The successful development of amyloid-based biomarkers and tests for Alzheimer's disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities, we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) > 80%, specificity (SP) > 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease, suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using non-amyloid based biomarkers.

中文翻译：

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使用支持向量机通过血浆蛋白早期检测阿尔茨海默病

基于淀粉样蛋白的生物标志物和阿尔茨海默病 (AD) 测试的成功开发代表了 AD 诊断的一个重要里程碑。但是，仍然存在两个主要限制。基于淀粉样蛋白的诊断生物标志物和测试提供的有关疾病过程的信息有限，并且它们无法在大脑中出现显着的淀粉样蛋白 β 积累之前识别出患有该疾病的个体。本研究的目的是开发一种方法来识别潜在的基于血液的非淀粉样蛋白生物标志物，用于早期 AD 检测。血液的使用很有吸引力，因为它容易获得且相对便宜。我们的方法主要基于机器学习 (ML) 技术（特别是支持向量机），因为它们能够通过从复杂数据中学习模式来创建多变量模型。使用新的特征选择和评估方式，我们确定了 5 个新的非淀粉样蛋白组，它们有可能作为早期 AD 的生物标志物。特别是，我们发现 A2M、ApoE、BNP、Eot3、RAGE 和 SGOT 的组合可能是早期疾病的关键生物标志物谱。基于已识别面板的疾病检测模型在前驱阶段实现了灵敏度 (SN) > 80%、特异性 (SP) > 70% 和接受者操作曲线下面积 (AUC) 至少为 0.80（在后期阶段具有更高的性能）这种病。与疾病的这个阶段相比，现有的 ML 模型表现不佳，这表明潜在的蛋白质组可能不适合早期疾病检测。我们的结果证明了使用非淀粉样蛋白生物标志物早期检测 AD 的可行性。