Nazeri Arash, Ganjgahi Habib, Roostaei Tina, Nichols Thomas, Zarei Mojtaba
Interdisciplinary Neuroscience Research Program, Tehran University of Medical Sciences, Tehran, 1417614411, Iran.
National Brain Mapping Centre, and Department of Neurology, Shahid Beheshti University of Medical Sciences, Tehran 4739, Iran; Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.
Neuroimage. 2014 Nov 15;102 Pt 2(Pt 2):657-65. doi: 10.1016/j.neuroimage.2014.08.041. Epub 2014 Aug 28.
Proteomic and imaging markers have been widely studied as potential biomarkers for diagnosis, monitoring and prognosis of Alzheimer's disease. In this study, we used Alzheimer Disease Neuroimaging Initiative dataset and performed parallel independent component analysis on cross sectional and longitudinal proteomic and imaging data in order to identify the best proteomic model for diagnosis, monitoring and prediction of Alzheimer disease (AD). We used plasma proteins measurement and imaging data from AD and healthy controls (HC) at the baseline and 1 year follow-up. Group comparisons at baseline and changes over 1 year were calculated for proteomic and imaging data. The results were fed into parallel independent component analysis in order to identify proteins that were associated with structural brain changes cross sectionally and longitudinally. Regression model was used to find the best model that can discriminate AD from HC, monitor AD and to predict MCI converters from non-converters. We showed that five proteins are associated with structural brain changes in the brain. These proteins could discriminate AD from HC with 57% specificity and 89% sensitivity. Four proteins whose change over 1 year were associated with brain structural changes could discriminate AD from HC with sensitivity of 93%, and specificity of 92%. This model predicted MCI conversion to AD in 2 years with 94% accuracy. This model has the highest accuracy in prediction of MCI conversion to AD within the ADNI-1 dataset. This study shows that combination of selected plasma protein levels and MR imaging is a useful method in identifying potential biomarker.
蛋白质组学和影像学标志物作为阿尔茨海默病诊断、监测和预后的潜在生物标志物已得到广泛研究。在本研究中，我们使用了阿尔茨海默病神经影像学计划数据集，并对横断面和纵向蛋白质组学及影像学数据进行了并行独立成分分析，以确定用于阿尔茨海默病（AD）诊断、监测和预测的最佳蛋白质组学模型。我们使用了AD患者和健康对照（HC）在基线和1年随访时的血浆蛋白测量值及影像学数据。计算了蛋白质组学和影像学数据在基线时的组间比较以及1年内的变化情况。将结果输入并行独立成分分析，以识别在横断面和纵向与脑结构变化相关的蛋白质。使用回归模型来寻找能够区分AD与HC、监测AD并预测MCI转化者与非转化者的最佳模型。我们发现有五种蛋白质与脑结构变化相关。这些蛋白质能够以57%的特异性和89%的敏感性区分AD与HC。四种在1年内变化与脑结构变化相关的蛋白质能够以93%的敏感性和92%的特异性区分AD与HC。该模型预测MCI在2年内转化为AD的准确率为94%。在ADNI - 1数据集中，该模型在预测MCI转化为AD方面具有最高的准确率。本研究表明，所选血浆蛋白水平与磁共振成像相结合是识别潜在生物标志物的一种有用方法。
