Prostate and gut: Any relationship? A narrative review on the available evidence and putative mechanisms
Bibliographic record
Abstract
BACKGROUND: Gut microbiome is a community of microorganisms that lives in the human intestine and exerts various functions on the host, including metabolic, immunoregulatory, and control over cell proliferation. Gut microbiome alterations have been associated with various pathological conditions, such as diabetes mellitus, obesity, and cardiovascular diseases. Gut-prostate axis is explained by the association between gut microbiome quantitative and functional alterations along with increased intestinal epithelial permeability with prostatediseases. However, the pathophysiological mechanisms and clinical importance of this association are not completely clarified yet. METHODS: We conducted a narrative review of the most relevant articles in the Medline (US National Library of Medicine, Bethesda, MD, USA), Scopus (Elsevier, Amsterdam, The Netherlands) and Web of Science Core Collection (Thomson Reuters, Toronto, ON, Canada) databases. No chronological restrictions were applied, and the most related papers published until December 2023 were included. RESULTS: Gut microbiota (GM) and its metabolites are capable of modifying host androgen level, as well as prostate cancer (PCa) therapy response. Moreover, patients with inflammatory bowel disease have higher rates of prostatitis-like symptoms and a potential risk of developing PCa. CONCLUSIONS: There is evidence that interventions on the GM and its metabolites have a high potential to serve as diagnostic and therapeutic tools for prostate diseases, including PCa.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".