.1) 189/464 1.1 (0.8, 1.4) 152/338 1.5 (1.1, 2.0) 75/464 1.1 (0.8, 1.6) 66/338 1.6 (1.1, 2.5)
 Q3 314/481 1.3 (1.0, 1.6) 217/322 1.6 (1.2, 2.1) 238/481 1.3 (1.0, 1.6) 162/322 1.7 (1.2, 2.3) 96/481 1.3 (0.9, 1.9) 85/322 2.1 (1.4, 3.2)
 Q4 365/478 1.3 (1.0, 1.6) 263/324 1.6 (1.2, 2.1) 268/478 1.2 (1.0, 1.6) 199/324 1.6 (1.2, 2.2) 112/478 1.3 (0.9, 1.9) 92/324 1.7 (1.1, 2.5)
 P-trend 0.015 0.001 0.027 0.002 0.064 0.009
 P-interaction 0.268 0.232 0.334
1

The median overall HCA-metabolizing score is 10 in control subjects. An overall HCA-metabolizing score ≤10 indicates low risk, and an overall HCA-metabolizing score >10 indicates high risk. DiMeIQx, 2-amino-3,4,8-trimethylimidazo-[4,5-f]-quinoxaline; HCA, heterocyclic amine; MeIQx, 2-amino-3,8-dimethylimidazo[4,5-f]-quinoxaline; PhIP, 2-amino-I-methyl-6-phenylimidazo-[4,5-b]-pyridine; Q, quartile; Ref, reference.

2

Cutoff for dietary intake was the quartile intake level of control subjects.

3

Defined as adenoma only or synchronous adenoma and hyperplastic polyp.

4

Reflects the number of cases/controls; counts may not sum to the total because of missing data.

5

Adjusted for age, sex, study site, educational attainment, smoking, alcohol consumption, BMI, physical activity, regular use of nonsteroidal antiinflammatory drugs, total energy intake, year of recruitment, and recruitment before or after colonoscopy.

DISCUSSION

In this study, we showed that increased consumption of red meat synergistically interacted with genetically controlled HCA-metabolizing pattern to increase colorectal polyp risk. These results suggest that the association between red meat intake and risk of colorectal neoplasia may be mediated through exposure to HCA.

In general, the association of polyp risk with individual HCA-exposure variables and overall meat mutageneity derived from dietary meat intake was weaker than that with red meat intake. One possible explanation may be the presence of considerable measurement errors in HCA-exposure assessment, as determined with a food questionnaire and the CHARRED program. Although no statistically significant interaction was found between HCA-metabolizing patterns and any HCA-exposure variables, we did observe a stronger association of these HCA-exposure variables with polyp risk in subjects with a high HCA-metabolizing risk score than in those with a low risk score. It is possible that the overall interaction test may not be statistically significant in an analysis that includes both significant and nonsignificant point estimates. Small sample size could have contributed to the nonsignificant OR in some subgroups. Interestingly, a marginally significant interaction between MeIQx exposure and HCA-metabolizing score was found for advanced/multiple adenomas. MeIQx was shown to be ∼3- to 7-fold more potent than PhIP in carcinogenicity (3). MeIQx is also more strongly associated with cancers than is PhIP (45). Most DiMeIQx and MeIQx were derived from red meat (3). For example, >85% of MeIQx in the current study originated from red meat (data not shown). The exposure level to DiMeIQx is very low (Table 2) and perhaps not informative in the analysis of its association with polyp risk. The mutageneity index is a measure of overall revertant colonies from HCA, benzo[a]pyrene, and other mutagens from well-done meat (27); thus, this index is not a measure of HCA-specific exposure.

Previous studies evaluated only a few HCA-metabolizing genetic variants and yielded inconsistent results for a potential modifying effect of genetic factors on the association of HCA exposure with adenoma (4652) and colorectal cancer risks (5365). The largely null findings reported from previous studies are not surprising because each genetic variant in the HCA-metabolizing pathway plays a small role in the activation or detoxification of HCAs. Therefore, it is important to combine information from multiple genetic variants to capture the HCA-metabolizing pattern of each individual. Additional functional variants likely will be identified in the future in the HCA-metabolizing pathway, which could improve the classification of participants into HCA-metabolizing risk categories. This means the true synergistic effect of genetic factors and HCA exposure could be stronger than what we observed in this study.

As discussed previously, observational studies may suffer from various biases; thus, it is difficult to make causal inference based on results from observational studies alone. It is unlikely that randomized intervention trials would be conducted to directly evaluate the association of HCA exposure and cancer risk. Mendelian randomization analysis provides unique opportunities for a nature's randomization trial, because it uses the random assortment of genetic variants during gamete formation. This is analogous to the random assignment of patients to placebo or treatment arms in a clinical intervention trial; thus, the analysis minimizes and even eliminates potential bias associated with observational studies (22). As discussed recently by Smith (24), gene-environment interaction analyses can be interpreted within Mendelian randomization when certain criteria are met. Our study, in general, fulfilled these criteria:

1) The genotypic profile, as summarized by the HCA-metabolizing risk score, was independent of factors that may confound the association between meat intake levels or HCA-exposure levels and risk of colorectal polyps. We evaluated this criterion by analyzing the distribution of demographic characteristics and risk factors presented in Table 1 by HCA-metabolizing risk categories (see Supplemental Table S3 under “Supplemental data” in the online issue). Our results show no differences between the low and high HCA-metabolizing risk categories regarding distributions of almost all demographic characteristics and risk factors. These findings support the successful Mendelian randomization of our study participants.

2) The HCA genetic-risk profile must only be related to risk of disease through the exposure of interest and not through other pathways. Because many metabolizing genes are pleiotropic, they may be involved in the metabolism of other carcinogens. We selected functional SNPs involved in HCA metabolism and combined them to construct the HCA-specific metabolizing pathway. Because it is highly unlikely that all of the HCA-metabolizing SNPs selected are also involved in the metabolism of another substrate, the metabolizing pathway derived in this study should be more HCA specific than any other substrates. Therefore, the HCA-metabolizing risk score constructed in this study should be more specific for HCA metabolism than for any single variant of HCA-metabolizing enzymes.

We also evaluated the interaction of HCA-metabolizing risk score with cigarette smoking, NSAID use, alcohol intake, BMI, and physical activity. We found no interactions between these factors and polyp risk. Thus, it is unlikely that the potential pleiotropic effects of certain genes could appreciably affect our study results. Finally, on the basis of HCA-metabolizing patterns, the HCA-metabolizing risk score derived from this study should affect the internal effective dose of HCA exposure and thus can further define internal exposure status for those with the same external exposure level of HCAs. Interestingly, the association of cancer risk with aromatic amines or HCA and carcinogen-metabolizing enzymes is among the examples provided by Smith (24) and by Thomas and Conti (23) for Mendelian randomization analyses of gene-environment interactions. However, residual bias might remain in Mendelian randomization analyses. Therefore, additional studies are needed to further clarify the association of red meat intake and HCA exposure in the risk of colorectal tumors.

This study's focus on precursors for colorectal cancer, ie, colorectal polyps, eliminates possible survival bias commonly encountered in cancer case-control studies as a result of failure to recruit patients with a short survival time. Other strengths of this study include the use of colonoscopy to define patient groups, use of a questionnaire specifically designed to capture intake patterns of well-done meat and meat-carcinogen exposures, and large sample size. With questions regarding meat intake by cooking method and degree of doneness, the questionnaire used in this study collects data more relevant for estimating HCA intake than a typical food-frequency questionnaire.

Because all polyps were benign lesions, recall bias and any subsequent lifestyle change after polyp diagnosis is unlikely to be substantial. Most participants (87.8%) were recruited before colonoscopy, and thus before polyp diagnosis, which reduces possible selection bias. Exclusion of participants recruited after colonoscopy (n = 894) did not appreciably change the associations observed. Response bias is always a possible limitation when response rates are not optimal; however, the diagnosis of polyps cannot influence the genotype of study participants, and the genotype is unlikely to be related to HCA external exposure, ie, dietary meat intake. As discussed previously, these potential biases were reduced in our study through Mendelian randomization analysis.

Our findings suggest that genetic variants in metabolic enzymes may modify the association of dietary meat intake and meat-derived mutagen exposures with colorectal polyp risk. Many explanations have been proposed for the association between meat intake and polyp risk (such as meat-derived fat, iron, and HCA). However, by defining the HCA-specific metabolizing risk pattern for each subject, our study specifically implicates a causal role of HCAs in the formation of colorectal cancer precursors. These findings may aid the understanding of colorectal cancer pathogenesis and the development of prevention strategies.

Acknowledgments

We thank the study participants and research staff of the Tennessee Colorectal Polyp Study for their support of this research.

The authors’ responsibilities were as follows—WZ, MJS, QC, and RMN: designed and directed the study; ZF: analyzed the data; QC and GL: directed the genotyping; GL: conducted the genotyping; ZF, WZ, and MJS: drafted the manuscript; RMN and WES: provided support for the clinical operation; DH: provided support to derive NAT1 and NAT2 phenotypes; and ZC and YS: helped with the statistical analysis and data cleaning. All authors reviewed and approved the final draft. None of the authors declared any conflicts of interest.

Footnotes

4

Abbreviations used: ABI, Applied Biosystems; AhR, aryl hydrocarbon receptor; CYP, cytochrome P450; DiMeIQx, 2-amino-3,4,8-trimethylimidazo-[4,5-f]-quinoxaline; GST, glutathione S-transferase; HCA, heterocyclic amine; MeIQx, 2-amino-3,8-dimethylimidazo[4,5-f]-quinoxaline; NAT, N-acetyltransferase; NSAID, nonsteroidal antiinflammatory drug; PCR, polymerase chain reaction; PhIP, 2-amino-I-methyl-6-phenylimidazo-[4,5-b]-pyridine; SNP, single nucleotide polymorphism; SULT, sulfotransferase; UGT, UDP-glucuronosyltransferase; VA, Veterans Affairs.

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