Chen-Plotkin Alice S
Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, 3 West Gates, 3400 Spruce Street, Philadelphia, PA 19104, USA.
Neuron. 2014 Nov 5;84(3):594-607. doi: 10.1016/j.neuron.2014.10.031.
Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and frontotemporal dementia have several important features in common. They are progressive, they affect a relatively inaccessible organ, and we have no disease-modifying therapies for them. For these brain-based diseases, current diagnosis and evaluation of disease severity rely almost entirely on clinical examination, which may be only a rough approximation of disease state. Thus, the development of biomarkers-objective, relatively easily measured, and precise indicators of pathogenic processes-could improve patient care and accelerate therapeutic discovery. Yet existing, rigorously tested neurodegenerative disease biomarkers are few, and even fewer biomarkers have translated into clinical use. To find new biomarkers for these diseases, an unbiased, high-throughput screening approach may be needed. In this review, I will describe the potential utility of such an approach to biomarker discovery, using Parkinson's disease as a case example.
诸如阿尔茨海默病、帕金森病、肌萎缩侧索硬化症和额颞叶痴呆等神经退行性疾病有几个重要的共同特征。它们是进行性的,影响一个相对难以触及的器官,而且我们没有针对它们的疾病修饰疗法。对于这些基于大脑的疾病,目前疾病严重程度的诊断和评估几乎完全依赖于临床检查,而这可能只是对疾病状态的大致估计。因此,生物标志物的开发——客观、相对易于测量且精确的致病过程指标——可以改善患者护理并加速治疗发现。然而,现有的经过严格测试的神经退行性疾病生物标志物很少,转化为临床应用的生物标志物更少。为了找到这些疾病的新生物标志物,可能需要一种无偏倚的高通量筛选方法。在这篇综述中,我将以帕金森病为例,描述这种方法在生物标志物发现中的潜在用途。