1. Alayoubi AM, Iqbal M, Aman H, Hashmi JA, Alayadhi L, Al-Regaiey K, Basit S. Loss-of-function variant in spermidine/spermine N1-acetyl transferase like 1 (SATL1) gene as an underlying cause of autism spectrum disorder. Sci Rep;2024 (Mar 8);14(1):5765.

Autism spectrum disorder (ASD) is a complicated, lifelong neurodevelopmental disorder affecting verbal and non-verbal communication and social interactions. ASD signs and symptoms appear early in development before the age of 3 years. It is unlikely for a person to acquire autism after a period of normal development. However, we encountered an 8-year-old child who developed ASD later in life although his developmental milestones were normal at the beginning of life. Sequencing the complete coding part of the genome identified a hemizygous nonsense mutation (NM_001367857.2):c.1803C>G; (p.Tyr601Ter) in the gene (SATL1) encoding spermidine/spermine N1-acetyl transferase like 1. Screening an ASD cohort of 28 isolated patients for the SATL1 gene identified another patient with the same variant. Although SATL1 mutations have not been associated with any human diseases, our data suggests that a mutation in SATL1 is the underlying cause of ASD in our cases. In mammals, mutations in spermine synthase (SMS), an enzyme needed for the synthesis of spermidine polyamine, have been reported in a syndromic form of the X-linked mental retardation. Moreover, SATL1 gene expression studies showed a relatively higher expression of SATL1 transcripts in ASD related parts of the brain including the cerebellum, amygdala and frontal cortex. Additionally, spermidine has been characterized in the context of learning and memory and supplementations with spermidine increase neuroprotective effects and decrease age-induced memory impairment. Furthermore, spermidine biosynthesis is required for spontaneous axonal regeneration and prevents α-synuclein neurotoxicity in invertebrate models. Thus, we report, for the first time, that a mutation in the SATL1 gene could be a contributing factor in the development of autistic symptoms in our patients.

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2. Bravo Balsa L, Abu-Akel A, Mevorach C. Dynamic functional connectivity in the right temporoparietal junction captures variations in male autistic trait expression. Autism Res;2024 (Mar 8)

Autistic individuals can experience difficulties with attention reorienting and Theory of Mind (ToM), which are closely associated with anterior and posterior subdivisions of the right temporoparietal junction. While the link between these processes remains unclear, it is likely subserved by a dynamic crosstalk between these two subdivisions. We, therefore, examined the dynamic functional connectivity (dFC) between the anterior and posterior temporoparietal junction, as a biological marker of attention and ToM, to test its contribution to the manifestation of autistic trait expression in Autism Spectrum Condition (ASC). Two studies were conducted, exploratory (14 ASC, 15 TD) and replication (29 ASC, 29 TD), using resting-state fMRI data and the Social Responsiveness Scale (SRS) from the Autism Brain Imaging Data Exchange repository. Dynamic Independent Component Analysis was performed in both datasets using the CONN toolbox. An additional sliding-window analysis was performed in the replication study to explore different connectivity states (from highly negatively to highly positively correlated). Dynamic FC was reduced in ASC compared to TD adults in both the exploratory and replication datasets and was associated with increased SRS scores (especially in ASC). Regression analyses revealed that decreased SRS autistic expression was predicted by engagement of highly negatively correlated states, while engagement of highly positively correlated states predicted increased expression. These findings provided consistent evidence that the difficulties observed in ASC are associated with altered patterns of dFC between brain regions subserving attention reorienting and ToM processes and may serve as a biomarker of autistic trait expression.

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3. Clarke L, Gesundheit N, Sherr EH, Hardan AY, Parker KJ. Vasopressin deficiency: a hypothesized driver of both social impairment and fluid imbalance in autism spectrum disorder. Mol Psychiatry;2024 (Mar 7)

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4. Eyuboglu D, Eyuboglu M, Yaylaci F, Guller B, Sahbudak B, Avunduk A, Dagli OO, Pala SC, Arslantas D. The Validity and Reliability of the Turkish Version of the Autism Family Experience Questionnaire (AFEQ). J Autism Dev Disord;2024 (Mar 8)

The aim of this study was to examine the reliability and validity of the Turkish version of the AFEQ for Turkish parents of children with ASD. The Turkish-translated version of the AFEQ was administered to 241 parents of children aged 2-12 years with ASD to examine the construct validity and internal consistencies. Parents completed the Autism Behavior Checklist (ABC), and Quality of Life in Autism Questionnaire Parent version, along with the AFEQ. The mean age of the children of 241 individuals in the study group was 7.63 ± 3.02 and 88.4% (n = 213) were male. Cronbach’s alpha coefficient was 0.921 of the total variance. Cronbach alpha coefficients are 0.813 for the « Experience of being a parent » subscale, 0.768 for the « Family Life » subscale, 0.810 for the « Child Development, Understanding and Social Relationships » subscale, and 0.804 for the « Child Symptoms (Feelings and Behaviour) » subscale. In conclusion, the translated and culturally adapted AFEQ shows good reliability and validity to measure the priorities of autistic children and their families in Turkey. It can also be useful in monitoring the effectiveness of intervention programs and changes in the child.

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5. Feng P, Zhang Y, Zhao Y, Zhao P, Li E. Combined repetitive transcranial magnetic stimulation and gut microbiota modulation through the gut-brain axis for prevention and treatment of autism spectrum disorder. Front Immunol;2024;15:1341404.

Autism spectrum disorder (ASD) encompasses a range of neurodevelopmental conditions characterized by enduring impairments in social communication and interaction together with restricted repetitive behaviors, interests, and activities. No targeted pharmacological or physical interventions are currently available for ASD. However, emerging evidence has indicated a potential association between the development of ASD and dysregulation of the gut-brain axis. Repetitive transcranial magnetic stimulation (rTMS), a noninvasive diagnostic and therapeutic approach, has demonstrated positive outcomes in diverse psychiatric disorders; however, its efficacy in treating ASD and its accompanying gastrointestinal effects, particularly the effects on the gut-brain axis, remain unclear. Hence, this review aimed to thoroughly examine the existing research on the application of rTMS in the treatment of ASD. Additionally, the review explored the interplay between rTMS and the gut microbiota in children with ASD, focusing on the gut-brain axis. Furthermore, the review delved into the integration of rTMS and gut microbiota modulation as a targeted approach for ASD treatment based on recent literature. This review emphasizes the potential synergistic effects of rTMS and gut microbiota interventions, describes the underlying mechanisms, and proposes a potential therapeutic strategy for specific subsets of individuals with ASD.

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6. Inge KJ, Wehman P, Avellone L, Broda M, McDonough J. The impact of customized employment on the competitive integrated employment outcomes of transition age youth with intellectual and developmental disabilities: A randomized controlled trial study. Work;2024;77(3):721-729.

BACKGROUND: Customized employment (CE) is recognized in the Workforce Innovation and Opportunity Act (2014) as a strategy for promoting competitive integrated employment. However, the existing body of evidence supporting CE is mainly descriptive rather than experimental research. OBJECTIVE: This study examined the impact of CE on the employment outcomes, hours worked per week, and wages of transition-age youth with intellectual and developmental disabilities. METHOD: The outcomes of transition-age youth participating in a CE intervention were compared to those receiving treatment-as-usual using a randomized controlled trial design. RESULTS: Participants receiving CE were significantly more likely to secure competitive integrated employment than controls who received treatment-as-usual. Participants in the intervention and control conditions earned similar wages. Participants in the control condition worked more hours per week than those in CE. CONCLUSION: The findings from this study demonstrate the effectiveness of CE to assist transition-age youth with intellectual and developmental disabilities in obtaining competitive integrated employment, but future research is needed to examine factors impacting weekly hours and wages of participants in CE.

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7. Koehler JC, Dong MS, Song DY, Bong G, Koutsouleris N, Yoo H, Falter-Wagner CM. Classifying autism in a clinical population based on motion synchrony: a proof-of-concept study using real-life diagnostic interviews. Sci Rep;2024 (Mar 7);14(1):5663.

Predictive modeling strategies are increasingly studied as a means to overcome clinical bottlenecks in the diagnostic classification of autism spectrum disorder. However, while some findings are promising in the light of diagnostic marker research, many of these approaches lack the scalability for adequate and effective translation to everyday clinical practice. In this study, our aim was to explore the use of objective computer vision video analysis of real-world autism diagnostic interviews in a clinical sample of children and young individuals in the transition to adulthood to predict diagnosis. Specifically, we trained a support vector machine learning model on interpersonal synchrony data recorded in Autism Diagnostic Observation Schedule (ADOS-2) interviews of patient-clinician dyads. Our model was able to classify dyads involving an autistic patient (n = 56) with a balanced accuracy of 63.4% against dyads including a patient with other psychiatric diagnoses (n = 38). Further analyses revealed no significant associations between our classification metrics with clinical ratings. We argue that, given the above-chance performance of our classifier in a highly heterogeneous sample both in age and diagnosis, with few adjustments this highly scalable approach presents a viable route for future diagnostic marker research in autism.

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8. Liu W, Zhang Y, Zhang B, Xiong Q, Zhao H, Li S, Liu J, Bian Y. Self-Guided DMT: Exploring a Novel Paradigm of Dance Movement Therapy in Mixed Reality for Children with ASD. IEEE Trans Vis Comput Graph;2024 (Mar 12);Pp

Children diagnosed with Autism Spectrum Disorder (ASD) often exhibit motor disorders. Dance Movement Therapy (DMT) has shown great potential for improving the motor control ability of children with ASD. However, traditional DMT methods often lack vividness and are difficult to implement effectively. To address this issue, we propose a Mixed Reality DMT approach, utilizing interactive virtual agents. This approach offers immersive training content and multi-sensory feedback. To improve the training performance of children with ASD, we introduce a novel training paradigm featuring a self-guided mode. This paradigm enables the rapid creation of a virtual twin agent of the child with ASD using a single photo to embody oneself, which can then guide oneself during training. We conducted an experiment with the participation of 24 children diagnosed with ASD (or ASD propensity), recording their training performance under various experimental conditions. Through expert rating, behavior coding of training sessions, and statistical analysis, our findings revealed that the use of the twin agent for self-guidance resulted in noticeable improvements in the training performance of children with ASD. These improvements were particularly evident in terms of enhancing movement quality and refining overall target-related responses. Our study holds clinical potential in the field of medical treatment and rehabilitation for children with ASD.

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9. Ma C, Li W, Ke S, Lv J, Zhou T, Zou L. Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network. Med Biol Eng Comput;2024 (Mar 8)

Presently, the combination of graph convolutional networks (GCN) with resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising approach for early diagnosis of autism spectrum disorder (ASD). However, the prevalent approach involves exclusively full-brain functional connectivity data for disease classification using GCN, while overlooking the prior information related to the functional connectivity of brain subnetworks associated with ASD. Therefore, in this study, the multiple functional connectivity-based graph convolutional network (MFC-GCN) framework is proposed, using not only full brain functional connectivity data but also the established functional connectivity data from networks of key brain subnetworks associated with ASD, and the GCN is adopted to acquire complementary feature information for the final classification task. Given the heterogeneity within the Autism Brain Imaging Data Exchange (ABIDE) dataset, a novel External Attention Network Readout (EANReadout) is introduced. This design enables the exploration of potential subject associations, effectively addressing the dataset’s heterogeneity. Experiments were conducted on the ABIDE dataset using the proposed framework, involving 714 subjects, and the average accuracy of the framework was 70.31%. The experimental results show that the proposed EANReadout outperforms the best traditional readout layer and improves the average accuracy of the framework by 4.32%.

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10. Mazel B, Delanne J, Garde A, Racine C, Bruel AL, Duffourd Y, Lopergolo D, Santorelli FM, Marchi V, Pinto AM, Mencarelli MA, Canitano R, Valentino F, Papa FT, Fallerini C, Mari F, Renieri A, Munnich A, Niclass T, Le Guyader G, Thauvin-Robinet C, Philippe C, Faivre L. FOXG1 variants can be associated with milder phenotypes than congenital Rett syndrome with unassisted walking and language development. Am J Med Genet B Neuropsychiatr Genet;2024 (Mar 8):e32970.

Since 2008, FOXG1 haploinsufficiency has been linked to a severe neurodevelopmental phenotype resembling Rett syndrome but with earlier onset. Most patients are unable to sit, walk, or speak. For years, FOXG1 sequencing was only prescribed in such severe cases, limiting insight into the full clinical spectrum associated with this gene. Next-generation sequencing (NGS) now enables unbiased diagnostics. Through the European Reference Network for Rare Malformation Syndromes, Intellectual and Other Neurodevelopmental Disorders, we gathered data from patients with heterozygous FOXG1 variants presenting a mild phenotype, defined as able to speak and walk independently. We also reviewed data from three previously reported patients meeting our criteria. We identified five new patients with pathogenic FOXG1 missense variants, primarily in the forkhead domain, showing varying nonspecific intellectual disability and developmental delay. These features are not typical of congenital Rett syndrome and were rarely associated with microcephaly and epilepsy. Our findings are consistent with a previous genotype-phenotype analysis by Mitter et al. suggesting the delineation of five different FOXG1 genotype groups. Milder phenotypes were associated with missense variants in the forkhead domain. This information may facilitate prognostic assessments in children carrying a FOXG1 variant and improve the interpretation of new variants identified with genomic sequencing.

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11. Mori D, Ikeda R, Sawahata M, Yamaguchi S, Kodama A, Hirao T, Arioka Y, Okumura H, Inami C, Suzuki T, Hayashi Y, Kato H, Nawa Y, Miyata S, Kimura H, Kushima I, Aleksic B, Mizoguchi H, Nagai T, Nakazawa T, Hashimoto R, Kaibuchi K, Kume K, Yamada K, Ozaki N. Phenotypes for general behavior, activity, and body temperature in 3q29 deletion model mice. Transl Psychiatry;2024 (Mar 7);14(1):138.

Whole genome analysis has identified rare copy number variations (CNV) that are strongly involved in the pathogenesis of psychiatric disorders, and 3q29 deletion has been found to have the largest effect size. The 3q29 deletion mice model (3q29-del mice) has been established as a good pathological model for schizophrenia based on phenotypic analysis; however, circadian rhythm and sleep, which are also closely related to neuropsychiatric disorders, have not been investigated. In this study, our aims were to reevaluate the pathogenesis of 3q29-del by recreating model mice and analyzing their behavior and to identify novel new insights into the temporal activity and temperature fluctuations of the mouse model using a recently developed small implantable accelerometer chip, Nano-tag. We generated 3q29-del mice using genome editing technology and reevaluated common behavioral phenotypes. We next implanted Nano-tag in the abdominal cavity of mice for continuous measurements of long-time activity and body temperature. Our model mice exhibited weight loss similar to that of other mice reported previously. A general behavioral battery test in the model mice revealed phenotypes similar to those observed in mouse models of schizophrenia, including increased rearing frequency. Intraperitoneal implantation of Nano-tag, a miniature acceleration sensor, resulted in hypersensitive and rapid increases in the activity and body temperature of 3q29-del mice upon switching to lights-off condition. Similar to the 3q29-del mice reported previously, these mice are a promising model animals for schizophrenia. Successive quantitative analysis may provide results that could help in treating sleep disorders closely associated with neuropsychiatric disorders.

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12. Ramazani Z, Nakhaee S, Sharafi K, Rezaei Z, Mansouri B. Autism spectrum disorder: Cadmium and mercury concentrations in different biological samples, a systematic literature review and meta-analysis of human studies. Heliyon;2024 (Mar 30);10(6):e27789.

The present study was conducted to investigate the differences in cadmium (Cd) and mercury (Hg) concentrations between children with autism spectrum disorder (ASD) and controls. In this systematic review and meta-analysis study, three thousand one hundred forty-five studies were collected from scientific databases including Web of Science, Scopus, PubMed, and Google Scholar from January 2000 to October 2022 and were investigated for eligibility. As a result, 37 studies published in the period from 2003 to 2022 met our inclusion criteria and were considered in the meta-analysis. The heterogeneity assumption was evaluated using the Chi-squared-based Q-test and I-squared (I(2)) statistics. The pooled estimates were shown in the forest plots with Hedges’ g (95% confidence interval) values. The random effects model demonstrated that there is no significant difference in the blood (Hedges’ g: 0.14, 95% CI: 0.45, 0.72, p > 0.05), hair (Hedges’ g: 0.12, 95% CI: 0.26, 0.50, p > 0.05), and urinary (Hedges’ g: 0.05, 95% CI: 0.86, 0.76, p > 0.05) Cd levels of the case group versus control subjects. Moreover, the pooled findings of studies showed no significant difference in the blood (Hedges’ g: 1.69, 95% CI: 0.09, 3.48, p > 0.05), hair (Hedges’ g: 3.42, 95% CI: 1.96, 8.80, p > 0.05), and urinary (Hedges’ g: 0.49, 95% CI: 1.29 – 0.30, p > 0.05) Hg concentrations. The results demonstrated no significant differences in Hg and Cd concentrations in different biological samples of children with ASD compared to control subjects.

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13. Ranaut A, Khandnor P, Chand T. Identifying Autism using EEG: Unleashing the Power of Feature Selection and Machine Learning. Biomed Phys Eng Express;2024 (Mar 8)

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is characterized by communication barriers, societal disengagement, and monotonous actions. Currently, the diagnosis of ASD is made by experts through a subjective and time-consuming qualitative behavioural examination using internationally recognized descriptive standards. In this paper, we present an EEG-based three-phase novel approach comprising 29 autistic subjects and 30 neurotypical people. In the first phase, preprocessing of data is performed from which we derived one continuous dataset and four condition-based datasets to determine the role of each dataset in the identification of autism from neurotypical people. In the second phase, time-domain and morphological features were extracted and four different feature selection techniques were applied. In the last phase, five-fold cross-validation is used to evaluate six different machine learning models based on the performance metrics and computational efficiency. The neural network outperformed when trained with maximum relevance and minimum redundancy (MRMR) algorithm on the continuous dataset with 98.10% validation accuracy and 0.9994 area under the curve (AUC) value for model validation, and 98.43% testing accuracy and AUC test value of 0.9998. The decision tree overall performed the second best in terms of computational efficiency and performance accuracy. The results indicate that EEG-based machine learning models have the potential for ASD identification from neurotypical people with a more objective and reliable method.

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14. Rubio-Martín S, García-Ordás MT, Bayón-Gutiérrez M, Prieto-Fernández N, Benítez-Andrades JA. Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing. Health Inf Sci Syst;2024 (Dec);12(1):20.

PURPOSE: The main aim of our study was to explore the utility of artificial intelligence (AI) in diagnosing autism spectrum disorder (ASD). The study primarily focused on using machine learning (ML) and deep learning (DL) models to detect ASD potential cases by analyzing text inputs, especially from social media platforms like Twitter. This is to overcome the ongoing challenges in ASD diagnosis, such as the requirement for specialized professionals and extensive resources. Timely identification, particularly in children, is essential to provide immediate intervention and support, thereby improving the quality of life for affected individuals. METHODS: We employed natural language processing (NLP) techniques along with ML models like decision trees, extreme gradient boosting (XGB), k-nearest neighbors algorithm (KNN), and DL models such as recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), bidirectional encoder representations from transformers (BERT and BERTweet). We extracted a dataset of 404,627 tweets from Twitter users using the platform’s API and classified them based on whether they were written by individuals claiming to have ASD (ASD users) or by those without ASD (non-ASD users). From this dataset, we used a subset of 90,000 tweets (45,000 from each classification group) for the training and testing of these models. RESULTS: The application of our AI models yielded promising results, with the predictive model reaching an accuracy of almost 88% when classifying texts that potentially originated from individuals with ASD. CONCLUSION: Our research demonstrated the potential of using AI, particularly DL models, in enhancing the accuracy of ASD detection and diagnosis. This innovative approach signifies the critical role AI can play in advancing early diagnostic techniques, enabling better patient outcomes and underlining the importance of early identification of ASD, especially in children.

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15. Saban-Bezalel R, Ben-Itzchak E, Zachor DA. Friendship in Autism Spectrum Disorder Is Related to Diverse Developmental Changes Between Toddlerhood and Adolescence. J Autism Dev Disord;2024 (Mar 8)

PURPOSE: Follow-up studies of children diagnosed with autism spectrum disorder (ASD) in early childhood that focus on friendship formation during adolescence are scarce. The present study focused on exploring characteristics possibly related to the ability to establish friendships during adolescence among children diagnosed with ASD in toddlerhood. METHODS: The cohort included 43 participants who underwent comprehensive assessments during toddlerhood and adolescence. Participants were divided into two groups [Friendship(+)/Friendship(-)] based on (1) adolescent social insight as assessed by professionals and (2) parental and adolescent self-reports regarding having or not having friends. No differences in IQ, ASD symptoms, or adaptive behavior during early childhood were found between the two groups. RESULTS: Different and better changes in social communication, adaptive socialization, and daily living skills were observed for the Friendship(+) group. Adolescents with ASD in the Friendship(+) group exhibited greater social independence. Attention-deficit/hyperactivity disorder incidence, anxiety symptom severity, and placement in mainstream or special education classes did not differ between the two groups. CONCLUSION: This long-term study highlights that for children with ASD, longitudinal growth in social communication and adaptive functioning is possible, highly important for and related to the development of the complex ability to establish friendship.

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16. Sano M, Hirosawa T, Yoshimura Y, Hasegawa C, An KM, Tanaka S, Yaoi K, Naitou N, Kikuchi M. Neural responses to syllable-induced P1m and social impairment in children with autism spectrum disorder and typically developing Peers. PLoS One;2024;19(3):e0298020.

In previous magnetoencephalography (MEG) studies, children with autism spectrum disorder (ASD) have been shown to respond differently to speech stimuli than typically developing (TD) children. Quantitative evaluation of this difference in responsiveness may support early diagnosis and intervention for ASD. The objective of this research is to investigate the relationship between syllable-induced P1m and social impairment in children with ASD and TD children. We analyzed 49 children with ASD aged 40-92 months and age-matched 26 TD children. We evaluated their social impairment by means of the Social Responsiveness Scale (SRS) and their intelligence ability using the Kaufman Assessment Battery for Children (K-ABC). Multiple regression analysis with SRS score as the dependent variable and syllable-induced P1m latency or intensity and intelligence ability as explanatory variables revealed that SRS score was associated with syllable-induced P1m latency in the left hemisphere only in the TD group and not in the ASD group. A second finding was that increased leftward-lateralization of intensity was correlated with higher SRS scores only in the ASD group. These results provide valuable insights but also highlight the intricate nature of neural mechanisms and their relationship with autistic traits.

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17. Smith JV, Menezes M, Brunt S, Pappagianopoulos J, Sadikova E, M OM. Understanding autism diagnosis in primary care: Rates of diagnosis from 2004 to 2019 and child age at diagnosis. Autism;2024 (Mar 8):13623613241236112.

The current demand for autism diagnostic services exceeds the ability of the workforce to assess and diagnose children in a timely manner. One solution may be to equip primary care providers (PCPs) with the tools and expertise needed to diagnose autism within their practice. PCPs are often trusted professionals who have many touchpoints with children during early development, in which they can identify early signs of autism. Recent initiatives have focused on bolstering PCPs’ diagnostic capabilities; however, no studies have examined how the rates of autism diagnosis in primary care have changed over time. We aimed to evaluate whether autism diagnosis in primary care has changed over time and how diagnosis in primary care relates to a child’s age at the time of diagnosis. We found that the likelihood of a child being diagnosed by a PCP decreased by about 2% with every passing year from 2004 to 2019 when accounting for demographic characteristics. In our sample, PCPs diagnosed children approximately 1 year earlier than non-PCPs (e.g., psychologists and psychiatrists). Further research is needed to understand why the proportion of children diagnosed by PCPs decreases over time. However, this decrease suggests more work is needed to get capacity-building initiatives into community primary care practice. Though we must continue to find effective ways to build community PCPs’ ability to diagnose autism, the present findings support the crucial role PCPs can play in early autism diagnosis.

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18. Wakuda T, Benner S, Uemura Y, Nishimura T, Kojima M, Kuroda M, Matsumoto K, Kanai C, Inada N, Harada T, Kameno Y, Munesue T, Inoue J, Umemura K, Yamauchi A, Ogawa N, Kushima I, Suyama S, Saito T, Hamada J, Kano Y, Honda N, Kikuchi S, Seto M, Tomita H, Miyoshi N, Matsumoto M, Kawaguchi Y, Kanai K, Ikeda M, Nakamura I, Isomura S, Hirano Y, Onitsuka T, Ozaki N, Kosaka H, Okada T, Kuwabara H, Yamasue H. Oxytocin-induced increases in cytokines and clinical effect on the core social features of autism: Analyses of RCT datasets. Brain Behav Immun;2024 (Mar 8);118:398-407.

Although oxytocin may provide a novel therapeutics for the core features of autism spectrum disorder (ASD), previous results regarding the efficacy of repeated or higher dose oxytocin are controversial, and the underlying mechanisms remain unclear. The current study is aimed to clarify whether repeated oxytocin alter plasma cytokine levels in relation to clinical changes of autism social core feature. Here we analyzed cytokine concentrations using comprehensive proteomics of plasmas of 207 adult males with high-functioning ASD collected from two independent multi-center large-scale randomized controlled trials (RCTs): Testing effects of 4-week intranasal administrations of TTA-121 (A novel oxytocin spray with enhanced bioavailability: 3U, 6U, 10U, or 20U/day) and placebo in the crossover discovery RCT; 48U/day Syntocinon or placebo in the parallel-group verification RCT. Among the successfully quantified 17 cytokines, 4 weeks TTA-121 6U (the peak dose for clinical effects) significantly elevated IL-7 (9.74, 95 % confidence interval [CI] 3.59 to 15.90, False discovery rate corrected P (P(FDR)) < 0.001), IL-9 (56.64, 20.46 to 92.82, P(FDR) < 0.001) and MIP-1b (18.27, 4.96 to 31.57, P(FDR) < 0.001) compared with placebo. Inverted U-shape dose-response relationships peaking at TTA-121 6U were consistently observed for all these cytokines (IL-7: P < 0.001; IL-9: P < 0.001; MIP-1b: P = 0.002). Increased IL-7 and IL-9 in participants with ASD after 4 weeks TTA-121 6U administration compared with placebo was verified in the confirmatory analyses in the dataset before crossover (P(FDR) < 0.001). Furthermore, the changes in all these cytokines during 4 weeks of TTA-121 10U administration revealed associations with changes in reciprocity score, the original primary outcome, observed during the same period (IL-7: Coefficient = -0.05, -0.10 to 0.003, P = 0.067; IL-9: -0.01, -0.02 to -0.003, P = 0.005; MIP-1b: -0.02, -0.04 to -0.007, P = 0.005). These findings provide the first evidence for a role of interaction between oxytocin and neuroinflammation in the change of ASD core social features, and support the potential role of this interaction as a novel therapeutic seed. Trial registration: UMIN000015264, NCT03466671/UMIN000031412.

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19. Wei Q, Xiao Y, Yang T, Chen J, Chen L, Wang K, Zhang J, Li L, Jia F, Wu L, Hao Y, Ke X, Yi M, Hong Q, Chen J, Fang S, Wang Y, Wang Q, Jin C, Xu X, Li T. Predicting autism spectrum disorder using maternal risk factors: A multi-center machine learning study. Psychiatry Res;2024 (Apr);334:115789.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a complex environmental etiology involving maternal risk factors, which have been combined with machine learning to predict ASD. However, limited studies have considered the factors throughout preconception, perinatal, and postnatal periods, and even fewer have been conducted in multi-center. In this study, five predictive models were developed using 57 maternal risk factors from a cohort across ten cities (ASD:1232, typically developing[TD]: 1090). The extreme gradient boosting model performed best, achieving an accuracy of 66.2 % on the external cohort from three cities (ASD:266, TD:353). The most important risk factors were identified as unstable emotions and lack of multivitamin supplementation using Shapley values. ASD risk scores were calculated based on predicted probabilities from the optimal model and divided into low, medium, and high-risk groups. The logistic analysis indicated that the high-risk group had a significantly increased risk of ASD compared to the low-risk group. Our study demonstrated the potential of machine learning models in predicting the risk for ASD based on maternal factors. The developed model provided insights into the maternal emotion and nutrition factors associated with ASD and highlighted the potential clinical applicability of the developed model in identifying high-risk populations.

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20. Will EA, Hills KJ, Smith K, McQuillin S, Roberts JE. Developmental associations between motor and communication outcomes in Fragile X syndrome: Variation in the context of co-occurring autism. Autism;2024 (Mar 8):13623613231225498.

Fragile X syndrome (FXS), the leading heritable cause of intellectual disability, has a co-occurrence rate of autism spectrum disorder (ASD) estimated at ~60%. Children with FXS experience delayed achievement and slower development of key motor abilities, which happens to an even greater extent for children with both FXS and ASD. A multitude of studies have demonstrated that motor abilities are foundational skills related to later communication outcomes in neurotypical development, as well as in the context of ASD. However, these associations remain unexamined in FXS, or FXS + ASD. In this study, we aimed to determine the associations between early motor skills and their rate of development on communication outcomes in FXS. Furthermore, we investigated whether these associations varied in the context of co-occurring FXS + ASD. Results revealed within-FXS variation in the context of co-occurring ASD between some aspects of motor development and communication outcomes, yet within-FXS consistency between others. Findings provide evidence for variability in developmental processes and outcomes in FXS in the context of co-occurring ASD and offer implications for intervention.

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