Journal article
A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, 592.7KB, Terms of use)
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- Publisher copy:
- 10.1126/sciadv.aau7220
Authors
- Publisher:
- American Association for the Advancement of Science
- Journal:
- Science Advances More from this journal
- Volume:
- 5
- Issue:
- 2
- Article number:
- eaau7220
- Place of publication:
- United States
- Publication date:
- 2019-02-06
- Acceptance date:
- 2018-12-19
- DOI:
- EISSN:
-
2375-2548
- Pmid:
-
30775436
- Language:
-
English
- Keywords:
- Pubs id:
-
974714
- Local pid:
-
pubs:974714
- Deposit date:
-
2020-03-20
Terms of use
- Copyright holder:
- Ashton et al.
- Copyright date:
- 2019
- Rights statement:
- Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
- Licence:
- CC Attribution-NonCommercial (CC BY-NC)
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