Investigation of interaction between N-acetyltransferase 2 and heterocyclic amines as potential risk factors for colorectal cancer

J.H. Barrett1,7, G. Smith2, R. Waxman3, N. Gooderham4, T. Lightfoot5, R.C. Garner5, K. Augustsson6, C.R. Wolf2, D.T. Bishop1, D. Forman3 and The Colorectal Cancer Study Group*

1 Genetic Epidemiology Division, Cancer Research UK Clinical Centre, St James’s University Hospital, Leeds,
2 Biomedical Research Centre, University of Dundee, Dundee,
3 University of Leeds, Leeds,
4 Imperial College, London,
5 University of York, York, UK and
6 Karolinska Institutet, Sweden


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Conclusions
 References
 
Fast N-acetyltransferase 2 (NAT2) acetylators may be at increased risk of colorectal cancer through the activation of carcinogenic heterocyclic amines (HA), which are produced by meat cooked at high temperatures and are found in cigarette smoke. A study of 500 incident colorectal cancer cases and population controls, matched for age, sex and general practitioner, was conducted in the UK to investigate this hypothesis. Usual meat intake and lifetime smoking habits were estimated using a detailed questionnaire administered by interview. Subjects also indicated how well cooked they ate their meat. Subjects were classified as fast or slow NAT2 acetylators on the basis of NAT2 genotype. Complete genotype data were available on 433 matched pairs. The risk of colorectal cancer showed a steady increase with meat intake, rising to an odds ratio of 1.51 [95% confidence interval (1.03, 2.23)] for the highest versus the lowest quartile, after adjustment for total energy intake, and this was even more pronounced for red meat [odds ratio 1.97 (1.30, 2.98)]. However, this effect was not influenced by the preference for well-done meat. Smoking was also associated with an increased risk [odds ratio 1.47 (1.10, 1.98) for ever- versus never-smokers]. In both cases and controls ~40% of subjects were classified as fast acetylators, and the risks associated with (red) meat intake and smoking did not vary with NAT2 status. This study provides no support for the hypothesis that fast NAT2 acetylators are at increased risk of colorectal cancer, even if exposed to high levels of HA from well-cooked meat or smoking.

Abbreviations: FFQ, food frequency questionnaire; HA, heterocyclic amines; NAT2, N-acetyltransferase 2; SNPs, single nucleotide polymorphisms.


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Conclusions
 References
 
Many epidemiological studies have been conducted investigating meat intake as a risk factor for colorectal cancer. Although there is inconsistency in the results, there is a broad consensus that high consumption, especially of red meat, increases risk (13). More inconsistent results have been found concerning the association between cigarette smoking and colorectal cancer risk, although most recent studies have shown an association with long-term exposure (4). One speculation is that people may be differentially susceptible to the effects of such exposures.

One possible mechanism for the risk associated with red meat consumption is through the potentially carcinogenic heterocyclic amines (HAs) found in meat cooked at high temperatures. Several studies have suggested that the fast phenotype of N-acetyltransferase 2 (NAT2) may confer susceptibility to colorectal cancer (ref. 5, for example), especially in individuals with a diet high in HAs. Fast acetylators may be at increased risk of colorectal cancer because of the activation of these and other procarcinogens. The HAs undergo hepatic N-oxidation and N-glucuronidation, resulting in conjugated N-hydroxy metabolites, which can be transported to the colonic lumen. Within the mucosa, these derivatives can be O-acetylated and can form covalent DNA adducts. The aromatic amine exposure could come from cooked meat or fish or from smoking.

A number of epidemiological studies have investigated this potential interaction between NAT2 and environmental exposures as risk factors for colorectal cancer (510; reviewed in ref. 11). These studies have produced conflicting results. Whereas most studies showed no overall difference in NAT2 acetylator status between cases and controls, some authors found evidence of interactions, although these were either not statistically significant or the results of subgroup analyses.

From the perspective of the limited and confusing literature on the interaction between dietary HAs and N-acetylation with respect to risk of colorectal cancer, we have performed a case-control study in the North of England (Leeds and York) and Scotland (Dundee).

The following primary hypotheses were addressed: does the risk associated with total meat consumption, red meat consumption, preference for well-done meat and smoking differ according to NAT2 genotype?

In response to reported interactions in the literature we also carried out subgroup analyses stratifying by age and gender.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Conclusions
 References
 
Case ascertainment
All histologically confirmed incident cases of colorectal cancer (ICD9 153, 154.0 or 154.1 or ICD10 C18, C19 or C20), aged between 45 and 80 years and diagnosed in the period 1997–2000, were identified in each of three centres: Dundee (Ninewells Hospital, Perth Royal Infirmary), Leeds (Leeds General Infirmary, St James’s Hospital) and York (York District Hospital). Dundee and York cases were identified via ward diaries and patient notes. Eligible patients were approached in hospital and given an information sheet. They were then re-approached within 1 week to obtain written consent to participate.

In Leeds, pathology departments at Leeds General Infirmary and St James’s Hospital each provided information monthly on patients diagnosed with bowel cancer in the previous 4 weeks. Patients in Leeds had been discharged from hospital by the time they were identified. Eligibility was then determined via patient notes. Those who returned a positive consent form were contacted by telephone to arrange an appointment for interview. Those who did not return a consent form within 10 days were re-contacted once by telephone.

The following exclusion criteria were used, and patients in these categories were not approached: a previous primary cancer, history of coeliac disease, familial adenomatous polyposis, diverticular disease <2 years previous to the current cancer diagnosis, colorectal cancer which was not an adenocarcinoma, ulcerative colitis diagnosed within the previous 3 years or non-Caucasian.

Eligible patients were considered inaccessible for the following reasons: history of alcoholism, a psychiatric or brain disorder, blind or deaf, moved or in prison, non-English speakers, involved in another trial, emergency admissions and where general practitioner (GP) or consultant consent was not obtained. These patients were not invited to participate. Those who died before being contacted were also classified as inaccessible.

Local research ethics committee approval was obtained for the study for each centre.

Control selection
An age- and sex-matched control was identified for each consented case. Nearly all age matches were within 1 year. Eligible controls were identified from the patient’s GP practice list. Contact was made initially by post, including a standard letter of invitation by or on behalf of their GP, an information sheet, and an addressed and stamped return envelope. The control who most closely matched the case by age was telephoned to arrange an interview at home. Some additional controls were recruited by this process.

Questionnaire data collection
Diet was assessed in cases and controls using an extensive, 18-page diet and lifestyle questionnaire, the Food Frequency and Epidemiology Questionnaire (FFEQ). The FFEQ was modelled on the questionnaire developed and validated for the European Prospective Investigation into Cancer and Nutrition (12).

Interviews were carried out by experienced research interviewers in hospital in York, in hospital or soon after discharge at home in Dundee, and at home or at a convenient location in Leeds. Interviewees were asked to estimate diet items and frequency of consumption in the year previous to diagnosis, or for controls in the year prior to interview. Completed questionnaires were sent to the University of Leeds for double entry into a customized database. Double entered questionnaires were subsequently checked for accuracy.

For 132 food items the frequency of consuming a ‘medium serving’ or, for some foods, a unit such as a slice or teaspoon was recorded on a nine-point Likert scale as follows: never or less than once per month, 1–3 per month, once a week, 2–4 per week, 5–6 per week, once a day, 2–3 per day, 4–5 per day, 6 or more per day. Interviewees were asked to estimate their average consumption of specific food items by ticking the appropriate box.

Meat consumption and heterocyclic amine intake
Meat items used from the FFEQ for the estimation of HA consumption were as follows: (i) beef: roast, steak, mince, stew or casserole; (ii) beefburgers; (iii) pork: roast, chops, stew or slices; (iv) lamb: roast, chops or stew; (v) chicken or other poultry, e.g. turkey; (vi) bacon and (vii) gravy made with meat juices.

Responses were converted into a monthly number of helpings and summed. Meat consumption was analysed by summing items (i)–(vii) and red meat by summing items (i)–(iv). Total energy intake in kilocalories was estimated from the FFEQ using published standard portion sizes and energy content as described in more detail elsewhere (13). Frequency of meat consumption was adjusted for total dietary intake by regressing on this and using the residuals (plus the overall mean) as a measure of energy-adjusted consumption; this was then categorized by quartile based upon the control distribution.

Meat ‘doneness’, or the outside appearance after cooking, was estimated using verbal and visual prompts. Interviewees were asked to estimate how well cooked they ate grilled or roast meat by ticking one of three possible responses: ‘well done/dark brown’, ‘medium’ or ‘lightly cooked/rare’. Subjects were also asked to select from one of four photographs representing four frying temperatures (225°C, 200°C, 175°C, 150°C) for each of five meat items (pork chops, beefsteak, bacon rashers, beefburgers and meatballs) (14). It has been found that the most marked difference in HA content is between very high and moderate cooking temperatures (15). The picture prompt was therefore used to identify the group of subjects who potentially had the highest HA consumption based upon their choice of the most blackened photograph for one or more meat items.

Exposure to smoking was assessed by asking subjects whether they had ever smoked as much as one cigarette a day for as long as a year. For those who answered positively a more detailed smoking history was taken, recording average number of cigarettes smoked daily at ages 20, 30, 40 and 50 years. Subjects were also asked at what age they started and (if applicable) stopped smoking. Current smokers (defined as those smoking 1 year prior to interview/diagnosis) were asked to estimate their current daily consumption. On the basis of these replies subjects were classified as current, ex- and non-smokers. Degree of exposure among those who had ever smoked was categorized by approximate tertiles based upon the control distribution and on the basis of (i) duration of smoking habit and (ii) lifetime pack-years. (n pack-years is the exposure from smoking one 20-cigarette pack per day for n years or any pattern leading to the same total exposure.) Time since first exposure to smoking was also examined.

NAT2 genotyping
Blood samples were collected from cases, matched controls and some unmatched controls and were stored at –20°C prior to analysis. All DNA analysis was carried out in Dundee. Extraction of EDTA anticoagulated whole blood samples was performed using the Qiagen 96 Spin Blood Kit, following the instructions of the manufacturer.

As reviewed in Hirvonen (16), at least 23 different NAT2 mutations have been identified to date. These include the following four nucleotide transitions: C to T at position 481, G to A at position 590, A to G at position 803 and G to A at 857. The first of these exerts no influence on the amino acid sequence, while the remaining three result in amino acid changes. In this study genotypes were assigned on the basis of these four single nucleotide polymorphisms (SNPs), from which six alleles could be identified (Table IGo).


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Table I. Frequencies of NAT2 alleles identified in the study
 
Some alleles are indistinguishable using this approach but have no impact on phenotype. For example allele NAT2*6A is only distinguished from NAT2*6B by the presence of a C to T substitution at position 282. A few rare alleles could not be detected which may influence NAT2 phenotype, and these are discussed later. In addition the genotypes NAT2*4/*5B and NAT2*5A/*5C could not be distinguished, as can be seen from Table IGo.

NAT2 genotype was determined at each of the polymorphic sites in Table IGo using ‘Taqman’ allele-specific PCR. PCR reactions for each site were performed in the presence of two sequence-specific fluorescent Taqman probes, representing Allele 1 (FAM-label) and Allele 2 (VIC-label) at each site, respectively. Probe and primer sequences and reaction conditions are given in Table IIGo. All reactions were performed in 1x Taqman Universal PCR Master Mix (PE Applied Biosystems, Warmington, UK) in a total volume of 10 µl in a PE Applied Biosystems 9700 Thermocycler (50°C 1 min, 95°C 10 min: 1 cycle, 95°C 15 s, 60/62°C 1 min: 40 cycles). Following PCR amplification, end-point fluorescence was read using an Applied Biosystems ABI PRISM 7700 Sequence Detector. Genotypes were assigned using Allelic Discrimination Software (Applied Biosystems SDS Software v1.7a). Appropriate controls representative of each genotype and no template controls were included. All fluorescent probes were synthesized by PE Applied Biosystems and oligonucleotides by MWG Biotech. The genotyping methods used have been validated previously (17).


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Table II. Taqman genotyping conditions
 
NAT2 phenotype
Previous studies have found that allele NAT2*4 (see Table IGo) acts dominantly to produce the fast acetylator phenotype (16,18). Carriers of one or more copies of this allele were therefore classified as fast acetylators and all other subjects as slow acetylators. Because of the relative frequencies of the NAT2*4/*5B and NAT2*5A/*5C genotypes, all subjects with this ambiguous genotype were classified as fast acetylators.

In addition, subjects in York were invited to undergo a caffeine test so that their NAT2 phenotype could be directly assessed. Subjects were asked to refrain from consuming caffeine-containing food and beverages for 24 h prior to the test and for the duration of the urine collection. First morning urines were voided. A cup of instant coffee (4 g) or pro-plus tablets containing an equivalent amount of caffeine were administered, and subjects were asked to collect their urine for the following 8 h. Urine samples were frozen (–20°C) as soon as possible after collection. Within 2 days of collection, samples were thawed, volumes measured and acidified to pH 3.5 (HCl) then stored at –20°C until analysis.

Caffeine and its metabolites were measured in urine using a modified version of the method described by Butler et al. (19). Briefly, 70% ammonium sulphate solution (200 µl) was added to a 200 µl aliquot of urine in a centrifuge tube and mixed for 2 min before adding 200 µl internal standard (0.12 mg/ml 4-acetamidophenol in chloroform). The mixture was extracted with chloroform: 2-propanol (3:1) and the organic phase was taken to dryness under N2. The sample was reconstituted in 0.05% acetic acid (500 µl) and analysed by HPLC using a Supelco LC-18-DB (5 micron) column, 25 cmx4.6 mm ID with a linear gradient comprising 0.045% acetic acid/9% methanol (A) and 100% methanol (B), at a flow rate of 1.2 ml/min. Individuals were phenotyped for NAT2 using the caffeine metabolite ratio (AAMU + AFMU)/1X (20).

Statistical analysis
Linkage disequilibrium between the four SNPs was assessed using the EH software (21,22). This implements an EM-algorithm (Expectation-Maximization) to estimate haplotype frequencies from genotype data and was used to confirm that no unusual combinations of SNPS are found.

Evidence against Hardy–Weinberg equilibrium was tested in controls using the likelihood ratio {chi}2 statistic. NAT2 allele frequencies were compared between cases and controls and between centres using Pearson’s {chi}2 test. In the subset undertaking the caffeine test, NAT2 phenotype was compared between cases and controls and between fast and slow acetylators (assigned on the basis of genotype) using the Mann–Whitney test.

Frequencies of potential risk factors for colorectal cancer were compared between all cases and controls using {chi}2 tests. The primary analysis was of the matched pairs, using conditional logistic regression. The pairs are matched for age, sex, study centre and general practitioner, which were not controlled for in the unmatched analysis. Effect sizes are presented as odds ratios (ORs), with 95% confidence intervals.

Interaction was investigated between meat consumption and cooking preferences and between HA intake (as measured by dietary and smoking variables) and NAT2 acetylator status. Evidence for interaction was assessed using the likelihood ratio test, by comparing conditional logistic regression models with and without an interaction term. In addition, stratified (unmatched) analyses are presented of the cases and controls from the matched pairs to facilitate comparison of risk estimates according to genotype.

All analyses were carried out using the statistical analysis software Stata (Stata Statistical Software: Release 7.0. College Station, TX: Stata Corporation, 2001).


    Results
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Conclusions
 References
 
In total 484 cases and 738 controls were interviewed. A further 16 cases and four controls could not be interviewed but had blood samples taken for DNA analysis. Of the 500 cases, 286 (57.2%) were diagnosed with cancer of the colon and the remainder with rectal cancer. In total 138 cases in the age range were ineligible for the study, the most common reason being a previous cancer, and 354 were inaccessible, the most common reasons being emergency admissions (84), deaths (74) and patient refusals (66). Across centres 69% of first-approached controls agreed to participate. The subjects recruited formed 461 matched pairs and a smaller number of unmatched cases and controls (Table IIIGo). The mean age at diagnosis was 67.0 years, and 60% of cases were male.


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Table III. Descriptive data on study participants
 
NAT2 genotypes were ascertained for 490 cases and 592 controls, including 433 matched pairs. There was no significant difference in allele frequencies between centres (P = 0.18). There was some weak evidence against Hardy–Weinberg equilibrium when tested in controls [likelihood ratio {chi}2 = 29.5, 5 degrees of freedom (df), P = 0.01], although this test is sensitive to small departures because of the very low expected frequencies of certain genotypes.

Analysis in EH using 592 genotyped controls confirmed the strong linkage disequilibrium between the four SNPs. Out of the 16 possible haplotypes it is estimated that only the six identified in Table IGo occur in this population.

Allele frequencies for NAT2 (Table IGo) did not differ between cases and controls ({chi}2 = 2.4, 5 df, P = 0.79). Overall 40% of subjects were fast acetylators and the proportion did not differ between cases and controls (Table IVGo).


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Table IV. Distribution of potential HA-related risk factors for colorectal cancer in cases and controls
 
Frequencies of potential dietary risk factors related to HA content are shown in Table IVGo. There is marginal evidence of greater meat consumption in cases than controls (P = 0.08) and this is much more marked when red meat only is considered (P = 0.003). Over 50% of subjects said they preferred meat well done, and <5% preferred it lightly done. In the matched analysis those who did not prefer well-done meat were therefore merged into one category. Less than 30% of subjects chose the photograph showing the highest cooking temperature for any of the five meat items, suggesting that the visual prompt might be more specific in identifying subjects eating meat with a high HA consumption. Unfortunately the pictures were not available at the start of the study, so many participants did not answer this question. Using either verbal or visual prompts no difference was found between cooking preferences in cases and controls.

Cases were more likely than controls to have been regular smokers at some point (68.3% compared with 58.5%, P < 0.001). Among those who had ever been regular smokers there was no difference in estimated pack-years of exposure (P = 0.32) or duration of smoking (P = 0.17) between cases and controls (Table IVGo). There was also no difference in time since first exposure to smoking (P = 0.64, data not shown).

NAT2 phenotyping based on the caffeine test was carried out on 47 cases and 49 controls, of whom 39 were ‘fast’ and 57 ‘slow’ acetylators according to genotype. Seven of the ‘fast’ and four of the ‘slow’ acetylators had unreadable values. The distributions of the remaining values are shown in Figure 1Go. As expected fast acetylators had significantly higher ratios (P < 0.0001), ranging from 0.4 to 17.9, than slow acetylators, whose values ranged from 0.03 to 4.3. Only four of the ‘fast’ acetylators were homozyogous for the NAT2*4 allele (putatively faster acetylators) and they had ratios of 1.6, 3.0, 5.5 and 17.9.



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Fig. 1 NAT2 caffeine test by genotype-based acetylator status.

 
Matched case-control pairs
Results of the analysis of the 461 matched case-control pairs are given in Table VGo. There was no association between NAT2 acetylator status and colorectal cancer [OR 0.89, 95% confidence interval (0.67, 1.17) for fast versus slow acetylators]. Colorectal cancer risk was associated with total meat consumption (422 pairs, P = 0.03 for trend), with an OR of 1.51 for those in the highest quartile compared to those in the lowest. This effect was even more marked for red meat consumption, where a trend in risk with consumption was observed (425 pairs, P = 0.0003), and those in the highest quartile were estimated to be at twice the risk of those in the lowest quartile. A preference for well-done meat showed no association with risk based on either verbal questioning [393 pairs, OR 1.02 (0.76, 1.37)] or the picture prompts [271 pairs, 0.95 (0.66, 1.36)].


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Table V. Estimated effects of potential risk factors for colorectal cancer based on analysis of matched pairs
 
Smoking was shown to be a risk factor for colorectal cancer, with an OR of 1.64 (1.20, 2.24) for ex-smokers compared with those who had never smoked (based on 446 pairs). The risk to current smokers however was not significantly increased. Quantifying smoking by either pack-years or duration of habit showed no evidence of a dose–response relationship, with an increased risk of disease to smokers in all categories.

The analyses presented in Table VGo were repeated stratifying by cancer site (colon or rectum). There was no evidence of risk associated with NAT2 (OR 0.95 and 0.80 for colon and rectal cancer, respectively). The risk estimates for meat and smoking were slightly higher for rectal than for colon cancer, but the differences were not statistically significant.

Interactions
The effects of total meat and red meat consumption on risk did not differ according to preference for well-done meat based on responses to either the verbal or picture prompt (data not shown). Analysis of the effect of (red) meat consumption, stratifying subjects according to whether or not they expressed a preference for well-done meat, yielded very similar risk estimates for the two groups. For example, the estimated ORs for the three higher categories of red meat consumption are 1.28, 1.47 and 1.90 for those who did not prefer very well-done meat according to the picture prompt and 1.23, 1.83 and 1.91 for those who did (P = 0.99 for interaction). As recorded cooking preferences had no observable effect on risk, they were not considered in the subsequent analysis.

Results of the analysis of potential interactions between surrogates of HA exposure and NAT2 genotype are shown in Table VIGo. There was no evidence of interaction between NAT2 and overall meat consumption, red meat consumption or smoking. Subgroup analyses were also carried out stratifying by age (less than median of 69 years, 69 years and over) and sex. It can be seen that the estimated risks for males and females separately (Table VIGo) vary more between fast and slow acetylators, but the estimates are much more unstable than for the overall group and there is again no evidence of interaction.


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Table VI. Potential interactions between HA intake and NAT2 genotype as risk factors for colorectal cancer, overall and in males and females
 
Results are presented without adjustment for potential confounders (other than the matching variables of age and sex and total energy intake for dietary variables). The overall analyses presented in Table VIGo were repeated adjusting for body-mass index, regular use of non-steroidal anti-inflammatory drugs, consumption of cruciferous vegetables and (in the meat analysis) smoking. Although there was some effect on the estimated coefficients, the results were similar and none of the tests for interaction approached statistical significance.

Data from the three centres were pooled for all analyses. Formal tests for interaction revealed no evidence of interaction between centre and any of the main risk factors studied.


    Discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Conclusions
 References
 
This case-control study has shown an increased risk of colorectal cancer with higher meat consumption. This was particularly marked for red meat, where subjects in the highest quartile of consumption were estimated to be at twice the risk of developing the disease as those in the lowest quartile, and this finding is in accord with previous studies (23). In the analysis presented here, we adjusted meat consumption for total energy intake, although there are differing views as to whether this is advisable (24). We carried out a similar analysis without adjustment and found even stronger relationships between meat intake and colorectal cancer (OR 2.34 for highest versus lowest quartile of red meat consumption), but there was still no evidence of interaction with NAT2.

A less clear-cut relationship with smoking was observed; an increased risk was found in people who had ever been regular smokers compared with those who had never smoked. This observed increase however was confined to ex-smokers, and, unlike meat consumption, no dose–response relationship was found. The evidence from most recent epidemiological studies is of a higher risk to long-term heavy smokers (4), and the lack of evident dose–response here may be due to sample size and potential inaccuracies in self-reported smoking.

Although meat cooked at high temperatures and cigarette smoke are both potential source of HA exposure, there was no evidence from this study that the observed increases in risk were due to HAs. High HA content is confined to meat cooked at high temperatures, but the risk associated with meat consumption did not differ according to self-reported cooking preferences. In addition, there was no evidence that NAT2 genotype modified the observed risks or was itself a risk factor for the disease.

NAT2 phenotype and genotype
Subjects were classified as fast or slow NAT2 acetylators on the basis of NAT2 genotype. The 40% frequency of fast acetylators is in agreement with previous studies in Caucasians (reviewed in ref. 16). There is known to be a close relationship between genotype and phenotype, but there is some evidence that the situation is more complex than a fast/slow dichotomy. In particular a more extreme fast phenotype has been observed in homozygotes for the NAT2*4 allele (18). In our data this genotype was not associated with disease: 26 (5.3%) cases and 32 (5.4%) controls were homozygous for the NAT2*4 allele. In the absence of any differences between fast and slow acetylators as originally defined, further subcategorizations were not considered.

Genotype was determined on the basis of four SNPs within the NAT2 gene as described. Two genotypes (NAT2*4/*5B and NAT2*5A/*5C), one ‘fast’ and one ‘slow’, were indistinguishable by this method (Table IGo), and in view of the relative frequencies of these alleles all such subjects were classified as fast acetylators (NAT2*4/*5B genotype). Altogether 211 (19.5%) subjects had this combination of SNPs. As the expected relative frequencies of the fast and slow genotypes assuming Hardy–Weinberg equilibrium are 123 to 1, it is expected that no more than two subjects will have been incorrectly assigned the fast acetylator phenotype on this basis.

Some additional alleles, not listed in Table IGo, have been identified in NAT2, which may influence phenotype. It is probable that only a very small number of subjects (<1%) would have been classified differently by testing for these additional SNPs. However, as new polymorphisms continue to be discovered it is clear that there is the potential for misclassification. This problem will persist until the functions of polymorphisms within NAT2 are more fully characterized and the gene is routinely sequenced.

In a recent meta-analysis of published studies looking at NAT2 and colorectal cancer risk (25) it was found that fast acetylator status determined by phenotyping was associated with increased colorectal cancer risk [OR 1.7 (1.23–2.37)]. However there was no overall evidence of increased risk from the much larger studies based on genotyping. Although our study confirms a close relationship between genotype and phenotype (Figure 1Go), the phenotype distributions do overlap. It is possible that assignment of phenotype on the basis of genotype introduces sufficient misclassification to mask a relationship with colorectal cancer. On the other hand it has been argued that studies based on genotype may more accurately reflect risk, as the presence or treatment of disease itself may influence the result of the phenotypic assay (11).

Heterocyclic amine exposure
This study did not attempt to directly measure dietary exposure to HAs. Subjects likely to have high exposure were identified as those who ate large amounts of meat, especially red meat, according to responses to a food frequency questionnaire (FFQ). In addition, the subgroup was examined who expressed a preference for ‘well done’ meat, as meat cooked at high temperatures has a higher HA content. FFQs do not provide an accurate measure of food consumption, but may be sufficiently reliable instruments to identify high and low consumers of particular foods. The observation of a trend in colorectal cancer risk by quartile of (red) meat intake in this study suggests that the FFQ has been successful in distinguishing high and low consumers. The lack of association between disease risk and cooking preference offers no support for the hypothesis that HAs account for the increase in risk. However, the validity of reporting cooking preferences, in response to either a verbal or picture prompt, is not known. The verbal prompt lacked specificity, as over 50% of subjects reported liking meat ‘well done’. The picture prompt was more specific, and the pictures used had a known relationship to HA content, but unfortunately this method was not used for everyone in the study. Our results are in agreement with Augustsson et al. (13), who developed and used the picture prompts and also found no evidence of an increase in risk in a Swedish case control study. An additional shortcoming is that the FFQ did not distinguish meat consumption by method of consumption (grilling, stewing, etc.), which may have a bearing on HA content.

Interaction analyses
In contrast to our study, some authors have reported evidence of interaction between environmental risk factors and NAT2 genotype. However, in most cases, although meat consumption was found to be a stronger risk factor in fast acetylators, the observed difference between slow and fast acetylators would not reach statistical significance. Formal tests for interaction are not reported in many studies. One problem with such formal tests is that they require specification of the joint effect of the two risk factors under the null hypothesis. We have chosen a multiplicative model, but other models could have been examined. We have therefore also presented results stratified by NAT2 genotype.

Roberts-Thomson et al. (5) found that the risk of cancer showed some increase with increasing intake of meat in fast but not in slow acetylators. Welfare et al. (6) showed that frequent fried meat intake was more common in fast-acetylator cases than matched controls [OR 6.0 (1.34–55)]. In contrast, recent smoking was more frequent in slow acetylator cases than matched controls [OR 2.31 (1.16–4.6)] but not in fast acetylators. Chen et al. (7) observed a stronger association of red meat intake with cancer risk among NAT2 fast acetylators especially among older men. Kampman et al. (8) reported that associations with processed meat and the Mutagen Index (an estimate for exposure to mutagenic or carcinogenic substances) were stronger for those with the intermediate or rapid NAT2 acetylator phenotype. The evidence for a modifying effect of NAT2 was however weak despite the large size of the study (1542 cases and 1860 controls). Slattery et al. (9) examined cigarette smoking and found no evidence for interaction with genotype.

Le Marchand et al. (10) reported a high risk of colorectal cancer [OR 8.8 (1.7–44.9)] among ever-smokers with high NAT2 and CYP1A2 activity who preferred well-done red meat compared with smokers with none of the other putative risk factors. In their study the interaction between NAT2, CYP1A2 and meat preference was statistically significant in this subgroup (P = 0.01), but the numbers in the high risk category were small (n = 14). CYP1A2 phenotype was only measured in a subset of individuals in our study (details not reported here). In total 64 smokers were phenotyped, so our data to address this question are very limited. However, we observed two cases and four controls among smokers with the putative ‘high risk’ combination of the three factors compared with nine cases and five controls with the ‘low risk’ combination. This yields an OR of 0.28 (0.02, 2.96), which, despite the small numbers, provides some evidence that the true OR is to the lower end of the confidence interval reported in Le Marchand et al. (10).

Assuming no excess risk associated with NAT2 genotype in the absence of high environmental exposure and a 1.5–2-fold increase in risk for those with the highest quartile of meat intake compared with all others in the slow acetylator group, we calculate that our study has 64% power to detect an interaction odds ratio of 2 and 87% power to detect an interaction odds ratio of 2.5 (two-sided test with significance level 0.05). As more is understood of the pathways through which potential carcinogens are metabolized, one way forward may be to test specific hypotheses regarding the joint effects of several genes in relation to environmental factors. Epidemiological studies may have to be much larger than the current norm to achieve sufficient power for such analyses.


    Conclusions
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Conclusions
 References
 
This study has confirmed earlier findings of an increased risk of colorectal cancer associated with meat consumption, especially red meat. This relationship does not necessarily indicate a harmful effect of red meat per se, but may be due to dietary or lifestyle correlates of high meat consumption. Many potential confounders could have been included in the analysis, but as the focus here is on interaction with NAT2 rather than main effects, adjustment is less important and we wished to avoid the risk of removing the main effect of interest by adjusting for highly correlated variables.

In view of the difficulties in measuring HA exposure, this study does not rule out a role for HAs in the diet as risk factors for colorectal cancer, but no evidence is found to support the hypothesis. Our results do show that any harmful effect of dietary HA exposure is not greatly modified by NAT2 genotype. Previous studies showing a trend towards greater risk among fast acetylators are based on small numbers (in the whole study or in subgroups) and do not provide convincing evidence of a modifying effect of NAT2.


    Notes
 
7 To whom correspondence should be addressed Email: j.barrett{at}cancer.org.uk Back

* The Colorectal Cancer Study Group: Dr Jenny Barrett (St James’s University Hospital, Leeds), Professor D.Timothy Bishop (St James’s University Hospital, Leeds), Professor Alan R.Boobis (Imperial College, London, UK), Professor David Forman (University of Leeds, Leeds, UK), Professor R.Colin Garner (University of York, York, UK), Dr Nigel Gooderham (Imperial College, London, UK), Dr Tracy Lightfoot (University of York, York, UK), Dr Christoph Sachse (Biomedical Research Centre, University of Dundee, Dundee, UK), Dr Gillian Smith (Biomedical Research Centre, University of Dundee, Dundee, UK)2, Ms Robin Waxman (University of Leeds, Leeds, UK) and Professor C.Roland Wolf (Biomedical Research Centre, University of Dundee, Dundee, UK). Back


    Acknowledgments
 
We thank all the research interviewers who were responsible for patient recruitment and data collection and the colorectal surgeons who enabled us to recruit their patients. Thanks also to Tim Key for advice on dietary measures and Paul Appleby for the calculation of total energy intake. We acknowledge financial support from the Food Standards Agency (Contracts T01003,T01004, T01005) and from Cancer Research UK.


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Conclusions
 References
 

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Received August 13, 2002; revised October 28, 2002; accepted November 1, 2002.