Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Canada.
Université de Montréal, Montreal, Canada.
Brain. 2020 May 1;143(5):1315-1331. doi: 10.1093/brain/awz384.
Aetiological and clinical heterogeneity is increasingly recognized as a common characteristic of Alzheimer's disease and related dementias. This heterogeneity complicates diagnosis, treatment, and the design and testing of new drugs. An important line of research is discovery of multimodal biomarkers that will facilitate the targeting of subpopulations with homogeneous pathophysiological signatures. High-throughput 'omics' are unbiased data-driven techniques that probe the complex aetiology of Alzheimer's disease from multiple levels (e.g. network, cellular, and molecular) and thereby account for pathophysiological heterogeneity in clinical populations. This review focuses on data reduction analyses that identify complementary disease-relevant perturbations for three omics techniques: neuroimaging-based subtypes, metabolomics-derived metabolite panels, and genomics-related polygenic risk scores. Neuroimaging can track accrued neurodegeneration and other sources of network impairments, metabolomics provides a global small-molecule snapshot that is sensitive to ongoing pathological processes, and genomics characterizes relatively invariant genetic risk factors representing key pathways associated with Alzheimer's disease. Following this focused review, we present a roadmap for assembling these multiomics measurements into a diagnostic tool highly predictive of individual clinical trajectories, to further the goal of personalized medicine in Alzheimer's disease.
病因和临床异质性日益被认为是阿尔茨海默病和相关痴呆的共同特征。这种异质性使诊断、治疗以及新药的设计和测试变得复杂。一个重要的研究方向是发现多模态生物标志物，这将有助于针对具有同质病理生理特征的亚人群进行靶向治疗。高通量“组学”是一种无偏见的数据驱动技术，可从多个层面（如网络、细胞和分子）探测阿尔茨海默病的复杂病因，从而解释临床人群中的病理生理异质性。这篇综述重点介绍了数据减少分析，这些分析确定了三种组学技术的互补疾病相关扰动：基于神经影像学的亚型、代谢组学衍生的代谢物图谱以及与基因组学相关的多基因风险评分。神经影像学可以跟踪累积的神经退行性变和其他网络损伤源，代谢组学提供了对正在进行的病理过程敏感的全局小分子快照，而基因组学则描述了相对不变的遗传风险因素，这些因素代表了与阿尔茨海默病相关的关键途径。在进行了这次重点综述后，我们提出了一个路线图，将这些多组学测量组合成一个具有高度预测个体临床轨迹的诊断工具，以推进阿尔茨海默病个体化医学的目标。
