Presurgical weight loss affects tumour traits and circulating biomarkers in men with prostate cancer
Bibliographic record
Abstract
Obesity is associated with aggressive prostate cancer. To explore whether weight loss favourably affects tumour biology and other outcomes, we undertook a presurgical trial among overweight and obese men with prostate cancer. This single-blinded, two-arm randomised controlled trial explored outcomes of a presurgical weight loss intervention (WLI) that promoted ∼ 1 kg per week loss via caloric restriction and increased physical activity (PA). Forty overweight/obese men with clinically confirmed prostate cancer were randomised to the WLI presurgery or to a control arm; changes in weight, body composition, quality-of-life, circulating biomarkers, gene expression, and immunohistochemical markers in tumour and benign prostatic tissue were evaluated. The study period averaged 50 days. Mean (s.d.) change scores for the WLI vs control arms were as follows: weight: −4.7 (3.1) kg vs −2.2 (4.4) kg ( P =0.0508); caloric intake: −500 (636) vs −159 (600) kcal per day ( P =0.0034); PA: +0.9 (3.1) vs +1.7 (4.6) MET-hours per day (NS); vitality: +5.3 (7.l4) vs −1.8 (8.1) ( P =0.0491); testosterone: +55.1 (86.0) vs −48.3 (203.7) ng dl −1 ( P =0.0418); sex hormone-binding globulin: +14.0 (14.6) vs +1.8 (7.6) nmol l −1 ( P =0.0023); and leptin: −2.16 (2.6) vs −0.03 (3.75) ( P =0.0355). Follow-up Ki67 was significantly higher in WLI vs control arms; median (interquartile range): 5.0 (2.5,10.0) vs 0.0 (0.0,2.5) ( P =0.0061) and several genes were upregulated, for example, CTSL, GSK3B, MED12, and LAMC2. Intentional weight loss shows mixed effects on circulating biomarkers, tumour gene expression, and proliferative markers. More study is needed before recommending weight loss, in particular rapid weight loss, among men with 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".