Autistic Disorder: A 20 Year Chronicle

Skinner, Cindy; Pauly, Rini; Skinner, Steven A.; Schroer, Richard J.; Simensen, Richard J.; Taylor, Henry; Friez, Michael J.; DuPont, Barbara R.; Stevenson, Roger E.

doi:10.1007/s10803-020-04568-3

Cited by 7 publications

(4 citation statements)

References 34 publications

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“…Although not explicitly addressed in this review (as adulthood is defined as 18+), it is vital to consider how these findings apply to older autistic people. Many autistic people—independent of IQ status—may be dependent on parents, 21 , 22 and with increasing age, older autistic people will have still older parents. With subsequent bereavement many older autistic people may therefore lose a vital support system.…”

mentioning

confidence: 99%

Healthcare barriers, what about older age? A comment on Malik-Soni et al.

Mason

2021

Pediatr Res

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confidence: 99%

Healthcare barriers, what about older age? A comment on Malik-Soni et al.

Mason

2021

Pediatr Res

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“…Autism is a stable neurodevelopmental disorder of varying severity in its primary features, such as difficulty communicating and relating to others. While some individuals with autism will lead an independent life, many will have significant intellectual disabilities, comorbid mental health disorders and be dependent on a caregiver (Rommelse et al, 2011; Simonoff et al, 2008; Skinner et al, 2021). Deriving a “universal” prevalence of autism has been made difficult due to evolving diagnostic criteria, burgeoning recognition in both the medical world and the community, and the difficulty of assessing autistic behaviors in individuals with intellectual disabilities (Xu et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a machine learning‐based tool to predict autism among children

et al. 2023

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Autism is a lifelong condition for which intervention must occur as early as possible to improve social functioning. Thus, there is great interest in improving our ability to diagnose autism as early as possible. We take a novel approach to this challenge by combining machine learning with maternal and infant health administrative data to construct a prediction model capable of predicting autism disorder (defined as ICD10 84.0) in the general population. The sample included all mother–offspring pairs from the Australian state of New South Wales (NSW) between January 2003 and December 2005 (n = 262,650 offspring), linked across three health administrative data sets including the NSW perinatal data collection (PDC); the NSW admitted patient data collection (APDC) and the NSW mental health ambulatory data collection (MHADC). Our most successful model was able to predict autism disorder with an area under the receiver operating curve of 0.73, with the strongest risk factors for diagnoses found to include offspring gender, maternal age at birth, delivery analgesia, maternal prenatal tobacco disorders, and low 5‐min APGAR score. Our findings indicate that the combination of machine learning and routinely collected admin data, with further refinement and increased accuracy than achieved by us, may play a role in the early detection of autism disorders.

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“…Since the first description of inborn “autistic disturbances of affective contact” by Leo Kanner, autism has been composed of enigmatic neurobehavioral disorders that frequently become apparent in childhood and then persist as lifelong neuronal impairment (Kanner, 1968 ; Skinner et al., 2021 ). The neurodevelopmental condition, also termed autism spectrum disorder (ASD), presents with severe social and communication impairment, restricted interests, and stereotypical and repetitive behaviors (McPartland & Volkmar, 2012 ; Shuid et al., 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Downregulation of TGIF2 is possibly correlated with neuronal apoptosis and autism‐like symptoms in mice

Lei

Deng

2022

Brain and Behavior

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Background TGFB‐induced factor homeobox 2 (TGIF2) has been reported to exert essential functions in brain development. This study aimed to elucidate the correlation of TGIF2 with autism, a neurodevelopmental condition which presents with severe communication problems. Methods An autism‐related gene expression dataset GSE36315 was used to analyze aberrantly expressed genes in autistic brain tissues. Maternal mice were treated with valproate (VPA), and their offspring were selected as model mice with autism. The functions of TGIF2 in autism‐like symptoms in mice were examined by behavioral tests and histological examination of their hippocampal tissues. Mouse hippocampal neurons were extracted for in vitro studies. A gene set enrichment analysis was performed to analyze the signaling pathways involved, and the upstream factors influencing TGIF2 expression were explored in the ENCODE database and validated by ChIP‐qPCR assays. Results TGIF2 was poorly expressed in autistic patients in the GSE36315 dataset as well as in the temporal cortex tissues of autistic mice. Adenovirus‐mediated overexpression of TGIF2 suppressed autism‐like symptoms and neuronal apoptosis in autistic mice. TGIF2 activated the Wnt/β‐catenin signaling pathway. TGIF2 could be regulated by monomethylation of histone H3 Lys4 (H3K4me1). The histone demethylase LSD1 was highly expressed in the tissues of autistic mice and bound to TGIF2 promoter, which was possibly responsible for TGIF2 downregulation. Conclusion This research suggests that the downregulation of TGIF2, possibly regulated by LSD1/H3K4me1, is correlated with neuronal apoptosis and development of autism in mice through the inactivation of the Wnt/β‐catenin pathway.

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Autistic Disorder: A 20 Year Chronicle

Cited by 7 publications

References 34 publications

Healthcare barriers, what about older age? A comment on Malik-Soni et al.

Healthcare barriers, what about older age? A comment on Malik-Soni et al.

Development and validation of a machine learning‐based tool to predict autism among children

Downregulation of TGIF2 is possibly correlated with neuronal apoptosis and autism‐like symptoms in mice

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