Kinetics of neurodegeneration based on a risk-related biomarker in animal model of glaucoma.
Kinetics of neurodegeneration based on a risk-related biomarker in animal model of glaucoma.
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DOI：
10.1186/1750-1326-8-4
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发表时间：
2013-01-18
影响因子：
15.1
通讯作者：
Onoe H
中科院分区：
文献类型：
--
作者：
Hayashi T;Shimazawa M;Watabe H;Ose T;Inokuchi Y;Ito Y;Yamanaka H;Urayama S;Watanabe Y;Hara H;Onoe H
神经退行性疾病，包括帕金森病和阿尔茨海默病，在数年或数十年内缓慢而稳定地进展。它们在进展和临床症状方面显示出显著的受试者间差异，这使得难以预测有或无治疗的长期疾病进展过程。生物标志物的最新技术进展通过测量或成像与发病机制相关的分子，促进了神经变性的早期临床前诊断。然而，目前还没有建立的“生物标志物模型”，人们可以通过它来定量预测神经退行性变的进展。在这里，我们展示了基于风险的神经退行性变动力学模型的可预测性，神经退行性变作为概率事件根据风险进行。我们使用了5只实验性青光眼动物，已知眼内压（IOP）升高和视觉通路神经变性之间的因果关系，并通过扩散张量成像（DTI）重复测量IOP以及白色物质完整性作为轴突变性的生物标志物。青光眼眼的IOP比正常眼显著增加，并且随时间和动物而变化;因此，我们测试了该测量是否可用于预测完整性的动力学。在四种神经变性模型（恒定速率、恒定风险、可变风险和异质性模型）中，模型的拟合优度和用于模型选择的F检验表明，视神经完整性的时间过程由可变风险模型最好地解释，其中神经变性动力学基于测量的IOP以累积风险的指数函数表示。具有拉伸指数衰减函数的异质性模型也能很好地拟合数据，但不具有变量风险模型的统计优势。可变风险模型还预测了视神经中存活轴突的数量，如通过免疫组织化学评估的，这也被证实与视神经的死前完整性相关。此外，可变风险模型确定了高阶视觉通路的不完整性，已知这是这种疾病中跨突触变性的基础。这些发现表明，使用风险相关生物标志物的可变风险模型可以预测神经变性的时空进展。该模型实际上相当于生存分析，可以使我们估计神经保护在延缓神经变性进展中的可能作用。
Neurodegenerative diseases including Parkinson’s and Alzheimer’s diseases progress slowly and steadily over years or decades. They show significant between-subject variation in progress and clinical symptoms, which makes it difficult to predict the course of long-term disease progression with or without treatments. Recent technical advances in biomarkers have facilitated earlier, preclinical diagnoses of neurodegeneration by measuring or imaging molecules linked to pathogenesis. However, there is no established “biomarker model” by which one can quantitatively predict the progress of neurodegeneration. Here, we show predictability of a model with risk-based kinetics of neurodegeneration, whereby neurodegeneration proceeds as probabilistic events depending on the risk. We used five experimental glaucomatous animals, known for causality between the increased intraocular pressure (IOP) and neurodegeneration of visual pathways, and repeatedly measured IOP as well as white matter integrity by diffusion tensor imaging (DTI) as a biomarker of axonal degeneration. The IOP in the glaucomatous eye was significantly increased than in normal and was varied across time and animals; thus we tested whether this measurement is useful to predict kinetics of the integrity. Among four kinds of models of neurodegeneration, constant-rate, constant-risk, variable-risk and heterogeneity models, goodness of fit of the model and F-test for model selection showed that the time course of optic nerve integrity was best explained by the variable-risk model, wherein neurodegeneration kinetics is expressed in an exponential function across cumulative risk based on measured IOP. The heterogeneity model with stretched exponential decay function also fit well to the data, but without statistical superiority to the variable-risk model. The variable-risk model also predicted the number of viable axons in the optic nerve, as assessed by immunohistochemistry, which was also confirmed to be correlated with the pre-mortem integrity of the optic nerve. In addition, the variable-risk model identified the disintegrity in the higher-order visual pathways, known to underlie the transsynaptic degeneration in this disease. These findings indicate that the variable-risk model, using a risk-related biomarker, could predict the spatiotemporal progression of neurodegeneration. This model, virtually equivalent to survival analysis, may allow us to estimate possible effect of neuroprotection in delaying progress of neurodegeneration.
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影响因子：
--
作者：
Craft, Suzanne
通讯作者：
Craft, Suzanne
影响因子：
2.5
作者：
El-Rafei, Ahmed;Engelhorn, Tobias;Michelson, Georg
通讯作者：
Michelson, Georg
影响因子：
2.2
作者：
Assaf, Y;Cohen, Y
通讯作者：
Cohen, Y
影响因子：
2
作者：
Clarke, G;Lumsden, CJ
通讯作者：
Lumsden, CJ
DOI：
10.1523/jneurosci.3785-09.2010
发表时间：
2010-02-10
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Fjell AM;Walhovd KB;Fennema-Notestine C;McEvoy LK;Hagler DJ;Holland D;Brewer JB;Dale AM;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative