Emerging biomarkers in autism spectrum disorder: a systematic review

Metabolomic changes in children with autism

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social communication and repetitive behaviors. Metabolomic profiling has emerged as a valuable tool for understanding the underlying metabolic dysregulations associated with ASD.

AIM

To comprehensively explore metabolomic changes in children with ASD, integrating findings from various research articles, reviews, systematic reviews, meta-analyses, case reports, editorials, and a book chapter.

METHODS

A systematic search was conducted in electronic databases, including PubMed, PubMed Central, Cochrane Library, Embase, Web of Science, CINAHL, Scopus, LISA, and NLM catalog up until January 2024. Inclusion criteria encompassed research articles (83), review articles (145), meta-analyses (6), systematic reviews (6), case reports (2), editorials (2), and a book chapter (1) related to metabolomic changes in children with ASD. Exclusion criteria were applied to ensure the relevance and quality of included studies.

RESULTS

The systematic review identified specific metabolites and metabolic pathways showing consistent differences in children with ASD compared to typically developing individuals. These metabolic biomarkers may serve as objective measures to support clinical assessments, improve diagnostic accuracy, and inform personalized treatment approaches. Metabolomic profiling also offers insights into the metabolic alterations associated with comorbid conditions commonly observed in individuals with ASD.

CONCLUSION

Integration of metabolomic changes in children with ASD holds promise for enhancing diagnostic accuracy, guiding personalized treatment approaches, monitoring treatment response, and improving outcomes. Further research is needed to validate findings, establish standardized protocols, and overcome technical challenges in metabolomic analysis. By advancing our understanding of metabolic dysregulations in ASD, clinicians can improve the lives of affected individuals and their families.

Intrinsic network abnormalities in children with autism spectrum disorder: an independent component analysis

Aberrant intrinsic brain networks are consistently observed in individuals with autism spectrum disorder. However, studies examining the strength of functional connectivity across brain regions have yielded conflicting results. Therefore, this study aimed to investigate the functional connectivity of the resting brain in children with low-functioning autism, including during the early developmental stages. We explored the functional connectivity of 43 children with autism spectrum disorder and 54 children with typical development aged 2 to 12 years using resting-state functional magnetic resonance imaging data. We used independent component analysis to classify the brain regions into six intrinsic networks and analyzed the functional connectivity within each network. Moreover, we analyzed the relationship between functional connectivity and clinical scores. In children with autism, the under-connectivities were observed within several brain networks, including the cognitive control, default mode, visual, and somatomotor networks. In contrast, we found over-connectivities between the subcortical, visual, and somatomotor networks in children with autism compared with children with typical development. Moderate effect sizes were observed in entire networks (Cohen's d = 0.43-0.77). These network alterations were significantly correlated with clinical scores such as the communication sub-score (r = - 0.442, p = 0.045) and the calibrated severity score (r = - 0.435, p = 0.049) of the Autism Diagnostic Observation Schedule. These opposing results observed based on the brain areas suggest that aberrant neurodevelopment proceeds in various ways depending on the functional brain regions in individuals with autism spectrum disorder.

Resting Heart Rate Variability and Emotion Dysregulation in Adolescents with Autism Spectrum Disorder

Emotion dysregulation is common among individuals with autism spectrum disorder (ASD). This study examined the relationship between emotion dysregulation and resting heart rate variability (HRV), a marker of the autonomic nervous system, in ASD adolescents. Resting HRV data were collected from ASD (n = 23) and typically developing (TD) adolescents (n = 32) via short-term electrocardiogram. Parents/caregivers reported participants' level of emotion dysregulation with the Emotion Dysregulation Inventory (EDI). Controlling for the effects of age and gender, regression analyses revealed moderating effects of group, suggesting that lower resting HRV was more strongly associated with greater emotion dysregulation in ASD than TD adolescents. The results support the view that disruptions in autonomic functioning may contribute to emotion dysregulation in ASD.

Metabolomic profiles in Jamaican children with and without autism spectrum disorder

Background

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a wide range of behavioral and cognitive impairments. While genetic and environmental factors are known to contribute to its etiology, the underlying metabolic perturbations associated with ASD which can potentially connect genetic and environmental factors, remain poorly understood. Therefore, we conducted a metabolomic case-control study and performed a comprehensive analysis to identify significant alterations in metabolite profiles between children with ASD and typically developing (TD) controls.

Objective

To elucidate potential metabolomic signatures associated with ASD in children and identify specific metabolites that may serve as biomarkers for the disorder.

Methods

We conducted metabolomic profiling on plasma samples from participants in the second phase of Epidemiological Research on Autism in Jamaica (ERAJ-2), which was a 1:1 age (±6 months)-and sex-matched cohort of 200 children with ASD and 200 TD controls (2-8 years old). Using high-throughput liquid chromatography-mass spectrometry techniques, we performed a targeted metabolite analysis, encompassing amino acids, lipids, carbohydrates, and other key metabolic compounds. After quality control and imputation of missing values, we performed univariable and multivariable analysis using normalized metabolites while adjusting for covariates, age, sex, socioeconomic status, and child's parish of birth.

Results

Our findings revealed unique metabolic patterns in children with ASD for four metabolites compared to TD controls. Notably, three of these metabolites were fatty acids, including myristoleic acid, eicosatetraenoic acid, and octadecenoic acid. Additionally, the amino acid sarcosine exhibited a significant association with ASD.

Conclusions

These findings highlight the role of metabolites in the etiology of ASD and suggest opportunities for the development of targeted interventions.

Oscillatory activity underlying cognitive performance in children and adolescents with autism: a systematic review

Autism spectrum disorder (ASD) is a neurodevelopmental condition that exhibits a widely heterogeneous range of social and cognitive symptoms. This feature has challenged a broad comprehension of this neurodevelopmental disorder and therapeutic efforts to address its difficulties. Current therapeutic strategies have focused primarily on treating behavioral symptoms rather than on brain psychophysiology. During the past years, the emergence of non-invasive brain stimulation techniques (NIBS) has opened alternatives to the design of potential combined treatments focused on the neurophysiopathology of neuropsychiatric disorders like ASD. Such interventions require identifying the key brain mechanisms underlying the symptomatology and cognitive features. Evidence has shown alterations in oscillatory features of the neural ensembles associated with cognitive functions in ASD. In this line, we elaborated a systematic revision of the evidence of alterations in brain oscillations that underlie key cognitive processes that have been shown to be affected in ASD during childhood and adolescence, namely, social cognition, attention, working memory, inhibitory control, and cognitive flexibility. This knowledge could contribute to developing therapies based on NIBS to improve these processes in populations with ASD.

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

BACKGROUND

Early diagnosis and therapeutic interventions can greatly enhance the developmental trajectory of children with autism spectrum disorder (ASD). However, the etiology of ASD is not completely understood. The presence of confounding factors from environment and genetics has increased the difficulty of the identification of diagnostic biomarkers for ASD.

AIM

To estimate and interpret the causal relationship between ASD and metabolite profile, taking into consideration both genetic and environmental influences.

METHODS

A two-sample Mendelian randomization (MR) analysis was conducted using summarized data from large-scale genome-wide association studies (GWAS) including a metabolite GWAS dataset covering 453 metabolites from 7824 European and an ASD GWAS dataset comprising 18381 ASD cases and 27969 healthy controls. Metabolites in plasma were set as exposures with ASD as the main outcome. The causal relationships were estimated using the inverse variant weight (IVW) algorithm. We also performed leave-one-out sensitivity tests to validate the robustness of the results. Based on the drafted metabolites, enrichment analysis was conducted to interpret the association via constructing a protein-protein interaction network with multi-scale evidence from databases including Infinome, SwissTargetPrediction, STRING, and Metascape.

RESULTS

Des-Arg(9)-bradykinin was identified as a causal metabolite that increases the risk of ASD (β = 0.262, SE = 0.064, PIVW = 4.64 × 10-5). The association was robust, with no significant heterogeneity among instrument variables (PMR Egger = 0.663, PIVW = 0.906) and no evidence of pleiotropy (P = 0.949). Neuroinflammation and the response to stimulus were suggested as potential biological processes mediating the association between Des-Arg(9) bradykinin and ASD.

CONCLUSION

Through the application of MR, this study provides practical insights into the potential causal association between plasma metabolites and ASD. These findings offer perspectives for the discovery of diagnostic or predictive biomarkers to support clinical practice in treating ASD.

Volume-based and Surface-Based Methods in Autism Compared with Healthy Controls Are Free surfer and CAT12 in Agreement?

Objectives

Autism Spectrum Disorder (ASD) encompasses a range of neurodevelopmental disorders, and early detection is crucial. This study aims to identify the Regions of Interest (ROIs) with significant differences between healthy controls and individuals with autism, as well as evaluate the agreement between FreeSurfer 6 (FS6) and Computational Anatomy Toolbox (CAT12) methods.

Materials & Methods

Surface-based and volume-based features were extracted from FS software and CAT12 toolbox for Statistical Parametric Mapping (SPM) software to estimate ROI-wise biomarkers. These biomarkers were compared between 18 males Typically Developing Controls (TDCs) and 40 male subjects with ASD to assess group differences for each method. Finally, agreement and regression analyses were performed between the two methods for TDCs and ASD groups.

Results

Both methods revealed ROIs with significant differences for each parameter. The Analysis of Covariance (ANCOVA) showed that both TDCs and ASD groups indicated a significant relationship between the two methods (p<0.001). The R2 values for TDCs and ASD groups were 0.692 and 0.680, respectively, demonstrating a moderate correlation between CAT12 and FS6. Bland-Altman graphs showed a moderate level of agreement between the two methods.

Conclusion

The moderate correlation and agreement between CAT12 and FS6 suggest that while some consistency is observed in the results, CAT12 is not a superior substitute for FS6 software. Further research is needed to identify a potential replacement for this method.

Resting-state EEG power differences in autism spectrum disorder: a systematic review and meta-analysis

Narrative reviews have described various resting-state EEG power differences in autism across all five canonical frequency bands, with increased power for low and high frequencies and reduced power for middle frequencies. However, these differences have yet to be quantified using effect sizes and probed robustly for consistency, which are critical next steps for clinical translation. Following PRISMA guidelines, we conducted a systematic review of published and gray literature on resting-state EEG power in autism. We performed 10 meta-analyses to synthesize and quantify differences in absolute and relative resting-state delta, theta, alpha, beta, and gamma EEG power in autism. We also conducted moderator analyses to determine whether demographic characteristics, methodological details, and risk-of-bias indicators might account for heterogeneous study effect sizes. Our literature search and study selection processes yielded 41 studies involving 1,246 autistic and 1,455 neurotypical individuals. Meta-analytic models of 135 effect sizes demonstrated that autistic individuals exhibited reduced relative alpha (g = -0.35) and increased gamma (absolute: g = 0.37, relative: g = 1.06) power, but similar delta (absolute: g = 0.06, relative: g = 0.10), theta (absolute: g = -0.03, relative: g = -0.15), absolute alpha (g = -0.17), and beta (absolute: g = 0.01, relative: g = 0.08) power. Substantial heterogeneity in effect sizes was observed across all absolute (I2: 36.1-81.9%) and relative (I2: 64.6-84.4%) frequency bands. Moderator analyses revealed that age, biological sex, IQ, referencing scheme, epoch duration, and use of gold-standard autism diagnostic instruments did not moderate study effect sizes. In contrast, resting-state paradigm type (eyes-closed versus eyes-open) moderated absolute beta, relative delta, and relative alpha power effect sizes, and resting-state recording duration moderated relative alpha power effect sizes. These findings support further investigation of resting-state alpha and gamma power as potential biomarkers for autism.

Towards a comprehensive approach to mentalization-based treatment for children with autism: integrating attachment, neurosciences, and mentalizing

Autism spectrum disorder (ASD) is diagnosed based on socio-communicative difficulties, which are believed to result from deficits in mentalizing, mainly evidenced by alterations in recognizing and responding to the mental states of others. In recent years, efforts have been made to develop mentalization-based treatment (MBT) models for this population. These models focus on enhancing individuals' ability to understand and reflect on their own mental states, as well as those of others. However, MBT approaches for people with ASD are limited by their existing theoretical background, which lacks a strong foundation grounded in neuroscience-based evidence properly integrated with attachment, and mentalizing. These are crucial aspects for understanding psychological processes in autism, and as such, they play a pivotal role in shaping the development of tailored and effective therapeutic strategies for this specific population. In this paper we review evidence related to the neurobiological, interpersonal, and psychological dimensions of autism and their implications for mentalizing processes. We also review previous mentalization-based frameworks on the psychosis continuum to provide a comprehensive understanding of attachment, neurobiology, and mentalization domains in therapeutic approaches for autism. After presenting a synthesis of the literature, we offer a set of clinical strategies for the work with children with autism. Finally, we provide recommendations to advance the field towards more robust models that can serve as a basis for evidence-based therapeutic strategies.

App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review

BACKGROUND

Diagnostic delays in autism are common, with the time to diagnosis being up to 3 years from the onset of symptoms. Such delays have a proven detrimental effect on individuals and families going through the process. Digital health products, such as mobile apps, can help close this gap due to their scalability and ease of access. Further, mobile apps offer the opportunity to make the diagnostic process faster and more accurate by providing additional and timely information to clinicians undergoing autism assessments.

OBJECTIVE

The aim of this scoping review was to synthesize the available evidence about digital biomarker tools to aid clinicians, researchers in the autism field, and end users in making decisions as to their adoption within clinical and research settings.

METHODS

We conducted a structured literature search on databases and search engines to identify peer-reviewed studies and regulatory submissions that describe app characteristics, validation study details, and accuracy and validity metrics of commercial and research digital biomarker apps aimed at aiding the diagnosis of autism.

RESULTS

We identified 4 studies evaluating 4 products: 1 commercial and 3 research apps. The accuracy of the identified apps varied between 28% and 80.6%. Sensitivity and specificity also varied, ranging from 51.6% to 81.6% and 18.5% to 80.5%, respectively. Positive predictive value ranged from 20.3% to 76.6%, and negative predictive value fluctuated between 48.7% and 97.4%. Further, we found a lack of details around participants' demographics and, where these were reported, important imbalances in sex and ethnicity in the studies evaluating such products. Finally, evaluation methods as well as accuracy and validity metrics of available tools were not clearly reported in some cases and varied greatly across studies. Different comparators were also used, with some studies validating their tools against the Diagnostic and Statistical Manual of Mental Disorders criteria and others through self-reported measures. Further, while in most cases, 2 classes were used for algorithm validation purposes, 1 of the studies reported a third category (indeterminate). These discrepancies substantially impact the comparability and generalizability of the results, thus highlighting the need for standardized validation processes and the reporting of findings.

CONCLUSIONS

Despite their popularity, systematic evaluations and syntheses of the current state of the art of digital health products are lacking. Standardized and transparent evaluations of digital health tools in diverse populations are needed to assess their real-world usability and validity, as well as help researchers, clinicians, and end users safely adopt novel tools within clinical and research practices.

Increased cerebral lactate levels in adults with autism spectrum disorders compared to non-autistic controls: a magnetic resonance spectroscopy study

INTRODUCTION

Autism spectrum disorder (ASD) encompasses a heterogeneous group with varied phenotypes and etiologies. Identifying pathogenic subgroups could facilitate targeted treatments. One promising avenue is investigating energy metabolism, as mitochondrial dysfunction has been implicated in a subgroup of ASD. Lactate, an indicator of energy metabolic anomalies, may serve as a potential biomarker for this subgroup. This study aimed to examine cerebral lactate (Lac+) levels in high-functioning adults with ASD, hypothesizing elevated mean Lac+ concentrations in contrast to neurotypical controls (NTCs).

MATERIALS AND METHODS

Magnetic resonance spectroscopy (MRS) was used to study cerebral Lac+ in 71 adults with ASD and NTC, focusing on the posterior cingulate cortex (PCC). After quality control, 64 ASD and 58 NTC participants remained. Lac+ levels two standard deviations above the mean of the control group were considered elevated.

RESULTS

Mean PCC Lac+ levels were significantly higher in the ASD group than in the NTC group (p = 0.028; Cohen's d = 0.404), and 9.4% of the ASD group had elevated levels as compared to 0% of the NTCs (p = 0.029). No significant correlation was found between blood serum lactate levels and MRS-derived Lac+ levels.

LIMITATIONS

A cautious interpretation of our results is warranted due to a p value of 0.028. In addition, a higher than anticipated proportion of data sets had to be excluded due to poor spectral quality.

CONCLUSION

This study confirms the presence of elevated cerebral Lac+ levels in a subgroup of adults with ASD, suggesting the potential of lactate as a biomarker for mitochondrial dysfunction in a subgroup of ASD. The lower-than-expected prevalence (20% was expected) and moderate increase require further investigation to elucidate the underlying mechanisms and relationships with mitochondrial function.

Altered markers of mitochondrial function in adults with autism spectrum disorder

Previous research suggests potential mitochondrial dysfunction and changes in fatty acid metabolism in a subgroup of individuals with autism spectrum disorder (ASD), indicated by higher lactate, pyruvate levels, and mitochondrial disorder prevalence. This study aimed to further investigate potential mitochondrial dysfunction in ASD by assessing blood metabolite levels linked to mitochondrial metabolism. Blood levels of creatine kinase (CK), alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate, pyruvate, free and total carnitine, as well as acylcarnitines were obtained in 73 adults with ASD (47 males, 26 females) and compared with those of 71 neurotypical controls (NTC) (44 males, 27 females). Correlations between blood parameters and psychometric ASD symptom scores were also explored. Lower CK (pcorr  = 0.045) levels were found exclusively in males with ASD compared to NTC, with no such variation in females. ALT and AST levels did not differ significantly between both groups. After correction for antipsychotic and antidepressant medication, CK remained significant. ASD participants had lower serum lactate levels (pcorr  = 0.036) compared to NTC, but pyruvate and carnitine concentrations showed no significant difference. ASD subjects had significantly increased levels of certain acylcarnitines, with a decrease in tetradecadienoyl-carnitine (C14:2), and certain acylcarnitines correlated significantly with autistic symptom scores. We found reduced serum lactate levels in ASD, in contrast to previous studies suggesting elevated lactate or pyruvate. This difference may reflect the focus of our study on high-functioning adults with ASD, who are likely to have fewer secondary genetic conditions associated with mitochondrial dysfunction. Our findings of significantly altered acylcarnitine levels in ASD support the hypothesis of altered fatty acid metabolism in a subset of ASD patients.

Apolipoprotein D as a Potential Biomarker in Neuropsychiatric Disorders

Neuropsychiatric disorders (NDs) are a diverse group of pathologies, including schizophrenia or bipolar disorders, that directly affect the mental and physical health of those who suffer from them, with an incidence that is increasing worldwide. Most NDs result from a complex interaction of multiple genes and environmental factors such as stress or traumatic events, including the recent Coronavirus Disease (COVID-19) pandemic. In addition to diverse clinical presentations, these diseases are heterogeneous in their pathogenesis, brain regions affected, and clinical symptoms, making diagnosis difficult. Therefore, finding new biomarkers is essential for the detection, prognosis, response prediction, and development of new treatments for NDs. Among the most promising candidates is the apolipoprotein D (Apo D), a component of lipoproteins implicated in lipid metabolism. Evidence suggests an increase in Apo D expression in association with aging and in the presence of neuropathological processes. As a part of the cellular neuroprotective defense machinery against oxidative stress and inflammation, changes in Apo D levels have been demonstrated in neuropsychiatric conditions like schizophrenia (SZ) or bipolar disorders (BPD), not only in some brain areas but in corporal fluids, i.e., blood or serum of patients. What is not clear is whether variation in Apo D quantity could be used as an indicator to detect NDs and their progression. This review aims to provide an updated view of the clinical potential of Apo D as a possible biomarker for NDs.

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.

Evidence of a reduced role for circumscribed interests in the social attention patterns of children with Autism Spectrum Disorder

Reduced social attention is characteristic of Autism Spectrum Disorder (ASD). It has been suggested to result from an early onset and excessive influence of circumscribed interests (CIs) on gaze behaviour, compared to typically developing (TYP) individuals. To date, these findings have been mixed. The current eye-tracking study utilised a visual preference paradigm to investigate the influence of CI versus non-CI objects on attention patterns in children with ASD (aged 3-12 years, n = 37) and their age-matched TYP peers (n = 30). Compared to TYP, social and object attention was reduced in the ASD group irrespective of the presence of CIs. Results suggest a reduced role for CIs and extend recent evidence of atypical attention patterns across social and non-social domains in ASD.

Serum levels of IL-6/IL-10/GLDH may be early recognition markers of anti-tuberculosis drugs (ATB) -induced liver injury

To explore the potential value of serum glutamate dehydrogenase (GLDH) combined with inflammatory cytokines as diagnostic biomarkers for anti-tuberculosis drug -induced liver injury (ATB-DILI). We collected the residual serum from the patients who met the criteria after liver function tests. We have examined these parameters including GLDH which were determined by enzyme-linked immunosorbent assay and cytokines which were determined by cytokine combination detection kit. Multivariate logistics stepwise forward regression was applied to establish regression models. A total of 138 tuberculosis patients were included in the diagnostic markers study of ATB-DILI, including normal liver function group (n = 108) and ATB-DILI group(n = 30). Serum GLDH, IL-6 and IL-10 levels were significantly increased in the ATB-DILI group. Receiver operating characteristic curve (ROC) curve showed that the area under curve (AUC) of serum GLDH, IL-6 and IL-10 for the diagnosis of ATB-DILI were 0.870, 0.714 and 0.811, respectively. In logistic regression modeling, the AUC of GLDH combined with IL-10 as an ATB-DILI marker is 0.912. Serum IL-6、IL-10 and GLDH levels began to rise preceded the increase in ALT by 7 days, with significant differences in IL-6 compared with 7 days. Serum GLDH, IL-6 and IL-10 levels were correlated with the severity of liver injury. In conclusion, we found that GLDH, IL-6 and IL-10 alone as diagnostic markers of ATB-DILI had good diagnostic efficacy. Logistic regression model established by GLDH and IL-10 had better diagnostic efficacy and IL-6 may be an early predictor of liver injury in the setting of ATB poisoning.

Mitochondria and Brain Disease: A Comprehensive Review of Pathological Mechanisms and Therapeutic Opportunities

Mitochondria play a vital role in maintaining cellular energy homeostasis, regulating apoptosis, and controlling redox signaling. Dysfunction of mitochondria has been implicated in the pathogenesis of various brain diseases, including neurodegenerative disorders, stroke, and psychiatric illnesses. This review paper provides a comprehensive overview of the intricate relationship between mitochondria and brain disease, focusing on the underlying pathological mechanisms and exploring potential therapeutic opportunities. The review covers key topics such as mitochondrial DNA mutations, impaired oxidative phosphorylation, mitochondrial dynamics, calcium dysregulation, and reactive oxygen species generation in the context of brain disease. Additionally, it discusses emerging strategies targeting mitochondrial dysfunction, including mitochondrial protective agents, metabolic modulators, and gene therapy approaches. By critically analysing the existing literature and recent advancements, this review aims to enhance our understanding of the multifaceted role of mitochondria in brain disease and shed light on novel therapeutic interventions.

Transcript levels of 4 genes in umbilical cord blood are predictive of later autism development: a longitudinal follow-up study

BACKGROUND

Over recent decades, autism spectrum disorder (ASD) has been of increasing epidemiological importance, given the substantial increase in its prevalence; at present, clinical diagnosis is possible only after 2 years of age. In this study, we sought to develop a potential predictive model for ASD screening.

METHODS

We conducted a longitudinal follow-up study of newborns over 3 years. We measured transcript levels of 4 genes (superoxide dismutase-2 [SOD2], retinoic acid-related orphan receptor-α [RORA], G protein-coupled estrogen receptor-1 [GPER], progesterone receptor [PGR]), 2 oxidative stress markers and epigenetic marks at the RORA promoter in case-control umbilical cord blood mononuclear cell (UCBMC) samples.

RESULTS

We followed 2623 newborns; we identified 41 children with ASD, 63 with delayed development and 2519 typically developing children. We matched the 41 children with ASD to 41 typically developing children for UCBMC measurements. Our results showed that children with ASD had significantly higher levels of H3K9me3 histone modifications at the RORA promoter and oxidative stress in UCBMC than typically developing children; children with delayed development showed no significant differences. Children with ASD had significantly lower expression of SOD2, RORA and GPER, but higher PGR expression than typically developing children. We established a model based on these 4 candidate genes, and achieved an area under the curve of 87.0% (standard deviation 3.9%) with a sensitivity of 1.000 and specificity of 0.854 to predict ASD in UCBMC.

LIMITATIONS

Although the gene combinations produced a good pass/fail cut-off value for ASD evaluation, relatively few children in our study sample had ASD.

CONCLUSION

The altered gene expression in UCBMC can predict later autism development, possibly providing a predictive model for ASD screening immediately after birth.

The first six months of life: A systematic review of early markers associated with later autism

There is now good evidence that behavioural signs of autism spectrum conditions (autism) emerge over the first two years of life. Identifying clear developmental differences early in life may facilitate earlier identification and intervention that can promote longer-term quality of life. Here we present a systematic review of studies investigating behavioural markers of later autism diagnosis or symptomology taken at 0-6 months. The following databases were searched for articles published between 01/01/2000 and 15/03/2022: Embase, Medline, Scopus, PubMed, PsycINFO, CINAHL, Web of Science and Proquest. Twenty-five studies met inclusion criteria: assessment of behaviour at 0-6 months and later assessment of autism symptomology or diagnosis. Studies examined behaviours of attention, early social and communication behaviours, and motor behaviours, as well as composite measures. Findings indicated some evidence of measures of general attention, attention to social stimuli, and motor behaviours associated with later autism diagnosis or symptomology. Findings were inconsistent regarding social and communication behaviours, with a lack of repeated or validated measures limiting drawing firm conclusions. We discuss implications of the findings and suggest recommendations for future research.

A machine learning approach for understanding the metabolomics response of children with autism spectrum disorder to medical cannabis treatment

Autism spectrum disorder (ASD) is a neurodevelopmental condition impacting behavior, communication, social interaction and learning abilities. Medical cannabis (MC) treatment can reduce clinical symptoms in individuals with ASD. Cannabis-responsive biomarkers are metabolites found in saliva that change in response to MC treatment. Previously we showed levels of these biomarkers in children with ASD successfully treated with MC shift towards the physiological levels detected in typically developing (TD) children, and potentially can quantify the impact. Here, we tested for the first time the capabilities of machine learning techniques applied to our dynamic, high-resolution and rich feature dataset of cannabis-responsive biomarkers from a limited number of children with ASD before and after MC treatment and a TD group to identify: (1) biomarkers distinguishing ASD and TD groups; (2) non-cannabinoid plant molecules with synergistic effects; and (3) biomarkers associated with specific cannabinoids. We found: (1) lysophosphatidylethanolamine can distinguish between ASD and TD groups; (2) novel phytochemicals contribute to the therapeutic effects of MC treatment by inhibition of acetylcholinesterase; and (3) THC- and CBD-associated cannabis-responsive biomarkers are two distinct groups, while CBG is associated with some biomarkers from both groups.

Evidence for an association of prenatal exposure to particulate matter with clinical severity of Autism Spectrum Disorder

Early-life exposure to air pollutants, including ozone (O₃), particulate matter (PM_2.5 or PM₁₀, depending on diameter of particles), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) has been suggested to contribute to the etiology of Autism Spectrum Disorder (ASD). In this study, we used air quality monitoring data to examine whether mothers of children with ASD were exposed to high levels of air pollutants during critical periods of pregnancy, and if higher exposure levels may lead to a higher clinical severity in their offspring. We used public data from the Portuguese Environment Agency to estimate exposure to these pollutants during the first, second and third trimesters of pregnancy, full pregnancy and first year of life of the child, for 217 subjects with ASD born between 2003 and 2016. These subjects were stratified in two subgroups according to clinical severity, as defined by the Autism Diagnostic Observational Schedule (ADOS). For all time periods, the average levels of PM_2.5, PM₁₀ and NO₂ to which the subjects were exposed were within the admissible levels defined by the European Union. However, a fraction of these subjects showed exposure to levels of PM_2.5 and PM₁₀ above the admissible threshold. A higher clinical severity was associated with higher exposure to PM_2.5 (p = 0.001), NO₂ (p = 0.011) and PM₁₀ (p = 0.041) during the first trimester of pregnancy, when compared with milder clinical severity. After logistic regression, associations with higher clinical severity were identified for PM_2.5 exposure during the first trimester (p = 0.002; OR = 1.14, 95%CI: 1.05-1.23) and full pregnancy (p = 0.04; OR = 1.07, 95%CI: 1.00-1.15) and for PM₁₀ (p = 0.02; OR = 1.07, 95%CI: 1.01-1.14) exposure during the third trimester. Exposure to PM is known to elicit neuropathological mechanisms associated with ASD, including neuroinflammation, mitochondrial disruptions, oxidative stress and epigenetic changes. These results offer new insights on the impact of early-life exposure to PM in ASD clinical severity.

A systematic review of non-coding RNA genes with differential expression profiles associated with autism spectrum disorders

AIMS

To identify differential expression of shorter non-coding RNA (ncRNA) genes associated with autism spectrum disorders (ASD).

BACKGROUND

ncRNA are functional molecules that derive from non-translated DNA sequence. The HUGO Gene Nomenclature Committee (HGNC) have approved ncRNA gene classes with alignment to the reference human genome. One subset is microRNA (miRNA), which are highly conserved, short RNA molecules that regulate gene expression by direct post-transcriptional repression of messenger RNA. Several miRNA genes are implicated in the development and regulation of the nervous system. Expression of miRNA genes in ASD cohorts have been examined by multiple research groups. Other shorter classes of ncRNA have been examined less. A comprehensive systematic review examining expression of shorter ncRNA gene classes in ASD is timely to inform the direction of research.

METHODS

We extracted data from studies examining ncRNA gene expression in ASD compared with non-ASD controls. We included studies on miRNA, piwi-interacting RNA (piRNA), small NF90 (ILF3) associated RNA (snaR), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), transfer RNA (tRNA), vault RNA (vtRNA) and Y RNA. The following electronic databases were searched: Cochrane Library, EMBASE, PubMed, Web of Science, PsycINFO, ERIC, AMED and CINAHL for papers published from January 2000 to May 2022. Studies were screened by two independent investigators with a third resolving discrepancies. Data was extracted from eligible papers.

RESULTS

Forty-eight eligible studies were included in our systematic review with the majority examining miRNA gene expression alone. Sixty-four miRNA genes had differential expression in ASD compared to controls as reported in two or more studies, but often in opposing directions. Four miRNA genes had differential expression in the same direction in the same tissue type in at least 3 separate studies. Increased expression was reported in miR-106b-5p, miR-155-5p and miR-146a-5p in blood, post-mortem brain, and across several tissue types, respectively. Decreased expression was reported in miR-328-3p in bloods samples. Seven studies examined differential expression from other classes of ncRNA, including piRNA, snRNA, snoRNA and Y RNA. No individual ncRNA genes were reported in more than one study. Six studies reported differentially expressed snoRNA genes in ASD. A meta-analysis was not possible because of inconsistent methodologies, disparate tissue types examined, and varying forms of data presented.

CONCLUSION

There is limited but promising evidence associating the expression of certain miRNA genes and ASD, although the studies are of variable methodological quality and the results are largely inconsistent. There is emerging evidence associating differential expression of snoRNA genes in ASD. It is not currently possible to say whether the reports of differential expression in ncRNA may relate to ASD aetiology, a response to shared environmental factors linked to ASD such as sleep and nutrition, other molecular functions, human diversity, or chance findings. To improve our understanding of any potential association, we recommend improved and standardised methodologies and reporting of raw data. Further high-quality research is required to shine a light on possible associations, which may yet yield important information.

A Potential Biomarker of Brain Activity in Autism Spectrum Disorders: A Pilot fNIRS Study in Female Preschoolers

Autism spectrum disorder (ASD) refers to a neurodevelopmental condition whose detection still remains challenging in young females due to the heterogeneity of the behavioral phenotype and the capacity of camouflage. The availability of quantitative biomarkers to assess brain function may support in the assessment of ASD. Functional Near-infrared Spectroscopy (fNIRS) is a non-invasive and flexible tool that quantifies cortical hemodynamic responses (HDR) that can be easily employed to describe brain activity. Since the study of the visual phenotype is a paradigmatic model to evaluate cerebral processing in many neurodevelopmental conditions, we hypothesized that visually-evoked HDR (vHDR) might represent a potential biomarker in ASD females. We performed a case-control study comparing vHDR in a cohort of high-functioning preschooler females with ASD (fASD) and sex/age matched peers. We demonstrated the feasibility of visual fNIRS measurements in fASD, and the possibility to discriminate between fASD and typical subjects using different signal features, such as the amplitude and lateralization of vHDR. Moreover, the level of response lateralization was correlated to the severity of autistic traits. These results corroborate the cruciality of sensory symptoms in ASD, paving the way for the validation of the fNIRS analytical tool for diagnosis and treatment outcome monitoring in the ASD population.

Epigenomic signatures reveal mechanistic clues and predictive markers for autism spectrum disorder

Autism spectrum disorder (ASD) comprises a heterogeneous group of neurodevelopmental outcomes in children with a commonality in deficits in social communication and language combined with repetitive behaviors and interests. The etiology of ASD is heterogeneous, as several hundred genes have been implicated as well as multiple in utero environmental exposures. Over the past two decades, epigenetic investigations, including DNA methylation, have emerged as a novel way to capture the complex interface of multivariate ASD etiologies. More recently, epigenome-wide association studies using human brain and surrogate accessible tissues have revealed some convergent genes that are epigenetically altered in ASD, many of which overlap with known genetic risk factors. Unlike transcriptomes, epigenomic signatures defined by DNA methylation from surrogate tissues such as placenta and cord blood can reflect past differences in fetal brain gene transcription, transcription factor binding, and chromatin. For example, the discovery of NHIP (neuronal hypoxia inducible, placenta associated) through an epigenome-wide association in placenta, identified a common genetic risk for ASD that was modified by prenatal vitamin use. While epigenomic signatures are distinct between different genetic syndromic causes of ASD, bivalent chromatin and some convergent gene pathways are consistently epigenetically altered in both syndromic and idiopathic ASD, as well as some environmental exposures. Together, these epigenomic signatures hold promising clues towards improved early prediction and prevention of ASD as well genes and gene pathways to target for pharmacological interventions. Future advancements in single cell and multi-omic technologies, machine learning, as well as non-invasive screening of epigenomic signatures during pregnancy or newborn periods are expected to continue to impact the translatability of the recent discoveries in epigenomics to precision public health.

Identification of atypical sleep microarchitecture biomarkers in children with autism spectrum disorder

Importance

Sleep disorders are one of the most frequent comorbidities in children with autism spectrum disorder (ASD). However, the link between neurodevelopmental effects in ASD children with their underlying sleep microarchitecture is not well understood. An improved understanding of etiology of sleep difficulties and identification of sleep-associated biomarkers for children with ASD can improve the accuracy of clinical diagnosis.

Objectives

To investigate whether machine learning models can identify biomarkers for children with ASD based on sleep EEG recordings.

Design setting and participants

Sleep polysomnogram data were obtained from the Nationwide Children' Health (NCH) Sleep DataBank. Children (ages: 8-16 yrs) with 149 autism and 197 age-matched controls without neurodevelopmental diagnosis were selected for analysis. An additional independent age-matched control group (n = 79) selected from the Childhood Adenotonsillectomy Trial (CHAT) was also used to validate the models. Furthermore, an independent smaller NCH cohort of younger infants and toddlers (age: 0.5-3 yr.; 38 autism and 75 controls) was used for additional validation.

Main outcomes and measures

We computed periodic and non-periodic characteristics from sleep EEG recordings: sleep stages, spectral power, sleep spindle characteristics, and aperiodic signals. Machine learning models including the Logistic Regression (LR) classifier, Support Vector Machine (SVM), and Random Forest (RF) model were trained using these features. We determined the autism class based on the prediction score of the classifier. The area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance.

Results

In the NCH study, RF outperformed two other models with a 10-fold cross-validated median AUC of 0.95 (interquartile range [IQR], [0.93, 0.98]). The LR and SVM models performed comparably across multiple metrics, with median AUC 0.80 [0.78, 0.85] and 0.83 [0.79, 0.87], respectively. In the CHAT study, three tested models have comparable AUC results: LR: 0.83 [0.76, 0.92], SVM: 0.87 [0.75, 1.00], and RF: 0.85 [0.75, 1.00]. Sleep spindle density, amplitude, spindle-slow oscillation (SSO) coupling, aperiodic signal's spectral slope and intercept, as well as the percentage of REM sleep were found to be key discriminative features in the predictive models.

Conclusion and relevance

Our results suggest that integration of EEG feature engineering and machine learning can identify sleep-based biomarkers for ASD children and produce good generalization in independent validation datasets. Microstructural EEG alterations may help reveal underlying pathophysiological mechanisms of autism that alter sleep quality and behaviors. Machine learning analysis may reveal new insight into the etiology and treatment of sleep difficulties in autism.

Predictors of placebo response in three large clinical trials of the V1a receptor antagonist balovaptan in autism spectrum disorder

High rates of placebo response are increasingly implicated in failed autism spectrum disorder (ASD) clinical trials. Despite this, there are limited investigations of placebo response in ASD. We sought to identify baseline predictors of placebo response and quantify their influence on clinical scales of interest for three harmonized randomized clinical trials of balovaptan, a V1a receptor antagonist. We employed a two-step approach to identify predictors of placebo response on the Vineland-II two-domain composite (2DC) (primary outcome and a caregiver measure) and Clinical Global Impression (CGI) scale (secondary outcome and a clinician measure). The initial candidate predictor set of variables pertained to participant-level, site-specific, and protocol-related factors. Step 1 aimed to identify influential predictors of placebo response using Least Absolute Shrinkage and Selection Operator (LASSO) regression, while Step 2 quantified the influence of predictors via linear regression. Results were validated through statistical bootstrapping approaches with 500 replications of the analysis dataset. The pooled participant-level dataset included individuals with ASD aged 5 to 62 years (mean age 21 [SD 10]), among which 263 and 172 participants received placebo at Weeks 12 and 24, respectively. Although no influential predictors were identified for CGI, findings for Vineland-II 2DC are robust and informative. Decreased placebo response was predicted by higher baseline Vineland-II 2DC (i.e., more advanced adaptive function), longer trial duration, and European (vs United States) sites, while increased placebo response was predicted by commercial (vs academic) sites, attention deficit hyperactivity disorder and depression. Identification of these factors may be useful in anticipating and mitigating placebo response in drug development efforts in ASD and across developmental and psychiatric conditions.

Autism Spectrum Disorder and Complementary-Integrative Medicine

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects 0.6%-1.7% of children. The etiology of autism is hypothesized to include both biological and environmental factors (Watts, 2008). In addition to the core symptoms of social-communication delay and restricted, repetitive interests, co-occurring irritability/aggression, hyperactivity, and insomnia negatively impact adaptive functioning and quality of life of patients and families. Despite years of effort, no pharmacologic agent has been found that targets the core symptoms of ASD. The only FDA-approved agents are risperidone and aripiprazole for agitation and irritability in ASD, not for core symptoms. Though they effectively reduce irritability/violence, they do so at the expense of problematic side effects: metabolic syndrome, elevated liver enzymes, and extrapyramidal side effects. Thus, it is not surprising that many families of children with ASD turn to nonallopathic treatment, including dietary interventions, vitamins, and immunomodulatory agents subsumed under complementary-integrative medicine (CIM). Per recent studies, 27% to 88% of families report using a CIM treatment. In an extensive population-based survey of CIM, families of children with more severe ASD, comorbid irritability, GI symptoms, food allergies, seizures, and higher parental education tend to use CIM at higher rates. The perceived safety of CIM treatments as "natural treatment" over allopathic medication increases parental comfort in using these agents. The most frequently used CIM treatments include multivitamins, an elimination diet, and Methyl B12 injections. Those perceived most effective are sensory integration, melatonin, and antifungals. Practitioners working with these families should improve their knowledge about CIM as parents currently perceive little interest in and poor knowledge of CIM by physicians. This article reviews the most popular complementary treatments preferred by families with children with autism. With many of them having limited or poor quality data, clinical recommendations about the efficacy and safety of each treatment are discussed using the SECS versus RUDE criteria.

A platform trial approach to proof-of-concept (POC) studies in autism spectrum disorder: Autism spectrum POC initiative (ASPI)

Background

Over the past decade, autism spectrum disorder (ASD) research has blossomed, and multiple clinical trials have tested potential interventions, with varying results and no clear demonstration of efficacy. Lack of clarity concerning appropriate biological mechanisms to target and lack of sensitive, objective tools to identify subgroups and measure symptom changes have hampered the efforts to develop treatments. A platform trial for proof-of-concept studies in ASD could help address these issues. A major goal of a platform trial is to find the best treatment in the most expeditious manner, by simultaneously investigating multiple treatments, using specialized statistical tools for allocation and analysis. We describe the setup of a platform trial and perform simulations to evaluate the operating characteristics under several scenarios. We use the Autism Behavior Inventory (ABI), a psychometrically validated web-based rating scale to measure the change in ASD core and associated symptoms.

Methods

Detailed description of the setup, conduct, and decision-making rules of a platform trial are explained. Simulations of a virtual platform trial for several scenarios are performed to compare operating characteristics. The success and futility criteria for treatments are based on a Bayesian posterior probability model.

Results

Overall, simulation results show the potential gain in terms of statistical properties especially for improved decision-making ability, while careful planning is needed due to the complexities of a platform trial.

Conclusions

Autism research, shaped particularly by its heterogeneity, may benefit from the platform trial approach for POC clinical studies.

Detection of autism spectrum disorder using graph representation learning algorithms and deep neural network, based on fMRI signals

Introduction

Can we apply graph representation learning algorithms to identify autism spectrum disorder (ASD) patients within a large brain imaging dataset? ASD is mainly identified by brain functional connectivity patterns. Attempts to unveil the common neural patterns emerged in ASD are the essence of ASD classification. We claim that graph representation learning methods can appropriately extract the connectivity patterns of the brain, in such a way that the method can be generalized to every recording condition, and phenotypical information of subjects. These methods can capture the whole structure of the brain, both local and global properties.

Methods

The investigation is done for the worldwide brain imaging multi-site database known as ABIDE I and II (Autism Brain Imaging Data Exchange). Among different graph representation techniques, we used AWE, Node2vec, Struct2vec, multi node2vec, and Graph2Img. The best approach was Graph2Img, in which after extracting the feature vectors representative of the brain nodes, the PCA algorithm is applied to the matrix of feature vectors. The classifier adapted to the features embedded in graphs is an LeNet deep neural network.

Results and discussion

Although we could not outperform the previous accuracy of 10-fold cross-validation in the identification of ASD versus control patients in this dataset, for leave-one-site-out cross-validation, we could obtain better results (our accuracy: 80%). The result is that graph embedding methods can prepare the connectivity matrix more suitable for applying to a deep network.

Novel maternal autoantibodies in autism spectrum disorder: Implications for screening and diagnosis

Introduction

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder for which early recognition is a major challenge. Autoantibodies against fetal brain antigens have been found in the blood of mothers of children with ASD (m-ASD) and can be transferred to the fetus where they can impact neurodevelopment by binding to fetal brain proteins. This study aims to identify novel maternal autoantibodies reactive against human fetal brain antigens, and explore their use as biomarkers for ASD screening and diagnosis.

Methods

A custom-made human fetal brain cDNA phage display library was constructed, and screened for antibody reactivity in m-ASD samples from the Simons Simplex Collection (SSC) of the Simons Foundation Autism Research Initiative (SFARI). Antibody reactivity against 6 identified antigens was determined in plasma samples of 238 m-ASD and 90 mothers with typically developing children (m-TD).

Results

We identified antibodies to 6 novel University Hasselt (UH)-ASD antigens, including three novel m-ASD autoantigens, i.e., ribosomal protein L23 (RPL23), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and calmodulin-regulated spectrin-associated protein 3 (CAMSAP3). Antibody reactivity against a panel of four of these targets was found in 16% of m-ASD samples, compared to 4% in m-TD samples (p = 0.0049).

Discussion

Maternal antibodies against 4 UH-ASD antigens could therefore provide a novel tool to support the diagnosis of ASD in a subset of individuals.

Digitally assisted diagnostics of autism spectrum disorder

Digital technologies have the potential to support psychiatric diagnostics and, in particular, differential diagnostics of autism spectrum disorder in the near future, making clinical decisions more objective, reliable and evidence-based while reducing clinical resources. Multimodal automatized measurement of symptoms at cognitive, behavioral, and neuronal levels combined with artificial intelligence applications offer promising strides toward personalized prognostics and treatment strategies. In addition, these new technologies could enable systematic and continuous assessment of longitudinal symptom development, beyond the usual scope of clinical practice. Early recognition of exacerbation and simplified, as well as detailed, progression control would become possible. Ultimately, digitally assisted diagnostics will advance early recognition. Nonetheless, digital technologies cannot and should not substitute clinical decision making that takes the comprehensive complexity of individual longitudinal and cross-section presentation of autism spectrum disorder into account. Yet, they might aid the clinician by objectifying decision processes and provide a welcome relief to resources in the clinical setting.

Explainable Gated Recurrent Unit to explore the effect of co-exposure to multiple air pollutants and meteorological conditions on mental health outcomes

Mental health conditions have the potential to be worsened by air pollution or other climate-sensitive factors. Few studies have empirically examined those associations when we faced to co-exposures, as well as interaction effects. There would be an urgent need to use deep learning to handle complex co-exposures that might interact in multiple ways, and the model performance reinforced by SHapely Additive exPlanations (SHAP) enabled our predictions interpretable and hence actionable. Here, to evaluate the mixed effect of short-term co-exposure, we conducted a time-series analysis using approximately 1.47 million hospital outpatient visits of mental disorders (i.e., depressive disorder-DD, Schizophrenia-SP, Anxiety Disorder-AD, Bipolar Disorder-BD, Attention Deficit and Hyperactivity Disorder-ADHD, Autism Spectrum Disorder-ASD), with matched meteorological observations from 2015 through 2019 in Nanjing, China. The global insights of gated recurrent unit model revealed that most of input features with similar effect size caused the illness risk of SP and ASD increase, and most markedly, 73% of relative humidity, 44.6 µg/m³ of NO₂, and 14.1 µg/m³ of SO₂ at 5-year average level associated with 2.27, 1.14, and 1.29 visits increase for DD, SP, and AD, respectively. Both synergic and antagonistic effect among informative paired-features were distinguished from local feature dependence. Interestingly, variation tendencies of excessive visits of bipolar disorder when atmospheric pressure, PM_2.5, and O₃ interacted with one another were inconsistent. Our results provided added qualitative and quantitative support for the conclusion that short-term co-exposure to ambient air pollutants and meteorological conditions posed threats to human mental health.

Framework for Testing Robustness of Machine Learning-Based Classifiers

There has been a rapid increase in the number of artificial intelligence (AI)/machine learning (ML)-based biomarker diagnostic classifiers in recent years. However, relatively little work has focused on assessing the robustness of these biomarkers, i.e., investigating the uncertainty of the AI/ML models that these biomarkers are based upon. This paper addresses this issue by proposing a framework to evaluate the already-developed classifiers with regard to their robustness by focusing on the variability of the classifiers' performance and changes in the classifiers' parameter values using factor analysis and Monte Carlo simulations. Specifically, this work evaluates (1) the importance of a classifier's input features and (2) the variability of a classifier's output and model parameter values in response to data perturbations. Additionally, it was found that one can estimate a priori how much replacement noise a classifier can tolerate while still meeting accuracy goals. To illustrate the evaluation framework, six different AI/ML-based biomarkers are developed using commonly used techniques (linear discriminant analysis, support vector machines, random forest, partial-least squares discriminant analysis, logistic regression, and multilayer perceptron) for a metabolomics dataset involving 24 measured metabolites taken from 159 study participants. The framework was able to correctly predict which of the classifiers should be less robust than others without recomputing the classifiers itself, and this prediction was then validated in a detailed analysis.

Modern Biomarkers for Autism Spectrum Disorder: Future Directions

Autism spectrum disorder is an increasingly prevalent neurodevelopmental disorder in the world today, with an estimated 2% of the population being affected in the USA. A major complicating factor in diagnosing, treating, and understanding autism spectrum disorder is that defining the disorder is solely based on the observation of behavior. Thus, recent research has focused on identifying specific biological abnormalities in autism spectrum disorder that can provide clues to diagnosis and treatment. Biomarkers are an objective way to identify and measure biological abnormalities for diagnostic purposes as well as to measure changes resulting from treatment. This current opinion paper discusses the state of research of various biomarkers currently in development for autism spectrum disorder. The types of biomarkers identified include prenatal history, genetics, neurological including neuroimaging, neurophysiologic, and visual attention, metabolic including abnormalities in mitochondrial, folate, trans-methylation, and trans-sulfuration pathways, immune including autoantibodies and cytokine dysregulation, autonomic nervous system, and nutritional. Many of these biomarkers have promising preliminary evidence for prenatal and post-natal pre-symptomatic risk assessment, confirmation of diagnosis, subtyping, and treatment response. However, most biomarkers have not undergone validation studies and most studies do not investigate biomarkers with clinically relevant comparison groups. Although the field of biomarker research in autism spectrum disorder is promising, it appears that it is currently in the early stages of development.

Maternal Levels of Cytokines in Early Pregnancy and Risk of Autism Spectrum Disorders in Offspring

Previous studies indicate a role of immune disturbances during early development in the etiology of autism spectrum disorders (ASD). Any potential disturbances during fetal development are best addressed by prospective evaluation of maternal markers of inflammation. Previous studies have investigated maternal cytokines, a group of powerful effectors of the immune system, with inconsistent results. In this study, we aimed to clarify the relationship between maternal cytokines and ASD by evaluating levels of 17 cytokines in first trimester maternal serum samples, from 318 mothers to ASD-cases and 429 mothers to ASD-unaffected controls, nested within the register-based Stockholm Youth Cohort. Overall, we observed no consistent associations between levels of maternal cytokines and ASD. While we observed a number of individual associations, the patterns varied across the diagnostic sub-groups. Levels above the 90th percentile of IL-1β (OR = 2.31, 95% CI 1.16–4.60), IL-7 (OR = 2.28, 95% CI 1.20–4.33), IL-13 (OR = 2.42, 95% CI 1.29–4.55), and MCP-1 (OR = 2.09, 95% CI 1.03–4.24) were associated with increased odds of ASD with co-occurring intellectual disability (ID), whereas GMCSF (OR = 2.06, 95% CI 1.03–4.11) and TNF-α (OR = 2.31, 95% CI 1.18–4.50) were associated with increased odds of ASD with ADHD but none survived correction for multiple comparisons. Also, none of the measured maternal cytokines were associated with ASD without co-occurring ID or ADHD. Implementing a data-driven approach using machine learning (Random Forest's Variable Importance measurement), we found no evidence to suggest that adding these cytokines and other markers of maternal immunity, to register-based maternal factors (e.g., psychiatric history) improves prediction of ASD. In summary, we found no robust evidence of an association between maternal immune markers during early pregnancy and ASD.

Multivariate Analysis of Metabolomic and Nutritional Profiles among Children with Autism Spectrum Disorder

There have been promising results regarding the capability of statistical and machine-learning techniques to offer insight into unique metabolomic patterns observed in ASD. This work re-examines a comparative study contrasting metabolomic and nutrient measurements of children with ASD (n = 55) against their typically developing (TD) peers (n = 44) through a multivariate statistical lens. Hypothesis testing, receiver characteristic curve assessment, and correlation analysis were consistent with prior work and served to underscore prominent areas where metabolomic and nutritional profiles between the groups diverged. Improved univariate analysis revealed 46 nutritional/metabolic differences that were significantly different between ASD and TD groups, with individual areas under the receiver operator curve (AUROC) scores of 0.6–0.9. Many of the significant measurements had correlations with many others, forming two integrated networks of interrelated metabolic differences in ASD. The TD group had 189 significant correlation pairs between metabolites, vs. only 106 for the ASD group, calling attention to underlying differences in metabolic processes. Furthermore, multivariate techniques identified potential biomarker panels with up to six metabolites that were able to attain a predictive accuracy of up to 98% for discriminating between ASD and TD, following cross-validation. Assessing all optimized multivariate models demonstrated concordance with prior physiological pathways identified in the literature, with some of the most important metabolites for discriminating ASD and TD being sulfate, the transsulfuration pathway, uridine (methylation biomarker), and beta-amino isobutyrate (regulator of carbohydrate and lipid metabolism).

Identification of Pathogenetic Brain Regions via Neuroimaging Data for Diagnosis of Autism Spectrum Disorders

Autism spectrum disorder (ASD) is a kind of neurodevelopmental disorder that often occurs in children and has a hidden onset. Patients usually have lagged development of communication ability and social behavior and thus suffer an unhealthy physical and mental state. Evidence has indicated that diseases related to ASD have commonalities in brain imaging characteristics. This study aims to study the pathogenesis of ASD based on brain imaging data to locate the ASD-related brain regions. Specifically, we collected the functional magnetic resonance image data of 479 patients with ASD and 478 normal subjects matched in age and gender and used a machine-learning framework named random support vector machine cluster to extract distinctive brain regions from the preprocessed data. According to the experimental results, compared with other existing approaches, the method used in this study can more accurately distinguish patients from normal individuals based on brain imaging data. At the same time, this study found that the development of ASD was highly correlated with certain brain regions, e.g., lingual gyrus, superior frontal gyrus, medial gyrus, insular lobe, and olfactory cortex. This study explores the effectiveness of a novel machine-learning approach in the study of ASD brain imaging and provides a reference brain area for the medical research and clinical treatment of ASD.

Biomarkers for the early detection of pressure injury: A systematic review and meta-analysis

BACKGROUND

Pressure injury imposes a significant burden for patients and healthcare systems and the majority of pressure injuries are preventable. The early identification of pressure injury is critical for its prevention. As an objective measure, biomarkers have preliminarily shown the potential to identify individuals at risk for developing pressure injury before it is visually observed to occur. However, these results have not been synthesized.

OBJECTIVE

To assess and synthesise the predictive effect of different biomarkers in the early detection of pressure injury formation.

DESIGN

A systematic review and meta-analysis.

DATA SOURCES

PubMed, EMBASE, CINAHL Complete and the Cochrane Library were comprehensively searched for articles up to June 2021. No restrictions were applied to study design type, language, country, race or date of publication.

REVIEW METHODS

Two reviewers independently extracted data from all original eligible studies using a specified data extraction form, resolved disagreements through discussion and the involvement of an additional reviewer. Methodological quality of all included studies was independently appraised by two authors with the Joanna Briggs Institute (JBI) Critical Appraisal Checklist and the Newcastle-Ottawa Quality Assessment Scale (NOS). Heterogeneity of each study was estimated using the I² statistic, and the data was synthesized using StataSE15.

RESULTS

Eight observational studies involving 10595 participants were included. The overall pooled area under curve (AUC) and the 95% confidence intervals (CIs) of Serum albumin (Alb) was 0.66(0.62-0.70), and the Serum haemoglobin (Hb) was 0.67(0.60-0.74). The AUC and 95% CI of C-reactive protein (CRP) was 0.62(0.50-0.74), Braden score was 0.56 (0.429-0.691), Waterlow score was 0.729(0.654-0.803), Alb with Waterlow was 0.741(0.694-0.787), and the combination of Hb, CRP, Alb, Age and Gender was 0.79(0.682-0.898). Besides, the chemokine interferon-γ-induced protein of 10kd/CXCL10, cytokine interferon-α, tumor necrosis factor-alpha (TNF-α), granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin-15 (IL-15) and combination of creatine kinase (CK), myoglobin (Mb), heart-type fatty acid binding protein (H-FABP) and CRP may prove potential for detecting pressure injury.

CONCLUSION

The findings suggest the combination of Hb, CRP, Alb, Age and Gender is superior to other biomarkers. However, the predictive effect of biomarkers needs to be confirmed by more researches and patient-level data.

Immune Dysregulation in Autism Spectrum Disorder: What Do We Know about It?

Autism spectrum disorder (ASD) is a group of complex multifactorial neurodevelopmental disorders characterized by a wide and variable set of neuropsychiatric symptoms, including deficits in social communication, narrow and restricted interests, and repetitive behavior. The immune hypothesis is considered to be a major factor contributing to autism pathogenesis, as well as a way to explain the differences of the clinical phenotypes and comorbidities influencing disease course and severity. Evidence highlights a link between immune dysfunction and behavioral traits in autism from several types of evidence found in both cerebrospinal fluid and peripheral blood and their utility to identify autistic subgroups with specific immunophenotypes; underlying behavioral symptoms are also shown. This review summarizes current insights into immune dysfunction in ASD, with particular reference to the impact of immunological factors related to the maternal influence of autism development; comorbidities influencing autism disease course and severity; and others factors with particular relevance, including obesity. Finally, we described main elements of similarities between immunopathology overlapping neurodevelopmental and neurodegenerative disorders, taking as examples autism and Parkinson Disease, respectively.

Antioxidant interventions in autism spectrum disorders: A meta-analysis

BACKGROUND

Autism spectrum disorder (ASD) might be associated with oxidative stress, and antioxidants are commonly used in the treatment of young people with ASD. However, the evidence about the effectiveness of these interventions remains debatable. We performed a meta-analysis to evaluate the effect of antioxidants on the symptoms of patients with autism.

METHODS

Data sources: PubMed and Web of Science databases.

STUDY SELECTION

We selected placebo-controlled, double-blind, randomized clinical trials published until February 2021 to evaluate the efficacy of antioxidant interventions on ASD.

DATA ANALYSIS

Aberrant Behavior Checklist (ABC), Repetitive Behavior Scale-Revised (RBS), Social Responsiveness Scale (SRS), Developmental Behavior Checklist (DBC) and Clinical Global Impressions Severity scale (CGIS) were used to evaluate the 22 different symptom outcomes. The Hedges-adjusted g value was used to estimate the effect of each dietary intervention relative to the placebo.

RESULTS

In this meta-analysis, we examined 13 double-blind randomized clinical trials, comprising a total of 570 patients with ASD: 293 in the intervention group and 277 in the placebo group. Antioxidants (N-acetylcysteine (NAC), other antioxidants) are more effective than placebos in improving the irritability among symptoms in the ABC and communication disturbance symptoms in the DBC. There was a good trend of improvement in the stereotypic behavior symptoms in the ABC. Treatment with NAC antioxidants showed a good trend of improvement in irritability in the ABC and symptoms of hyperactivity. The effect size was small, and there was a low risk of statistical heterogeneity and publication bias.

LIMITATIONS

The number of studies in this meta-analysis was small and the sample size was small.

CONCLUSION

This meta-analysis suggests that antioxidant intervention has a potential role in the management of some symptoms in patients with ASD, and indicates the feasibility of using antioxidants to treat autism in the future.

Efficacy and safety of balovaptan for socialisation and communication difficulties in autistic adults in North America and Europe: a phase 3, randomised, placebo-controlled trial

BACKGROUND

There are no approved pharmacological therapies to support treatment of the core communication and socialisation difficulties associated with autism spectrum disorder in adults. We aimed to assess the efficacy, safety, and pharmacokinetics of balovaptan, a vasopressin 1a receptor antagonist, versus placebo in autistic adults.

METHODS

V1aduct was a phase 3, randomised, placebo-controlled, double-blind trial, conducted at 46 sites across six countries (the USA, the UK, France, Italy, Spain, and Canada). Eligible participants were aged 18 years or older with an intelligence quotient (IQ) of 70 or higher, and met the criteria for moderate-to-severe autism spectrum disorder (DSM-5 and Autism Diagnostic Observation Schedule). Participants were randomly allocated (1:1), with an independent interactive voice or web-based response system, to receive balovaptan (10 mg) or placebo daily for 24 weeks. Randomisation was stratified by an individual's baseline Vineland-II two-domain composite (2DC) score (<60 or ≥60), sex, region (North America or rest of world), and age (<25 years or ≥25 years). Participants, study site personnel, and the sponsor were masked to treatment assignment. The primary endpoint was change from baseline in Vineland-II 2DC score (the mean composite score across the Vineland-II socialisation and communication domains) at week 24. The primary analysis was done with ANCOVA in the intention-to-treat population. The V1aduct study was terminated for futility after around 50% of participants completed the week 24 visit. This trial is registered with ClinicalTrials.gov (NCT03504917).

FINDINGS

Between Aug 8, 2018, and July 1, 2020, 540 people were screened for eligibility, of whom 322 were allocated to receive balovaptan (164 [51%]) or placebo (158 [49%]). One participant from the balovaptan group was not treated before trial termination and was excluded from the analysis. 60 participants in the balovaptan group and 55 in the placebo group discontinued treatment before week 24. The sample consisted of 64 (20%) women and 257 (80%) men, with 260 (81%) participants from North America and 61 (19%) from Europe. At baseline, mean age was 27·6 years (SD 9·7) and mean IQ score was 104·8 (18·1). Two (1%) participants were American Indian or Alaska Native, eight (2%) were Asian, 15 (5%) were Black or African American, 283 (88%) were White, four (1%) were of multiple races, and nine (3%) were of unknown race. Mean baseline Vineland-II 2DC scores were 67·2 (SD 15·3) in the balovaptan group and 66·2 (17·7) in the placebo group. The interim futility analysis showed no improvement for balovaptan versus placebo in terms of Vineland-II 2DC score at week 24 compared with baseline, with a least-squares mean change of 2·91 (SE 1·52) in the balovaptan group (n=79) and 4·75 (1·60) in the placebo group (n=71; estimated treatment difference -1·84 [95% CI -5·15 to 1·48]). In the final analysis, mean change from baseline in Vineland-II 2DC score at week 24 was 4·56 (SD 10·85) in the balovaptan group (n=111) and 6·83 (12·18) in the placebo group (n=99). Balovaptan was well tolerated, with similar proportions of participants with at least one adverse event in the balovaptan group (98 [60%] of 163) and placebo group (104 [66%] of 158). The most common adverse events were nasopharyngitis (14 [9%] in the balovaptan group and 19 [12%] in the placebo group), diarrhoea (11 [7%] and 14 [9%]), upper respiratory tract infection (ten [6%] and nine [6%]), insomnia (five [3%] and eight [5%]), oropharyngeal pain (five [3%] and eight [5%]), and dizziness (two [1%] and ten [6%]). Serious adverse events were reported for two (1%) participants in the balovaptan group (one each of suicidal ideation and schizoaffective disorder), and five (3%) participants in the placebo group (one each of suicidal ideation, panic disorder, limb abscess, urosepsis, colitis [in the same participant with urosepsis], and death by suicide). No treatment-related deaths occurred.

INTERPRETATION

Balovaptan did not improve social communication in autistic adults. This study provides insights into challenges facing autism spectrum disorder trials, including the considerable placebo response and the selection of appropriate outcome measures.

FUNDING

F Hoffmann-La Roche.

Investigation of Relaxin-3 Serum Levels in terms of Social Interaction, Communication, and Appetite as a Biomarker in Children with Autism

Objective

To investigate the possible relationship between relaxin-3 and autism spectrum disorder (ASD).

Methods

Serum relaxin-3 was measured in 80 children (50 children diagnosed with ASD and 30 controls). Symptom severity in the ASD group was evaluated by the Childhood Autism Rating Scale (CARS). Behavioral and nutritional problems in the groups were evaluated using the Abnormal Behavior Checklist (ABC) and the Childrenʼs Eating Behavior Questionnaire (CEBQ).

Results

Our findings showed that serum relaxin-3 levels were higher in children with ASD than in the controls. The listening response sub-scale score of the CARS scale was found to decrease as the level of relaxin-3 increased. However, as relaxin-3 levels increased in children with ASD, it was found that the speech problem sub-scale score on the ABC scale and the desire to drink score on the CEBQ scale increased, but the satiety responsiveness and food fussiness scores decreased.

Conclusion

This study the first to investigate serum levels of relaxin-3, which has a role in regulating social behavior and nutritional behavior in children with ASD.

Decreased in vivo glutamate/GABA ratio correlates with the social behavior deficit in a mouse model of autism spectrum disorder

To diagnose autism spectrum disorder (ASD), researchers have sought biomarkers whose alterations correlate with the susceptibility to ASD. However, biomarkers closely related to the pathophysiology of ASD are lacking. Even though excitation/inhibition (E/I) imbalance has been suggested as an underlying mechanism of ASD, few studies have investigated the actual ratio of glutamate (Glu) to γ-aminobutyric acid (GABA) concentration in vivo. Moreover, there are controversies in the directions of E/I ratio alterations even in extensively studied ASD animal models. Here, using proton magnetic resonance spectroscopy (¹H-MRS) at 9.4T, we found significant differences in the levels of different metabolites or their ratios in the prefrontal cortex and hippocampus of Cntnap2^−/− mice compared to their wild-type littermates. The Glu/GABA ratio, N-acetylaspartate (NAA)/total creatine (tCr) ratio, and tCr level in the prefrontal cortex were significantly different in Cntnap2^−/− mice compared to those in wild-type mice, and they significantly correlated with the sociability of mice. Moreover, receiver operating characteristic (ROC) analyses indicated high specificity and selectivity of these metabolites in discriminating genotypes. These results suggest that the lowered Glu/GABA ratio in the prefrontal cortex along with the changes in the other metabolites might contribute to the social behavior deficit in Cntnap2^−/− mice. Our results also demonstrate the utility of ¹H-MRS in investigating the underlying mechanisms or the diagnosis of ASD.

The amplitude of fNIRS hemodynamic response in the visual cortex unmasks autistic traits in typically developing children

Autistic traits represent a continuum dimension across the population, with autism spectrum disorder (ASD) being the extreme end of the distribution. Accumulating evidence shows that neuroanatomical and neurofunctional profiles described in relatives of ASD individuals reflect an intermediate neurobiological pattern between the clinical population and healthy controls. This suggests that quantitative measures detecting autistic traits in the general population represent potential candidates for the development of biomarkers identifying early pathophysiological processes associated with ASD. Functional near-infrared spectroscopy (fNIRS) has been extensively employed to investigate neural development and function. In contrast, the potential of fNIRS to define reliable biomarkers of brain activity has been barely explored. Features of non-invasiveness, portability, ease of administration, and low-operating costs make fNIRS a suitable instrument to assess brain function for differential diagnosis, follow-up, analysis of treatment outcomes, and personalized medicine in several neurological conditions. Here, we introduce a novel standardized procedure with high entertaining value to measure hemodynamic responses (HDR) in the occipital cortex of adult subjects and children. We found that the variability of evoked HDR correlates with the autistic traits of children, assessed by the Autism-Spectrum Quotient. Interestingly, HDR amplitude was especially linked to social and communication features, representing the core symptoms of ASD. These findings establish a quick and easy strategy for measuring visually-evoked cortical activity with fNIRS that optimize the compliance of young subjects, setting the background for testing the diagnostic value of fNIRS visual measurements in the ASD clinical population.

The Value of Brain Imaging and Electrophysiological Testing for Early Screening of Autism Spectrum Disorder: A Systematic Review

Given the significance of validating reliable tests for the early detection of autism spectrum disorder (ASD), this systematic review aims to summarize available evidence of neuroimaging and neurophysiological changes in high-risk infants to improve ASD early diagnosis. We included peer-reviewed, primary research in English published before May 21, 2021, involving the use of magnetic resonance imaging (MRI), electroencephalogram (EEG), or functional near-infrared spectroscopy (fNIRS) in children with high risk for ASD under 24 months of age. The main exclusion criteria includes diagnosis of a genetic disorder and gestation age of less the 36 weeks. Online research was performed on PubMed, Web of Science, PsycINFO, and CINAHL. Article selection was conducted by two reviewers to minimize bias. This research was funded by Massachusetts General Hospital Sundry funding. IRB approval was not submitted as it was deemed unnecessary. We included 75 primary research articles. Studies showed that high-risk infants had divergent developmental trajectories for fractional anisotropy and regional brain volumes, increased CSF volume, and global connectivity abnormalities on MRI, decreased sensitivity for familiar faces, atypical lateralization during facial and auditory processing, and different spectral powers across multiple band frequencies on EEG, and distinct developmental trajectories in functional connectivity and regional oxyhemoglobin concentrations in fNIRS. These findings in infants were found to be correlated with the core ASD symptoms and diagnosis at toddler age. Despite the lack of quantitative analysis of the research database, neuroimaging and electrophysiological biomarkers have promising value for the screening of ASD as early as infancy with high accuracy, which warrants further investigation.

Neonatal chemokine markers predict subsequent diagnosis of autism spectrum disorder and delayed development

Immune dysregulation has been found to be related to a diagnosis of autism spectrum disorder (ASD). However, investigations in very early childhood examining immunological abnormalities such as altered neonatal cytokine/chemokine profiles in association with an aberrant developmental trajectory, are sparse. We assessed neonatal blood spots from 398 children, including 171 with ASD, which were subdivided according to severity (121 severe, 50 mild/moderate) and cognitive/adaptive levels (144 low-functioning, 27 typical to high-functioning). The remainder were 69 children with developmental delay (DD), and 158 with typical development (TD), who served as controls in the Childhood Autism Risks from Genetics and the Environment (CHARGE) study. Exploratory analysis suggested that, in comparisons with TD and DD, CTACK (CCL27) and MPIF-1 (CCL23), respectively, were independently associated with ASD. Higher neonatal levels of CTACK were associated with decreased odds of ASD compared to TD (odds ratio [OR] = 0.40, 95% confidence interval [Cl] 0.21, 0.77), whereas higher levels of MPIF-1 were associated with increased odds of ASD (OR = 2.38, 95% Cl 1.42, 3.98) compared to DD but not to TD. MPIF-1 was positively associated with better scores in several developmental domains. Dysregulation of chemokine levels in early life can impede normal immune and neurobehavioral development, which can lead to diagnosis of ASD or DD. This study collectively suggests that certain peripheral chemokines at birth are associated with ASD progression during childhood and that children with ASD and DD have distinct neonatal chemokine profiles that can differentiate their diagnoses.

Evidence from Characteristics and Comorbidities Suggesting That Asperger Syndrome Is a Subtype of Autism Spectrum Disorder

The current version of the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM-V) does not consider Asperger syndrome a diagnostic category. This study was undertaken to see if there is evidence that this diagnosis should be reinstated. An online survey was conducted to examine symptoms and behaviors associated with the current diagnostic criteria of autism spectrum disorders (ASD) (DSM-V), and those associated with Asperger syndrome based on the previous version (DSM-IV-TR). The study also examined other characteristics historically associated with autism, as well as impairments often reported in infancy/young childhood and medical comorbidities frequently associated with autism. The sample included 251 individuals who had received a diagnosis of Asperger syndrome and 1888 who were diagnosed with autism or ASD. Numerous similarities and differences were found between the two groups. The findings are discussed in relation to reestablishing Asperger syndrome as a valid diagnostic category as well as a subtype of ASD.

Identification of Autism in Children Using Static Facial Features and Deep Neural Networks

Autism spectrum disorder (ASD) is a complicated neurological developmental disorder that manifests itself in a variety of ways. The child diagnosed with ASD and their parents’ daily lives can be dramatically improved with early diagnosis and appropriate medical intervention. The applicability of static features extracted from autistic children’s face photographs as a biomarker to distinguish them from typically developing children is investigated in this study paper. We used five pre-trained CNN models: MobileNet, Xception, EfficientNetB0, EfficientNetB1, and EfficientNetB2 as feature extractors and a DNN model as a binary classifier to identify autism in children accurately. We used a publicly available dataset to train the suggested models, which consisted of face pictures of children diagnosed with autism and controls classed as autistic and non-autistic. The Xception model outperformed the others, with an AUC of 96.63%, a sensitivity of 88.46%, and an NPV of 88%. EfficientNetB0 produced a consistent prediction score of 59% for autistic and non-autistic groups with a 95% confidence level.

Investigation of the Relation between Epithelial Barrier Function and Autism Symptom Severity in Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by limitations in mutual communication and social interaction as well as restricted, repetitive patterns of behaviors, interests, or activities. The possible role of biological abnormalities in the etiopathogenesis of this disorder arouses research interest in this area. This is a case-control study evaluating epithelial barrier function by comparing serum concentrations of occludin and zonulin in children with ASD (n = 60) and controls (n = 30). The Childhood Autism Rating Scale (CARS) was used to evaluate autism symptom levels in all children. Serum occludin and zonulin levels were analyzed using an enzyme-linked immunosorbent assay. Serum occludin was significantly lower in children with ASD than in control subjects. In children with ASD, a decrease in occludin level was significantly associated with the disorder symptom levels items mean score (CARS total scores). Our findings showed that children with ASD had alterations in epithelial barrier function compared to the control group. The investigation of the mechanism underlying the different levels of occludin between ASD and controls may be of importance in clarifying the etiopathogenesis of ASD, as well as its follow-up and treatment.