›› 2017, Vol. 37 ›› Issue (7): 950-.doi: 10.3969/j.issn.1674-8115.2017.07.011

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Study on detection biomarkers of Alzheimer’s disease based on plasma immune and inflammatory proteins#br#

HE Lu1, WANG Ying1, 2, WANG Ying1, XU Wei1, CHEN Sheng-di1, ZHAO Jian2, DING Jian-qing1   

  1. 1. Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025; 2. Translational Medical Center for Stem Cell Therapy, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200123
  • Online:2017-07-28 Published:2017-08-25
  • Supported by:
    National Program on Key Basic Research Project of China, “973”Program,  2014CB965002; National Natural Science Foundation of China, 81171200; Project from Science and Technology Commission of Shanghai Municipality, 13JC1401502, 13140904000

Abstract:  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.

Key words:  Alzheimer's disease, biomarker, plasma, immune and inflammatory proteins