Characterization of sleep difficulties in maltreated children and adolescents.

Numerous studies have identified connections between child maltreatment and sleep-related issues. However, poor is known on potential links between sleep patterns and day-to-day functioning, along with psychopathology in maltreated youths. Additionally, existing research on the relationship between sleep difficulties and maltreatment often lacks investigation into specific sleep difficulty profiles across different age ranges. The current study aimed to determine the prevalence of diverse sleep disturbance profiles in a sample of maltreated children and adolescents, exploring distinct sleep disorder profiles based on sex, age, and the type of maltreatment experienced. Potential variations in adaptive and psychopathological profiles between maltreated children with and without sleep disturbances were also explored. This retrospective study included 91 children and adolescents (56% males, 44% females), aged 6 to 17, with a history of maltreatment (physical maltreatment, sexual abuse, psychological abuse, or neglect), referring for a neuropsychiatric and psychological evaluation at a pediatric hospital. Data were obtained through a retrospective file review. Sleep difficulties were measured through the Sleep Disturbance Scale for Children; cognitive abilities, adaptive skills, and emotional and behavioral features were also investigated. Among maltreated youth, difficulties in initiating and maintaining sleep were the most frequently observed by caregivers. Poor sex differences emerged, whereas adolescents exhibited more daytime somnolence than school-age children. Children with sleep difficulties exhibited more anxiety symptoms and worse global functioning in comparison with children without sleep difficulties.Conclusion: Considering the vital impact of sleep quality on healthy development, practitioners should offer tailored services to child maltreatment victims. Enhancing the sleep quality of these children could help foster their resilience.

What Factors Predict Adaptive Functioning in Preschool Children with Autism Spectrum Disorder? A Longitudinal Study.

Adaptive functioning constitutes a fundamental aspect of the phenotype associated with autism spectrum disorder (ASD) in preschool-aged children, exerting a significant influence on both the child and the family's overall quality of life. The aim of this study was to investigate the predictors of the adaptive functioning domains in preschool-age children with ASD at two time points, providing a snapshot of this fundamental developmental step. Methods: Ninety-five children with ASD (M = 3.89, SD = 1.13) were included in the study and longitudinal data (the mean length of the longitudinal data collection was 1 year) on ASD features such as social communication and social interaction, repetitive and restricted behavior, cognitive level, and adaptive functioning were collected. We considered autistic features, cognitive level, and sociodemographic factors as possible predictors of the different adaptive functioning domains one year later. Results: Data obtained showed a worsening of the ASD features and adaptive functioning after one year. Furthermore, the severity of repetitive and restricted behavior predicted adaptive functioning, especially in the social and practical domains of the child, one year later. This prediction was observed alongside the child's cognitive level. Conclusions: The study identifies some potential predictive factors of specific adaptive functioning domains in preschoolers with ASD. Considering how critical adaptive functioning is for the well-being of both the child and their family, it becomes imperative to design early-stage interventions focused on nurturing adaptive skills in children with ASD.

Autism Spectrum Disorder and Early Psychosis: a narrative review from a neurodevelopmental perspective.

Autism Spectrum Disorder (ASD), characterized by socio-communicative abnormalities and restricted, repetitive, and stereotyped behaviors, is part of Neurodevelopmental Disorders (NDDs), a diagnostic category distinctly in accordance with the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, (DSM-5), clearly separated from Schizophrenia Spectrum Disorder (SSD) (schizophrenia, schizophreniform disorder, schizoaffective disorder, schizotypal personality disorder). Over the last four decades, this clear distinction is gradually being replaced, describing ASD and SSD as two heterogeneous conditions but with neurodevelopmental origins and overlaps. Referring to the proposal of a neurodevelopmental continuum model, the current research's aim is to provide an update of the knowledge to date on the course of clinical symptoms and their overlaps among ASD and SSD. A narrative review of the literature published between January 2010 and June 2023 was conducted. Five studies were included. All studies show a global impairment in both conditions. Two studies show a focus on neurodevelopmental perspective in ASD and SSD. Only one study of these adopts a longitudinal prospective in terms of prognostic markers among ASD and SSD. Three studies underline the overlap between ASD and SSD in terms of negative, disorganized and positive symptomatology. To date, there is a gap in the current scientific literature focused on ASD-SSD course of clinical symptoms and their overlaps from a neurodevelopmental perspective. Future longitudinal studies to identify risk markers and tailored treatments are needed.

Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors.

Background: Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder. Major interplays between the gastrointestinal (GI) tract and the central nervous system (CNS) seem to be driven by gut microbiota (GM). Herein, we provide a GM functional characterization, based on GM metabolomics, mapping of bacterial biochemical pathways, and anamnestic, clinical, and nutritional patient metadata. Methods: Fecal samples collected from children with ASD and neurotypical children were analyzed by gas-chromatography mass spectrometry coupled with solid phase microextraction (GC-MS/SPME) to determine volatile organic compounds (VOCs) associated with the metataxonomic approach by 16S rRNA gene sequencing. Multivariate and univariate statistical analyses assessed differential VOC profiles and relationships with ASD anamnestic and clinical features for biomarker discovery. Multiple web-based and machine learning (ML) models identified metabolic predictors of disease and network analyses correlated GM ecological and metabolic patterns. Results: The GM core volatilome for all ASD patients was characterized by a high concentration of 1-pentanol, 1-butanol, phenyl ethyl alcohol; benzeneacetaldehyde, octadecanal, tetradecanal; methyl isobutyl ketone, 2-hexanone, acetone; acetic, propanoic, 3-methyl-butanoic and 2-methyl-propanoic acids; indole and skatole; and o-cymene. Patients were stratified based on age, GI symptoms, and ASD severity symptoms. Disease risk prediction allowed us to associate butanoic acid with subjects older than 5 years, indole with the absence of GI symptoms and low disease severity, propanoic acid with the ASD risk group, and p-cymene with ASD symptoms, all based on the predictive CBCL-EXT scale. The HistGradientBoostingClassifier model classified ASD patients vs. CTRLs by an accuracy of 89%, based on methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, ethanol, butanoic acid, octadecane, acetic acid, skatole, and tetradecanal features. LogisticRegression models corroborated methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, skatole, and acetic acid as ASD predictors. Conclusion: Our results will aid the development of advanced clinical decision support systems (CDSSs), assisted by ML models, for advanced ASD-personalized medicine, based on omics data integrated into electronic health/medical records. Furthermore, new ASD screening strategies based on GM-related predictors could be used to improve ASD risk assessment by uncovering novel ASD onset and risk predictors.

Assessing cortical features in early stage ASD children.

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental disorder largely investigated in the neurologic field. Recently, neuroimaging studies have been conducted in order to investigate cerebral morphologic alterations in ASD patients, demonstrating an atypical brain development before the clinical manifestations of the disorder. Cortical Thickness (CT) and Local Gyrification Index (LGI) distribution for ASD children were investigated in this study, with the aim to evaluate possible relationship between brain measures and individual characteristics (i.e., IQ and verbal ability). 3D T1-w sequences from 129 ASD and 58 age-matched Healthy Controls (HC) were acquired and processed in order to assess CT and LGI for each subject. Intergroup differences between ASD and HC were investigated, including analyses of 2 ASD subgroups, split according to patient verbal ability and IQ. When compared to HC, ASD showed increased CT and LGI within several brain areas, both as an overall group and as verbal ability an IQ subgroups. Moreover, when comparing language characteristics of the ASD subjects, those patients with verbal ability exhibit significant CT and LGI increase was found within the occipital lobe of right hemisphere. No significant results occurred when comparing ASD patients according to their IQ value. These results support the hypothesis of abnormal brain maturation in ASD since early childhood with differences among clinical subgroups suggesting different anatomical substrates underlying an aberrant connectivity.