Richens, J.L., Vere, K.-A., Light, R.A., Soria, D., Garibaldi, J., Smith, A.D., Warden, D., Wilcock, G., Bajaj, N., Morgan, K., and others. (2014) Practical detection of a definitive biomarker panel for Alzheimer's disease; comparisons between matched plasma and cerebrospinal fluid. International Journal of Molecular Epidemiology and Genetics, 5 (2). pp. 53-70. (KAR id:98887)
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Abstract
Previous mass spectrometry analysis of cerebrospinal fluid (CSF) has allowed the identification of a panel of molecular markers that are associated with Alzheimer's disease (AD). The panel comprises Amyloid beta, Apoli-poprotein E, Fibrinogen alpha chain precursor, Keratin type I cytoskeletal 9, Serum albumin precursor, SPARC-like 1 protein and Tetranectin. Here we report the development and implementation of immunoassays to measure the abundance and diagnostic capacity of these putative biomarkers in matched lumbar CSF and blood plasma samples taken in life from individuals confirmed at post-mortem as suffering from AD (n = 10) and from screened 'cognitively healthy' subjects (n = 18). The inflammatory components of Alzheimer's disease were also investigated. Employment of supervised learning techniques permitted examination of the interrelated expression patterns of the putative biomarkers and identified inflammatory components, resulting in biomarker panels with a diagnostic accuracy of 87.5 and 86.7 for the plasma and CSF datasets respectively. This is extremely important as it offers an ideal high-throughput and relatively inexpensive population screening approach. It appears possible to determine the presence or absence of AD based on our biomarker panel and it seems likely that a cheap and rapid blood test for AD is feasible.
Item Type: | Article |
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Additional information: | cited By 17 |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Funders: |
University of Nottingham (https://ror.org/01ee9ar58)
Medical Research Council (https://ror.org/03x94j517) |
Depositing User: | Daniel Soria |
Date Deposited: | 07 Dec 2022 17:20 UTC |
Last Modified: | 05 Nov 2024 13:04 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/98887 (The current URI for this page, for reference purposes) |
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