Autism Spectrum Disorder Children Research Articles

The Resilience and Lived Experiences of the Parents of Children With Autism in India: A Mixed-method Study

Background and Objectives: Families dealing with autism spectrum disorder (ASD) children try to adapt with different strategies. 1) To assess the resilience of parents dealing with children having ASD and its associated factors. 2) To explore the perspective and strategies of families dealing with children having ASD Methods: This was a mixed-methods study done over a period of 3 months among parents having children diagnosed with ASD; undergoing institutional therapy for more than 6 months in duration. Data collection was done using the PREQ questionnaire and key informant interviews (KII). Informed consent was taken prior to each interview and audio recordings. Relevant tests of significance and deductive analysis followed. Results: There was an equal divide observed between the low (50%) and high resilience (50%) groups. Binary logistic regression showed that the upper socio-economic class showed higher odds of better resilience (OR=4.038, 95% CI, 2.857%, 4.894%) as compared to the lower socio-economic class, and those with higher levels of education showed better resilience (OR=9.903, 95% CI, 2.502%, 3.483% than the lesser educated. KII explored different perspectives on personal experiences, concerns, challenges, attitudes, etc. where both perspectives such as benefits and hardships were addressed on an individual as well as a family basis, respectively Conclusion: Education and socio-economic backgrounds played a role in mending the resilience and other active measures such as peer support mechanisms and self-coping strategies also influenced the attitude and improved determination of parents dealing with children having ASD.

Early diagnosis of autism spectrum disorders in children: what pediatricians should know

The issue of autism spectrum disorder (ASD) in children is a significant concern at present. In the Russian Federation, there has been a rapid increase in the incidence of ASD and the number of children diagnosed with this condition, making childhood autism a major medical and social issue. Pediatricians need to be trained in the diagnostic criteria for early identification of this disorder. The main diagnostic methods involve monitoring the child’s development, with the district pediatrician playing a crucial role. The knowledge and expertise of the doctor significantly influences the timeliness of diagnosis and the prescription of appropriate interventions, which can lead to positive outcomes and prevent further symptom progression. It is essential for pediatricians to carefully listen to families’ concerns about any developmental or behavioral issues in their children. According to our research, 32% of families did not receive timely assistance. The most common complaint made by parents was delayed speech development and impaired communication. To identify autism spectrum disorders, doctors should regularly assess a child’s neuropsychiatric development and monitor them in various situations to assess their behavior and interaction with others. If any deviations are detected, a questionnaire such as the M-CHAT (a modified list of questions for detecting autism in children) should be used and the patient referred to a psychiatrist for further consultation. Our study highlighted the need for pediatricians to better understand the signs and symptoms of autism spectrum disorders.

Utilizing Artificial Intelligence for Microbiome Decision-Making: Autism Spectrum Disorder in Children from Bosnia and Herzegovina

Background/Objectives: The study of microbiome composition shows positive indications for application in the diagnosis and treatment of many conditions and diseases. One such condition is autism spectrum disorder (ASD). We aimed to analyze gut microbiome samples from children in Bosnia and Herzegovina to identify microbial differences between neurotypical children and those with ASD. Additionally, we developed machine learning classifiers to differentiate between the two groups using microbial abundance and predicted functional pathways. Methods: A total of 60 gut microbiome samples (16S rRNA sequences) were analyzed, with 44 from children with ASD and 16 from neurotypical children. Four machine learning algorithms (Random Forest, Support Vector Classification, Gradient Boosting, and Extremely Randomized Tree Classifier) were applied to create eight classification models based on bacterial abundance at the genus level and KEGG pathways. Model accuracy was evaluated, and an external dataset was introduced to test model generalizability. Results: The highest classification accuracy (80%) was achieved with Random Forest and Extremely Randomized Tree Classifier using genus-level taxa. The Random Forest model also performed well (78%) with KEGG pathways. When tested on an independent dataset, the model maintained high accuracy (79%), confirming its generalizability. Conclusions: This study identified significant microbial differences between neurotypical children and children with ASD. Machine learning classifiers, particularly Random Forest and Extremely Randomized Tree Classifier, achieved strong accuracy. Validation with external data demonstrated that the models could generalize across different datasets, highlighting their potential use.

Investigating frank autism: clinician initial impressions and autism characteristics.

"Frank autism," recognizable through the first minutes of an interaction, describes a behavioral presentation of a subset of autistic individuals that is closely tied to social communication challenges, and may be linked to so-called "prototypical autism." To date, there is no research on frank autism presentations of autistic adolescents and young adults, nor individuals diagnosed with autism spectrum disorder (ASD) in childhood who do not meet diagnostic criteria during or after adolescence (loss of autism diagnosis, LAD). In addition, there are currently no data on the factors that drive frank autism impressions in these adolescent groups. This study quantifies initial impressions of autistic characteristics in 24 autistic, 24 LAD and 26 neurotypical (NT) individuals ages 12 to 39 years. Graduate student and expert clinicians completed five-minute impressions, rated confidence in their own impressions, and scored the atypicality of behaviors associated with impressions; impressions were compared with current gold-standard diagnostic outcomes. Overall, clinicians' impressions within the first five minutes generally matched current gold-standard diagnostic status (clinical best estimate), were highly correlated with ADOS-2 CSS, and were driven primarily by prosodic and facial cues. However, this brief observation did not detect autism in all cases. While clinicians noted some subclinical atypicalities in the LAD group, impressions of the LAD and NT groups were similar. The brief observations in this study were conducted during clinical research, including some semi-structured assessments. While results suggest overall concordance between initial impressions and diagnoses following more thorough evaluation, findings may not generalize to less structured, informal contexts. In addition, our sample was demographically homogeneous and comprised only speaking autistic participants. They were also unmatched for sex, with more females in the non-autistic group. Future studies should recruit samples that are diverse in demographic variables and ability level to replicate these findings and explore their implications. Results provide insights into the behavioral characteristics that contribute to the diagnosis of adolescents and young adults and may help inform diagnostic decision making in the wake of an increase in the demand for autism evaluations later than childhood. They also substantiate claims of an absence of apparent autistic characteristics in individuals who have lost the diagnosis.

Large-scale metagenomic analysis of oral microbiomes reveals markers for autism spectrum disorders.

The link between the oral microbiome and neurodevelopmental disorders remains a compelling hypothesis, still requiring confirmation in large-scale datasets. Leveraging over 7000 whole-genome sequenced salivary samples from 2025 US families with children diagnosed with autism spectrum disorders (ASD), our cross-sectional study shows that the oral microbiome composition can discriminate ASD subjects from neurotypical siblings (NTs, AUC = 0.66), with 108 differentiating species (q < 0.005). The relative abundance of these species is highly correlated with cognitive impairment as measured by Full-Scale Intelligence Quotient (IQ). ASD children with IQ < 70 also exhibit lower microbiome strain sharing with parents (p < 10-6) with respect to NTs. A two-pronged functional enrichment analysis suggests the contribution of enzymes from the serotonin, GABA, and dopamine degradation pathways to the distinct microbial community compositions observed between ASD and NT samples. Although measures of restrictive eating diet and proxies of oral hygiene show relatively minor effects on the microbiome composition, the observed associations with ASD and IQ may still represent unaccounted-for underlying differences in lifestyleamong groups. While causal relationships could not be established, our study provides substantial support to the investigation of oral microbiome biomarkers in ASD.

Educational Mobile Apps Enhancing Communication Skills in Children With Autism in Arab Countries

This exploratory literature review investigates the landscape of educational mobile applications designed for children with autism spectrum disorder (ASD) in Arab countries, with a specific focus on their efficacy in improving communication skills. After a comprehensive search of academic databases and digital libraries, nine studies were reviewed. These studies discussed seven Arabic-language applications designed for ASD children. Consistent with previous research findings, these applications were found to be effective in enhancing communication skills among ASD individuals. By shedding light on the few existing Arabic-language educational mobile applications for ASD children in Arab countries, this comprehensive review contributes significantly to the discourse surrounding ASD interventions. Additionally, it highlights the immediate need for linguistically and culturally sensitive educational applications, designed for a specific population of Arab children with ASD.

Comparative Efficacy of Family-Mediated Intervention Versus Early Intensive Behavioral Intervention on Developmental Domains in Children with Autism Spectrum Disorder: A Randomized Controlled Trial

Background: Autism spectrum disorder is a neurodevelopmental disorder of childhood in which there is a deficit in social and communicative skill, and the child shows restricted and repetitive behavior. For children diagnosed with autism spectrum disorder, family-mediated intervention and early intensive behavioral intervention are considered standard of care. The study aimed to compare their efficacy on social skills and other developmental domains in our cultural context. Methods: This study was a parallel-arm, open-label, randomized active-controlled clinical trial. Fifty children diagnosed with autism spectrum disorder were randomly assigned to either the family-mediated intervention or early intensive behavioral intervention groups. Communication DEALL Developmental Checklist was applied at 0, 12, and 24 weeks. Results: The difference in the mean value of Communication DEALL Developmental Checklist at the end of 24 weeks from the initial value was significantly better in the family-mediated intervention as compared to the early intensive behavioral intervention group in social (mean difference = −16.8, 95% CI = −27.18, −6.49, p = .002) as well as fine motor, cognitive, and overall development. In other developmental domains of Communication DEALL Developmental Checklist, the family-mediated intervention group performed better in comparison to the early intensive behavioral intervention group at different time periods although not statistically significant. Conclusion: The results of the study indicated that, throughout the course of 24 weeks of treatment, autism spectrum disorder children in family-mediated intervention showed a distinct improvement in major developmental domains. Family-mediated intervention can be advocated for autism spectrum disorder children in our cultural context in view of improved functioning in major developmental domains in the background of our study findings.

Relationship between global warming and autism spectrum disorder from 1990 to 2019.

Despite mounting evidence linking neurological diseases with climate change, the link between autism spectrum disorder (ASD) and global warming has yet to be explored. To examine the relationship between the incidence of ASD and global warming from 1990 to 2019 and estimate the trajectory of ASD incidence from 2020 to 2100 globally. We extracted meteorological data from TerraClimate between 1990 and 2019. To estimate the association between global ASD incidence and temperature variation, we adopted a two-stage analysis strategy using a generalised additive regression model. Additionally, we projected future ASD incidence under four representative shared socioeconomic pathways (SSPs: 126, 245, 370 and 585) by bootstrapping. Between 1990 and 2019, the global mean incidence of ASD in children under 5 years old was 96.9 per 100 000. The incidence was higher in males (147.5) than in females (46.3). A 1.0 °C increase in the temperature variation was associated with a 3.0% increased risk of ASD incidence. The association was stronger in boys and children living in a low/low-middle sociodemographic index region, as well as in low-latitude areas. According to the SSP585 scenario, by 2100, the children living in regions between 10 and 20° latitude, particularly in Africa, will experience a 68.6% increase in ASD incidence if the association remains. However, the SSP126 scenario is expected to mitigate this increase, with a less than 10% increase in incidence across all latitudes. Our study highlights the association between climate change and ASD incidence worldwide. Prospective studies are warranted to confirm the association.

Adverse food reactions and alterations in nutritional status in children with autism spectrum disorders: results of the NAFRA project

BackgroundTo assess the adverse food reactions (AFR) prevalence in children with autism spectrum disorder (ASD) and in non-ASD healthy controls (NASD). Nutritional status alterations, food selectivity and adherence to Mediterranean Diet (MD) were also evaluated.MethodsThe NAFRA (Nutritional status and Adverse Food Reactions in children with Autism Spectrum Disorder) project was an observational, case-control, comparative study conducted at a tertriary center for pediatrics involving Caucasian patients of both sexes, aged 18 months-7 years, with a diagnosis of ASD, and matched NASD controls.ResultsFrom October 2017 to December 2023, 100 ASD patients [79 male, mean (± SD) age 49.9 months (± 15.4)] and 100 NASD controls [75 male, mean (± SD) age 49.8 months (± 17.7)] were enrolled at the Pediatric Section of the Department of Translational Medical Science of the University of Naples Federico II. A significantly higher prevalence of AFR was observed in ASD patients if compared with NASD (16% vs. 2%, p = 0.001), mainly due to a higher prevalence of food allergy (7% vs. 1%, p = 0.03). A significantly higher prevalence of food intolerance and celiac disease was also observed in ASD children. The rate of obesity was significantly higher in ASD patients compared to NASD. Food selectivity and low MD-adherence were more frequent in ASD children (26% vs. 2%, p < 0.0001 and 28% vs. 16%, p = 0.041, respectively).ConclusionsThe high rate of AFR, obesity and unhealthy dietary habits observed in ASD children strongly suggest the importance of a multidisciplinary approach, providing early diagnosis of AFR and appropriate nutritional management to improve core and associated ASD-related conditions.Trial registrationThe NAFRA Project was registered on https://clinicaltrials.gov/ with the identifier NCT04719923. Registered 18 January 2021. https://clinicaltrials.gov/study/NCT04719923.

Recognition of psychoneurological state of children with autism spectrum disorder by features of speech: acoustic and perceptual features

The work is devoted to studying of recognition of psychoneurological state of children aged 5–14 with autism spectrum disorder (ASD), n = 35, and typically developing (TD) children, n = 45, by adults. A perceptual analysis was carried out, in which adult native speakers of the Russian language (auditors), n = 206, took part. Test sequences (audiotests) with words and phrases of ASD and TD children selected from spontaneous speech recordings were created for perceptual research. Auditors’ task was to determine psychoneurological state of children (typical/atypical development) based on auditory perception. A spectrographic analysis of phrases of children was carried out. Phrases of ASD children in comparison with TD children are characterized by lower speech rate, fewer words, great stressed and unstressed vowel duration, great values of pitch in phrase, word, stressed and unstressed vowel.

Enhancing early detection of autistic spectrum disorder in children using machine learning approaches

Diagnosing Autism Spectrum Disorder (ASD) presents a multifaceted challenge, demanding accurate and efficient screening methods. Applying machine learning techniques offers a promising avenue for enhancing diagnostic accuracy and efficiency. This research investigates the efficiency of machine learning in distinguishing individuals with ASD from those without, utilizing a comprehensive dataset comprising screening questions, demographic factors, and ASD related diagnostic classifications. We applied chi-square feature selection technique and also tested Random Forest, Logistic Regression, Gradient Boosting Classifier, and Extra Trees Classifier. Each model showed optimal performance and exhibit high precision, recall, and F1-score for both ASD-positive and ASD-negative instances. Additionally, AUROC curves further validated the models’ exceptional discriminatory abilities, with exceptional results. Our findings highlight the potential of machine learning algorithms for enhancing ASD diagnosis accuracy and efficiency in clinical settings. Further research and validation on larger datasets are required to understand the importance of machine learning methods in ASD diagnosis.

Exploring the effect of diet programs on the behavior of Sudanese children with autism and the prevalence and association of gastrointestinal symptoms: a multi-center cross-sectional study.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social difficulties, speech and nonverbal communication issues, and restricted behaviors. Nutritional issues, such as food allergies and intolerances, can affect children with ASD. This study aimed to evaluate the impact of diet programs on ASD behavior and gastrointestinal symptoms, which would be considered as a starting point to increase the family's knowledge about how to practice healthy and suitable dieting for their children. The study was a cross-sectional observational study on 45 children with an autism spectrum disorder in four centers aged 2-18 in Khartoum state. Data was collected through an interview questionnaire, which included sociodemographic, diet, gastrointestinal, and behavioral assessments. The data was analyzed using SPSS to find the correlation between the various variables. Independent t-test, Analysis of Variance, and Mann-Whitney test were used in univariant analysis to assess the association between study variables, while multiple linear regression was used in multivariant analysis for the same purpose. The study involved a large number of children with autism spectrum disorder (ASD), with 80% being male and 73.3% being school-aged. Most had parents as guardians and 71.1% were in intermediate financial status. About 20% were overweight or obese, and 57.8% did not have a specific diet program. Food selectivity was prevalent, with 22.7% having allergies to milk and wheat. ASD children experienced vomiting, gastric reflux, abdominal pain, and changes in stool characteristics. A significant link was found between financial status, behavioral status and gastrointestinal changes. High financial status was significantly different from intermediate and low statuses based on behavioral changes. However, no significant association was found in multivariant analysis. The study found that ASD children generally have good nutritional health, with a higher degree of dietary selection. The financial status of guardians significantly influenced behavioral and gastrointestinal changes in the children. Further interventional studies are recommended to assess the direct impact of diet programs on these symptoms.

Parental age at birth, telomere length, and autism spectrum disorders in the UK Biobank cohort.

Older parental age at birth is associated with increased risk of autism spectrum disorders (ASD) in offspring. Independently, shorter telomere length (TL) has also been shown to be associated with ASD in children. However, older paternal age at birth, with or without controlling for maternal age, has been associated with longer TL, a seemingly contradictory finding. Here, we conducted a retrospective cohort study among participants in the UK Biobank to disentangle associations between leukocyte TL and ASD status in adults, and the potential moderation by parental age on adult offspring's TL. Participants with ASD diagnosis (N = 87) with a mean age of 46.0 (SD 4.4) years were matched to participants without ASD diagnosis (N = 870) based on age, sex, ethnicity, education, household income, and assessment center. No statistically significant differences were seen in TL between participants with and without ASD when parental age at birth was not considered. However, there was a significant interaction between ASD diagnostic status and parental age on participants' TL, such that older paternal or maternal age at birth was more strongly associated with longer TL in participants with ASD. This study suggests that the shortened TL observed in children with ASD in previous research may partially depend on parental age at birth. Future studies tracking TL attrition before ASD diagnosis are warranted to depict temporal associations and the interacting effects of parental age at birth and ASD status on TL across the lifespan.

Autism Spectrum Disorder in Children: Early Signs and Therapeutic Interventions

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in communication, social interaction difficulties, and repetitive behaviors that can hinder a child’s development. The growing prevalence of autism necessitates early detection and effective intervention strategies. This review summarizes the current knowledge of early indicators of ASD, including brain development markers and behavioral signs visible in infants. It investigates diagnostic processes, emphasizing the importance of timely detection at 18 to 24 months using established screening tools. We discuss a variety of therapeutic approaches, including behavioral interventions, educational strategies such as music therapy, and technological advancements such as speech-generating devices. Furthermore, we investigate pharmacological options for treating associated symptoms, emphasizing the lack of targeted medications for core ASD symptoms. Finally, we present evidence highlighting the positive effects of early intervention on developmental outcomes, advocating for individualized treatment plans to enhance the well-being of children with ASD. This comprehensive overview aims to inform ongoing ASD research and clinical practices.

Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis

BackgroundThe use of the deep learning (DL) approach has been suggested or applied to identify childhood autism spectrum disorder (ASD). The capacity to predict ASD, however, differs across investigations. Our study’s objective was to conduct a meta-analysis to determine the DL for ASD in children’s classification accuracy.MethodsEligibility criteria were designed according to the purpose of the meta-analysis; PubMed, EMBASE, Cochrane Library, and Web of Science Database were searched for articles published up to April 16, 2023, on the accuracy of DL methods for ASD classification. Using the Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) to assess the quality of the included studies. Sensitivity, specificity, areas under the curve (AUC), summary receiver operating characteristic (SROC), and corresponding 95% confidence intervals (CIs) were compiled by using the bivariate random-effects models.ResultsA total of 11 predictive trials based on DL models were included, involving 9495 ASD patients from 6 different databases. According to bivariate random-effects models’ results, the overall sensitivity, specificity, and AUC of the DL technique for ASD were, 0.95 (95% CI = 0.88–0.98), 0.93 (95% CI = 0.85–0.97), and 0.98 (95%CI: 0.97–0.99), respectively. Subgroup analysis results found that different datasets did not cause heterogeneity (meta-regression P = 0.55). The Kaggle dataset’s sensitivity and specificity were 0.94 (95%CI: 0.82-1.00) and 0.91 (95%CI: 0.76-1.00), and with 0.97 (95%CI: 0.92-1.00) and 0.97 (95%CI: 0.92-1.00) for ABIDE dataset.ConclusionsDL techniques has satisfactory sensitivity, specificity, and AUC in ASD classification. However, the major heterogeneity of the included studies limited the effectiveness of this meta-analysis. Further trials need to be performed to demonstrate the clinical practicability of DL diagnosis.

A longitudinal study on the development trajectory of auditory processing and its relationship with language development in Chinese preschool children with autism spectrum disorder: study protocol

BackgroundIt was reported that more than 96% of autism spectrum disorder (ASD) children are accompanied with different degrees of sensory processing abnormalities, and up to 50% of ASD children exhibit abnormal auditory response. Studies have confirmed that some ASD children’s abnormal auditory response may be related to their abnormal auditory processing. Prior research demonstrated that ASD children’s auditory processing has high heterogeneity, thus, ASD children’s auditory processing may have different developmental trajectories. However, no study has concentrated on the developmental trajectories of ASD children’s auditory processing. In addition, auditory processing plays a crucial role in ASD children’s language development, thus, ASD children’s different language development outcomes may be related to different auditory processing development tracks. Therefore, this study aims to explore the developmental trajectory of auditory processing in ASD children and analyze the relationship between different developmental trajectories of auditory processing and language impairment.Methods/DesignIn this study, 220 ASD children aging 3 years and 0 months to 4 years and 11 months are recruited as the research objects, and their demographic characteristics are collected. The subjects are tested for peripheral hearing, intelligence, and autism symptoms. Furthermore, ASD children’s auditory processing and language development are evaluated at baseline, 1 year, and 2 years later. In addition, ASD children’s auditory processing is evaluated by electrophysiological test and the Preschool Auditory Processing Assessment Scale. Moreover, ASD children’s language skills are assessed using the Language Development Assessment Scale for Children Aged 1–6. The various categories of the developmental trajectory of ASD children’s auditory processing are examined through the latent category growth model. Additionally, a hierarchical regression model is developed to analyze the predictive impact of different auditory processing development trajectories on language impairment in ASD children.DiscussionThis longitudinal study will explore the categories of auditory processing developmental trajectories in ASD children, and analyze the relationship between different categories of auditory processing developmental trajectories and language development, providing new ideas and targeted targets for the rehabilitation training of language impairment in ASD children, as well as promoting early and accurate interventions for ASD children.

Estimation of Plasma Concentration of L-Carnosine and its Correlation with Core Symptoms of Autism Spectrum Disorder Children: A Pilot Clinical Trial.

Literature indicates that L-carnosine may be deficient in autism spectrum disorder (ASD) children. The aim of the present study was to estimate the level of L-carnosine in plasma and correlate it with the Autism Treatment Evaluation Checklist (ATEC) and Childhood Autism Rating Scale 2nd Edition, Standard Version (CARS2-ST) scores. To measure L-carnosine level, a bio-analytical method was developed using reverse phase high- liquid chromatography and validated as per International Conference on Harmonization guidelines. Children were supplemented with L-carnosine (10-15 mg/kg) along with standard care therapies for 2 months. Before and after supplementation, scores on the ATEC, CARS2-ST, BEARS sleep screening tool, 6-item Gastrointestinal Severity Index, and Parental Stress Scale were evaluated, and L-carnosine was measured at the end of the trial. The calibration curve was linear in the range of 100-600 ng/mL (R2 = 0.998). The level of L-carnosine quantified was 33.7 ± 0.2 ng/mL. There was no significant difference found in any of the outcome measures (p > 0.05). Despite the fact that L-carnosine is detectable in the blood, it was found to be ineffective in the management of ASD in children. The study was registered in the Clinical Trial Registry-India, registration number: CTRI/2019/07/020102.

AST-001 versus placebo for social communication in children with autism spectrum disorder: A randomized clinical trial.

This study examined the efficacy of AST-001 for the core symptoms of autism spectrum disorder (ASD) in children. This phase 2 clinical trial consisted of a 12-week placebo-controlled main study, a 12-week extension, and a 12-week follow-up in children aged 2 to 11 years with ASD. The participants were randomized in a 1:1:1 ratio to a high-dose, low-dose, or placebo-to-high-dose control group during the main study. The placebo-to-high-dose control group received placebo during the main study and high-dose AST-001 during the extension. The a priori primary outcome was the mean change in the Adaptive Behavior Composite (ABC) score of the Korean Vineland Adaptive Behavior Scales II (K-VABS-II) from baseline to week 12. Among 151 enrolled participants, 144 completed the main study, 140 completed the extension, and 135 completed the follow-up. The mean K-VABS-II ABC score at the 12th week compared with baseline was significantly increased in the high-dose group (P = 0.042) compared with the placebo-to-high-dose control group. The mean CGI-S scores were significantly decreased at the 12th week in the high-dose (P = 0.046) and low-dose (P = 0.017) groups compared with the placebo-to-high-dose control group. During the extension, the K-VABS-II ABC and CGI-S scores of the placebo-to-high-dose control group changed rapidly after administration of high-dose AST-001 and caught up with those of the high-dose group at the 24th week. AST-001 was well tolerated with no safety concern. The most common adverse drug reaction was diarrhea. Our results provide preliminary evidence for the efficacy of AST-001 for the core symptoms of ASD.

Unraveling the relative abundance of psychobiotic bacteria in children with Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by social deficits. Accumulated evidence has shown a link between alterations in the composition of gut microbiota and both neurobehavioural and gastrointestinal symptoms in children with ASD which are related to the genera Lactobacillus and Bifidobacterium. These genera have been recently categorized as “psychobiotics”. Moreover, this study aimed to compare the relative abundance of psychobiotics (L. plantarum, L. reuteri, and B. longum) to the total gut microbiome in typically developing (TD) children and those with ASD in order to correlate the distribution of psychobiotic with the severity and sensory impairments in autism. The ASD children were assessed using the Childhood Autism Rating Scale (CARS), while sensory impairments were evaluated using the Short Sensory Profile (SSP). Furthermore, the gut microbiome was analyzed using the quantitative real-time PCR. The study revealed a statistically significant increase in the relative abundance of L. reuteri and L. plantarum in the TD group in comparison to ASD children. Regarding the SSP total score of ASD children, a statistically significant negative correlation was found between both Lactobacillus and L. plantarum with the under-responsive subscale. For the Autism Treatment Evaluation Checklist (ATEC) score, B. longum and Lactobacillus showed a significant positive correlation with Health/Physical/Behaviour.

Causal Metabolomic and Lipidomic Analysis of Circulating Plasma Metabolites in Autism: A Comprehensive Mendelian Randomization Study with Independent Cohort Validation.

The increasing prevalence of autism spectrum disorder (ASD) highlights the need for objective diagnostic markers and a better understanding of its pathogenesis. Metabolic differences have been observed between individuals with and without ASD, but their causal relevance remains unclear. Bidirectional two-sample Mendelian randomization (MR) was used to assess causal associations between circulating plasma metabolites and ASD using large-scale genome-wide association study (GWAS) datasets-comprising 1091 metabolites, 309 ratios, and 179 lipids-and three European autism datasets (PGC 2015: n = 10,610 and 10,263; 2017: n = 46,351). Inverse-variance weighted (IVW) and weighted median methods were employed, along with robust sensitivity and power analyses followed by independent cohort validation. Higher genetically predicted levels of sphingomyelin (SM) (d17:1/16:0) (OR, 1.129; 95% CI, 1.024-1.245; p = 0.015) were causally linked to increased ASD risk. Additionally, ASD children had higher plasma creatine/carnitine ratios. These MR findings were validated in an independent US autism cohort using machine learning analysis. Utilizing large datasets, two MR approaches, robust sensitivity analyses, and independent validation, our novel findings provide evidence for the potential roles of metabolomics and circulating metabolites in ASD diagnosis and etiology.