Association of Pre-diagnostic Antibody Responses to Escherichia coli and Bacteroides fragilis Toxin Proteins with Colorectal Cancer in a European Cohort
Bibliographic record
Abstract
Experimental evidence has implicated genotoxic Escherichia coli (E. coli) and enterotoxigenic Bacteroides fragilis (ETBF) in the development of colorectal cancer (CRC). However, evidence from epidemiological studies is sparse. We therefore assessed the association of serological markers of E. coli and ETBF exposure with odds of developing CRC in the European Prospective Investigation into Nutrition and Cancer (EPIC) study.Serum samples of incident CRC cases and matched controls (n = 442 pairs) were analyzed for immunoglobulin (Ig) A and G antibody responses to seven E. coli proteins and two isoforms of the ETBF toxin via multiplex serology. Multivariable-adjusted conditional logistic regression analyses were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association of sero-positivity to E. coli and ETBF with CRC.The IgA-positivity of any of the tested E. coli antigens was associated with higher odds of developing CRC (OR: 1.42; 95% CI: 1.05–1.91). Dual-positivity for both IgA and IgG to E. coli and ETBF was associated with >1.7-fold higher odds of developing CRC, with a significant association only for IgG (OR: 1.75; 95% CI: 1.04, 2.94). This association was more pronounced when restricted to the proximal colon cancers (OR: 2.62; 95% CI: 1.09, 6.29) compared to those of the distal colon (OR: 1.24; 95% CI: 0.51, 3.00) (pheterogeneity = 0.095). Sero-positivity to E. coli and ETBF was associated with CRC development, suggesting that co-infection of these bacterial species may contribute to colorectal carcinogenesis. These findings warrant further exploration in larger prospective studies and within different population groups.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".