Autism risk linked to prematurity is more accentuated in girls

Association of preterm birth and birth size status with neurodevelopmental and psychiatric disorders in spontaneous births

Preterm birth (PTB) or small birth size are risk factors for certain neurodevelopmental disorders. The magnitude of these associations in spontaneous births, and of associations for combined PTB and birth size status on neurodevelopmental and psychiatric disorders is unexplored. We investigated whether PTB and small/large for gestational age (SGA/LGA), separately or combined, in spontaneous births, are associated with a wide spectrum of neurodevelopmental and psychiatric disorders. In this population-based registry cohort study, all singleton spontaneous births in Finland from 1996 to 2014 were followed until 2018 (n = 819 764). We show that PTB across gestational ages, and SGA, were associated with higher risks for anxiety disorders, intellectual disabilities, specific developmental disorders (SDD), autism spectrum disorders (ASD), attention-deficit/hyperactivity disorders (ADHD) and other emotional and behavioural disorders (F98). Most of these associations were not attributed to familial factors. Larger effect sizes were observed with lower gestational ages. Extremely PTB was associated at highest risks with intellectual disabilities (HR, 10.70 [95%CI, 8.69-13.17]) and SDD (HR, 8.91 [95%CI, 8.18-9.71]). Moreover, very preterm birth combined with SGA was associated with a higher risk for SDD (HR, 7.55 [95%CI, 6.61-8.62]) than that of very preterm or SGA birth alone. Conversely, LGA birth lowered the risk for SDD and other emotional and behavioural disorders among individuals born very preterm. In conclusion, PTB along with SGA is associated with higher risks for SDD than one exposure alone, whereas LGA lowers the risks for SDD and other emotional and behavioural disorders in individuals born spontaneously.

Impact of sex, race, and social determinants of health on neonatal outcomes

Despite the global improvements in neonatal outcomes, mortality and morbidity rates among preterm infants are still unacceptably high. Therefore, it is crucial to thoroughly analyze the factors that affect these outcomes, including sex, race, and social determinants of health. By comprehending the influence of these factors, we can work towards reducing their impact and enhancing the quality of neonatal care. This review will summarize the available evidence on sex differences, racial differences, and social determinants of health related to neonates. This review will discuss sex differences in neonatal outcomes in part I and racial differences with social determinants of health in part II. Research has shown that sex differences begin to manifest in the early part of the pregnancy. Hence, we will explore this topic under two main categories: (1) Antenatal and (2) Postnatal sex differences. We will also discuss long-term outcome differences wherever the evidence is available. Multiple factors determine health outcomes during pregnancy and the newborn period. Apart from the genetic, biological, and sex-based differences that influence fetal and neonatal outcomes, racial and social factors influence the health and well-being of developing humans. Race categorizes humans based on shared physical or social qualities into groups generally considered distinct within a given society. Social determinants of health (SDOH) are the non-medical factors that influence health outcomes. These factors can include a person's living conditions, access to healthy food, education, employment status, income level, and social support. Understanding these factors is essential in developing strategies to improve overall health outcomes in communities.

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.

Early Motor Delays During the First 2 Years of Life in Autism Spectrum Disorder: A Scoping Review

PURPOSE

To summarize and appraise the emerging evidence on early motor skills of infants later diagnosed with autism spectrum disorder (ASD), and the association of early motor delays to later ASD diagnosis/characteristics.

METHODS

A literature search was conducted for studies published from 2000 to 2023 on the motor skills of infants later diagnosed with ASD, followed by screening and data extraction.

RESULTS

Current evidence suggested presence of early motor deficits including poor anticipatory movements, postural control, and gross/fine motor skills during the first 2 years of ASD. However, there was variability among studies with regard to study sample and methodology.

CONCLUSION

Although motor deficits are evident in infants, it is unclear whether these are specific to ASD or a consequence of general developmental disorder. Future research is needed on the investigation of specificity and severity of early motor delays, which can potentially assist in early identification of ASD.

A Comparison of the Clinical Presentation of Preterm Birth and Autism Spectrum Disorder: Commonalities and Distinctions in Children Under 3

Premature infants and infants later diagnosed with autism spectrum disorder (ASD) share many commonalities in clinical presentations. However, prematurity and ASD also have differences in clinical presentation. These overlapping phenotypes can lead to misdiagnoses of ASD or missing a diagnosis of ASD in preterm infants. We document these commonalities and differences in various developmental domains with the hope of aiding in the accurate early detection of ASD and timely intervention implementation in children born premature. Given the degree of similarities in presentation, evidence-based interventions designed specifically for preterm toddlers or toddlers with ASD may ultimately aid both populations.

Exploring brainstem auditory evoked potentials and mental development index as early indicators of autism spectrum disorders in high-risk infants

This study of infants from Hubei Province, China examined brainstem auditory evoked potentials (BAEP) and mental development index (MDI) as possible early indicators associated with autism spectrum disorders (ASD). The 34 ASD cases and 102 controls who had recovered from perinatal conditions were matched for age, sex, gestational age, birth weight and maternal age. BAEP absolute latencies (AL) I, III, V and interpeak latencies (IPL) I-III, III-V, I-V were compared in ASD cases and controls at ages 1, 3 and 6 months. MDI scores were compared in these infants from 1 month to 2 years old. Multiple logistic regression analysis was performed to test associations among ASD, BAEP and MDI. Results showed BAEP AL I, V and IPL III-V prolonged in the ASD group (p < 0.001), and MDI scores in ASD cases sharply declining from 12 to 24 months (p < 0.001). Regression analysis revealed odds ratios (OR) indicating that ASD was likely associated with abnormal values of BAEP AL I at 1 and 3 months (OR_{AL I} : 4.27; OR_{AL I} : 4.13), and AL V at 6 months (OR_{AL V} : 7.85). Lower MDI scores (MDI < 80) in infants at 1, 3, and 6 months were likely associated with ASD (OR_MDI : 2.58; OR_MDI : 3.83; OR_MDI : 4.87). These data show that abnormal BAEP values and low MDI scores are independent factors associated with ASD, and that monitoring of BAEP and MDI during infancy might facilitate screening for ASD development.

Gestational Age in Autistic Children and Adolescents: Prevalence and Effects on Autism Phenotype

Pre- and post-term children show increased autism risk. Little is known about gestational age (GA) prevalence among autistic children, and their respective autism phenotype. We compared prevalence of pre-, full- and post-term birth between a population-derived sample of N = 606 (137 females, 22.61%) autistic children and adolescents (mean age = 14.01, SD = 3.63, range 3–24) from the Netherlands Autism Register, and matched controls from the Dutch birth register. Autism phenotype and comorbid symptoms were assessed with the AQ-short and SDQ questionnaires. Using logistic regression, we found higher prevalence of pre- and post-term birth among autistic individuals but no phenotypical differences across GA groups. Autism risk was particularly elevated for post-term children, highlighting the need for closer investigation of autism on the whole GA range.

Artificial intelligence and placental DNA methylation: newborn prediction and molecular mechanisms of autism in preterm children

BACKGROUND

Autism Spectrum Disorder (ASD) represents a heterogeneous group of disorders with a complex genetic and epigenomic etiology. DNA methylation is the most extensively studied epigenomic mechanism and correlates with altered gene expression. Artificial intelligence (AI) is a powerful tool for group segregation and for handling the large volume of data generated in omics experiments.

METHODS

We performed genome-wide methylation analysis for differential methylation of cytosine nucleotide (CpG) was performed in 20 postpartum placental tissue samples from preterm births. Ten newborns went on to develop autism (Autistic Disorder subtype) and there were 10 unaffected controls. AI including Deep Learning (AI-DL) platforms were used to identify and rank cytosine methylation markers for ASD detection. Ingenuity Pathway Analysis (IPA) to identify genes and molecular pathways that were dysregulated in autism.

RESULTS

We identified 4870 CpG loci comprising 2868 genes that were significantly differentially methylated in ASD compared to controls. Of these 431 CpGs met the stringent EWAS threshold (p-value <5 × 10^-8) along with ≥10% methylation difference between CpGs in cases and controls. DL accurately predicted autism with an AUC (95% CI) of 1.00 (1-1) and sensitivity and specificity of 100% using a combination of 5 CpGs [cg13858611 (NRN1), cg09228833 (ZNF217), cg06179765 (GPNMB), cg08814105 (NKX2-5), cg27092191 (ZNF267)] CpG markers. IPA identified five prenatally dysregulated molecular pathways linked to ASD.

CONCLUSIONS

The present study provides substantial evidence that epigenetic differences in placental tissue are associated with autism development and raises the prospect of early and accurate detection of the disorder.

Placental DNA methylation changes and the early prediction of autism in full-term newborns

Autism spectrum disorder (ASD) is associated with abnormal brain development during fetal life. Overall, increasing evidence indicates an important role of epigenetic dysfunction in ASD. The placenta is critical to and produces neurotransmitters that regulate fetal brain development. We hypothesized that placental DNA methylation changes are a feature of the fetal development of the autistic brain and importantly could help to elucidate the early pathogenesis and prediction of these disorders. Genome-wide methylation using placental tissue from the full-term autistic disorder subtype was performed using the Illumina 450K array. The study consisted of 14 cases and 10 control subjects. Significantly epigenetically altered CpG loci (FDR p-value <0.05) in autism were identified. Ingenuity Pathway Analysis (IPA) was further used to identify molecular pathways that were over-represented (epigenetically dysregulated) in autism. Six Artificial Intelligence (AI) algorithms including Deep Learning (DL) to determine the predictive accuracy of CpG markers for autism detection. We identified 9655 CpGs differentially methylated in autism. Among them, 2802 CpGs were inter- or non-genic and 6853 intragenic. The latter involved 4129 genes. AI analysis of differentially methylated loci appeared highly accurate for autism detection. DL yielded an AUC (95% CI) of 1.00 (1.00-1.00) for autism detection using intra- or intergenic markers by themselves or combined. The biological functional enrichment showed, four significant functions that were affected in autism: quantity of synapse, microtubule dynamics, neuritogenesis, and abnormal morphology of neurons. In this preliminary study, significant placental DNA methylation changes. AI had high accuracy for the prediction of subsequent autism development in newborns. Finally, biologically functional relevant gene pathways were identified that may play a significant role in early fetal neurodevelopmental influences on later cognition and social behavior.

Sex influences on the neurocognitive outcome of preterm children

This article presents a revision of the literature regarding the influence of sex differences on the recovery and long-term behavioral and cognitive outcomes of preterm children. After initial discussion of some methodological concerns, the literature regarding the concept of "male disadvantage," which is often used when talking about early neurological and psychomotor outcomes in preterm children, is presented. Subsequently, the literature data on sex-related differences in preterm children are discussed, focusing on their influence on the developmental pathways of cognition, language, executive function, behavior and affect, and response to rehabilitation therapies. Finally, evidence about brain structural and connectivity correlates of sex differences in the brain of preterm survivors is taken into account. Although visuo-spatial and visuo-perceptual functioning is widely studied in the preterm child and is strongly sex specific, little to no data are available regarding male-female differences in preterm children and the interaction effect between sex and preterm birth. For this reason, original data analyses of male-female differences in visuo-spatial performance from a small sample of preterm children are also presented.

The Weak Link: Hypotonia in Infancy and Autism Early Identification

Background: Presenting symptoms and age specific differential diagnosis of Autism Spectrum Disorder (ASD), determine the age of initial assessment and the age of a definite diagnosis. The AAP recommends screening all children for ASD at 18 and 24 months followed by a comprehensive evaluation for children with developmental concerns. More recently it has been recommended that the evaluation should be performed at a younger age, with a diagnosis being made as early as the beginning of the second year of life resulting in earlier intensive intervention. Objective: To assess early developmental milestones in a cohort of children diagnosed with Autism Spectrum Disorder (ASD) in order to find an objective and reliable early marker. We suggest that low muscle tone- hypotonia, is a sign that meets the above criteria of consistency and reliability and may be related to early diagnosis. Methods: We compared age distributions of ASD diagnosis in the presence of hypotonia in a dataset of 5,205 children diagnosed at Keshet Center. One thousand, one hundred eighty-two children (953 males) were diagnosed with ASD and compared to other developmental diagnoses. Within the ASD cohort we further analyzed for gender and pre-maturity differences. Results: In the presence of hypotonia, the mean age for ASD diagnosis was significantly lower (by 1.5 years for males and females) and this effect increased in children born at term as compared to pre-maturity. Conclusions: Hypotonia is a recognizable marker of ASD and may serve as a "red flag" to prompt earlier recognition and neurodevelopmental evaluation toward an autism diagnosis.

Prevention in Autism Spectrum Disorder: A Lifelong Focused Approach

Autism Spectrum Disorder (ASD) is a complex highly heritable disorder, in which multiple environmental factors interact with the genes to increase its risk and lead to variable clinical presentations and outcomes. Furthermore, the inherent fundamental deficits of ASD in social attention and interaction critically diverge children from the typical pathways of learning, "creating" what we perceive as autism syndrome during the first three years of life. Later in life, training and education, the presence and management of comorbidities, as well as social and vocational support throughout the lifespan, will define the quality of life and the adaptation of an individual with ASD. Given the overall burden of ASD, prevention strategies seem like a cost-effective endeavour that we have to explore. In this paper, we take a life course approach to prevention. We will review the possibilities of the management of risk factors from preconception until the perinatal period, that of early intervention in the first three years of life and that of effective training and support from childhood until adulthood.