Autism Spectrum Disorder Cases Research Articles

Short tandem repeat expansions in cortical layer-specific genes implicate in phenotypic severity and adaptability of autism spectrum disorder.

Short tandem repeats (STRs) are repetitive DNA sequences and highly mutable in various human disorders. While the involvement of STRs in various genetic disorders has been extensively studied, their role in autism spectrum disorder (ASD) remains largely unexplored. In this study, we aimed to investigate genetic association of STR expansions with ASD using whole genome sequencing (WGS) and identify risk loci associated with ASD phenotypes. We analyzed WGS data of 634 ASD families and performed genome-wide evaluation for 12,929 STR loci. We found rare STR expansions that exceeded normal repeat lengths in autism cases compared to unaffected controls. By integrating single cell RNA and ATAC sequencing datasets of human postmortem brains, we prioritized STR loci in genes specifically expressed in cortical development stages. A deep learning method was used to predict functionality of ASD-associated STR loci. In ASD cases, rare STR expansions predominantly occurred in early cortical layer-specific genes involved in neurodevelopment, highlighting the cellular specificity of STR-associated genes in ASD risk. Leveraging deep learning prediction models, we demonstrated that these STR expansions disrupted the regulatory activity of enhancers and promoters, suggesting a potential mechanism through which they contribute to ASD pathogenesis. We found that individuals with ASD-associated STR expansions exhibited more severe ASD phenotypes and diminished adaptability compared to non-carriers. Short tandem repeat expansions in cortical layer-specific genes are associated with ASD and could potentially be a risk genetic factor for ASD. Our study is the first to show evidence of STR expansion associated with ASD in an under-investigated population.

The Association Between Gastrointestinal Issues and Psychometric Scores in Children with Autism Spectrum Disorder, Developmental Delays, Down Syndrome, and Typical Development.

Investigate the association between gastrointestinal (GI) issues and psychometric scores among children with developmental delays and typical development. We examined the association between GI issues and the Mullen Scale of Early Learning (MSEL), Vineland Adaptive Behavior Subscales (VABS), and Aberrant Behavior Checklist (ABC) scores from participants with autism spectrum disorder (ASD), Down syndrome (DS),other developmental delays (DD) and typical development (TD) from the CHildhood Autism Risk from Genetics and Environment (CHARGE) Study (n = 1603). Approximately 32% of children with ASD, 31% of children with DD, and 20% of children with DS reported at least one GI issue, compared to 7% of TD controls.Constipation was the most frequently reported symptom for the entire population, including controls. In general, GI issues correlated with poorer behavioral scores (decreased communication, daily living, socialization, and motor skills on the VABS, and increased irritability/agitation, lethargy/social withdrawal, stereotypic behavior, and hyperactivity/noncompliance on the ABC) among ASD cases. Analysis by sex indicated that GI issues also correlated with poorer cognitive scores (fine motor, receptive language, expressive language, and MSEL composite scores), and adaptive behavior (communication skills, daily living skills, motor, and VABS composite scores) among boys with DD, but not girls with DD-suggesting sex differences among DD cases. Even TD controls showed increased stereotypic behavior and social withdrawal in association with GI issues. However, GI issues were not correlated with impairments in psychometric scores among DS cases. Given that GI issues correlate with deficits in behavioral and cognitive scores, future studies should investigate the treatment of GI symptoms in children with ASD and DD.

Prenatal & Perinatal Risk Factors of Autism Spectrum Disorder

Background: Autism spectrum disorder (ASD) is lifelong neurodevelopmental disorder characterized by impairments in social function, communication, and certain behaviors. The etiology of ASD is unclear & it is believed to be multi-factorial, among those several prenatal & perinatal factors are found to be significant. So, the necessity of this study is to detect the presence of known risk factors of ASD which in turn may help in the early diagnosis and intervention for better long-term outcomes. Objective: To find out prenatal and perinatal risk factors of ASD. Method: A case control study was conducted with 112 cases of ASD and 201 controls of 2-6 years of age. The study was carried out in Center for Neurodevelopment and Autism in Children (CNAC), in the department of Paediatrics, Bangabandhu Sheikh Mujib Medical University and Autism Welfare Foundation (AWF). ASD was diagnosed by DSM-IV TR criteria. Mothers were interviewed and data were collected by semistructured questionnaires. Results: Total 5 risk factors were identified as the risk factors of ASD. They were: advanced paternal age (OR=2.84) and maternal age (OR=1.87); male baby (OR=2.01); environmental stress during pregnancy (OR=1.61) and preterm delivery (OR=1.80). Conclusions: It is concluded that advanced paternal age, maternal age, male baby, environmental stress during pregnancy and preterm delivery are associated with increased risk of ASD. BANGLADESH J CHILD HEALTH 2022; VOL 46 (3) : 115-121

Novel protein-truncating variants of a chromatin-modifying gene MSL2 in syndromic neurodevelopmental disorders.

Numerous large scale genomic studies have uncovered rare but recurrent pathogenetic variants in a significant number of genes encoding epigenetic machinery in cases with neurodevelopmental disorders (NDD) especially autism spectrum disorder (ASD). These findings provide strong support for the functional importance of epigenetic regulators in neurodevelopment. After the clinical genomics evaluation of the patients using exome sequencing, we have identified, three novel protein-truncating variants (PTVs) in the MSL2 gene (OMIM: 614802) which encodes a chromatin modifying enzyme. MSL2 modifies chromatin through both mono-ubiquitination of histone 2B on lysine 34 (K34) and acetylation of histone H4 on lysine 16 (K16). We reported first time the detailed clinical features associated with 3 MSL2 PTVs. There are 15 PTVs (13 de novo) reported from the large genomics studies (12 cases) or ClinVar (3 cases) of NDD, ASD, and developmental disorders (DD) but the specific clinical features for these cases are not described. Taken together, our descriptions of dysmorphic face and other features support the causal role of MSL2 in a likely syndromic neurodevelopmental disorder and add MSL2 to a growing list of epigenetic genes implicated in ASD.

Trends in the Prevalence of Autism Spectrum Disorder in California: Disparities by Sociodemographic Factors and Region Between 1990-2018.

Autism Spectrum Disorders (ASD) prevalence has risen globally, with regional variation and sociodemographic disparities affecting diagnosis and intervention. This study examines ASD trends from 1990 to 2018 in California (CA), focusing on sociodemographic factors that may inform policy/interventions. Using CA Department of Public Health birth records (1990-2018) and Developmental Services ASD cases (1994-2022), we analyzed diagnosis incidence by age 4 or 8, stratified by sociodemographic and regional factors. From 1990 to 2018, for each birth year the cumulative incidence of ASD by 4 and 8 years of age in CA increased while the diagnosis age decreased. Distinct patterns emerged over these three decades. Children born to White and Asian and Pacific Islander (API) mothers, or to mothers with higher education or living in high socioeconomic status (SES) neighborhoods exhibited higher ASD cumulative incidences throughout the 1990s and early 2000s. However, in the mid-2000s, ASD incidence in children born to Black or Hispanic mothers, with low education, or living in low SES neighborhoods surpassed that of White/API children or those living in high SES neighborhoods. Black or Hispanic children now have the highest ASD cumulative incidence, even though age at first diagnosis remains lowest in high SES regions, for the highly educated, and for White/API children. ASD cumulative incidence in CA from 1990 to 2018 exhibited demographic reversals with higher rates in children born to Black or Hispanic mothers or lower SES neighborhoods. Black and Hispanic children still have delayed age at diagnosis compared to White/API children.

Interactions between circulating inflammatory factors and autism spectrum disorder: a bidirectional Mendelian randomization study in European population.

Extensive observational studies have reported an association between inflammatory factors and autism spectrum disorder (ASD), but their causal relationships remain unclear. This study aims to offer deeper insight into causal relationships between circulating inflammatory factors and ASD. Two-sample bidirectional Mendelian randomization (MR) analysis method was used in this study. The genetic variation of 91 circulating inflammatory factors was obtained from the genome-wide association study (GWAS) database of European ancestry. The germline GWAS summary data for ASD were also obtained (18,381 ASD cases and 27,969 controls). Single nucleotide polymorphisms robustly associated with the 91 inflammatory factors were used as instrumental variables. The random-effects inverse-variance weighted method was used as the primary analysis, and the Bonferroni correction for multiple comparisons was applied. Sensitivity tests were carried out to assess the validity of the causal relationship. The forward MR analysis results suggest that levels of sulfotransferase 1A1, natural killer cell receptor 2B4, T-cell surface glycoprotein CD5, Fms-related tyrosine kinase 3 ligand, and tumor necrosis factor-related apoptosis-inducing ligand are positively associated with the occurrence of ASD, while levels of interleukin-7, interleukin-2 receptor subunit beta, and interleukin-2 are inversely associated with the occurrence of ASD. In addition, matrix metalloproteinase-10, caspase 8, tumor necrosis factor-related activation-induced cytokine, and C-C motif chemokine 19 were considered downstream consequences of ASD. This MR study identified additional inflammatory factors in patients with ASD relative to previous studies, and raised a possibility of ASD-caused immune abnormalities. These identified inflammatory factors may be potential biomarkers of immunologic dysfunction in ASD.

Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes.

Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are transparent and in line with clinical, diagnostic rules. We demonstrate our approach for autism spectrum disorders (ASD), a neurodevelopmental condition with increasing prevalence. We use unstructured data from the Centers for Disease Control and Prevention (CDC) surveillance records labeled by a CDC-trained clinician with ASD A1-3 and B1-4 criterion labels per sentence and with ASD cases labels per record using Diagnostic and Statistical Manual of Mental Disorders (DSM5) rules. One rule-based and three deep ML algorithms and six ensembles were compared and evaluated using a test set with 6773 sentences (N = 35 cases) set aside in advance. Criterion and case labeling were evaluated for each ML algorithm and ensemble. Case labeling outcomes were compared also with seven traditional tests. Performance for criterion labeling was highest for the hybrid BiLSTM ML model. The best case labeling was achieved by an ensemble of two BiLSTM ML models using a majority vote. It achieved 100% precision (or PPV), 83% recall (or sensitivity), 100% specificity, 91% accuracy, and 0.91 F-measure. A comparison with existing diagnostic tests shows that our best ensemble was more accurate overall. Transparent ML is achievable even with small datasets. By focusing on intermediate steps, deep ML can provide transparent decisions. By leveraging data redundancies, ML errors at the intermediate level have a low impact on final outcomes.

Innate Immune Dysfunction and Neuroinflammation in Autism Spectrum Disorder (ASD).

Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder characterized by communication and social behavior deficits. The presence of restricted and repetitive behaviors often accompanies these deficits, and these characteristics can range from mild to severe. The past several decades have seen a significant rise in the prevalence of ASD. The etiology of ASD remains unknown; however, genetic and environmental risk factors play a role. Multiple hypotheses converge to suggest that neuroinflammation, or at least the interaction between immune and neural systems, may be involved in the etiology of some ASD cases or groups. Repeated evidence of innate immune dysfunction has been seen in ASD, often associated with worsening behaviors. This evidence includes data from circulating myeloid cells and brain resident macrophages/microglia in both human and animal models. This comprehensive review presents recent findings of innate immune dysfunction in ASD, including aberrant innate cellular function, evidence of neuroinflammation, and microglia activation. Appeared originally in Brain Behav Immun 2023; 108:245-254.

Augmented Reality Mobile Application for Autism Spectrum Disorder Rehabilitation

Autism Spectrum Disorder (ASD) is a neurological disorder in which a diagnosed child may have difficulty communicating socially or may have a repeated or limited set of behaviors. Recently, ASD cases have increased dramatically. This makes scientists interested in finding various drugs or therapies that can be used for children with autism. Social behavior therapy is one of therapy that has shown effectiveness in improving fundamental symptoms of ASD. With the rapid development of technology, social behavior therapy was then expanded by utilizing some of the latest technologies that can be beneficial for children with autism. One of the therapies using the latest technology that can be utilized is making a quiz-based game by utilizing Augmented Reality (AR) to create 3D objects virtually. Spectrum Quiz is an example of an AR-based therapy game. This game is expected to improve the ability of children with autism to communicate socially. In the development of the Spectrum Quiz AR application, several tools are needed such as Adobe Illustrator, Unity, and Blender 3D.

Prevalence of autism spectrum disorder among children in Southeast Asia from 2002 to 2022: An updated systematic review and meta-analysis.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that impacts the brain, characterized by challenges in social communication and interaction, often accompanied by repetitive behaviors or focused interests. This study sheds light on the prevalence of ASDwithin the Southeast Asian region. The study protocol was registered in PROSPERO (Registration No: CRD42023413915). Appropriate search terms and Boolean operators were employed to explore electronic databases for relevant articles. Data thus extracted were prepared in Excel and analyzed in Comprehensive Meta-Analysis Software. The effect measure utilized in the study was represented by the proportion, and the choice between a fixed or random-effect model depended on the observed heterogeneity. Visual feedback was provided through the use of forest plots and funnel plots. A total of 14studies were included in the qualitative and quantitative synthesis after screening the imported studies. The prevalence of ASDwas sixper 1000 population (proportion: 0.006; CI: 0.002-0.017; I 2: 99.263%). Among the ASD cases, 64.4% (proportion: 0.644; CI: 0.590-0.693; I 2: 9.937%) were males and 35.6% (proportion: 0.356; CI: 0.307-0.410; I 2: 9.937%) were females. The prevalence ofASDin Southeast Asia was estimated to be sixcases per 1000 individuals, with a higher prevalence among males. This study contributes to our understanding of ASD prevalence in the region, although it is essential to note certain limitations in estimating prevalence.

The genetic landscape of autism spectrum disorder in the Middle Eastern population.

Introduction: Autism spectrum disorder (ASD) is characterized by aberrations in social interaction and communication associated with repetitive behaviors and interests, with strong clinical heterogeneity. Genetic factors play an important role in ASD, but about 75% of ASD cases have an undetermined genetic risk. Methods: We extensively investigated an ASD cohort made of 102 families from the Middle Eastern population of Qatar. First, we investigated the copy number variations (CNV) contribution using genome-wide SNP arrays. Next, we employed Next Generation Sequencing (NGS) to identify de novo or inherited variants contributing to the ASD etiology and its associated comorbid conditions in families with complete trios (affected child and the parents). Results: Our analysis revealed 16 CNV regions located in genomic regions implicated in ASD. The analysis of the 88 ASD cases identified 41 genes in 39 ASD subjects with de novo (n = 24) or inherited variants (n = 22). We identified three novel de novo variants in new candidate genes for ASD (DTX4, ARMC6, and B3GNT3). Also, we have identified 15 de novo variants in genes that were previously implicated in ASD or related neurodevelopmental disorders (PHF21A, WASF1, TCF20, DEAF1, MED13, CREBBP, KDM6B, SMURF1, ADNP, CACNA1G, MYT1L, KIF13B, GRIA2, CHM, and KCNK9). Additionally, we defined eight novel recessive variants (RYR2, DNAH3, TSPYL2, UPF3B KDM5C, LYST, and WNK3), four of which were X-linked. Conclusion: Despite the ASD multifactorial etiology that hinders ASD genetic risk discovery, the number of identified novel or known putative ASD genetic variants was appreciable. Nevertheless, this study represents the first comprehensive characterization of ASD genetic risk in Qatar's Middle Eastern population.

Socio-demographic correlates of autism spectrum disorder: A study in Odisha

ABSTRACT Background: There is very little data available on socio-demographic factors associated with the early detection of autism spectrum disorder (ASD). Aim: This study sought to investigate the socio-demographic correlates of ASD. Materials and Methods: A cross-sectional study of 201 first-time reported cases of ASD using a semi-structured socio-demographic datasheet was conducted to understand the socio-demographic correlates. The Statistical Package for the Social Sciences (SPSS) software (version 26) was used to calculate frequencies, percentages, and Chi-square values. Results: Descriptive analysis revealed an upward trend in the number of reported cases among both genders. Most cases were five years old or above, from middle socioeconomic background, with parents who had studied above matriculation, and lived in an urban area. Chi-square test results revealed a significant relationship between the number of first-time reported cases of ASD and that of parental education, socioeconomic status (SES), and living conditions. Conclusion: A prominent finding of this study was parents’ lack of awareness regarding their child’s condition. Measures for awareness and early detection have been recommended.

Autism spectrum disorder detection with kNN imputer and machine learning classifiers via questionnaire mode of screening.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. ASD cannot be fully cured, but early-stage diagnosis followed by therapies and rehabilitation helps an autistic person to live a quality life. Clinical diagnosis of ASD symptoms via questionnaire and screening tests such as Autism Spectrum Quotient-10 (AQ-10) and Quantitative Check-list for Autism in Toddlers (Q-chat) are expensive, inaccessible, and time-consuming processes. Machine learning (ML) techniques are beneficial to predict ASD easily at the initial stage of diagnosis. The main aim of this work is to classify ASD and typical developed (TD) class data using ML classifiers. In our work, we have used different ASD data sets of all age groups (toddlers, adults, children, and adolescents) to classify ASD and TD cases. We implemented One-Hot encoding to translate categorical data into numerical data during preprocessing. We then used kNN Imputer with MinMaxScaler feature transformation to handle missing values and data normalization. ASD and TD class data is classified using Support vector machine, k-nearest-neighbor (KNN), random forest (RF), and artificial neural network classifiers. RF gives the best performance in terms of the accuracy of 100% with different training and testing data split for all four types of data sets and has no over-fitting issue. We have also examined our results with already published work, including recent methods like Deep Neural Network (DNN) and Convolution Neural Network (CNN). Even using complex architectures like DNN and CNN, our proposed methods provide the best results with low-complexity models. In contrast, existing methods have shown accuracy upto 98% with log-loss upto 15%. Our proposed methodology demonstrates the improved generalization for real-time ASD detection during clinical trials.

Associations between brake and tire wear-related PM2.5 metal components, particulate oxidative stress potential, and autism spectrum disorder in Southern California

BackgroundAir pollution is a global health concern, with fine particulate matter (PM2.5) constituents posing potential risks to human health, including children's neurodevelopment. Here we investigated associations between exposure during pregnancy and infancy to specific traffic-related PM2.5 components with Autism Spectrum Disorder (ASD) diagnosis. MethodsFor exposure assessment, we estimated PM2.5 components related to traffic exposure (Barium [Ba] as a marker of brake dust and Zinc [Zn] as a tire wear marker, Black Carbon [BC]) and oxidative stress potential (OSP) markers (Hydroxyl Radical [OPOH] formation, Dithiothreitol activity [OPDTT], reactive oxygen species [ROS]) modeled with land use regression with co-kriging based on an intensive air monitoring campaign. We assigned exposures to a cohort of 444,651 children born in Southern California between 2016 and 2019, among whom 11,466 ASD cases were diagnosed between 2018 and 2022, Odds ratios (ORs) and 95% confidence intervals (CIs) were obtained with logistic regression for single pollutant and PM2.5 mass co-adjusted models, also adjusting for sociodemographic characteristics. ResultsAmong PM2.5 components, we found the strongest positive association with ASD for our brake wear marker Ba (ORper IQR = 1.29, 95 % CI: 1.24, 1.34). This was followed by an increased risk for all PM2.5 oxidative stress potential markers; the strongest association was with ROS formation (ORper IQR = 1.22, 95 % CI: 1.18, 1.25). PM2.5 mass was linked to ASD in Hispanic and Black children, but not White children, while traffic-related PM2.5 and OSP markers increased ASD risk across all groups. In neighborhoods with the lowest socioeconomic status (SES), associations with ASD were stronger for all examined pollutants compared to higher SES areas. ConclusionsOur findings suggest that brake wear-related PM2.5 and PM2.5 OSP are associated with ASD diagnosis in Southern California. These results suggest that strategies aimed at reducing the public health impacts of PM2.5 need to consider specific sources.

Causal effects of gut microbiota on autism spectrum disorder: A two-sample mendelian randomization study.

There is increasing evidence that alterations in gut microbiota (GM) composition are associated with autism spectrum disorder (ASD), but no reliable causal relationship has been established. Therefore, a 2-sample Mendelian randomization (MR) study was conducted to reveal a potential causal relationship between GM and ASD. Instrumental variables for 211 GM taxa were obtained from genome-wide association studies (GWAS) and Mendelian randomization studies to estimate their impact on ASD risk in the iPSYCH-PGC GWAS dataset (18,382 ASD cases and 27,969 controls). Inverse variance weighted (IVW) is the primary method for causality analysis, and several sensitivity analyses validate MR results. Among 211 GM taxa, IVW results confirmed that Tenericutes (P value = .0369), Mollicutes (P value = .0369), Negativicutes (P value = .0374), Bifidobacteriales (P value = .0389), Selenomonadales (P value = .0374), Bifidobacteriaceae (P value = .0389), Family XIII (P value = .0149), Prevotella7 (P value = .0215), Ruminococcaceae NK4A214 group (P value = .0205) were potential protective factors for ASD. Eisenbergiella (P value = .0159) was a possible risk factor for ASD. No evidence of heterogeneous, pleiotropic, or outlier single-nucleotide polymorphism was detected. Additionally, further sensitivity analysis verified the robustness of the above results. We confirm a potential causal relationship between certain gut microbes and ASD, providing new insights into how gut microbes mediate ASD. The association between them needs to be further explored and will provide new ideas for the prevention and treatment of ASD.

Neuroimmune mechanisms in autism etiology - untangling a complex problem using human cellular models.

Autism spectrum disorder (ASD) affects 1 in 36 people and is more often diagnosed in males than in females. Core features of ASD are impaired social interactions, repetitive behaviors and deficits in verbal communication. ASD is a highly heterogeneous and heritable disorder, yet its underlying genetic causes account only for up to 80% of the cases. Hence, a subset of ASD cases could be influenced by environmental risk factors. Maternal immune activation (MIA) is a response to inflammation during pregnancy, which can lead to increased inflammatory signals to the fetus. Inflammatory signals can cross the placenta and blood brain barriers affecting fetal brain development. Epidemiological and animal studies suggest that MIA could contribute to ASD etiology. However, human mechanistic studies have been hindered by a lack of experimental systems that could replicate the impact of MIA during fetal development. Therefore, mechanisms altered by inflammation during human pre-natal brain development, and that could underlie ASD pathogenesis have been largely understudied. The advent of human cellular models with induced pluripotent stem cell (iPSC) and organoid technology is closing this gap in knowledge by providing both access to molecular manipulations and culturing capability of tissue that would be otherwise inaccessible. We present an overview of multiple levels of evidence from clinical, epidemiological, and cellular studies that provide a potential link between higher ASD risk and inflammation. More importantly, we discuss how stem cell-derived models may constitute an ideal experimental system to mechanistically interrogate the effect of inflammation during the early stages of brain development.

Relationship between autism and brain cortex surface area: genetic correlation and a two-sample Mendelian randomization study

BackgroundAlterations in surface area (SA) in specific regions of the cortex have been reported in many individuals with autism spectrum disorder (ASD), however, the genetic background between ASD and SA is still unclear. This study estimated the genetic correlation and causal effect of ASD and cortical SA.MethodsSummarized data of genome-wide association studies (GWAS) were separately downloaded from the Psychiatric Genomics Consortium (18,381 cases of ASD, and 27,969 controls) and the Enhancing Neuroimaging Genetics through Meta-Analysis Consortium (33,992 participants of Europeans). We used Linkage disequilibrium score regression (LDSC) and Heritability Estimation from Summary Statistics (HESS) to calculate the heritability of each trait. As for the genetic correlation between ASD and SA, LDSC was used for global correlation and HESS was used to examine the local genetic covariance further. We used three Mendelian randomization (MR) methods, Inverse-variance weighted, MR-Egger, and weighted median to estimate the causal relationship.ResultsLDSC observed a nominal significant genetic correlation (rg = 0.1229, P-value = 0.0346) between ASD and SA of the rostral anterior cingulate gyrus whereas analysis through HESS did not reveal any significant loci having genetic covariance. Based on MR results, statistically meaningful estimations were found in the following areas, postcentral cortex (β (SE) = 21.82 (7.84) mm, 95% CI: 6.46 to 37.19 mm, PIVW = 5.38 × 10− 3, PFDR = 3.09 × 10− 2), posterior cingulate gyrus (β (SE) = 6.23 (2.69) mm, 95% CI: 0.96 to 11.49 mm, PIVW = 2.05 × 10− 2, PFDR = 4.26 × 10− 2), supramarginal gyrus (β (SE) = 19.25 (8.43) mm, 95% CI: 29.29 to 35.77 mm, PIVW = 2.24 × 10− 2, PFDR = 4.31 × 10− 2).ConclusionOur results provided genetic evidence to support the opinion that individuals with ASD tend to develop differences in cortical SA of special areas. The findings contributed to understanding the genetic relationship between ASD and cortical SA.

Neurobiology of cognitive abilities in early childhood autism

AbstractThis perspective considers complexities in the relationship between impaired cognitive abilities and autism from a maturational, developmental perspective, and aims to serve as a helpful guide for the complex and growing investigation of cognitive abilities and Autism Spectrum Disorder (ASD). Low Intelligence Quotient (IQ) and ASD are frequently co‐occurring. About 37% of 8‐year old children and 48% of 4‐year old children diagnosed with ASD also have Intellectual Disability, with IQ below 70. And, low IQ in early infancy, including below 1 year of age, carries a 40% greater chance of receiving ASD diagnosis in early childhood. We consider the evidence that may explain this co‐occurrence, including the possibility that high IQ may “rescue” the social communication issues, as well as the possible role of critical periods during growth and development. We consider how early low IQ may subsume a part of a subgroup of individuals with ASD, in particular, those diagnosed with autism in very early childhood, and we provide neurobiological evidence in support of this subtype. Moreover, we distinguish the concept of early low IQ from the delay in speech onset in preschool and school‐aged children, based on (i) age and (ii) impairments in both verbal and non‐verbal domains. The etiology of these early‐diagnosed, early low IQ ASD cases is different from later‐diagnosed, average or higher‐IQ cases, and from children with speech delay onset. Given recent interest in formulating new subtypes of autism, rather than continuing to conceive of ASD as a spectrum, as well as new subtypes that vary in the degree of severity along the spectrum, we identify gaps in knowledge and directions for future work in this complex and growing area.

Des-Arg(9) bradykinin as a causal metabolite for autism spectrum disorder.

Early diagnosis and therapeutic interventions can greatly enhance the developmental trajectory of children with autism spectrum disorder (ASD). However, the etiology of ASD is not completely understood. The presence of confounding factors from environment and genetics has increased the difficulty of the identification of diagnostic biomarkers for ASD. To estimate and interpret the causal relationship between ASD and metabolite profile, taking into consideration both genetic and environmental influences. A two-sample Mendelian randomization (MR) analysis was conducted using summarized data from large-scale genome-wide association studies (GWAS) including a metabolite GWAS dataset covering 453 metabolites from 7824 European and an ASD GWAS dataset comprising 18381 ASD cases and 27969 healthy controls. Metabolites in plasma were set as exposures with ASD as the main outcome. The causal relationships were estimated using the inverse variant weight (IVW) algorithm. We also performed leave-one-out sensitivity tests to validate the robustness of the results. Based on the drafted metabolites, enrichment analysis was conducted to interpret the association via constructing a protein-protein interaction network with multi-scale evidence from databases including Infinome, SwissTargetPrediction, STRING, and Metascape. Des-Arg(9)-bradykinin was identified as a causal metabolite that increases the risk of ASD (β = 0.262, SE = 0.064, PIVW = 4.64 × 10-5). The association was robust, with no significant heterogeneity among instrument variables (PMR Egger = 0.663, PIVW = 0.906) and no evidence of pleiotropy (P = 0.949). Neuroinflammation and the response to stimulus were suggested as potential biological processes mediating the association between Des-Arg(9) bradykinin and ASD. Through the application of MR, this study provides practical insights into the potential causal association between plasma metabolites and ASD. These findings offer perspectives for the discovery of diagnostic or predictive biomarkers to support clinical practice in treating ASD.

Pregnancy Planning and its Association with Autism Spectrum Disorder: Findings from the Study to Explore Early Development.

To examine associations between pregnancy planning and autism spectrum disorder (ASD) in offspring. The Study to Explore Early Development (SEED), a multi-site case-control study, enrolled preschool-aged children with ASD, other DDs, and from the general population (POP). Some children with DDs had ASD symptoms but did not meet the ASD case definition. We examined associations between mother's report of trying to get pregnant (pregnancy planning) and (1) ASD and (2) ASD symptomatology (ASD group, plus DD with ASD symptoms group combined) (each vs. POP group). We computed odds ratios adjusted for demographic, maternal, health, and perinatal health factors (aORs) via logistic regression. Due to differential associations by race-ethnicity, final analyses were stratified by race-ethnicity. Pregnancy planning was reported by 66.4%, 64.8%, and 76.6% of non-Hispanic White (NHW) mothers in the ASD, ASD symptomatology, and POP groups, respectively. Among NHW mother-child pairs, pregnancy planning was inversely associated with ASD (aOR = 0.71 [95% confidence interval 0.56-0.91]) and ASD symptomatology (aOR = 0.67 [0.54-0.84]). Pregnancy planning was much less common among non-Hispanic Black mothers (28-32% depending on study group) and Hispanic mothers (49-56%) and was not associated with ASD or ASD symptomatology in these two race-ethnicity groups. Pregnancy planning was inversely associated with ASD and ASD symptomatology in NHW mother-child pairs. The findings were not explained by several adverse maternal or perinatal health factors. The associations observed in NHW mother-child pairs did not extend to other race-ethnicity groups, for whom pregnancy planning was lower overall.