Early Autism Research Articles

Developmental and Functional Outcomes Amongst Culturally and Linguistically Diverse Autistic Children.

Children typically learn by attending to other people. Autism traits may impact access to social stimuli fundamental to early learning, increasing children's likelihood of a learning disability. Recent reports have highlighted that Autistic children from minority backgrounds have a higher likelihood of co-occurring intellectual disability. This study aimed to firstly confirm that early autism traits are concurrently and prospectively associated with lower developmental quotients, and secondly to examine the developmental and functional gains in Culturally and Linguistically Diverse (CALD) and non-CALD Autistic children following one year of Group-based Early Start Denver Model (G-ESDM). Secondary data on 114 non-CALD and 91 CALD Autistic preschoolers receiving the G-ESDM was utilised to address the study aims. Children were administered the Autism Diagnostic Observation Schedule (ADOS) at entry into the service. The Mullen Scales of Early Learning and the Vineland Adaptive Behaviour Scales-II were administered at both entry (Time 1) and 12 months later (Time 2). While no concurrent associations were found between autism traits and Developmental Quotient (DQ), significant associations were found prospectively with ADOS scores at Time 1 and DQ at Time 2, holding for both groups after controlling for DQ at Time 1. Autistic children made significant gains in cognition and functional behaviour following one year of early therapy, with no differences between the two groups. The results indicate that young Autistic children make significant gains in early development and add to the sparse research on the developmental and functional outcomes of Autistic children from CALD communities.

A Closed-Loop Ear-Worn Wearable EEG System with Real-Time Passive Electrode Skin Impedance Measurement for Early Autism Detection

Autism spectrum disorder (ASD) is a chronic neurological disorder with the severity directly linked to the diagnosis age. The severity can be reduced if diagnosis and intervention are early (age < 2 years). This work presents a novel ear-worn wearable EEG system designed to aid in the early detection of ASD. Conventional EEG systems often suffer from bulky, wired electrodes, high power consumption, and a lack of real-time electrode–skin interface (ESI) impedance monitoring. To address these limitations, our system incorporates continuous, long-term EEG recording, on-chip machine learning for real-time ASD prediction, and a passive ESI evaluation system. The passive ESI methodology evaluates impedance using the root mean square voltage of the output signal, considering factors like pressure, electrode surface area, material, gel thickness, and duration. The on-chip machine learning processor, implemented in 180 nm CMOS, occupies a minimal 2.52 mm² of active area while consuming only 0.87 µJ of energy per classification. The performance of this ML processor is validated using the Old Dominion University ASD dataset.

Emotion dysregulation in autism: Severity and correlates in early childhood.

Emotion dysregulation (ED) is common and severe in older autistic youth, but is rarely the focus of early autism screening or intervention. Moreover, research characterizing ED in the preschool years (when autism is typically diagnosed) is limited. This study aimed to characterize ED in autistic children by examining (1) prevalence and severity of ED as compared to children without an autism diagnosis; and (2) correlates of ED in autistic children. A sample of 1864 parents (Mean child age = 4.21 years, SD = 1.16 years; 37% female) of 2-5 year-old children with (1) autism; (2) developmental concerns, but no autism; and (3) no developmental concerns or autism completed measures via an online questionnaire. ED was measured using the Emotion Dysregulation Inventory-Young Child, a parent report measure characterizing ED across two dimensions: Reactivity (fast, intense emotional reactions) and dysphoria (low positive affect, sadness, unease). Autistic preschoolers, compared to peers without developmental concerns, had more severe ED (+1.12 SD for reactivity; +0.60 SD for dysphoria) and were nearly four and three times more likely to have clinically significant reactivity and dysphoria, respectively. Autistic traits, sleep problems, speaking ability, and parent depression were the strongest correlates of ED in the autism sample. While more work is needed to establish the prevalence, severity, and correlates of ED in young autistic children, this study represents an important first step. Results highlight a critical need for more high-quality research in this area as well as the potential value of screening and intervention for ED in young autistic children.

Validating the Child Behavior Checklist 1.5-5 as a screening tool for autism spectrum disorder.

Early identification of Autism Spectrum Disorder is very important, especially in low and middle-income countries, where access to resources is often limited. The Child Behavior Checklist 1.5-5 is a tool that has been used to help identify children with autism spectrum disorder through specific behavior patterns. However, its effectiveness in low- and middle-income country settings has not been thoroughly studied. This research focused on evaluating the Child Behavior Checklist 1.5-5 as a screening tool for autism spectrum disorder among Brazilian children. The study involved 1292 children aged 3-5 years from the general population and 70 children with autism spectrum disorder aged 1-5 years. Using advanced statistical methods, the study tested how well the Child Behavior Checklist identified children with autism spectrum disorder and how reliable it was in this context. The findings showed that the Child Behavior Checklist 1.5-5 performed well in identifying autism spectrum disorder, with high reliability and consistency in the results. Although one item in each of the autism spectrum problems and withdrawn syndrome subscales did not perform as strongly, the overall tool was effective. In summary, the Child Behavior Checklist 1.5-5 proves to be a reliable and valid tool for early autism spectrum disorder screening in Brazilian children. This can help ensure that more children in low- and middle-income country settings are identified early and receive the necessary support and interventions to help them thrive. Future research should continue to test this tool in different contexts to confirm its usefulness across various populations.

Prevalence, incidence, and characteristics of autism spectrum disorder among children in Beijing, China.

It is the first study to explore the prevalence, incidence, and co-occurring conditions of autism spectrum disorder for the preschoolers in China. The prevalence and incidence of autism spectrum disorder has increased in recent decades. Autism spectrum disorder has become an important public concern worldwide. In this study, all hospital confirmed cases had an associated diagnosis (International Classification of Diseases, 10th revision (ICD-10) codes: F84.0, 84.5, F84.9). In total, 4457 children aged 4-6 years were identified as having autism spectrum disorder. In 2021, 1 in 95 children aged 6 years, 1 in 115 children aged 5 years, and 1 in 130 children aged 4 years were estimated to have autism spectrum disorder in Beijing. The incidence was 0.11% in 2019 and increased to 0.18% in 2021. There has been a great emphasis on the importance of early autism spectrum disorder diagnosis in large cities in China.

EEG-Based Early Detection of Autism Using Convolutional Neural Networks

This Timely intervention and improved management of autism spectrum disorder (ASD) are contingent upon early detection. In this paper a novel method for early autism identification using EEG signals and convolutional neural networks (CNNs) is proposed. Preprocessing, wavelet transform, Discrete Cosine Transform (DCT) , energy and entropy function feature extraction, and CNN classifier classification are some of the phases in the suggested approach. EEG signals are first pre-processed to get rid of artifacts and noise. Then, to extract pertinent characteristics from the EEG signals, wavelet transform and DCT are used. For feature extraction, energy and entropy calculations are used to identify unique patterns suggestive of ASD. After then, a CNN classifier receives these features and divides them into two categories: Autism identified or normal identified. The accuracy, specificity, sensitivity, and precision of the suggested approach are among the performance measures that are used to assess its effectiveness. With an accuracy of 92.34%, specificity of 92.95%, sensitivity of 92.65%, and precision of 92.65%, the experimental findings show encouraging performance. When compared to current systems, the suggested approach performs significantly better, outperforming the 91.78% accuracy of the current system.

Maternal Prenatal Cannabis Use and Child Autism Spectrum Disorder

Despite an increase in maternal prenatal cannabis use and associations with adverse neonatal outcomes, research on child neurodevelopmental outcomes is limited. To evaluate the association between maternal cannabis use in early pregnancy and child autism spectrum disorder (ASD). This population-based retrospective birth cohort study included children born between 2011 and 2019 to pregnant Kaiser Permanente Northern California members screened for prenatal cannabis use during pregnancy. Statistical analysis was conducted February 2023 to March 2024. Maternal prenatal cannabis use was assessed at entrance to prenatal care (approximately 8- to 10-weeks' gestation) via self-report and/or positive urine toxicology test. Use frequency was assessed. Child ASD was defined by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis codes ascertained from the electronic health record. Associations between maternal prenatal cannabis use and child ASD were modeled using Cox proportional hazards regression adjusted for maternal sociodemographic, other substance use and disorders, prenatal care initiation, comorbidities, and clustering among maternal siblings. The study cohort included 178 948 singleton pregnancies among 146 296 unique pregnant individuals, including 48 880 (27.3%) Asian or Pacific Islander, 42 799 (23.9%) Hispanic, 9742 (5.4%) non-Hispanic Black, and 70 733 (39.5%) non-Hispanic White pregnancies. The median (IQR) maternal age at pregnancy onset was 31 (6) years; 8486 (4.7%) screened positive for cannabis use, 7054 (3.9%) via urine toxicology testing and 3662 (2.0%) by self-report. In the total study population, the frequency of self-reported use was monthly or less for 2003 pregnancies (1.1%), weekly for 918 pregnancies (0.5%), daily for 741 pregnancies (0.4%), and unknown for 4824 pregnancies (2.7%). ASD was diagnosed in 3.6% of children. After adjustment for maternal characteristics, maternal prenatal cannabis use was not associated with child ASD (hazard ratio [HR], 1.05; 95% CI, 0.84-1.32). When self-reported frequency of use was assessed, no statistically significant associations were observed after confounder adjustment. No sex-specific associations were documented (males: HR, 1.01; 95% CI, 0.77-1.32; and females: HR, 1.19; 95% CI, 0.77-1.85). In this cohort study, maternal cannabis use assessed in early pregnancy was not associated with child ASD. Additional studies are needed to evaluate different patterns of use throughout pregnancy. Given the known adverse neonatal health effects of maternal prenatal cannabis use, clinicians should follow national guidelines and advise against use.

DNA methylation signatures of prenatal socioeconomic position associated with 36-month language outcomes

BackgroundSocioeconomic position (SEP), which reflects one’s position in society and access to resources, is strongly tied to neurodevelopment and is associated with epigenetic changes. AIM: This study examined whether DNA methylation signatures of prenatal SEP, measured in birth samples, are associated with child neurodevelopmental outcomes at 36 months of age. METHODS: Prenatal SEP DNA methylation scores were derived using 97 placenta and 127 cord blood biospecimens in the Early Autism Risk Longitudinal Investigation cohort. Participants completed the Mullen Scales of Early Learning (MSEL) and Vineland Adaptive Behavior Scales (VABS) at 36 months of age. Generalized regression analyses, adjusting for maternal age and race, were performed to test the association between SEP methylation score, for each birth biospecimen type, and MSEL and VABS scores. RESULTS: Significant associations were observed between placenta SEP methylation score and MSEL Expressive Language outcomes (beta = −2.7, p = 0.046, 95 % CI [- 5.43, −0.05]) and Receptive Language outcomes (beta = −2.5, p = 0.037, 95 % CI [-4.82, −0.16]). In cord blood, methylation-SEP scores were significantly associated with Receptive Language outcomes (beta = −2.0, p = 0.037, 95 % CI [-3.85, −0.12]). No significant associations were observed with VABS scores. CONCLUSION: Our results confirm associations between prenatal SEP and early childhood language development using a novel empiric DNA methylation measure of exposure.

Intelligent models for early Autism detection from MRI images

Autism Spectrum Disorders (ASDs) are neurodevelopmental conditions that usually manifest during childhood. It is a multi-symptom disorder, and its symptoms overlap with several other disorders. The early detection of these disorders is conducive to more successful treatment outcomes, as treatment is more effective before the disorder becomes severe. However, the conventional diagnostic procedures are quite time-consuming, with a typical confirmation period of several months with different specialists in speech and neurology. Recent advances in artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), have demonstrated considerable potential in addressing the diagnostic challenges associated with autism spectrum disorders (ASDs). In this paper, we propose a range of machine learning models, including a support vector machine (SVM), convolutional neural network (CNN), residual networks (ResNet), and vision transformers (ViT), to detect ASDs based on magnetic resonance images (MRI). The SVM model, in terms of accuracy, outperforms the other similar works, achieving a score of 94.03%.

Commonly used genomic arrays may lose information due to imperfect coverage of discovered variants for autism spectrum disorder

BackgroundCommon genetic variation has been shown to account for a large proportion of ASD heritability. Polygenic scores generated for autism spectrum disorder (ASD-PGS) using the most recent discovery data, however, explain less variance than expected, despite reporting significant associations with ASD and other ASD-related traits. Here, we investigate the extent to which information loss on the target study genome-wide microarray weakens the predictive power of the ASD-PGS.MethodsWe studied genotype data from three cohorts of individuals with high familial liability for ASD: The Early Autism Risk Longitudinal Investigation (EARLI), Markers of Autism Risk in Babies-Learning Early Signs (MARBLES), and the Infant Brain Imaging Study (IBIS), and one population-based sample, Study to Explore Early Development Phase I (SEED I). Individuals were genotyped on different microarrays ranging from 1 to 5 million sites. Coverage of the top 88 genome-wide suggestive variants implicated in the discovery was evaluated in all four studies before quality control (QC), after QC, and after imputation. We then created a novel method to assess coverage on the resulting ASD-PGS by correlating a PGS informed by a comprehensive list of variants to a PGS informed with only the available variants.ResultsPrior to imputations, None of the four cohorts directly or indirectly covered all 88 variants among the measured genotype data. After imputation, the two cohorts genotyped on 5-million arrays reached full coverage. Analysis of our novel metric showed generally high genome-wide coverage across all four studies, but a greater number of SNPs informing the ASD-PGS did not result in improved coverage according to our metric.Limitations.The studies we analyzed contained modest sample sizes. Our analyses included microarrays with more than 1-million sites, so smaller arrays such as Global Diversity and the PsychArray were not included. Our PGS metric for ASD is only generalizable to samples of European ancestries, though the coverage metric can be computed for traits that have sufficiently large-sized discovery findings in other ancestries.ConclusionsWe show that commonly used genotyping microarrays have incomplete coverage for common ASD variants, and imputation cannot always recover lost information. Our novel metric provides an intuitive approach to reporting information loss in PGS and an alternative to reporting the total number of SNPs included in the PGS. While applied only to ASD here, this metric can easily be used with other traits.

Evaluating mental chronometry as a quantitative measure of information processing in early childhood autism

Objectives Mental chronometry is the scientific study of cognitive processing speed measured by reaction time (RT), which is the elapsed time between the onset of a stimulus and an individual’s response. This study aims at measuring the RT among young children with autism spectrum disorders (ASD) and comparing it with normal (typically developing) children. Methods 60 ASD children were selected from different ASD centers, and 60 normal children were selected from different kindergartens for participation in this study. Participants were aged 3–6 years old. The RT was measured using the Fitlight trainer device. The findings were statistically evaluated using independent t-tests and ANOVA tests. Result Significant differences (p < 0.0001) were found between both groups in all tasks, and ASD children demonstrated slower RT compared to the normal group. The RT measured through three senses (visual, auditory, and touch) for ASD and normal were 3.64 ± 2.16, 13.19 ± 2.41(trial), 1835.23 ± 757.95, 697.12 ± 87.83 (second), and 1550.89 ± 499.76, 752.67 ± 124.02 (second) respectively. Conclusion The evaluated RT showed significant impairment in RT among ASD in comparison to normal children and this was true for the three senses. The Fitlight trainer could be used to assess RT and stimulus-response among ASD children in various cognitive tasks. Similar studies, involving larger samples from different areas and involving other sense organs, are indicated to confirm the results.

More Intensive Early Autism Treatment Not Tied to Better Outcomes

More Intensive Early Autism Treatment Not Tied to Better Outcomes

Face perception and social cognitive development in early autism: A prospective longitudinal study from 3 months to 7 years of age.

Autism spectrum condition (ASC) is characterized by atypical attention to eyes and faces, but the onset and impact of these atypicalities remain unclear. This prospective longitudinal study examined face perception in infants who develop ASC (N = 22, female = 5, 100% White) compared with typically developing infants (N = 131, female = 65, 55.6% White), tracking social-cognitive and ASC development through age seven. Reduced interest in direct gaze and eyes during infancy correlated with atypical development of adaptive behavior at age four and theory of mind at age seven. Principal component analyses revealed less integrated processing of facial features and eye-gaze information in ASC infants, potentially impacting their childhood social functioning. These findings highlight the intertwined nature of social-cognitive development and ASC.

Training and Sustaining: Training and Learning Collaborative Outcomes Across a Statewide Network for Early Autism Diagnosis.

The objective of this study was to describe the development of a primary care professional (PCP) autism diagnosis training model and to report on outcomes related to PCP training and sustained engagement in a longitudinal learning collaborative. We developed Accelerating the Diagnosis of Autism with Primary care Training (ADAPT), a training program to prepare PCPs to develop independent competency in evaluation of autism in children aged 14 to 48 months. ADAPT includes didactic and case-based modules and practice-based coaching delivered by an expert diagnostic specialist; after training, PCPs participate in a longitudinal learning collaborative. Aligned with competency-based medical education standards, measures of autism evaluation knowledge and diagnostic competency are collected. From 2021 to 2023, 13 PCPs completed ADAPT didactic and practicum training to reach competency in independent autism evaluation. Clinicians demonstrated significant improvement in total autism knowledge after didactic training (p = 0.02). Scoring agreement on an autism observational assessment tool between clinicians and expert diagnosticians improved over case observations and practicum evaluations. Similarly, PCPs demonstrated improved evaluation competence, moving on average from Advanced Beginner to Competent Performer as rated by expert diagnosticians. After training, PCPs attended 57% of monthly learning collaborative sessions. Training PCPs to deliver autism evaluations for young children as part of tiered community-based models of care is a promising solution to address access and waitlist challenges. ADAPT is an intensive, standardized PCP training model that results in achievement of independent competency and sustained engagement in autism evaluation. Effectiveness-implementation studies are needed to ensure scalability and sustainability of training models.

Prenatal air pollution and children's autism traits score: Examination of joint associations with maternal intake of vitamin D, methyl donors, and polyunsaturated fatty acids using mixture methods.

Maternal nutrient intake may moderate associations between environmental exposures and children's neurodevelopmental outcomes, but few studies have assessed joint effects. We aimed to evaluate whether prenatal nutrient intake influences the association between air pollutants and autism-related trait scores. We included 126 participants from the EARLI (Early Autism Risk Longitudinal Investigation, 2009-2012) cohort, which followed US pregnant mothers who previously had a child with autism. Bayesian kernel machine regression and traditional regression models were used to examine joint associations of prenatal nutrient intake (vitamins D, B12, and B6; folate, choline, and betaine; and total omega 3 and 6 polyunsaturated fatty acids, reported via food frequency questionnaire), air pollutant exposure (particulate matter <2.5 μm [PM2.5], nitrogen dioxide [NO2], and ozone [O3], estimated at the address level), and children's autism-related traits (measured by the Social Responsiveness Scale [SRS] at 36 months). Most participants had nutrient intakes and air pollutant exposures that met US standards. Bayesian kernel machine regression mixture models and traditional regression models provided little evidence of individual or joint associations of nutrients and air pollutants with SRS scores or of an association between the overall mixture and SRS scores. In this cohort with a high familial likelihood of autism, we did not observe evidence of joint associations between air pollution exposures and nutrient intake with autism-related traits. Future work should examine the use of these methods in larger, more diverse samples, as our results may have been influenced by familial liability and/or relatively high nutrient intakes and low air pollutant exposures.

Assessment of maternal phthalate exposure in urine across three trimesters and at delivery (umbilical cord blood and placenta) and its influence on birth anthropometric measures

Phthalates, commonly used in plastic manufacturing, have been linked to adverse reproductive effects. Our research from the Saudi Early Autism and Environment Study (2019–2022), involving 672 participants, focused on the impacts of maternal phthalate exposure on birth anthropometric measures. We measured urinary phthalate metabolites in 390 maternal samples collected during each of the three trimesters of pregnancy and in cord serum and placental samples obtained at delivery. We employed various statistical methods to analyze our data. Intraclass correlation coefficients were used to assess the consistency of phthalate measurements, generalized estimating equations were used to explore temporal variations across the trimesters, and linear regression models, adjusted for significant confounders and Bonferroni correction, were used for each birth outcome. Exposure to six phthalates was consistently high across trimesters, with 82 %–100 % of samples containing significant levels of all metabolites, except for mono-benzyl phthalate. We found a 3.15 %–3.73 % reduction in birth weight (BWT), 1.39 %–1.69 % reduction in head circumference (HC), and 3.63 %–5.45 % reduction in placental weight (PWT) associated with a one-unit increase in certain urinary di(2-ethylhexyl) phthalate (DEHP) metabolites during the first trimester. In the second trimester, exposure to MEP, ∑7PAE, and ∑LMW correlated with a 3.15 %–4.5 % increase in the APGAR 5-min score and increases in PWT by 8.98 % for ∑7PAE and 9.09 % for ∑LMW. Our study also highlighted the maternal-to-fetal transfer of DEHP metabolites, indicating diverse impacts on birth outcomes and potential effects on developmental processes. Our study further confirmed the transfer of DEHP metabolites from mothers to fetuses, evidenced by variable rates in the placenta and cord serum, with an inverse relationship suggesting a passive transfer mechanism. Additionally, we observed distinct phthalate profiles across these matrices, adversely impacting birth outcomes. In serum, we noticed increases associated with DEHP metabolites, with birth gestational age rising by 1.01 % to 1.11 %, HC by 2.84 % to 3.67 %, and APGAR 5-min scores by 3.77 % to 3.87 %. Conversely, placental analysis revealed a different impact: BWT decreased by 3.54 % to 4.69 %, HC reductions ranged from 2.57 % to 4.69 %, and chest circumference decreased by 7.13 %. However, the cephalization index increased by 3.67 %–5.87 %. These results highlight the complex effects of phthalates on fetal development, indicating their potential influence on crucial developmental processes like sexual maturation and brain development.

Associations Among Race, Ethnicity, and Clinical Profiles of Young Children Evaluated for Autism in the Primary Care Setting.

Despite long-standing racial and ethnic disparities in autism spectrum (AS) diagnosis, recent research suggests that overall, greater numbers of Black and Latine children are now diagnosed with AS as compared with non-Latine White (NLW) children in some US regions. However, gaps remain in the equitable detection of Black and Latine children with AS without significant developmental impairment. The objective of this study was to determine whether the clinical profiles of young children evaluated for AS across a statewide system of early autism diagnosis in Indiana vary by race and ethnicity. We examined racial and ethnic differences in: (1) AS symptom severity, (2) developmental functioning, (3) adaptive functioning, and (4) behavior problems in a sample of 147 children, aged 14 to 48 months (M = 2.6 years), referred for AS evaluation. Clinical profiles of young children evaluated differed significantly by race and ethnicity, with Black and Latine children exhibiting lower developmental ( p = 0.008) and adaptive abilities ( p = 0.01) and higher AS symptoms ( p = 0.03) as compared with NLW children. Potential explanations for findings include racial and ethnic differences in family and community awareness and knowledge about AS and follow-through on evaluation referral, both driven by social determinants of health (SDOH) affecting minoritized children. Bias in screening and assessment instruments and clinician surveillance, screening, and referral practices may also underlie differences in clinical profiles of children evaluated. Future research is needed to understand the SDOH that influence AS detection and diagnosis to improve equitable access to early diagnosis and intervention.

Considerations and Actionable Steps to Promote Scaling of Early Autism Diagnosis in Community Primary Care Practice.

Considerations and Actionable Steps to Promote Scaling of Early Autism Diagnosis in Community Primary Care Practice.

Applying a User-Centered Design Framework to Develop a Remote Research Assessment Protocol for a Randomized Clinical Trial for Toddlers with Early Autism Characteristics.

COVID-19 required many research teams to shift from in-person to remote assessments, which posed both procedural and theoretical challenges. While research has explored the utility of remote assessments for autism diagnosis from the perspective of families and clinicians, less is known about their application in clinical trials. This paper describes the development of a remote research assessment protocol for a randomized clinical trial focusing on the implementation of reciprocal imitation teaching (RIT) with toddlers in Part C early intervention. This project spans two phases. For Phase 1, our team developed and documented a series of steps utilizing user-centered design (UCD) strategies (e.g., recruiting potential users, creating a prototype, engaging in iterative development) for the purpose of redesigning an assessment protocol for a remote environment. For Phase 2, we examined preliminary outcomes of the redesign process. Primary end users (assessors) rated post-redesign usability and acceptability, while acceptability was examined using attrition data from secondary end users (family participants). Preliminary fidelity of implementation was also examined. The iterative redesign process allowed the research team to refine aspects of the assessment that ultimately led to promising preliminary ratings of usability, acceptability, and feasibility, as well as high fidelity. Preliminary data suggest that the redesigned assessment appears to be an acceptable, feasible, and usable tool for autism clinical trial research and that assessors can use it with fidelity. Further research is needed to examine the reliability and validity of the assessment, as well as implementation characteristics on a larger scale.

Exposure to heavy metals in utero and autism spectrum disorder at age 3: a meta-analysis of two longitudinal cohorts of siblings of children with autism

BackgroundAutism spectrum disorder (ASD) is a prevalent and heterogeneous neurodevelopmental disorder. Risk is attributed to genetic and prenatal environmental factors, though the environmental agents are incompletely characterized.MethodsIn Early Autism Risk Longitudinal Investigation (EARLI) and Markers of Autism Risk in Babies Learning Early Signs (MARBLES), two pregnancy cohorts of siblings of children with ASD, urinary metals concentrations during two pregnancy time periods (< 28 weeks and ≥ 28 weeks of gestation) were measured using inductively coupled plasma mass spectrometry. At age three, clinicians assessed ASD with DSM-5 criteria. In an exposure-wide association framework, using multivariable log binomial regression, we examined each metal for association with ASD status, adjusting for gestational age at urine sampling, child sex, age at pregnancy, race/ethnicity and education. We meta-analyzed across the two cohorts.ResultsIn EARLI (n = 170) 17% of children were diagnosed with ASD, and 44% were classified as having non-neurotypical development (Non-TD). In MARBLES (n = 231), 21% were diagnosed with ASD, and 14% classified as Non-TD. During the first and second trimester period (< 28 weeks), having cadmium concentration over the level of detection was associated with 1.69 (1.08, 2.64) times higher risk of ASD, and 1.29 (0.95, 1.75)times higher risk of Non-TD.A doubling of first and second trimester cesium concentration was marginally associated with 1.89 (0.94, 3.80) times higher risk of ASD, and a doubling of third trimester cesium with 1.69 (0.97, 2.95) times higher risk of ASD.ConclusionExposure in utero to elevated levels of cadmium and cesium, as measured in urine collected during pregnancy, was associated with increased risk of developing ASD.