摘要
目的·探索可作为阿尔茨海默病(AD)潜在筛查标志物的血浆免疫及炎症相关蛋白。方法·纳入对照组19例,AD组19例,收集血浆样本并检测70种免疫及炎症相关蛋白。运用Mann-Whitney U检验、偏相关分析筛选与AD强相关的免疫炎症蛋白。采用Wilks'lambda逐步分析法建立多蛋白联合判别算法,建立受试者工作特征(ROC)曲线评价判别算法的诊断效能。结果·70种蛋白中,23种在AD患者血浆中表达显著升高(P<0.05),其中19种与AD强相关(P<0.05)。用Wilks'lambda逐步分析法建立的多蛋白联合判别算法显示,由11种血浆免疫及炎症蛋白(EGF、GRO、MDC、MCP-1、MCP-2、MCP-4、TARC、SCF、TRAIL、CTACK、GCP-2)联合建立的判别方程具有最优的诊断效能(AUC=0.994),其最佳截断值为-0.609。取最佳截断值时,方程诊断敏感度可达100%,特异度可达94.7%。结论·由上述11种血浆免疫炎症相关蛋白组成的判别方程具有辅助AD筛查的潜力。
Objective · To explore plasma immune and inflammatory proteins that could serve as potential screening markers for Alzheimer＇s disease （AD）. Methods · Healthy controls （n=19） and AD patients （n=19） were enrolled. Plasma samples were collected and 70 kinds of immune and inflammatory proteins were detected. The immune and inflammatory proteins associated with AD were screened by Mann-Whitney U test and partial correlation analysis. Discriminant analysis was used to develop multi-protein combined algorithm to distinguish plasma samples of AD patients from those of healthy controls. Receiver operating characteristic （ROC） curve was used to evaluate the diagnostic efficacy for the multi-protein combined algorithm. Results · Among the 70 proteins analyzed, 23 were significantly higher in AD patients （P〈0.05）, among which 19 were strongly correlated with AD （P〈0.05）. These 19 proteins were analyzed with Wilks＇ lambda stepwise analysis to develop discriminant algorithm for detecting plasma samples of AD. Finally, the discriminant algorithm established by 11 plasma immune and inflammatory proteins （EGF, GRO, MDC, MCP-1, MCP-2, MCP-4, TARC, SCF, TRAIL, CTACK, GCP2） was found to have an optimal diagnostic efficacy （AUC=0.994）. The optimal cutoff value of the algorithm was -0.609. When the optimal cutoff value was obtained, the sensitivity of the equation could reach 100% and the specificity could reach 94.7%. Conclusion · The discriminant equation composed of the above 11 plasma immune and inflammatory proteins has the potential to assist AD screening.
出处
《上海交通大学学报（医学版）》
CSCD
北大核心
2017年第7期950-954,共5页
Journal of Shanghai Jiao tong University:Medical Science
基金
国家重点基础研究发展计划(“973”计划)(2014CB965002)
国家自然科学基金(81171200)
上海市科学技术委员会项目(13JC1401502,13140904000)~~
