gupta-plasmaproteinclassifier-2019.pdf (649.78 kB)
A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease
journal contribution
posted on 2019-02-06, 00:00 authored by Nicholas J Ashton, Alejo J Nevado-Holgado, Imelda S Barber, Steven Lynham, Veer GuptaVeer 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 HyeA 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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Journal
Science AdvancesVolume
5Issue
2Article number
eaau7220Publisher
American Association for the Advancement of Science (AAAS)Location
Washington, D.C.Publisher DOI
ISSN
2375-2548eISSN
2375-2548Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2019, The AuthorsUsage metrics
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