Title : A machine learning approach to identify a circulating microRNA signature for Alzheimer disease
Accurate diagnosis of Alzheimer’s disease (AD) involving less invasive molecular procedures, and at reasonable cost is an unmet medical need. From the Oxford Project to Investigate Memory and Ageing (OPTIMA) study, a cohort of serum samples was profiled by a multiplex microRNA (miRNA) reverse transcription quantitative PCR analysis. Clinical diagnosis of a subset of AD and the controls was confirmed by post-mortem (PM) histologic examination of brain tissue. In a machine learning approach, a 12-miRNA signature for AD identification was constructed. Using a subset of AD and control subjects with PM confirmed diagnosis status, a separate 12-miRNA signature was constructed demonstrating improved accuracy. The miRNA signature appears to be a promising blood test to diagnose AD.