Prostate Cancer and Dietary Sugar Intake: A Systematic Review
Bibliographic record
Abstract
Background: Prostate cancer is a leading malignancy among men globally, with its incidence expected to rise due to aging populations and shifting lifestyles. While established risk factors include age, ethnicity, and genetics, the role of modifiable dietary factors, particularly sugar intake, remains less clear. Emerging evidence suggests that high sugar consumption may promote carcinogenesis through insulin resistance, chronic inflammation, and hormonal dysregulation. This systematic review aimed to evaluate the current evidence on the association between dietary sugar intake and prostate cancer risk. Methods: A systematic search was conducted across six databases for observational studies published between January 2005 and April 2025. Eligible studies assessed the associations between quantitative sugar intake and prostate cancer outcomes. Screening, data extraction, and a risk of bias assessment (using ROBINS-E) were performed independently by multiple reviewers. Results: Six studies met the inclusion criteria, comprising four prospective cohorts, one case–control study, and one cross-sectional study, with a combined sample of 11,583 men from the USA, Canada, Sweden, and France. Three studies reported a significant positive association between a high intake of dietary sugars and prostate cancer risk, two found no association, and one showed mixed findings depending on the type of sugar. Heterogeneity in the exposure assessments and confounder control limited the comparability. Conclusions: This review suggests a possible association between high dietary sugar intake and increased prostate cancer risk, especially from added sugars and sugar-sweetened beverages. However, inconsistent findings and methodological limitations highlight the need for robust, prospective studies with standardized assessments to understand this relationship better.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".