自閉症類群障礙症患者血漿代謝體特徵與臨床表現關聯性研究

Abstract

背景 代謝體學研究在精神疾病研究中越來越重要，因其更接近患者之行為表現。自閉症類群障礙症（ASD）是一種廣泛研究的神經發展障礙，但其病理生理機制仍然未知。透過分析生理與行為表現的關聯性，代謝體學研究可以為ASD的病理生理機制提供新見解，且此類型研究仍不足。本研究旨在比較ASD患者與典型發展控制組（TDC）之間的代謝體，並探索ASD中代謝物與臨床量表之間的關聯。 方法 研究對象包括2019年至2023年間招募的242名ASD患者和80名TDC。透過Swanson, Nolan and Pelham, version IV（SNAP-IV）、Social Responsiveness Scale（SRS）、Children Behavior Checklist（CBCL）和Autism Diagnostic Observation Schedule（ADOS）測量行為表型。使用非標靶超高效液相色譜/質譜（UHPLC/MS）分析血漿樣本。若變量重要性（VIP）值≥1，p≤0.05，且變化倍數≥1.05或≤0.95，則該代謝物被認為具有統計學意義。使用MetaboAnalyst 6.0進行網絡分析。 結果 共21種顯著代謝物符合以下標準：（1）變化倍數≥1.05和≤0.95，（2）p值≤0.05，（3）VIP分數>1。網絡分析進一步確定了與代謝調控和途徑動態顯著互動的五種代謝物——GABA、phenylethylamine、 citrulline、glutaric acid和cortisol，這些代謝物與不同行為相關：GABA增加與過動、衝動和固化行為相關；phenylethylamine減少與創造力減退相關；cortisol減少與語言和溝通障礙相關；glutaric acid增加與思維問題相關；citrulline減少與社交問題相關。 結論 本研究探索與ASD不同行為表現相關的關鍵代謝物，並展示了代謝體學如何幫助發現新的ASD生物標記，後將代謝體與特定臨床特徵聯繫起來，為未來的臨床和研究應用奠定基礎。

Background Metabolomic is the study of downstream products closer to behavioral expressions and has increasing importance in mental disorder research. Autism spectrum disorder (ASD) is a widely studied neurodevelopmental disorder; however, its pathophysiological mechanisms remain largely unknown. By providing crucial information linking physiological and behavioral manifestations, metabolomics research may provide new insights into the pathophysiological mechanisms behind ASD behaviors. Research exploring metabolomics and its connections with clinical phenotypes in individuals with ASD is notably lacking. This study aimed to compare the metabolomic profiles between ASD individuals and typically developing controls (TDC) and explore the associations between identified metabolites and clinical measures in ASD. Methods Participants included 242 ASD patients and 80 TDCs, recruited from 2019 to 2023. Behavioral phenotypes were measured using the Swanson, Nolan, and Pelham, version IV (SNAP-IV), Social Responsiveness Scale (SRS), Children Behavior Checklist (CBCL), and Autism Diagnostic Observation Schedule (ADOS). Plasma samples were analyzed by untargeted ultra-high-performance liquid chromatography/mass spectrometry (UHPLC/MS). Metabolites were considered statistically significant if variable importance in projection (VIP) ≥ 1, p ≤ 0.05, and fold change ≥ 1.05 or ≤ 0.95. Network analysis was performed on MetaboAnalyst 6.0. Results A total of 21 significant metabolites met the following criteria: (1) a fold change ≥ 1.05 and ≤ 0.95, (2) a p-value ≤ 0.05 , and 3) a VIP score > 1. Network analysis further identified five metabolites—GABA, phenylethylamine, citrulline, glutaric acid, and cortisol with significant interactions related to metabolic regulation and pathway dynamics. Distinct behaviors correlated with these metabolites: upregulated GABA was associated with hyperactivity, impulsivity, and oppositional behaviors; downregulated phenylethylamine was linked to decreased creativity impairments; downregulated cortisol was related to deficits in language and communication; upregulated glutaric acid was correlated with thought problems; and downregulated citrulline was connected with social problems. Conclusion The study identified key metabolites linked to distinct behavioral phenotypes in ASD and demonstrated how metabolomics can help discover novel ASD biomarkers and connect metabolic profiles to specific clinical features, setting the stage for future clinical and research applications.