Abstract
Background: The evidence for meat intake and renal cell carcinoma (RCC) risk is inconsistent. Mutagens related to meat cooking and processing, and variation by RCC subtype may be important to consider.
Objective: In a large US cohort, we prospectively investigated intake of meat and meat-related compounds in relation to risk of RCC, as well as clear cell and papillary RCC histologic subtypes.
Design: Study participants (492,186) completed a detailed dietary assessment linked to a database of heme iron, heterocyclic amines (HCA), polycyclic aromatic hydrocarbons (PAHs), nitrate, and nitrite concentrations in cooked and processed meats. Over 9 (mean) y of follow-up, we identified 1814 cases of RCC (498 clear cell and 115 papillary adenocarcinomas). HRs and 95% CIs were estimated within quintiles by using multivariable Cox proportional hazards regression.
Results: Red meat intake [62.7 g (quintile 5) compared with 9.8 g (quintile 1) per 1000 kcal (median)] was associated with a tendency toward an increased risk of RCC [HR: 1.19; 95% CI: 1.01, 1.40; P-trend = 0.06] and a 2-fold increased risk of papillary RCC [P-trend = 0.002]. Intakes of benzo(a)pyrene (BaP), a marker of PAHs, and 2-amino-1-methyl-6-phenyl-imidazo[4,5-b]pyridine (PhIP), an HCA, were associated with a significant 20–30% elevated risk of RCC and a 2-fold increased risk of papillary RCC. No associations were observed for the clear cell subtype.
Conclusions: Red meat intake may increase the risk of RCC through mechanisms related to the cooking compounds BaP and PhIP. Our findings for RCC appeared to be driven by strong associations with the rarer papillary histologic variant. This study is registered at clinicaltrials.gov as NCT00340015.
INTRODUCTION
Adenocarcinoma of the renal parenchyma, also known as RCC5, accounts for ∼3% of adult malignancies and >90% of adult kidney cancers (1). RCC constitutes a group of heterogeneous epithelial tumors of the proximal renal tubule that differ with respect to morphology, clinical behavior, and prognosis. The most predominant, well-defined histologic variant is clear cell, followed by papillary adenocarcinomas and other RCC subtypes (1).
The incidence of RCC has been steadily increasing in the United States over the past 3 decades (1). Major risk factors include smoking, obesity, and hypertension, but dietary risk factors for RCC are not well established, and the epidemiologic evidence for meat intake and RCC risk remains inconsistent. In 2007, the World Cancer Research Fund/American Institute for Cancer Research report deemed the evidence for meat intake and RCC risk to be inconclusive (2), whereas a meta-analysis of case-control studies reported a positive association for red and processed meat intake and RCC (3). By 2009, another meta-analysis (4), and a pooled analysis of 13 prospective studies (5), concluded that intakes of red meat, processed meat, poultry, and seafood were not associated with RCC. Earlier findings were suspected of recall bias and/or confounding by emergent risk factors, such as obesity and hypertension (4, 5). Epidemiologic data on RCC histologic subtypes remain sparse and have not shown consistent incidence or risk factor patterns (1).
Previous investigations of RCC and meat intake have suggested that endogenous compounds and mutagens related to cooking and processing may be important to consider (6–9), but no prospective cohort has investigated these exposures. The kidney is one of the main organs targeted by iron metabolism, and meat is a key source of bioavailable heme iron—a pro-oxidant involved in carcinogenesis (10, 11). Furthermore, meat cooked by methods such as grilling or pan-frying results in the formation of carcinogenic compounds, such as HCAs and PAHs (12). Intake of red and processed meats also results in exposure to NOCs, which can form endogenously (13) and exogenously in nitrite-preserved meats (14). Heme iron in red and processed meats may further increase endogenous NOC formation (15). To clarify the potential role of meat intake in the development of renal adenocarcinomas, we prospectively investigated dietary intake of meat, components of meat, and compounds related to cooking and processing in relation to RCC incidence in a large US cohort. We further investigated associations with the 2 most common histologic variants: clear cell and papillary RCC.
SUBJECTS AND METHODS
Study cohort
The NIH-AARP Diet and Health Study is a large prospective cohort of US men and women, aged 50–71 y, residing in 6 states (California, Florida, Louisiana, New Jersey, North Carolina, and Pennsylvania) and 2 metropolitan areas (Atlanta, GA, and Detroit, MI). At baseline in 1995–1996, participants completed a self-administered questionnaire about demographics, diet, and lifestyle; details of the study design were described previously (16). Within 6 mo, a second questionnaire with queries for meat-cooking methods and doneness levels and more detailed medical history information was sent to participants without self-reported cancer at baseline and who still resided in one of the study areas.
Of those who completed the baseline questionnaire satisfactorily (n = 566,401), we excluded proxy respondents (n = 15,760) and participants with prevalent cancer (as noted by cancer registry or self-report; n = 51,223) or end-stage renal disease (n = 997) at baseline, a mortality report only for any cancer (n = 1,804), zero person-years of follow-up (n = 36), or total energy intake outside of 2 interquartile ranges above the 75th or below the 25th percentile (n = 4395). After the exclusions, the baseline analytic cohort included 492,186 (n = 293,466 men and 198,720 women) participants. The analysis of compounds in cooked and processed meats was restricted to a subcohort that responded to the second questionnaire and met the inclusion criteria above (n = 302,162; 176,179 men and 125,983 women). The conduct of the NIH-AARP Diet and Health Study was reviewed and approved by the Special Studies Institutional Review Board of the US National Cancer Institute, and all participants gave informed consent by virtue of completing and returning the questionnaire.
Dietary assessment
Participants were asked to report their usual dietary intake of foods and beverages over the past year in both frequency of intake and portion size in a 124-item food-frequency questionnaire developed and validated by the National Cancer Institute (17, 18). Nutrient and total energy intakes were calculated by using the 1994–1996 US Department of Agriculture's Continuing Survey of Food Intakes by Individuals (19, 20). The second questionnaire, herein referred to as the Risk Factor Questionnaire, additionally included a validated meat-cooking module that ascertained the participant's usual cooking method (pan-fried, grilled or barbecued, oven-broiled, sautéed, baked, or microwaved) and internal and external appearance (browning) categorized into doneness levels (21, 22). Meat intake, cooking method, and degree 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 (PhIP, MeIQx, and DiMeIQx), one PAH (BaP), and heme iron by using an exposure index described in detail elsewhere (22, 23). Similarly, intake of nitrate and nitrite was estimated by using a database of measured values from 10 types of processed meat, constituting 90% of the processed meat types consumed in the United States (22).
The total red meat variable contained all types of fresh (beef, pork, hamburger, steak, and liver) and processed red meat (bacon, cold cuts, ham, hot dogs, and sausage, excluding low-fat versions made from poultry products). White meat included fresh (chicken, turkey, and ground poultry) and processed poultry (poultry cold cuts, low-fat sausages, and low-fat hot dogs) and fish (finfish/shellfish and canned tuna). The meat variables also included meats added to complex food mixtures, such as casseroles and sandwiches (24).
Case ascertainment
Cancer cases were ascertained through linkage with the 8 original state cancer registries plus an additional 2 states (Arizona and Texas), where participants commonly migrated. The cancer registries are certified by the North American Association of Central Cancer Registries as being ≥90% complete within 2 y of cancer incidence. The high quality of the NIH-AARP Diet and Health study case ascertainment methods are described in detail elsewhere (25). Vital status was ascertained though periodic linkage of the cohort to the US Social Security Administration Death Master File, follow-up searches of the National Death Index Plus for participants matched to the Social Security Administration Death Master File, cancer registry linkage, questionnaire responses, and responses to other mailings. Follow-up for each subject began on the date of questionnaire return and continued until the date of cancer diagnosis, date of censoring due to loss to follow-up, death, movement out of the registry areas, or 31 December 2006, whichever came first.
RCC endpoints were defined by anatomic site and histologic code per the International Classification of Diseases for Oncology, third edition (ICD-0-3) (26). We restricted our definition of primary adenocarcinoma of the kidney (C649) to the following histology codes: 8140, 8141, 8190, 8200, 8211, 8251, 8255, 8260, 8270, 8280, 8310, 8312, 8316–8320, 8323, 8370, 8440, 8450, 8480, 8481, 8490, 8500, 8504, 8510, 8521, 8550, 8570, 8940, and 8959. The 2 most common and distinctly defined histomorphologic subtypes of RCC were also investigated: clear cell (histology code 8310) and papillary (8260) adenocarcinomas (27). RCCs not otherwise specified (NOS; histology code 8312) were not included in the subtype analysis (27), and there were <50 cases each of the other defined subtypes.
Statistical analysis
All dietary variables were adjusted for total energy intake by using the nutrient density method and were presented for ease of interpretability as g/1000 kcal total energy intake. Residual energy adjustment (28) produced similar results. We evaluated the association between meat intake and risk of RCC by using Cox proportional hazards regression models with time since entry (person-years) as the underlying time metric. HRs, 95% CIs, and P values for linear trend (using the median value within quintiles) are reported across sex-specific quintiles of intake with the lowest intake quintile representing the referent group. We confirmed that the Cox proportional hazards assumption was met through assessment of interaction terms for the exposures with follow-up time. Multivariable-adjusted models included the following covariables: age (modeled as a continuous covariable), education (<8 y or unknown, 8–11 y, high school graduate, some college, or college graduate), marital status, family history of any cancer (first-degree relative), race [non-Hispanic white, non-Hispanic black, other (Hispanic, Asian/Pacific Islander, American Indian/Alaskan Native, or unknown)], BMI (in kg/m2; <18.5, 18.5 to <25 (reference), 25 to <30, 30 to <35, or ≥35], smoking status (never, quit ≥10 y ago, quit 5–9 y ago, quit 1–4 y ago, quit <1 y ago or currently smoking and smoked ≤20 cigarettes/d, quit <1 y ago or currently smoking and smoked >20 cigarettes/d), history of diabetes (yes or no), history of hypertension (yes or no), alcohol intake (none, >0 to <5, 5 to <15, 15 to <30, or ≥30 g/d), and fruit and vegetable intake [MyPyramid equivalents database (29) servings/1000 kcal; modeled separately in quintiles]. Indicator variables were created for covariables with missing values (smoking status and personal history of hypertension). Exclusion of participants with missing values produced similar results. Risk estimates were adjusted for quintiles of other meat intake, such that the sum of all the meat variables in the model would represent total meat intake (ie, red meat intake was adjusted for poultry and fish intake and vice versa; 30). Additional adjustment for physical activity, menopausal hormone therapy (women), and parity (women) and intakes of total fat, saturated fat, or whole grains did not appreciably change the risk estimates and, thus, were not included in the final multivariate models.
We assessed whether associations varied by sex, smoking status, race, BMI, history of hypertension, history of diabetes, or alcohol intake and conducted a lag analysis excluding the first 2-y of follow-up. Statistical tests for interaction evaluated the significance of categorical cross-product terms in the multivariate-adjusted models. All statistical tests were 2-sided and were considered statistically significant at P < 0.05. All statistical analyses were conducted by using SAS version 9.2 (SAS Institute Inc).
RESULTS
Over a mean follow-up of 9 y, we ascertained 1814 cases of RCC (n = 498 clear cell; n = 115 papillary cell; n = 1071 NOS; n = 147 otherwise specified) in the baseline analysis and 1,089 cases of RCC (n = 306 clear cell; n = 83 papillary cell; n = 617 NOS; n = 90, otherwise specified) in the subcohort analysis. Individuals in the highest, compared with the lowest, category of red meat intake were more likely to be non-Hispanic white, to be current smokers, and to have a higher BMI and history of diabetes (Table 1). The median (10th, 90th percentiles) of total red meat intakes in men and women, respectively, were 35.1 (12.0, 67.0) and 26.3 (7.8, 54.7) g/1000 kcal. Red meat intake was significantly correlated with intake of heme iron (r = 0.82), PhIP (r = 0.42), MeIQx (r = 0.52), and BaP (r = 0.36).
TABLE 1.
Means and proportions for selected characteristics of the NIH-AARP Diet and Health Study cohort by red meat intake (n = 492,186)1
| Quintile of red meat intake | |||||
| Characteristic | 1 | 2 | 3 | 4 | 5 |
| Red meat (g/1000 kcal) | 9.7 ± 0.022 | 21.8 ± 0.01 | 31.6 ± 0.02 | 42.9 ± 0.02 | 66.8 ± 0.06 |
| Age (y) | 62.4 ± 0.02 | 62.4 ± 0.02 | 62.1 ± 0.02 | 61.9 ± 0.02 | 61.3 ± 0.02 |
| Male (%) | 59.6 | 59.6 | 59.6 | 59.6 | 59.6 |
| White, non-Hispanic (%) | 87.5 | 90.7 | 92.0 | 93.1 | 92.8 |
| Black, non-Hispanic (%) | 5.7 | 4.3 | 3.7 | 3.7 | 2.8 |
| College and postcollege (%) | 46.4 | 40.6 | 37.9 | 35.6 | 32.4 |
| Currently married (%) | 63.3 | 67.3 | 69.7 | 71.1 | 71.3 |
| Positive family history of cancer (%) | 47.6 | 48.8 | 49.2 | 49.2 | 48.3 |
| Never smoker (%) | 40.8 | 40.1 | 37.0 | 37.5 | 33.8 |
| Current smoker or quit <1 y ago (%) | 7.4 | 11.3 | 13.0 | 16.0 | 18.6 |
| Alcohol intake (g/d) | 14.5 ± 0.14 | 14.1 ± 0.11 | 12.5 ± 0.09 | 11.2 ± 0.08 | 9.4 ± 0.06 |
| BMI (kg/m2) | 25.8 ± 0.01 | 26.7 ± 0.02 | 27.1 ± 0.02 | 27.6 ± 0.02 | 28.3 ± 0.02 |
| History of hypertension (%)3 | 36.2 | 38.1 | 39.0 | 39.8 | 41.9 |
| History of diabetes (%) | 6.4 | 7.3 | 8.3 | 9.8 | 13.0 |
| Dietary intakes | |||||
| White meat (g/1000 kcal) | 36.6 ± 0.10 | 33.4 ± 0.08 | 32.4 ± 0.07 | 32.3 ± 0.07 | 32.8 ± 0.07 |
| Processed meat (g/1000 kcal) | 5.3 ± 0.02 | 8.2 ± 0.02 | 10.6 ± 0.02 | 13.6 ± 0.03 | 19.1 ± 0.04 |
| Fruit (MPED servings/1000 kcal) | 1.7 ± 0.003 | 1.3 ± 0.003 | 1.1 ± 0.002 | 1.0 ± 0.002 | 0.8 ± 0.002 |
| Vegetables (MPED servings/1000 kcal) | 1.3 ± 0.003 | 1.1 ± 0.002 | 1.1 ± 0.002 | 1.1 ± 0.002 | 1.0 ± 0.002 |
| Total energy intake (kcal/d) | 1757 ± 2.5 | 1789 ± 2.5 | 1825 ± 2.5 | 1873 ± 2.6 | 1929 ± 2.7 |
MPED, MyPyramid Equivalents Database.
Mean ± SE (all such values).
Ascertained from a second questionnaire mailed to a subcohort (Risk Factor Questionnaire).
Meat intake by type and risk of RCC is presented in Table 2. For participants in the highest compared with the lowest quintiles of total red meat intake, we observed a tendency toward an increased risk of total RCC (HR: 1.19; 95% CI: 1.01, 1.40; P-trend = 0.06). Although the risks were elevated for both subtypes investigated, statistical significance was only attained for papillary RCC (HR: 2.20; 95% CI: 1.11, 3.09; P-trend = 0.002). In a continuous analysis, every 10-g (per 1000 kcal) increase in red meat intake was associated with a statistically significant 13% higher risk of papillary RCC, which remained when we excluded processed components from the red meat variable (HR: 1.15; 95% CI: 1.04, 1.26; per 10-g/1000 kcal). We found no association for total processed meat intake and total RCC, but observed a positive trend for intake of processed red meat and the clear cell subtype (P-trend = 0.04). On further evaluation of specific types of processed red meat, we found a statistically significant positive association for sausage intake and clear cell RCC (HRs and 95% CIs) across quintiles: 1.00 (reference), 1.21 (0.88, 1.68), 1.14 (0.83, 1.58), 1.56 (1.14, 2.14), and 1.54 (1.14, 2.12); P-trend = 0.002 (data presented in the text only). Intake of other types of meat, poultry, and fish were not related to risk of RCC or either histologic subtype.
TABLE 2.
Intake of different types of meat and risk of RCC: NIH-AARP Diet and Health Study (n = 492,186)1
| Dietary intake(g/1000 kcal) | Total RCC | Clear cell RCC | Papillary cell RCC | |||||||||
| No. of cases | HR | 95% CI | P-trend2 | No. of cases | HR | 95% CI | P-trend2 | No. of cases | HR | 95% CI | P-trend2 | |
| Red meat, total | ||||||||||||
| Model 13 | 1814 | 1.05 | 1.03, 1.08 | 498 | 1.08 | 1.03, 1.12 | 115 | 1.10 | 1.02, 1.18 | |||
| Model 24 | 1.02 | 1.00, 1.04 | 1.03 | 0.99, 1.07 | 1.13 | 1.04, 1.23 | ||||||
| Q1 (9.8) | 310 | 1.00 | 78 | 1.00 | 21 | 1.00 | ||||||
| Q2 (21.4) | 370 | 1.14 | 0.98, 1.33 | 94 | 1.16 | 0.85, 1.57 | 17 | 0.90 | 0.47, 1.74 | |||
| Q3 (31.4) | 327 | 1.00 | 0.84, 1.16 | 92 | 1.11 | 0.81, 1.51 | 21 | 1.22 | 0.64, 2.30 | |||
| Q4 (42.9) | 386 | 1.14 | 0.97, 1.34 | 108 | 1.25 | 0.91, 1.69 | 26 | 1.61 | 0.86, 3.01 | |||
| Q5 (62.7) | 421 | 1.19 | 1.01, 1.40 | 0.06 | 126 | 1.33 | 0.97, 1.80 | 0.14 | 30 | 2.02 | 1.11, 3.09 | 0.002 |
| Red meat, not processed | ||||||||||||
| Model 13 | 1814 | 1.04 | 1.02, 1.07 | 498 | 1.05 | 1.00, 1.10 | 115 | 1.13 | 1.04, 1.24 | |||
| Model 24 | 1.01 | 0.98, 1.04 | 1.01 | 0.96, 1.06 | 1.15 | 1.04, 1.26 | ||||||
| Q1 (6.8) | 327 | 1.00 | 86 | 1.00 | 21 | 1.00 | ||||||
| Q2 (15.3) | 385 | 1.12 | 0.96, 1.31 | 102 | 1.14 | 0.84, 1.54 | 22 | 1.00 | 0.53, 1.87 | |||
| Q3 (22.7) | 327 | 0.93 | 0.78, 1.09 | 92 | 0.98 | 0.71, 1.35 | 15 | 0.72 | 0.35, 1.46 | |||
| Q4 (31.6) | 366 | 1.01 | 0.86, 1.20 | 95 | 0.96 | 0.69, 1.32 | 26 | 1.33 | 0.70, 2.55 | |||
| Q5 (48.1) | 409 | 1.08 | 0.92, 1.28 | 0.99 | 123 | 1.10 | 0.80, 1.52 | 0.69 | 31 | 1.79 | 0.94, 3.42 | 0.008 |
| Processed meat, total | ||||||||||||
| Model 13 | 1814 | 1.07 | 1.03, 1.12 | 498 | 1.10 | 1.02, 1.19 | 115 | 1.02 | 0.86, 1.21 | |||
| Model 24 | 1.03 | 0.98, 1.07 | 1.05 | 0.97, 1.13 | 1.06 | 0.89, 1.25 | ||||||
| Q1 (2.2) | 321 | 1.00 | 86 | 1.00 | 16 | 1.00 | ||||||
| Q2 (5.3) | 357 | 1.06 | 0.91, 1.24 | 89 | 0.97 | 0.72, 1.31 | 31 | 2.00 | 1.09, 3.70 | |||
| Q3 (8.6) | 365 | 1.05 | 0.90, 1.23 | 94 | 0.98 | 0.72, 1.32 | 27 | 1.83 | 0.97, 3.46 | |||
| Q4 (13.3) | 371 | 1.04 | 0.89, 1.22 | 113 | 1.13 | 0.84, 1.52 | 18 | 1.26 | 0.63, 2.54 | |||
| Q5 (23.6) | 400 | 1.09 | 0.93, 1.27 | 0.50 | 116 | 1.12 | 0.83, 1.51 | 0.30 | 23 | 1.68 | 0.86, 3.27 | 0.62 |
| Red meat, processed | ||||||||||||
| Model 13 | 1814 | 1.09 | 1.03, 1.14 | 498 | 1.14 | 1.05, 1.25 | 115 | 0.98 | 0.80, 1.21 | |||
| Model 24 | 1.04 | 0.98, 1.09 | 1.09 | 0.99, 1.19 | 1.05 | 0.85, 1.29 | ||||||
| Q1 (1.4) | 333 | 1.00 | 92 | 1.00 | 17 | 1.00 | ||||||
| Q2 (3.7) | 330 | 0.96 | 0.82, 1.12 | 77 | 0.80 | 0.58, 1.10 | 29 | 1.78 | 0.95, 3.36 | |||
| Q3 (6.4) | 371 | 1.05 | 0.89, 1.23 | 99 | 1.00 | 0.73, 1.36 | 23 | 1.43 | 0.72, 2.84 | |||
| Q4 (10.1) | 347 | 0.95 | 0.80, 1.12 | 94 | 0.92 | 0.67, 1.27 | 24 | 1.48 | 0.74, 2.99 | |||
| Q5 (19.9) | 433 | 1.12 | 0.95, 1.32 | 0.16 | 136 | 1.26 | 0.92, 1.71 | 0.04 | 22 | 1.42 | 0.69, 2.93 | 0.91 |
| Poultry | ||||||||||||
| Model 13 | 1814 | 1.02 | 1.00, 1.04 | 498 | 1.03 | 0.99, 1.07 | 115 | 1.00 | 0.91, 1.10 | |||
| Model 24 | 1.01 | 0.99, 1.04 | 1.02 | 0.98, 1.07 | 0.99 | 0.89, 1.10 | ||||||
| Q1 (4.4) | 368 | 1.00 | 105 | 1.00 | 23 | 1.00 | ||||||
| Q2 (10.3) | 353 | 0.94 | 0.81, 1.10 | 99 | 0.90 | 0.68, 1.19 | 22 | 0.89 | 0.49, 1.61 | |||
| Q3 (16.7) | 361 | 0.97 | 0.83, 1.12 | 94 | 0.84 | 0.63, 1.12 | 26 | 1.01 | 0.57, 1.80 | |||
| Q4 (26.0) | 356 | 0.96 | 0.82, 1.12 | 88 | 0.78 | 0.58, 1.05 | 23 | 0.89 | 0.49, 1.62 | |||
| Q5 (47.1) | 376 | 1.01 | 0.87, 1.18 | 0.48 | 112 | 0.99 | 0.75, 1.32 | 0.75 | 21 | 0.86 | 0.46, 1.60 | 0.61 |
| Fish | ||||||||||||
| Model 13 | 1814 | 1.01 | 0.97, 1.05 | 498 | 1.01 | 0.93, 1.09 | 115 | 0.91 | 0.74, 1.11 | |||
| Model 24 | 1.01 | 0.97, 1.05 | 1.01 | 0.93, 1.09 | 0.88 | 0.72, 1.08 | ||||||
| Q1 (2.1) | 347 | 1.00 | 99 | 1.00 | 23 | 1.00 | ||||||
| Q2 (4.9) | 55 | 1.03 | 0.89, 1.19 | 106 | 1.18 | 0.89, 1.57 | 21 | 0.85 | 0.47, 1.54 | |||
| Q3 (7.9) | 361 | 1.05 | 0.90, 1.22 | 102 | 1.14 | 0.85, 1.52 | 20 | 0.78 | 0.42, 1.43 | |||
| Q4 (12.5) | 374 | 1.10 | 0.94, 1.28 | 94 | 1.05 | 0.78, 1.42 | 28 | 1.08 | 0.61, 1.91 | |||
| Q5 (23.1) | 377 | 1.14 | 0.95, 1.29 | 0.21 | 106 | 1.18 | 0.88, 1.58 | 0.66 | 23 | 0.90 | 0.49, 1.64 | 0.93 |
Q, quintile; RCC, renal cell carcinoma.
Categorical multivariable model (model 2); median intake within quintile.
Cox proportional hazards regression continuous (10 g/1000 kcal) model adjusted for age, sex, total energy intake, and mutually adjusted for other types of meat intake.
Cox proportional hazards regression continuous (10 g/1000 kcal) multivariable model additionally adjusted for education, marital status, family history of cancer, race, BMI, smoking status, history of diabetes, history of hypertension, and intakes of alcohol, fruit, and vegetables.
Intakes of meat-derived compounds and RCC risk are presented in Table 3. We observed a positive trend for heme iron intake and total RCC (P-trend = 0.03); however, investigation of this association by subtype showed no association for clear cell RCC, but a >2-fold increase in risk of the papillary subtype (HR: 2.36; 95% CI: 1.16, 4.83; P-trend = 0.003). We found no association between intake of nitrate and/or nitrite from processed meats and risk of RCC in the multivariable-adjusted analysis. High intake of 2 meat-cooking compounds, BaP and PhIP, was associated with a significant increased risk of total RCC, but the excess risk was confined to the fifth quintile of BaP intake (HR: 1.23; 95% CI: 1.01, 1.48; P-trend = 0.03) and to the fourth (HR: 1.32; 95% CI: 1.09, 1.61) and fifth (HR: 1.30; 95% CI: 1.07, 1.58) quintiles of PhIP intake (P-trend = 0.04). This was consistent with the weak positive association we observed for intake of grilled or barbecued meat intake and total RCC (HR: 1.19; 95% CI: 1.01, 1.41; P-trend = 0.09; data in text only). We found no associations between intake of meat-cooking compounds and clear cell RCC. However, the highest compared with the lowest intake quintiles of BaP and PhIP were associated with approximately a 2-fold or greater increased risk of papillary RCC. We also observed a positive trend for MeIQx intake and papillary RCC (P-trend = 0.03), but the effect estimates were not statistically significant. Results did not appear to be modified by sex, race, smoking status, BMI, history of hypertension, history of diabetes, or alcohol intake (P-interaction for all >0.05).
TABLE 3.
Intake of endogenous meat compounds and meat mutagens formed during high-temperature cooking and risk of RCC: NIH-AARP Diet and Health Study subcohort (n = 302,162)1
| Dietary intake (per 1000 kcal) | Total RCC | Clear cell RCC | Papillary cell RCC | |||||||||
| No. of cases2 | HR | 95% CI | P-trend3 | No. of cases2 | HR | 95% CI | P-trend3 | No. of cases2 | HR | 95% CI | P-trend3 | |
| Heme iron (μg) | ||||||||||||
| Model 1 (100 μg)4 | 1089 | 1.10 | 1.05, 1.15 | 306 | 1.10 | 1.02, 1.19 | 83 | 1.24 | 1.11, 1.39 | |||
| Model 2 (100 μg)5 | 1.05 | 1.00, 1.10 | 1.02 | 0.93, 1.11 | 1.21 | 1.07, 1.37 | ||||||
| Q1 (48.1) | 193 | 1.00 | 58 | 1.00 | 14 | 1.00 | ||||||
| Q2 (100.9) | 184 | 0.89 | 0.72, 1.09 | 50 | 0.75 | 0.52, 1.10 | 15 | 1.15 | 0.55, 2.40 | |||
| Q3 (151.3) | 223 | 1.04 | 0.86, 1.27 | 56 | 0.78 | 0.54, 1.14 | 11 | 0.91 | 0.40, 1.99 | |||
| Q4 (212.7) | 231 | 1.05 | 0.86, 1.28 | 70 | 0.92 | 0.64, 1.32 | 18 | 1.57 | 0.76, 3.26 | |||
| Q5 (336.0) | 258 | 1.15 | 0.94, 1.40 | 0.03 | 72 | 0.86 | 0.59, 1.24 | 0.91 | 25 | 2.36 | 1.16, 4.83 | 0.003 |
| Nitrate and nitrite (mg)6 | ||||||||||||
| Model 14 | 1089 | 1.44 | 1.10, 1.88 | 306 | 2.06 | 1.32, 3.23 | 83 | 0.84 | 0.29, 2.46 | |||
| Model 25 | 1.03 | 0.77, 1.38 | 1.41 | 0.85, 2.35 | 0.61 | 0.19, 1.93 | ||||||
| Q1 (0.02) | 208 | 1.00 | 61 | 1.00 | 9 | 1.00 | ||||||
| Q2 (0.07) | 213 | 1.02 | 0.94, 1.24 | 45 | 0.66 | 0.45, 0.97 | 23 | 0.67 | 0.29, 1.54 | |||
| Q3 (0.11) | 192 | 0.97 | 0.81, 1.17 | 41 | 0.56 | 0.37, 0.84 | 21 | 1.58 | 0.82, 3.05 | |||
| Q4 (0.17) | 223 | 0.93 | 0.69, 1.01 | 75 | 0.97 | 0.68, 1.38 | 15 | 1.43 | 0.73, 2.77 | |||
| Q5 (0.29) | 253 | 0.93 | 0.78, 1.12 | 0.76 | 84 | 1.02 | 0.72, 1.45 | 0.09 | 15 | 1.02 | 0.50, 2.09 | 0.85 |
| BaP (ng) | ||||||||||||
| Model 1 (10 mg)4 | 1089 | 1.02 | 1.00, 1.04 | 306 | 1.02 | 0.98, 1.06 | 83 | 1.08 | 1.04, 1.12 | |||
| Model 2 (10 ng)5 | 1.02 | 1.00, 1.04 | 1.00 | 0.97, 1.04 | 1.07 | 1.03, 1.12 | ||||||
| Q1 (0.2) | 193 | 1.00 | 45 | 1.00 | 14 | 1.00 | ||||||
| Q2 (1.5) | 211 | 1.04 | 0.86, 1.27 | 61 | 1.26 | 0.85, 1.85 | 11 | 0.79 | 0.36, 1.75 | |||
| Q3 (6.2) | 215 | 1.09 | 0.90, 1.32 | 67 | 1.43 | 0.98, 2.09 | 18 | 1.26 | 0.63, 2.54 | |||
| Q4 (16.8) | 225 | 1.14 | 0.93, 1.38 | 66 | 1.37 | 0.93, 2.00 | 11 | 0.79 | 0.36, 1.75 | |||
| Q5 (44.0) | 245 | 1.23 | 1.01, 1.48 | 0.03 | 67 | 1.31 | 0.89, 1.91 | 0.61 | 29 | 2.17 | 1.13, 4.17 | 0.002 |
| PhIP (ng) | ||||||||||||
| Model 1 (10 ng)4 | 1089 | 1.01 | 1.00, 1.01 | 306 | 1.00 | 0.99, 1.02 | 83 | 1.02 | 1.01, 1.03 | |||
| Model 2 (10 ng)5 | 1.01 | 1.00, 1.01 | 1.00 | 0.99, 1.01 | 1.02 | 1.01, 1.03 | ||||||
| Q1 (2.1) | 172 | 1.00 | 50 | 1.00 | 11 | 1.00 | ||||||
| Q2 (10.9) | 218 | 1.22 | 1.00, 1.50 | 67 | 1.24 | 0.86, 1.80 | 20 | 1.86 | 0.89, 3.90 | |||
| Q3 (24.7) | 213 | 1.17 | 0.96, 1.43 | 62 | 1.10 | 0.75, 1.59 | 9 | 0.82 | 0.34, 1.98 | |||
| Q4 (49.4) | 242 | 1.32 | 1.09, 1.61 | 62 | 1.08 | 0.74, 1.56 | 19 | 1.75 | 0.82, 3.71 | |||
| Q5 (123.6) | 244 | 1.30 | 1.07, 1.58 | 0.04 | 65 | 1.09 | 0.74, 1.57 | 0.82 | 24 | 2.17 | 1.05, 4.47 | 0.03 |
| MeIQx (ng) | ||||||||||||
| Model 1 (10 ng)4 | 1,089 | 1.03 | 1.01, 1.06 | 306 | 1.00 | 0.93, 1.07 | 83 | 1.04 | 0.98, 1.10 | |||
| Model 2 (10 ng)5 | 1.02 | 0.99, 1.04 | 0.95 | 0.88, 1.03 | 1.03 | 0.96, 1.10 | ||||||
| Q1 (0.5) | 185 | 1.00 | 50 | 1.00 | 16 | 1.00 | ||||||
| Q2 (2.4) | 197 | 1.03 | 0.84, 1.26 | 58 | 1.09 | 0.74, 1.59 | 15 | 0.94 | 0.46, 1.91 | |||
| Q3 (5.3) | 233 | 1.16 | 0.96, 1.41 | 78 | 1.36 | 0.95, 1.95 | 15 | 0.95 | 0.47, 1.95 | |||
| Q4 (10.3) | 218 | 1.05 | 0.86, 1.28 | 57 | 0.93 | 0.63, 1.37 | 12 | 0.78 | 0.36, 1.67 | |||
| Q5 (24.4) | 256 | 1.16 | 0.96, 1.42 | 0.18 | 63 | 0.93 | 0.64, 1.37 | 0.22 | 25 | 1.76 | 0.91, 3.40 | 0.03 |
| DiMeIQx (ng) | ||||||||||||
| Model 14 | 1089 | 1.02 | 1.00, 1.04 | 306 | 0.98 | 0.90, 1.07 | 83 | 1.02 | 0.95, 1.09 | |||
| Model 25 | 1.01 | 0.98, 1.04 | 0.94 | 0.86, 1.04 | 1.01 | 0.93, 1.10 | ||||||
| Q1 (0) | 408 | 1.00 | 117 | 1.00 | 35 | 1.00 | ||||||
| Q2 (0.04) | 41 | 0.85 | 0.62, 1.17 | 8 | 0.60 | 0.29, 1.23 | 3 | 0.72 | 0.22, 2.34 | |||
| Q3 (0.19) | 184 | 0.79 | 0.66, 0.94 | 53 | 0.81 | 0.59, 1.12 | 11 | 0.53 | 0.27, 1.05 | |||
| Q4 (0.58) | 111 | 0.91 | 0.77, 1.08 | 66 | 0.94 | 0.69, 1.27 | 16 | 0.79 | 0.44, 1.43 | |||
| Q5 (1.74) | 240 | 0.96 | 0.81, 1.12 | 0.79 | 62 | 0.82 | 0.60, 1.11 | 0.41 | 18 | 0.94 | 0.53, 1.67 | 0.77 |
BaP, benzo(a)pyrene; DiMeIQx, 2-amino-3,4,8-trimethylimidazo(4,5-f)quinoxaline; MeIQx, 2-amino-3,8-dimethylimidazo(4,5-f)quinoxaline; PhIP, 2-amino-1-methyl-6-phenyl-imidazo(4,5-b)pyridine; Q, quintile; RCC, renal cell carcinoma.
A subcohort of participants completed a second questionnaire (Risk Factor Questionnaire) with queries for meat-cooking and doneness.
Categorical multivariable model (model 2); median intake within quintile.
Cox proportional hazards regression continuous model adjusted for age, sex, and total energy intake.
Cox proportional hazards regression continuous multivariable model additionally adjusted for education, marital status, family history of cancer, race, BMI, smoking status, history of diabetes, history of hypertension, and intakes of alcohol, fruit, and vegetables.
Sum of nitrate and nitrite intake from processed meat sources.
DISCUSSION
In this large prospective investigation of middle-aged US adults, we found evidence that red meat intake may increase the risk of RCC through potential mechanisms related to carcinogenic cooking compounds and heme iron, a prooxidant compound that lends red meat its color. We observed modest associations, ranging from a 20% to 30% increase in risk, between red meat, BaP, and PhIP intakes and total RCC. By histologic subtype, we observed no significant associations with clear cell RCC, but the highest compared with the lowest intake quintiles of red meat, heme iron, BaP, and PhIP were associated with a 2-fold or greater increased risk of papillary RCC.
The weak positive association we observed between red meat intake and total RCC, as well as the strong association between red meat intake and papillary RCC, contrasts with the decidedly null findings for red meat intake from a pooled analysis of 13 prospective studies (5). However, the NIH-AARP Diet and Health Study was not part of the previous analysis and currently exceeds the number of incident RCC cases included in the pooled sample. We were able to investigate associations across a wide range of red meat consumption, within a single cohort, using a standardized protocol and dietary questionnaire validated within the study population (16, 18). Although the range of red and processed meat intake in our study was similar to that of the pooled sample, the dietary assessment methods and median intakes for red meat varied across the 13 cohorts (from 15 to 100 g/d) (5). Furthermore, our prospective findings were not subject to recall bias, a potential limitation in the previous case-control investigations that reported positive associations for red meat intake (3, 31).
The positive association we observed between red meat intake and RCC was supported by our findings for intake of heme iron and for BaP and PhIP found in meat cooked by methods such as grilling or pan-frying. BaP and PhIP intakes were associated with a statistically significant 20–30% elevated risk of total RCC and a 2-fold increased risk of the papillary subtype. Experimental studies suggest that HCA and PAH metabolites, processed by the kidney through the course of metabolism, are a biologically plausible risk factor for RCC tumors (32–34). Although no previous prospective investigations of meat-cooking compounds and RCC risk have been conducted, previous case-control studies have found positive associations for BaP intake, as well as “well-done or charred” usual degree of meat doneness (8) and intake of meat that was fried or sautéed (7, 8)—proxies of HCA and/or PAH exposure from meat consumption. To our knowledge, no other studies of heme iron intake and RCC risk have been conducted. However, increasing evidence indicates that the development of cancer within the kidney coincides with genetic and epigenetic changes in pathways involved in nutrient and xenobiotic metabolism and in iron and oxygen sensing (35, 36). Thus, it seems plausible that the epithelial cells within the renal tubule would be sensitive to metabolic stress from heme iron and other dietary carcinogens related to meat intake. Interestingly, we observed no association between estimated intake of nitrate and/or nitrite from processed meats and RCC. However, NOCs may be derived from a variety of dietary sources, including plant foods and drinking water, and further investigation of these compounds is warranted. In a prospective cohort of Iowa women, no association was found for nitrate in public drinking water and RCC risk (37), whereas a case-control study of RCC and nitrate in Iowa's public water supplies observed a positive association among participants with high red meat intake—a factor known to increase endogenous NOC formation (38).
Previous prospective studies of diet and RCC have not investigated potential associations by histologic subtype, possibly because of low incidence. We observed significant associations with both red meat intake and related exposures, such as heme iron, BaP, and PhIP for papillary RCC; however, the number of cases was relatively small, and a dose-response relation was not always clear. Histologic variants of RCC differ with respect to both clinical features and genetic determinants (39), but little is known regarding their etiology. Papillary adenocarcinomas originate from chromosomal aberrations, whereas clear cell tumors are associated with a loss of function mutations in the VHL tumor suppressor gene. Loss of VHL function in sporadic clear cell RCC, as well as potential VHL protein degradation in sporadic papillary RCC, has been shown to disrupt iron homeostasis and regulation of hypoxia-inducible factor concentrations in RCC cells, which leads to aberrations in proliferation and oxidant sensitivity (35, 40). It is not clear why we observed associations between meat-related exposures and papillary RCC, but not the clear cell subtype. Previous epidemiologic investigations of VHL mutations in RCC patients (>90% clear cell) and dietary factors have found a potential protective effect of vegetables and citrus fruit, but no association for meat intake (41). Similarly, we observed no significant associations for red meat or heme iron intake with the clear cell subtype in multivariable models adjusted for other diet and lifestyle risk factors. Our findings will need to be replicated in studies with standardized classification of histologic subtypes (27, 42) and biologically plausible mechanisms for diet and papillary RCC explored.
The large size, prospective design, and detailed meat questionnaire allowed us to examine previously uninvestigated aspects of the relation between meat and RCC, such as risks by histologic subtype and associations with meat-related compounds to shed light on potential mechanisms. Most of the significant associations we observed were confined to the top quintile (20%) of intake, and the distribution of meat-mutagen intake was skewed with most of the study population consuming relatively low concentrations of these compounds (43). We used a strict subtype definition (27); thus, we only had a sufficient number of cases to investigate the 2 main histologic variants. Some of our results should be viewed with caution, because our most robust findings occurred within a limited number of cases of the papillary subtype and just more than half of the total RCC cases lacked a more-specific histomorphologic determination. However, the proportion of RCC NOS in our study population is consistent with overall Surveillance, Epidemiology, and End Results data (44), and we did not observe any inconsistencies by date of cancer diagnosis. We examined and adjusted for a multitude of key confounders; however, the possibility of residual confounding by unmeasured or unknown risk factors remains. Exposure data were self-reported, and the study questionnaires did not ascertain information regarding some suggested risk factors for RCC, including family history of RCC (specifically) and occupational exposures. Because of a large number of comparisons, it is possible that some of the more modestly significant results may be attributable to chance. Because this is the first prospective study to report the associations between meat-related compounds and RCC, our findings will need to be replicated in other prospective studies with similar exposure measures and in studies of RCC subtypes with central pathologic review.
Our findings from the largest prospective investigation of meat intake and RCC thus far suggest that red meat intake may play a role in the development of RCC through potential mechanisms related to mutagenic cooking compounds (eg, BaP and PhIP) and heme iron intake. Most of our findings appeared to be driven by strong associations with the rarer papillary histologic variant of RCC. This finding warrants further investigation of diet and RCC histologies, because none of the previous prospective studies of meat and RCC have reported findings by subtype.
Acknowledgments
Cancer incidence data from the Atlanta metropolitan area were collected by the Georgia Center for Cancer Statistics, Department of Epidemiology, Rollins School of Public Health, Emory University. Cancer incidence data from California were collected by the California Department of Health Services, Cancer Surveillance Section. Cancer incidence data from the Detroit metropolitan area were collected by the Michigan Cancer Surveillance Program, Community Health Administration, State of Michigan. The Florida cancer incidence data used in this report were collected by the Florida Cancer Data System (FCDC) under contract with the Florida Department of Health (FDOH). Cancer incidence data from Louisiana were collected by the Louisiana Tumor Registry, Louisiana State University Medical Center in New Orleans. Cancer incidence data from New Jersey were collected by the New Jersey State Cancer Registry, Cancer Epidemiology Services, New Jersey State Department of Health and Senior Services. Cancer incidence data from North Carolina were collected by the North Carolina Central Cancer Registry. Cancer incidence data from Pennsylvania were supplied by the Division of Health Statistics and Research, Pennsylvania Department of Health, Harrisburg, PA. The Pennsylvania Department of Health specifically disclaims responsibility for any analyses, interpretations or conclusions. Cancer incidence data from Arizona were collected by the Arizona Cancer Registry, Division of Public Health Services, Arizona Department of Health Services. Cancer incidence data from Texas were collected by the Texas Cancer Registry, Cancer Epidemiology and Surveillance Branch, Texas Department of State Health Services.
We are indebted to the participants in the NIH-AARP Diet and Health Study for their outstanding cooperation. We thank Sigurd Hermansen and Kerry Grace Morrissey from Westat for study outcome ascertainment and management and Leslie Carroll and Adam Risch at Information Management Services for data support and analysis. We wish to acknowledge the loss and memory of Arthur Schatzkin—our leader, colleague, mentor, and friend. The authors’ responsibilities were as follows—CRD: performed statistical analysis and drafted the manuscript; CRD, RS, AJC, and W-HC: conceived of the project; and RS, AJC, W-HC, BIG, YP, ARH, NR, and MHW: contributed to the design of the study, analysis, and/or its components. All authors critically reviewed and approved of the final manuscript. None of the authors had any conflicts of interest.
Footnotes
Abbreviations used: BaP, benzo(a)pyrene; DiMeIQx, 2-amino-3,4,8-trimethylimidazo[4,5-f]quinoxaline; HCA, heterocyclic amine; MeIQx, 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline; MPED, My Pyramid Equivalents Database; NOC, N-nitroso compound; NOS, not otherwise specified; PAH, polycyclic aromatic hydrocarbon; PhIP, 2-amino-1-methyl-6-phenyl-imidazo[4,5-b]pyridine; RCC, renal cell carcinoma; VHL, von Hippel-Lindau.
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