Biomarkers of the transsulfuration pathway and risk of renal cell carcinoma in the European Prospective Investigation into Cancer and Nutrition (<scp>EPIC</scp>) study
Bibliographic record
Abstract
Previous studies have suggested that components of one-carbon metabolism, particularly circulating vitamin B6, have an etiological role in renal cell carcinoma (RCC). Vitamin B6 is a cofactor in the transsulfuration pathway. We sought to holistically investigate the role of the transsulfuration pathway in RCC risk. We conducted a nested case-control study (455 RCC cases and 455 matched controls) within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Plasma samples from the baseline visit were analyzed for metabolites of the transsulfuration pathway, including pyridoxal 5'-phosphate (PLP, the biologically active form of vitamin B6), homocysteine, serine, cystathionine, and cysteine, in addition to folate. Bayesian conditional logistic regression was used to estimate associations of metabolites with RCC risk as well as interactions with established RCC risk factors. Circulating PLP and cysteine were inversely associated with RCC risk, and these associations were not attenuated after adjustment for other transsulfuration metabolites (odds ratio (OR) and 90% credible interval (CrI) per 1 SD increase in log concentration: 0.76 [0.66, 0.87]; 0.81 [0.66, 0.96], respectively). A comparison of joint metabolite profiles suggested substantially greater RCC risk for the profile representative of low overall transsulfuration function compared to high function (OR 2.70 [90% CrI 1.26, 5.70]). We found some statistical evidence of interactions of cysteine with body mass index, and PLP and homocysteine with smoking status, on their associations with RCC risk. In conclusion, we found evidence suggesting that the transsulfuration pathway may play a role in metabolic dysregulation leading to RCC 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".