Background: Previous epidemiologic studies found inconsistent results for the association between red meat intake, nitrosamines [NDMA: N-nitrosodimethylamine, and ENOC (endogenous nitroso compounds)], and the risk of bladder cancer. We investigated the association between red meat consumption, dietary nitrosamines, and heme iron and the risk of bladder cancer among participants of the European Prospective Investigation into Cancer and Nutrition (EPIC).

Methods: Data on food consumption and complete follow-up for cancer occurrence were available for a total of 481,419 participants, recruited in 10 European countries. Estimates of HRs were obtained by proportional hazard models, stratified by age at recruitment, gender, and study center and adjusted for total energy intake, smoking status, lifetime intensity of smoking, duration of smoking, educational level, and BMI.

Results: After a mean follow-up of 8.7 years, 1,001 participants were diagnosed with bladder cancer. We found no overall association between intake of red meat (log2 HR: 1.06; 95% CI: 0.99–1.13), nitrosamines (log2 HR: 1.09; 95% CI: 0.92–1.30 and HR: 0.98; 95% CI: 0.92–1.05 for ENOC and NDMA, respectively) or heme iron

(log2 HR: 1.05; 95 CI: 0.99–1.12) and bladder cancer risk. The associations did not vary by sex, high- versus low-risk bladder cancers, smoking status, or occupation (high vs. low risk).

Conclusions: Our findings do not support an effect of red meat intake, nitrosamines (endogenous or exogenous), or heme iron intake on bladder cancer risk. Cancer Epidemiol Biomarkers Prev; 20(3); 555–9. ©2011 AACR.

Meat may be involved in bladder carcinogenesis via several biological mechanisms (1). One possible mechanism involves the formation of endogenous nitrosamines from heme contained in fresh and processed meat (2). Nitrosamines have been shown to cause a wide range of tumors, including cancer of the bladder, in more than 40 animal species (3). Intake of red meat (the most important source of heme iron) has shown inconsistent results with bladder cancer in epidemiologic studies (4, 5). A recent study has shown a positive association between dietary nitrite from processed meat and bladder cancer risk (6). The aim of the present study was to examine the association between red meat intake, dietary nitrosamines (endogenous and exogenous), heme iron intake, and bladder cancer incidence in the European Prospective Investigation into Cancer and Nutrition (EPIC).

EPIC rationale and details were described elsewhere (7). Briefly, the EPIC study involves more than a half million people from 23 centers in 10 European countries. A lifestyle questionnaire was used to collect information about sociodemographic characteristics, lifestyle factors, and medical history. Dietary data collected by validated country-specific questionnaires recording the usual diet over the previous 12 months (8) were used to estimate red meat intake (fresh and processed meat). NDMA (N-nitrosodimethylamine), the most available exogenous nitroso compound, and ENOC (endogenous nitroso compounds), both in micrograms per day, were obtained from previous estimation in the EPIC-EURGAST study (9). Published information on measured values of different types of meat (10, 11) was used to estimate heme iron. The proportion of heme iron from total iron was 65% in cooked beef, 39% in pork and chicken, and 26% in fish. For those foods with mixed composition of meat (beef and pork), we applied an average factor.

After a mean follow-up of 8.7 years, 1,049 bladder cancer cases were identified. Bladder cancer ascertainment was described previously (12). We excluded 6,220 individuals without dietary or nondietary exposure information and 3,220 participants with implausible dietary data (below or above 3 SD sex-specific log of mean energy intake). We also excluded 3,500 individuals with an extreme heme iron intake (>4 mg/d). After the exclusion, we had a sample of 481,419 subjects, of which 1,001 subjects developed primary bladder cancer. Other variables such as occupational exposures were defined as previously (12). Proportional hazards models were used to estimate the association between meat intake, nitrosamines (NDMA and ENOC), and heme iron and bladder cancer risk. The time scale was age; the entry time was age at recruitment and the exit time was, for cases, age at first bladder tumor, and for noncases, age at death or last complete follow-up. All models were stratified by age, sex, and center and adjusted for educational level (none, primary school, technical/professional school, secondary school, longer education, and unknown), BMI (as continuous variable), smoking status (never, former, and current), lifetime intensity of smoking (for current smokers, <20 cigarettes per day and >20) and time since quitting (for former smokers, <10 years and >10 years), and total energy intake. The Statistical Analysis System (version 9; SAS Institute) was used for analyses.

In multivariable models, the HR for highest versus lowest quartile of intake was 1.15 (95% CI: 0.90–1.45) for read meat, 1.12 (95% CI: 0.89–1.42) for ENOC, 1.12 (95% CI: 0.88–1.44 for NDMA, and 1.10 (95 CI: 0.88–1.39) for heme iron. This lack of association was supported by analyses of continuous variables (Table 1).

Table 1.

Adjusted HRs for full cohort and prognostically high- and low-risk bladder cancer by quartiles of heme iron and red meat intake, ENOC, and NDMAa

 Heme ironbRed meat intakec (fresh and processed meat)ENOCdNDMAe
CasesHRCasesHRCasesHRCasesHR
Full cohort (n = 481,419) 
 Q1 171 1.00 144 1.00 157 1.00 165 1.00 
 Q2 219 1.04 (0.84–1.28) 233 1.20 (0.96–1.49) 214 1.05 (0.84–1.30) 190 1.14 (0.91–1.42) 
 Q3 268 1.09 (0.88–1.35) 269 1.14 (0.91–1.42) 274 1.12 (0.90–1.39) 226 1.07 (0.85–1.34) 
 Q4 343 1.10 (0.88–1.39) 355 1.15 (0.90–1.45) 354 1.12 (0.89–1.42) 420 1.12 (0.88–1.44) 
 Ptrend  0.39  0.49  0.30  0.49 
 log2 1,001 1.05 (0.99–1.12) 1,001 1.06 (0.99–1.13) 999f 1.09 (0.92–1.30) 1,001 0.98 (0.92–1.05) 
High riskg 
 Q1 72 1.00 64 1.00 67 1.00 89 1.00 
 Q2 111 1.16 (0.86–1.56) 111 1.16 (0.86–1.58) 113 1.15 (0.85–1.55) 97 1.11 (0.83–1.49) 
 Q3 140 1.19 (0.88–1.59) 137 1.13 (0.84–1.53) 144 1.18 (0.87–1.59) 113 1.03 (0.76–1.40) 
 Q4 161 1.00 (0.73–1.37) 172 1.05 (0.76–1.44) 160 1.00 (0.73–1.37) 185 0.99 (0.72–1.36) 
 Ptrend  0.88  0.15  0.92  0.98 
 log2 484 1.07 (0.97–1.19) 484 1.09 (0.98–1.21) 484 0.95 (0.73–1.22) 484 1.00 (0.91–1.10) 
Low riskg 
 Q1 77 1.00 64 1.00 73 1.00 69 1.00 
 Q2 110 1.04 (0.77–1.40) 112 1.19 (0.87–1.62) 100 0.95 (0.70–1.29) 87 1.11 (0.81–1.53) 
 Q3 112 0.92 (0.68–1.25) 120 1.07 (0.78–1.47) 118 0.92 (0.67–1.25) 105 1.04 (0.75–1.44) 
 Q4 172 1.10 (0.81–1.51) 175 1.16 (0.84–1.61) 179 1.04 (0.76–1.43) 210 1.16 (0.83–1.63) 
 Ptrend  0.22  0.28  0.37  0.16 
 log2 471 1.05 (0.95–1.15) 471 1.06 (0.96–1.17) 470h 1.24 (0.96–1.59) 471 0.99 (0.90–1.09) 
 Heme ironbRed meat intakec (fresh and processed meat)ENOCdNDMAe
CasesHRCasesHRCasesHRCasesHR
Full cohort (n = 481,419) 
 Q1 171 1.00 144 1.00 157 1.00 165 1.00 
 Q2 219 1.04 (0.84–1.28) 233 1.20 (0.96–1.49) 214 1.05 (0.84–1.30) 190 1.14 (0.91–1.42) 
 Q3 268 1.09 (0.88–1.35) 269 1.14 (0.91–1.42) 274 1.12 (0.90–1.39) 226 1.07 (0.85–1.34) 
 Q4 343 1.10 (0.88–1.39) 355 1.15 (0.90–1.45) 354 1.12 (0.89–1.42) 420 1.12 (0.88–1.44) 
 Ptrend  0.39  0.49  0.30  0.49 
 log2 1,001 1.05 (0.99–1.12) 1,001 1.06 (0.99–1.13) 999f 1.09 (0.92–1.30) 1,001 0.98 (0.92–1.05) 
High riskg 
 Q1 72 1.00 64 1.00 67 1.00 89 1.00 
 Q2 111 1.16 (0.86–1.56) 111 1.16 (0.86–1.58) 113 1.15 (0.85–1.55) 97 1.11 (0.83–1.49) 
 Q3 140 1.19 (0.88–1.59) 137 1.13 (0.84–1.53) 144 1.18 (0.87–1.59) 113 1.03 (0.76–1.40) 
 Q4 161 1.00 (0.73–1.37) 172 1.05 (0.76–1.44) 160 1.00 (0.73–1.37) 185 0.99 (0.72–1.36) 
 Ptrend  0.88  0.15  0.92  0.98 
 log2 484 1.07 (0.97–1.19) 484 1.09 (0.98–1.21) 484 0.95 (0.73–1.22) 484 1.00 (0.91–1.10) 
Low riskg 
 Q1 77 1.00 64 1.00 73 1.00 69 1.00 
 Q2 110 1.04 (0.77–1.40) 112 1.19 (0.87–1.62) 100 0.95 (0.70–1.29) 87 1.11 (0.81–1.53) 
 Q3 112 0.92 (0.68–1.25) 120 1.07 (0.78–1.47) 118 0.92 (0.67–1.25) 105 1.04 (0.75–1.44) 
 Q4 172 1.10 (0.81–1.51) 175 1.16 (0.84–1.61) 179 1.04 (0.76–1.43) 210 1.16 (0.83–1.63) 
 Ptrend  0.22  0.28  0.37  0.16 
 log2 471 1.05 (0.95–1.15) 471 1.06 (0.96–1.17) 470h 1.24 (0.96–1.59) 471 0.99 (0.90–1.09) 

aThe Cox regression model stratified by age at recruitment, sex, and center and adjusted for educational level, BMI (as continuous variable), smoking status, lifetime intensity of smoking (number of cigarettes per day), time since quitting or duration of smoking, and total energy intake.

bHeme iron (mg/d): Q1, 0–<0.6; Q2, >0.6–<1.02; Q3, >1.02–<1.53; Q4, >1.53–4.

cRed meat intake (g/d): Q1, 0–57.86; Q2, 57.86–91.42; Q3, 91.42–130.63; Q4, 130.63–754.79.

dENOC (μg/d): Q1, 40.52; Q2, 62.91–78.27; Q3, 78.27–98.45; Q4, 98.46–201.41.

eNDMA (μg/d): Q1, 0–0.05; Q2, 0.05–0.1; Q3, 0.10–0.19; Q4, 0.19–21.11.

fTwo missing values for ENOC.

gExcludes 46 cases that could not be classified into prognostically high- or low-risk bladder cancer.

hOne missing value for ENOC.

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No differences were observed when fresh meat and processed red meat were assessed separately (data not shown). Calibration of dietary variables did not change our findings (data not shown), Furthermore, similar findings were found when stratifying bladder cancer cases by risk of progression (13). There was no association between heme iron and bladder cancer risk (Table 2) within strata of sex (male vs. female), tobacco smoking (never, former, current), and occupational exposure (high vs. low risk).

Table 2.

Adjusteda HRs for bladder cancer by heme iron and red meat intake, ENOC, and NDMA

 Heme ironRed meat intakebENOCNDMA
CasesHRCasesHRCasesHRCasesHR
Tobacco smoking 
Current smokers (n = 108,011) 
 Q1 41 1.00 43 1.00 40 1.00 48 1.00 
 Q2 87 1.16 (0.79–1.71) 80 1.04 (0.70–1.52) 79 1.04 (0.71–1.54) 63 0.96 (0.65–1.43) 
 Q3 105 1.03 (0.70–1.53) 101 0.92 (0.63–1.35) 112 1.07 (0.73–1.57) 94 0.99 (0.67–1.45) 
 Q4 187 1.20 (0.80–1.79) 196 1.12 (0.76–1.65) 189 1.18 (0.79–1.75) 215 1.06 (0.71–1.58) 
 Ptrend  0.43  0.54  0.35  0.67 
 log2 420 1.07 (0.94–1.23) 420 1.06 (0.92–1.21) 420 1.12 (0.85–1.48) 420 0.98 (0.89–1.09) 
Former smokers (n = 128,094) 
 Q1 76 1.00 57 1.00 72 1.00 60 1.00 
 Q2 81 0.94 (0.67–1.32) 87 1.16 (0.82–1.66) 79 0.94 (0.67–1.33) 69 1.29 (0.90–1.21) 
 Q3 106 1.12 (0.81–1.56) 112 1.25 (0.88–1.79) 107 1.07 (0.76–1.50) 87 1.21 (0.83–1.75) 
 Q4 105 0.95 (0.65–1.37) 112 1.01 (0.69–1.49) 110 0.94 (0.64–1.37) 152 1.24 (0.83–1.86) 
 Ptrend  0.87  0.95  0.90  0.41 
 log2 368 1.03 (0.94–1.14) 368 1.07 (0.97–1.18) 368 0.97 (0.73–1.30) 368 1.01 (0.91–1.12) 
Never smokers (n = 235,401) 
 Q1 51 1.00 41 1.00 41 1.00 55 1.00 
 Q2 49 1.01 (0.67–1.54) 64 1.50 (1.00–2.26) 55 1.30 (0.85–2.00) 55 1.32 (0.88–1.97) 
 Q3 57 1.22 (0.81–1.85) 55 1.35 (0.86–2.10) 55 1.38 (0.88–2.16) 45 1.11 (0.70–1.76) 
 Q4 50 1.13 (0.71–1.80) 47 1.25 (0.75–2.08) 54 1.36 (0.83–2.21) 52 1.06 (0.62–1.80) 
 Ptrend  0.51  0.52  0.24  0.94 
 log2 207 1.06 (0.94–1.20) 207 1.04 (0.93–1.17) 205 1.24 (0.86–1.79) 207 0.95 (0.82–1.10) 
Sex 
Male (n = 142,010) 
 Q1 90 1.00 70 1.00 76 1.00 78 1.00 
 Q2 128 1.09 (0.82–1.45) 135 1.29 (0.95–1.75) 130 1.01 (0.60–1.70) 106 1.23 (0.90–1.67) 
 Q3 185 1.16 (0.87–1.54) 190 1.26 (0.93–1.71) 188 0.90 (0.53–1.54) 158 1.23 (0.91–1.66) 
 Q4 302 1.22 (0.91–1.64) 310 1.26 (0.92–1.72) 309 0.94 (0.54–1.61) 363 1.24 (0.90–1.69) 
 Ptrend  0.18  0.36  0.80  0.30 
 log2 705 1.06 (0.97–1.15) 705 1.05 (0.97–1.14) 703 0.99 (0.88–1.12) 705 1.00 (0.93–1.08) 
Female (n = 339,409) 
 Q1 81 1.00 74 1.00 81 1.00 87 1.00 
 Q2 91 1.05 (0.76–1.44) 98 1.20 (0.87–1.64) 84 0.93 (0.67–1.28) 84 1.18 (0.85–1.64) 
 Q3 83 1.10 (0.79–1.54) 79 1.06 (0.75–1.50) 86 1.10 (0.79–1.54) 68 0.96 (0.66–1.41) 
 Q4 41 0.89 (0.58–1.36) 45 0.98 (0.64–1.50) 45 0.91 (0.60–1.34) 57 1.02 (0.66–1.57) 
 Ptrend  0.70  0.87  0.99  0.85 
 log2 296 1.05 (0.94–1.16) 296 1.07 (0.97–1.19) 296 0.95 (0.68–1.32) 296 0.95 (0.84–1.08) 
Occupational exposure risk 
High (n = 389,996)c 
 Q1 21 1.00 12 1.00 19 1.00 29 1.00 
 Q2 25 0.61 (0.33–1.12) 32 1.28 (0.65–2.53) 35 0.92 (0.52–1.64) 31 0.97 (0.56–1.69) 
 Q3 44 0.66 (0.37–1.17) 52 1.27 (0.66–2.45) 46 0.73 (0.41–1.31) 30 0.58 (0.32–1.06) 
 Q4 113 0.93 (0.52–1.67) 107 1.32 (0.68–2.57) 103 0.90 (0.50–1.63) 113 0.77 (0.43–1.37) 
 Ptrend  0.27  0.54  0.85  0.33 
 log2 203 1.10 (0.89–1.35) 203 1.09 (0.88–1.34) 203 0.98 (0.64–1.50) 203 0.98 (0.85–1.14) 
Low (n = 167,236) 
 Q1 64 1.00 54 1.00 64 1.00 66 1.00 
 Q2 77 0.78 (0.56–1.11) 85 0.91 (0.64–1.29) 85 0.88 (0.63–1.23) 66 1.19 (0.82–1.72) 
 Q3 117 0.96 (0.69–1.34) 106 0.91 (0.64–1.28) 115 0.95 (0.68–1.32) 105 1.45 (1.00–2.10) 
 Q4 147 0.91 (0.63–1.31) 160 1.01 (0.70–1.45) 140 0.96 (0.67–1.38) 168 1.39 (0.93–2.09) 
 Ptrend  0.99  0.8  0.97  0.11 
 log2 405 1.00 (0.89–1.12) 405 0.99 (0.89–1.09) 404 1.10 (0.82–1.47) 405 1.01 (0.91–1.12) 
 Heme ironRed meat intakebENOCNDMA
CasesHRCasesHRCasesHRCasesHR
Tobacco smoking 
Current smokers (n = 108,011) 
 Q1 41 1.00 43 1.00 40 1.00 48 1.00 
 Q2 87 1.16 (0.79–1.71) 80 1.04 (0.70–1.52) 79 1.04 (0.71–1.54) 63 0.96 (0.65–1.43) 
 Q3 105 1.03 (0.70–1.53) 101 0.92 (0.63–1.35) 112 1.07 (0.73–1.57) 94 0.99 (0.67–1.45) 
 Q4 187 1.20 (0.80–1.79) 196 1.12 (0.76–1.65) 189 1.18 (0.79–1.75) 215 1.06 (0.71–1.58) 
 Ptrend  0.43  0.54  0.35  0.67 
 log2 420 1.07 (0.94–1.23) 420 1.06 (0.92–1.21) 420 1.12 (0.85–1.48) 420 0.98 (0.89–1.09) 
Former smokers (n = 128,094) 
 Q1 76 1.00 57 1.00 72 1.00 60 1.00 
 Q2 81 0.94 (0.67–1.32) 87 1.16 (0.82–1.66) 79 0.94 (0.67–1.33) 69 1.29 (0.90–1.21) 
 Q3 106 1.12 (0.81–1.56) 112 1.25 (0.88–1.79) 107 1.07 (0.76–1.50) 87 1.21 (0.83–1.75) 
 Q4 105 0.95 (0.65–1.37) 112 1.01 (0.69–1.49) 110 0.94 (0.64–1.37) 152 1.24 (0.83–1.86) 
 Ptrend  0.87  0.95  0.90  0.41 
 log2 368 1.03 (0.94–1.14) 368 1.07 (0.97–1.18) 368 0.97 (0.73–1.30) 368 1.01 (0.91–1.12) 
Never smokers (n = 235,401) 
 Q1 51 1.00 41 1.00 41 1.00 55 1.00 
 Q2 49 1.01 (0.67–1.54) 64 1.50 (1.00–2.26) 55 1.30 (0.85–2.00) 55 1.32 (0.88–1.97) 
 Q3 57 1.22 (0.81–1.85) 55 1.35 (0.86–2.10) 55 1.38 (0.88–2.16) 45 1.11 (0.70–1.76) 
 Q4 50 1.13 (0.71–1.80) 47 1.25 (0.75–2.08) 54 1.36 (0.83–2.21) 52 1.06 (0.62–1.80) 
 Ptrend  0.51  0.52  0.24  0.94 
 log2 207 1.06 (0.94–1.20) 207 1.04 (0.93–1.17) 205 1.24 (0.86–1.79) 207 0.95 (0.82–1.10) 
Sex 
Male (n = 142,010) 
 Q1 90 1.00 70 1.00 76 1.00 78 1.00 
 Q2 128 1.09 (0.82–1.45) 135 1.29 (0.95–1.75) 130 1.01 (0.60–1.70) 106 1.23 (0.90–1.67) 
 Q3 185 1.16 (0.87–1.54) 190 1.26 (0.93–1.71) 188 0.90 (0.53–1.54) 158 1.23 (0.91–1.66) 
 Q4 302 1.22 (0.91–1.64) 310 1.26 (0.92–1.72) 309 0.94 (0.54–1.61) 363 1.24 (0.90–1.69) 
 Ptrend  0.18  0.36  0.80  0.30 
 log2 705 1.06 (0.97–1.15) 705 1.05 (0.97–1.14) 703 0.99 (0.88–1.12) 705 1.00 (0.93–1.08) 
Female (n = 339,409) 
 Q1 81 1.00 74 1.00 81 1.00 87 1.00 
 Q2 91 1.05 (0.76–1.44) 98 1.20 (0.87–1.64) 84 0.93 (0.67–1.28) 84 1.18 (0.85–1.64) 
 Q3 83 1.10 (0.79–1.54) 79 1.06 (0.75–1.50) 86 1.10 (0.79–1.54) 68 0.96 (0.66–1.41) 
 Q4 41 0.89 (0.58–1.36) 45 0.98 (0.64–1.50) 45 0.91 (0.60–1.34) 57 1.02 (0.66–1.57) 
 Ptrend  0.70  0.87  0.99  0.85 
 log2 296 1.05 (0.94–1.16) 296 1.07 (0.97–1.19) 296 0.95 (0.68–1.32) 296 0.95 (0.84–1.08) 
Occupational exposure risk 
High (n = 389,996)c 
 Q1 21 1.00 12 1.00 19 1.00 29 1.00 
 Q2 25 0.61 (0.33–1.12) 32 1.28 (0.65–2.53) 35 0.92 (0.52–1.64) 31 0.97 (0.56–1.69) 
 Q3 44 0.66 (0.37–1.17) 52 1.27 (0.66–2.45) 46 0.73 (0.41–1.31) 30 0.58 (0.32–1.06) 
 Q4 113 0.93 (0.52–1.67) 107 1.32 (0.68–2.57) 103 0.90 (0.50–1.63) 113 0.77 (0.43–1.37) 
 Ptrend  0.27  0.54  0.85  0.33 
 log2 203 1.10 (0.89–1.35) 203 1.09 (0.88–1.34) 203 0.98 (0.64–1.50) 203 0.98 (0.85–1.14) 
Low (n = 167,236) 
 Q1 64 1.00 54 1.00 64 1.00 66 1.00 
 Q2 77 0.78 (0.56–1.11) 85 0.91 (0.64–1.29) 85 0.88 (0.63–1.23) 66 1.19 (0.82–1.72) 
 Q3 117 0.96 (0.69–1.34) 106 0.91 (0.64–1.28) 115 0.95 (0.68–1.32) 105 1.45 (1.00–2.10) 
 Q4 147 0.91 (0.63–1.31) 160 1.01 (0.70–1.45) 140 0.96 (0.67–1.38) 168 1.39 (0.93–2.09) 
 Ptrend  0.99  0.8  0.97  0.11 
 log2 405 1.00 (0.89–1.12) 405 0.99 (0.89–1.09) 404 1.10 (0.82–1.47) 405 1.01 (0.91–1.12) 

aThe Cox regression model stratified by age at recruitment, sex, and center and adjusted for educational level, BMI, smoking status, lifetime intensity of smoking (number of cigarettes per day), time since quitting or duration of smoking, and total energy intake.

bIncluding fresh and processed meat.

CWorked in foundries, electroplating, refinery, dye production, rubber industry, textile dying, fiber preparation or weaving, leather and tanning industry, turning/metal engineering, welding shop, as asphalt worker, as truck driver, as bus/taxi driver, as welder, as hairdresser, as gasoline station worker, and as car repair worker.

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In this prospective study, we found no overall association between intake of red meat (fresh and processed meat), nitrosamines, or heme iron and bladder cancer risk. The results did not vary by sex, risk of progression, smoking status, or occupational exposure (high vs. low risk). Although there was no association between the intake of red meat and bladder cancer risk in a previously published analysis in EPIC (14), it does not preclude an association with certain aspects of meat intake, such as its content of heme iron, or nitrosamines. However, we were not able to find an association with any particular meat-related compound.

To our knowledge, this is the first prospective study of heme iron intake and bladder cancer risk. The strengths of this study include its large size, prospective design, and inclusion of potential confounding variables.

In summary, our findings do not support the hypothesis that red meat and meat-related compounds are associated with the risk of developing bladder cancer. Considering the biological plausibility of an association between red meat intake and bladder cancer risk, further investigation is warranted, given the limited available evidence.

No potential conflicts of interest were disclosed.

The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue contre le Cancer, 3M, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Medicale (France); Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); Ministry of Health and Social Solidarity, Stavros Niarchos Foundation and Hellenic Health Foundation (Greece); Italian Association for Research on Cancer (AIRC) and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF; the Netherlands); Statistics Netherlands; Norwegian Cancer Society (Norway); Health Research Fund (FIS) of the Spanish Ministry of Health (Exp P10710130), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, La Caixa (BM 06-130), RTICCC-C03/10, R06/0020; Spain); Swedish Cancer Society, Swedish Scientific Council and Regional Government of Skåne and Västerbotten (Sweden); Cancer Research UK, Medical Research Council, Stroke Association, British Heart Foundation, Department of Health, Food Standards Agency, and Wellcome Trust (United Kingdom).

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2011