Department of Laboratory Medicine, Southwest Hospital, Third Military Medical University, Chongqing, 400038, China.
State Key Laboratory of Military Stomatology and National Clinical Research Center for Oral Disease and Shaanxi Clinical Research Center for Oral Disease, Department of Laboratory Medicine, School of Stomatology, Fourth Military Medical University, Xi'an, Shaanxi Province, 710000, China.
Mol Neurobiol. 2018 May;55(5):3999-4008. doi: 10.1007/s12035-017-0609-0. Epub 2017 May 31.
As a multi-stage disorder, Alzheimer's disease (AD) is quickly becoming one of the most prevalent neurodegenerative diseases worldwide. Thus, a non-invasive, serum-based diagnostic platform is eagerly awaited. The goal of this study was to identify a serum-based biomarker panel using a predictive protein-based algorithm that is able to confidently distinguish AD patients from control subjects. One hundred and fifty-six patients with AD and the same number of gender- and age-matched control participants with standardized clinical assessments and neuroimaging measures were evaluated. Serum proteins of interest were quantified using a magnetic bead-based immunofluorescent assay, and a total of 33 analytes were examined. All of the subjects were then randomized into a training set containing 70% of the total samples and a validation set containing 30%, with each containing an equal number of AD and normal samples. Logistic regression and random forest analyses were then applied to develop a desirable algorithm for AD detection. The random forest method was found to generate a more robust predictive model than the logistic regression analysis. Furthermore, an eight-protein-based algorithm was found to be the most robust with a sensitivity of 97.7%, specificity of 88.6%, and AUC of 99%. Our study developed a novel eight-protein biomarker panel that can be used to distinguish AD and control multi-source candidates regardless of age. It is hoped that these results provide further insight into the applicability of serum-based screening methods and contribute to the development of lower-cost, less invasive methods for diagnosing AD and monitoring progression.
作为一种多阶段的疾病，阿尔茨海默病（AD）正在迅速成为全球最普遍的神经退行性疾病之一。因此，人们急切地期待着一种非侵入性的、基于血清的诊断平台。本研究的目的是使用基于预测蛋白的算法来确定一个基于血清的生物标志物谱，该算法能够自信地将 AD 患者与对照受试者区分开来。对 156 名 AD 患者和相同数量的性别和年龄匹配的对照参与者进行了评估，这些参与者进行了标准化的临床评估和神经影像学测量。使用基于磁珠的免疫荧光测定法对感兴趣的血清蛋白进行定量，共检查了 33 种分析物。然后，所有受试者被随机分为一个包含总样本 70%的训练集和一个包含 30%的验证集，每个样本集都包含等量的 AD 和正常样本。然后应用逻辑回归和随机森林分析来开发一个理想的 AD 检测算法。随机森林方法被发现比逻辑回归分析产生更稳健的预测模型。此外，发现基于 8 种蛋白的算法是最稳健的，具有 97.7%的敏感性、88.6%的特异性和 99%的 AUC。我们的研究开发了一种新的基于 8 种蛋白的生物标志物谱，可以用于区分 AD 和对照多源候选者，而与年龄无关。希望这些结果能进一步深入了解基于血清的筛选方法的适用性，并有助于开发成本更低、侵入性更小的 AD 诊断和监测进展的方法。
