One‐carbon metabolism biomarkers and risk of urothelial cell carcinoma in the European prospective investigation into cancer and nutrition
Bibliographic record
Abstract
Published associations between dietary folate and bladder cancer risk are inconsistent. Biomarkers may provide more accurate measures of nutrient status. This nested case-control analysis within the European Prospective Investigation into Cancer and Nutrition (EPIC) investigated associations between pre-diagnostic serum folate, homocysteine, vitamins B6 and B12 and the risk of urothelial cell carcinomas of the bladder (UCC). A total of 824 patients with newly diagnosed UCC were matched with 824 cohort members. Serum folate, homocysteine, and vitamins B6 and B12 were measured. Odds ratios (OR) and 95% confidence intervals (CI) for total, aggressive, and non-aggressive UCC were estimated using conditional logistic regression with adjustment for smoking status, smoking duration and intensity, and other potential confounders. Additionally, statistical interaction with smoking status was assessed. A halving in serum folate concentrations was moderately associated with risk of UCC (OR: 1.18; 95% CI: 0.98-1.43), in particular aggressive UCC (OR: 1.34; 95% CI: 1.02-1.75; p-heterogeneity = 0.19). Compared to never smokers in the highest quartile of folate concentrations, this association seemed only apparent among current smokers in the lowest quartile of folate concentrations (OR: 6.26; 95% CI: 3.62-10.81, p-interaction = 0.07). Dietary folate was not associated with aggressive UCC (OR: 1.26; 95% CI: 0.81-1.95; p-heterogeneity = 0.14). No association was observed between serum homocysteine, vitamins B6 and B12 and risk of UCC. This study suggests that lower serum folate concentrations are associated with increased UCC risk, in particular aggressive UCC. Residual confounding by smoking cannot be ruled out and these findings require confirmation in future studies with multiple measurements.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".