scholarly journals Polychlorinated Biphenyls (PCBs): Risk Factors for Autism Spectrum Disorder?

Toxics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 70
Author(s):  
Harmanpreet Kaur Panesar ◽  
Conner L. Kennedy ◽  
Kimberly P. Keil Stietz ◽  
Pamela J. Lein
Keyword(s):  
Risk Factors ◽  
Autism Spectrum Disorder ◽  
Polychlorinated Biphenyls ◽  
Genetic Factors ◽  
The United States ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Individual Risk ◽  
Social Reciprocity

Autism spectrum disorder (ASD) includes a group of multifactorial neurodevelopmental disorders defined clinically by core deficits in social reciprocity and communication, restrictive interests and repetitive behaviors. ASD affects one in 54 children in the United States, one in 89 children in Europe, and one in 277 children in Asia, with an estimated worldwide prevalence of 1–2%. While there is increasing consensus that ASD results from complex gene x environment interactions, the identity of specific environmental risk factors and the mechanisms by which environmental and genetic factors interact to determine individual risk remain critical gaps in our understanding of ASD etiology. Polychlorinated biphenyls (PCBs) are ubiquitous environmental contaminants that have been linked to altered neurodevelopment in humans. Preclinical studies demonstrate that PCBs modulate signaling pathways implicated in ASD and phenocopy the effects of ASD risk genes on critical morphometric determinants of neuronal connectivity, such as dendritic arborization. Here, we review human and experimental evidence identifying PCBs as potential risk factors for ASD and discuss the potential for PCBs to influence not only core symptoms of ASD, but also comorbidities commonly associated with ASD, via effects on the central and peripheral nervous systems, and/or peripheral target tissues, using bladder dysfunction as an example. We also discuss critical data gaps in the literature implicating PCBs as ASD risk factors. Unlike genetic factors, which are currently irreversible, environmental factors are modifiable risks. Therefore, data confirming PCBs as risk factors for ASD may suggest rational approaches for the primary prevention of ASD in genetically susceptible individuals.

scholarly journals Genotype-Based Deep Learning in Autism Spectrum Disorder: Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants (Preprint)

2020 ◽  
Author(s):  
Haishuai Wang ◽  
Paul Avillach
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
The United States ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Screening Tools ◽  
Common Variants ◽  
Autism Screening

BACKGROUND In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. OBJECTIVE Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. METHODS After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants. RESULTS The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic from nonautistic individuals. Our classifier demonstrated a significant improvement over standard autism screening tools by average 13% in terms of classification accuracy. CONCLUSIONS Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.

scholarly journals Risk Factors for Unhealthy Weight Gain and Obesity among Children with Autism Spectrum Disorder

2019 ◽  
Vol 20 (13) ◽  
pp. 3285 ◽  
Author(s):  
Khushmol K. Dhaliwal ◽  
Camila E. Orsso ◽  
Caroline Richard ◽  
Andrea M. Haqq ◽  
Lonnie Zwaigenbaum
Keyword(s):  
Risk Factors ◽  
Autism Spectrum Disorder ◽  
Weight Gain ◽  
Autism Spectrum ◽  
Lifestyle Factors ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Future Research ◽  
Children With Asd ◽  
Unhealthy Weight

Autism Spectrum Disorder (ASD) is a developmental disorder characterized by social and communication deficits and repetitive behaviors. Children with ASD are also at a higher risk for developing overweight or obesity than children with typical development (TD). Childhood obesity has been associated with adverse health outcomes, including insulin resistance, diabetes, heart disease, and certain cancers. Importantly some key factors that play a mediating role in these higher rates of obesity include lifestyle factors and biological influences, as well as secondary comorbidities and medications. This review summarizes current knowledge about behavioral and lifestyle factors that could contribute to unhealthy weight gain in children with ASD, as well as the current state of knowledge of emerging risk factors such as the possible influence of sleep problems, the gut microbiome, endocrine influences and maternal metabolic disorders. We also discuss some of the clinical implications of these risk factors and areas for future research.

scholarly journals Review Impact of environmental pollution, dietary factors and diabetes mellitus on Autism Spectrum Disorder (ASD)

2019 ◽  
Vol 35 (4) ◽  
Author(s):  
Abdulrahman Mohammed Alhowikan ◽  
Laila Yousef AL-Ayadhi ◽  
Dost Muhammad Halepoto
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Autism Spectrum Disorder ◽  
Environmental Pollution ◽  
Autism Spectrum ◽  
Dietary Factors ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Creative Commons ◽  
Related Risk

Autism spectrum disorder (ASD) is complex neurodevelopmental condition described by impairments in three main behavioral areas: social deficits, impaired communication, and repetitive behaviors. Despite many years of vast study, the causes of ASD are still unknown. Various risk factors including genetic, infectious, metabolic and immunological have been investigated however, environmental, nutritional and diabetes related risk factors have not received sufficient attention. This study has provided an insight into the comprehensive interaction between environmental pollution, dietary factors and diabetes mellitus that could lead to the advancement of this debilitating neurodevelopment disorder. The literature search was done using PubMed and Google Scholar databases up to October 2018. Key words “Environmental Pollution”, “Nutritional Factors”, “Diabetes Mellitus”, “Autism Spectrum Disorder” were selected. doi: https://doi.org/10.12669/pjms.35.4.269 How to cite this:Alhowikan AM, AL-Ayadhi LY, Halepoto DM. Impact of environmental pollution, dietary factors and diabetes mellitus on Autism Spectrum Disorder (ASD). Pak J Med Sci. 2019;35(4):---------. doi: https://doi.org/10.12669/pjms.35.4.269 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals Risk Factors of Children with Autism Spectrum Disorder (ASD) in Palembang, Indonesia

2020 ◽  
Vol 1 (4) ◽  
pp. 1-8
Author(s):  
Ziske Maritska ◽  
Leonardo Satria ◽  
Nita Parisa
Keyword(s):  
Risk Factors ◽  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Nervous System Development ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Paternal Age ◽  
Primary Data ◽  
Polygenic Inheritance ◽  
Children With Asd

Abstract Background: Autism Spectrum Disorder (ASD) is a group of nervous system development disorders with polygenic inheritance patterns, characterized by a type of dysfunctional in social communication and limited also repetitive behaviors.  Risk factors for ASD can be divided into two categories in general: genetic, and environmental factors. To date, a study about risk factors of ASD in Indonesia, let alone Palembang, is limited.  Therefore, this study wished to investigate the risk factors of children with ASD in Dr. Mohammad Hoesin Hospital, Palembang. Method: This study is an observational descriptive study.  Samples were children with ASD who went to Dr. Mohammad Hoesin Hospital, Palembang.  The primary data obtained from a semi-structured interview with parents/guardians of children with ASD, while secondary data obtained from their medical records. Result: The most common risk factors identified in this study are the paternal age and maternal age ≥ 30 years at the time of conception (59,8% and 40.2%), and the history of cesarean delivery (27,8%). Conclusion: This study concludes that the occurrence of ASD in Palembang is multifactorial, involving both genetic and environmental risk factors.

scholarly journals Genetic contributions to autism spectrum disorder

2021 ◽  
pp. 1-14
Author(s):  
A. Havdahl ◽  
M. Niarchou ◽  
A. Starnawska ◽  
M. Uddin ◽  
C. van der Merwe ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Rare Variants ◽  
De Novo ◽  
Point Mutations ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Individual Risk ◽  
Future Research ◽  
Risk Genes

Abstract Autism spectrum disorder (autism) is a heterogeneous group of neurodevelopmental conditions characterized by early childhood-onset impairments in communication and social interaction alongside restricted and repetitive behaviors and interests. This review summarizes recent developments in human genetics research in autism, complemented by epigenetic and transcriptomic findings. The clinical heterogeneity of autism is mirrored by a complex genetic architecture involving several types of common and rare variants, ranging from point mutations to large copy number variants, and either inherited or spontaneous (de novo). More than 100 risk genes have been implicated by rare, often de novo, potentially damaging mutations in highly constrained genes. These account for substantial individual risk but a small proportion of the population risk. In contrast, most of the genetic risk is attributable to common inherited variants acting en masse, each individually with small effects. Studies have identified a handful of robustly associated common variants. Different risk genes converge on the same mechanisms, such as gene regulation and synaptic connectivity. These mechanisms are also implicated by genes that are epigenetically and transcriptionally dysregulated in autism. Major challenges to understanding the biological mechanisms include substantial phenotypic heterogeneity, large locus heterogeneity, variable penetrance, and widespread pleiotropy. Considerable increases in sample sizes are needed to better understand the hundreds or thousands of common and rare genetic variants involved. Future research should integrate common and rare variant research, multi-omics data including genomics, epigenomics, and transcriptomics, and refined phenotype assessment with multidimensional and longitudinal measures.

scholarly journals Role of environmental and genetic factors in autism spectrum disorder

2017 ◽  
Vol 10 (2) ◽  
pp. 76 ◽  
Author(s):  
Zakia Sultana ◽  
Sarder Nasir Uddin ◽  
Asif Ahmed
Keyword(s):  
Risk Factors ◽  
Autism Spectrum Disorder ◽  
Environmental Factors ◽  
Premature Birth ◽  
Genetic Factors ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Multiple Factors ◽  
Control Association

<p>The aim of this study was to find out the environmental as well as genetic factors responsible for increasing the number of autism spectrum disorder (ASD) patients in Bangladesh. A questionnaire was developed based on 12 environmental factors and genetic aspects. Sixty six patients of ASD and 66 non-ASD control were selected randomly. Among the environmental factors, the age of the mother, premature birth, air pollution, age of the father, hypoxia during childbirth and oral contraceptive came out as significant (p&lt;0.05) factors for ASD incidence compared to the control. Association of multiple factors on an individual was found to be crucial to enhance the risk and exposure to five and six factors was statistically significant (p&lt;0.05) for ASD development. Prospective parents should try to keep the number of risk factors as low as possible before 1-2 months of pregnancy, during pregnancy and 1-2 years after the child birth (for child only).</p>

scholarly journals The Alert Collector: Autism Spectrum Disorder: A Guide to the Latest Resources

2015 ◽  
Vol 55 (2) ◽  
pp. 113
Author(s):  
Michele Frasier-Robinson
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Communication ◽  
Neurodevelopmental Disorders ◽  
The United States ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Statistical Manual ◽  
Diagnostic And Statistical Manual ◽  
Dsm 5

Since the early 1990s there has been a steady escalation in the numbers of children diagnosed with autism spectrum disorder (ASD)—today it is considered the fastest growing developmental disability in the United States. In 2010, it was estimated that 1 in 68 children were affected by autism spectrum disorder. This is an increase of approximately 120 percent from the data collected ten years earlier. Identifying it as one of six neurodevelopmental disorders, the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM-5) describes autism spectrum disorder as “a series of developmental disabilities characterized by impaired social communication and interaction skills, accompanied by the existence of repetitive behaviors or activities, such as rocking movements, hand clapping or obsessively arranging personal belongings.”

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