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

Research output: Contribution to journalArticle

Nicholas James Ashton, Alejo J. Nevado-Holgado, Imelda S Barber, Steven Lynham, Veer Bala Gupta, Pratishtha Chatterjee, Kathryn Goozee, Eugene Hone, Steve Pedrini, Kaj Blennow, Michael Schöll, Henrik Zetterberg, Kathryn A. Ellis, Ashley I. Bush, Christopher Rowe, Victor L Villemagne, David Ames, Colin L Masters, Dag Aarsland, John F Powell & 3 others Simon Lovestone, Ralph N. Martins, Abdul Hye

Original languageEnglish
Article numbereaau7220
JournalScience Advances
Volume5
Issue number2
Publication statusPublished - 6 Feb 2019

King's Authors

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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