MétaCan
Menu
Back to cohort
Record W4400673369 · doi:10.1016/j.ajcnut.2024.07.012

Folate intake and colorectal cancer risk according to genetic subtypes defined by targeted tumor sequencing

2024· article· en· W4400673369 on OpenAlexaff
Elom K. Aglago, Conghui Qu, Sophia Harlid, Amanda I. Phipps, Robert S. Steinfelder, Shuji Ogino, Claire E. Thomas, Li Hsu, Amanda E. Toland, Hermann Brenner, Sonja I. Berndt, Daniel D. Buchanan, Peter T. Campbell, Yin Cao, Andrew T. Chan, David A. Drew, Jane C. Figueiredo, Amy J. French, Steven Gallinger, Peter Georgeson, Marios Giannakis, Ellen L. Goode, Stephen B. Gruber, Marc J. Gunter, Tabitha A. Harrison, Michael Hoffmeister, Wen‐Yi Huang, Meredith A.J. Hullar, Jeroen R. Huyghe, Mark A. Jenkins, Brigid M. Lynch, Victor Moreno, Neil Murphy, Christina C. Newton, Jonathan A. Nowak, Mireia Obón‐Santacana, Wei Sun, Tomotaka Ugai, Caroline Y. Um, Syed Hassan Ejaz Zaidi, Konstantinos K. Tsilidis, Bethany Van Guelpen

Bibliographic record

VenueAmerican Journal of Clinical Nutrition · 2024
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteOntario Institute for Cancer ResearchUniversity of TorontoMount Sinai Hospital
FundersOffice of Research Infrastructure Programs, National Institutes of HealthNational Cancer InstituteWorld Cancer Research FundWereld Kanker Onderzoek FondsMinistry of Science and Innovation, New ZealandWorld Cancer Research Fund InternationalWorld Health Organization
KeywordsColorectal cancerAXIN2Odds ratioOncologyInternal medicineMedicineGeneticsBiologyCancerKRASConfidence intervalWnt signaling pathwayGene

Abstract

fetched live from OpenAlex

BACKGROUND: Folate is involved in multiple genetic, epigenetic, and metabolic processes, and inadequate folate intake has been associated with an increased risk of cancer. OBJECTIVE: We examined whether folate intake is differentially associated with colorectal cancer (CRC) risk according to somatic mutations in genes linked to CRC using targeted sequencing. DESIGN: Participants within 2 large CRC consortia with available information on dietary folate, supplemental folic acid, and total folate intake were included. Colorectal tumor samples from cases were sequenced for the presence of nonsilent mutations in 105 genes and 6 signaling pathways (IGF2/PI3K, MMR, RTK/RAS, TGF-β, WNT, and TP53/ATM). Multinomial logistic regression models were analyzed comparing mutated/nonmutated CRC cases to controls to compute multivariable-adjusted odds ratios (ORs) with 95% confidence interval (CI). Heterogeneity of associations of mutated compared with nonmutated CRC cases was tested in case-only analyses using logistic regression. Analyses were performed separately in hypermutated and nonhypermutated tumors, because they exhibit different clinical behaviors. RESULTS: We included 4339 CRC cases (702 hypermutated tumors, 16.2%) and 11,767 controls. Total folate intake was inversely associated with CRC risk (OR = 0.93; 95% CI: 0.90, 0.96). Among hypermutated tumors, 12 genes (AXIN2, B2M, BCOR, CHD1, DOCK3, FBLN2, MAP3K21, POLD1, RYR1, TET2, UTP20, and ZNF521) showed nominal statistical significance (P < 0.05) for heterogeneity by mutation status, but none remained significant after multiple testing correction. Among these genetic subtypes, the associations between folate variables and CRC were mostly inverse or toward the null, except for tumors mutated for DOCK3 (supplemental folic acid), CHD1 (total folate), and ZNF521 (dietary folate) that showed positive associations. We did not observe differential associations in analyses among nonhypermutated tumors, or according to the signaling pathways. CONCLUSIONS: Folate intake was not differentially associated with CRC risk according to mutations in the genes explored. The nominally significant differential mutation effects observed in a few genes warrants further investigation.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.382
Teacher spread0.352 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Clinical NutritionSame topicFolate and B Vitamins ResearchFrench-language works237,207