Antioxidant vitamin and mineral supplementation and prostate cancer prevention in the SU.VI.MAX trial
Bibliographic record
Abstract
Randomized trials have shown, unexpectedly, that supplementation with selenium or vitamin E is associated with a reduction of prostate cancer risk. We assess whether a supplementation with low doses of antioxidant vitamins and minerals could reduce the occurrence of prostate cancer and influence biochemical markers. The SU.VI.MAX trial comprised 5,141 men randomized to take either a placebo or a supplementation with nutritional doses of vitamin C, vitamin E, beta-carotene, selenium and zinc daily for 8 years. Biochemical markers of prostate cancer risk such as prostate-specific antigen (PSA) and insulin-like growth factors (IGFs) were measured on plasma samples collected at enrollment and at the end of follow-up from 3,616 men. Cox regression models were used to estimate the hazard ratio and related 95% confidence interval of prostate cancer associated with the supplementation and to examine whether the effect differed among predetermined susceptible subgroups. During the follow-up, 103 cases of prostate cancer were diagnosed. Overall, there was a moderate nonsignificant reduction in prostate cancer rate associated with the supplementation (hazard ratio = 0.88; 95% CI = 0.60-1.29). However, the effect differed significantly between men with normal baseline PSA (< 3 microg/L) and those with elevated PSA (p = 0.009). Among men with normal PSA, there was a marked statistically significant reduction in the rate of prostate cancer for men receiving the supplements (hazard ratio = 0.52; 95% CI = 0.29-0.92). In men with elevated PSA at baseline, the supplementation was associated with an increased incidence of prostate cancer of borderline statistical significance (hazard ratio = 1.54; 95% CI = 0.87-2.72). The supplementation had no effect on PSA or IGF levels. Our findings support the hypothesis that chemoprevention of prostate cancer can be achieved with nutritional doses of antioxidant vitamins and minerals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".