O'Bryant Sid E, Zhang Fan, Silverman Wayne, Lee Joseph H, Krinsky-McHale Sharon J, Pang Deborah, Hall James, Schupf Nicole
Department of Pharmacology & Neuroscience I Institute for Translational Research University of North Texas Health Science Center Fort Worth Texas USA.
Vermont Genetics Network University of Vermont Burlington Vermont USA.
Alzheimers Dement (Amst). 2020 May 21;12(1):e12033. doi: 10.1002/dad2.12033. eCollection 2020.
We sought to determine if proteomic profiles could predict risk for incident mild cognitive impairment (MCI) and Alzheimer's disease (AD) among adults with Down syndrome (DS).
In a cohort of 398 adults with DS, a total of n = 186 participants were determined to be non-demented and without MCI or AD at baseline and throughout follow-up; n = 103 had incident MCI and n = 81 had incident AD. Proteomics were conducted on banked plasma samples from a previously generated algorithm.
The proteomic profile was highly accurate in predicting incident MCI (area under the curve [AUC] = 0.92) and incident AD (AUC = 0.88). For MCI risk, the support vector machine (SVM)-based high/low cut-point yielded an adjusted hazard ratio (HR) = 6.46 ( < .001). For AD risk, the SVM-based high/low cut-point score yielded an adjusted HR = 8.4 ( < .001).
The current results provide support for our blood-based proteomic profile for predicting risk for MCI and AD among adults with DS.
我们试图确定蛋白质组学特征是否能够预测唐氏综合征（DS）成年人发生轻度认知障碍（MCI）和阿尔茨海默病（AD）的风险。
在一个包含398名DS成年人的队列中，共有n = 186名参与者在基线期及整个随访过程中被确定为无痴呆、无MCI或AD；n = 103名发生了MCI，n = 81名发生了AD。对先前生成的算法中储存的血浆样本进行蛋白质组学分析。
蛋白质组学特征在预测MCI发生（曲线下面积[AUC]=0.92）和AD发生（AUC = 0.88）方面具有高度准确性。对于MCI风险，基于支持向量机（SVM）的高/低切点产生的调整后风险比（HR）= 6.46（< .001）。对于AD风险，基于SVM的高/低切点评分产生的调整后HR = 8.4（< .001）。
目前的结果为我们基于血液的蛋白质组学特征用于预测DS成年人发生MCI和AD的风险提供了支持。
