Associations between circulating metabolites and pca: a bidirectional two-sample Mendelian randomization study
Bibliographic record
Abstract
Abstract Background Prostate cancer (PCa) remains the most prevalent cancer among male globally. Despite the critical role of genetic factors in PCa pathogenesis, recent advances in metabolomics have highlighted the significant contributions of circulating metabolites to genetic risk profiles for PCa. However, the causal relationship between metabolites and PCa is not yet unclear. Methods We utilized a bidirectional two-sample Mendelian randomization (MR) approach, analyzing metabolite datasets from the Canadian Longitudinal Study of Aging (CLSA), the Cooperative Health Research in the Region of Augsburg (KORA) study, and the TwinsUK study and PCa dataset from the Oncoarray. Replication analyses were performed with the UK Biobank. Instrumental variables (IVs) were selected based on established MR criteria and analyzed using methods including the Wald ratio, inverse-variance weighted (IVW), MR-Egger, and weighted median. To ensure robustness, sensitivity analyses were performed using Cochrane’s Q, Egger’s intercept, MR-PRESSO, and leave-one-out (LOO) methods. Results We identified causal relationships between circulating metabolites and PCa risk. After removing high influential SNPs and outliers and reanalysis, we obtained the levels of N6-carbamoylthreonyladenosine (OR 0.61, 95% CI 0.37–1.01, p = 0.054) and 4-ethylphenylsulfate (OR 0.66, 95% CI 0.47–0.92, p = 0.015) causally associated with PCa. All results passed FDR correction; 4-ethylphenylsulfate also remained significant after Bonferroni adjustment. Reverse MR analysis highlighted robust causal relationships of PCa to homovanillate (OR 1.07, 95% CI 1.03–1.10, p = 5.49 × 10 − 5) and X-12,627 (OR 1.03, 95% CI 1.01–1.04, p = 7.54 × 10−5) levels. Conclusion These insights underscore the etiology and risk factors of PCa, providing genetic evidence for the development of therapeutic targets and contributing to elucidating disease mechanisms, suggesting potential diagnostic 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 | 0.028 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".