Genetic variants associated with circulating C‐reactive protein levels and colorectal cancer survival: Sex‐specific and lifestyle factors specific associations
Bibliographic record
Abstract
Abstract Elevated blood levels of C‐reactive protein (CRP) have been linked to colorectal cancer (CRC) survival. We evaluated genetic variants associated with CRP levels and their interactions with sex and lifestyle factors in association with CRC‐specific mortality. Our study included 16 142 CRC cases from the International Survival Analysis in Colorectal Cancer Consortium. We identified 618 common single nucleotide polymorphisms (SNPs) associated with CRP levels from the NHGRI‐EBI GWAS Catalog. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between SNPs and CRC‐specific mortality adjusting for age, sex, genotyping platform/study and principal components. We investigated their interactions with sex and lifestyle factors using likelihood ratio tests. Of 5472 (33.9%) deaths accrued over up to 10 years of follow‐up, 3547 (64.8%) were due to CRC. No variants were associated with CRC‐specific mortality after multiple comparison correction. We observed strong evidence of interaction between variant rs1933736 at FRK gene and sex in relation to CRC‐specific mortality (corrected Pinteraction = .0004); women had higher CRC‐specific mortality associated with the minor allele (HR = 1.11, 95% CI = 1.04‐1.19) whereas an inverse association was observed for men (HR = 0.88, 95% CI = 0.82‐0.94). There was no evidence of interactions between CRP‐associated SNPs and alcohol, obesity or smoking. Our study observed a significant interaction between sex and a CRP‐associated variant in relation to CRC‐specific mortality. Future replication of this association and functional annotation of the variant are needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".