Frequency of cerebral energy-metabolic anomalies in autism spectrum disorder - a tool for subgroup definition?

被诊断为自闭症谱系障碍(ASD)的个体在社会交流和互动的情况下受到严重挑战。行为的僵化和社交方面的问题往往会造成严重的痛苦，影响到他们生活的各个领域。ASD被认为是一种严重的器质性神经发育疾病，具有很强的遗传影响。但是，尽管有大量研究报道了磁共振(MR)、遗传和生理信号的差异，但到目前为止，还没有发现ASD的共同病因或至少是生物标志物。ASD综合征的确切病因只被描述为各种各样的，但非常小的亚群，大多是罕见的遗传变异。然而，在大多数ASD病例中，遗传影响是非常复杂和多样的，这导致了ASD被认为是数百种遗传和基因组疾病的行为表现，通常与大多数未知的环境决定因素相互作用。这种病因的异质性被认为汇聚到下游，形成自闭症信息处理的连接模式，影响远程神经元回路之间的通信，这最终体现在自闭症表型上。关于自闭症连接模式如何出现的问题来自不同的科学领域，报告了解剖学、新陈代谢、免疫学和神经递质异常，以损害大脑信息处理。这些信号中的大多数只适用于亚群，并不是非常具体。然而，它们在努力将不同的致病途径与潜在的遗传变异联系起来的努力中构成了一个重要的工具。与诊断亚型的定义类似，在遗传、分子、神经元和行为水平上的共性应该导致对各自亚型的定义。这些亚群理想地由客观标记物信号定义，可以建立将病因上游因素与下游症状联系起来的基础。对于这种连锁，中游标记在不同病因的几条不同病理机制途径汇聚的水平上发挥着重要作用。定义ASD可能致病亚群的一个有希望的融合中游标记是能量代谢。这项拟议的研究的目的是测量大脑和血清乳酸浓度，作为ASD患者受扰能量代谢的替代指标，并评估其作为ASD成人亚群线粒体功能不足的标记的潜力。我们将使用3D兆谱成像(MEGA-SI)的方法，这使得在体内评估大脑乳酸具有前所未有的灵敏度，跨越不同的大脑区域。具体地说，我们将调查MEGA-SI是否是一种临床上可行的工具，用于识别有能量代谢紊乱证据的ASD患者亚组。

Individuals diagnosed with Autism Spectrum Disorder (ASD) are severely challenged in situations of social communication and interaction. Behavioral inflexibility together with problems in social communication often cause severe distress affecting various areas of their lives. ASD is regarded as a severe neurodevelopmental condition of organic origin that has strong genetic influences. But, in spite of the high number of studies reporting differences in magnetic-resonance (MR), genetic, and physiological signals, no common etiological factors or at least biological markers of ASD could be identified so far. Definite etiologies of the ASD syndrome have only been described for miscellaneous, but very small subgroups mostly of rare genetic variants. In most ASD cases, however, the genetic influence is very complex and diverse, which led to the view of ASD as the behavioral manifestation of hundreds of genetic and genomic conditions, often in interaction with mostly unknown environmental determinants. This heterogeneity of etiologies is thought to converge down-stream into an autistic connectivity pattern of information processing affecting the communication between remote neuronal circuits, which finally manifests in the autistic phenotype. Input about the question how the autistic connectivity pattern emerges arrives from various scientific fields, reporting anatomical, metabolic, immunologic, and neurotransmitter anomalies to compromise cerebral information processing. Most of these signals apply only to subgroups and are not very specific. Nevertheless, they constitute an important tool in the endeavor to relate different pathogenetic pathways to the underlying genetic variants. Similar to the definition of diagnostic subtypes, commonalities on the genetic, molecular, neuronal, and behavioral levels should lead to the definition of respective subgroups. Such subgroups, which are ideally defined by objective marker signals, can build the basis to link etiological upstream factors to downstream symptoms. For this linkage, midstream markers at a level where several different pathomechanistic pathways of different etiologies converge play an important role. One promising converging midstream marker defining a putative pathogenetic subgroup of ASD is energy metabolism. The objective of the proposed study is to measure both cerebral and serum lactate concentrations as a proxy-measure of perturbed energy metabolism in individuals with ASD and to evaluate its potential as a marker of insufficient mitochondrial functioning in a subpopulation of adults with ASD. We will employ the method of 3D MEGA- spectroscopic imaging (MEGA-SI), which allows the in vivo assessment of cerebral lactate with unprecedented sensitivity across various brain areas. Specifically, we will investigate whether MEGA-SI is a clinically feasible tool for identifying subgroups of ASD patients with evidence of disturbed energy metabolism.