Abstract
BACKGROUND: Autism spectrum disorder (ASD) imposes a significant burden on both patients and society. Amino acid metabolism abnormalities are particularly relevant to ASD pathology due to their crucial role in neurotransmitter synthesis, synaptic function, and overall neurodevelopment. This study aims to explore the association between amino acid metabolic abnormalities and the severity of ASD by analyzing the amino acid concentrations in the blood of children with ASD. METHODS: Fasting peripheral blood samples were collected from 344 children with ASD, and amino acid concentrations were measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS) while strictly following quality control measures. The association between amino acid concentrations and ASD severity was evaluated using logistic regression and restricted cubic spline (RCS) analysis. The ROC (receiver operating characteristic) curve, decision curve analysis (DCA), and calibration curve were used to construct and validate predictive models and nomograms, thereby assessing their predictive performance. RESULTS: Multivariate logistic regression analysis showed that aspartic acid (OR = 1.037, 95% CI: 1.009-1.068, P = 0.01), glutamic acid (OR = 1.009, 95% CI: 1.001-1.017, P = 0.03), phenylalanine (OR = 1.036, 95% CI: 1.003-1.072, P = 0.04), and leucine/isoleucine (OR = 1.021, 95% CI: 1.006-1.039, P = 0.01) were significantly positively correlated with the severity of ASD. On the other hand, tryptophan (OR = 0.935, 95% CI: 0.903-0.965, P < 0.01) and valine (OR = 0.987, 95% CI: 0.977-0.997, P = 0.01) were significantly negatively correlated with the severity of ASD. RCS analysis further revealed a nonlinear relationship between the concentrations of aspartic acid, proline, and glutamic acid and the risk of ASD. ROC curve analysis showed that the combined model achieved an AUC (area under the curve) of 0.806, indicating high diagnostic accuracy. Calibration and decision curve analysis further validated the predictive effectiveness and clinical utility of the model. CONCLUSIONS: This study identifies potential amino acid biomarkers that may contribute to ASD severity assessment. Further research is needed to validate these findings and explore their clinical utility.