Premium
O2‐05‐01: A DATA‐DRIVEN MODEL OF BIOMARKER CHANGES IN SPORADIC ALZHEIMER'S DISEASE
Author(s) -
Young Alexandra L.,
Oxtoby Neil P.,
Daga Pankaj,
Cash David M.,
Fox Nick,
Ourselin Sebastien,
Schott Jonathan M.,
Alexander Daniel C.
Publication year - 2014
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.2014.04.180
Subject(s) - biomarker , abnormality , atrophy , disease , population , medicine , oncology , cognition , psychology , pathology , neuroscience , psychiatry , biology , biochemistry , environmental health
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