Causal relationships between plasma metabolites and prostate cancer: A Mendelian randomization study exploring immune and inflammatory mediators
Bibliographic record
Abstract
Abstract Background Metabolic alterations and inflammatory processes contribute substantially to the pathogenesis of prostate cancer (PCa). This study used Mendelian randomization (MR) to investigate the causal relationships between plasma metabolites and PCa and to identify potential mediators, including immune cell traits and circulating inflammatory proteins. Materials and methods A 2-sample MR analysis was conducted using data from the Canadian Longitudinal Study on Aging and a diverse genome-wide association study of PCa. A total of 1400 plasma metabolites were analyzed. Single-nucleotide polymorphisms were carefully selected and refined using linkage disequilibrium clumping. The inverse variance weighting method was used for primary analysis, supplemented by sensitivity analyses, including MR-Egger, weighted median, and MR-Pleiotropy RESidual Sum and Outlier, to ensure the robustness of the results. Results Eight metabolites were significantly associated with PCa. Specifically, a higher phosphate-to-uridine ratio was associated with a decreased risk of PCa, whereas higher levels of N -acetyl-arginine were linked to an increased risk. Other significant metabolites included the phosphate-to-2′-deoxyuridine ratio; N6-methyl-lysine, N -acetyl-leucine, N -succinyl-phenylalanine, and cysteinylglycine disulfide levels; and the α-ketoglutarate-to-ornithine ratio. Sensitivity analyses and the MR-Steiger test confirmed the robustness and causal direction of these associations. In addition, further analysis indicated that certain metabolites may influence PCa risk by modulating the expression of inflammatory markers, such as leukemia inhibitory factor receptor, interleukin-8, and CD33-related markers. Conclusions This study identified plasma metabolites that exert causal effects on the risk of PCa and highlighted the mediating role of immune traits and inflammatory proteins. These findings underscore the complexity of the biological pathways involved and suggest potential targets for therapeutic interventions.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".