Soy Consumption and the Risk of Prostate Cancer in Men: An Updated Systematic Review and Meta‐analysis
Bibliographic record
Abstract
Background Prostate cancer (PCa) is the third most commonly diagnosed cancer and the sixth most common cause of cancer‐related deaths in the United States. Worldwide, PCa is the second most commonly diagnosed cancer and the fifth leading cause of cancer‐related deaths. PCa incidence is higher in more developed countries, with Australia/New Zealand and North America having the highest rates, while rates remain lowest in Asian countries. The lower incidence of PCa in Asian populations has been associated with the consumption of soy foods. Soy has been under intensifying scrutiny in recent years for its potential role in the prevention of hormone‐driven cancers. The purpose of this study is to provide an updated systematic review and meta‐analysis focused on the association of soy foods on the risk of PCa in men. Methods A systematic review and meta‐analysis to determine the influence of soy food consumption on the risk of PCa in men is currently underway. Eligible studies were published before October 10, 2016 and were identified from PubMed, Web of Science, and the Cochrane Library. Articles were identified using the following key words and their variants: prostate cancer, prostate neoplasm, soy, soymilk, soy milk, isoflavone, bean curd, tofu, soy protein, daidzein, and genistein. For studies to have been included in this meta‐analysis, they must have met the following criteria: (a) evaluated the association between soy food consumption and PCa risk by using randomized control trials and cohort, cross‐sectional, retrospective, prospective, or case‐control studies; (b) methodology was documented in replicable detail; (c) evaluated the relationship between soy and prostate cancer risk; (d) included relative risk ratio with 95% confidence intervals for exposure categories; (e) were written in English; and (f) peer‐reviewed publications or theses. We will utilize a Newcastle‐Ottawa Scale in order to assess the quality of this data. Additionally, data from included articles were utilized for comparisons of highest to lowest consumption, dose‐response relationships, and for potential publication bias. From these comparisons, we will estimate pooled relative risk ratios (RR) and 95% confidence intervals (CI) using random and fixed effects models. Results After screening the literature, a total of 3,309 articles were identified. Eight hundred and thirty‐six articles were immediately removed as duplicates. Of the 2,473 remaining articles, 2,444 were removed through the abstract screening process, and 29 articles were identified for full‐text review. After reviewing the full text, 22 articles met the inclusion criteria and will be analyzed in the meta‐analysis. Significance Data gleaned from this study will result in an updated systematic review and meta‐analysis of the effect of soy consumption on the risk of PCa, providing support or nonsupport for the potential beneficial effect of increased soy food consumption on PCa risk.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".