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Complement Biomarkers as Predictors of Disease Progression in Alzheimer's Disease

Lookup NU author(s): Professor Claire Harris

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by IOS Press, 2016.

For re-use rights please refer to the publisher's terms and conditions.


Abstract

© 2016 - IOS Press and the authors. All rights reserved. There is a critical unmet need for reliable markers of disease and disease course in mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The growing appreciation of the importance of inflammation in early AD has focused attention on inflammatory biomarkers in cerebrospinal fluid or plasma; however, non-specific inflammation markers have disappointed to date. We have adopted a targeted approach, centered on an inflammatory pathway already implicated in the disease. Complement, a core system in innate immune defense and potent driver of inflammation, has been implicated in pathogenesis of AD based on a confluence of genetic, histochemical, and model data. Numerous studies have suggested that measurement of individual complement proteins or activation products in cerebrospinal fluid or plasma is useful in diagnosis, prediction, or stratification, but few have been replicated. Here we apply a novel multiplex assay to measure five complement proteins and four activation products in plasma from donors with MCI, AD, and controls. Only one complement analyte, clusterin, differed significantly between control and AD plasma (controls, 295 mg/l; AD, 388 mg/l: p < 10- 5). A model combining clusterin with relevant co-variables was highly predictive of disease. Three analytes (clusterin, factor I, terminal complement complex) were significantly different between MCI individuals who had converted to dementia one year later compared to non-converters; a model combining these three analytes with informative co-variables was highly predictive of conversion. The data confirm the relevance of complement biomarkers in MCI and AD and build the case for using multi-parameter models for disease prediction and stratification.


Publication metadata

Author(s): Hakobyan S, Harding K, Aiyaz M, Hye A, Dobson R, Baird A, Liu B, Harris CL, Lovestone S, Morgan BP

Publication type: Article

Publication status: Published

Journal: Journal of Alzheimer's Disease

Year: 2016

Volume: 54

Issue: 2

Pages: 707-716

Online publication date: 06/09/2016

Acceptance date: 14/06/2016

Date deposited: 20/02/2019

ISSN (print): 1387-2877

ISSN (electronic): 1875-8908

Publisher: IOS Press

URL: https://doi.org/10.3233/JAD-160420

DOI: 10.3233/JAD-160420

PubMed id: 27567854


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