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. 2025 Apr 4;25(1):332.
doi: 10.1186/s12888-025-06771-x.

Associations between amino acid levels and autism spectrum disorder severity

Jing Li #  1   2 , Panpan Zhai #  1   2 , Liangliang Bi  1   2 , Ying Wang  1   2 , Xiaoqing Yang  1   2 , Yueli Yang  1   2 , Nan Li  3 , Weili Dang  4   5 , Gang Feng  6   7 , Pei Li  3 , Yuan Liu  3 , Qiushuang Zhang  1   2 , Xiaofeng Mei  1   2
Affiliations

Associations between amino acid levels and autism spectrum disorder severity

Jing Li et al. BMC Psychiatry. .

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.

Keywords: Amino acid metabolism; Autism spectrum disorder; Early diagnosis; Glutamic acid; Restricted cubic spline analysis; Risk assessment.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This project was reviewed by the Ethics Committee of the First Affiliated Hospital of Henan University of Chinese Medicine and complies with the relevant provisions of the Measures for the Ethical Review of Biomedical Research Involving Humans and the Declaration of Helsinki regarding human biological experiments. The study protocol has been approved (Approval No.: 2023HL-427-01), and informed consent was obtained from all primary guardians. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Clinical trial: Not applicable.

Figures

Fig. 1
Fig. 1
Univariate logistic regression analysis: the association between amino acid concentrations, age, gender, and the severity of ASD
Fig. 2
Fig. 2
The nonlinear relationship between amino acid concentrations and the severity of ASD (Phenylalanine, Tryptophan, Leucine/Isoleucine, Valine, Aspartic Acid, Glutamic Acid, Ornithine, and Proline)
Fig. 3
Fig. 3
ASD severity prediction model based on multivariable analysis: ROC Curve, Nomogram, Calibration Plot, and Decision Curve Analysis (Phenylalanine, Tryptophan, Leucine/Isoleucine, Valine, Aspartic Acid, Glutamic Acid, Ornithine, and Proline)
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