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

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...

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Bibliographic Details
Published in:Science advances 2019-02, Vol.5 (2), p.eaau7220-eaau7220
Main Authors: 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
Format: Article
Language:English
Subjects:
Aged
Aged, 80 and over
Alzheimer Disease - blood
Alzheimer Disease - diagnosis
Alzheimer Disease - etiology
Alzheimer Disease - metabolism
Amyloid beta-Peptides - metabolism
Biomarkers
Biomedical Laboratory Science/Technology
Biomedicinsk laboratorievetenskap/teknologi
Blood Proteins - metabolism
Cognitive Dysfunction - diagnosis
Female
Humans
Male
Medicin och hälsovetenskap
Neuroscience
Neurosciences
Neurovetenskaper
Proteomics - methods
SciAdv r-articles
Severity of Illness Index
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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.
ISSN:2375-2548
2375-2548
DOI:10.1126/sciadv.aau7220