Openly accessible

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

Ashton, Nicholas J., Nevado-Holgado, Alejo J., Barber, Imelda S., Lynham, Steven, Gupta, Veer, Chatterjee, Pratishtha, Goozee, Kathryn, Hone, Eugene, Pedrini, Steve, Blennow, Kaj, Schöll, Michael, Zetterberg, Henrik, Ellis, Kathryn A., Bush, Ashley I., Rowe, Christopher C., Villemagne, Victor L., Ames, David, Masters, Colin L., Aarsland, Dag, Powell, John, Lovestone, Simon, Martins, Ralph and Hye, Abdul 2019, A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease, Science Advances, vol. 5, no. 2, doi: 10.1126/sciadv.aau7220.

Attached Files
Name Description MIMEType Size Downloads

Title A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease
Author(s) Ashton, Nicholas J.
Nevado-Holgado, Alejo J.
Barber, Imelda S.
Lynham, Steven
Gupta, Veer
Chatterjee, Pratishtha
Goozee, Kathryn
Hone, Eugene
Pedrini, Steve
Blennow, Kaj
Schöll, Michael
Zetterberg, Henrik
Ellis, Kathryn A.
Bush, Ashley I.
Rowe, Christopher C.
Villemagne, Victor L.
Ames, David
Masters, Colin L.
Aarsland, Dag
Powell, John
Lovestone, Simon
Martins, Ralph
Hye, Abdul
Journal name Science Advances
Volume number 5
Issue number 2
Article ID eaau7220
Total pages 12
Publisher American Association for the Advancement of Science (AAAS)
Place of publication Washington, D.C.
Publication date 2019-02-06
ISSN 2375-2548
2375-2548
Keyword(s) Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
NEUROFILAMENT LIGHT-CHAIN
PRECURSOR PROTEIN
BETA
BIOMARKERS
NEUROGENIN-2
BRAIN
Summary 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.
Language eng
DOI 10.1126/sciadv.aau7220
Indigenous content off
Field of Research MD Multidisciplinary
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, The Authors
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30144660

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 30 times in TR Web of Science
Scopus Citation Count Cited 32 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 55 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Thu, 29 Oct 2020, 07:38:30 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.