Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY.
Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY; Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY.
Semin Pediatr Neurol. 2020 Jul;34:100803. doi: 10.1016/j.spen.2020.100803. Epub 2020 Mar 5.
An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.
对自闭症谱系障碍 (ASD) 病理生理学的认识在不断发展，这就要求诊断标准也从基于观察的方法发展为包括可量化的临床测量方法。ASD 的多系统性质促使人们使用多元统计分析方法而不是常用的单变量方法来发现与这一目标相关的临床生物标志物。除了对重要行为模式进行特征描述以改进当前的诊断工具外，多元分析迄今为止还允许对自闭症患者的神经影像学、遗传学和代谢异常进行全面研究。这篇综述重点介绍了目前使用多元统计分析来量化这些行为和生理标志物对 ASD 诊断价值的研究。还详细讨论了一种使用特定代谢物浓度的基于血液的 ASD 诊断测试。ASD 生物标志物研究的进展有望为该疾病提供更早、更准确的诊断。
