Mendelian randomization analysis of C-reactive protein on colorectal cancer risk
Bibliographic record
Abstract
BACKGROUND: Chronic inflammation is a risk factor for colorectal cancer (CRC). Circulating C-reactive protein (CRP) is also moderately associated with CRC risk. However, observational studies are susceptible to unmeasured confounding or reverse causality. Using genetic risk variants as instrumental variables, we investigated the causal relationship between genetically elevated CRP concentration and CRC risk, using a Mendelian randomization approach. METHODS: Individual-level data from 30 480 CRC cases and 22 844 controls from 33 participating studies in three international consortia were used: the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), the Colorectal Transdisciplinary Study (CORECT) and the Colon Cancer Family Registry (CCFR). As instrumental variables, we included 19 single nucleotide polymorphisms (SNPs) previously associated with CRP concentration. The SNP-CRC associations were estimated using a logistic regression model adjusted for age, sex, principal components and genotyping phases. An inverse-variance weighted method was applied to estimate the causal effect of CRP on CRC risk. RESULTS: Among the 19 CRP-associated SNPs, rs1260326 and rs6734238 were significantly associated with CRC risk (P = 7.5 × 10-4, and P = 0.003, respectively). A genetically predicted one-unit increase in the log-transformed CRP concentrations (mg/l) was not associated with increased risk of CRC [odds ratio (OR) = 1.04; 95% confidence interval (CI): 0.97, 1.12; P = 0.256). No evidence of association was observed in subgroup analyses stratified by other risk factors. CONCLUSIONS: In spite of adequate statistical power to detect moderate association, we found genetically elevated CRP concentration was not associated with increased risk of CRC among individuals of European ancestry. Our findings suggested that circulating CRP is unlikely to be a causal factor in CRC development.
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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 | 0.053 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".