Genetic Correlation Between Male Infertility and Prostate Cancer: An In-depth Meta-analysis
Bibliographic record
Abstract
Introduction: Male infertility and prostate cancer are both significant global health issues. Male infertility refers to the failure to conceive after a year of unprotected sexual intercourse. Prostate cancer, primarily found in older men, poses a substantial health risk globally. Recent research suggests a possible connection between male reproductive dysfunction and prostate carcinoma. Methodology: A thorough analysis of existing literature was undertaken utilizing automated databases, which included Embase, PubMed, and Google Scholar. In conducting this meta-analysis, we implemented the standards charted by the PRISMA guidelines. Findings were included investigating the link between GSTP1 gene variants, estrogen receptor genes (PvuII, XbaI, and AluI), male infertility, and prostate cancer. Criteria for inclusion required case vs. control study designs providing data on genotype and allele frequencies. The Newcastle Ottawa Scale (NOS) and assessment of Hardy-Weinberg equilibrium (HWE) were employed for evaluating methodological quality. Statistical analysis was performed using Review Manager 5.4, with significance defined as p < 0.05. Results: The meta-analysis encompassed 24 case-control studies. Significant associations were observed between GSTP1 gene polymorphisms, particularly the Ile105Val genotype, and male infertility and prostate cancer in the allelic and homozygote models. However, substantial correlations involving the estrogen receptor gene polymorphisms (PvuII, XbaI, and AluI) and male infertility or prostate cancer were not found. Conclusion: The meta-analysis shows a significant correlation between GSTP1 gene variations and both impaired male fertility and prostatic carcinoma. These results indicate a possible genetic predisposition to these conditions, underscoring the demand for additional exploration to elucidate the function of genetic influences in male reproductive health.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.049 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".