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A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer's disease.

Journal article
Authors Nicholas Ashton
Alejo J Nevado-Holgado
Imelda S Barber
Steven Lynham
Veer Gupta
Pratishtha Chatterjee
Kathryn Goozee
Eugene Hone
Steve Pedrini
Kaj Blennow
Michael Schöll
Henrik Zetterberg
Kathryn A Ellis
Ashley I Bush
Christopher C Rowe
Victor L Villemagne
David Ames
Colin L Masters
Dag Aarsland
John Powell
Simon Lovestone
Ralph Martins
Abdul Hye
Published in Science advances
Volume 5
Issue 2
Pages eaau7220
ISSN 2375-2548
Publication year 2019
Published at Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry
Pages eaau7220
Language en
Links dx.doi.org/10.1126/sciadv.aau7220
www.ncbi.nlm.nih.gov/entrez/query.f...
Subject categories Biomedical Laboratory Science/Technology, Neurosciences

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 Aβ negative or Aβ 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 Aβ-positive participants using feature selection and machine learning in AIBL and independently assessed in KARVIAH. A 12-feature model for predicting Aβ-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.

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