Is phytoestrogen intake associated with decreased risk of prostate cancer? A systematic review of epidemiological studies based on 17,546 cases
Bibliographic record
Abstract
This study uses current epidemiological data to evaluate whether phytoestrogen intake is associated with a reduced risk of prostate cancer. We performed a random-effect meta-analysis of published data retrieved from PubMed, Web of Science, ProQuest, and CNKI, which was supplemented by a manual search of relevant references. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). Subgroup analysis and meta-regression were performed to explore the source of heterogeneity. Sensitivity analysis was evaluated to assess the stability of the results. Egger's test and funnel plots were used to detect the existence of publication bias. We retrieved 507 papers, and 29 studies were ultimately confirmed as eligible. The meta-analysis showed that phytoestrogen intake was significantly associated with a reduced risk of prostate cancer, with an odds ratio (OR) of 0.77 (95% CI 0.66-0.88; I(2) = 77.6%). The food/nutritional sources that were significantly associated with a reduced risk of prostate cancer included soy and soy products, tofu, legumes, daidzein, and genistein. Subgroup analysis indicated that the associations were significant among Asians and Caucasians, but not among Africans. Meta-regression revealed that the pooled OR increased with the number of cases in the studies. The results might be affected by publication bias based on the Eggers' test (p = 0.011) and the asymmetry of the funnel plot. Phytoestrogen intake may reduce the risk of prostate cancer in Asians and Caucasians. Regular intake of food that is rich in phytoestrogens, such as soy/soy products or legumes, should be recommended.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.024 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".