Changes in periprostatic adipose tissue induced by 5α‐reductase inhibitors
Bibliographic record
Abstract
There is increasing interest in periprostatic fat and its influence on prostate cancer aggressiveness. In vitro data suggest that adipose stromal/stem cells (ASCs) can increase production of cytokines and growth factors resulting in invasive growth and metastasis in prostate cancer. The objective of the study was to determine the interaction between 5α-reductase inhibitors (5ARIs) and periprostatic adipose tissue (PPAT) and factors of prostate cancer aggressiveness. In this retrospective study, we identified 61 patients treated with 5ARIs for a period of ≥12 months before undergoing radiation therapy (brachytherapy or external beam radiotherapy). The control group consisted of 117 patients without any exposure to 5ARIs. Prior to being treated, all patients underwent abdominal computed tomography (CT). To measure PPAT, we defined the fat pad anteriorly to the prostate, as well as the intra-abdominal visceral adipose tissue (VAT) and subcutaneous tissue (SAT) at the level of L4/L5. All contours were performed manually. These adipose tissue measurements were correlated with the Cancer of the Prostate Risk Assessment (CAPRA) score using Pearson correlation coefficient. Differences in fat contents were evaluated using Student's t-test. Median time on 5ARIs for the 61 patients was 12 months (range 12-96). Patient on 5ARIs had a significantly (p < 0.001) smaller PPAT (0.4, SD 0.5) than patients without a 5ARI (0.6 cc, SD 0.4). There was no significant correlation between the CAPRA score and fat measurements when adjusted for 5ARI use (p = 0.18). In non-5ARI users, BMI was not correlated with PPAT but was correlated with SAT and VAT volume and its density. There were no significant differences in diabetics (p = 0.3), metformin users (p = 0.4) or statin users (p = 0.09) between both groups. 5ARIs taken for at least 12 months induce changes in PPAT volume. Whether these changes or the extent of changes will have an influence on outcome remains unknown.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".