Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer's Disease Neuroimaging (ADNI) database
Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer's Disease Neuroimaging (ADNI) database
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DOI：
10.1371/journal.pone.0235663
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发表时间：
2020-07-27
期刊：
影响因子：
3.7
通讯作者：
Kline, Richard P.
中科院分区：
文献类型：
--
作者：
Beltran, Juan Felipe;Wahba, Brandon Malik;Kline, Richard P.
阿尔茨海默病神经影像学（ADNI）数据库是政府，学术界和工业界的一项广泛事业，旨在汇集阿尔茨海默病症状严重程度不同阶段的受试者的资源和数据。正如预期的那样，磁共振成像是该项目的主要组成部分。在每6个月的访视时获得全脑图像。一系列研究执行功能和记忆的认知测试被较少采用。两次抽血（基线，6个月）提供了测量约145种血浆生物标志物浓度的样本。此外，还进行其他诊断测量，包括PET成像，淀粉样蛋白β和tau肽的脑脊液测量，以及遗传测试，人口统计学和生命体征。ADNI数据在审查应用程序时可用。已有许多关于AD进展期间各种过程如何演变的报告，包括代谢和神经内分泌活动、细胞存活和认知行为的改变。由于一开始缺乏分析模型，我们利用了机器学习的最新进展，这使我们能够处理具有许多变量的大型非线性系统。特别值得注意的是，研究如何从简单的非侵入性测量（如依赖于血液样本的测量）中学习未来疾病状态的二进制预测。这样的测量对医务人员或患者的时间和精力的要求相对较小。我们报告了应用CART，随机森林，梯度提升和支持向量机后的召回率/精度/接收器操作曲线下面积的结果，我们的结果表明：（i）随机森林和梯度提升对这些数据的工作非常好，（ii）应用于相对容易获得的测量时的预测质量（认知评分、遗传风险和血浆生物标志物）实现了与磁共振技术竞争的结果。这绝不是一个详尽的研究，而是一个探索的可行性，定义一系列相对便宜，广泛的人口为基础的测试。
The Alzheimer's Disease Neuroimaging (ADNI) database is an expansive undertaking by government, academia, and industry to pool resources and data on subjects at various stage of symptomatic severity due to Alzheimer's disease. As expected, magnetic resonance imaging is a major component of the project. Full brain images are obtained at every 6-month visit. A range of cognitive tests studying executive function and memory are employed less frequently. Two blood draws (baseline, 6 months) provide samples to measure concentrations of approximately 145 plasma biomarkers. In addition, other diagnostic measurements are performed including PET imaging, cerebral spinal fluid measurements of amyloid-beta and tau peptides, as well as genetic tests, demographics, and vital signs. ADNI data is available upon review of an application. There have been numerous reports of how various processes evolve during AD progression, including alterations in metabolic and neuroendocrine activity, cell survival, and cognitive behavior. Lacking an analytic model at the onset, we leveraged recent advances in machine learning, which allow us to deal with large, non-linear systems with many variables. Of particular note was examining how well binary predictions of future disease states could be learned from simple, non-invasive measurements like those dependent on blood samples. Such measurements make relatively little demands on the time and effort of medical staff or patient. We report findings with recall/precision/area under the receiver operator curve after application of CART, Random Forest, Gradient Boosting, and Support Vector Machines, Our results show (i) Random Forests and Gradient Boosting work very well with such data, (ii) Prediction quality when applied to relatively easily obtained measurements (Cognitive scores, Genetic Risk and plasma biomarkers) achieve results that are competitive with magnetic resonance techniques. This is by no means an exhaustive study, but instead an exploration of the plausibility of defining a series of relatively inexpensive, broad population based tests.