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Record W4407837517 · doi:10.1158/1055-9965.epi-24-0970

Association of Genetic Liability to Allergic Diseases with Overall and Early-Onset Colorectal Cancer Risk: A Mendelian Randomization Study

2025· article· en· W4407837517 on OpenAlexafffund
Saleh Alduhayh, Ruhina Shirin Laskar, Xia Jiang, Zhaozhong Zhu, Emma E. Vincent, Andrei‐Emil Constantinescu, Daniel D. Buchanan, Robert C. Grant, Amanda I. Phipps, Hermann Brenner, Wen‐Yi Huang, Sun‐Seog Kweon, Li Li, Rachel Pearlman, Sergi Castellvı́-Bel, Stephen B. Gruber, Christopher I. Li, Andrew J. Pellatt, Elizabeth A. Platz, Bethany Van Guelpen, Wei Zheng, Andrew T. Chan, Jane C. Figueiredo, Shuji Ogino, Cornelia M. Ulrich, Marc J. Gunter, Philip Haycock, Gianluca Severi, Neil Murphy, Niki Dimou

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsPrincess Margaret Cancer Centre
FundersOffice of Research Infrastructure Programs, National Institutes of HealthNational Center for Advancing Translational SciencesNational Institute of Environmental Health SciencesNational Institute on AgingNIHR Imperial Biomedical Research CentreInstituto de Salud Carlos IIINational Human Genome Research InstituteCancer Council VictoriaNational Health and Medical Research CouncilMedical Research CouncilCanadian Institutes of Health ResearchJunta de Castilla y LeónConseil Régional des Pays de la LoireXunta de GaliciaGroupement des Entreprises Françaises dans la lutte contre le CancerCentre Hospitalier Universitaire de NantesMutuelle Générale de l'Education NationaleCenters for Disease Control and PreventionChonnam National University Hwasun HospitalInstitut National Du CancerSchool of Public Health, Imperial College LondonDeutsche KrebshilfeAssociazione Italiana per la Ricerca sul CancroVetenskapsrådetUniversity of PittsburghStockholms Läns LandstingNational Institute of Diabetes and Digestive and Kidney DiseasesWorld Cancer Research FundKnut och Alice Wallenbergs StiftelseHarvard T.H. Chan School of Public HealthKarolinska InstitutetBundesministerium für Bildung und ForschungMinisterio de Economía y CompetitividadChonnam National UniversityCentres de Recerca de CatalunyaUmeå UniversitetMinisterstvo Zdravotnictví Ceské RepublikyCancerfondenNational Cancer InstituteFundación Científica Asociación Española Contra el CáncerInstitut Gustave-RoussyHerzfelder'sche FamilienstiftungGrantová Agentura České RepublikyCanadian Cancer Society Research InstituteMarshfield Clinic Research FoundationInstitut National de la Santé et de la Recherche MédicaleImperial College LondonGeneralitat de CatalunyaFood Standards AgencyUniversity of South FloridaSwedish Cancer FoundationAgència de Gestió d'Ajuts Universitaris i de RecercaNational Institute for Health and Care ResearchDivision of Cancer Prevention, National Cancer InstituteNational Heart, Lung, and Blood InstituteFlorida Department of HealthNational Institutes of HealthMike and Josie Harper Cancer Research InstituteNational Institute of Allergy and Infectious DiseasesVictorian Cancer AgencyUniversity of CambridgeAssociation Anne de Bretagne GenetiqueLigue Contre le CancerUniverzita Karlova v PrazeDeutsches KrebsforschungszentrumBiobanco VascoFred Hutchinson Cancer Research CenterMoffitt Cancer CenterPelotoniaBrigham and Women's HospitalEmory UniversityDamon Runyon Cancer Research FoundationAmerican Cancer SocietyMemorial Sloan-Kettering Cancer CenterCancer Research UKXarxa de Bancs de Tumors de CatalunyaWorld Health OrganizationFondation ARC pour la Recherche sur le CancerU.S. Department of Health and Human Services
KeywordsMedicineHay feverMendelian randomizationColorectal cancerInternal medicineOdds ratioAllergyAsthmaCancerGenome-wide association studyOncologyImmunologySingle-nucleotide polymorphismGenotypeGeneticsBiology

Abstract

fetched live from OpenAlex

BACKGROUND: The tumor immunosurveillance theory supports that allergic conditions could decrease cancer risk. However, observational evidence yielded inconsistent results for the association between allergic diseases and colorectal cancer risk. We used Mendelian randomization (MR) to examine potential causal associations of allergies with the risk of overall and early-onset colorectal cancer. METHODS: Genome-wide association study summary statistical data were used to identify genetic variants associated with allergic diseases (Nvariants = 65) and individual allergic conditions (asthma, hay fever/allergic rhinitis, and eczema). Using two-sample MR, we examined these variants in relation to incident overall (Ncases = 52,775 cases) and early-onset colorectal cancer (Ncases = 6,176). The mediating role of white blood cells was examined using multivariable MR. RESULTS: In inverse-variance-weighted models, genetic liability to allergic diseases was inversely associated with overall {OR per log (odds) = 0.90 [95% confidence interval (CI), 0.85-0.96]; P < 0.01} and early-onset colorectal cancer [OR = 0.83 (95% CI, 0.73-0.95); P = 0.01]. Similar inverse associations were found for hay fever/allergic rhinitis or eczema, whereas no evidence of association was found between liability to asthma-related phenotypes and colorectal cancer risk. Multivariable MR adjustment for eosinophils weakened the inverse associations for liability to allergic diseases for overall [OR = 0.96 (95% CI, 0.89-1.03); P = 0.26] and early-onset colorectal cancer [OR = 0.86 (95% CI, 0.73-1.01); P = 0.06]. CONCLUSIONS: Our study supports a potential causal association between liability to allergic diseases, specifically hay fever/allergic rhinitis or eczema, and colorectal cancer, possibly at least in part mediated via eosinophil counts. IMPACT: Our results provide evidence that allergic responses may also have a role in immunosurveillance against colorectal cancer.

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)0.012
metaresearch head score (Gemma)0.029
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 score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.308
Teacher spread0.298 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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