Using the Griffiths Mental Development Scales to Evaluate a Developmental Profile of Children with Autism Spectrum Disorder and Their Symptomatologic Severity

Overt hypothyroidism in pregnancy and language development in offspring: is there an association?

PURPOSE

Overt hypothyroidism during pregnancy is linked to various obstetric complications, such as premature birth and fetal death. While some studies have shown that maternal hypothyroidism can impact a child's Intelligence Quotient (IQ) and language development, findings are controversial. The aim of this study was to explore the connection between treated maternal hypothyroidism during pregnancy and offspring neurodevelopment, focusing on learning and language and examining related maternal obstetric complications.

METHODS

Group 1 included 31 hypothyroid women with elevated thyroid stimulating hormone (TSH) (> 10 mU/L, > 10 µIU/mL) during pregnancy, and Group 2 had 21 euthyroid women with normal TSH levels (0.5-2.5 mU/L, 0.5-2.5 µIU/mL). Children underwent neuropsycological assessments using the Griffiths-II scale.

RESULTS

Pregnancy outcome showed an average gestational age at delivery of 38.2 weeks for hypothyroid women, compared to 40 weeks for controls, and average birth weight of 2855.6 g versus 3285 g for controls, with hypothyroid women having children with higher intrauterine growth restriction (IUGR) prevalence and more caesarean sections. The 1-min APGAR score was lower for the hypothyroid group's children, at 8.85 versus 9.52. Neuropsychological outcomes showed children of hypothyroid mothers scored lower in neurocognitive development, particularly in the learning and language subscale (subscale C), with a notable correlation between higher maternal TSH levels and lower subscale scores.

CONCLUSION

Fetuses born to hypothyroid mothers appeared to be at higher risk of IUGR and reduced APGAR score at birth. Neurocognitive development seemed to affect language performance more than the developmental quotient. This alteration appeared to correlate with the severity of hypothyroidism and its duration.

The Developmental Autism Early Screening (DAES): A Novel Test for Screening Autism Spectrum Disorder

This study was undertaken to set a novel developmental screening test for autism spectrum disorder (ASD) using the Griffiths Scales of Child Development (Griffith III) (Green et al., 2016; Stroud et al., 2016), in order to intercept the early atypical developmental patterns indicating ASD risk in the first 3 years of age. An observational and interactive ASD screener, the Developmental Autism Early Screening (DAES), was developed by detecting Griffiths III items differentiating toddlers with ASD risk from those with global developmental delay (DD) or neurotypical development. The DAES was validated with ASD-specific diagnostic instruments (ADOS-2) and the cut-off score based on sensitivity, specificity and positive predictive value that best differentiates between ASD and non-ASD children was identified. We enrolled a total sample of 297 subjects, including children at risk for ASD or DD and neurotypical children. At a cut-off score of 12.5, the DAES had a sensitivity of 93%, specificity of 98.4%, positive predictive value of 96.3% and negative predictive value of 96.9% for identifying children at risk for ASD from non-ASD participants (DD/neurotypical children). The DAES total score correlated significantly with the ADOS-2 calibrated severity scores (CSS) (R = 0.53, p < 0.001). Three ASD risk ranges were identified according to DAES total and ADOS-2 CSS: Little-to-no risk (CSS: 1-3, DAES: 1-7); Mild-to-moderate risk (CSS: 4-5, DAES: 8-14); Moderate-to-severe risk (CSS: 6-10, DAES ≥ 15). The DAES provides a direct approach based on developmental profiles to stratify risk for ASD in early childhood ensuring at risk children the most appropriate diagnostic procedures and targeted intervention.

Food Selectivity in Children with Autism Spectrum Disorder: A Statistical Analysis in Southern Italy

This paper focuses on autism spectrum disorder (ASD) and food selectivity, both of which are prevalent in the pediatric population. In this context, the authors paid attention to food selectivity and its possible correlation with the atypicality of sensory processes, outlining the useful rehabilitation treatments to draw on. This research included the parents or caregivers of pediatric patients diagnosed with autism spectrum disorder and placed within a therapeutic clinic. The sample is composed of 111 children, males and females, aged between 2 and 10 years, and includes 60 children diagnosed with autism and 51 children with normotypical development, similar in characteristics but without the disorder. The standardized questionnaire, "Brief Autism Mealtime Behavior Inventory", was developed to examine behavior during meals, especially in children with ASD. The "Brief Sensory Profile", and the "Child Oral and Motor Proficiency Scale", were also administered. The results obtained from the analysis lead to evidence of eating and food selectivity difficulty. Additionally, our study demonstrates that food selectivity can be caused by extreme sensory modulation and sensory problems related to the smell, texture, color, and temperature of food. In fact, the results obtained emphasize the correlation between food selectivity and the sensory domains of taste and smell. Furthermore, this research highlights a correlation between motor skills and eating skills, particularly regarding food selectivity, which is closely associated with atypical and disruptive behaviors during meals.

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.