Document Type

Journal Article

Publisher

Nature Publishing Group

Faculty

Faculty of Health, Engineering and Science

School

School of Medical Sciences / Centre of Excellence for Alzheimer's Disease Research and Care

RAS ID

16877

Comments

This is an Author's Accepted Manuscript of: Burnham, S., Faux, N., Wilson, W., Laws, S. , Ames, D., Bedo, J., Bush, A., Doecke, J., Ellis, K., Head, R., Jones, G., Kiiveri, H., Martins, R. N., Rembach, A., Rowe, C., Salvado, O., Macaulay, S., Masters, C., & Villemagne, V. (2014). A blood-based predictor for neocortical Aβ burden in Alzheimer's disease: results from the AIBL study. Molecular Psychiatry, 19(4), 519-526. Available here

Abstract

Dementia is a global epidemic with Alzheimer’s disease (AD) being the leading cause. Early identification of patients at risk of developing AD is now becoming an international priority. Neocortical Aβ (extracellular β-amyloid) burden (NAB), as assessed by positron emission tomography (PET), represents one such marker for early identification. These scans are expensive and are not widely available, thus, there is a need for cheaper and more widely accessible alternatives. Addressing this need, a blood biomarker-based signature having efficacy for the prediction of NAB and which can be easily adapted for population screening is described. Blood data (176 analytes measured in plasma) and Pittsburgh Compound B (PiB)-PET measurements from 273 participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study were utilised. Univariate analysis was conducted to assess the difference of plasma measures between high and low NAB groups, and cross-validated machine-learning models were generated for predicting NAB. These models were applied to 817 non-imaged AIBL subjects and 82 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) for validation. Five analytes showed significant difference between subjects with high compared to low NAB. A machine-learning model (based on nine markers) achieved sensitivity and specificity of 80 and 82%, respectively, for predicting NAB. Validation using the ADNI cohort yielded similar results (sensitivity 79% and specificity 76%). These results show that a panel of blood-based biomarkers is able to accurately predict NAB, supporting the hypothesis for a relationship between a blood-based signature and Aβ accumulation, therefore, providing a platform for developing a population-based screen

DOI

10.1038/mp.2013.40

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