O2‐05‐01: A DATA‐DRIVEN MODEL OF BIOMARKER CHANGES IN SPORADIC ALZHEIMER'S DISEASE

Background: Determining the sequence in which Alzheimer’s disease (AD) biomarkers become abnormal would provide important insights into disease biology and a mechanism for disease staging. Here we implement a probabilistic event-based model (EBM) (Fonteijn et al. 2012) to determine the sequence of biomarker abnormality in sporadic AD, characterise the uncertainty in the ordering, and provide a natural patient staging system. Unlike previous attempts to construct such a model, our method does not rely on a-priori clinical diagnostic information, or explicit biomarker cut-points, and it allows for a proportion of cases and controls to be misdiagnosed. Methods: We included 285 ADNI subjects (92 cognitively normal (CN), 129 mild cognitive impairment (MCI), 64 AD) with measurements of 14 AD-related biomarkers including CSF A b 1-42, p-tau, t-tau, whole brain