Abstract
Background
Meat-cooking mutagens may be associated with renal cell carcinoma (RCC) risk. We examined associations between meat-cooking mutagens, genetic susceptibility variants and RCC risk.
Methods
We used 659 newly diagnosed RCC cases and 699 healthy controls to investigate the association between dietary intake of meat-cooking mutagens and RCC. We examined whether associations varied by risk factors for RCC and genetic susceptibility variants previously identified from genome-wide association studies (GWAS). Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using tertiles of dietary PAH/HCA intake.
Results
Dietary intake of the mutagenic compounds 2-amino-3,8-dimethylimidazo-[4,5-f] quinoxaline (MeIQx) and 2-Amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) were significantly associated with an increased risk of RCC [(OR and 95% CI across tertiles:1.00 (ref), 1.28 (0.94–1.74), 1.95 (1.43–2.66); P-trend <0.001 and 1.00 (ref), 1.41 (1.04–1.90), 1.54 (1.14–2.07); P-trend =0.02) respectively]. We observed evidence for interactions between PhIP and RCC susceptibility variants in two genes, ITPR2 (rs718314 multiplicative Pinteraction = 0.03, additive Pinteraction = 0.002) and EPAS1 (rs7579899 additive Pinteraction =0.06).
Conclusions
Intake of meat may increase RCC risk through mechanisms related to the cooking compounds MeIQx and PhIP. These associations may be modified by genetic susceptibility to RCC. Further research is necessary to understand the biological mechanisms underlying these interactions.
Keywords: renal cell carcinoma, meat-cooking mutagens, polycyclic aromatic hydrocarbons, heterocyclic amines, gene-environment interaction
The increase in renal cell carcinoma (RCC) incidence in the United States and other developing nations suggests that factors related to western lifestyle, such as a diet high in meats, processed foods, and starches, may play an important role in RCC etiology 1. While previous studies have linked meat intake with increased RCC risk, the underlying mechanism for this association remains unclear 1–6. Cooking of meat at high temperatures, particularly barbequing or pan-frying, results in the formation and ingestion of carcinogenic compounds including heterocyclic amines (HCAs) such as 2-amino-1-methyl-6-phenyl-imidazo(4,5-b)pyridine (PhIP), amino-3,8-dimethylimidazo(4,5-f) quinoxaline (MeIQx), 2-amino-3,4,8-trimethylimidazo(4,5-f) quinoxaline (DiMeIQx) and polycyclic aromatic hydrocarbons (PAHs), specifically benzo-a-pyrene (BaP) 1, 6. Few epidemiologic studies to date have investigated the association between these carcinogenic compounds and RCC risk 1, 6.
The kidney is a biochemically active organ that contributes significantly to metabolism of xenobiotics and is therefore exposed to higher concentrations of carcinogens than other organs 7, 8. Genome-wide association studies (GWAS) have implicated common variants involved in the cellular response to changes in oxygen, iron, nutrients or energy as playing an important role in RCC etiology 9. Recently, one single nucleotide polymorphism (SNP) located near the ITPR2 gene related to lipid metabolism and obesity was found to modulate the association between an adherence to a Western dietary pattern and increased RCC risk 10. Therefore, genetic variants related to RCC susceptibility may play a role in modifying the association between dietary intake of carcinogens and RCC risk.
In the present study, we investigated whether dietary intake of meat-cooking mutagens, such as BaP, MeIQx, DiMeIQx and PhIP, played a role in RCC risk in a large case-control study of newly diagnosed RCC cases and healthy controls. We examined whether these associations were modified by known or suspected risk factors for RCC, including smoking, and previously identified GWAS-identified RCC genetic susceptibility variants. This is the first study to investigate potential interactions between dietary intake of meat-cooking mutagens and RCC susceptibility variants in a case-control study of newly diagnosed RCC patients and healthy controls.
Methods
Study Population and Recruitment
Patients were drawn from an ongoing case-control study of RCC initiated in 2002. The study was approved by the MD Anderson Institutional Review Board. Procedures for subject recruitment and eligibility criteria have been previously described 11. Briefly, all case subjects were newly diagnosed and with histologically confirmed RCC. Healthy control subjects without a history of cancer, except non-melanoma skin cancer, were identified and recruited via random digit dialing 12. Control subjects were frequency matched to case subjects according to age (± 5 years), gender, ethnicity and county of residence. All participants provided written informed consent prior to participation in the study.
Exclusion and inclusion criteria for the parent case-control study have been described in detail elsewhere 11. Out of 1,516 matched cases and controls, we additionally excluded individuals with outlying total energy intake by excluding men (N=33) and women (N=34) with values that fell outside the interval delimited by the 25th percentile minus 1.5 times the interquartile range and the 75th percentile plus 1.5 times the interquartile range. Due to small number of minority participants (N=91), we limited the analysis to non-Hispanic whites only, leaving 1,358 individuals (659 cases and 699 controls) for inclusion in the present study.
Data Collection
Epidemiologic data were collected by MD Anderson staff interviewers via in-person interview including history of hypertension (yes/no), physical activity, alcohol use, and smoking status. After the interview, a 40-ml blood sample was collected from each participant and delivered to the laboratory for molecular analysis. Alcohol intake was adjusted for total energy intake using the nutrient density method 13. An individual who had never smoked or had smoked less than 100 cigarettes in his or her lifetime was defined as a never smoker. An individual who had smoked at least 100 cigarettes in his or her lifetime but had quit at least 12 months prior to diagnosis (for cases) or interview (for controls) was classified as a former smoker. Current smokers were those who were currently smoking or quit less than 12 months prior to diagnosis (for cases) or before the interview (for controls).
Weight at diagnosis (for cases) or recruitment (for control subjects) was recorded. Body mass index (BMI; kg/m2) was derived from height and weight. BMI was categorized according to the standard classifications of the World Health Organization (normal = <25kg/m2; overweight = ≤25–29.9kg/m2; obese = ≥30kg/m2). Participants also reported the average frequency they spent on five broad groups of physical activities. A metabolic equivalent value (MET) was assigned to each activity group 14. Energy expenditure from physical activity was calculated as the MET value of each activity multiplied by the frequency of each activity and then summed across all activities.
Dietary Assessment
We used a previously validated and modified version of the National Cancer Institute Health Habits and History Questionnaire 15. The validity and reliability of this food frequency questionnaire (FFQ) has been previously documented 16. The questionnaire queried the frequency of intake, method of preparation, and portion size of a wide range of foods and beverages, including cereals, grains, fruits, vegetables, meats, dairy, dessert, fast foods, juices, alcohol and water. Total energy intake and grams per day of consumption for each food item were estimated using the USDA Food and Nutrient Database for Dietary Studies (FNDDS)17. Foods items were standardized using the nutrient density method (g per 1000 kcal) for total energy intake.
Mutually exclusive categories were created from the meat, poultry and fish items queried in the modified FFQ. These categories included fresh red meat (beef: hamburger, beefsteak, ground beef, beef stew, roast beef, pot roast and beef ribs and pork: ham/ham steak, pork chops roast, pork ribs), processed red meat (bacon, sausage, hamhock, smoked meats, hot dogs), fresh white meat ( poultry: chicken, turkey fried chicken), processed white meat (low-fat hot dogs, low-fat sausages, low-fat lunch meat), and fish (canned/smoked/salted/fresh/fried).
The meat portion of the FFQ ascertained cooking methods (e.g. pan-fried, broiled, microwaved, baked, grilled, brown and serve, other, or do not know). Photographic models were used to help participants determine a level of doneness (inside and outside) of the meat. Meat intake, cooking methods, and level of doneness were linked to the Computerized Heterocyclic Amines Resource for Research In Epidemiology of Disease (CHARRED) database (http://charred.cancer.gov/) to estimate values (ng/d) of 3 HCAs (2-amino-1-methyl-6-phenyl-imidazo(4,5-b)pyridine (PhIP), 2-amino-3,8-dimethylimidazo(4,5-f)quinoxaline (MeIQx), 2-amino-3,4,8-trimethylimidazo(4,5-f)quinoxaline (DiMeIQx)), and one PAH, BaP (Benzo-a-pyrene). The exposure index has been previously described elsewhere 18, 19.
Genotyping and Selection of Single Nucleotide Polymorphisms (SNPs)
We selected 6 SNPs (rs12105918; chromosome 2; ZEB2, rs10054504; chromosome 5; PDZD2, rs718314; chromosome 12; ITPR2, rs7579899; chromosome 2; EPAS1, rs7105934; chromosome 11; CCND1 and rs4765623; chromosome 12; SCARB1 20–22) previously identified through GWAS showing significant or marginally significant associations (p<10−6) with RCC risk. Genomic DNA was extracted from peripheral blood using QIAmp DNA extraction kit (Qiagen, Valencia, CA) and genotyped by Taqman genotyping assays on the 7900HT Sequence Detection System (Life Technologies, Grand Island, NY) according to the specified protocol. Runs included negative controls (water) and 5% of samples as replicates. All SNPs were in Hardy Weinberg equilibrium (P>0.05) and concordance was 100%.
Statistical Analysis
METs of physical activity and alcohol intake were categorized into tertiles based on the distribution in control subjects. Missing physical activity was consistent between cases and controls and therefore coded as a separate “unknown” category. All continuous intake variables were categorized into tertiles based on the distribution among the controls and by gender with the reference group comprised of individuals in the lowest category of intake. Quintiles of intake were also assessed for the overall analysis, however due to limited power for strata and genetic interaction, we limited the analysis to tertiles. Results for the overall analysis using quintiles were consistent and are therefore not presented here.
Comparisons for case-control characteristics were performed using generalized linear models and Pearson χ2 tests. We evaluated the association between both meat intake and PAH/HCA exposure and risk of RCC by using unconditional logistic regression models. Age and gender, as well as multivariable adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were reported across gender-specific tertiles of intake. Multivariable-adjusted models included the following covariates: age (continuous), gender, BMI (categories), total energy intake (tertiles), total fruit and vegetable intake (tertiles), smoking status (never smoker, ever smoker), physical activity (low: <30 METs/week, medium 30–48 METs/week, and intense 48+ METs/week), and history of hypertension (yes/no). Alcohol intake did not contribute meaningfully to the model and was not included. Risk estimates for the meat consumption models were also adjusted for tertiles of other meat intake, so that the sum of all the meat variables in each model represents total meat intake (eg. fresh red meat intake was adjusted for processed red meat intake, total white meat intake and fish intake). Tests for trend were obtained by including an ordinal exposure variable in the model. Models evaluating processed and fresh red and white meats individually were consistent with the models for overall red, white and processed meats and therefore not presented here. Fish intake was not associated with RCC risk and is not included in the present analysis.
We conducted multivariable-adjusted analyses stratified by gender, age (<60, ≥60), obesity, smoking, hypertension, and physical activity in addition to 6 previously identified genetic variants using the homozygous major genotype as the reference category. We included the cross-product term of the dichotomous risk factor variables and ordinal mutagen variables in the logistic regression model to test for multiplicative interaction (Wald statistic). Additive interaction was determined using the relative excess risk due to interaction (RERI) measure 23. P-values and 95% confidence intervals (bias corrected and accelerated) were determined by using 1,000 bootstrap samples 24. All p-values were two-sided with a significance level of 0.05. All analyses were carried out using STATA 13.0 (College Station, TX).
Results
Participant characteristics are described in Table 1. Cases were more likely to have a history of hypertension, be obese, and have low levels of physical activity compared to healthy controls. Dietary intake of red, white, and fresh meat and intakes of MeIQx, PhIP and DiMeIQx were significantly higher in the cases versus controls (p<0.03 for each). Case subjects had overall higher daily total energy intake and lower overall total fruit and vegetable intake.
Table 1.
Characteristics for Cases and Controls
| Case | Control | Chi-2 | ||
|---|---|---|---|---|
| (N=659) | (N=699) | p-value | ||
| N(%) | N(%) | |||
| Age1 | 59.27 (10.36) | 60.70 (10.50) | 0.01 | |
| Gender | ||||
| Female | 220 (33.38) | 237 (33.91) | 0.83 | |
| Male | 439 (66.62) | 462 (66.09) | ||
| Smoking Status | ||||
| Never Smoker | 341 (51.75) | 353 (50.50) | 0.65 | |
| Ever Smoker | 318 (48.25) | 346 (49.50) | ||
| High Blood Pressure/Hypertension | ||||
| Yes | 388 (58.88) | 299 (42.78) | <0.001 | |
| No | 271 (41.12) | 400 (57.22) | ||
| BMI (kg/m2) | ||||
| Normal/Overweight (<25 kg/m2) | 171 (25.95) | 224 (32.05) | 0.002 | |
| Overweight (25–29 kg/m2) | 240 (36.42) | 276 (39.48) | ||
| Obese (≥30 kg/m2) | 248 (37.63) | 199 (28.47) | ||
| Physical Activity2 | ||||
| Intensive (48+ METs /week) | 94 (15.11) | 221 (32.50) | <0.001 | |
| Medium (30–48 METs /week) | 196 (31.51) | 253 (37.21) | ||
| Low (<30 METs/week) | 332 (53.38) | 206 (30.29) | ||
| Dietary Intakes3 | ||||
| Total Energy Intake (KcaL) | 2350.96 (913.19) | 1994.22 (806.73) | 0.001 | |
| Total Fruit and Vegetable Intake (g/day) | 246.57 (140.45) | 292.11 (159.00) | 0.001 | |
| Red Meat (g/day) | 55.25 (31.47) | 39.03 (22.59) | 0.001 | |
| White meat (g/day) | 23.03 (16.84) | 21.23 (17.15) | 0.01 | |
| All Fresh Meat (g/day) | 59.37 (32.03) | 43.53 (25.65) | 0.001 | |
| Fish (g/day) | 13.14 (13.66) | 13.68 (13.63) | 0.46 | |
| MeIQx (ng/day) | 16.95 (134.23) | 9.45 (40.68) | <0.001 | |
| PhIP (ng/day) | 48.65 (179.54) | 43.94 (153.82) | 0.001 | |
| DiMeIQx (ng/day ) | 1.22 (15.35) | 0.73 (4.91) | 0.03 | |
| BaP (ng/day) | 12.54 (42.93) | 13.18 (80.61) | 0.06 | |
Mean (Standard Deviation), t-test p-value
Individuals with missing physical activity not included in Table 1 (N=56)
Daily energy-adjusted (g/1000kcal) values for dietary intake. Intake values represented as Mean (Standard deviation);
p-values derived from non-parametric Kurskal-Wallis test
Age and gender adjusted and multivariable adjusted models for risk of RCC by meat-related exposures are described in Table 2. Higher intakes of all red meat (p-trend <0.001), all white meat (p-trend = 0.01), and all fresh meat (p-trend <0.001) were associated with increased RCC risk. High dietary intake of MeIQx and PhIP were associated with a significantly increased risk of RCC in (Table 2). High intake of MeIQx was associated with increased RCC risk (age- and gender adjusted OR= 2.61, 95% CI 1.99–3.44, multivariable adjusted OR = 1.95, 95% CI 1.43–2.66, p-trend <0.001 for both). Increased intake of PhIP was significantly associated with RCC risk across all tertiles for both models (highest versus lowest tertile OR= 1.88, 95% CI 1.42–2.49 in the age and gender adjusted model, p-trend <0.001 and highest versus lowest tertile OR=1.54, 95% CI 1.14–2.07 in the multivariable adjusted model, p-trend= 0.02). Results using a composite HCA value (sum of MeIQx. PhIP and DiMeIQx) were consistent with these significant findings (not shown).
Table 2.
Risk Estimates for the Association Between Meat-Related Exposures and RCC Risk, overall
| Dietary Intake (g/1000 kcal) | ||||
|---|---|---|---|---|
| Red meat ( All) | Cases/ Controls |
Age and Gender-Adjusted | Multivariate-Adjusted1,2 | |
| T1 | 120/232 | OR (95% CI) (REF) |
OR (95% CI) (REF) |
|
| T2 | 161/232 | 1.31 (0.97–1.77) | 1.22 (0.87–1.66) | |
| T3 | 378/235 | 3.06 (2.32–4.03) | 2.28 (1.67–3.10) | |
| P-trend | <0.001 | <0.001 | ||
| White meat (all) | ||||
| T1 | 163/226 | (REF) | (REF) | |
| T2 | 242/236 | 1.39 (1.06–1.83) | 1.37 (1.01–1.86) | |
| T3 | 254/237 | 1.43 (1.09–1.88) | 1.50 (1.10–2.04) | |
| P-trend | 0.01 | 0.01 | ||
| Fresh meats (all) | ||||
| T1 | 127/237 | (REF) | (REF) | |
| T2 | 157/224 | 1.26 (0.93–1.70) | 1.23 (0.89–1.71) | |
| T3 | 375/238 | 2.84 (2.16–3.74) | 2.43 (1.80–3.28) | |
| P-trend | <0.001 | <0.001 | ||
| Dietary intake of HCA from meat (ng per day) | ||||
| MelQx | ||||
| T1 | 129/234 | (REF) | (REF) | |
| T2 | 176/228 | 1.37 (1.03–1.84) | 1.28 (0.94–1.74) | |
| T3 | 354/237 | 2.61 (1.99–3.44) | 1.95 (1.43–2.66) | |
| P-trend | <0.001 | <0.001 | ||
| PhIP | ||||
| T1 | 139/239 | (REF) | (REF) | |
| T2 | 247/223 | 1.83 (1.39–2.43) | 1.41 (1.04–1.90) | |
| T3 | 273/237 | 1.88 (1.42–2.49) | 1.54 (1.14–2.07) | |
| P-trend | <0.001 | 0.02 | ||
| DiMelQx | ||||
| T1 | 221/244 | (REF ) | (REF) | |
| T2 | 171/221 | 0.81 (0.62–1.07) | 0.83 (0.62–1.11) | |
| T3 | 267/234 | 1.18 (0.91–1.53) | 1.03 (0.78–1.35) | |
| P-trend | 0.18 | 0.71 | ||
| Dietary intake of PAH from meat (ng per day) | ||||
| BaP | ||||
| T1 | 196/237 | (REF) | (REF) | |
| T2 | 227/222 | 1.19 (0.91–1.56) | 1.06 (0.80–1.40) | |
| T3 | 236/240 | 1.11 (0.85–1.44) | 0.89 (0.66–1.19) | |
| P-trend | 0.48 | 0.73 | ||
Unconditional logistic regression model adjusted for age, gender, BMI, history of hypertension, smoking status, total energy intake, total fruit and vegetable intake
Missing physical activity included as "unknown"
The associations between dietary intake of meat-cooking mutagens and RCC risk stratified by smoking status are presented in Table 3. Results suggest a trend between PhIP intake and RCC risk may be more profound in ever smokers (P=0.02 versus 0.18 in never smokers). The association between MeIQx and RCC were largely consistent between the two groups. Results did not yield significant interactions between meat-mutagen intake and smoking status. The remaining RCC risk factors did not modify these associations (results not shown).
Table 3.
Risk Estimates for the Association Between Meat-Related Exposures and RCC Risk, Stratified by Smoking Status
| Dietary intake of HCA from meat (ng per day) | ||||||
|---|---|---|---|---|---|---|
| Never Smoker | Ever Smoker | P-interaction | ||||
| MelQx (Overall) | Cases/ Controls |
OR (95% CI) | Cases/ Controls | OR(95% CI) | ||
| T1 | 60/118 | (REF) | 69/116 | (REF) | ||
| T2 | 101/114 | 1.75 (1.09–2.81) | 75/114 | 1.06 (0.66–1.70) | ||
| T3 | 180/121 | 2.51 (1.54–4.11) | 174/116 | 1.94 (1.22–3.08) | ||
| P-trend | <0.001 | 0.003 | 0.54 | |||
| PhIP (overall) | ||||||
| T1 | 66/105 | (REF) | 73/134 | (REF) | ||
| T2 | 133/118 | 1.62 (1.01–2.57) | 114/105 | 1.59 (1.02–2.50) | ||
| T3 | 142/130 | 1.45 (0.90–2.36) | 131/107 | 1.77 (1.12–2.79) | ||
| P-trend | 0.18 | 0.02 | 0.43 | |||
| DiMelQx (overall) | ||||||
| T1 | 111/107 | (REF) | 110/137 | (REF) | ||
| T2 | 91/117 | 0.78 (0.51–1.20) | 80/104 | 0.83 (0.54–1.26) | ||
| T3 | 139/129 | 0.93 (0.62–1.40) | 128/105 | 1.15 (0.77–1.72) | ||
| P-trend | 0.77 | 0.5 | 0.44 | |||
| Dietary intake of PAH from meat (ng per day) | ||||||
| BaP (overall) | ||||||
| T1 | 95/107 | (REF) | 99/125 | (REF) | ||
| T2 | 137/116 | 0.65 (0.39–1.04) | 90/106 | 0.99 (0.58–1.37) | ||
| T3 | 108/127 | 1.16 (0.76–1.76) | 128/113 | 1.19 (0.78–1.80) | ||
| P-trend | 0.08 | 0.4 | 0.09 | |||
Multivariable logistic regression model adjusted for age, gender, BMI, history of hypertension, smoking status, total energy intake, total fruit and vegetable intake
Missing physical activity included as "unknown"
Age and gender-adjusted associations between the selected GWAS SNPs and RCC risk in this population have been shown previously 10. Only one meat-cooking mutagen yielded evidence of a significant interaction with GWAS susceptibility loci for RCC (Table 4). Significant interactions were observed between PhIP and rs718314 (highest versus lowest tertile OR = 1.14, 95% CI: 0.73–1.76 among individuals with no minor alleles and OR = 2.19, 95% CI: 1.37–3.49 for individuals with at least one copy of the minor allele; multiplicative Pinteraction = 0.03, additive Pinteraction= 0.002) and rs7579899 (highest versus lowest tertile OR =1.25, 95% CI: 0.75–2.08 among individuals with no minor alleles and OR = 1.89, 95% CI: 1.25–2.85 among individuals with at least one copy of the minor allele, additive Pinteraction = 0.06). No interactions were observed between the remaining SNPs and meat-cooking mutagens (Supplementary Tables 1–4).
Table 4.
Risk Estimates for Association Between Meat-Related Mutagen Exposure and RCC risk Stratified by Genotype
| rs718314 | |||||||
|---|---|---|---|---|---|---|---|
| AA (N=736) | AG/GG (N=622) | ||||||
|
Dietary intake of HCA from meat(ng per day) |
Cases/ Controls |
OR (95% CI) | Cases/ Controls |
OR (95% CI) | Multiplicative P-interaction |
Additive P- interaction |
|
| PhIP | |||||||
| T1 | 72/134 | (REF) | 67/105 | (REF) | 0.03 | 0.002 | |
| T2 | 120/132 | 1.37 (0.89–2.11) | 127/91 | 1.96 (1.25–3.08) | |||
| T3 | 128/150 | 1.14 (0.73–1.76) | 145/87 | 2.19 (1.37–3.49) | |||
| P-trend | 0.65 | 0.001 | |||||
| rs7579899 | |||||||
| GG(N=499) | GA/AA (N=859) | ||||||
| PhIP | |||||||
| T1 | 54/98 | (REF) | 85/141 | (REF) | 0.15 | 0.06 | |
| T2 | 84/79 | 1.66 (1.00–2.76) | 163/144 | 1.60 (1.08–2.78) | |||
| T3 | 83/101 | 1.25 (0.75–2.08) | 190/136 | 1.89 (1.25–2.85) | |||
| P-trend | 0.5 | 0.003 | |||||
Multivariable logistic regression model adjusted for age, gender, BMI, history of hypertension, smoking status, total energy intake, total fruit and vegetable intake
Missing physical activity included as "unknown"
Discussion
In the present case-control study, we observed an almost 2-fold increase in RCC risk associated with dietary MeIQx intake and a 54% increased risk associated with PhIP intake, suggesting that intake of meat cooked at high temperatures may impact the risk of RCC through mechanisms related to mutagenic cooking compounds. This is the first study to investigate potential interactions between known RCC susceptibility variants and dietary intake of meat cooking-mutagens. We found a significant interaction between PhIP and rs718314 located downstream of the ITPR2 gene, and evidence for a marginal synergistic interaction between PhIP and rs7579899, located near EPAS1.
Previous studies of RCC risk have suggested positive associations with red meat and poultry intake while others have found null associations with RCC risk 1, 6, 25–27. A large prospective investigation of meat intake, related mutagens, and risk of RCC also found a significant association between red meat and RCC risk (fifth versus first quintile of consumption HR = 2.02, 95% CI: 1.11–3.09) 1. Additionally, this study found significant associations between meat mutagen intake and RCC risk, specifically BaP and PhIP 1. The present study provides additional evidence for the role of red meat, white meat and PhIP in RCC etiology and is the first study of dietary intake of mutagenic compounds and RCC risk to suggest an association with MeIQx, one of the most abundant HCAs commonly created in the grilling, barbequing and panfrying meats at high temperatures 6, 28. Further adjustment for total fresh meat intake (not shown) yielded slightly attenuated, but consistent, associations between meat-cooking mutagens and RCC risk. This suggests an independent effect of meat-cooking mutagens on RCC risk. However, other potentially tumorigenic components of meat including heme iron and N-nitroso compound (NOC) exposure, not measured here, may also play a role in RCC etiology 6, 29. Our study yields a similar increase in RCC risk associated with intake of white meat, generally low in heme iron, suggesting that some of these other mechanisms may not play a large role in increasing RCC risk.
We have previously shown that the ITPR2 gene interacts significantly with an American/Western dietary pattern to confer increased risk of RCC 10. The American/Western dietary pattern consists largely of red and processed meats 10, 30 and the present study suggests that the association between this dietary pattern and cancer may be in part explained by exposure to meat-cooking mutagens. The ITPR2 gene encodes inositol-1,4,5-triphosphate receptor type 2, involved in nutrient and lipid metabolism and is suspected to play an important role in carcinogenesis via cell migration, proliferation and apoptosis 31, 32.
We also found evidence of a marginal synergistic interaction between PhIP and the variant located near the EPAS1 gene on chromosome 2. This gene has been implicated in RCC susceptibility and encodes the hypoxia- inducible factor 2α (HIF-2α), a key gene in the VHL-HIF pathway 21. A previous study of colorectal neoplasia suggests that genetic variation in the bioactivation pathway PAHs and HCAs, in conjunction with interacting molecules such as HIF-2α, form heterodimeric complexes which mediate cellular responses to environmental toxins such as PAHs and HCAs 33. Further research is necessary to investigate the role of the EPAS1 gene in modifying the impact of PAH/HCA exposure on RCC risk.
This study is the first to evaluate the impact of RCC susceptibility variants, identified via GWAS, on the association between intake of mutagenic compounds and RCC risk. The use of histologically confirmed RCC cases, a validated FFQ with a meat cooking module, a wide range of dietary intake values, and the inclusion of several important potential confounders for this association, including history of hypertension, BMI, smoking status and physical activity, allowed us to investigate possible mechanisms underlying the association between meat intake and RCC etiology.
Several limitations of the present study should also be addressed. Residual confounding by unmeasured or unknown risk factors is possible, despite inclusion of important a priori confounders in analysis. Due to the highly correlated nature of the individual components and nutrients in whole foods, isolating the impacts of the mutagenic compounds remains difficult. The present study is a retrospective case-control design and is potentially limited by recall bias and non-differential misclassification. Additionally, analyses stratified by genotype may be limited by sample size. The present study does address the issue of multiple testing, therefore some of the more modestly significant findings may be attributable to chance. Finally, we limited the analysis to non-Hispanic whites, therefore, these results may not be generalizable to other population subgroups.
Our findings provide further evidence for the role of meat intake and mutagenic cooking compounds in RCC etiology, specifically PhIP and MeIQx. The results of the present study also suggest that interindividual variation in RCC risk may be due to both genetic susceptibility and modifiable dietary risk factors such as mutagenic compounds caused by various methods of cooking meat. Because of the limited epidemiologic evidence linking carcinogenic compounds, such as MeIQx to RCC risk, our findings will need to be replicated in future studies of diet and cancer utilizing similar assessments of meat intake and cooking methodologies. Future studies with greater power are needed to simultaneously examine combinations of relevant genetic polymorphisms, intake of meat cooking mutagens, and RCC risk.
Supplementary Material
Acknowledgments
Funding: This work was supported in part by grants from the National Institutes of Health (R01 CA170298) and Center for Translational and Public Health Genomics, Duncan Family Institute for Cancer Prevention, the University of Texas MD Anderson Cancer Center and the NCI R25T Postdoctoral Fellowship in Cancer Prevention.
Footnotes
No conflicts of interest to disclose
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