The impact of moderate wine consumption on the risk of developing prostate cancer
Bibliographic record
Abstract
Mihai Dorin Vartolomei,1,2,* Shoji Kimura,2,3,* Matteo Ferro,4 Beat Foerster,2,5 Mohammad Abufaraj,2,6 Alberto Briganti,7 Pierre I Karakiewicz,8 Shahrokh F Shariat2,9,10,11 1Department of Cell and Molecular Biology, University of Medicine and Pharmacy, Tirgu Mures, Romania; 2Department of Urology, Medical University of Vienna, Vienna, Austria; 3Department of Urology, Jikei University School of Medicine, Tokyo, Japan; 4Division of Urology, European Institute of Oncology, Milan, Italy; 5Department of Urology, Kantonsspital Winterthur, Winterthur, Switzerland; 6Division of Urology, Department of Special Surgery, Jordan University Hospital, The University of Jordan, Amman, Jordan; 7Department of Urology, Vita Salute San Raffaele University, Milan, Italy; 8Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Centre, Montreal, Canada; 9Karl Landsteiner Institute of Urology and Andrology, Vienna, Austria; 10 Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA; 11Department of Urology, Weill Cornell Medical College, New York, NY, USA *These authors contributed equally to this work Objective: To investigate the impact of moderate wine consumption on the risk of prostate cancer (PCa). We focused on the differential effect of moderate consumption of red versus white wine.Design: This study was a meta-analysis that includes data from case–control and cohort studies.Materials and methods: A systematic search of Web of Science, Medline/PubMed, and Cochrane library was performed on December 1, 2017. Studies were deemed eligible if they assessed the risk of PCa due to red, white, or any wine using multivariable logistic regression analysis. We performed a formal meta-analysis for the risk of PCa according to moderate wine and wine type consumption (white or red). Heterogeneity between studies was assessed using Cochrane’s Q test and I2 statistics. Publication bias was assessed using Egger’s regression test.Results: A total of 930 abstracts and titles were initially identified. After removal of duplicates, reviews, and conference abstracts, 83 full-text original articles were screened. Seventeen studies (611,169 subjects) were included for final evaluation and fulfilled the inclusion criteria. In the case of moderate wine consumption: the pooled risk ratio (RR) for the risk of PCa was 0.98 (95% CI 0.92–1.05, p=0.57) in the multivariable analysis. Moderate white wine consumption increased the risk of PCa with a pooled RR of 1.26 (95% CI 1.10–1.43, p=0.001) in the multivariable analysis. Meanwhile, moderate red wine consumption had a protective role reducing the risk by 12% (RR 0.88, 95% CI 0.78–0.999, p=0.047) in the multivariable analysis that comprised 222,447 subjects.Conclusions: In this meta-analysis, moderate wine consumption did not impact the risk of PCa. Interestingly, regarding the type of wine, moderate consumption of white wine increased the risk of PCa, whereas moderate consumption of red wine had a protective effect. Further analyses are needed to assess the differential molecular effect of white and red wine conferring their impact on PCa risk. Keywords: wine, prostate cancer, alcohol, risk of cancer, meta-analysis
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".