Systematic Review and Meta-Analysis of the Effects of Endocrine Disrupting Chemicals on Circadian Clock Gene Expression
Bibliographic record
Abstract
Background: Endocrine-disrupting chemicals (EDCs) such as bisphenol A (BPA), PFOS, PCBs, DEHP, and dioxins are known to interfere with hormonal systems. Emerging evidence suggests that EDCs can also disrupt circadian rhythm by altering the expression of core clock genes like BMAL1, PER1, CRY1, and CLOCK. However, no prior meta-analysis has comprehensively quantified this impact across multiple biological models. Objective: To systematically review and meta-analyze the effects of EDC exposure on circadian clock gene expression across human, animal, and in vitro studies. Methods: This review followed a PROSPERO-registered protocol (CRD420251068975). Databases searched included PubMed, Scopus, GEO, and ToxNet from January 2000 to June 2025. Inclusion criteria encompassed in vivo, in vitro, or epidemiological studies reporting gene expression data for BMAL1, PER1, CRY1, and CLOCK after EDC exposure. Random-effects meta-analysis was performed using standardized mean differences (SMDs). Risk of bias was assessed using OHAT and the Newcastle-Ottawa Scale. Results: From 342 screened records, 19 studies met inclusion criteria, and 10 were eligible for meta-analysis. EDC exposure was associated with significant downregulation of circadian genes, particularly BMAL1 and PER1. The pooled effect size was SMD = -0.48 (95% CI: -0.59 to -0.37; p <0.001), with moderate heterogeneity (12 = 41%). Funnel plots showed no substantial publication bias. Conclusion: This meta-analysis demonstrates consistent and statistically significant suppression of core circadian genes by chronic EDC exposure. These findings highlight the importance of including chronodisruption markers in toxicological and occupational health surveillance frameworks.. KEYWORDS: Endocrine Disrupting Chemicals, Circadian Rhythm, Gene Expression, BMAL1, PER1, CRY1, CLOCK, Chronodisruption, Toxicogenomics. References 1. Bottalico LN, Weljie AM. 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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".