Early Diagnosis Of Autism Spectrum Disorder Research Articles

Autism spectrum disorder: perspectives from paediatricians

Aims: Since the first medical contact of children and families is mostly with paediatricians, the knowledge and experience of paediatricians on Autism Spectrum Disorder (ASD) is extremely significant. It was aimed to investigate paediatricians’ general knowledge on ASD, daily practices in the outpatient clinic and ability to recognize warning signs. 
 Methods: A 40-item questionnaire prepared by the researchers was completed with 116 specialists or subspecialties working in public and private hospitals of two cities in Türkiye. 
 Results: While 41.7% of paediatricians received rotation training in residency, 32.8 % reported they have enough experience. While 87% considered ASD screening among their duties, only 12.8% stated they have enough time. The number of patients referred for formal assessment of ASD was low. Gender, type of expertise, duration of experience, and sense of competence were not associated with referral frequency. It’s been determined paediatricians have a lack of knowledge about ASD. While not responding to name and difficulty following the instructions were seen as most warning symptoms, other diagnostic criteria and accompanying symptoms were recognized insufficiently.
 Conclusions: In addition to increasing the duration and quality of child psychiatry education during residency, regulating the outpatient clinic conditions can overcome an important obstacle in the early diagnosis of ASD.

Glucose transporter 1 deficiency, AMP-activated protein kinase activation and immune dysregulation in autism spectrum disorder: Novel biomarker sources for clinical diagnosis

The neurophysiological basis of autism spectrum disorder (ASD) is still uncertain. Nevertheless, studies support the hypotheses that oxidative stress, neuroinflammation, immune dysregulation, and metabolic stress are contributors. In this study, the serum levels of 3-nitrotyrosine (3-NT), hypoxia-inducible factor 1 α (HIF-1 α), heat shock protein 70 (HSP-70), interleukin-17A (IL-17A), IL-35, vitamin D3 (VITD), glucose transporter-1 (GUT1), and AMP-activated protein kinase (AMPK) were estimated in Saudi ASD children versus age-matched neurotypical controls, aiming to investigate whether these parameters have potential roles in the pathophysiologic mechanisms of ASD and hoping to find a reliable marker for early ASD diagnosis. This study included 25 ASD children and 25 typically developing children (3–11 years old). The diagnosis of ASD cases was made based on the Autism Diagnostic Observation Schedule (ADOS) and the Statistical Manual of Mental Disorders (DSM-5). ASD subjects were commonly male and revealed an intelligence quotient (IQ) < 70.The results detected that ASD children have remarkable greater serum levels of nitrosative stress (3-NT), hypoxia (HIF-1 α), inflammatory (HSP-70, IL-17A, and AMPK) biomarkers and lower serum levels of anti-inflammatory (IL-35 and VITD) and metabolic stress (GUT-1) biomarkers versus age-matched controls (P ≤ 0.0001). Pearson's correlation study revealed that 3-NT was positively associated with HIF-1 α and HSP-70. HIF-1 α was also positively correlated with HSP-70. AMPK was positively associated with GUT-1, however, IL-17A was negatively correlated with IL-35 and VITD.Limitation:No specific therapeuticdrugs were administered in this study, and further studies are required to confirm the role of the selected biomarkers in ASD managements. ConclusionChanges in concentrations of different biomarkers indicate that they are involved in oxidative stress, metabolic stress, immune dysregulation and ASD pathogenesis. Hence, these parameters can prove to be promising biomarkers as well as therapeutic targets for the timely diagnosis and treatment of ASD patients.

Identification of diagnostic markers for ASD: a restrictive interest analysis based on EEG combined with eye tracking.

Electroencephalography (EEG) functional connectivity (EFC) and eye tracking (ET) have been explored as objective screening methods for autism spectrum disorder (ASD), but no study has yet evaluated restricted and repetitive behavior (RRBs) simultaneously to infer early ASD diagnosis. Typically developing (TD) children (n = 27) and ASD (n = 32), age- and sex-matched, were evaluated with EFC and ET simultaneously, using the restricted interest stimulus paradigm. Network-based machine learning prediction (NBS-predict) was used to identify ASD. Correlations between EFC, ET, and Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) were performed. The Area Under the Curve (AUC) of receiver-operating characteristics (ROC) was measured to evaluate the predictive performance. Under high restrictive interest stimuli (HRIS), ASD children have significantly higher α band connectivity and significantly more total fixation time (TFT)/pupil enlargement of ET relative to TD children (p = 0.04299). These biomarkers were not only significantly positively correlated with each other (R = 0.716, p = 8.26e-4), but also with ADOS total scores (R = 0.749, p = 34e-4) and RRBs sub-score (R = 0.770, p = 1.87e-4) for EFC (R = 0.641, p = 0.0148) for TFT. The accuracy of NBS-predict in identifying ASD was 63.4%. ROC curve demonstrated TFT with 91 and 90% sensitivity, and 78.7% and 77.4% specificity for ADOS total and RRB sub-scores, respectively. Simultaneous EFC and ET evaluation in ASD is highly correlated with RRB symptoms measured by ADOS-2. NBS-predict of EFC offered a direct prediction of ASD. The use of both EFC and ET improve early ASD diagnosis.

Investigation of LncRNA PVT1 and MiR-21-5p Expression as Promising Novel Biomarkers for Autism Spectrum Disorder.

The characteristics of ncRNA in children with autism spectrum disorder (ASD) were observed to disclose a theoretical basis for further research on molecular markers for early warning of ASD. Children with ASD and normal control children were recruited to collect peripheral blood RNA samples. The concentration of PVT1 and miR-21-5p was quantitatively analyzed by qRT-PCR. Pearson correlation coefficient method was used to evaluate the link between PVT1 level and miR-21-5p level of the children. Receiver operating characteristic (ROC) curves were applied to reckon the predictive value of PVT1, miR-21-5p, and their combination in ASD. The interconnection of PVT1 with miR-21-5p was represented by luciferase reporter assay. The targeted genes of miR-21-5p were predicted. The enrichment and protein interaction analysis of these genes was carried out to find the important core genes and analyze their value in ASD. In the disease group, the level of PVT1 was downregulated, while the content of miR-21-5p was upregulated. The expression level of serum miR-21-5p was negatively correlated with the level of PVT1. Luciferase reporter gene assay documented that PVT1 directly targeted miR-21-5p. ROC curve showed that PVT1, miR-21-5p, and their combination showed clinical value for disease diagnosis. The functional enrichment analysis showed that the targets of miR-21-5p participated in ASD by regulating related functions and pathways. Reduced expression of PVT1 and raised miR-21-5p were good diagnostic markers for ASD, which would provide a basis for effective prevention, early diagnosis, and early intervention of ASD.

Machine Learning Algorithms Applied to Predict Autism Spectrum Disorder Based on Gut Microbiome Composition.

The application of machine learning (ML) techniques stands as a reliable method for aiding in the diagnosis of complex diseases. Recent studies have related the composition of the gut microbiota to the presence of autism spectrum disorder (ASD), but until now, the results have been mostly contradictory. This work proposes using machine learning to study the gut microbiome composition and its role in the early diagnosis of ASD. We applied support vector machines (SVMs), artificial neural networks (ANNs), and random forest (RF) algorithms to classify subjects as neurotypical (NT) or having ASD, using published data on gut microbiome composition. Naive Bayes, k-nearest neighbors, ensemble learning, logistic regression, linear regression, and decision trees were also trained and validated; however, the ones presented showed the best performance and interpretability. All the ML methods were developed using the SAS Viya software platform. The microbiome's composition was determined using 16S rRNA sequencing technology. The application of ML yielded a classification accuracy as high as 90%, with a sensitivity of 96.97% and specificity reaching 85.29%. In the case of the ANN model, no errors occurred when classifying NT subjects from the first dataset, indicating a significant classification outcome compared to traditional tests and data-based approaches. This approach was repeated with two datasets, one from the USA and the other from China, resulting in similar findings. The main predictors in the obtained models differ between the analyzed datasets. The most important predictors identified from the analyzed datasets are Bacteroides, Lachnospira, Anaerobutyricum, and Ruminococcus torques. Notably, among the predictors in each model, there is the presence of bacteria that are usually considered insignificant in the microbiome's composition due to their low relative abundance. This outcome reinforces the conventional understanding of the microbiome's influence on ASD development, where an imbalance in the composition of the microbiota can lead to disrupted host-microbiota homeostasis. Considering that several previous studies focused on the most abundant genera and neglected smaller (and frequently not statistically significant) microbial communities, the impact of such communities has been poorly analyzed. The ML-based models suggest that more research should focus on these less abundant microbes. A novel hypothesis explains the contradictory results in this field and advocates for more in-depth research to be conducted on variables that may not exhibit statistical significance. The obtained results seem to contribute to an explanation of the contradictory findings regarding ASD and its relation with gut microbiota composition. While some research correlates higher ratios of Bacillota/Bacteroidota, others find the opposite. These discrepancies are closely linked to the minority organisms in the microbiome's composition, which may differ between populations but share similar metabolic functions. Therefore, the ratios of Bacillota/Bacteroidota regarding ASD may not be determinants in the manifestation of ASD.

Iron metabolism in autism spectrum disorder; inference through single nucleotide polymorphisms in key iron metabolism genes

Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental problems with various genetic and environmental components. The ASD diagnosis is based on symptom expression without reliance on any biomarkers. The genetic contributions in ASD remain elusive. Various studies have linked ASD with iron. Since iron plays a crucial role in brain development, neurotransmitter synthesis, neuronal myelination and mitochondrial function, we hypothesized that iron dysregulation in the brain could play a role and contribute to the pathogenesis of ASD. In this study, we investigated single nucleotide polymorphisms in ASD in various iron metabolism genes, including the Transferrin Receptor (TFRC) gene (rs11915082), the Solute Carrier Family 11 Member 2 (SLC11A2) gene (rs1048230 and rs224589), the Solute Carrier Family 40 Member 1 (SLC40A1) gene (rs1439816), and hepcidin antimicrobial peptide (HAMP) gene (rs10421768). We recruited 48 patients with ASD and 88 matched non-ASD controls. Our results revealed a significant difference between ASD and controls in the G allele of the TFRC gene rs11915082, and in the C allele of the SLC40A1 gene rs1439816. In silico analysis demonstrated potential positive role of the indicated genetic variations in ASD development and pathogenesis. These results suggest that specific genetic variations in iron metabolism genes may represent part of early genetic markers for early diagnosis of ASD. A significant effect of SNPs, groups (ASD/control) as well as interaction between SNPs and groups was revealed. Follow-up post hoc tests showed a significant difference between the ASD and control groups in rs11915082 (TFRC gene) and rs1439816 (SLC40A1 gene). Backward conditional logistic regression using both the genotype and allele data showed similar ability in detecting ASD using allel model (Nagelkerke R2 = 0.350 p = 0.967; Variables: rs1439816, rs11915082) compared to genotype model (Nagelkerke R2 = 0.347, p = 0.430; Variables: rs1439816 G, rs1439816 C, rs10421768 A). ROC curve showed 54% sensitivity in detecting ASD compared to 47% for the genotype model. Both models differentiated controls with high accuracy; the allele model had a specificity of 91% compared to 92% for the genotype model.In conclusion, our findings suggest that specific genetic variations in iron metabolism may represent early biomarkers for a diagnosis of ASD. Further research is needed to correlate these markers with specific blood iron indicators and their contribution to brain development and behavior.

Application of Multimodal MRI in the Early Diagnosis of Autism Spectrum Disorders: A Review.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder in children. Early diagnosis and intervention can remodel the neural structure of the brain and improve quality of life but may be inaccurate if based solely on clinical symptoms and assessment scales. Therefore, we aimed to analyze multimodal magnetic resonance imaging (MRI) data from the existing literature and review the abnormal changes in brain structural-functional networks, perfusion, neuronal metabolism, and the glymphatic system in children with ASD, which could help in early diagnosis and precise intervention. Structural MRI revealed morphological differences, abnormal developmental trajectories, and network connectivity changes in the brain at different ages. Functional MRI revealed disruption of functional networks, abnormal perfusion, and neurovascular decoupling associated with core ASD symptoms. Proton magnetic resonance spectroscopy revealed abnormal changes in the neuronal metabolites during different periods. Decreased diffusion tensor imaging signals along the perivascular space index reflected impaired glymphatic system function in children with ASD. Differences in age, subtype, degree of brain damage, and remodeling in children with ASD led to heterogeneity in research results. Multimodal MRI is expected to further assist in early and accurate clinical diagnosis of ASD through deep learning combined with genomics and artificial intelligence.

Artificial intelligence and bioinformatics analyze markers of children's transcriptional genome to predict autism spectrum disorder.

Autism spectrum disorder (ASD), characterized by difficulties in social interaction and communication as well as restricted interests and repetitive behaviors, is extremely challenging to diagnose in toddlers. Early diagnosis and intervention are crucial however. In this study, we developed a machine learning classification model based on mRNA expression data from the peripheral blood of 128 toddlers with ASD and 126 controls. Differentially expressed genes (DEGs) between ASD and controls were identified. We identified genes such as UBE4B, SPATA2 and RBM3 as DEGs, mainly involved in immune-related pathways. 21 genes were screened as key biomarkers using LASSO regression, yielding an accuracy of 86%. A neural network model based on these 21 genes achieved an AUC of 0.88. Our findings suggest that the identified neurotransmitters and 21 immune-related biomarkers may facilitate the early diagnosis of ASD. The mRNA expression profile sheds light on the biological underpinnings of ASD in toddlers and potential biomarkers for early identification. Nevertheless, larger samples are needed to validate these biomarkers.

Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey.

Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging-more specifically, magnetic resonance imaging (MRI)-has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.

A Multi-Site Anti-Interference Neural Network for ASD Classification

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that can reduce quality of life and burden families. However, there is a lack of objectivity in clinical diagnosis, so it is very important to develop a method for early and accurate diagnosis. Multi-site data increases sample size and statistical power, which is convenient for training deep learning models. However, heterogeneity between sites will affect ASD recognition. To solve this problem, we propose a multi-site anti-interference neural network for ASD classification. The resting state brain functional image data provided by the multi-site is used to train the ASD classification model. The model consists of three modules. First, the site feature extraction module is used to quantify the inter-site heterogeneity, in which the autoencoder is used to reduce the feature dimension. Secondly, the presentation learning module is used to extract classification features. Finally, the anti-interference classification module uses the output of the first two modules as labels and inputs for multi-task adversarial training to complete the representation learning that is not affected by the confounding of sites, so as to realize the adaptive anti-interference ASD classification. The results show that the average accuracy of ten-fold cross validation is 75.56%, which is better than the existing studies. The innovation of our proposed method lies in the problem that the traditional single-task deep learning ASD classification model will be affected by the heterogeneity of multi-site data and interfere with the classification. Our method eliminates the influence of multi-site factors on feature extraction through multi-task adversarial training, so that the model can better adapt to the heterogeneity of multi-site data. Meanwhile, large-scale 1DconV is introduced to extract features of brain functional network, which provides support for the interpretability of the model. This method is expected to take advantage of multiple sites and provide reference for early diagnosis and treatment of ASD.

COMPARISON OF MACHINE LEARNING MODELS FOR AUTOMATED AUTISM DIAGNOSIS

Autism Spectrum Disorder (ASD) is characterized by impairment in communication and language skills as well as repetitive and stereotyped behaviors. Early ASD diagnosis helps in developing a meaningful outcome in its treatment. Machine learning (ML) models can provide faster diagnostic capacity to determine patterns not observable by humans through behavioral analysis. We applied the ML classification models, including random forest, logistic regression, K-nearest neighbor intuition, support vector machine, decision tree, kernel support vector machine, and Naive Bayes, for each data set (children, teenagers, and adults). Our results show that ML models are powerful tools that can assist healthcare professionals in diagnosing ASD. Our model predicts non-autism cases with 97.9% accuracy. We believe that performing a logistic regression analysis indicating which factors increase or decrease the probability of diagnosis is a significant contribution. We hope to elucidate alternative ways to objectively diagnose ASD for timely treatment purposes.

Detection of autism spectrum disorder (ASD) in children and adults using machine learning

Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults).

Metallomic profiling and natural copper isotopic signatures of childhood autism in serum and red blood cells

Excessive exposure to metals directly threatens human health, including neurodeve lopment. Autism spectrum disorder (ASD) is a neurodevelopmental disorder, leaving great harms to children themselves, their families, and even society. In view of this, it is critical to develop reliable biomarkers for ASD in early childhood. Here we used inductively coupled plasma mass spectrometry (ICP-MS) to identify the abnormalities in ASD-associated metal elements in children blood. Multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) was applied to detect isotopic differences in copper (Cu) for further assessment on account of its core role in the brain. We also developed a machine learning classification method for unknown samples based on a support vector machine (SVM) algorithm. The results indicated significant differences in the blood metallome (chromium (Cr), manganese (Mn), cobalt (Co), magnesium (Mg), and arsenic (As)) between cases and controls, and a significantly lower Zn/Cu ratio was observed in the ASD cases. Interestingly, we found a strong association of serum copper isotopic composition (δ65Cu) with autistic serum. SVM was successfully applied to discriminate cases and controls based on the two-dimensional Cu signatures (Cu concentration and δ65Cu) with a high accuracy (94.4%). Overall, our findings revealed a new biomarker for potential early diagnosis and screening of ASD, and the significant alterations in the blood metallome also helped to understand the potential pathogenesis of ASD in terms of metallomics.

Risk of psychiatric comorbidity with autism spectrum disorder and its association with diagnosis timing using a nationally representative cohort

BackgroundA major issue of individuals with autism spectrum disorder (ASD) is having a comorbid, psychiatric disorder. However, the mental health of those with ASD is understudied. This study this study utilized a nationally representative population-based cohort data, to investigate the risk of psychiatric comorbidity with ASD and its association with, diagnosis timing. MethodsCharacteristics of those without ASD, ASD (overall), early-diagnosed ASD, and latediagnosed, ASD were compared using a chi-squared test. Subsequently, a multiple, logistic regression was performed to estimate the risk of psychiatric comorbidity in, those with ASD, early-diagnosed ASD, and late-diagnosed ASD compared to those, without ASD, after controlling for covariates. Moreover, the association of psychiatric, comorbidity and diagnosis timing of ASD was investigated using multiple logistics while controlling for covariates. ResultsIndividuals with ASD had a higher risk of having a psychiatric disorder than those, without ASD. Specifically, those with ASD showed a higher risk of schizophrenia and, related disorders, mood disorders, anxiety and stress disorders, intellectual disability, and behavioral and emotional disorders with onset usually occurring during childhood, and adolescence compared to those without ASD. Moreover, individuals with latediagnosed, ASD showed a higher risk of having all comorbid psychiatric disorder than, early-diagnosed ASD when compared to non-ASD, except for schizophrenia and related disorders. ConclusionsChildren with ASD are at a higher risk of psychiatric comorbidity than typically, developed children. Especially early ASD diagnosis may be important in alleviating the, risk of psychiatric comorbidity of those with ASD.

ASDC-Net: Optimized Convolutional Neural Network-Based Automatic Autism Spectrum Disorder Classification Using rs-fMRI Data

Progressive neurological disorders such as Autism Spectrum Disorder (ASD) affects one in every 160 children across the country. The latest diagnostic criteria for ASD are concentrated primarily on assessing behavior. The ASD diagnostic process, on the other hand, is often time-consuming and costly, putting a significant financial burden on patient families. This paper presents an automated clinical diagnosis system for classifying ASD and typical controls (TC) using a generalized end-to-end Convolutional neural network (CNN), a deep learning (DL)-based model referred to as ASDC-Net. The proposed model provides accelerated training through Batch Normalization (BN), reducing the internal covariate shift. The resulting classification system uses functional connectivity patterns or connectomes of CC400 brain parcellation atlas of resting-state functional MRI data (rs-fMRI). The performance of the proposed model has been assessed using 1035 subjects from the Autism Brain Imaging Data Exchange (ABIDE), including 530 TC and 505 ASD. The model achieves an accuracy of (Acc) of 76.72% as compared to state-of-the-art (SOTA) methods in classifying ASD outperforming several peer machine learning (ML) and DL models. Furthermore, the Diagnostic Odd Ratio (DOR) and Matthews Correlation Coefficient (MCC) have been introduced to evaluate the effectiveness of the classifier. The evaluation metrics indicate that the suggested method is efficient and raises the prospect of helping the clinician team with the early diagnosis of ASD.

The use of data independent acquisition based proteomic analysis and machine learning to reveal potential biomarkers for autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurological developmental disorder in children, and is associated with social isolation and restricted interests. The etiology of this disorder is still unknown. There is neither any confirmed laboratory test nor any effective therapeutic strategy to diagnose or cure it. We performed data independent acquisition (DIA) and multiple reaction monitoring (MRM) analysis of plasma from children with ASD and controls. The result showed that 45 differentially expressed proteins (DEPs) were identified between autistic subjects and controls. Among these, only one DEP was down-regulated in ASD; other DEPs were up-regulated in ASD children's plasma. These proteins are found associated with complement and coagulation cascades, vitamin digestion and absorption, cholesterol metabolism, platelet degranulation, selenium micronutrient network, extracellular matrix organization and inflammatory pathway, which have been reported to be related to ASD. After MRM verification, five key proteins in complement pathway (PLG, SERPINC1, and A2M) and inflammatory pathway (CD5L, ATRN, SERPINC1, and A2M) were confirmed to be significantly up-regulated in ASD group. Through the screening of machine learning model and MRM verification, we found that two proteins (biotinidase and carbonic anhydrase 1) can be used as early diagnostic markers of ASD (AUC = 0.8, p = 0.0001). SignificanceASD is the fastest growing neurodevelopmental disorder in the world and has become a major public health problem worldwide. Its prevalence has been steadily increasing, with a global prevalence rate of 1%. Early diagnosis and intervention can achieve better prognosis. In this study, data independent acquisition (DIA) and multiple reaction monitoring (MRM) analysis was applied to analyze the plasma proteome of ASD patients (31 (±5) months old), and 378 proteins were quantified. 45 differentially expressed proteins (DEPs) were identified between the ASD group and the control group. They mainly were associated with platelet degranulation, ECM proteoglycar, complement and coagulation cascades, selenium micronutrient network, regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs), cholesterol metabolism, vitamin metabolism, and inflammatory pathway. Through the integrated machine learning methods and the MRM verification of independent samples, it is considered that biotinidase and carbon anhydrase 1 have the potential to become biomarkers for the early diagnosis of ASD. These results complement proteomics database of the ASD patients, broaden our understanding of ASD, and provide a panel of biomarkers for the early diagnosis of ASD.

Autism spectrum disorder diagnosis using a new panel of immune- and inflammatory-related serum biomarkers: A case-control multicenter study.

Children with autism spectrum disorder (ASD) present with distinctive clinical features. No objective laboratory assay has been developed to establish a diagnosis of ASD. Considering the known immunological associations with ASD, immunological biomarkers might enable ASD diagnosis and intervention at an early age when the immature brain has the highest degree of plasticity. This work aimed to identify diagnostic biomarkers discriminating between children with ASD and typically developing (TD) children. A multicenter, diagnostic case-control study trial was conducted in Israel and Canada between 2014 and 2021. In this trial, a single blood sample was collected from 102 children with ASD as defined in Diagnostic Statistical Manual of Mental Disorders [DSM)-IV (299.00) or DSM-V (299.00)], and from 97 typically developing control children aged 3-12 years. Samples were analyzed using a high-throughput, multiplexed ELISA array which quantifies 1,000 human immune/inflammatory-related proteins. Multiple logistic regression analysis was used to obtain a predictor from these results using 10-fold cross validation. Twelve biomarkers were identified that provided an overall accuracy of 0.82 ± 0.09 (sensitivity: 0.87 ± 0.08; specificity: 0.77 ± 0.14) in diagnosing ASD with a threshold of 0.5. The resulting model had an area under the curve of 0.86 ± 0.06 (95% CI: 0.811-0.889). Of the 102 ASD children included in the study, 13% were negative for this signature. Most of the markers included in all models have been reported to be associated with ASD and/or autoimmune diseases. The identified biomarkers may serve as the basis of an objective assay for early and accurate diagnosis of ASD. In addition, the markers may shed light on ASD etiology and pathogenesis. It should be noted that this was only a pilot, case-control diagnostic study, with a high risk of bias. The findings should be validated in larger prospective cohorts of consecutive children suspected of ASD.

Level of Attention to Motherese Speech as an Early Marker of Autism Spectrum Disorder

Caregivers have long captured the attention of their infants by speaking in motherese, a playful speech style characterized by heightened affect. Reduced attention to motherese in toddlers with autism spectrum disorder (ASD) may be a contributor to downstream language and social challenges and could be diagnostically revealing. To investigate whether attention toward motherese speech can be used as a diagnostic classifier of ASD and is associated with language and social ability. This diagnostic study included toddlers aged 12 to 48 months, spanning ASD and non-ASD diagnostic groups, at a research center. Data were collected from February 2018 to April 2021 and analyzed from April 2021 to March 2022. Gaze-contingent eye-tracking test. Using gaze-contingent eye tracking wherein the location of a toddler's fixation triggered a specific movie file, toddlers participated in 1 or more 1-minute eye-tracking tests designed to quantify attention to motherese speech, including motherese vs traffic (ie, noisy vehicles on a highway) and motherese vs techno (ie, abstract shapes with music). Toddlers were also diagnostically and psychometrically evaluated by psychologists. Levels of fixation within motherese and nonmotherese movies and mean number of saccades per second were calculated. Receiver operating characteristic (ROC) curves were used to evaluate optimal fixation cutoff values and associated sensitivity, specificity, positive predictive value (PPV), and negative predictive value. Within the ASD group, toddlers were stratified based on low, middle, or high levels of interest in motherese speech, and associations with social and language abilities were examined. A total of 653 toddlers were included (mean [SD] age, 26.45 [8.37] months; 480 males [73.51%]). Unlike toddlers without ASD, who almost uniformly attended to motherese speech with a median level of 82.25% and 80.75% across the 2 tests, among toddlers with ASD, there was a wide range, spanning 0% to 100%. Both the traffic and techno paradigms were effective diagnostic classifiers, with large between-group effect sizes (eg, ASD vs typical development: Cohen d, 1.0 in the techno paradigm). Across both paradigms, a cutoff value of 30% or less fixation on motherese resulted in an area under the ROC curve (AUC) of 0.733 (95% CI, 0.693-0.773) and 0.761 (95% CI, 0.717-0.804), respectively; specificity of 98% (95% CI, 95%-99%) and 96% (95% CI, 92%-98%), respectively; and PPV of 94% (95% CI, 86%-98%). Reflective of heterogeneity and expected subtypes in ASD, sensitivity was lower at 18% (95% CI, 14%-22%) and 29% (95% CI, 24%-34%), respectively. Combining metrics increased the AUC to 0.841 (95% CI, 0.805-0.877). Toddlers with ASD who showed the lowest levels of attention to motherese speech had weaker social and language abilities. In this diagnostic study, a subset of toddlers showed low levels of attention toward motherese speech. When a cutoff level of 30% or less fixation on motherese speech was used, toddlers in this range were diagnostically classified as having ASD with high accuracy. Insight into which toddlers show unusually low levels of attention to motherese may be beneficial not only for early ASD diagnosis and prognosis but also as a possible therapeutic target.

Serum Neurofilament Light Chain and Glial Fibrillary Acidic Protein as Potential Diagnostic Biomarkers in Autism Spectrum Disorders: A Preliminary Study.

Autism spectrum disorder (ASD) is one of the most common neurodevelopment disorders, characterized by a multifactorial etiology based on the interaction of genetic and environmental factors. Recent evidence supports the neurobiological hypothesis based on neuroinflammation theory. To date, there are no sufficiently validated diagnostic and prognostic biomarkers for ASD. Therefore, we decided to investigate the potential diagnostic role for ASD of two biomarkers well known for other neurological inflammatory conditions: the glial fibrillary acidic protein (GFAP) and the neurofilament (Nfl). Nfl and GFAP serum levels were analyzed using SiMoA technology in a group of ASD patients and in a healthy control group (CTRS), age- and gender-matched. Then we investigated the distribution, frequency, and correlation between serum Nfl and GFAP levels and clinical data among the ASD group. The comparison of Nfl and GFAP serum levels between ASD children and the control group showed a mean value of these two markers significantly higher in the ASD group (sNfL mean value ASD pt 6.86 pg/mL median value ASD pt 5.7 pg/mL; mean value CTRS 3.55 pg/mL; median value CTRS 3.1 pg; GFAP mean value ASD pt 205.7 pg/mL median value ASD pt 155.4 pg/mL; mean value CTRS 77.12 pg/mL; median value CTRS 63.94 pg/mL). Interestingly, we also found a statistically significant positive correlation between GFAP levels and hyperactivity symptoms (p-value <0.001). Further investigations using larger groups are necessary to confirm our data and to verify in more depth the potential correlation between these biomarkers and ASD clinical features, such as the severity of the core symptoms, the presence of associated symptoms, and/or the evaluation of a therapeutic intervention. However, these data not only might shed a light on the neurobiology of ASD, supporting the neuroinflammation and neurodegeneration hypothesis, but they also might support the use of these biomarkers in the early diagnosis of ASD, to longitudinally monitor the disease activity, and even more as future prognostic biomarkers.

Machine Learning-Based Blood RNA Signature for Diagnosis of Autism Spectrum Disorder.

Early diagnosis of autism spectrum disorder (ASD) is crucial for providing appropriate treatments and parental guidance from an early age. Yet, ASD diagnosis is a lengthy process, in part due to the lack of reliable biomarkers. We recently applied RNA-sequencing of peripheral blood samples from 73 American and Israeli children with ASD and 26 neurotypically developing (NT) children to identify 10 genes with dysregulated blood expression levels in children with ASD. Machine learning (ML) analyzes data by computerized analytical model building and may be applied to building diagnostic tools based on the optimization of large datasets. Here, we present several ML-generated models, based on RNA expression datasets collected during our recently published RNA-seq study, as tentative tools for ASD diagnosis. Using the random forest classifier, two of our proposed models yield an accuracy of 82% in distinguishing children with ASD and NT children. Our proof-of-concept study requires refinement and independent validation by studies with far larger cohorts of children with ASD and NT children and should thus be perceived as starting point for building more accurate ML-based tools. Eventually, such tools may potentially provide an unbiased means to support the early diagnosis of ASD.