Molecular Neurobiology

, Volume 55, Issue 5, pp 3999–4008 | Cite as

Serum Protein-Based Profiles as Novel Biomarkers for the Diagnosis of Alzheimer’s Disease

  • Shu Yu
  • Yue-Ping Liu
  • Hai-Liang Liu
  • Jie Li
  • Yang Xiang
  • Yu-Hui Liu
  • Shu-Sheng Jiao
  • Lu Liu
  • Yajiang Wang
  • Weiling Fu
Article

Abstract

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.

Keywords

Alzheimer’s disease Serum-based biomarkers Algorithm Diagnosis 

Notes

Acknowledgements

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.

Supplementary material

12035_2017_609_Fig5_ESM.jpg (43 kb)
Fig. S1

Expression of candidate analytes. Heatmap displaying the proteins examined in AD and controls samples. Proteins found to de differentially expressed are colored red, while others are colored green. (JPEG 43 kb)

12035_2017_609_MOESM1_ESM.eps (2 mb)
High Resolution Image (EPS 2097 kb)
12035_2017_609_Fig6_ESM.jpg (35 kb)
Fig. S2

Selection of the analysis method. Comparison of Random Forest and logistic regression methods for the serum biomarkers determination: a. Random Forest, and b. logistic regression. (JPEG 35 kb)

12035_2017_609_MOESM2_ESM.eps (1019 kb)
High Resolution Image (EPS 1018 kb)
12035_2017_609_MOESM3_ESM.xlsx (18 kb)
ESM 1 (XLSX 17 kb)

References

  1. 1.
    Wortmann M (2012) Dementia: a global health priority—highlights from an ADI and World Health Organization report. Alzheimers Res Ther 4(5):40. doi: 10.1186/alzrt143 PubMedPubMedCentralGoogle Scholar
  2. 2.
    Blennow K, de Leon MJ, Zetterberg H (2006) Alzheimer’s disease. Lancet 368(9533):387–403. doi: 10.1016/S0140-6736(06)69113-7 CrossRefPubMedGoogle Scholar
  3. 3.
    Scheltens P, Blennow K, Breteler MM, de Strooper B, Frisoni GB, Salloway S, Van der Flier WM (2016) Alzheimer’s disease. Lancet 388(10043):505–517. doi: 10.1016/S0140-6736(15)01124-1 CrossRefPubMedGoogle Scholar
  4. 4.
    Chiam JT, Dobson RJ, Kiddle SJ, Sattlecker M (2015) Are blood-based protein biomarkers for Alzheimer’s disease also involved in other brain disorders? A systematic review. J Alzheimers Dis 43(1):303–314. doi: 10.3233/jad-140816 PubMedGoogle Scholar
  5. 5.
    Zhang R, Miller RG, Madison C, Jin X, Honrada R, Harris W, Katz J, Forshew DA et al (2013) Systemic immune system alterations in early stages of Alzheimer’s disease. J Neuroimmunol 256(1–2):38–42. doi: 10.1016/j.jneuroim.2013.01.002 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Yu S, Liu YP, Liu YH, Jiao SS, Liu L, Wang YJ, Fu WL (2016) Diagnostic utility of VEGF and soluble CD40L levels in serum of Alzheimer’s patients. Clin Chim Acta 453:154–159. doi: 10.1016/j.cca.2015.12.018 CrossRefPubMedGoogle Scholar
  7. 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
  8. 8.
    Kim HO, Kim HS, Youn JC, Shin EC, Park S (2011) Serum cytokine profiles in healthy young and elderly population assessed using multiplexed bead-based immunoassays. J Transl Med 9:113. doi: 10.1186/1479-5876-9-113 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Stuart MJ, Singhal G, Baune BT (2015) Systematic review of the neurobiological relevance of chemokines to psychiatric disorders. Front Cell Neurosci 9:357. doi: 10.3389/fncel.2015.00357 PubMedPubMedCentralGoogle Scholar
  10. 10.
    Stuart MJ, Baune BT (2014) Chemokines and chemokine receptors in mood disorders, schizophrenia, and cognitive impairment: a systematic review of biomarker studies. Neurosci Biobehav Rev 42:93–115. doi: 10.1016/j.neubiorev.2014.02.001 CrossRefPubMedGoogle Scholar
  11. 11.
    Baumgart M, Snyder HM, Carrillo MC, Fazio S, Kim H, Johns H (2015) Summary of the evidence on modifiable risk factors for cognitive decline and dementia: a population-based perspective. Alzheimers Dement 11(6):718–726. doi: 10.1016/j.jalz.2015.05.016 CrossRefPubMedGoogle Scholar
  12. 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. 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
  14. 14.
    Ma J, Zhang W, Wang HF, Wang ZX, Jiang T, Tan MS, Yu JT, Tan L (2016) Peripheral blood adipokines and insulin levels in patients with Alzheimer’s disease: a replication study and meta-analysis. Curr Alzheimer Res 13(3):223–233CrossRefPubMedGoogle Scholar
  15. 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
  16. 16.
    Pepe MS, Janes H, Li CI, Bossuyt PM, Feng Z, Hilden J (2016) Early-phase studies of biomarkers: what target sensitivity and specificity values might confer clinical utility? Clin Chem 62(5):737–742. doi: 10.1373/clinchem.2015.252163 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Bateman R (2015) Alzheimer’s disease and other dementias: advances in 2014. Lancet Neurol 14(1):4–6. doi: 10.1016/s1474-4422(14)70301-1 CrossRefPubMedGoogle Scholar
  18. 18.
    Henriksen K, O Bryant SE, Hampel H, Trojanowski JQ, Montine TJ, Jeromin A, Blennow K, Lönneborg A et al (2014) The future of blood-based biomarkers for Alzheimer’s disease. Alzheimers Dement 10(1):115–131. doi: 10.1016/j.jalz.2013.01.013 CrossRefPubMedGoogle Scholar
  19. 19.
    Khan TK, Alkon DL (2015) Peripheral biomarkers of Alzheimer’s disease. J Alzheimers Dis 44(3):729–744. doi: 10.3233/jad-142262 PubMedGoogle Scholar
  20. 20.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32. doi: 10.1023/A:1010933404324
  21. 21.
    O Bryant SE (2010) A serum protein-based algorithm for the detection of Alzheimer disease. Arch Neurol 67(9):1077. doi: 10.1001/archneurol.2010.215 CrossRefGoogle Scholar
  22. 22.
    Britschgi M, Wyss-Coray T (2009) Blood protein signature for the early diagnosis of Alzheimer disease. Arch Neurol 66(2):161–165. doi: 10.1001/archneurol.2008.530 CrossRefPubMedGoogle Scholar
  23. 23.
    Frisoni GB, Hansson O (2016) Clinical validity of CSF biomarkers for Alzheimer’s disease: necessary indeed, but sufficient? Lancet Neurol 15(7):650–651. doi: 10.1016/S1474-4422(16)30040-0 CrossRefPubMedGoogle Scholar
  24. 24.
    Ray S, Britschgi M, Herbert C, Takeda-Uchimura Y, Boxer A, Blennow K, Friedman LF, Galasko DR et al (2007) Classification and prediction of clinical Alzheimer’s diagnosis based on plasma signaling proteins. Nat Med 13(11):1359–1362. doi: 10.1038/nm1653 CrossRefPubMedGoogle Scholar
  25. 25.
    Khemka VK, Bagchi D, Bandyopadhyay K, Bir A, Chattopadhyay M, Biswas A, Basu D, Chakrabarti S (2014) Altered serum levels of adipokines and insulin in probable Alzheimer’s disease. J Alzheimers Dis 41(2):525–533. doi: 10.3233/JAD-140006 PubMedGoogle Scholar
  26. 26.
    Kizilarslanoglu MC, Kara O, Yesil Y, Kuyumcu ME, Ozturk ZA, Cankurtaran M, Rahatli S, Pakasticali N et al (2015) Alzheimer disease, inflammation, and novel inflammatory marker: resistin. Turk J Med Sci 45(5):1040–1046CrossRefPubMedGoogle Scholar
  27. 27.
    Magalhães CA, Carvalho MG, Sousa LP, Caramelli P, Gomes KB (2015) Leptin in Alzheimer’s disease. Clin Chim Acta 450:162–168. doi: 10.1016/j.cca.2015.08.009 CrossRefPubMedGoogle Scholar
  28. 28.
    Oh J, Lee HJ, Song JH, Park SI, Kim H (2014) Plasminogen activator inhibitor-1 as an early potential diagnostic marker for Alzheimer’s disease. Exp Gerontol 60:87–91. doi: 10.1016/j.exger.2014.10.004 CrossRefPubMedGoogle Scholar
  29. 29.
    Alvarez A, Cacabelos R, Sanpedro C, Garcia-Fantini M, Aleixandre M (2007) Serum TNF-alpha levels are increased and correlate negatively with free IGF-I in Alzheimer disease. Neurobiol Aging 28(4):533–536. doi: 10.1016/j.neurobiolaging.2006.02.012 CrossRefPubMedGoogle Scholar
  30. 30.
    Perry RT, Collins JS, Wiener H, Acton R, Go RC (2001) The role of TNF and its receptors in Alzheimer’s disease. Neurobiol Aging 22(6):873–883CrossRefPubMedGoogle Scholar
  31. 31.
    Cacabelos R, Alvarez XA, Franco-Maside A, Fernandez-Novoa L, Caamano J (1994) Serum tumor necrosis factor (TNF) in Alzheimer’s disease and multi-infarct dementia. Methods Find Exp Clin Pharmacol 16(1):29–35PubMedGoogle Scholar
  32. 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
  33. 33.
    Heneka MT, Carson MJ, El Khoury J, Landreth GE, Brosseron F, Feinstein DL, Jacobs AH, Wyss-Coray T et al (2015) Neuroinflammation in Alzheimer’s disease. Lancet Neurol 14(4):388–405. doi: 10.1016/S1474-4422(15)70016-5 CrossRefPubMedGoogle Scholar
  34. 34.
    Lista S, Faltraco F, Prvulovic D, Hampel H (2013) Blood and plasma-based proteomic biomarker research in Alzheimer’s disease. Prog Neurobiol 101-102:1–17. doi: 10.1016/j.pneurobio.2012.06.007 CrossRefPubMedGoogle Scholar
  35. 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. 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

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Laboratory Medicine, Southwest HospitalThird Military Medical UniversityChongqingChina
  2. 2.State Key Laboratory of Military Stomatology and National Clinical Research Center for Oral Disease and Shaanxi Clinical Research Center for Oral Disease, Department of Laboratory Medicine, School of StomatologyFourth Military Medical UniversityXi’anChina
  3. 3.Department of Laboratory Medicine477th Hospital of PLAXiangyangChina
  4. 4.CapitalBio Genomics Co., Ltd.DongguanChina
  5. 5.Department of Neurology and Center for Clinical Neuroscience, Daping HospitalThird Military Medical UniversityChongqingChina

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