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A multimodal approach to identify clinically relevant biomarkers to comprehensively monitor disease progression in a mouse model of pediatric neurodegenerative disease.
Progress in Neurobiology ( IF 6.7 ) Pub Date : 2020-03-18 , DOI: 10.1016/j.pneurobio.2020.101789
Tyler B Johnson 1 , Jon J Brudvig 1 , Kimmo K Lehtimäki 2 , Jacob T Cain 1 , Katherine A White 1 , Timo Bragge 2 , Jussi Rytkönen 2 , Tuulia Huhtala 2 , Derek Timm 1 , Maria Vihma 2 , Jukka T Puoliväli 2 , Pekka Poutiainen 3 , Antti Nurmi 2 , Jill M Weimer 4
Affiliation  

While research has accelerated the development of new treatments for pediatric neurodegenerative disorders, the ability to demonstrate the long-term efficacy of these therapies has been hindered by the lack of convincing, noninvasive methods for tracking disease progression both in animal models and in human clinical trials. Here, we unveil a new translational platform for tracking disease progression in an animal model of a pediatric neurodegenerative disorder, CLN6-Batten disease. Instead of looking at a handful of parameters or a single "needle in a haystack", we embrace the idea that disease progression, in mice and patients alike, is a diverse phenomenon best characterized by a combination of relevant biomarkers. Thus, we employed a multi-modal quantitative approach where 144 parameters were longitudinally monitored to allow for individual variability. We use a range of noninvasive neuroimaging modalities and kinematic gait analysis, all methods that parallel those commonly used in the clinic, followed by a powerful statistical platform to identify key progressive anatomical and metabolic changes that correlate strongly with the progression of pathological and behavioral deficits. This innovative, highly sensitive platform can be used as a powerful tool for preclinical studies on neurodegenerative diseases, and provides proof-of-principle for use as a potentially translatable tool for clinicians in the future.

中文翻译:

识别临床相关生物标记物以全面监测小儿神经退行性疾病小鼠模型中疾病进展的多模式方法。

尽管研究加速了小儿神经退行性疾病新疗法的开发,但由于缺乏在动物模型和人类临床试验中缺乏令人信服的非侵入性方法来追踪疾病进展的能力,因而无法证明这些疗法的长期疗效。在这里,我们推出了一个新的翻译平台,用于跟踪儿童神经退行性疾病CLN6-Batten疾病的动物模型中疾病的进展。我们不去看少数参数或只看一堆“大海捞针”,而是接受这样一种想法,即老鼠和患者中的疾病进展是一种以相关生物标志物的组合为特征的多样化现象。从而,我们采用了一种多模式定量方法,其中纵向监测了144个参数,以允许个体差异。我们使用一系列非侵入性神经影像学方法和运动步态分析,所有与临床常用方法相似的方法,然后使用功能强大的统计平台,以识别与病理和行为缺陷的进展密切相关的关键进行性解剖和代谢变化。这个创新的高度敏感的平台可用作神经退行性疾病的临床前研究的有力工具,并提供原理证明,供将来临床医生用作潜在的可翻译工具。所有与临床上常用的方法平行的方法,然后是功能强大的统计平台,用于识别与病理和行为缺陷的进展密切相关的关键进行性解剖和代谢变化。这个创新的高度敏感的平台可用作神经退行性疾病的临床前研究的有力工具,并提供原理证明,供将来临床医生用作潜在的可翻译工具。所有与临床上常用的方法平行的方法,然后是功能强大的统计平台,用于识别与病理和行为缺陷的进展密切相关的关键进行性解剖和代谢变化。这个创新的高度敏感的平台可以用作神经退行性疾病的临床前研究的有力工具,并提供原理证明,供将来临床医生使用。
更新日期:2020-03-18
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