阿尔茨海默病确定生物标志物面板的实际检测匹配血浆和脑脊液的比较。

International journal of molecular epidemiology and genetics Pub Date : 2014-05-29 eCollection Date: 2014-01-01
Joanna L Richens, Kelly-Ann Vere, Roger A Light, Daniele Soria, Jonathan Garibaldi, A David Smith, Donald Warden, Gordon Wilcock, Nin Bajaj, Kevin Morgan, Paul O'Shea
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引用次数: 0

摘要

先前对脑脊液(CSF)的质谱分析已经确定了与阿尔茨海默病(AD)相关的一组分子标记。该小组包括淀粉样蛋白、载脂蛋白E、纤维蛋白原α链前体、角蛋白I型细胞骨架9、血清白蛋白前体、sparc样1蛋白和四联蛋白。在这里,我们报告了免疫测定的发展和实施,以测量这些假定的生物标志物在匹配的腰椎脑脊液和血浆样本中的丰度和诊断能力,这些样本来自死后确认患有AD的个体(n = 10)和筛选的“认知健康”受试者(n = 18)。阿尔茨海默病的炎症成分也进行了研究。使用监督学习技术可以检查推定的生物标志物和已识别的炎症成分的相关表达模式,从而使血浆和脑脊液数据集的生物标志物面板的诊断准确性分别达到87.5%和86.7%。这是非常重要的,因为它提供了一种理想的高通量和相对便宜的人群筛查方法。根据我们的生物标志物小组,似乎有可能确定AD的存在与否,而且一种廉价而快速的AD血液检测似乎是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Practical detection of a definitive biomarker panel for Alzheimer's disease; comparisons between matched plasma and cerebrospinal fluid.

Practical detection of a definitive biomarker panel for Alzheimer's disease; comparisons between matched plasma and cerebrospinal fluid.

Practical detection of a definitive biomarker panel for Alzheimer's disease; comparisons between matched plasma and cerebrospinal fluid.

Practical detection of a definitive biomarker panel for Alzheimer's disease; comparisons between matched plasma and cerebrospinal fluid.

Previous mass spectrometry analysis of cerebrospinal fluid (CSF) has allowed the identification of a panel of molecular markers that are associated with Alzheimer's disease (AD). The panel comprises Amyloid beta, Apolipoprotein E, Fibrinogen alpha chain precursor, Keratin type I cytoskeletal 9, Serum albumin precursor, SPARC-like 1 protein and Tetranectin. Here we report the development and implementation of immunoassays to measure the abundance and diagnostic capacity of these putative biomarkers in matched lumbar CSF and blood plasma samples taken in life from individuals confirmed at post-mortem as suffering from AD (n = 10) and from screened 'cognitively healthy' subjects (n = 18). The inflammatory components of Alzheimer's disease were also investigated. Employment of supervised learning techniques permitted examination of the interrelated expression patterns of the putative biomarkers and identified inflammatory components, resulting in biomarker panels with a diagnostic accuracy of 87.5% and 86.7% for the plasma and CSF datasets respectively. This is extremely important as it offers an ideal high-throughput and relatively inexpensive population screening approach. It appears possible to determine the presence or absence of AD based on our biomarker panel and it seems likely that a cheap and rapid blood test for AD is feasible.

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