Endocrine Disrupting Chemicals and Health in Europe: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Endocrine-disrupting chemicals (EDCs) are synthetic and natural compounds that interfere with hormonal regulation and are linked to obesity, diabetes, thyroid disorders, reproductive dysfunction, and hormone-sensitive cancers. Despite European regulatory measures, human exposure persists through food, consumer products, and the environment, raising significant public health concerns. Objectives: This systematic review and meta-analysis aimed to estimate the prevalence of EDC exposure in European populations and evaluate associations with major endocrine-related health outcomes. Methodology: A comprehensive search of PubMed, EMBASE, Web of Science, and Scopus identified observational studies published between January 2000 and July 2025. Eligible studies reported biomonitoring data on key EDCs (bisphenol A (BPA), phthalates, per- and polyfluoroalkyl substances (PFAS), dioxins, and organochlorine pesticides) and/or statistically assessed associations with endocrine outcomes. Data extraction and risk-of-bias assessment were conducted independently by two reviewers using the Newcastle-Ottawa Scale. Random-effects meta-analysis generated pooled prevalence estimates and effect sizes, with subgroup analyses by region, sex, age, and chemical class. Heterogeneity was quantified using I², and publication bias was assessed via funnel plots and Egger’s test. Results: Preliminary findings indicate widespread detection of PFAS and phthalates, particularly in Western and Northern Europe, with growing evidence of links to metabolic and reproductive outcomes. Conclusion: EDC exposure remains a significant and under-recognised public health issue in Europe. This review highlights the need for strengthened regulatory frameworks, ongoing surveillance, and further research to mitigate the long-term health risks associated with EDCs.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".