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Enregistrement W4251616168 · doi:10.1093/humrep/deab130.295

P–296 Examining the link between environmental toxin exposure and uterine leiomyoma: a systematic review

2021· review· en· W4251616168 sur OpenAlexaffabout
Jasmine Sodhi, L Chan, Ryan Chow, I Chen

Notice bibliographique

RevueHuman Reproduction · 2021
Typereview
Langueen
DomaineSocial Sciences
ThématiqueEducation and Social Development in Ukraine
Établissements canadiensOttawa HospitalUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésLeiomyomaMedicineUterine fibroidsCohort studyGynecologyPhysiologyObstetricsInternal medicinePathology

Résumé

récupéré en direct d'OpenAlex

Abstract Study question Is there an association between exposure to certain environmental toxins and the prevalence of uterine leiomyoma in women? Summary answer Some evidence was obtained to suggest an association between phthalate esters, bisphenol A, heavy metals, persistent organic pollutants and the prevalence of uterine fibroids. What is known already Environmental toxins are naturally occurring, or human made chemicals that can act as endocrine disrupting chemicals (EDCs) by binding and activating estrogen receptors in the body. Uterine fibroids, often called leiomyoma are non-cancerous growths occurring in the uterus. Though often asymptomatic, they can cause pain, infertility, pregnancy complications and are a leading cause for hysterectomy. The aetiology of leiomyoma is not fully understood but both estrogen and progesterone have been implicated in their growth. We aimed to investigate the epidemiological evidence for the association between EDCs and the prevalence of fibroids. Study design, size, duration We undertook a systematic review and in keeping with PRISMA guidelines, a structured search of Medline, Embase, Scopus, and Web of Science was conducted (to October 2020). Case-control, cross-sectional, cohort and experimental studies were included. Participants/materials, setting, methods The included studies analyzed the association between one or more toxins and the occurrence, or growth of leiomyoma in humans, including human cell lines. The types of toxins, patient characteristics, association and outcome, body concentration of toxin and confounding variables were extracted and analyzed. Quality assessment was performed using the Newcastle-Ottawa Scale. Main results and the role of chance In total, 34 studies were included. The majority (76%) of studies revealed a significant association between the exposure studied and the prevalence of uterine leiomyoma. In examining body burden in cases vs controls, phthalate esters showed an association with increased odds of uterine leiomyoma, except in one case where a negative association was observed. In vitro experimental studies examining the effect of alkyl-phenols such as bisphenol A (BPA), octylphenol (OP) and nonylphenol (NP) demonstrated that these environmental estrogens can act to promote the proliferation of leiomyoma cells through a number of mechanisms, typically including the estrogen receptor alpha (ERa) signalling pathway. There were conflicting results for the association between alkyl-phenols and fibroids in case-control studies. A positive association between cadmium was demonstrated in only two studies. There were conflicting results for the association between lead, mercury, arsenic and uterine fibroids. Several metabolites of organophosphate esters, alternative plasticizers, and persistent organic pollutants were associated with an increased risk of uterine fibroids. Limitations, reasons for caution Separating these exposures from the multiple other factors that could affect the outcome of leiomyoma is challenging, but an important issue for future research. Wider implications of the findings: The link between some environmental toxins and uterine fibroids discussed is in agreement with previous literature. However, our review provides a more in depth analysis on specific dosage effects, odds ratios, and potential gene mechanisms of the exposures. This information could contribute to more accurate preventative measures. Trial registration number Not applicable

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,879
Score d'incertitude au seuil0,878

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,129
Tête enseignante GPT0,396
Écart entre enseignants0,267 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission2
Résumé présentoui

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