Polycystic Ovarian Syndrome and the Risk of Ovarian, Endometrial, and Breast Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder in women that may be associated with an increased risk of various cancers.This meta-analysis aims to evaluate the relationship between PCOS and the risk of breast, ovarian, and endometrial cancers.Methods: We performed a comprehensive search of databases, including PubMed (Medline), Scopus, Web of Science, and Embase (Elsevier), covering the period from 1983 to November 2023.The review included cohort studies (both retrospective and prospective) and case-control studies.Study quality was assessed using the Newcastle-Ottawa Scale (NOS) for nonrandomized studies.Data analysis was conducted with Stata 17.Results: Our meta-analysis reviewed 13 studies on the association between Polycystic Ovary Syndrome (PCOS) and endometrial cancer, revealing a combined Relative Risk (RR) of 2.91 (95% CI: 2.29 -3.70; I: 61.97%; P < 0.001), indicating a significantly increased risk of endometrial cancer among women with PCOS.For breast cancer, the pooled RR from the studies was 1.09 (95% CI: 0.97-1.22;I: 55.81%; P = 0.012), suggesting a modestly elevated risk.In the case of ovarian cancer, seven studies contributed to the analysis, resulting in a combined RR of 1.52 (95% CI: 1.17-1.97;I: 49.22%; P = 0.072).This finding indicates a moderate increase in risk for ovarian cancer in women with PCOS.These results collectively point to an elevated risk of endometrial, breast, and ovarian cancers in women with PCOS, although the magnitude of risk varies among different types of cancer.Conclusion: This meta-analysis supports an elevated risk of endometrial, breast, and ovarian cancers in women with PCOS.These findings highlight the need for increased surveillance and preventive measures for women with PCOS to mitigate their cancer risk.Further research with larger sample sizes is needed to confirm these associations and explore underlying mechanisms.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".