Diet is thought to play a key role in cancer prevention, with more than half of colorectal cancers (CRC) potentially preventable through diet and lifestyle modifications( 1 ). However, the role of diet on CRC survival is unclear. With an estimated 244 000 CRC patients in the UK, this is an urgent area of research. There are a number of plausible mechanisms to link diet to CRC, including formation of N-nitroso compounds( Reference Cross, Pollock and Bingham 2 ), anti-inflammatory effects( Reference Fung, Cosgrove and Lockett 3 ), contributions to DNA synthesis and methylation( Reference Pufulete, Al-Ghnaniem and Khushal 4 ), inhibition of cancer cell proliferation( Reference Bostick, Fosdick and Wood 5 , Reference Hamer, Jonkers and Venema 6 ), binding to free fatty acids and bile acids( Reference Newmark, Wargovich and Bruce 7 ), production of anticancer SCFA( Reference Burkitt 8 ), insulin lowering( Reference Pereira, O’Reilly and Augustsson 9 ) and metabolic activity attributed to adiposity( Reference Calle and Kaaks 10 ). In particular, convincing evidence has been presented for foods containing dietary fibre (lower CRC risk) and for red and processed meat (higher CRC risk)( 11 ); these pathways may also be relevant for CRC recurrence and survival outcomes. CRC patients frequently request dietary recommendations( Reference Pullar, Chisholm and Jackson 12 ), but there is insufficient evidence at present to provide guidance( 13 ). This is an increasingly necessary area of research as CRC survival rates have improved because of earlier detection and advances in treatment in many Western countries( Reference Edwards, Noone and Mariotto 14 , Reference Quaresma, Coleman and Rachet 15 ); between 1989 and 2011, mortality has decreased among European CRC patients by 13 % in men and 27 % in women( Reference Ouakrim, Pizot and Boniol 16 ).
At present, the few available studies on CRC survival and consumption of meat and fibre have yielded inconclusive findings. In the Cancer Prevention Study II, post-diagnostic intake of red and processed meat was not associated with survival among CRC patients( Reference McCullough, Gapstur and Shah 17 ). However, pre-diagnostic intake of red and processed meat was associated with greater all-cause mortality, and those with high intake of red and processed meat during both pre- and post-diagnostic dietary assessments were at greater risk of CRC-specific mortality( Reference McCullough, Gapstur and Shah 17 ). In a smaller US study, post-diagnosis red meat intake was associated with higher mortality risk, but only among those with familial CRC( Reference Zell, Ignatenko and Yerushalmi 18 ). Results for post-diagnostic fibre intake and CRC survival are similarly mixed. Among Mormons in Utah (USA), higher fibre intake was associated with poorer mortality outcomes( Reference Slattery, French and Egger 19 ), whereas another US study( Reference Zell, Ignatenko and Yerushalmi 18 ) yielded null results. Pre-diagnostic fibre intake was also unrelated to survival outcomes among CRC patients in studies from France( Reference Dray, Boutron-Ruault and Bertrais 20 ) and Scandinavia( Reference Skeie, Braaten and Olsen 21 ). However, many of the aforementioned studies had relatively small sample sizes (<500 CRC cases( Reference Zell, Ignatenko and Yerushalmi 18 , Reference Slattery, French and Egger 19 , Reference Skeie, Braaten and Olsen 21 )) and may have been underpowered to detect statistically significant associations. Drawing on the large European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, the objective of the present analysis was to further characterise the role of pre-diagnostic meat and fibre intakes on survival among CRC cases, with separate consideration for red meat, processed meat and poultry.
Methods
Study population
EPIC is a multi-centre, prospective cohort study, which recruited 519 978 volunteers (mostly ages 25–70 years) from twenty-three centres in ten countries (Sweden, Denmark, Norway, The Netherlands, UK, France, Germany, Spain, Italy and Greece) between 1992 and 2000. The study has been described in detail previously( Reference Riboli and Kaaks 22 , Reference Riboli, Hunt and Slimani 23 ). EPIC was approved by the Institutional Review Board at the International Agency for Research on Cancer (Lyon, France) and local ethics committees, and informed consent was obtained from all participants.
Data collection and definitions
At baseline, participants reported dietary intake using country-specific validated questionnaires. In most centres, a self-administered FFQ was used to assess intake over the past 12 months (eighty-eight to 266 food items). A questionnaire plus food record was used in Malmö, Sweden, and interviewer-administered diet questionnaires were used in Greece, Spain and Ragusa, Italy. For this analysis, red meat included beef, pork, mutton/lamb, horse, goat; processed meat included all meat products including ham, bacon, sausages and a small part of minced meat in ready-to-eat products; and poultry included chicken, hen, turkey, duck, goose, unclassified poultry and rabbit (domestic). Dietary intake of fibre was calculated from the standardised EPIC Nutrient Data Base( Reference Slimani, Deharveng and Unwin 24 ). Questionnaires on education, occupation, previous illnesses, alcohol and tobacco consumption, and physical activity were also completed by participants. Anthropometric variables were measured in most centres; as exceptions, self-reported data were used in France and Norway, and in Oxford self-reported data were adjusted based on measurements collected from a subset of participants( Reference Spencer, Appleby and Davey 25 ).
Population cancer registries were used in Denmark, Italy, The Netherlands, Norway, Spain, Sweden and the UK to identify incident cancers. In France, Germany and Greece, cancer cases were identified through active follow-up, directly through study participants or next of kin, and confirmed by a combination of methods including health insurance records as well as cancer and pathology registries. CRC cases were identified according to the second edition of the International Classification of Diseases for Oncology (ICD-O), either as located in the colon (C18.0–C18.7), rectum (C19 and C20) or overlapping/unspecified (C18.8 and 18.9).
The date of CRC diagnosis was used as the start date of follow-up for the present study. Participants were censored at date of death, last date of contact or the date at which follow-up data were considered to be complete at each study centre (between June 2005 and June 2009). Loss to follow-up across all countries was low (<2 %). Harmonisation of tumour stage data was required to combine TNM (Tumour, Node, Metastasis) classification classification, Dukes’s classification or centre-specific classification; this process has been described previously( Reference Fedirko, Riboli and Tjonneland 26 ).
A total of 4701 CRC cases were identified. Cases excluded from the present analysis were as follows: 426 cases diagnosed with CRC after vital status censoring date, 172 cases with in situ or metastatic tumour, 144 non-adenocarcinoma or tumour of unknown morphology, twenty-one because of missing date of death or diagnosis, eight missing cause of death, seventy-four within the extreme ranking (top and bottom 1 %) of the ratio energy intake:energy requirement and sixty-seven with missing data on diet. The final sample included 3789 CRC cases (1603 men and 2186 women; 2383 colon cancer cases and 1406 rectal cancer cases).
Availability of data on tumour stage and grade varied between centres: there were 970 participants with missing stage data, including all participants from Malmö (n 353) and Oxford (n 271), and there were 2080 participants with missing grade data, including all participants from Denmark (n 705), Malmö (n 353), Cambridge (n 270) and Oxford (n 271), as well as over 85 % of participants from Umeå (n 155).
Mortality data were obtained at the regional or national level. In Denmark, Italy, The Netherlands, Norway, Spain, Sweden and the UK, vital status and the causes and dates of death were ascertained by death indices, cancer registry records and national health statistics. Active follow-up was adopted in Germany, Greece and France. Causes of death were coded according to the International Classification of Diseases, 10th Revision (ICD-10). Up to six qualifiers of the cause of death were reviewed. Death from CRC was assigned based on the underlying cause of death.
Statistical analysis
The primary end point in the present analysis was death due to CRC (with other causes of death modelled as competing risks, using SAS macro PSHREG( Reference Kohl, Plischke and Leffondre 27 )); the secondary end point was all-cause mortality. The pre-diagnostic dietary exposures in the present analysis, rescaled to reflect approximately 1 sd, were fibre (10 g/d), red meat (40 g/d), processed meat (30 g/d), combined red and processed meat (50 g/d) and poultry (20 g/d); separate models were built for each dietary exposure. Sex-specific quartiles (Q) of the dietary variables were also derived for analysis. Fibre was further analysed by source (cereal, fruit and vegetable) as quartile and continuous grams per day. Tests for trend across quartiles were conducted by assigning participants the sex-specific median value per quartile.
The proportional hazards models used age at CRC diagnosis and age at censoring as the underlying time variables, and the models were stratified by country; two models are presented: (i) a model that adjusts for age at diagnosis and sex and (ii) a multivariable model adjusting for additional confounders. Potentially confounding variables were selected on the basis of possible associations with both the outcome and the dietary exposures, and were entered into the model stepwise to determine whether they changed the association between dietary exposure and mortality by 10 % or more. The following variables were identified for one or more of the dietary exposures under study: education level (primary school, technical/professional school, secondary school, longer education (including university degree), not specified/missing), smoking status (current, former, never, unknown), BMI (kg/m2, continuous), total energy intake (kJ/d (kcal/d)), Ca intake (mg/d), folate intake (mg/d), alcohol intake (g/d: 0, >0–6 (M)/>0–3 (W), >6–12 (M)/>3–12 (W), >12–24, >24–60, >60), tumour grade (well differentiated, moderately differentiated, poorly/undifferentiated, unknown), tumour stage (localised (I), localised with invasion (II), metastatic regional (III), metastatic distant (IV), unknown) and year of diagnosis (continuous, calendar year). Adjustment for physical activity (the Cambridge Index categories( Reference Wareham, Jakes and Rennie 28 )) and cancer site (colon v. rectum) did not change any associations by 10 % or more, and were therefore not included. The independent effect of red meat, processed meat, poultry and fibre was explored by mutually adjusting for each other in an additional model. The validity of the proportional hazards assumption was tested by inclusion of time-varying covariates; there was no evidence of deviation from proportionality.
Potential interactions with pre-diagnostic red meat, poultry, processed meat and fibre as continuous variables were tested for education level (primary school, technical/professional/secondary school, longer education) smoking status (current, former, never), BMI (<25, 25–30, >30 kg/m2), intake of total energy (kJ/d (kcal/d)), Ca intake (mg/d), folate intake (mg/d), alcohol intake (g/d: 0, >0–6 (M)/>0–3 (W), >6–24 (M)/>3–24 (W), ≥24), stage (I–III v. IV), age at diagnosis (<60, 60–69, ≥70 years), year of diagnosis (1992–1999, 2000–2004, 2005–2009), median length of follow-up after CRC diagnosis (<3·3 v. ≥3·3 years) and median length of time between dietary assessment and CRC diagnosis (<6·5 v. ≥6·5 years). The significance of interaction terms was assessed by likelihood ratio tests comparing nested models with and without the interaction terms.
A number of sensitivity analyses were conducted. Waist circumference was identified as a potential confounder, but was examined in a supplementary model rather than the main analysis because of a relatively high proportion of missing data (n 566). In order to explore the potential influence of advanced CRC, the main models were reanalysed (i) among participants with stage I, II or III tumours and (ii) restricted to deaths occurring more than 6 months after recruitment. The effect of replacing red and processed meat with poultry was estimated by substitution models, where total intake of red meat, processed meat and poultry was held constant and an increase in poultry intake represented an equal decrease in red and processed meat intake( Reference Kipnis, Freedman and Brown 29 ).
Tumour stage data were missing for 25·6 % of participants. The influence of missing stage data was examined by three ways: (i) creation of a separate ‘missing’ category for Malmö and Oxford, (ii) exclusion of Oxford and Malmö and (iii) imputation of missing values using SAS imputation command PROC MI. Stage was assumed to be missing at random, and imputation was based on sex, age at CRC diagnosis, year of diagnosis, tumour site, period between CRC diagnosis and death or censoring and vital status as covariates (five iterations).
Results
Among 1603 men and 2186 women, there were 1008 deaths due to CRC (1262 deaths from all causes). The average length of follow-up since CRC diagnosis was 4·1 years. Baseline characteristics according to low (Q1) and high (Q4) pre-diagnostic intakes of red and processed meat, poultry and fibre are presented separately for men and women in Table 1. For men and women, those in the highest quartile of red and processed meat intake had relatively higher waist circumference measurements and included a larger proportion of smokers and those with lower levels of education. In contrast, those in the highest quartile of poultry intake had lower waist circumference values than those in Q1. Among men, there was a higher proportion of rectal cancers in Q4 for red and processed meat intake (47·4 %) compared with Q1 (38·5 %): the corresponding distribution among women was less varied (34·5 v. 31·5 % for Q4 and Q1 of red and processed meat intake, respectively).
Table 1 Demographic, anthropometric and lifestyle characteristics according to pre-diagnostic meat and fibre intakes among colorectal cancer cases in European Prospective Investigation into Cancer and Nutrition
M, men; W, women; Q, quartile.
In both the age-adjusted and multivariable analysis, there was no evidence of an association between pre-diagnostic intakes of red meat, poultry or fibre and death due to CRC (Table 2). For processed meat and CRC mortality, a marginally significant test for trend was detected across quartiles (P=0·053, Table 2), but no association was detected among the individual quartiles or in the continuous analysis of grams per day. The corresponding results for all-cause mortality were also null, with the exception of a positive association in the highest quartile of processed meat that was limited to the age- and sex-adjusted model (hazard ratio (HR) 1·23; 95 % CI 1·04, 1·46)) (Table 3).
Table 2 Colorectal cancer (CRC)-specific mortality in relation to pre-diagnostic fibre and meat intakes among CRC survivors in European Prospective Investigation into Cancer and Nutrition (Hazard ratios (HR) and 95 % confidence intervals)
Q, quartile; Ref., referent values; M, men; W, women.
* Model 1: adjusted for age at diagnosis (1-year increments) and sex; stratified by country.
† Model 2: adjusted for age at diagnosis (1-year increments), sex, BMI (continuous), smoking status (current, former, never, unknown), tumour grade (well differentiated, moderately differentiated, poorly/undifferentiated, unknown), tumour stage (I, II, III, IV, unknown), year of tumour diagnosis (continuous), energy intake (kJ/d (kcal/d)), Ca intake (mg/d), folate intake (mg/d), alcohol intake (g/d: 0, >0–6 (M)/>0–3 (W), >6–12 (M)/>3–12 (W), >12–24, >24–60, >60) and education (primary school, technical/professional school, secondary school, longer education (including university, unknown)); stratified by country.
Table 3 All-cause mortality in relation to pre-diagnostic fibre and meat intakes among colorectal cancer survivors in European Prospective Investigation into Cancer and Nutrition (Hazard ratios (HR) and 95 % confidence intervals)
Q, quartile; Ref., referent values; M, men; W, women.
* Model 1: adjusted for age at diagnosis (1-year increments) and sex; stratified by country.
† Model 2: adjusted for age at diagnosis (1-year increments), sex, BMI (continuous), smoking status (current, former, never, unknown), tumour grade (well differentiated, moderately differentiated, poorly/undifferentiated, unknown), tumour stage (I, II, III, IV, unknown), year of tumour diagnosis (continuous), energy intake (kJ/d (kcal/d)), Ca intake (mg/d), folate intake (mg/d), alcohol intake (g/d: 0, >0–6 (M)/>0–3 (W), >6–12 (M)/>3–12 (W), >12–24, >24–60, >60), and education (primary school, technical/professional school, secondary school, longer education (including university, unknown)); stratified by country.
Testing for interactions between the main dietary exposures (modelled as continuous variables) and other covariates yielded mostly null results (online Supplementary Table S1). A marginal interaction between poultry intake (continuous g/d) and sex was detected in relation to all-cause mortality (P value 0·10); stratification by sex revealed a significant inverse association among women (HR/20 g 0·92; 95 % CI 0·84, 1·00) but not among men (HR 1·00; 95 % CI 0·91, 1·09) (online Supplementary Table S2). For both CRC-specific and all-cause mortality, interactions were detected between processed meat intake (30 g/d) and folate intake (P values 0·016 and 0·06, respectively, online Supplementary Table S1); however, the associations for processed meat remained null when the sample was divided by tertiles of folate intake. For CRC-specific mortality, a marginal interaction was detected between poultry and BMI group (P value 0·06); stratification by BMI group yielded an inverse relationship among overweight adults (HR/20 g 0·89; 95 % CI 0·80, 1·00) but no associations among normal-weight (HR 0·99; 95 % CI 0·87, 1·12) or obese adults (HR 1·10; 95 % CI 0·95, 1·27) (online Supplementary Table S1). There was no evidence of interactions by length of follow-up or lag-time between dietary assessment and CRC diagnosis. Owing to the large number of interactions tested, significant or marginally significant results may have been due to chance.
Further adjustment of the multivariable models to include waist circumference did not alter the null associations detected, nor did mutual adjustment for red meat, processed meat, poultry and fibre intakes. The effect of substituting poultry for red and processed meat was not significant for all-cause mortality (HR/20 g 0·95; 95 % CI 0·87, 1·03) or CRC-specific death (HR/20 g 0·96; 95 % CI 0·88, 1·06) (data not shown in tables). Fibre from cereal, vegetable or fruit sources was not associated with either CRC-specific or all-cause mortality in multivariable models (online Supplementary Table S3 and S4); these results remained null in further models where all fibre sources were examined simultaneously (data not shown). Similarly, results were not altered by the exclusion of deaths that occurred within 6 months of CRC diagnosis, limiting the analysis to those with stage I, II or III tumours or exploring the influence of missing stage data as described in the methods section (data not shown).
Discussion
In the present analysis, pre-diagnostic intake of red meat or fibre was not associated with survival after CRC diagnosis. Suggestive evidence was found for an inverse association between poultry intake and mortality among women and for a positive association between processed meat intake and risk of CRC-specific mortality. The associations were unchanged throughout a variety of sensitivity analyses and missing data exploration. Overall, these results are in contrast to the relatively consistent evidence regarding CRC prevention, where higher fibre and lower red or processed meat intakes are associated with lower CRC risk( 11 ). However, the number of studies available on meat and fibre intakes and CRC survival is still considerably smaller than that for CRC incidence; therefore, further research is required to determine whether dietary recommendations for CRC prevention can be extended to CRC survival.
The results of the present study can be compared with the available evidence for meat and fibre as individual exposures and for dietary patterns that feature meat and fibre as predominant components. The null result for fibre intake and CRC survival in the present study is consistent with several earlier studies( Reference Dray, Boutron-Ruault and Bertrais 20 , Reference Skeie, Braaten and Olsen 21 , Reference Zell, McEligot and Ziogas 30 ). The predominantly Mormon population among whom higher pre-diagnostic fibre intake was associated with poorer CRC survival has unique characteristics (prohibited from alcohol, caffeine and tobacco), which render it less comparable with the EPIC population( Reference Slattery, French and Egger 19 ). For meat intake, a positive association in previous studies was limited to subgroups that we were not able to examine within EPIC: those with a first-degree relative with CRC( Reference Zell, Ignatenko and Yerushalmi 18 ) (post-diagnostic intake) and those who had meat intakes consistently above the median before and after diagnosis( Reference McCullough, Gapstur and Shah 17 ). In observational studies of post-diagnostic dietary patterns, a Western pattern characterised by high intake of red and processed meat was found to be associated with poorer outcomes among CRC survivors( Reference Fung, Kashambwa and Sato 31 – Reference Zhu, Wu and Wang 33 ). The results for post-diagnostic dietary patterns are not entirely consistent: one study found that the Alternative Healthy Eating Index (A-HEI)-2010 was associated with lower mortality, but found no association for the DASH (Dietary Approaches to Stop Hypertension), Alternate Med Score, ‘Western’ pattern or ‘Prudent’ pattern( Reference Fung, Kashambwa and Sato 31 ). Among the individual components of the A-HEI, a significant association with all-cause mortality was found only for greater post-diagnostic consumption of sugar-sweetened beverages (SSB)( Reference Fung, Kashambwa and Sato 31 ). SSB also contributed to the data-derived patterns associated with poorer CRC survival outcomes in studies from Canada( Reference Zhu, Wu and Wang 33 ) and the USA( Reference Meyerhardt, Niedzwiecki and Hollis 32 ). In an earlier analysis of CRC survival in EPIC, SSB were included in an index based on pre-diagnostic adherence to World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) cancer prevention recommendations; greater adherence to the recommendations was associated with improved CRC survival( Reference Romaguera, Ward and Wark 34 ). However, exclusion of SSB from the index did not attenuate the protective effect of higher WCRF/AICR scores among CRC survivors( Reference Romaguera, Ward and Wark 34 ), and thus it is unlikely that the null results of the present analysis can be explained by a failure to account for SSB intake. In contrast to the present results, an inverse association was detected between the pre-diagnostic plant product component of the WCRF/AICR score and CRC-specific mortality; however, for the animal products component, results were similarly non-significant( Reference Romaguera, Ward and Wark 34 ). Overall, the interpretation of index components remains complex; in one study, the pre-diagnostic red and processed meat pattern associated with poorer CRC survival included fish as a component( Reference Zhu, Wu and Wang 33 ), which has previously been associated with lower risk of CRC incidence( Reference Norat, Bingham and Ferrari 35 ).
It is thought that cancer recurrence can be partly attributed to proliferation of micrometastatic disease that was not removed at the time of surgery. Accordingly, it is possible that some dietary factors associated with the development and progression of incident CRC tumours may also be associated with recurrence; one of the mechanisms by which diet could be related to CRC survival outcomes is through modification of circulating insulin levels, which is positively associated with greater production of insulin-like growth factor (IGF)-1( Reference LeRoith and Roberts 36 ). For example, ‘prudent’ dietary patterns (characterised by higher intake of fruits, vegetables, poultry, fish, whole grains and legumes) have been associated with lower levels of insulin, whereas ‘Western’ dietary patterns (characterised by higher intakes of red meat, processed meat, French fries, eggs, high-fat dairy products, sweets and refined grains) were positively associated with insulin and C-peptide levels( Reference Fung, Rimm and Spiegelman 37 ). Evidence from animal models indicates that greater IGF-1 exposure is associated with an increased rate of tumour progression( Reference Wu, Yakar and Zhao 38 ). In human studies, the results are more complex: there was no association between post-diagnostic IGF-1 levels and overall survival, but post-diagnostic insulin-like growth factor binding protein 3 (IGFBP-3) was inversely associated with tumour progression( Reference Fuchs, Goldberg and Sargent 39 ). In addition, a diet high in fibre can result in higher levels of SCFA in the lumen, which can serve to induce apoptosis in tumour cells( Reference Hague, Elder and Hicks 40 ).
Despite these potential pathways, we found no association between pre-diagnostic red meat or fibre intakes and CRC survival, and only marginally suggestive results for processed meat. An implicit assumption of the present analysis is that pre-diagnostic dietary intake is predictive of post-diagnostic diet. It is plausible that for some participants surgical treatment of CRC results in complications that require temporary or permanent dietary adjustments to avoid intestinal discomfort. A study on CRC survivors reported that, although the majority of respondents resumed comfort with their diet within 12 months, avoidance of foods such as fruits and vegetables was higher among those with a permanent ostomy (surgically created opening in the body for the discharge of body waste, 9·1 %) v. those who had their ostomy reversed (3·6 %)( Reference Sun, Grant and Wendel 41 ). In contrast, a comparison of repeat dietary assessments in a cohort of Norwegian women yielded no differences in consumption of fibre, red meat or poultry between the pre-and post-diagnostic period among CRC survivors( Reference Skeie, Hjartaker and Braaten 42 ). Intake of total meat products was lower during the post-diagnostic dietary assessment among the Norwegian CRC survivors, but the change was comparable with that observed in cancer-free women in the cohort( Reference Skeie, Hjartaker and Braaten 42 ). In principle, studies with the greatest potential to influence the development of cancer survival recommendations would be those with multiple dietary assessments both before and after diagnosis. Some of the aforementioned limitations may be addressed in the future by a multistage CRC survival-specific cohort that is underway at present( Reference Winkels, Heine-Broring and van 43 ). In the interim, the present analysis contributes to limited literature on diet and CRC survival.
The design of the present study had a number of strengths in terms of capacity to examine CRC survival. The collection of pre-diagnostic dietary data avoided the issue dietary variability close to the time of diagnosis, and thus avoided reverse causality, and standardised dietary assessment was undertaken. A range of sensitivity analyses was undertaken, including the exclusion of early mortality events, advanced stage tumours and stratification by duration since dietary assessment. The sample size was large, and there were data on a wide range of potentially confounding covariates including general and abdominal obesity (BMI and waist circumference), Ca, folate and alcohol intakes, and smoking status.
In conclusion, we found no evidence of an association between pre-diagnostic intakes of red meat or fibre and CRC survival. There is suggestive evidence of an association between poultry intake and all-cause mortality among female CRC survivors, and between processed meat intake and CRC-specific mortality; however, further research using post-diagnostic dietary data is required to confirm this relationship.
Acknowledgements
The coordination of EPIC is financially supported by the European Commission (Directorate General for Health and Consumer Affairs; DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale and Institut National de la Santé et de la Recherche Médicale (France); Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); the Hellenic Health Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro (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 and Statistics Netherlands (The Netherlands); Nordic Center of Excellence programme on Food, Nutrition and Health (Norway); Health Research Fund (FIS), PI13/00061 to Granada), Regional Governments of Andalucía, Asturias, Basque Country, Murcia (no. 6236) and Navarra and ISCIII RETIC (Instituto de Salud Carlos III Las Redes temáticas de investigación cooperativa en salud) (RD06/0020) (Spain); World Cancer Research Foundation ref. 2011/428, AGAUR (Agency for Management of University and Research Grants), Generalitat de Catalunya (exp. 2014 SGR 726), Health Research Funds (FIS Exp PI11/1486) and RD12/0036/0018 and Catalan Institute of Oncology (ICO-IDIBELL)(Barcelona); Swedish Cancer Society, Swedish Scientific Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 to EPIC-Oxford) and Medical Research Council (1000143 to EPIC-Norfolk) (UK).
H. A. W. and T. N. conceived the idea, drafted the manuscript, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. H. A. W. performed the statistical analyses. H. A. W., T. N., K. O., C. C. D., H. B. B.-d.-M., V. F., M. J., F. J. B. v. D., G. S., D. R.-B. and N. M. interpreted the results. K. O., H. B. B.-d.-M., M. J., F. J. B. v. D., G. S., A. Tj., A. O., M.-C. B.-R., V. K., T. K., K. A., H. B., A. Tr., P. L., C. B., D. P., S. S., R. T., P. H. P., E. W., P. J., J. R. Q., M.-J. S., M. D., J.-M. H., A. B., I. J., K. E. B., K.-T. K., N. J. W., A. J. C. and E. R. are EPIC investigators and were responsible for the study design and data collection. All authors critically revised the manuscript for intellectual content and approved the final version of the manuscript.
Data sharing: for information on how to submit an application for gaining access to EPIC data and/or biospecimens, please follow the instructions at http://epic.iarc.fr/access/index.php
The authors declare that there are no conflicts of interest.
Supplementary material
For supplementary material/s referred to in this article, please visit http://dx.doi.org/doi:10.1017/S0007114516001859
