Autism Spectrum Disorder Screening Research Articles

Cross-Cultural Generalizability of the First Year Inventory for Early ASD Screening in China.

The First Year Inventory (FYI) is a parent report screening measure, aimed at identifying the risk of autism spectrum disorder (ASD) in 12-month-old infants. This study aimed to investigate the utility of FYI within the Chinese community and develop a short version, encompassing both a low-risk sample and a high-risk sample comprising infants with older siblings diagnosed with ASD. Parents of 53 high-risk (HR) infants and 519 low-risk (LR) infants, aged 11 to 13 months, were recruited. After comparing response distributions across Chinese and American samples, a new factorial structure was developed according to the factor analyses. The construct validity and internal consistency of the two FYI versions were examined. The implementation of FYI in the HR sample was also assessed. Noteworthy disparities in response distribution were observed between the Chinese and American samples. Both FYI 2.0 and the FYI short version demonstrated moderate construct validity and internal consistency, with the FYI short version exhibiting better predictive ability in the HR sample. Significant lower risk scores was observed in the HR sample compared to the LR sample. These findings substantiate the applicability and validity of the Chinese short version of FYI. Future research should include follow-up assessments with the Chinese sample to evaluate cutoff scores, considering the cutoff between sensitivity and specificity and the sample?s characteristics.

Cortical dysmorphology and reduced cortico-collicular projections in an animal model of autism spectrum disorder.

Autism spectrum disorder is a neurodevelopmental disability that includes sensory disturbances. Hearing is frequently affected and ranges from deafness to hypersensitivity. In utero exposure to the antiepileptic valproic acid is associated with increased risk of autism spectrum disorder in humans and timed valproic acid exposure is a biologically relevant and validated animal model of autism spectrum disorder. Valproic acid-exposed rats have fewer neurons in their auditory brainstem and thalamus, fewer calbindin-positive neurons, reduced ascending projections to the midbrain and thalamus, elevated thresholds, and delayed auditory brainstem responses. Additionally, in the auditory cortex, valproic acid exposure results in abnormal responses, decreased phase-locking, elevated thresholds, and abnormal tonotopic maps. We therefore hypothesized that in utero, valproic acid exposure would result in fewer neurons in auditory cortex, neuronal dysmorphology, fewer calbindin-positive neurons, and reduced connectivity. We approached this hypothesis using morphometric analyses, immunohistochemistry, and retrograde tract tracing. We found thinner cortical layers but no changes in the density of neurons, smaller pyramidal and non-pyramidal neurons in several regions, fewer neurons immunoreactive for calbindin-positive, and fewer cortical neurons projecting to the inferior colliculus. These results support the widespread impact of the auditory system in autism spectrum disorder and valproic acid-exposed animals and emphasize the utility of simple, noninvasive auditory screening for autism spectrum disorder.

Screening for Autism Spectrum Disorder in children and adolescents with Leber's congenital amaurosis

PurposeTo investigate autism spectrum disorder (ASD) indicators in children with Leber congenital amaurosis (LCA). MethodsCross-sectional, correlational, and prospective study. Participants included patients aged 2 to 16 years with LCA confirmed by genetic testing. There were 20 individuals with ciliopathies (LCA cilio) and 26 with other gene mutations (LCA other). The instrument used for ASD screening was the Autism Behavior Checklist (ABC). Marginal descriptive analyses, non-parametric tests, and a linear regression model were conducted. ResultsOf the 46 participants, 6 had ASD scores. There was no statistically significant correlation between the different groups (LCA cilio and LCA other) (p=0.438). There was no statistically significant correlation between age and ASD (p=0.308). However, there was a statistically significant correlation between visual acuity and ASD (p=0.008) and between male gender and ASD (p=0.025). ConclusionsThis study suggests that there is no correlation between LCA cilio, LCA other and ASD. These findings bring new insights to the existing literature, which previously lacked robust data on the relationship between LCA and ASD. These data demonstrate that visual acuity plays a crucial role in the development of children with visual impairment as poorer visual acuity is associated with a higher incidence of ASD. Based on this study, early interventions can be designed, especially for individuals without light perception, with the aim of maximizing their developmental outcomes. Furthermore, such data indicates that any improvement in visual acuity outcomes in treatment clinical trials become relevant for child development.

KCNH5 deletion increases autism susceptibility by regulating neuronal growth through Akt/mTOR signaling pathway

Recent clinical studies have highlighted mutations in the voltage-gated potassium channel Kv10.2 encoded by the KCNH5 gene among individuals with autism spectrum disorder (ASD). Our preliminary study found that Kv10.2 was decreased in the hippocampus of valproic acid (VPA) - induced ASD rats. Nevertheless, it is currently unclear how KCNH5 regulates autism-like features, or becomes a new target for autism treatment. We employed KCNH5 knockout (KCNH5-/-) rats and VPA - induced ASD rats in this study. Then, we used behavioral assessments, combined with electrophysiological recordings and hippocampal brain slice, to elucidate the impact of KCNH5 deletion and environmental factors on neural development and function in rats. We found that KCNH5-/- rats showed early developmental delay, neuronal overdevelopment, and abnormal electroencephalogram (EEG) signals, but did not exhibit autism-like behavior. KCNH5-/- rats exposed to VPA (KCNH5-/--VPA) exhibit even more severe autism-like behaviors and abnormal neuronal development. The absence of KCNH5 excessively enhances the activity of the Protein Kinase B (Akt)/Mechanistic Target of Rapamycin (mTOR) signaling pathway in the hippocampus of rats after exposure to VPA. Overall, our findings underscore the deficiency of KCNH5 increases the susceptibility to autism under environmental exposures, suggesting its potential utility as a target for screening and diagnosis in ASD.

Implementing an Early Detection Program for Autism Spectrum Disorders in the Polish Primary Healthcare Setting-Possible Obstacles and Experiences from Online ASD Screening.

A screening questionnaire for autism symptoms is not yet available in Poland, and there are no recommendations regarding screening for developmental disorders in Polish primary healthcare. The aim of this study was to assess the opinions of parents and physicians on the legitimacy and necessity of screening for autism spectrum disorders, potential barriers to the implementation of the screening program, and the evaluation and presentation of the process of online ASD screening, which was part of the validation program for the Polish version of one of the screening tools. This study involved 418 parents whose children were screened online and 95 primary care physicians who expressed their opinions in prepared surveys. The results indicate that both parents and doctors perceive the need to screen children for ASD in the general population without a clear preference as to the screening method (online or in person). Moreover, online screening is considered by respondents as a satisfactory diagnostic method. Therefore, online screening may prove to be at least a partial method of solving numerous obstacles indicated by participants' systemic difficulties including time constraints, the lack of experienced specialists in the field of developmental disorders and organizational difficulties of healthcare systems.

A Prediction Model of Autism Spectrum Diagnosis from Well-Baby Electronic Data Using Machine Learning.

Early detection of autism spectrum disorder (ASD) is crucial for timely intervention, yet diagnosis typically occurs after age three. This study aimed to develop a machine learning model to predict ASD diagnosis using infants' electronic health records obtained through a national screening program and evaluate its accuracy. A retrospective cohort study analyzed health records of 780,610 children, including 1163 with ASD diagnoses. Data encompassed birth parameters, growth metrics, developmental milestones, and familial and post-natal variables from routine wellness visits within the first two years. Using a gradient boosting model with 3-fold cross-validation, 100 parameters predicted ASD diagnosis with an average area under the ROC curve of 0.86 (SD < 0.002). Feature importance was quantified using the Shapley Additive explanation tool. The model identified a high-risk group with a 4.3-fold higher ASD incidence (0.006) compared to the cohort (0.001). Key predictors included failing six milestones in language, social, and fine motor domains during the second year, male gender, parental developmental concerns, non-nursing, older maternal age, lower gestational age, and atypical growth percentiles. Machine learning algorithms capitalizing on preventative care electronic health records can facilitate ASD screening considering complex relations between familial and birth factors, post-natal growth, developmental parameters, and parent concern.

Autism Spectrum Disorder Detection in Children Via Deep Learning Models Based on Facial Images

Autism spectrum disorder (ASD) is a complicated psychiatric disease that causes difficulty in communicating with others, and restricted behavior, speech, as well as nonverbal interaction. Children with autism have unique facial characteristics that distinguish them from ordinarily developing children. Therefore, there is a requirement for a precise and automated system capable of early detection of autism in children, yielding accurate results. The objective of this research is to assist both families and psychiatrists in diagnosing autism through a straightforward approach. Specifically, the study employs a deep learning method that utilizes experimentally validated facial features. The technique involves a convolutional neural network along with transfer learning for the detection of autism. MobileNetv2, Xception, ResNet-50, VGG16 and DenseNet-121 were the pretrained models used for detection of autism. The evaluation of these models utilized a dataset sourced from Kaggle, comprising 2,940 facial images. We evaluated the five deep learning models using standard measures like recall, precision, accuracy, F1 score, and ROC curve. The proposed DenseNet-121 model outperformed existing transfer learning models, with 96% accuracy rate. With respect to performance evaluation, the proposed method exhibited superiority over the most recent models. Our model possesses the capability to support healthcare professionals in validating the precision of their initial screening for Autism Spectrum Disorders (ASDs) in pediatric patients.

Biomarkers of preschool children with autism spectrum disorder: quantitative analysis of whole-brain tissue component volumes, intelligence scores, ADOS-CSS, and ages of first-word production and walking onset.

Preschooling is a critical time for intervention in children with autism spectrum disorder (ASD); thus, we analyzed brain tissue component volumes (BTCVs) and clinical indicators in preschool children with ASD to identify new biomarkers for early screening. Eighty preschool children (3-6 years) with ASD were retrospectively included. The whole-brain myelin content (MyC), white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and non-WM/GM/MyC/CSF brain component volumes were obtained using synthetic magnetic resonance imaging (SyMRI). Clinical data, such as intelligence scores, autism diagnostic observation schedule-calibrated severity scores, age at first production of single words (AFSW), age at first production of phrases (AFP), and age at walking onset (AWO), were also collected. The correlation between the BTCV and clinical data was evaluated, and the effect of BTCVs on clinical data was assessed by a regression model. WM and GM volumes were positively correlated with intelligence scores (both P < 0.001), but WM and GM did not affect intelligence scores (P = 0.116, P = 0.290). AWO was positively correlated with AFSW and AFP (both P < 0.001). The multivariate linear regression analysis revealed that MyC, AFSW, AFP, and AWO were significantly different (P = 0.005, P < 0.001, P < 0.001). This study revealed positive correlations between WM and GM volumes and intelligence scores. Whole-brain MyC affected AFSW, AFP, and AWO in preschool children with ASD. Noninvasive quantification of BTCVs via SyMRI revealed a new visualizable and quantifiable biomarker (abnormal MyC) for early ASD screening in preschool children.

Evaluating screening for autism spectrum disorder using cluster randomization

We evaluated the rate of autism spectrum disorder (ASD) in a group invited to a screening program compared to the rates in two groups who received usual care. The population eligible for screening was all children in Iceland registered for their 30-month well-child visits at primary healthcare centers (PHCs) from March 1, 2016, to October 31, 2017 (N = 7173). The PHCs in the capital area of Reykjavik were the units of cluster randomization. Nine PHCs were selected for intervention (invited group), while eight PHCs received usual care (control group 1). PHCs outside the capital area were without randomization (control group 2). An interdisciplinary team, including a pediatrician contributing with physical and neurological examination, a psychologist evaluating autism symptoms using a diagnostic instrument, and a social worker interviewing the parents, reached a consensus on the clinical diagnosis of ASD according to the ICD-10 diagnostic system. Children in the population were followed up for at least two years and 119 cases were identified. The overall cumulative incidence of ASD was 1.66 (95% confidence interval (CI): 1.37, 1.99). In the invited group the incidence rate was 2.13 (95% CI: 1.60, 2.78); in control group 1, the rate was 1.83 (95% CI: 1.31, 2.50); and in control group 2, the rate was 1.02 (95% CI: 0.66, 1.50). Although the rate of ASD was higher in the invited group than in the control groups, the wide confidence intervals prevented us from concluding definitively that the screening detected ASD more readily than usual care.

Social Communication Traits in Typical Hispanic Infants and Toddlers for Use in Autism Screening

The Centers for Disease Control and Prevention recommend that the first Autism Spectrum Disorder (ASD) screening of development and behavior for all children can be completed as early as 9 months of age. To address this recommendation this study was aimed at describing the gestural and oral receptive and expressive social communicative behavior of typical Puerto Rican Hispanic children, using skills in the Early Social-Communication Scales (ESCS). Twenty Puerto Rican infants and toddlers, 10-14 months of age, were studied. Each participant was examined and observed (using video-audio recordings), by a trained speech-language pathology graduate student and a speech-language pathologist of the FILIUS Center. One clinician elicited while the other observed each child’s responses to the ESCS items. At the end, the recorded sessions were analyzed and discussed by the two observers to assign values to observed abilities on a scale of from 1 to 5 for each skill (1= does not execute; 5=very frequent execution). The strongest indicators of typical social communication in these Hispanic infants and toddlers demonstrate that, at that very early age, infants and toddlers are driven to interact with a stranger when accompanied by their mothers as a confirmation of their empathic dispositions. These strong indicators of social communication in typical Hispanic infants and toddlers can be observed by health professionals to identify difficulties in interaction skills as signs to refer children at-risk of autism.

Sex Differences in Autistic Youth Born Extremely Preterm.

To evaluate sex differences in autistic traits in youth born extremely preterm (EP; 23-27 weeks) who were later diagnosed with autism spectrum disorder (ASD) at 10-years. A longitudinal cohort design from the Extremely Low Gestational Age Newborn Study (ELGAN) followed N = 857 EP infants from birth through 10-years. EP infants later diagnosed with ASD (N = 61, 20 females) participated in the study. Group differences were evaluated via inferential and Bayesian statistics (values > 1 suggest evidence for alternate hypothesis) on ASD screeners (M-CHAT at 2-years, SCQ and SRS-2 at 10-years), and gold-standard diagnostic measures (ADOS-2, ADI-R) at 10-years. Males scored significantly higher than females on measures of Social Affect from the ADOS-2, t(34.27)=-2.20, BF10 = 2.33, and measures of Repetitive and Restricted Behaviors from the ADI-R, t(40.52)=-2.85, BF10 = 5.26. Bayesian estimates suggested marginal evidence for sex differences in Nonverbal Communication, t(30.66)=-1.81, BF10 = 1.25, and Verbal Communication, t(24.64)=-1.89, BF10 = 1.39, from the ADI-R, wherein males scored higher than females. No statistically significant sex differences were identified on any of the ASD screeners at 2 (M-CHAT) or 10 years (SCQ). No significant sex differences were observed on any subscales of the SRS at 10 years. EP autistic males present with more autistic traits than EP autistic females on gold-standard diagnostic measures of autism at 10-years of age, despite not presenting with higher autistic traits on screeners at either age. These results align with sex differences observed in full-term, autistic youth. These results suggest ASD screeners may under identify autism in EP youth, particularly females.

Identifying Autism Spectrum Disorder in a High-risk Follow-up Program through Quality Improvement Methodology.

Children born prematurely are at increased risk for autism spectrum disorder (ASD). ASD can be diagnosed between 18 and 24 months of age, but access barriers and medical complexity can delay diagnosis. ASD screening was implemented in a high-risk infant follow-up program using QI methodology. The project aimed to screen 60% of children and refer 90% of those with positive screens. The team developed a standardized workflow to administer the M-CHAT-R/F to HRIF patients between the ages of 16-22 months. Telehealth ASD assessment, using the TELE-ASD-PEDS, was conducted for those who screened positive. Monthly team meetings were held to implement change cycles and review the impact of the previous month's change. Within 7 months of program implementation, ASD screening exceeded the 60% aim. The program referred 72% of patients who screened as medium/high risk on the M-CHAT-R/F. The remaining patients were not referred per provider discretion. Twenty-seven percent of patients who received an autism evaluation received an ASD diagnosis. The average age at diagnosis was 22.5 months. An ASD screening protocol was implemented for patients enrolled in a high-risk infant follow-up program. Patients identified as at risk for ASD received an expedited telehealth ASD evaluation. The screening protocol was maintained for 13 months and is now part of the standard workflow. Screening has been expanded to other HRIF clinics, and evaluation appointments have been added to meet access needs. QI methodology is an effective tool for implementing ASD screening and referral in multidisciplinary HRIF programs.

Harnessing the Power of Mobile Phone Technology: Screening and Identifying Autism Spectrum Disorder With Smartphone Apps.

Integrating smartphone applications into screening and identifying autism spectrum disorder (ASD) represents a promising and innovative frontier within healthcare. This forward-looking paper examines the current landscape of ASD screening apps, shedding light on their potential advantages and addressing and navigating significant challenges. One of the most compelling aspects of these apps lies in their potential to democratize access to ASD screening, effectively breaking down geographical barriers. By using the widespread availability of smartphones, these apps make it possible for individuals, caregivers, and healthcare providers to engage in early ASD screening from virtually anywhere. This accessibility is especially crucial in underserved areas or regions with limited access to specialized healthcare services. Moreover, these apps offer a degree of objectivity that traditional screening methods may need help to match. By relying on data-driven algorithms and machine learning, they can provide a more impartial assessment of a child's behavior, minimizing the potential for subjective bias. This objectivity, combined with the ability to monitor and assess a child's development over time, empowers caregivers with valuable insights into their child's progress. However, as with any technological advancement in healthcare, integrating smartphone apps for ASD screening is not without its share of ethical and privacy considerations. Ensuring informed consent is obtained, especially when collecting data from children, is complex and critical. Striking the right balance between collecting necessary data and protecting an individual's privacy requires careful thought and transparent communication. Additionally, the "digital divide" represents a challenge that needs to be acknowledged and addressed. Not all individuals and families have equal access to smartphones or the technological literacy required to use these apps effectively. This disparity in access must be considered when developing and implementing app-based screening solutions to prevent exacerbating existing healthcare inequalities. Nevertheless, the future of ASD screening apps holds significant promise. Advancements in technology, including integrating advanced sensors, wearables, augmented reality, and machine learning, can further enhance the accuracy and depth of screening. Interdisciplinary collaboration between researchers, developers, clinicians, and educators is crucial to ensure that these apps are effective, culturally sensitive, and user-friendly. Furthermore, integrating these apps into broader healthcare systems, including electronic health records and telehealth platforms, can streamline the screening process and enable a more seamless transition from screening to diagnosis and intervention.

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths.

Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of affected individuals. Yet these are distinct neurological conditions that can exhibit diverse characteristics which require different management strategies. It is desirable to develop tools to assist with early distinction so that appropriate early interventions and support may be tailored to an individual's specific requirements. The current diagnostic procedures for ASD and ADHD require a multidisciplinary approach and can be lengthy. This study investigated the potential of electroretinogram (ERG), an eye test measuring retinal responses to light, for rapid screening of ASD and ADHD. Previous studies identified differences in ERG amplitude between ASD and ADHD, but this study explored time-frequency analysis (TFS) to capture dynamic changes in the signal. ERG data from 286 subjects (146 control, 94 ASD, 46 ADHD) was analyzed using two TFS techniques. Key features were selected, and machine learning models were trained to classify individuals based on their ERG response. The best model achieved 70% overall accuracy in distinguishing control, ASD, and ADHD groups. The ERG to the stronger flash strength provided better separation and the high frequency dynamics (80-300Hz) were more informative features than lower frequency components. To further improve classification a greater number of different flash strengths may be required along with a discrimination comparison to participants who meet both ASD and ADHD classifications and carry both diagnoses.

Machine learning algorithms for identifying autism spectrum disorder through eye-tracking in different intention videos

To investigate the differences in visual perception between children with autism spectrum disorder (ASD) and typically developing (TD) children when watching different intention videos, and to explore the feasibility of machine learning algorithms in objectively distinguishing between ASD children and TD children. A total of 58 children with ASD and 50 TD children were enrolled and were asked to watch the videos containing joint intention and non-joint intention, and the gaze duration and frequency in different areas of interest were used as original indicators to construct classifier-based models. The models were evaluated in terms of the indicators such as accuracy, sensitivity, and specificity. When using eight common classifiers, including support vector machine, linear discriminant analysis, decision tree, random forest, and K-nearest neighbors (with K values of 1, 3, 5, and 7), based on the original feature indicators, the highest classification accuracy achieved was 81.90%. A feature reconstruction approach with a decision tree classifier was used to further improve the accuracy of classification, and then the model showed the accuracy of 91.43%, the specificity of 89.80%, and the sensitivity of 92.86%, with an area under the receiver operating characteristic curve of 0.909 (P<0.001). The machine learning model based on eye-tracking data can accurately distinguish ASD children from TD children, which provides a scientific basis for developing rapid and objective ASD screening tools.

Parental Perspectives on Early Life Screening and Genetic Testing for ASD: A Systematic Review.

Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental condition for which no prenatal or early life screening tests exist. Early life recognition of ASD is key to accessing behavioral intervention when brain plasticity is at its peak. The purpose of our study was to systematically review the literature researching parental perspectives around early life screening for autism and specifically genetic testing. A total of 30 studies were included and coded against the following variables: parental characteristics, child characteristics, research design, data collection and data analysis methods, type of early screening, and parental perspectives towards early life screening and genetic testing. The outcomes of the review showed that caregivers need more knowledge about ASD genetic testing, they are in general in favor of early life screening, and they prefer to access ASD genetic testing and early behavioral intervention as early as possible. As emerging genetic tests are likely to increase diagnostic accuracy for ASD in the near future, it is of paramount importance for research and practice to embrace parental needs and preferences. Healthcare providers can be pivotal in empowering parents to make informed decisions through clear, compassionate communication and counseling. Future research should seek to fill in an essential gap in the literature, which is to capture parental views from a diverse population.

Using visual attention estimation on videos for automated prediction of autism spectrum disorder and symptom severity in preschool children.

Atypical visual attention in individuals with autism spectrum disorders (ASD) has been utilised as a unique diagnosis criterion in previous research. This paper presents a novel approach to the automatic and quantitative screening of ASD as well as symptom severity prediction in preschool children. We develop a novel computational pipeline that extracts learned features from a dynamic visual stimulus to classify ASD children and predict the level of ASD-related symptoms. Experimental results demonstrate promising performance that is superior to using handcrafted features and machine learning algorithms, in terms of evaluation metrics used in diagnostic tests. Using a leave-one-out cross-validation approach, we obtained an accuracy of 94.59%, a sensitivity of 100%, a specificity of 76.47% and an area under the receiver operating characteristic curve (AUC) of 96% for ASD classification. In addition, we obtained an accuracy of 94.74%, a sensitivity of 87.50%, a specificity of 100% and an AUC of 99% for ASD symptom severity prediction.

Engaging the AQ10 to Predict Professional Burnout or Poor Work-Related Psychological Wellbeing Among Anglican Clergy in Wales

The ten-item Autism Spectrum Quotient (AQ10) is a self-report instrument originally designed to identify referrals for professional diagnosis for Autism Spectrum Disorders (ASD). Recent studies suggest that this instrument may also be tapping more generalised affective disorders. Working with this interpretation, this study examines the predictive power of the AQ10 to account for additional variance, after personal and personality factors have been taken into account, on the two scales of the Francis Burnout Inventory. Data provided by 220 Anglican clergy serving in Wales demonstrated that 8.6% of the participants recorded six or more red flags on the AQ10 (and so qualified for referral for specialist diagnostic assessment) and that higher scores on the AQ10 are associated with significantly lower levels of satisfaction in ministry and with significantly higher levels of emotional exhaustion in ministry. These data suggest that screening with the AQ10 may be helpful in identifying clergy vulnerable to professional burnout and to poor work-related psychological wellbeing, in addition to its primary purpose of screening for ASD.

Validating Clinical Exome Sequencing (CES) Test for Copy Number Variation (CNV) Analysis in Patients with Autism Spectrum Disorder

A routine methodologies to detect germline copy number variants (CNVs) are ArrayCGH, and SNParray, while clinical exome sequencing (CES) it is not a standard methods used in clinical settings. This study aims to investigate the efficiency of CNVs identification by CES in the diagnosis of Autism spectrum disorder (ASD) based on a Italian cohort. In this cohort study, 50 patients with ASD diagnosis were recruited for ASD screening. Simultaneous CNVs and single nucleotide variation (SNV) analysis was conducted to identify the anomalies that can cause ASD phenotype. A total of 13 patients were identified with a causative CNVs among 15 (86.67%) SNP array positive patients. Showing that CES data can be used for detecting clinically relevant CNVs with high sensitivity for use in clinical diagnostic settings.

Validation of plasma protein glycation and oxidation biomarkers for the diagnosis of autism.

Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder in children. It is currently diagnosed by behaviour-based assessments made by observation and interview. In 2018 we reported a discovery study of a blood biomarker diagnostic test for ASD based on a combination of four plasma protein glycation and oxidation adducts. The test had 88% accuracy in children 5-12 years old. Herein, we present an international multicenter clinical validation study (N = 478) with application of similar biomarkers to a wider age range of 1.5-12 years old children. Three hundred and eleven children with ASD (247 male, 64 female; age 5.2 ± 3.0 years) and 167 children with typical development (94 male, 73 female; 4.9 ± 2.4 years) were recruited for this study at Sidra Medicine and Hamad Medical Corporation hospitals, Qatar, and Hospital Regional Universitario de Málaga, Spain. For subjects 5-12 years old, the diagnostic algorithm with features, advanced glycation endproducts (AGEs)-Nε-carboxymethyl-lysine (CML), Nω-carboxymethylarginine (CMA) and 3-deoxyglucosone-derived hydroimidazolone (3DG-H), and oxidative damage marker, o,o'-dityrosine (DT), age and gender had accuracy 83% (CI 79 - 89%), sensitivity 94% (CI 90-98%), specificity 67% (CI 57-76%) and area-under-the-curve of receiver operating characteristic plot (AUROC) 0.87 (CI 0.84-0.90). Inclusion of additional plasma protein glycation and oxidation adducts increased the specificity to 74%. An algorithm with 12 plasma protein glycation and oxidation adduct features was optimum for children of 1.5-12 years old: accuracy 74% (CI 70-79%), sensitivity 75% (CI 63-87%), specificity 74% (CI 58-90%) and AUROC 0.79 (CI 0.74-0.84). We conclude that ASD diagnosis may be supported using an algorithm with features of plasma protein CML, CMA, 3DG-H and DT in 5-12 years-old children, and an algorithm with additional features applicable for ASD screening in younger children. ASD severity, as assessed by ADOS-2 score, correlated positively with plasma protein glycation adducts derived from methylglyoxal, hydroimidazolone MG-H1 and Nε(1-carboxyethyl)lysine (CEL). The successful validation herein may indicate that the algorithm modifiable features are mechanistic risk markers linking ASD to increased lipid peroxidation, neuronal plasticity and proteotoxic stress.