Novel causal associations between plasma metabolites and prostate cancer risk revealed by mendelian randomization
Bibliographic record
Abstract
Background: Prostate cancer (PCa) is a major global health concern for men, yet its underlying metabolic mechanisms are not fully understood. Identifying causal metabolites could reveal novel pathways for risk assessment and prevention. Methods: We conducted a comprehensive two-sample Mendelian randomization (TSMR) study following STROBE-MR guidelines. Genetic instruments for plasma metabolites were derived from two independent sources, including the METSIM study, a cohort exclusively comprising Finnish men, and the Canadian Longitudinal Study on Aging (CLSA). Summary-level data for PCa were obtained from the PRACTICAL consortium and FinnGen. Inverse variance weighted (IVW) was the primary analysis method, supplemented by sensitivity analyses and Bayesian colocalization (coloc) to assess shared causal genetic variants, a key methodological strength enhancing causal inference. Results: Our analysis identified four plasma metabolites with a significant causal relationship with PCa risk. Ribitol was associated with a reduced risk, while N2,N5-diacetylornithine, N-acetylarginine, and N-acetylcitrulline were associated with an elevated risk. These findings were consistent across datasets and robust in sensitivity analyses. Colocalization analysis provided strong evidence (PP.H4 > 0.8) for a shared causal variant at the rs10201159 locus between N2,N5-diacetylornithine and PCa. Conclusion: This study provides robust genetic evidence supporting a causal role of specific plasma metabolites in prostate cancer development. The incorporation of a male-exclusive metabolomic dataset (METSIM) strengthens the validity of our findings for this male-specific cancer. These metabolites represent promising candidates for further mechanistic investigation into prostate cancer etiology and potential translation into clinical biomarkers.
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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 | 0.039 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".