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1H NMR Metabolomics and Lipidomics To Monitor Positive Responses in Children with Autism Spectrum Disorder Following a Guided Parental Intervention: A Pilot Study

脂类学 自闭症谱系障碍 代谢组学 自闭症 医学 心理学 临床心理学 神经科学 生物信息学 精神科 生物
作者
Banny Silva Barbosa Correia,João Guilherme de Moraes Pontes,João V. Nani,Fabian Villalta,Natalia Cristina Mor,Daniela Bordini,Décio Brunoni,Helena Brentani,Jair de Jesus Mari,Mirian A.F. Hayashi,Ljubica Tasić
出处
期刊:ACS Chemical Neuroscience [American Chemical Society]
卷期号:14 (6): 1137-1145 被引量:3
标识
DOI:10.1021/acschemneuro.2c00735
摘要

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that is characterized by patients displaying at least two out of the classical symptoms, such as impaired social communication, impaired interactions, and restricted repetitive behavior. Early parent-mediated interventions, such as video modeling for parental training, were demonstrated to be a successful low-cost way to deliver care for children with ASD. Nuclear magnetic resonance (NMR)-based metabolomics/lipidomics has been successfully employed in several mental disorder studies. Metabolomics and lipidomics of 37 ASD patients (children, aged 3–8 years), who were divided into two groups, one control group with no parental-training intervention ( N = 18) and the other in which the parents were trained by a video modeling intervention (ASD parental training, N = 19), were analyzed by proton NMR spectroscopy. Patients in the ASD parental-training group sera were seen to have increased glucose, myo-inositol, malonate, proline, phenylalanine, and gangliosides in their blood serum, while cholesterol, choline, and lipids were decreased, compared to the control group, who received no parental-training. Taken together, we demonstrated here significant changes in serum metabolites and lipids in ASD children, previously demonstrated to show clinical positive effects following a parental training intervention based on video modeling, delivered over 22 weeks. We demonstrate the value of applying metabolomics and lipidomics to identify potential biomarkers for clinical interventions follow-up in ASD.
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