Serum Protein-Based Profiles as Novel Biomarkers for the Diagnosis of Alzheimer’s Disease
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.
KeywordsAlzheimer’s disease Serum-based biomarkers Algorithm Diagnosis
We would like to thank the participants from CapitalBio Genomics Co., Ltd., Dongguan 523808, China. We would also like to thank Professor Yan-Jiang Wang from the Department of Neurology and Center for Clinical Neuroscience, Daping Hospital, for his support and consultation. This work was supported in part by a grant from the Chongqing Nature Science Foundation (cstc2014jcyjA10117) and the Chongqing Post-Doctoral Research Project (Xm2014123), along with the support from the National Science and Technology Major Project (2012ZX10004801-003).
Compliance with Ethical Standards
This study was approved by the Institutional Ethics Committee of Southwest Hospital Daping Hospital and the Chengdu Military General Hospital, and informed consent was obtained from all participants or their caregivers.
Conflict of Interest
The authors declare that they have no conflict of interests.
- 7.Dursun E, Gezen-Ak D, Hanagasi H, Bilgic B, Lohmann E, Ertan S, Atasoy IL, Alaylioglu M et al (2015) The interleukin 1 alpha, interleukin 1 beta, interleukin 6 and alpha-2-macroglobulin serum levels in patients with early or late onset Alzheimer’s disease, mild cognitive impairment or Parkinson’s disease. J Neuroimmunol 283:50–57. doi: 10.1016/j.jneuroim.2015.04.014 CrossRefPubMedGoogle Scholar
- 12.Yin F, Sancheti H, Patil I, Cadenas E (2016) Energy metabolism and inflammation in brain aging and Alzheimer’s disease. Free Radic Biol Med. doi: 10.1016/j.freeradbiomed.2016.04.200
- 13.Mittal K, Katare DP (2016) Shared links between type 2 diabetes mellitus and Alzheimer’s disease: a review. Diabetes Metab Syndr Clin Res Rev. doi: 10.1016/j.dsx.2016.01.021
- 15.Zheng C, Zhou X-W, Wang J-Z (2016) The dual roles of cytokines in Alzheimer’s disease: update on interleukins, TNF-α, TGF-β and IFN-γ. Transl Neurodegener 5(1). doi: 10.1186/s40035-016-0054-4
- 20.Breiman L (2001) Random forests. Mach Learn 45(1):5–32. doi: 10.1023/A:1010933404324
- 32.Hall JR, Wiechmann AR, Johnson LA, Edwards M, Barber RC, Cunningham R, Singh M, O’Bryant SE (2014) The impact of APOE status on relationship of biomarkers of vascular risk and systemic inflammation to neuropsychiatric symptoms in Alzheimer’s disease. J Alzheimer’s Dis: JAD 40(4):887–896. doi: 10.3233/JAD-131724 Google Scholar
- 35.O’Bryant SE, Xiao G, Barber R, Reisch J, Doody R, Fairchild T, Adams P, Waring S et al, Texas Alzheimer’s Research C (2010) A serum protein-based algorithm for the detection of Alzheimer disease. Arch Neurol 67(9):1077–1081. doi: 10.1001/archneurol.2010.215
- 36.Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonca A (2011) Data mining methods in the prediction of dementia: a real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res notes 4:299. doi: 10.1186/1756-0500-4-299 CrossRefPubMedPubMedCentralGoogle Scholar