Increasing the predictive accuracy of amyloid-β blood-borne biomarkers in alzheimer's disease

Watt, Andrew D., Perez, Keyla A., Faux, Noel G., Pike, Kerryn E., Rowe, Christopher C., Bourgeat, Pierrick, Salvado, Olivier, Masters, Colin L., Villemagne, Victor L. and Barnham, Kevin J. (2011) Increasing the predictive accuracy of amyloid-β blood-borne biomarkers in alzheimer's disease. Journal of Alzheimers Disease, 24 1: 47-59. doi:10.3233/JAD-2010-101722

Author Watt, Andrew D.
Perez, Keyla A.
Faux, Noel G.
Pike, Kerryn E.
Rowe, Christopher C.
Bourgeat, Pierrick
Salvado, Olivier
Masters, Colin L.
Villemagne, Victor L.
Barnham, Kevin J.
Title Increasing the predictive accuracy of amyloid-β blood-borne biomarkers in alzheimer's disease
Journal name Journal of Alzheimers Disease   Check publisher's open access policy
ISSN 1387-2877
Publication date 2011-01-01
Year available 2010
Sub-type Article (original research)
DOI 10.3233/JAD-2010-101722
Open Access Status
Volume 24
Issue 1
Start page 47
End page 59
Total pages 13
Place of publication Amsterdam, Netherlands
Publisher I O S Press
Language eng
Abstract Diagnostic measures for Alzheimer's disease (AD) commonly rely on evaluating the levels of amyloid-β (Aβ) peptides within the cerebrospinal fluid (CSF) of affected individuals. These levels are often combined with levels of an additional non-Aβ marker to increase predictive accuracy. Recent efforts to overcome the invasive nature of CSF collection led to the observation of Aβ species within the blood cellular fraction, however, little is known of what additional biomarkers may be found in this membranous fraction. The current study aimed to undertake a discovery-based proteomic investigation of the blood cellular fraction from AD patients (n = 18) and healthy controls (HC; n = 15) using copper immobilized metal affinity capture and Surface Enhanced Laser Desorption/Ionisation Time-Of-Flight Mass Spectrometry. Three candidate biomarkers were observed which could differentiate AD patients from HC (ROC AUC > 0.8). Bivariate pairwise comparisons revealed significant correlations between these markers and measures of AD severity including; MMSE, composite memory, brain amyloid burden, and hippocampal volume. A partial least squares regression model was generated using the three candidate markers along with blood levels of Aβ. This model was able to distinguish AD from HC with high specificity (90%) and sensitivity (77%) and was able to separate individuals with mild cognitive impairment (MCI) who converted to AD from MCI non-converters. While requiring further characterization, these candidate biomarkers reaffirm the potential efficacy of blood-based investigations into neurodegenerative conditions. Furthermore, the findings indicate that the incorporation of non-amyloid markers into predictive models, function to increase the accuracy of the diagnostic potential of Aβ.
Keyword Amyloid-β
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status Non-UQ
Additional Notes Online Date: 14 December 2010.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Non HERDC
School of Information Technology and Electrical Engineering Publications
Centre for Advanced Imaging Publications
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