Hakobyan, S; Harding, K; Aiyaz, M; Hye, A; Dobson, R; Baird, A; Liu, B; ... Morgan, BP; + view all Hakobyan, S; Harding, K; Aiyaz, M; Hye, A; Dobson, R; Baird, A; Liu, B; Harris, CL; Lovestone, S; Morgan, BP; - view fewer (2016) Complement Biomarkers as Predictors of Disease Progression in Alzheimer's Disease. Journal of Alzheimer's Disease , 54 (2) pp. 707-716. 10.3233/JAD-160420.

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Abstract

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.

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