Paternal age contribution to brain white matter aberrations in autism spectrum disorder

Yassin, Walid; Kojima, Mitsuharu; Owada, Keiho; Kuwabara, Hitoshi; Gonoi, Wataru; Aoki, Yuta; Takao, Hidemasa; Natsubori, Tatsunobu; Iwashiro, Norichika; Kasai, Kiyoto; Kano, Yukiko; Abe, Osamu; Yamasue, Hidenori

doi:10.1111/pcn.12909

Cited by 10 publications

(5 citation statements)

References 83 publications

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“…Our study also found significant differences in FA of the bilateral UF between the ASD and control groups; this finding confirmed the abnormalities of the UF in ASD individuals, including decreased FA (Jou et al, 2016;Koshiyama et al, 2020;Samson et al, 2016) and increased MD (Catani et al, 2016;Yassin et al, 2019), reported in a number of previous studies. The UF participates in the second functional network of the limbic system, the temporo-amygdala-orbitofrontal network, and plays an important role in the integration of visceral and emotional states with semantic memory and behavior (Catani et al, 2013).…”

Section: Discussionsupporting

confidence: 91%

Heritability of abnormalities in limbic networks of autism spectrum disorder children: Evidence from an autism spectrum disorder twin study

et al. 2022

Autism Research

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Although the limbic system is closely related to emotion and social behaviors, little is known about the integrity of limbic pathways and how genetics influence the anatomical abnormalities of limbic networks in children with autism spectrum disorder (ASD). Therefore, we used an ASD twin study design to evaluate the microstructural integrity and autism-related differences in limbic pathways of young children with ASD and to estimate the heritability of limbic tracts microstructure variance. We obtained diffusion tensor imaging scans from 33 pairs of twins with ASD aged 2-9 years and 20 age-matched typically developing children. The ACE model was used to estimate the relative effects of additive genetic factors (A), shared environmental factors (C) and specific environmental factors (E) on the variability of diffusivity measurements. We found a significant decrease in fractional anisotropy (FA) in the bilateral fornix and uncinate fasciculus (UF), as well as increased mean diffusivity (MD) and radial diffusivity (RD) in the bilateral fornix and right UF of ASD children. Correlation analysis showed that FA, MD, and lateralization indices of UF were correlated with autism diagnostic observation schedule scores. The ACE model revealed that genetic effects may drive some of the variability of microstructure in the bilateral fornix, cingulum, and left UF. In conclusion, in children with ASD, there are abnormalities in the white matter microstructure of the limbic system, which is related to the core symptoms; these abnormalities may be related to the relative contribution of genetic and environmental effects on specific tracts. Lay SummaryAutism spectrum disorder (ASD) children have abnormal white matter structure in limbic system related to ASD symptoms, and genetic factors play an important role in the development of limbic tracts.

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Section: Discussionsupporting

confidence: 91%

Heritability of abnormalities in limbic networks of autism spectrum disorder children: Evidence from an autism spectrum disorder twin study

et al. 2022

Autism Research

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“…An example is the suggestion-assisted reproduction and in vitro fertilization is involved, but follow-up studies demonstrated no risk of ASD in children born after assisted reproduction [23,24]. One factor that has been correlated with autism is paternal age [4,9,15] and sperm DNA methylation alterations [11,25]. Previous studies have shown a hypermethylation of sperm DNA is associated with male infertility, abnormal sperm parameters and increasing age [13,26,27].…”

Section: Discussionmentioning

confidence: 99%

“…Although there are both paternal and maternal transmission of ASD, the prevalence of paternal transmission is higher in most populations. One of the main factors proposed to be involved is paternal age [9], with an increased percentage risk of 28% between 40-49 years and nearly 70% when greater than 50 years of age [4]. Increased paternal age has been associated with epigenetic DNA methylation alterations in sperm [10], including specific genes associated with autism [11,12].…”

Section: Introductionmentioning

confidence: 99%

Sperm DNA methylation epimutation biomarker for paternal offspring autism susceptibility

Garrido¹,

Cruz²,

Egea³

et al. 2021

Clin Epigenet

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Background Autism spectrum disorder (ASD) has increased over tenfold over the past several decades and appears predominantly associated with paternal transmission. Although genetics is anticipated to be a component of ASD etiology, environmental epigenetics is now also thought to be an important factor. Epigenetic alterations, such as DNA methylation, have been correlated with ASD. The current study was designed to identify a DNA methylation signature in sperm as a potential biomarker to identify paternal offspring autism susceptibility. Methods and results Sperm samples were obtained from fathers that have children with or without autism, and the sperm then assessed for alterations in DNA methylation. A genome-wide analysis (> 90%) for differential DNA methylation regions (DMRs) was used to identify DMRs in the sperm of fathers (n = 13) with autistic children in comparison with those (n = 13) without ASD children. The 805 DMR genomic features such as chromosomal location, CpG density and length of the DMRs were characterized. Genes associated with the DMRs were identified and found to be linked to previously known ASD genes, as well as other neurobiology-related genes. The potential sperm DMR biomarkers/diagnostic was validated with blinded test sets (n = 8–10) of individuals with an approximately 90% accuracy. Conclusions Observations demonstrate a highly significant set of 805 DMRs in sperm that can potentially act as a biomarker for paternal offspring autism susceptibility. Ancestral or early-life paternal exposures that alter germline epigenetics are anticipated to be a molecular component of ASD etiology.

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“…All participants are ethnically Japanese and were recruited at The University of Tokyo Hospital. The diagnostic criteria for schizophrenia, and ASD and inclusion and exclusion criteria can be found elsewhere 29 – 31 , additionally, a comprehensive explanation was added to the supplemental materials. The ethical review board of The University of Tokyo Hospital approved this study (Nos.…”

Section: Methodsmentioning

confidence: 99%

Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis

et al. 2020

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Neuropsychiatric disorders are diagnosed based on behavioral criteria, which makes the diagnosis challenging. Objective biomarkers such as neuroimaging are needed, and when coupled with machine learning, can assist the diagnostic decision and increase its reliability. Sixty-four schizophrenia, 36 autism spectrum disorder (ASD), and 106 typically developing individuals were analyzed. FreeSurfer was used to obtain the data from the participant's brain scans. Six classifiers were utilized to classify the subjects. Subsequently, 26 ultra-high risk for psychosis (UHR) and 17 first-episode psychosis (FEP) subjects were run through the trained classifiers. Lastly, the classifiers' output of the patient groups was correlated with their clinical severity. All six classifiers performed relatively well to distinguish the subject groups, especially support vector machine (SVM) and Logistic regression (LR). Cortical thickness and subcortical volume feature groups were most useful for the classification. LR and SVM were highly consistent with clinical indices of ASD. When UHR and FEP groups were run with the trained classifiers, majority of the cases were classified as schizophrenia, none as ASD. Overall, SVM and LR were the best performing classifiers. Cortical thickness and subcortical volume were most useful for the classification, compared to surface area. LR, SVM, and DT's output were clinically informative. The trained classifiers were able to help predict the diagnostic category of both UHR and FEP Individuals.

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Paternal age contribution to brain white matter aberrations in autism spectrum disorder

Cited by 10 publications

References 83 publications

Heritability of abnormalities in limbic networks of autism spectrum disorder children: Evidence from an autism spectrum disorder twin study

Heritability of abnormalities in limbic networks of autism spectrum disorder children: Evidence from an autism spectrum disorder twin study

Sperm DNA methylation epimutation biomarker for paternal offspring autism susceptibility

Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis

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