Association between exposure to polycyclic aromatic hydrocarbons and reproductive health outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Polycyclic aromatic hydrocarbons (PAHs) are widespread environmental pollutants with known harmful effects on human health. However, their specific impact on reproductive outcomes, both cancer-related and non-cancer-related, has not been comprehensively assessed. This study systematically reviews and synthesizes existing evidence on PAH exposure and reproductive health in men and women. METHODS: A comprehensive literature search was conducted in Web of Science, Cochrane Library, PubMed/MEDLINE, ProQuest, and Scopus through May 31, 2024, following PRISMA guidelines. Study quality was assessed using the Newcastle-Ottawa Scale, and the strength of evidence was evaluated using the GRADE framework. The certainty of evidence was rated as moderate for male reproductive organ cancers and low for female reproductive organ cancers, based on GRADE assessment, primarily due to imprecision in the latter. RESULTS: Of the 4,546 articles screened, 30 met the inclusion criteria for the systematic review. Among these, 9 studies were included in a meta-analysis. In men, PAH exposure was consistently associated with reduced semen quality, including lower sperm count, motility, viability, and morphology, as well as increased DNA damage and hormone disruption. The meta-analysis of seven studies found a 13% increased risk of male reproductive cancers, primarily prostate cancer, associated with PAH exposure (standardized incidence ratio [SIR]: 1.13; 95% CI: 1.04-1.23; P < 0.001). For women, PAH metabolites were linked to infertility and possibly endometriosis, but these associations were weakened after controlling for confounders. No significant association was found between PAH exposure and female reproductive cancers (SIR: 1.01; 95% CI: 0.91-1.12; P = 0.91). CONCLUSION: PAH exposure may be associated with adverse male reproductive outcomes, including impaired semen quality and a potential increase in reproductive cancer risk. However, the evidence is limited by methodological heterogeneity, observational designs, and imprecision. In contrast, evidence in females is sparse and of very low certainty, underscoring the need for more rigorous, targeted research on female reproductive outcome.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.044 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".