Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012

Christensen, Deborah; Baio, Jon; Braun, Kim Van Naarden; Bilder, Deborah A.; Charles, Jane M.; Constantino, John N.; Daniels, Julie L.; Durkin, Maureen S.; Fitzgerald, Robert T.; Kurzius-Spencer, Margaret; Lee, Li Ching; Pettygrove, Sydney; Robinson, Cordelia; Schulz, Eldon G.; Wells, Chris; Wingate, Martha S.; Zahorodny, Walter; Yeargin‐Allsopp, Marshalyn

doi:10.15585/mmwr.ss6503a1

Cited by 1,519 publications

(1,011 citation statements)

References 28 publications

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“…The most recent (2012) estimates in the United States indicate a rate of 14.6 per 1000 children (one in 68) aged eight (Christensen et al, 2016). With increased prevalence in the population has come increased level of attention to the causes of ASD, which remain elusive yet legion.…”

Section: Introductionmentioning

confidence: 99%

The Role of the Immune System in Autism Spectrum Disorder

Meltzer

Water

2016

Neuropsychopharmacol

405

293

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Autism is a neurodevelopmental disorder characterized by deficits in communication and social skills as well as repetitive and stereotypical behaviors. While much effort has focused on the identification of genes associated with autism, research emerging within the past two decades suggests that immune dysfunction is a viable risk factor contributing to the neurodevelopmental deficits observed in autism spectrum disorders (ASD). Further, it is the heterogeneity within this disorder that has brought to light much of the current thinking regarding the subphenotypes within ASD and how the immune system is associated with these distinctions. This review will focus on the two main axes of immune involvement in ASD, namely dysfunction in the prenatal and postnatal periods. During gestation, prenatal insults including maternal infection and subsequent immunological activation may increase the risk of autism in the child. Similarly, the presence of maternally derived anti-brain autoantibodies found in~20% of mothers whose children are at risk for developing autism has defined an additional subphenotype of ASD. The postnatal environment, on the other hand, is characterized by related but distinct profiles of immune dysregulation, inflammation, and endogenous autoantibodies that all persist within the affected individual. Further definition of the role of immune dysregulation in ASD thus necessitates a deeper understanding of the interaction between both maternal and child immune systems, and the role they have in diagnosis and treatment.

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Section: Introductionmentioning

confidence: 99%

The Role of the Immune System in Autism Spectrum Disorder

Meltzer

Water

2016

Neuropsychopharmacol

405

293

View full text Add to dashboard Cite

show abstract

“…Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a prevalence rate of 1:68 [1] and is characterized by impairments across multiple domains including socio-communication ability, and restrictive and stereotyped behaviors [2]. The high heterogeneity and complexity of ASD has previously limited the capacity of neuroimaging to produce reliable and consistent biomarkers that can be applied in a standard clinical evaluation.…”

Section: Introductionmentioning

confidence: 99%

Functional SPECT neuroimaging using machine learning algorithms distinguishes autism spectrum disorder from healthy subjects

Amen¹,

Sarabi²,

Willeumier³

et al. 2017

J Syst Integr Neurosci

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The diagnosis of Autism Spectrum Disorder (ASD) relies on history and behavioral observation, lacking reliable biomarkers. We performed a retrospective analysis using machine learning algorithms of 928 persons with ASD (mean age: 17 ± 10.8 years; age range 4-67) obtained from a multisite psychiatric database with rest and on-task brain SPECT scans to investigate whether or not these scans distinguish ASD from healthy controls (HC, n=101; mean age: 43 ± 17.2 years; age range 13-84). Using 128 regions of interest extracts (ROIs), we applied multiple machine learning algorithms for binary classification. Due to an unbalanced sample size between ASD and controls, we then sub-sampled the data prior to feature selection and classification. Using a subsampled dataset, least absolute shrinkage and selection operator (LASSO) feature selection with Random Forest method baseline accuracy results of approximately 81% were achieved, based on optimal classifier settings with the top selected features. We applied machine learning algorithms to ASD adults only, the majority of our sample, and selected subjects in both AD and HC groups with age range of 13-67 years and found the results consistent with the combined data. These machine learning results identified potential diagnostic biomarkers differentiating ASD from HC in the regions of the cerebellum and vermis, anterior cingulate gyrus, amygdala, thalamus, frontal, and temporal lobes.

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“…According to recent statistics by Centers of Disease Control and Prevention (CDC), 1 in every 68 children in the US is affected by ASD [3]. This could be due to true increase in prevalence or increased awareness and diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…This could be due to true increase in prevalence or increased awareness and diagnosis. ASD also shows higher prevalence in boys [3], familial patterns and sibling learning issues [4,5], and high monozygotic twin concordance [6]. Numerous underlying causes for ASD have been indicated such as genetic mutations, neurotoxicity and inflammation, impaired immune response, dysbiosis, nutrient imbalance, and oxidative stress [2].…”

Section: Introductionmentioning

confidence: 99%

Pharmacobiological Treatments in Autism Spectrum Disorders

Jayaraman¹,

Mundkur²

2017

IJCRR

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Autism spectrum disorders (ASD) refer to a group of neuro-developmental disorders affecting young children and adults. Currently, the treatment options for ASD are mostly restricted to treating its symptoms. Among the various approaches for treating ASD, pharmacobiology-based treatments are numerous. Our objective is to review the up-to-date information on different types of medications and briefly discuss the evidence-based potential of these treatments in ASD therapy. PubMed searches for reports and reviews on clinical data from the last 15 years, between 2002-2017, using search terms of each category of treatment along with the terms "autism", "mechanism", and/or "side effects" were conducted. Several pharmacobiological interventions have been prescribed for ASD, including antipsychotics, stimulants, antidepressants, supplements, and special diets. However, none of these methods is an effective ASD treatment, and only few show much promise. We provide a brief overview of the current pharmacobiological treatments for ASD, their mechanisms of action, and clinical research-based evidence on their effectiveness. Based on our review, we recommend that caution should be exercised when choosing a pharmacobiological treatment method for ASD as majority of existing evidence is not from large-scale long-term high quality studies.Future research should focus on rigorous investigative design, long-term implementation, and meaningful uniform outcome measurements.

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Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012

Cited by 1,519 publications

References 28 publications

The Role of the Immune System in Autism Spectrum Disorder

The Role of the Immune System in Autism Spectrum Disorder

Functional SPECT neuroimaging using machine learning algorithms distinguishes autism spectrum disorder from healthy subjects

Pharmacobiological Treatments in Autism Spectrum Disorders

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