Systematic Review and Meta-Analysis of the Effects of Endocrine Disrupting Chemicals on Circadian Clock Gene Expression
Notice bibliographique
Résumé
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. Cross-species physiological interactions of endocrine disrupting chemicals with the circadian clock. Gen Comp Endocrinol. 2020;292:113466. 2. Ono M, Dai Y, Fujiwara T, Fujiwara H, Daikoku T, Ando H, et al. Influence of lifestyle and the circadian clock on reproduction. Reprod Med Biol. 2025;24(1):1-11. 3. Sen A, Sellix MT. The circadian timing system and environmental circadian disruption: From follicles to fertility. Endocrinology. 2016;157(10):3364-3376. 4. Yuan W, Liu L, Wei C, Li X, Sun D, Dai C, et al. Identification and meta- analysis of copy number variation-driven circadian clock genes for colorectal cancer. Oncol Lett. 2019;18(6):6090-6098. 5. Rashed N, Liu W, Zhou X, Bode AM, Luo X. The role of circadian gene CLOCK in cancer. Biochim Biophys Acta Mol Cell Res. 2024;1871(1):119097. 6. Škrlec I, Talapko J, Džijan S, Cesar V, Lazić N, Lepeduš H. The association between circadian clock gene polymorphisms and metabolic syndrome: A systematic review and meta-analysis. 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Sun C, Li C, Liu W, Schiöth HB. Generation of endogenous promoter- driven luciferase reporter system using CRISPR/Cas9 for investigating transcriptional regulation of the core clock gene BMAL1. Biomedicines. 2022;10(10):2440. 13. Leso V, Battistini B, Vetrani I, Reppuccia L, Fedele M, Ruggieri F, et al. The endocrine disrupting effects of nanoplastic exposure: A systematic review. Toxicol Ind Health. 2023;39(5):222-236. 14. Kopp R, Martínez 10, Legradi J, Legler J. Exposure to endocrine disrupting chemicals perturbs lipid metabolism and circadian rhythms. J Environ Sci (China). 2017;61:61-72. 15. Shetty V, Adelman ZN, Slotman MA. Effects of circadian clock disruption on gene expression and biological processes in Aedes aegypti. BMC Genomics. 2024;25(1):102. 16. Ding L, Weger BD, Liu J, Zhou L, Lim Y, Wang D, et al. Maternal high fat diet induces circadian clock-independent endocrine alterations impacting the metabolism of the offspring. iScience. 2024;27(5):107642. 17. Caballero-Gallardo K, Olivero-Verbel J, Freeman JL. Toxicogenomics to evaluate endocrine disrupting effects of environmental chemicals using the zebrafish model. Curr Genomics. 2016;17(3):206-219. 18. Bertram MG, Gore AC, Tyler CR, Brodin T. Endocrine-disrupting chemicals. Curr Biol. 2022;32(15):R869-R874. 19. Wong KH, Durrani TS. Exposures to endocrine disrupting chemicals in consumer products: A guide for pediatricians. Curr Probl Pediatr Adolesc Health Care. 2017;47(5):107-118. 20. Thakkar S, Seetharaman B, Kumar H, Vasantharekha R. Endocrine- disrupting chemicals exposure alter neuroendocrine factors, disrupt cardiac functions and provoke hypoxia conditions in zebrafish model. Arch Environ Contam Toxicol. 2022;82(3):459-468.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,019 | 0,036 |
| Bibliométrie | 0,009 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».