Prostatic and Dietary Omega-3 Fatty Acids and Prostate Cancer Progression during Active Surveillance
Bibliographic record
Abstract
The association between omega-3 (ω-3) fatty acids and prostate cancer has been widely studied. However, little is known about the impact of prostate tissue fatty acid content on prostate cancer progression. We hypothesized that compared with the estimated dietary ω-3 fatty acids intake and the ω-3 fatty acids levels measured in red blood cells (RBC), the prostate tissue ω-3 fatty acid content is more strongly related to prostate cancer progression. We present the initial observations from baseline data of a phase II clinical trial conducted in a cohort of 48 untreated men affected with low-risk prostate cancer, managed under active surveillance. These men underwent a first repeat biopsy session within 6 months after the initial diagnosis of low-risk prostate cancer, at which time 29% of the men had progressed from a Gleason score of 6 to a Gleason score of 7. At the first repeat biopsy session, fatty acid levels were assessed with a food-frequency questionnaire, and determined in the RBC and in the prostate tissue biopsy. We found that eicosapentaenoic acid (EPA) was associated with a reduced risk of prostate cancer progression when measured directly in the prostate tissue. Thus, this initial interim study analysis suggests that prostate tissue ω-3 fatty acids, especially EPA, may be protective against prostate cancer progression in men with low-risk prostate cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".