Causal Relationship Between Blood Metabolites and Prostate Cancer Risk: A Two‐Sample Mendelian Randomization Study
Bibliographic record
Abstract
Recent research has increasingly suggested an association between changes in specific blood metabolites and prostate cancer (PCa) development. However, it remains unclear whether these observed associations represent a causal relationship. To reveal the potential causal associations between blood metabolites and PCa risk, we conducted a comprehensive two-sample Mendelian randomization (MR) analysis. We used genetic instruments for 514 and 490 metabolites from two independent comprehensive genome-wide association studies. These studies included 14,295 individuals of European ancestry from the INTERVAL/EPIC-Norfolk cohorts and 8299 individuals of European ancestry from the Canadian Longitudinal Study on Aging cohort. Summary statistics of PCa risk involving 122,188 cases and 604,640 controls of European ancestry individuals were analyzed. The associations between metabolites and PCa risk were evaluated using the inverse-variance weighted method, supplemented by sensitivity analyses including MR-Egger and MR-PRESSO tests. Additionally, we conducted a phenome-wide MR analysis to assess the potential side effects of targeting the identified metabolites for PCa intervention. Our analysis revealed 107 unique blood metabolites significantly associated with PCa risk, with 43 of these associations consistently replicated using instruments from two independent data sets. This study provides novel insights into the potential role of specific metabolites in the etiology of PCa, which warrants further investigations.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".