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Enregistrement W2132957742 · doi:10.1038/tp.2014.4

Environmental toxicants and autism spectrum disorders: a systematic review

2014· review· en· W2132957742 sur OpenAlex

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Notice bibliographique

RevueTranslational Psychiatry · 2014
Typereview
Langueen
DomaineNeuroscience
ThématiqueAutism Spectrum Disorder Research
Établissements canadiensUniversity of Alberta
Organismes subventionnairesAutism Research Institute
Mots-clésToxicantAutismEnvironmental healthPollutantAutism spectrum disorderCohort studyMedicineToxicologyPsychiatryBiologyToxicityEcologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

Although the involvement of genetic abnormalities in autism spectrum disorders (ASD) is well-accepted, recent studies point to an equal contribution by environmental factors, particularly environmental toxicants. However, these toxicant-related studies in ASD have not been systematically reviewed to date. Therefore, we compiled publications investigating potential associations between environmental toxicants and ASD and arranged these publications into the following three categories: (a) studies examining estimated toxicant exposures in the environment during the preconceptional, gestational and early childhood periods; (b) studies investigating biomarkers of toxicants; and (c) studies examining potential genetic susceptibilities to toxicants. A literature search of nine electronic scientific databases through November 2013 was performed. In the first category examining ASD risk and estimated toxicant exposures in the environment, the majority of studies (34/37; 92%) reported an association. Most of these studies were retrospective case-control, ecological or prospective cohort studies, although a few had weaker study designs (for example, case reports or series). Toxicants implicated in ASD included pesticides, phthalates, polychlorinated biphenyls (PCBs), solvents, toxic waste sites, air pollutants and heavy metals, with the strongest evidence found for air pollutants and pesticides. Gestational exposure to methylmercury (through fish exposure, one study) and childhood exposure to pollutants in water supplies (two studies) were not found to be associated with ASD risk. In the second category of studies investigating biomarkers of toxicants and ASD, a large number was dedicated to examining heavy metals. Such studies demonstrated mixed findings, with only 19 of 40 (47%) case-control studies reporting higher concentrations of heavy metals in blood, urine, hair, brain or teeth of children with ASD compared with controls. Other biomarker studies reported that solvent, phthalate and pesticide levels were associated with ASD, whereas PCB studies were mixed. Seven studies reported a relationship between autism severity and heavy metal biomarkers, suggesting evidence of a dose-effect relationship. Overall, the evidence linking biomarkers of toxicants with ASD (the second category) was weaker compared with the evidence associating estimated exposures to toxicants in the environment and ASD risk (the first category) because many of the biomarker studies contained small sample sizes and the relationships between biomarkers and ASD were inconsistent across studies. Regarding the third category of studies investigating potential genetic susceptibilities to toxicants, 10 unique studies examined polymorphisms in genes associated with increased susceptibilities to toxicants, with 8 studies reporting that such polymorphisms were more common in ASD individuals (or their mothers, 1 study) compared with controls (one study examined multiple polymorphisms). Genes implicated in these studies included paraoxonase (PON1, three of five studies), glutathione S-transferase (GSTM1 and GSTP1, three of four studies), δ-aminolevulinic acid dehydratase (one study), SLC11A3 (one study) and the metal regulatory transcription factor 1 (one of two studies). Notably, many of the reviewed studies had significant limitations, including lack of replication, limited sample sizes, retrospective design, recall and publication biases, inadequate matching of cases and controls, and the use of nonstandard tools to diagnose ASD. The findings of this review suggest that the etiology of ASD may involve, at least in a subset of children, complex interactions between genetic factors and certain environmental toxicants that may act synergistically or in parallel during critical periods of neurodevelopment, in a manner that increases the likelihood of developing ASD. Because of the limitations of many of the reviewed studies, additional high-quality epidemiological studies concerning environmental toxicants and ASD are warranted to confirm and clarify many of these findings.

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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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,254
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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 Opus0,028
Tête enseignante GPT0,324
Écart entre enseignants0,296 · 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