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P2‐059: Identification of blood‐based complement biomarkers in Alzheimer's disease
Author(s) -
Aiyaz Mohammed,
Hakobyan Svetlana,
Harris Claire,
Stretton Alexandra,
Baird Alison,
Ashton Nicholas,
Hye Abdul,
RiddochContreras Joanna,
Settlecker Martina,
Dobson Richard,
Morgan Paul,
Lovestone Simon
Publication year - 2012
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2012.05.763
Subject(s) - multiplex , factor h , biomarker , complement system , complement factor i , immunology , disease , pathogenesis , medicine , antibody , oncology , biology , bioinformatics , genetics
Background: Evidence for the role of inflammation in Alzheimer’s disease (AD) pathogenesis is increasing, with particular attention given to complement proteins that are involved in the clearance of amyloid plaque in addition to a causal role through chronic activation of the inflammatory response. Multiple studies suggest a role for both early and acute phase proteins within the complement network including Factor I, Factor H, C3 and C1 inhibitor all of which are altered in AD patient plasma. It is now necessary to study plasma complement proteome in depth to determine whether variation in complement proteins have sufficiently discriminatory power as an AD biomarker. Methods: Subjects were recruited from 2 large multi-centre cohorts with 399 cognitively normal subjects, 165 with mild cognitive impairment, and 411 Alzheimer’s disease subjects that were genderand age matched with an average age of 76.9 6 7.5 years. Plasma collected in EDTA tubes were anonymised and randomized. The concentration of 19 analytes was quantified using antibody-based multiplex assays on the Luminex and MSD platforms in 975 longitudinal samples using a case versus control design. Using the multiplex assays, we have at least 70% coverage of the complement cascade. Results: The intra-assay precision of the Luminex andMSD assays was calculated using coefficient of variation (%CV) showing<15% variation in duplicate samples. Univariate analyses to correlate protein expression levels with clinical parameters (MMSE, ADAS-cog, CDR) will be followed by multivariate analyses to identify the set of complement proteins that have discriminatory power. Conclusions: Peripheral inflammatory markers predictive of disease progression or with diagnostic power are of critical relevance for AD. This work aims to identify such markers within the complement cascade.