A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease
Document Type
Journal Article
Publication Title
Science Advances
PubMed ID
30775436
Publisher
American Association for the Advancement of Science
School
School of Medical and Health Sciences
RAS ID
28901
Abstract
A blood-based assessment of preclinical disease would have huge potential in the enrichment of participants for Alzheimer’s disease (AD) therapeutic trials. In this study, cognitively unimpaired individuals from the AIBL and KARVIAH cohorts were defined as A negative or A positive by positron emission tomography. Nontargeted proteomic analysis that incorporated peptide fractionation and high-resolution mass spectrometry quantified relative protein abundances in plasma samples from all participants. A protein classifier model was trained to predict A-positive participants using feature selection and machine learning in AIBL and independently assessed in KARVIAH. A 12-feature model for predicting A-positive participants was established and demonstrated high accuracy (testing area under the receiver operator characteristic curve = 0.891, sensitivity = 0.78, and specificity = 0.77). This extensive plasma proteomic study has unbiasedly highlighted putative and novel candidates for AD pathology that should be further validated with automated methodologies. Copyright © 2019 The Authors.
DOI
10.1126/sciadv.aau7220
Access Rights
free_to_read
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Comments
Ashton, N. J., Nevado-Holgado, A. J., Barber, I. S., Lynham, S., Gupta, V., Chatterjee, P., . . . Hye, A. (2019). A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease. Science Advances, 5(2). Available here