Ruggeri Barbara, Sarkans Ugis, Schumann Gunter, Persico Antonio M
MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, De Crespigny Park, London, SE5 8AF, UK.
Psychopharmacology (Berl). 2014 Mar;231(6):1201-16. doi: 10.1007/s00213-013-3290-7. Epub 2013 Oct 6.
Autism spectrum disorder (ASD) is a complex heterogeneous neurodevelopmental disorder with onset during early childhood and typically a life-long course. The majority of ASD cases stems from complex, 'multiple-hit', oligogenic/polygenic underpinnings involving several loci and possibly gene-environment interactions. These multiple layers of complexity spur interest into the identification of biomarkers able to define biologically homogeneous subgroups, predict autism risk prior to the onset of behavioural abnormalities, aid early diagnoses, predict the developmental trajectory of ASD children, predict response to treatment and identify children at risk for severe adverse reactions to psychoactive drugs.
The present paper reviews (a) similarities and differences between the concepts of 'biomarker' and 'endophenotype', (b) established biomarkers and endophenotypes in autism research (biochemical, morphological, hormonal, immunological, neurophysiological and neuroanatomical, neuropsychological, behavioural), (c) -omics approaches towards the discovery of novel biomarker panels for ASD, (d) bioresource infrastructures and (e) data management for biomarker research in autism.
Known biomarkers, such as abnormal blood levels of serotonin, oxytocin, melatonin, immune cytokines and lymphocyte subtypes, multiple neuropsychological, electrophysiological and brain imaging parameters, will eventually merge with novel biomarkers identified using unbiased genomic, epigenomic, transcriptomic, proteomic and metabolomic methods, to generate multimarker panels. Bioresource infrastructures, data management and data analysis using artificial intelligence networks will be instrumental in supporting efforts to identify these biomarker panels.
Biomarker research has great heuristic potential in targeting autism diagnosis and treatment.
自闭症谱系障碍（ASD）是一种复杂的异质性神经发育障碍，起病于儿童早期，通常病程持续终生。大多数ASD病例源于复杂的“多重打击”、寡基因/多基因基础，涉及多个基因座，可能还存在基因-环境相互作用。这些多层次的复杂性激发了人们对识别生物标志物的兴趣，这些生物标志物能够定义生物学上同质的亚组，在行为异常出现之前预测自闭症风险，辅助早期诊断，预测ASD儿童的发育轨迹，预测治疗反应，并识别对精神活性药物有严重不良反应风险的儿童。
本文综述了（a）“生物标志物”和“内表型”概念之间的异同，（b）自闭症研究中已确立的生物标志物和内表型（生化、形态学、激素、免疫、神经生理和神经解剖学、神经心理学、行为学方面），（c）用于发现ASD新型生物标志物组合的“组学”方法，（d）生物资源基础设施，以及（e）自闭症生物标志物研究的数据管理。
已知的生物标志物，如血液中血清素、催产素、褪黑素、免疫细胞因子和淋巴细胞亚群水平异常，多种神经心理学、电生理和脑成像参数，最终将与使用无偏倚的基因组、表观基因组、转录组、蛋白质组和代谢组学方法鉴定出的新型生物标志物相结合，以生成多标志物组合。生物资源基础设施、数据管理以及使用人工智能网络进行数据分析，将有助于支持识别这些生物标志物组合的工作。
生物标志物研究在针对自闭症诊断和治疗方面具有巨大的启发潜力。
