Is air pollution a plausible candidate for prenatal exposure in autism spectrum disorder (ASD)? : a systematic review

AbstractBackground:Selective serotonin reuptake inhibitors (SSRIs) are the most common antidepressants used to preclude maternal pregnancy depression. There is a growing body of literature assessing the association of prenatal exposure to SSRIs with autism spectrum disorder (ASD). The present systematic review and meta-analysis reviewed the medical literature and pooled the results of the association of prenatal exposure to SSRIs with ASD.Methods:Published investigations in English by June 2016 with keywords of selective serotonin reuptake inhibitors, SSRI, autism spectrum disorder, ASD, pregnancy, childhood, children, neurodevelopment were identified using databases PubMed and PMC, MEDLINE, EMBASE, SCOPUS, and Google Scholar. Cochran's Q statistic-value (Q), degree of freedom (df), and I2 indices (variation in odds ratio [OR] attributable to heterogeneity) were calculated to analyze the risk of heterogeneity of the within- and between-study variability. Pooled odds ratio (OR) and 95% confidence interval (CI) were reported by a Mantel–Haenszel test.Results:There was a non-significant heterogeneity for the included studies ([Q = 3.61, df = 6, P = 0.730], I2 = 0%). The pooled results showed a significant association between prenatal SSRI exposure and ASD (OR = 1.82, 95% CI = 1.59–2.10, Z = 8.49, P = 0.00).Conclusion:The evidence from the present study suggests that prenatal exposure to SSRIs is associated with a higher risk of ASD.

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Prenatal Exposure to Air Pollution and Autism Spectrum Disorder: Sensitive Windows of Exposure and Sex Differences

ISEE Conference Abstracts ◽

10.1289/isee.2021.p-263 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Md Mostafijur Rahman ◽

Yu Hsiang Shu ◽

Ting Chow ◽

Frederick W. Lurmann ◽

Xin Yu ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Air Pollution ◽

Sex Differences ◽

Prenatal Exposure ◽

Autism Spectrum ◽

Spectrum Disorder

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Association of Prenatal Exposure to Air Pollution With Autism Spectrum Disorder

JAMA Pediatrics ◽

10.1001/jamapediatrics.2018.3101 ◽

2019 ◽

Vol 173 (1) ◽

pp. 86 ◽

Cited By ~ 16

Author(s):

Lief Pagalan ◽

Celeste Bickford ◽

Whitney Weikum ◽

Bruce Lanphear ◽

Michael Brauer ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Air Pollution ◽

Prenatal Exposure ◽

Autism Spectrum ◽

Spectrum Disorder

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Endocrine Disruptors and Autism Spectrum Disorder in Pregnancy: A Review and Evaluation of the Quality of the Epidemiological Evidence

Children ◽

10.3390/children5120157 ◽

2018 ◽

Vol 5 (12) ◽

pp. 157 ◽

Cited By ~ 1

Author(s):

Salvador Marí-Bauset ◽

Carolina Donat-Vargas ◽

Agustín Llópis-González ◽

Amelia Marí-Sanchis ◽

Isabel Peraita-Costa ◽

...

Keyword(s):

Systematic Review ◽

Autism Spectrum Disorder ◽

Endocrine Disruptors ◽

Prenatal Exposure ◽

Positive Association ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Systematic Review Methodology ◽

Adverse Health Outcomes

Exposure to environmental contaminants during pregnancy has been linked to adverse health outcomes later in life. Notable among these pollutants are the endocrine disruptors chemicals (EDCs), which are ubiquitously present in the environment and they have been measured and quantified in the fetus. In this systematic review, our objective was to summarize the epidemiological research on the potential association between prenatal exposure to EDCs and Autism Spectrum Disorder (ASD) published from 2005 to 2016. The Navigation Guide Systematic Review Methodology was applied. A total of 17 studies met the inclusion criteria for this review, including: five cohorts and 12 case-control. According to the definitions specified in the Navigation Guide, we rated the quality of evidence for a relationship between prenatal exposure to EDCs and ASD as “moderate”. Although the studies generally showed a positive association between EDCs and ASD, after considering the strengths and limitations, we concluded that the overall strength of evidence supporting an association between prenatal exposure to EDCs and later ASD in humans remains “limited” and inconclusive. Further well-conducted prospective studies are warranted to clarify the role of EDCs on ASD development.

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Autism spectrum disorder and prenatal exposure to selective serotonin reuptake inhibitors: A systematic review and meta-analysis

Reproductive Toxicology ◽

10.1016/j.reprotox.2016.07.016 ◽

2016 ◽

Vol 65 ◽

pp. 170-178 ◽

Cited By ~ 41

Author(s):

Tohru Kobayashi ◽

Tasuku Matsuyama ◽

Masanobu Takeuchi ◽

Shinya Ito

Keyword(s):

Systematic Review ◽

Autism Spectrum Disorder ◽

Prenatal Exposure ◽

Serotonin Reuptake ◽

Selective Serotonin Reuptake Inhibitors ◽

Meta Analysis ◽

Autism Spectrum ◽

Serotonin Reuptake Inhibitors ◽

Spectrum Disorder ◽

Selective Serotonin

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Parent Education in Studies With Nonverbal and Minimally Verbal Participants With Autism Spectrum Disorder: A Systematic Review

American Journal of Speech-Language Pathology ◽

10.1044/2019_ajslp-19-00007 ◽

2020 ◽

Vol 29 (2) ◽

pp. 890-902

Author(s):

Lynn Kern Koegel ◽

Katherine M. Bryan ◽

Pumpki Lei Su ◽

Mohini Vaidya ◽

Stephen Camarata

Keyword(s):

Systematic Review ◽

Autism Spectrum Disorder ◽

Parent Education ◽

Autism Spectrum ◽

Verbal Communication ◽

Fidelity Of Implementation ◽

Spectrum Disorder ◽

Education Programs ◽

Minimally Verbal ◽

Parent Education Programs

Purpose The purpose of this systematic review was to identify parent education procedures implemented in intervention studies focused on expressive verbal communication for nonverbal (NV) or minimally verbal (MV) children with autism spectrum disorder (ASD). Parent education has been shown to be an essential component in the habilitation of individuals with ASD. Parents of individuals with ASD who are NV or MV may particularly benefit from parent education in order to provide opportunities for communication and to support their children across the life span. Method ProQuest databases were searched between the years of 1960 and 2018 to identify articles that targeted verbal communication in MV and NV individuals with ASD. A total of 1,231 were evaluated to assess whether parent education was implemented. We found 36 studies that included a parent education component. These were reviewed with regard to (a) the number of participants and participants' ages, (b) the parent education program provided, (c) the format of the parent education, (d) the duration of the parent education, (e) the measurement of parent education, and (f) the parent fidelity of implementation scores. Results The results of this analysis showed that very few studies have included a parent education component, descriptions of the parent education programs are unclear in most studies, and few studies have scored the parents' implementation of the intervention. Conclusions Currently, there is great variability in parent education programs in regard to participant age, hours provided, fidelity of implementation, format of parent education, and type of treatment used. Suggestions are made to provide both a more comprehensive description and consistent measurement of parent education programs.

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Herbal Medicine as an Alternative Treatment in Autism Spectrum Disorder: A Systematic Review

Current Drug Metabolism ◽

10.2174/1389200219666171227202332 ◽

2018 ◽

Vol 19 (5) ◽

pp. 454-459 ◽

Cited By ~ 2

Author(s):

Francielly Mourao Gasparotto ◽

Francislaine Aparecida dos Reis Lívero ◽

Sara Emilia Lima Tolouei Menegati ◽

Arquimedes Gasparotto Junior

Keyword(s):

Systematic Review ◽

Autism Spectrum Disorder ◽

Herbal Medicine ◽

Alternative Treatment ◽

Autism Spectrum ◽

Spectrum Disorder

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Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

10.2196/preprints.14108 ◽

2019 ◽

Author(s):

Sun Jae Moon ◽

Jin Seub Hwang ◽

Rajesh Kana ◽

John Torous ◽

Jung Won Kim

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Autism Spectrum Disorder ◽

Meta Analysis ◽

Learning Algorithms ◽

Structural Mri ◽

Autism Spectrum ◽

Machine Learning Algorithms ◽

Spectrum Disorder ◽

Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

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