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Record W4391750585 · doi:10.1016/j.ebiom.2024.105010

Body size and risk of colorectal cancer molecular defined subtypes and pathways: Mendelian randomization analyses

2024· article· en· W4391750585 on OpenAlexafffund
Nikos Papadimitriou, Conghui Qu, Tabitha A. Harrison, Alaina M. Bever, Richard M. Martin, Konstantinos K. Tsilidis, Polly A. Newcomb, Stephen N. Thibodeau, Christina C. Newton, Caroline Y. Um, Mireia Obón‐Santacana, Vı́ctor Moreno, Hermann Brenner, Marko Mandic, Jenny Chang‐Claude, Michael Hoffmeister, Andrew J. Pellatt, Robert E. Schoen, Sophia Harlid, Shuji Ogino, Tomotaka Ugai, Daniel D. Buchanan, Brigid M. Lynch, Stephen B. Gruber, Yin Cao, Li Hsu, Jeroen R. Huyghe, Yi Lin, Robert S. Steinfelder, Wei Sun, Bethany Van Guelpen, Syed Hassan Ejaz Zaidi, Amanda E. Toland, Sonja I. Berndt, Wen‐Yi Huang, Elom K. Aglago, David A. Drew, Amy J. French, Peter Georgeson, Marios Giannakis, Meredith A.J. Hullar, Johnathan A. Nowak, Claire E. Thomas, Loı̈c Le Marchand, Iona Cheng, Steven Gallinger, Mark A. Jenkins, Marc J. Gunter, Peter T. Campbell, Ulrike Peters, Mingyang Song, Amanda I. Phipps, Neil Murphy

Bibliographic record

VenueEBioMedicine · 2024
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalUniversity of TorontoOntario Institute for Cancer Research
FundersDepartment of Epidemiology and Biostatistics, University of California, San FranciscoDepartment of Pathology, Northwestern UniversityNational Heart, Lung, and Blood InstituteNational Health and Medical Research CouncilOntario Ministry of Research and InnovationSchool of Public Health, Imperial College LondonMedical Research CouncilVetenskapsrådetUniversity of Maryland School of Public HealthNational Institute of General Medical SciencesCenters for Disease Control and PreventionNational Institutes of HealthUmeå UniversitetHjärnfondenOntario Ministry of Research, Innovation and ScienceVästerbotten Läns LandstingUniversity Hospitals Bristol NHS Foundation TrustCanadian Institutes of Health ResearchCancerfondenBundesministerium für Bildung und ForschungDeutsche ForschungsgemeinschaftNational Cancer InstituteKnut och Alice Wallenbergs StiftelseUniversity of BristolImperial College LondonCentre International de Recherche sur le CancerNIHR Bristol Biomedical Research CentreJohns Hopkins UniversityCanadian Cancer SocietyHarvard T.H. Chan School of Public HealthNational Institute for Health and Care ResearchGovernment of South AustraliaCancer Council VictoriaAustralian Lions Childhood Cancer Research FoundationMarshfield Clinic Research FoundationNIHR Imperial Biomedical Research CentreOntario Research FoundationLung Cancer Research FoundationPrevent Cancer FoundationVictorian Cancer AgencyAmerican Cancer SocietyVicHealthBrigham and Women's HospitalEmory UniversityFred Hutchinson Cancer Research CenterWorld Health OrganizationCancer Research UKOffice of Research Infrastructure Programs, National Institutes of HealthAmerican Institute for Cancer ResearchU.S. Department of Health and Human Services
KeywordsMendelian randomizationColorectal cancerRandomizationBiologyGeneticsMedicineOncologyBioinformaticsInternal medicineCancerGeneClinical trialGenotypeGenetic variants

Abstract

fetched live from OpenAlex

Background Obesity has been positively associated with most molecular subtypes of colorectal cancer (CRC); however, the magnitude and the causality of these associations is uncertain. Methods We used Mendelian randomization (MR) to examine potential causal relationships between body size traits (body mass index [BMI], waist circumference, and body fat percentage) with risks of Jass classification types and individual subtypes of CRC (microsatellite instability [MSI] status, CpG island methylator phenotype [CIMP] status, BRAF and KRAS mutations). Summary data on tumour markers were obtained from two genetic consortia (CCFR, GECCO). Findings A 1-standard deviation (SD:5.1 kg/m 2 ) increment in BMI levels was found to increase risks of Jass type 1 MSI-high,CIMP-high,BRAF-mutated,KRAS-wildtype (odds ratio [OR]: 2.14, 95% confidence interval [CI]: 1.46, 3.13; p-value = 9 × 10 −5 ) and Jass type 2 non-MSI-high,CIMP-high,BRAF-mutated,KRAS-wildtype CRC (OR: 2.20, 95% CI: 1.26, 3.86; p-value = 0.005). The magnitude of these associations was stronger compared with Jass type 4 non-MSI-high,CIMP-low/negative,BRAF-wildtype,KRAS-wildtype CRC (p-differences: 0.03 and 0.04, respectively). A 1-SD (SD:13.4 cm) increment in waist circumference increased risk of Jass type 3 non-MSI-high,CIMP-low/negative,BRAF-wildtype,KRAS-mutated (OR 1.73, 95% CI: 1.34, 2.25; p-value = 9 × 10 −5 ) that was stronger compared with Jass type 4 CRC (p-difference: 0.03). A higher body fat percentage (SD:8.5%) increased risk of Jass type 1 CRC (OR: 2.59, 95% CI: 1.49, 4.48; p-value = 0.001), which was greater than Jass type 4 CRC (p-difference: 0.03). Interpretation Body size was more strongly linked to the serrated (Jass types 1 and 2) and alternate (Jass type 3) pathways of colorectal carcinogenesis in comparison to the traditional pathway (Jass type 4). Funding Cancer Research UK, National Institute for Health Research, Medical Research Council, National Institutes of Health, National Cancer Institute, American Institute for Cancer Research, Brigham and Women's Hospital, Prevent Cancer Foundation, Victorian Cancer Agency, Swedish Research Council, Swedish Cancer Society, Region Västerbotten, Knut and Alice Wallenberg Foundation, Lion's Cancer Research Foundation, Insamlingsstiftelsen, Umeå University. Full funding details are provided in acknowledgements.

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)0.029
metaresearch head score (Gemma)0.038
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 score0.029
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.038
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.017
GPT teacher head0.300
Teacher spread0.284 · 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

Citations10
Published2024
Admission routes2
Has abstractyes

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