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The autism spectrum disorder (ASD) is an etiologically heterogeneous disorder. Dysfunctions of the intermediate metabolism have been described in some patients. We speculate these metabolic abnormalities are associated with brain insulin resistance (IR), i.e., the reduced glucose metabolism at the level of the nervous central system. The Homeostasis model assessment of insulin resistance (HOMA-IR) is very often used in population studies as estimate of peripheral IR and it has been recently recognized as proxy of brain IR. We investigated HOMA-IR in 60 ASD patients aged 4–18 years and 240 healthy controls, also aged 4–18 years, but unmatched for age, sex, body weight, or body mass index (BMI). At multivariable linear regression model, the HOMA-IR was 0.31 unit higher in ASD individuals than in controls, after having adjusted for sex, age, BMI z-score category, and lipids that are factors known to influence HOMA-IR. Findings of this preliminary study suggest it is worth investigating brain glucose metabolism in larger population of patients with ASD by using gold standard technique. The recognition of a reduced glucose metabolism in some areas of the brain as marker of autism might have tremendous impact on our understanding of the pathogenic mechanisms of the disease and in terms of public health.

Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.

Autism spectrum disorder (ASD) is characterized by psychiatric and behavioral comorbidities. The Child Behavior Checklist (CBCL) provides valid and well-established measures of emotional, behavioral, and social problems in children and adolescents. The aim of the present study was to verify whether emotional, behavioral, and social problems were modulated by ASD symptom severity, cognitive development, gender, and age by analyzing the CBCL in a large group of children and adolescents with ASD. The results show that around 30% of participants with ASD exhibited internalizing problems and only 6% externalizing problems, with males exhibiting more internalizing problems than females. No correlation was found between CBCL scores and indices of ASD severity. However, higher CBCL Total Problems scores were found in older children and in children with lower cognitive abilities. The detection of behavioral and emotional problems allows children with ASD to undergo specific and individualized treatment that takes into account their psychopathological problems.

An increased prevalence of psychiatric comorbidity (PC) in individuals with Autism Spectrum Disorders (ASD) is consistently reported. While several studies have examined PC in school-aged children, adolescents and adults with ASD, investigations on PC in preschoolers are less common. In this study, we explore the prevalence and the type of PC in a sample of 989 preschoolers with ASD through the DSM-Oriented Scales (DOS) of the Child Behavior Checklist (CBCL 11⁄2-5) and their possible links with the core features of ASD and cognitive functioning. Results indicated that 37.8% of the sample had at least one PC in addition to ASD; these subjects displayed significantly higher Total score (p = 0.02) and Social Affect score (p = 0.003) on the ADOS-based calibrated severity scores (CSS), as well as lower (p ≤ 0.0001) performance IQ (pIQ) compared to ASD individuals without PC. As far as the specific DOS, Affective Problems (AP) were detected in 23.4% of the whole sample, ADHD Problems (ADHD) in 17.3%, Anxiety Problems (AXP) in 16.7%, and Oppositional Problems (OP) in 7.9%. These different comorbidities were isolated in 195 subjects (Mono-comorbid group: 19.7% of the whole sample), while 179 subjects (18.1% of the whole sample) had two or more types of PC (Multi-comorbid group). One-way ANOVA revealed that subjects with multi-comorbidity have statistically significant lower pIQ and higher Total score and Social Affect score on CSS-ADOS. Specific differences for each type of comorbidity and gender differences were also discussed. Taken together, results indicate a considerable presence of PC in preschoolers with ASD that should be accurately considered during the assessment and diagnosis process in order to plan a tailored intervention based not only on core symptoms of ASD, but also on comorbid psychiatric condition since preschool age.

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