Intake of dietary fruit, vegetables, and fiber and risk of colorectal cancer according to molecular subtypes: A pooled analysis of 9 studies

Akihisa Hidaka; Tabitha A Harrison; Yin Cao; Lori C Sakoda; Richard Barfield; Marios Giannakis; Mingyang Song; Amanda I Phipps; Jane C Figueiredo; Syed H Zaidi; Amanda E Toland; Efrat L Amitay; Sonja I Berndt; Ivan Borozan; Andrew T Chan; Steven Gallinger; Marc J Gunter; Mark A Guinter; Sophia Harlid; Heather Hampel; Mark A Jenkins; Yi Lin; Victor Moreno; Polly A Newcomb; Reiko Nishihara; Shuji Ogino; Mireia Obón-Santacana; Patrick S Parfrey; John D Potter; Martha L Slattery; Robert S Steinfelder; Caroline Y Um; Xiaoliang Wang; Michael O Woods; Bethany Van Guelpen; Stephen N Thibodeau; Michael Hoffmeister; Wei Sun; Li Hsu; Daniel D Buchanan; Peter T Campbell; Ulrike Peters

doi:10.1158/0008-5472.CAN-20-0168

. Author manuscript; available in PMC: 2021 Apr 15.

Published in final edited form as: Cancer Res. 2020 Aug 14;80(20):4578–4590. doi: 10.1158/0008-5472.CAN-20-0168

Intake of dietary fruit, vegetables, and fiber and risk of colorectal cancer according to molecular subtypes: A pooled analysis of 9 studies

Akihisa Hidaka ¹, Tabitha A Harrison ¹, Yin Cao ^2,^3,⁴, Lori C Sakoda ^1,⁵, Richard Barfield ¹, Marios Giannakis ⁶, Mingyang Song ^7,^8,⁹, Amanda I Phipps ^1,¹⁰, Jane C Figueiredo ^11,¹², Syed H Zaidi ¹³, Amanda E Toland ¹⁴, Efrat L Amitay ¹⁵, Sonja I Berndt ¹⁶, Ivan Borozan ¹⁷, Andrew T Chan ^8,^9,^18,^19,^20,²¹, Steven Gallinger ²², Marc J Gunter ²³, Mark A Guinter ²⁴, Sophia Harlid ²⁵, Heather Hampel ¹⁴, Mark A Jenkins ²⁶, Yi Lin ¹, Victor Moreno ^27,^28,^29,³⁰, Polly A Newcomb ^1,³¹, Reiko Nishihara ^32,³³, Shuji Ogino ^19,^20,^32,³⁴, Mireia Obón-Santacana ^27,^30,³⁵, Patrick S Parfrey ³⁶, John D Potter ¹, Martha L Slattery ³⁷, Robert S Steinfelder ¹, Caroline Y Um ²⁴, Xiaoliang Wang ¹, Michael O Woods ³⁸, Bethany Van Guelpen ^25,³⁹, Stephen N Thibodeau ⁴⁰, Michael Hoffmeister ¹⁵, Wei Sun ¹, Li Hsu ^1,⁴¹, Daniel D Buchanan ^42,^43,⁴⁴, Peter T Campbell ²⁴, Ulrike Peters ^1,¹⁰

^1.Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA

^2.Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, Missouri, USA

^3.Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine, St. Louis, MO, USA

^4.Division of Gastroenterology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA

^5.Division of Research, Kaiser Permanente Northern California, Oakland, California, USA

^6.Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

^7.Departments of Epidemiology and Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA

^8.Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA

^9.Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA

^10.Department of Epidemiology, University of Washington, Seattle, WA, USA

^11.Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

^12.Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA

^13.Ontario Institute for Cancer Research, Toronto, Ontario, Canada

^14.Departments of Cancer Biology and Genetics and Internal Medicine, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA

^15.Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany

^16.Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA

^17.Ontario Institute for Cancer Research, Toronto, Canada

^18.Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA

^19.Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA

^20.Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA

^21.Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA

^22.Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada

^23.Nutrition and Metabolism Section, International Agency for Research on Cancer, World Health Organization, Lyon, France

^24.Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, Georgia, USA

^25.Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden

^26.Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia

^27.Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain

^28.CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

^29.Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain

^30.ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain

^31.School of Public Health, University of Washington, Seattle, Washington, USA

^32.Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA

^33.Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

^34.Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

^35.Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP),’ ‘ Madrid, Spain

^36.Memorial University, Faculty of Medicine, Newfoundland, Canada

^37.Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA

^38.Memorial University of Newfoundland, Discipline of Genetics, St. John’s, Canada

^39.Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden

^40.Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA

^41.Department of Biostatistics, University of Washington, Seattle, Washington, USA

^42.University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia

^43.Genetic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia

^44.Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia

Authors’ Contributions:

Conception and design: A. Hidaka, R. Peters, T. Harrison

Development of methodology: A. Hidaka, T. Harrison, Peters, L. Hsu

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): T. Harrison, S. Berndt, D. Buchanan, P. Campbell, A. Chan, S. Gallinger, M. Gunter, S. Thibodeau, M. Hoffmeister, M. O Woods, B. Van Guelpen, M. Slattery, J. Potter, P. Parfrey, S. Ogino, P. Newcomb, V. Moreno, M. Jenkins, L. Sakoda, H. Hampel, A. Phipps, Y. Lin

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A. Hidaka, R. Peters, L. Hsu, W. Sun

Writing, review, and/or revision of the manuscript: A. Hidaka, T. Harrison, H. Zaidi, R. Peters, M. Giannakis, M. Song, B. Van Guelpen, L. Hsu, Y. Cao, R. Barfield, J. Figueiredo, A. Toland, I. Borozan, M. Guinter, S. Harlid, R. Nishihara, M. Obon-Santacana, C. Um, E. Amitay, X. Wang ((INCLUDE THOSE THAT GAVE YOU COMMENTS))

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): Y. Lin, R. Steinfelder

Study supervision: T. Harrison and R. Peters

^✉

For correspondence: Dr Akihisa Hidaka, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA, Telephone: +1-206-667-6600, Fax: +1-206-667-5268, ahidaka@fredhutch.org

PMCID: PMC7572895 NIHMSID: NIHMS1618738 PMID: 32816852

Abstract

Protective associations of fruits, vegetables, and fiber intake with colorectal cancer (CRC) risk have been shown in many, but not all epidemiological studies. One possible reason for study heterogeneity is that dietary factors may have distinct effects by CRC molecular subtypes. Here we investigate the association of fruit, vegetables, and fiber intake with four well-established CRC molecular subtypes separately and in combination. Nine observational studies including 9,592 cases with molecular subtypes for microsatellite instability (MSI), CpG island methylator phenotype (CIMP), and somatic mutations in BRAF and KRAS genes, and 7,869 controls were analyzed. Both case-only logistic regression analyses and polytomous logistic regression analyses (with one control set and multiple case groups) were used. Higher fruit intake was associated with a trend towards decreased risk of BRAF-mutated tumors [Odds ratio 4th vs. 1st quartile = 0.82 (95% confidence interval = 0.65-1.04)] but not BRAF-wildtype tumors [1.09 (0.97-1.22); P-difference as shown in case-only analysis = 0.02]. This difference was observed in case-control studies and not in cohort studies. Compared with controls, higher fiber intake showed negative association with CRC risk for cases with microsatellite stable (MSS)/MSI-low, CIMP-negative, BRAF-wildtype, and KRAS-wildtype tumors (Ptrend range from 0.03 to 3.4e-03), which is consistent with the traditional adenoma-CRC pathway. These negative associations were stronger compared with MSI-high, CIMP-positive, BRAF-mutated, or KRAS-mutated tumors, but the differences were not statistically significant. These inverse associations for fruit and fiber intake may explain, in part, inconsistent findings between fruit or fiber intake and CRC risk that have previously been reported.

Keywords: Fruit, Vegetables, Fiber, Colorectal cancer risk, Pooled analysis

INTRODUCTION

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide, and the number of cases is predicted to increase to 2.2 million new cases per year by 2030 (1). To date, various dietary factors have been investigated in relation to CRC risk in many epidemiological studies. Meta-analyses report that higher intake of fruit (2), vegetables (2), and fiber (3) is associated with decreased CRC risk. However, the World Cancer Research Fund and the American Institute for Cancer Research (WCRF/AICR) concluded that there is no “convincing strong evidence” for these dietary factors. High intake of foods containing dietary fiber only have “probable strong evidence” for decreasing the risk of CRC, and low intake of fruit and vegetables only have “limited-suggestive evidence” for increasing risk (https://www.wcrf.org/dietandcancer/colorectal-cancer) (4) because these preventive associations have been shown in many, but not all epidemiological studies (5–7). One possible reason for study heterogeneity is differential effects of diet on distinct CRC molecular subtypes.

Four molecular markers in particular have been well-studied with regard to CRC heterogeneity: microsatellite instability (MSI), the CpG island methylator phenotype (CIMP), and oncogenic mutations in BRAF and KRAS genes (8). Increasing evidence showed that some lifestyle risk factors can be differentially associated with these molecular markers (9). Probably the evidence is currently strongest for smoking which is more strongly associated with MSI-high, CIMP-positive and BRAF-mutated tumors (10,11). Furthermore, there is some evidence that body mass index and hormone replacement therapy are associated with MSI status (12). These findings provide a strong rationale to investigate if dietary risk factors may also be differentially associated with CRC molecular markers. Although several epidemiological studies have evaluated the association of fruit (13–19), vegetables (13–20), and fiber (13,16–22) intake with risk of CRC molecular subtypes, results from these studies have been inconsistent. Additionally, there has been no study to this point that has investigated all four characteristic molecular markers together despite the fact that these can point to differential pathways. Specifically, three different pathways to CRC development may be affected by the combination of molecular subtypes: a) a serrated pathway, b) an alternate pathway, and c) a traditional pathway (23,24). Only one meta-analysis reported the association of fiber intake with MSI status of CRC (12). However, this meta-analysis of three case-control studies used heterogeneous fiber definitions across studies (12). The direction of the association between fiber intake and MSI status in each study was different because one study found higher fiber intake was associated with decreased risk of MSS tumors (13), but another study reported decreased risk of both MSI-high and MSS/MSI-low tumors (16). On the contrary, one study found increased risk of MSS/MSI-low tumors with low fiber intake (17). To better address the hypothesis that these dietary factors may impact risk of CRC molecular subtypes differently, here we pooled 9 population-based studies with individual-level data harmonized in a consistent manner.

We analyzed data from 9,592 CRC cases and 7,869 controls within the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the Colon Cancer Family Registry (CCFR) to assess the association of fruit, vegetables, and fiber intake with CRC risk by molecular subtypes using data on MSI, CIMP, BRAF mutation, and KRAS mutation status. Identification of associations with specific molecular subtypes may point to specific diet-CRC associations and help inform underlying biological mechanisms relevant to CRC risk.

MATERIALS AND METHODS

Study Participants

This study population consisted of 8,783 CRC cases and 7,869 controls from 9 observational studies within GECCO and the CCFR with available tumor marker, fruit, fiber, vegetables, and total energy intake data. Additionally, 809 population-based CRC cases from the Mayo Clinic CCFR, which did not enroll population-based controls, were included in case-only analyses. Among study participants, 3,258 CRC cases and 3,984 controls were from cohort studies, and 6,334 CRC cases and 3,885 controls were from case-control studies. Descriptive characteristics of each study and mean intake of quartile cut points of fruits, vegetables, and fiber in each study are shown in Supplementary Tables S1 and S2. Additionally, details regarding each observational study are described in Supplementary Text. All CRC cases were defined by colorectal adenocarcinoma and confirmed by pathological records, medical records, and/or death certificate information. All study participants provided written informed consent, and each study was approved by their relevant research ethics committee or institutional review board.

Definition of Tumor Subtypes

Testing for MSI, CIMP, and mutations in the BRAF and KRAS gene was conducted previously by each study and according to individual study protocols. Details regarding marker testing in each study are described in Supplementary Text. Especially, Supplementary Table S3 shows study-specific markers used to assess MSI and definition of MSI status. Additionally, Supplementary Table S4 also shows study-specific panels used to assess CIMP status. We defined marker combinations for molecular subtypes, consistent with previously suggested classifications (23,25): Types 1-10. Details of the defined marker combinations are given in Figure 1 and Supplementary Text. Subtype classifications with fewer than 50 cases were excluded from analyses. With regard to colorectal carcinogenic pathways, previous studies reported that three different pathways to CRC development may be affected by the combination of molecular subtypes: a) a serrated pathway [Type 1: MSI-high, CIMP-positive, BRAF-mutated, KRAS-wildtype and Type 2: MSS/MSI-low, CIMP-positive, BRAF-mutated, KRAS-wildtype], b) an alternate pathway [Type 3: MSS/MSI-low, CIMP-negative, BRAF-wildtype, KRAS-mutated], and c) a traditional pathway [Type 4: MSS/MSI-low, CIMP-negative, BRAF-wildtype, KRAS-wildtype and Type 5: MSI-high, CIMP-negative, BRAF-wildtype, KRAS-wildtype] (23,24).

Exposure Data

In each study, demographic and lifestyle risk factor information was assessed via in-person interviews or structured self-administered questionnaires, as described in the Supplementary Text. Dietary variables were ascertained using food frequency questionnaires (FFQ) or diet history. Data were collected at study entry, at blood draw, or one to two years prior to sample ascertainment. A multistep, iterative data harmonization procedure was applied, reconciling each study’s unique protocols and data collection instruments (26). Multiple quality-control checks were performed, and outlying values of variables were truncated to the minimum or maximum value of an established range for each variable. Variables were combined into a single dataset using common definition, standardized coding, and standardized permissible values.

In our study analysis, we selected dietary intake variables for fruit (servings/day), vegetables (servings/day), and fiber (measured as g/day). Sex- and study-specific quartiles for dietary variables were created based on the distribution in the controls. Data harmonization was performed using SAS and T-SQL.

Statistical Analyses

We used the Chi-square and Mann-Whitney t tests to compare baseline characteristics between cases and controls. To test if the association of fruit, vegetables, and fiber intake with each molecular subtype differed, we conducted case-only logistic regression analysis. We also estimated the strength of the association between the dietary variables and risk for the specific molecular subtypes using polytomous logistic regression models with two case groups and one control set. The odds ratios (ORs) and their corresponding 95% confidential intervals (CIs) for the quartiles of dietary variables were compared with the lowest intake category as a reference. P-values for linear trends were calculated by treating quartiles of intake as a continuous variable. Participants with missing values of each dietary intake for fruit (n=1,933), vegetables (n=1,775), and fiber (n=5,422) were excluded from the analyses. In our primary analyses, minimally adjusted models (minimally adjusted OR) included study site, age at diagnosis, total energy consumption (kcal/day), and sex as covariates. To determine confounding factors for multivariate adjusted models (multivariate adjusted OR), we evaluated the association of the following CRC-related parameters: tobacco smoking, alcohol, body mass index, physical activity, history of diabetes mellitus, red meat intake, processed meat intake, and aspirin/non-steroidal anti-inflammatory drugs use, with dietary factors on the risk of CRC overall. We included those factors that changed the beta estimate of the dietary factors by more than 10% when compared with the minimally adjusted models (27). Based on this we included tobacco smoking, red meat intake, and processed meat intake in the multivariate adjusted models. Missing covariates were imputed by sex-specific mean for each study for age at diagnosis (n=8 missing), total energy consumption (n=5,473 missing), tobacco smoking (n=1,375 missing), red meat intake (n=1,709 missing), and processed meat intake (n=6,565 missing). Processed meat was not imputed for NFCCR because it was missing for all subjects (n=1,045 missing). We conducted a sub-group analysis stratified by study design (case-control or cohort study) as sensitivity analyses to evaluate differences in effects between study designs.

All P-values are two-sided. A P-value <0.05 was considered statistically significant for association with CRC risk. For assessing the heterogeneous association with molecular subtypes a Bonferroni corrected P-value < 0.05/10 was considered statistically significant to account for the 10 subtypes being tested. All statistical analyses were performed using R version 3.6.0.

RESULTS

Cases were more likely to be men; younger; past or current smokers; have a higher intake of red meat, processed meat, and energy; and have a lower intake of fruits and vegetables (Table 1). In a case-control analysis of all CRC cases combined, we observed an inverse association between fiber intake and overall CRC risk [multivariate adjusted OR _{4th vs 1st quartile} = 0.85 (95% CI = 0.76-0.97); P_trend = 6.2e-03], but no statistically significant association between CRC risk with fruit intake [multivariate adjusted OR _{4th vs 1st quartile} = 1.04 (95% CI = 0.93-1.15); P_trend = 0.99] and vegetable intake [multivariate adjusted OR _{4th vs 1st quartile} = 0.92 (95% CI = 0.82-1.03); P_trend = 0.09] (Supplementary Table S5). Among CRC cases, 15.6% were MSI-high, 18.9% CIMP-positive, 12.8% BRAF-mutated, and 32.8% KRAS-mutated.

Table 1.

Baseline characteristics of cases and controls.

Characteristics	Cases^a	Controls	P value^b
n	9,592	7,869
Age, mean (SD)^c	58.6 (11.5)	60.9 (10.3)	<2.2e-16
Sex (%)
Men	4,883 (50.9)	3,713 (47.2)
Women	4,709 (49.1)	4,156 (52.8)	9.9e-07
Tobacco smoking (%)^d
Never smoker	3,790 (41.8)	3,292 (46.9)
Past or current smoker	5,273 (58.2)	3,731 (53.1)	1.6e-10
Dietary intake
Red meat, servings/day, mean (SD)^e	0.77 (0.68)	0.73 (0.65)	5.0e-06
processed meat, servings/day, mean (SD)^f	0.29 (0.34)	0.25 (0.29)	1.8e-08
Total energy, kcal/day, mean (SD)^g	2118.3 (851.1)	2024.2 (768.0)	5.4e-07
Fruits, servings/day, mean (SD)^h	1.82 (1.63)	2.09 (1.75)	<2.2e-16
Vegetables, servings/day, mean (SD)ⁱ	2.51 (1.98)	2.95 (2.15)	<2.2e-16
Fiber, g/day, mean (SD)^j	22.9 (10.7)	23.1 (10.4)	0.30
Location of colorectal cancer (%)
Proximal	3.670 (38.3)	-
Distal	2,975 (31.0)	-
Rectum	2,526 (26.4)	-
Missing	421 (4.4)	-	-
Colorectal cancer stage (%)
I	2,038 (21.2)	-
II	1,780 (18.6)	-
III	1,847 (19.3)	-
IV	817 (8.5)	-
Missing	3,110 (32.4)	-	-
Microsatellite instability (%)
High	1,417 (15.6)	-
Stable/low	7,639 (84.4)	-	-
CpG island methylator phenotype (%)
High	1,298 (18.9)	-
Low	5,569 (81.1)	-	-
BRAF (%)
Mutated	1,109 (12.8)	-
Wildtype	7,566 (87.2)	-	-
KRAS (%)
Mutated	2,355 (32.8)	-
Wildtype	4,831 (67.2)	-	-

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The number of cases includes 809 colorectal cancer cases from the Mayo Clinic CCFR, which is not included controls.

Based on Chi-square test or Mann-Whitney t test.

Age is missing for 4 cases and 4 controls.

Tobacco smoking is missing for 529 cases and 846 controls.

Red meat intake is missing for 743 cases and 966 controls.

Processed meat intake is missing for 4,606 cases and 1,959 controls.

Total energy intake is missing for 4,067 cases and 1,406 controls.

Fruit intake is missing for 850 cases and 1,083 controls.

ⁱ

Vegetables intake is missing for 731 cases and 1,044 controls.

Fiber intake is missing for 4,038 cases and 1,384 controls.

Abbreviations: SD: standard deviation.

When we analyzed the association of dietary variables with risk of each molecular CRC subtype in a case-only analysis (Table 2), we observed that fruit intake was differentially associated with BRAF-mutated compared with BRAF-wildtype tumors [multivariate adjusted OR _{4th vs 1st quartile} = 0.75 (95% CI = 0.60-0.94), P-difference = 0.02]. Additionally, a polytomous logistic regression analysis (Table 3) comparing BRAF-mutated and BRAF-wildtype tumors with controls showed that a trend towards inverse association between fruit intake and CRC risk was limited to BRAF-mutated tumors [multivariate adjusted OR _{4th vs 1st quartile} = 0.82 (95% CI = 0.65-1.04), P_trend =0.06] as compared with controls, while there was no association between fruit intake and BRAF-wildtype tumors [multivariate adjusted OR _{4th vs 1st quartile} = 1.09 (95% CI = 0.97-1.22), P_trend = 0.54] as compared with controls. In sub-group analyses, these associations were found statistically significant in case-control studies, but not in cohort studies (Table 4 and Supplementary Table S6). Fruit intake did not show a statistically significant differential association with other molecular subtypes. Neither vegetable nor fiber intake showed significant differential associations for any of the molecular markers as shown by the case-only analysis (Table 2). However, in the sub-group analyses, we found that fiber intake was differentially associated with CIMP-negative tumors as compared with CIMP-positive tumors in cohort studies (Table 4).

Table 2.

ORs and 95% CIs for the association of fruits, vegetables, and fiber intake with the risk of molecular subtypes of CRC in case-only analysis.

	Quartile

	Lowest (Q1)	Second (Q2)	Third (Q3)	Highest (Q4)	P_trend
Fruits (servings/day)

MSI-high vs MSS/MSI-low
High (n)/Stable/low cases (n)	354/1844	383/2076	322/1701	238/1338
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.12 (0.94-1.33)	0.99 (0.84-1.17)	0.88 (0.73-1.07)	0.14
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.14 (0.96-1.35)	1.02 (0.86-1.21)	0.91 (0.75-1.10)	0.27
CIMP-positive vs CIMP-negative
High (n)/Low cases (n)	274/1293	334/1559	274/1278	232/1066
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.09 (0.90-1.33)	0.97 (0.80-1.19)	0.95 (0.77-1.18)	0.43
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.10 (0.91-1.34)	1.00 (0.82-1.23)	0.99 (0.80-1.23)	0.73
BRAF-mutated vs BRAF-wildtype
Mutated (n)/Wildtype cases (n)	284/1780	277/2161	254/1691	169/1336
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	0.91 (0.75-1.11)	0.87 (0.72-1.06)	0.71 (0.57-0.88)	2.9e-03
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	0.93 (0.76-1.13)	0.91 (0.75-1.10)	0.75 (0.60-0.94)	0.02
KRAS-mutated vs KRAS-wildtype
Mutated (n)/Wildtype cases (n)	502/1093	703/1410	544/1070	430/853
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.16 (1.00-1.36)	1.12 (0.96-1.30)	1.06 (0.90-1.25)	0.55
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.16 (0.99-1.35)	1.11 (0.95-1.29)	1.04 (0.88-1.22)	0.75

Vegetables (servings/day)

MSI-high vs MSS/MSI-low
High (n)/Stable/low cases (n)	267/1438	470/2535	377/1976	204/1101
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.02 (0.86-1.21)	1.03 (0.86-1.23)	0.97 (0.79-1.20)	0.87
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.03 (0.87-1.22)	1.05 (0.87-1.25)	0.98 (0.80-1.21)	0.99
CIMP-positive vs CIMP-negative
High (n)/Low cases (n)	261/1074	351/1715	294/1529	214/921
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	0.99 (0.82-1.21)	1.02 (0.83-1.25)	1.08 (0.86-1.35)	0.47
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.00 (0.82-1.22)	1.05 (0.86-1.29)	1.10 (0.88-1.38)	0.33
BRAF-mutated vs BRAF-wildtype
Mutated (n)/Wildtype cases (n)	203/1442	362/2545	276/1982	150/1104
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.12 (0.92-1.36)	1.08 (0.88-1.33)	0.99 (0.78-1.26)	0.89
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.13 (0.93-1.38)	1.12 (0.91-1.38)	1.01 (0.79-1.29)	0.90
KRAS-mutated vs KRAS-wildtype
Mutated (n)/Wildtype cases (n)	444/974	785/1531	598/1196	378/756
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.12 (0.97-1.30)	1.07 (0.92-1.25)	1.06 (0.89-1.26)	0.68
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.12 (0.97-1.30)	1.06 (0.91-1.24)	1.06 (0.89-1.26)	0.75

Fiber (g/day)

MSI-high vs MSS/MSI-low
High (n)/Stable/low cases (n)	199/1092	202/1094	191/1095	235/1101
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	0.98 (0.78-1.21)	0.91 (0.72-1.14)	1.11 (0.87-1.41)	0.51
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	0.99 (0.79-1.23)	0.94 (0.74-1.18)	1.15 (0.90-1.49)	0.35
CIMP-positive vs CIMP-negative
High (n)/Low cases (n)	195/822	231/849	218/826	235/863
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.21 (0.96-1.52)	1.15 (0.90-1.46)	1.18 (0.92-1.52)	0.30
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.23 (0.98-1.54)	1.18 (0.93-1.50)	1.22 (0.94-1.57)	0.20
BRAF-mutated vs BRAF-wildtype
Mutated (n)/Wildtype cases (n)	166/1070	171/1056	175/1049	183/1072
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	0.98 (0.77-1.24)	0.98 (0.77-1.26)	0.99 (0.76-1.29)	0.97
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	0.99 (0.78-1.26)	1.02 (0.80-1.31)	1.05 (0.80-1.37)	0.69
KRAS-mutated vs KRAS-wildtype
Mutated (n)/Wildtype cases (n)	375/735	375/743	383/723	402/735
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	0.98 (0.82-1.17)	1.04 (0.86-1.25)	1.09 (0.89-1.32)	0.34
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	0.97 (0.81-1.17)	1.02 (0.84-1.23)	1.04 (0.85-1.27)	0.60

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Minimally adjusted OR was adjusted for age, sex, study, and total energy (continuous).

In addition to minimally adjusted OR, multivariate adjusted OR was further adjusted for tobacco smoking (never-, past and current smoker <25, 25-<50, 50-<75, ≥75 pack-years), red meat intake (study- and sex-specific quartiles as continuous), and processed meat intake (study- and sex-specific quartiles as continuous).

Abbreviations: CI: confidence interval; OR: odds ratio.

Table 3.

ORs and 95% CIs for the association of fruits, vegetables, and fiber intake with the risk of molecular subtypes of CRC in case-control analysis.

	Quartile

	Lowest (Q1)	Second (Q2)	Third (Q3)	Highest (Q4)	P_trend	P_diff^a
Fruits (servings/day)

MSI-high vs Controls
Cases (n)/Controls (n)	275/1938	404/1736	297/1682	199/1430
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.27 (1.06-1.52)	1.01 (0.84-1.22)	0.85 (0.69-1.04)	0.047
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.32 (1.10-1.58)	1.09 (0.91-1.32)	0.95 (0.77-1.16)	0.40
MSS/MSI-low vs Controls
Cases (n)/Controls (n)	1447/1938	2199/1736	1556/1682	1128/1430
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.22 (1.10-1.35)	1.02 (0.92-1.13)	0.98 (0.87-1.09)	0.24	0.19
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.25 (1.13-1.39)	1.07 (0.96-1.19)	1.06 (0.94-1.18)	0.83	0.37

CIMP-positive vs Controls
Cases (n)/Controls (n)	242/1938	360/1736	268/1682	205/1430
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.18 (0.97-1.42)	0.95 (0.78-1.15)	0.91 (0.73-1.12)	0.14
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.21 (1.00-1.47)	1.02 (0.84-1.25)	1.02 (0.82-1.26)	0.77
CIMP-negative vs Controls
Cases (n)/Controls (n)	1124/1938	1656/1736	1250/1682	933/1430
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.26 (1.13-1.41)	1.06 (0.95-1.19)	0.94 (0.83-1.06)	0.11	0.59
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.30 (1.16-1.46)	1.12 (0.99-1.25)	1.02 (0.90-1.16)	0.82	0.87

BRAF-mutated vs Controls
Cases (n)/Controls (n)	233/1938	321/1736	235/1682	145/1430
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.09 (0.89-1.32)	0.86 (0.70-1.06)	0.72 (0.57-0.91)	1.7e-03
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.13 (0.93-1.38)	0.94 (0.77-1.16)	0.82 (0.65-1.04)	0.06
BRAF-wildtype vs Controls
Cases (n)/Controls (n)	1407/1938	2298/1736	1577/1682	1117/1430
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.27 (1.14-1.41)	1.05 (0.94-1.17)	1.01 (0.90-1.13)	0.43	6.5e-03
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.31 (1.18-1.45)	1.10 (0.99-1.23)	1.09 (0.97-1.22)	0.54	0.03

KRAS-mutated vs Controls
Cases (n)/Controls (n)	438/1938	755/1736	548/1682	369/1430
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.39 (1.20-1.61)	1.16 (0.99-1.34)	1.04 (0.88-1.22)	0.93
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.43 (1.23-1.66)	1.21 (1.04-1.41)	1.12 (0.95-1.33)	0.38
KRAS-wildtype vs Controls
Cases (n)/Controls (n)	883/1938	1577/1736	1070/1682	738/1430
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.31 (1.17-1.47)	1.06 (0.94-1.20)	1.05 (0.93-1.20)	0.84	0.94
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.36 (1.21-1.53)	1.13 (1.00-1.28)	1.16 (1.02-1.32)	0.18	0.63

Vegetables (servings/day)

MSI-high vs Controls
Cases (n)/Controls (n)	255/1772	441/1987	319/1828	178/1238
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.08 (0.90-1.29)	0.98 (0.81-1.19)	0.87 (0.70-1.09)	0.17
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.10 (0.92-1.31)	1.03 (0.85-1.25)	0.91 (0.73-1.13)	0.35
MSS/MSI-low vs Controls
Cases (n)/Controls (n)	1397/1772	2301/1987	1779/1828	929/1238
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.03 (0.93-1.14)	0.97 (0.87-1.08)	0.89 (0.79-1.00)	0.046	0.81
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.03 (0.93-1.15)	0.99 (0.89-1.11)	0.91 (0.81-1.03)	0.13	0.93

CIMP-positive vs Controls
Cases (n)/Controls (n)	261/1772	380/1987	246/1828	191/1238
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.15 (0.95-1.38)	1.00 (0.82-1.22)	1.05 (0.84-1.31)	0.96
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.17 (0.97-1.41)	1.05 (0.86-1.28)	1.09 (0.88-1.36)	0.67
CIMP-negative vs Controls
Cases (n)/Controls (n)	1060/1772	1711/1987	1451/1828	784/1238
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.05 (0.94-1.18)	0.98 (0.87-1.11)	0.90 (0.79-1.03)	0.09	0.35
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.06 (0.95-1.19)	1.00 (0.89-1.13)	0.93 (0.81-1.06)	0.22	0.26

BRAF-mutated vs Controls
Cases (n)/Controls (n)	207/1772	370/1987	228/1828	135/1238
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.17 (0.96-1.42)	0.99 (0.80-1.22)	0.90 (0.71-1.15)	0.23
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.19 (0.98-1.45)	1.04 (0.84-1.28)	0.94 (0.74-1.20)	0.45
BRAF-wildtype vs Controls
Cases (n)/Controls (n)	1408/1772	2374/1987	1793/1828	914/1238
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.01 (0.91-1.12)	0.93 (0.84-1.05)	0.87 (0.77-0.98)	0.01	0.94
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.02 (0.92-1.13)	0.96 (0.86-1.07)	0.89 (0.79-1.01)	0.04	0.77

KRAS-mutated vs Controls
Cases (n)/Controls (n)	448/1772	812/1987	561/1828	315/1238
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.10 (0.95-1.27)	1.01 (0.87-1.18)	0.95 (0.80-1.13)	0.40
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.11 (0.96-1.29)	1.04 (0.89-1.21)	0.98 (0.82-1.16)	0.65
KRAS-wildtype vs Controls
Cases (n)/Controls (n)	968/1772	1616/1987	1104/1828	611/1238
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.00 (0.89-1.13)	0.96 (0.85-1.09)	0.90 (0.79-1.04)	0.12	0.71
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.02 (0.90-1.14)	0.99 (0.88-1.12)	0.93 (0.81-1.07)	0.33	0.76

Fiber (g/day)

MSI-high vs Controls
Cases (n)/Controls (n)	214/1606	200/1621	184/1624	230/1634
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	0.88 (0.71-1.09)	0.74 (0.59-0.92)	0.86 (0.68-1.08)	0.11
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	0.91 (0.73-1.12)	0.79 (0.63-0.99)	0.95 (0.75-1.20)	0.47
MSS/MSI-low vs Controls
Cases (n)/Controls (n)	1161/1606	1079/1621	1050/1624	1092/1634
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	0.88 (0.79-0.99)	0.81 (0.72-0.91)	0.78 (0.69-0.88)	4.3e-05	0.56
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	0.90 (0.80-1.01)	0.84 (0.74-0.94)	0.83 (0.73-0.95)	3.4e-03	0.40

CIMP-positive vs Controls
Cases (n)/Controls (n)	212/1606	226/1621	209/1624	233/1634
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	1.09 (0.88-1.34)	0.93 (0.75-1.16)	1.01 (0.80-1.27)	0.69
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	1.12 (0.91-1.38)	0.99 (0.80-1.24)	1.11 (0.88-1.41)	0.63
CIMP-negative vs Controls
Cases (n)/Controls (n)	899/1606	836/1621	786/1624	840/1634
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	0.90 (0.80-1.02)	0.79 (0.69-0.90)	0.79 (0.69-0.91)	1.8e-04	0.08
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	0.92 (0.81-1.04)	0.82 (0.72-0.94)	0.85 (0.74-0.98)	9.0e-03	0.049

BRAF-mutated vs Controls
Cases (n)/Controls (n)	178/1606	170/1621	167/1624	181/1634
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	0.89 (0.71-1.12)	0.79 (0.62-1.00)	0.80 (0.62-1.03)	0.053
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	0.92 (0.73-1.16)	0.86 (0.67-1.09)	0.90 (0.70-1.16)	0.36
BRAF-wildtype vs Controls
Cases (n)/Controls (n)	1139/1606	1038/1621	1012/1624	1059/1634
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	0.87 (0.78-0.97)	0.80 (0.71-0.90)	0.78 (0.68-0.88)	4.0e-05	0.88
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	0.88 (0.79-0.99)	0.83 (0.74-0.94)	0.83 (0.73-0.95)	3.5e-03	0.58

KRAS-mutated vs Controls
Cases (n)/Controls (n)	403/1606	370/1621	372/1624	391/1634
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	0.89 (0.76-1.05)	0.84 (0.71-0.99)	0.84 (0.71-1.01)	0.047
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	0.90 (0.77-1.06)	0.87 (0.73-1.03)	0.89 (0.74-1.07)	0.18
KRAS-wildtype vs Controls
Cases (n)/Controls (n)	791/1606	733/1621	674/1624	739/1634
Minimally adjusted OR (95%CI)^b	1.00 (Reference)	0.91 (0.80-1.03)	0.78 (0.68-0.89)	0.80 (0.70-0.93)	4.7e-04	0.44
Multivariate adjusted OR (95%CI)^c	1.00 (Reference)	0.93 (0.82-1.06)	0.82 (0.72-0.94)	0.88 (0.76-1.02)	0.03	0.72

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Pdiff test the difference of the dietary risk factor and colorectal cancer association for the two cancer subtypes, such as MSI-high vs MSS/MSI-low. This is based on the case-only analysis testing the difference in the trend across the 4 quartiles.

Minimally adjusted OR was adjusted for age, sex, study, and total energy (continuous).

Abbreviations: CI: confidence interval; OR: odds ratio.

Table 4.

ORs and 95% CIs for the association of fruits, vegetables, and fiber intake with the risk of molecular subtypes of CRC in case-only analysis, stratified by study design.

	Quartile

	Lowest (Q1)	Second (Q2)	Third (Q3)	Highest (Q4)	P_trend
Cohort studies

Fruits (servings/day)

MSI-high vs MSS/MSI-low
High (n)/Stable/low cases (n)	114/591	94/500	103/505	94/502
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	0.92 (0.67-1.25)	0.93 (0.69-1.26)	0.83 (0.60-1.15)	0.31
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	0.92 (0.68-1.26)	0.94 (0.69-1.27)	0.83 (0.60-1.15)	0.31
CIMP-positive vs CIMP-negative
High (n)/Low cases (n)	126/575	129/479	112/512	119/495
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.15 (0.86-1.55)	0.90 (0.66-1.22)	1.04 (0.76-1.43)	0.81
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.15 (0.85-1.54)	0.90 (0.66-1.22)	1.03 (0.75-1.41)	0.74
BRAF-mutated vs BRAF-wildtype
Mutated (n)/Wildtype cases (n)	109/586	86/508	100/510	90/501
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	0.88 (0.65-1.22)	0.99 (0.73-1.35)	0.96 (0.69-1.34)	0.97
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	0.88 (0.64-1.22)	0.99 (0.72-1.35)	0.94 (0.67-1.32)	0.89
KRAS-mutated vs KRAS-wildtype
Mutated (n)/Wildtype cases (n)	205/457	219/349	214/366	194/375
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.32 (1.03-1.68)	1.24 (0.97-1.58)	1.05 (0.81-1.35)	0.79
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.31 (1.03-1.66)	1.22 (0.96-1.56)	1.02 (0.79-1.32)	0.93

Vegetables (servings/day)

MSI-high vs MSS/MSI-low
High (n)/Stable/low cases (n)	123/603	98/516	91/538	93/440
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	0.88 (0.65-1.19)	0.78 (0.57-1.07)	0.95 (0.69-1.32)	0.55
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	0.90 (0.66-1.22)	0.80 (0.59-1.09)	0.96 (0.70-1.33)	0.59
CIMP-positive vs CIMP-negative
High (n)/Low cases (n)	146/573	126/505	105/528	109/454
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.13 (0.84-1.51)	0.93 (0.68-1.26)	1.12 (0.82-1.54)	0.79
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.16 (0.86-1.55)	0.94 (0.69-1.28)	1.14 (0.83-1.57)	0.75
BRAF-mutated vs BRAF-wildtype
Mutated (n)/Wildtype cases (n)	114/611	106/501	86/539	79/454
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.24 (0.91-1.68)	0.94 (0.68-1.29)	1.00 (0.71-1.41)	0.65
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.27 (0.94-1.73)	0.96 (0.69-1.32)	1.01 (0.71-1.42)	0.67
KRAS-mutated vs KRAS-wildtype
Mutated (n)/Wildtype cases (n)	215/484	227/348	219/369	171/345
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.33 (1.05-1.69)	1.23 (0.97-1.57)	1.01 (0.78-1.31)	0.94
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.34 (1.05-1.70)	1.23 (0.97-1.57)	1.00 (0.77-1.30)	0.99

Fiber (g/day)

MSI-high vs MSS/MSI-low
High (n)/Stable/low cases (n)	95/533	95/542	106/531	125/528
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	0.91 (0.66-1.25)	1.01 (0.73-1.38)	1.15 (0.84-1.58)	0.28
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	0.92 (0.67-1.27)	1.05 (0.76-1.44)	1.19 (0.86-1.65)	0.20
CIMP-positive vs CIMP-negative
High (n)/Low cases (n)	112/516	125/525	137/507	153/519
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.12 (0.83-1.52)	1.29 (0.95-1.76)	1.35 (0.99-1.85)	0.04
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.13 (0.83-1.53)	1.33 (0.98-1.81)	1.35 (0.98-1.85)	0.04
BRAF-mutated vs BRAF-wildtype
Mutated (n)/Wildtype cases (n)	95/544	99/535	105/527	109/536
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.06 (0.77-1.46)	1.15 (0.83-1.58)	1.17 (0.84-1.62)	0.31
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.05 (0.77-1.45)	1.17 (0.85-1.61)	1.15 (0.82-1.61)	0.34
KRAS-mutated vs KRAS-wildtype
Mutated (n)/Wildtype cases (n)	200/409	206/399	200/395	228/404
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.03 (0.81-1.32)	1.00 (0.78-1.29)	1.14 (0.89-1.47)	0.35
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.02 (0.80-1.30)	0.98 (0.76-1.26)	1.08 (0.84-1.40)	0.64

Case-control studies

Fruits (servings/day)

MSI-high vs MSS/MSI-low
High (n)/Stable/low cases (n)	240/1253	289/1576	219/1196	144/836
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.12 (0.94-1.33)	0.99 (0.84-1.17)	0.88 (0.73-1.07)	0.58
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.24 (1.01-1.54)	1.05 (0.85-1.28)	0.93 (0.73-1.17)	0.42
CIMP-positive vs CIMP-negative
High (n)/Low cases (n)	148/718	205/1080	162/766	113/571
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.06 (0.81-1.39)	1.03 (0.80-1.34)	0.87 (0.65-1.17)	0.41
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.08 (0.83-1.42)	1.10 (0.84-1.43)	0.96 (0.71-1.29)	0.89
BRAF-mutated vs BRAF-wildtype
Mutated (n)/Wildtype cases (n)	175/1194	191/1653	154/1181	79/835
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	0.92 (0.71-1.19)	0.83 (0.65-1.06)	0.58 (0.43-0.77)	3.5e-04
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	0.94 (0.73-1.22)	0.88 (0.69-1.12)	0.64 (0.47-0.86)	4.9e-04
KRAS-mutated vs KRAS-wildtype
Mutated (n)/Wildtype cases (n)	297/636	484/1061	330/704	236/478
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.06 (0.87-1.29)	1.04 (0.85-1.26)	1.07 (0.87-1.33)	0.58
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.05 (0.86-1.28)	1.02 (0.84-1.24)	1.05 (0.84-1.30)	0.76

Vegetables (servings/day)

MSI-high vs MSS/MSI-low
High (n)/Stable/low cases (n)	144/835	372/2019	286/1438	111/661
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.12 (0.90-1.39)	1.17 (0.93-1.46)	0.97 (0.74-1.28)	0.87
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.12 (0.91-1.39)	1.19 (0.95-1.50)	0.98 (0.75-1.30)	0.75
CIMP-positive vs CIMP-negative
High (n)/Low cases (n)	115/501	225/1210	189/1001	105/467
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	0.92 (0.70-1.20)	1.07 (0.81-1.41)	1.04 (0.75-1.42)	0.46
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	0.91 (0.70-1.19)	1.11 (0.84-1.46)	1.06 (0.77-1.46)	0.31
BRAF-mutated vs BRAF-wildtype
Mutated (n)/Wildtype cases (n)	89/831	256/2044	190/1443	71/650
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.07 (0.82-1.40)	1.16 (0.88-1.54)	0.98 (0.69-1.38)	0.77
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.07 (0.82-1.40)	1.21 (0.91-1.60)	1.00 (0.71-1.41)	0.58
KRAS-mutated vs KRAS-wildtype
Mutated (n)/Wildtype cases (n)	229/490	558/1183	379/827	207/411
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.01 (0.83-1.22)	0.96 (0.78-1.18)	1.10 (0.87-1.39)	0.64
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.00 (0.83-1.21)	0.95 (0.78-1.17)	1.09 (0.86-1.38)	0.70

Fiber (g/day)

MSI-high vs MSS/MSI-low
High (n)/Stable/low cases (n)	104/559	107/552	85/564	110/573
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.01 (0.75-1.38)	0.77 (0.55-1.10)	0.99 (0.68-1.45)	0.65
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.03 (0.75-1.39)	0.80 (0.56-1.13)	1.02 (0.70-1.51)	0.79
CIMP-positive vs CIMP-negative
High (n)/Low cases (n)	83/306	106/324	81/319	82/344
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	1.30 (0.92-1.85)	0.90 (0.61-1.34)	0.87 (0.55-1.37)	0.28
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	1.32 (0.93-1.88)	0.95 (0.64-1.42)	0.94 (0.60-1.50)	0.50
BRAF-mutated vs BRAF-wildtype
Mutated (n)/Wildtype cases (n)	71/526	72/521	70/522	74/536
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	0.85 (0.59-1.23)	0.73 (0.49-1.10)	0.70 (0.45-1.11)	0.11
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	0.88 (0.61-1.27)	0.80 (0.53-1.21)	0.80 (0.50-1.28)	0.33
KRAS-mutated vs KRAS-wildtype
Mutated (n)/Wildtype cases (n)	175/326	169/344	183/328	174/331
Minimally adjusted OR (95%CI)^a	1.00 (Reference)	0.93 (0.71-1.21)	1.08 (0.81-1.45)	1.03 (0.74-1.44)	0.66
Multivariate adjusted OR (95%CI)^b	1.00 (Reference)	0.92 (0.70-1.21)	1.07 (0.79-1.43)	1.00 (0.71-1.41)	0.79

Open in a new tab

Minimally adjusted OR was adjusted for age, sex, study, and total energy (continuous).

Abbreviations: CI: confidence interval; OR: odds ratio.

When we combined markers to define subtypes in polytomous logistic regression analyses (Figure 1, Supplementary Table S7), we observed a linear trend for increased fiber intake in relation to CRC risk among Type 4 tumors, MSS/MSI-low, CIMP-negative, BRAF-wildtype, and KRAS-wildtype [multivariate adjusted OR = 0.94 (95% CI = 0.88-0.99), P_trend = 0.03)] compared with controls, although the difference between Type 4 and each of the other Types was not statistically significant. This finding was consistent with results in the single marker analyses (Table 3), where the same trend was seen for MSS/MSI-low tumors (P_trend = 3.4e-03), CIMP-negative tumors (P_trend =9.0e-03), BRAF-wildtype tumors (P_trend = 3.5e-03), and KRAS-wildtype tumors (P_trend = 0.03) compared with controls.

DISCUSSION

This is the largest analysis using individual level of dietary data to investigate their association with well-described molecular subtypes for CRC. While higher fruit intake was not associated with overall CRC risk, we observed that fruit intake was statistically significantly associated with a decreased risk for BRAF-mutated tumors but not BRAF-wildtype tumors. This finding was observed only among case-control studies. Additionally, we observed in single-marker polytomous logistic regression analyses that higher fiber intake was associated with a decreased risk for MSS/MSI-low, CIMP-negative, BRAF-wildtype, and KRAS-wildtype molecular subtypes compared with controls. The association in the combined-marker analysis also showed a trend towards a negative association. These negative associations were stronger than they were for MSI-high, CIMP-positive, BRAF-mutated or KRAS-mutated tumors, but the differences were not statistically significant. Increased fiber intake was differentially associated with a decreased risk of CIMP-negative compared with CIMP-positive tumors in cohort studies, but not in case-control studies. We did not identify any differences in vegetable intake with CRC risk in subtype analyses examining MSI, CIMP, and KRAS and BRAF mutations separately or in combination.

CRC development is caused by different etiological pathways underlying different genetic and epigenetic aberrations, which have been defined by specific molecular subtypes associated with distinct development trajectories. If dietary risk factors for CRC, such as fruit, vegetable, and fiber intake, impact specific etiologic pathways, then we can expect that the specific dietary factors are differentially associated with these molecular subtypes. For specific molecular subtypes, the mutation in the KRAS oncogene has been widely known as an acting driver of CRC development (28). In addition, mutation of the BRAF oncogene induces proliferation and inhibits normal apoptosis of colonic epithelial cells (29). Both KRAS and BRAF are key players of the mitogen-activated protein kinase (MAPK) pathway (24). Another driving force of CRC development is CIMP. Widespread methylation of numerous promoter CpG island loci are responsible for inactivation of tumor suppressor genes and other tumor-related genes (28). CIMP is also strongly related to the serrated pathway. Silencing of tumor suppressor genes such as p16INK4a and IGFBP7 via the synergistic effects of mutation of BRAF and CIMP resulting from hypermethylation could facilitate progression to serrated colorectal polyps (24). MSI is recognized by high-frequency of genetic alterations in repeated microsatellite sequences of DNA resulting from a DNA mismatch repair deficiency (30).

Fruit intake was associated in BRAF-mutated tumors, which is of interest given that BRAF-mutated tumors have particularly poor survival (31). Differences in findings for fruit intake by study design may be due to differences in sample size, given that approximately two-thirds of cases were from case-control studies. To evaluate study-specific differences among case-control studies, we explored consistency of the case-control study findings. The meta-analysis OR from case-controls studies was similar to the pooled estimate from all studies, and we did not observe substantial heterogeneity among the case-control studies [OR_{case-control meta-analysis} = 0.85 (95% CI = 0.78-0.93), P-heterogeneity = 0.52]. However, selection and recall biases, which are more likely to occur in case-control studies, may also explain observed differences due to study design. Selection bias could occur when CRC cases and/or controls are not representative of the target population. While it could be possible that controls with a healthy diet are more willing to participate in studies, this is unlikely to explain the observed differential effect of fruit intake specifically on BRAF-mutated vs BRAF-wildtype tumors in the case-only analysis. For this case-only finding to be explained by selection bias, the participation of BRAF-mutated vs BRAF-wildtype CRC cases would need to differ by fruit intake. This may be possible if survival after CRC diagnosis impacts participation and differs by BRAF mutational status and by fruit intake. However, we recently showed in a pooled analysis that BRAF-mutational status is not associated with survival after CRC diagnosis (32) and previous studies do not show strong evidence for the impact of fruit intake on survival (33,34). For recall bias to explain the case-only finding, cases with and without BRAF mutation would need to recall their fruit intake differently. If treatment with BRAF-inhibitors could have an impact is unknown; however, the majority of CRC cases included in this study were diagnosed before treatment with BRAF-inhibitors were introduced (35). As BRAF mutation are measured by standard methods, it is probably also less likely that measurement error could explain the observed difference in the case-only analysis for BRAF-mutated vs wildtype tumors.

To our knowledge, only one study has published findings on fruit intake and CRC risk by BRAF mutation status (15). This cohort study of 186 cases found no clear differences in risk. Several meta-analyses of studies not accounting for tumor markers have been published. A meta-analysis published in 2003 reported statistically significant inverse associations between fruit intake and CRC risk in case-control studies, but the relative risk was null when restricted to cohort studies (36). Additionally, a previous pooled analysis of 14 cohort studies published in 2007 also showed no overall association (37). However, a more recent meta-analysis of 19 cohort studies published in 2018 reported statistically significant inverse associations between fruit intake and CRC risk, and reported nonlinear relationships between fruit intake and CRC risk (2). It is possible as sample sizes of meta-analyses increase that the power becomes sufficient to capture a significant association in a subset of the cancers (in this case BRAF-mutated tumors). Additional studies ideally conducting in cohorts are needed to replicate our finding.

Fruit intake may plausibly play a role in inhibiting events of BRAF-mutated tumors. Fruits are rich in many nutrients and biologically active compounds, such as vitamins, carotenoids, and folic acid, which may be cancer-preventive (38). Accordingly, a previous laboratory study showed that high levels of vitamin C specifically kill BRAF-mutated CRC cells, but not BRAF wildtype cells (39). This effect is due to uptake of dehydroascorbate (DHA), which is the oxidized form of vitamin C. Increased intracellular DHA uptake accumulates cellular reactive oxygen species and inactivates glyceraldehyde 3-phosphate dehydrogenase (GAPDH). Inhibition of GAPDH in BRAF-mutated CRC cells leads to an energetic crisis and cell death through inhibiting glycolysis and depleting ATP (39). However, two randomized, double-blind, placebo-controlled trials with a mean follow-up period ranging from 8.0 to 9.4 years did not find that 500mg of vitamin C supplementation daily was associated with a decreased CRC incidence in both men and women (40,41). Fruit intake may also impact BRAF-mutated CRC development through impacting the MAPK-pathway. It has been previously reported that Lycium barbarum fruit, also known as Chinese Wolfberry, inhibits cancer cell growth by cell cycle arrest and apoptosis through regulating the activation of MAPK-signaling pathway (42). Furthermore, previous studies have shown that fisetin, a dietary flavonoid found in apples, strawberries, kiwi and other fruits inhibits cell invasion by targeting MAPK signaling pathway (43) and reduces the anti-invasive and anti-metastatic effects of BRAF-mutated cells (44). Accordingly, fruit intake may reduce BRAF-mutated CRC through multiple nutrients and biologically active compounds.

With regard to colorectal carcinogenic pathways, we investigated three different pathways: a) a serrated pathway [Types 1 and 2], b) an alternate pathway [Type 3], and c) a traditional pathway [Types 4 and 5] (23,24). Because this colorectal carcinogenic hypothesis only shows the predominant pathways, there is overlap between them (24). We observed that higher intake of fiber was associated with a decreased risk of combined CRC subtypes that were MSS/MSI-low, CIMP-negative, BRAF-wildtype and KRAS-wildtype [Types 4 and 5: traditional pathway]. Moreover, higher fiber intake was associated with a decreased risk for CIMP-negative tumors, but not for CIMP-positive tumors in cohort studies. Higher dietary fiber intake has “probable strong evidence” for decreasing the risk of CRC as defined by WCRF/AICR (4), which is in line with our findings given that the traditional pathway is the predominate pathway for CRC development. A previous meta-analysis of 7 observational studies investigating how lifestyle exposures may increase or decrease the risk of serrated colorectal polyps presented a suggestive trend toward an inverse association between fiber intake and risk of serrated colorectal polyps, but findings were not statistically significant (45), which is not consistent with our finding. Currently functional data are missing that could explain why the association with fiber intake is restricted to a subset of molecularly-defined CRC tumors.

A strength of our pooled analysis from 9 observational studies with up to 9,592 CRC cases and 7,869 controls is that we were well powered to detect associations of fruit, vegetables, and fiber intake with risk of major CRC molecular subtypes. Due to this sizable dataset, we could further assess the association of CRC molecular subtype markers in combination. Furthermore, we used a consistent approach to harmonize all dietary variables across the studies to enable a pooled analysis of individual level data. Additionally, we have robust data on other factors including age at diagnosis, total energy consumption, and sex as covariates.

Our study has some limitations. First, it is difficult to exclude potential selection bias. While molecular subtypes are ideally available for all cases, there may be tissue retrieval biases with tissue availability potentially associated with tumor size and stage (46). However, some of the included studies previously have shown that there were no differences in age, diet, or other lifestyle characteristics between cases with and without available tumor tissue (47,48). Second, fruit, vegetables, and fiber intake are likely measured with error because they were assessed via in-person interviews or self-administered questionnaires resulting in exposure misclassification. To best account for differences between dietary assessment methods we harmonized data as study- and sex-specific quartiles rather than the continuous intake value, as it is commonly done for pooled dietary analyses (37). As exposure misclassification would likely be nondifferential, it would result in an underestimation of the association of these dietary variables with CRC risk. Third, we analyzed the CRC risk only using the lifestyle information measured at a single point in time, while lifestyle habits of study participants might change during the relevant etiologic time window. However, as such changes are not differential between cases and controls, this would have led to an attenuation of the associations. Fourth, some studies had a sizable number of missing covariate data; however, the missing covariate data were imputed by sex-specific mean for each study to ensure no samples were missed in the analysis due to missing confounders.

In summary, we found in our large pooled analysis for well-defined molecular subtypes that higher fruit intake was associated with a decreased risk of BRAF-mutated CRC but not with BRAF-wildtype CRC. Additionally, higher fiber intake may be associated with a stronger decreased risk of CRC developing by the traditional adenoma-CRC pathway that is MSS/MSI-low, CIMP-negative, BRAF-wildtype and KRAS-wildtype tumors. These results potentially explain in part the inconsistent findings between fruit or fiber intake and overall CRC risk that have previously been reported.

Supplementary Material

NIHMS1618738-supplement-1.pdf^{(133.3KB, pdf)}

NIHMS1618738-supplement-2.xlsx^{(13.2KB, xlsx)}

NIHMS1618738-supplement-3.xlsx^{(12.4KB, xlsx)}

NIHMS1618738-supplement-4.xlsx^{(11.3KB, xlsx)}

NIHMS1618738-supplement-5.xlsx^{(11KB, xlsx)}

NIHMS1618738-supplement-6.xlsx^{(11.9KB, xlsx)}

NIHMS1618738-supplement-7.xlsx^{(23.4KB, xlsx)}

NIHMS1618738-supplement-8.xlsx^{(23.7KB, xlsx)}

Significance:

These analyses by colorectal cancer molecular subtypes potentially explain the inconsistent findings between dietary fruit or fiber intake and overall colorectal cancer risk that have previously been reported.

Acknowledgements:

CCFR: The Colon CFR graciously thanks the generous contributions of their 42,505 study participants, dedication of study staff, and the financial support from the U.S. National Cancer Institute, without which this important registry would not exist. The content of this manuscript does not necessarily reflect the views or policies of the NIH or any of the collaborating centers in the CCFR, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government, any cancer registry, or the CCFR.

CPS-II: The authors thank the CPS-II participants and Study Management Group for their invaluable contributions to this research. The authors would also like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention National Program of Cancer Registries, and cancer registries supported by the National Cancer Institute Surveillance Epidemiology and End Results program.

Harvard cohorts (HPFS, NHS, PHS): The study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required. We would like to thank the participants and staff of the HPFS, NHS and PHS for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data.

NSHDS investigators thank the Biobank Research Unit at Umeå University, the Västerbotten Intervention Programme, the Northern Sweden MONICA study and Region Västerbotten for providing data and samples and acknowledge the contribution from Biobank Sweden, supported by the Swedish Research Council (VR 2017-00650).

Financial support:

This work was supported by the Uehara Memorial Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO): National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services (U01 CA164930, U01 CA137088, R01 CA059045, U01 CA164930, R21 CA191312, R01 CA201407).

Genotyping/Sequencing services were provided by the Center for Inherited Disease Research (CIDR) (X01-HG008596 and X-01-HG007585). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268201200008I. This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA015704.

The Colon Cancer Family Registry (CCFR, www.coloncfr.org) was supported in part by funding from the National Cancer Institute (NCI), National Institutes of Health (NIH) (award U01 CA167551) and through U01/U24 cooperative agreements from NCI with the following CCFR centers: Australasian (CA074778 and CA097735) , Ontario (OFCCR) (CA074783), Seattle (SFCCR) (CA074794 and R01 CA076366 to PAN), and the Mayo Clinic (CA074800). Support for case ascertainment was provided in part from the Surveillance, Epidemiology, and End Results (SEER) Program, the Minnesota Cancer Surveillance System (MCSS); the Victoria Cancer Registry (Australia) and the Ontario Cancer Registry (Canada).The content of this manuscript does not necessarily reflect the views or policies of the NIH or any of the collaborating centers in the CCFR, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government, any cancer registry, or the CCFR.

CPS-II: The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II (CPS-II) cohort. This study was conducted with Institutional Review Board approval.

DALS: National Institutes of Health (R01 CA48998 to M. L. Slattery).

EPIC: The coordination of EPIC is financially supported by the European Commission (DGSANCO) 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, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF), Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); the Hellenic Health Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRCItaly 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), Statistics Netherlands (The Netherlands); ERC-2009-AdG 232997 and Nordforsk, Nordic Centre of Excellence programme on Food, Nutrition and Health (Norway); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden).

Harvard cohorts (HPFS, NHS: HPFS is supported by the National Institutes of Health (P01 CA055075, UM1 CA167552, U01 CA167552, R01 CA137178, R01 CA151993, R35 CA197735, K07 CA190673, and P50 CA127003), NHS by the National Institutes of Health (R01 CA137178, P01 CA087969, UM1 CA186107, R01 CA151993, R35 CA197735, K07 CA190673, and P50 CA127003).

MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 509348, 209057, 251553 and 504711 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database.

NFCCR: This work was supported by an Interdisciplinary Health Research Team award from the Canadian Institutes of Health Research (CRT 43821); the National Institutes of Health, U.S. Department of Health and Human Serivces (U01 CA74783); and National Cancer Institute of Canada grants (18223 and 18226). The authors wish to acknowledge the contribution of Alexandre Belisle and the genotyping team of the McGill University and Génome Québec Innovation Centre, Montréal, Canada, for genotyping the Sequenom panel in the NFCCR samples. Funding was provided to Michael O. Woods by the Canadian Cancer Society Research Institute.

NSHDS: Swedish Research Council; Swedish Cancer Society; Cutting-Edge Research Grant and other grants from Region Västerbotten; Knut and Alice Wallenberg Foundation; Lion’s Cancer Research Foundation at Umeå University; the Cancer Research Foundation in Northern Sweden; and the Faculty of Medicine, Umeå University, Umeå, Sweden.

Conflict of Interest Statement: M.G. receives research funding from Bristol-Myers Squibb and Merck. These Funds had no role in the design, data collection, analysis, interpretation, or manuscript drafting or in the decision to submit the manuscript for publication. The remaining authors declared no conflicts of interest related to the study.

Abbreviations:

CCFR: Colon Cancer Family Registry
CI: confidence interval
CIMP: CpG island methylator phenotype
CRC: colorectal cancer
DHA: dehydroascorbate
FFQ: food frequency questionnaire
GAPDH: glyceraldehyde 3-phosphate dehydrogenase
GECCO: Genetics and Epidemiology of Colorectal Cancer Consortium
IGFBP7: insulin-like growth factor binding protein 7
MAPK: mitogen-activated protein kinase
MSI: microsatellite instability
OR: odds ratio
SD: standard deviation
WCRF/AICR: World Cancer Research Fund and the American Institute for Cancer Research

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39.Yun J, Mullarky E, Lu C, Bosch KN, Kavalier A, Rivera K, et al. Vitamin C selectively kills KRAS and BRAF mutant colorectal cancer cells by targeting GAPDH. Science. 2015;350:1391–1396. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Gaziano JM, Glynn RJ, Christen WG, Kurth T, Belanger C, MacFadyen J, et al. Vitamins E and C in the prevention of prostate and total cancer in men: the Physicians’ Health Study II randomized controlled trial. JAMA. 2009;301:52–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
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43.Pal HC, Sharma S, Strickland LR, Katiyar SK, Ballestas ME, Athar M, et al. Fisetin inhibits human melanoma cell invasion through promotion of mesenchymal to epithelial transition and by targeting MAPK and NFκB signaling pathways. PLoS One. 2014;9:e86338. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Pal HC, Diamond AC, Strickland LR, Kappes JC, Katiyar SK, Elmets CA, et al. Fisetin, a dietary flavonoid, augments the anti-invasive and anti-metastatic potential of sorafenib in melanoma. Oncotarget. 2016;7:1227–1241. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Bailie L, Loughrey MB, Coleman HG. Lifestyle Risk Factors for Serrated Colorectal Polyps: A Systematic Review and Meta-analysis. Gastroenterology. 2017;152:92–104. [DOI] [PubMed] [Google Scholar]
46.Hoppin JA, Tolbert PE, Taylor JA, Schroeder JC, Holly EA. Potential for selection bias with tumor tissue retrieval in molecular epidemiology studies. Ann Epidemiol. 2002;12:1–6. [DOI] [PubMed] [Google Scholar]
47.Chan AT, Ogino S, Fuchs CS. Aspirin and the risk of colorectal cancer in relation to the expression of COX-2. N Engl J Med. 2007;356:2131–2142. [DOI] [PubMed] [Google Scholar]
48.Slattery ML, Edwards SL, Palmer L, Curtin K, Morse J, Anderson K, et al. Use of archival tissue in epidemiologic studies: collection procedures and assessment of potential sources of bias. Mutat Res. 2000;432:7–14. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

NIHMS1618738-supplement-1.pdf^{(133.3KB, pdf)}

NIHMS1618738-supplement-2.xlsx^{(13.2KB, xlsx)}

NIHMS1618738-supplement-3.xlsx^{(12.4KB, xlsx)}

NIHMS1618738-supplement-4.xlsx^{(11.3KB, xlsx)}

NIHMS1618738-supplement-5.xlsx^{(11KB, xlsx)}

NIHMS1618738-supplement-6.xlsx^{(11.9KB, xlsx)}

NIHMS1618738-supplement-7.xlsx^{(23.4KB, xlsx)}

NIHMS1618738-supplement-8.xlsx^{(23.7KB, xlsx)}

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PERMALINK

Intake of dietary fruit, vegetables, and fiber and risk of colorectal cancer according to molecular subtypes: A pooled analysis of 9 studies

Akihisa Hidaka

Tabitha A Harrison

Yin Cao

Lori C Sakoda

Richard Barfield

Marios Giannakis

Mingyang Song

Amanda I Phipps

Jane C Figueiredo

Syed H Zaidi

Amanda E Toland

Efrat L Amitay

Sonja I Berndt

Ivan Borozan

Andrew T Chan

Steven Gallinger

Marc J Gunter

Mark A Guinter

Sophia Harlid

Heather Hampel

Mark A Jenkins

Yi Lin

Victor Moreno

Polly A Newcomb

Reiko Nishihara

Shuji Ogino

Mireia Obón-Santacana

Patrick S Parfrey

John D Potter

Martha L Slattery

Robert S Steinfelder

Caroline Y Um

Xiaoliang Wang

Michael O Woods

Bethany Van Guelpen

Stephen N Thibodeau

Michael Hoffmeister

Wei Sun

Li Hsu

Daniel D Buchanan

Peter T Campbell

Ulrike Peters

Abstract

INTRODUCTION

MATERIALS AND METHODS

Study Participants

Definition of Tumor Subtypes

Exposure Data

Statistical Analyses

RESULTS

Table 1.

Table 2.

Table 3.

Table 4.

Figure 1. Forest plot of the association between fiber intake and colorectal cancer risk by combined molecular subtypes using polytomous logistic regression analysis.

DISCUSSION

Supplementary Material

Significance:

Acknowledgements:

Abbreviations:

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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