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Title: Predicting Alzheimer disease from a blood-based biomarker profile: A 54-month follow-up
Austin Authors: Burnham, Samantha C;Rowe, Christopher C ;Baker, David;Bush, Ashley I;Doecke, James D;Faux, Noel G;Laws, Simon M;Martins, Ralph N;Maruff, Paul;Macaulay, S Lance;Rainey-Smith, Stephanie R;Savage, Greg;Ames, David;Masters, Colin L ;Wilson, William;Villemagne, Victor L 
Affiliation: Austin Health, Heidelberg, Victoria, Australia
eHealth, CSIRO Health and Biosecurity, Floreat, Western Australia, Australia
Department of Nuclear Medicine and Centre for PET, Austin Health, Heidelberg, Victoria, Australia
Department of Medicine, Austin Health, the University of Melbourne, Heidelberg, Victoria, Australia
Janssen Research and Development, Titusville, NJ, USA
Mental Health Research Institute, Academic Unit for Psychiatry of Old Age, the University of Melbourne, Parkville, Victoria, Australia
Department of Psychiatry, the University of Melbourne, Parkville, Victoria, Australia
The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
eHealth, CSIRO Health and Biosecurity, Herston, Queensland, Australia
Centre of Excellence for Alzheimer's Disease Research & Care, School of Medical Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
School of Biomedical Sciences, Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia
Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital), Perth, Western Australia, Australia
Cogstate, Melbourne, Victoria, Australia
CSIRO Food and Nutrition Flagship, Melbourne, Victoria, Australia
Department of Psychology, Macquarie University, Sydney, NSW, Australia
eHealth, CSIRO Health and Biosecurity, North Ryde, NSW, Australia
Issue Date: 13-Sep-2016 2016-08-17
Publication information: Neurology 2016; 87(11): 1093-1101
Abstract: Objective: We assessed a blood-based signature, which previously demonstrated high accuracy at stratifying individuals with high or low neocortical β-amyloid burden (NAB), to determine whether it could also identify individuals at risk of progression to Alzheimer disease (AD) within 54 months. Methods: We generated the blood-based signature for 585 healthy controls (HCs) and 74 participants with mild cognitive impairment (MCI) from the Australian Imaging, Biomarkers and Lifestyle Study who underwent clinical reclassification (blinded to biomarker findings) at 54-month follow-up. The individuals were split into estimated high and low NAB groups based on a cutoff of 1.5 standardized uptake value ratio. We assessed the predictive accuracy of the high and low NAB groupings based on progression to mild cognitive impairment or AD according to clinical reclassification at 54-month follow-up. Results: Twelve percent of HCs with estimated high NAB progressed in comparison to 5% of HCs with estimated low NAB (odds ratio = 2.4). Forty percent of the participants with MCI who had estimated high NAB progressed in comparison to 5% of the participants with MCI who had estimated low NAB (odds ratio = 12.3). These ratios are in line with those reported for Pittsburgh compound B–PET results. Individuals with estimated high NAB had faster rates of memory decline than those with estimated low NAB. Conclusion: These findings suggest that a simple blood-based signature not only provides estimates of NAB but also predicts cognitive decline and disease progression, identifying individuals at risk of progressing toward AD at the prodromal and preclinical stages.
DOI: 10.1212/WNL.0000000000003094
ORCID: 0000-0003-3910-2453
PubMed URL:
Type: Journal Article
Appears in Collections:Journal articles

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