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Record W4361823241 · doi:10.1158/0008-5472.c.6512209

Data from Intake of Dietary Fruit, Vegetables, and Fiber and Risk of Colorectal Cancer According to Molecular Subtypes: A Pooled Analysis of 9 Studies

2023· preprint· en· W4361823241 on OpenAlexafffund
Akihisa Hidaka, Tabitha A. Harrison, Yin Cao, Lori C. Sakoda, Richard Barfield, Marios Giannakis, Mingyang Song, Amanda I. Phipps, Jane C. Figueiredo, Syed Hassan Ejaz 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, Vı́ctor 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

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsMemorial University of NewfoundlandLunenfeld-Tanenbaum Research InstituteOntario Institute for Cancer Research
FundersNational Cancer InstituteInstituto de Salud Carlos IIINational Health and Medical Research CouncilWorld Cancer Research FundHellenic Health FoundationInstitut Gustave-RoussyDeutsche KrebshilfeCanadian Institutes of Health ResearchAssociazione Italiana per la Ricerca sul CancroCancer Council VictoriaCancerfondenLigue Contre le CancerBundesministerium für Bildung und ForschungEuropean CommissionNordForskKnut och Alice Wallenbergs StiftelseDeutsches KrebsforschungszentrumMcGill UniversityInstitut National de la Santé et de la Recherche MédicaleVetenskapsrådetCanadian Cancer Society Research InstituteNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsColorectal cancerKRASInternal medicineMicrosatellite instabilityMedicineConfidence intervalOncologyQuartileCancerGastroenterologyBiologyGeneticsAlleleMicrosatelliteGene

Abstract

fetched live from OpenAlex

Abstract Protective associations of fruits, vegetables, and fiber intake with colorectal cancer risk have been shown in many, but not all epidemiologic studies. One possible reason for study heterogeneity is that dietary factors may have distinct effects by colorectal cancer molecular subtypes. Here, we investigate the association of fruit, vegetables, and fiber intake with four well-established colorectal cancer 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 toward decreased risk of BRAF-mutated tumors [OR 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 colorectal cancer risk for cases with microsatellite stable/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-colorectal cancer 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 colorectal cancer risk that have previously been reported. 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.017
Bibliometrics0.0050.006
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.090
GPT teacher head0.364
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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