Developmental and Intelligence Quotient in Autism: A Brief Report on the Possible Long-Term Relation

A longitudinal characterization of the adaptive and behavioral profile in Sotos syndrome

Delineation of a developmental and behavioral trajectory is a key-topic in the context of a genetic syndrome. Short- and long-term implications concerning school outcome, independent living, and working opportunities are strictly linked to the cognitive and behavioral profile of an individual. For the first time, we present a longitudinal characterization of the adaptive and behavioral profile of a pediatric sample of 32 individuals with Sotos Syndrome (SoS) (18 males, 14 females; mean age 9.7 ± 4 years, eight carrying the NSD1 5q35 microdeletion and 24 with an intragenic mutation). We performed two clinical assessments: at baseline (T0) and at distance evaluation (T1) of adaptive and behavioral skills with a mean distance of 1.56 ± 0.95 years among timepoints. Our study reports a stability over the years-meant as lack of statistically significant clinical worsening or improvement-of both adaptive and behavioral skills investigated, regardless the level of Intellectual Quotient and chronological age at baseline. However, participants who did not discontinue intervention among T0 and T1, were characterized by a better clinical profile in terms of adaptive skills and behavioral profile at distance, emphasizing that uninterrupted intervention positively contributes to the developmental trajectory.

Short report: Autistic symptoms in Sotos syndrome, preliminary results from a case-control study

BACKGROUND

An extremely heterogeneous neuropsychological phenotype has been reported in Sotos Syndrome (SoS), including socio-communicative and behavioral difficulties referred to Autism Spectrum Disorder (ASD). Nonetheless, to date, only few data are available on the topic.

AIM

To investigate ASD symptoms within a sample of children with SoS in comparison to a matched control group of individuals with idiopathic ASD.

METHODS

A convenience sample of SoS (n = 33, age: 9.8 ± 4.1) and ASD (n = 33, age: 9.9 ± 4.1), was included. Autistic symptoms' assessment was performed through the administration of the Autism Diagnostic Observation Schedule-Second Edition- ADOS-2, the Social Responsiveness Scale -SRS and the Social Communication Questionnaire-SCQ.

RESULTS

72.7% of SoS children presented mild to moderate levels of ASD symptoms as measured by the ADOS-2. Oneway ANOVA analysis showed that SoS individuals presenting lower IQ demonstrated higher ASD symptom's level (p = 0.01). No statistically significant differences emerged between the SoS and ASD groups within the SRS total score domain (p = 0.95).

CONCLUSIONS AND IMPLICATIONS

Our results support the evidence for an increased risk for ASD in SoS, suggesting that the ASD symptoms' assessment should be regularly performed in SoS children, with subsequent important implications in terms of therapeutic strategies and later outcome.

A Machine Learning Approach to the Diagnosis of Autism Spectrum Disorder and Multi-Systemic Developmental Disorder Based on Retrospective Data and ADOS-2 Score

Early and accurate diagnosis of autism spectrum disorders (ASD) and tailored therapeutic interventions can improve prognosis. ADOS-2 is a standardized test for ASD diagnosis. However, owing to ASD heterogeneity, the presence of false positives remains a challenge for clinicians. In this study, retrospective data from patients with ASD and multi-systemic developmental disorder (MSDD), a term used to describe children under the age of 3 with impaired communication but with strong emotional attachments, were tested by machine learning (ML) models to assess the best predictors of disease development as well as the items that best describe these two autism spectrum disorder presentations. Maternal and infant data as well as ADOS-2 score were included in different ML testing models. Depending on the outcome to be estimated, a best-performing model was selected. RIDGE regression model showed that the best predictors for ADOS social affect score were gut disturbances, EEG retrievals, and sleep problems. Linear Regression Model showed that term pregnancy, psychomotor development status, and gut disturbances were predicting at best for the ADOS Repetitive and Restricted Behavior score. The LASSO regression model showed that EEG retrievals, sleep disturbances, age at diagnosis, term pregnancy, weight at birth, gut disturbances, and neurological findings were the best predictors for the overall ADOS score. The CART classification and regression model showed that age at diagnosis and weight at birth best discriminate between ASD and MSDD.