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Published on: April 12, 2018
Selective Plasmatic Amino Acid Alterations as a Potential Biomarker for Pathological Stratification in Autism
Andrea De Giacomo1, Nicoletta Lionetti1, Maria Grazia Di Lago1
1Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", 70121 Bari, Italy.
Abstract:
Background: Autism Spectrum Disorders (ASD) are neurodevelopmental disorders characterized by repetitive behaviors and social interaction deficits. While the severity of ASD is classified into levels (1-3) by the DSM-5, reliable circulating biomarkers to differentiate these levels are lacking. This retrospective pilot study examines plasma amino acid levels in children with ASD to identify the potential biomarkers of disease severity. Methods: Plasma samples from 30 children diagnosed with ASD (24 males, 6 females, aged 3-12 years) were analyzed. Participants were stratified into two groups based on the Autism Diagnostic Observation Schedule Calibrated Severity Score (ADOS CSS): Group 1, presenting with mild symptoms (Level 1, n = 11), and Group 2, characterized by moderate-to-severe symptoms (Levels 2-3, n = 19). This was further confirmed by the identification of electroencephalogram (EEG) anomalies (21.1%) and magnetic resonance imaging (MRI) abnormalities (5.3%), which were detected exclusively in Group 2 and absent in Group 1. Amino acid levels were measured by ion-exchange chromatography. Statistical analyses (Mann-Whitney U test and chi-square test) were used to compare AA levels between groups. Results: Statistically significant differences were observed in the levels of phosphoethanolamine, aspartic acid, and glutamic acid between the two groups. These amino acids (AA) were significantly higher in the moderate-to-severe symptoms group (Levels 2-3) compared to the mild symptoms group (Level 1) (p < 0.05). All AA values remained within age-appropriate reference ranges. Conclusions: Plasma levels of phosphoethanolamine, aspartic acid, and glutamic acid may serve as potential biomarkers for ASD severity in children. Results from this exploratory analysis suggest that AA profiling could differentiate ASD severity and identify specific metabolic pathways, such as excitatory neurotransmission and phospholipid turnover. Further studies with larger cohorts are necessary to validate these findings and explore the role of AAs in ASD pathophysiology.
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