Format

Send to

Choose Destination
Mol Psychiatry. 2014 Apr;19(4):519-26. doi: 10.1038/mp.2013.40. Epub 2013 Apr 30.

A blood-based predictor for neocortical Aβ burden in Alzheimer's disease: results from the AIBL study.

Collaborators (148)

Ames D, Rembach A, Rainey-Smith S, Ellis K, Macaulay L, Martins R, Masters C, Rowe C, Appannah A, Barnes M, Barnham K, Bedo J, Bellingham S, Bon L, Bourgeat P, Brown B, Buckley R, Burnham S, Bush A, Chandler G, Chatterjee P, Chen K, Clarnette R, Collins S, Cooke I, Cowie T, Cox K, Creegan R, Cuningham E, Cyarto E, Darby D, Deruyck K, Desmond P, Ding H, Doecke J, Dore V, Downing H, Dridan B, Duesing K, Fahey M, Farrow M, Faux N, Fenech M, Fowler C, Francois M, Fripp J, Frost S, Gardener S, Gibson S, Goth D, Graham P, Gupta V, Hannan G, Hansen D, Harrington K, Head R, Hill A, Hor M, Horne M, Huckstepp B, Jones A, Kamer A, Kanagasingam Y, Karunanithi M, Keam L, Kowalczyk A, Krause D, Krivdic B, Lachovitzki R, Lam CP, Lamb F, Lautenschlager N, Laws S, Leifert W, Lenzo N, Leroux H, Lftikhar F, Li QX, Lim F, Lintern T, Lockett L, Lucas K, Mano M, Marczak C, Martins G, Maruff P, Matsumoto Y, Matthaes S, McBride S, McKay R, Mondal A, Mulligan R, Nash T, Nigro J, Nuttall S, O'Callaghan N, O'Keefe G, Ong K, Osborne L, Parker B, Patten G, Peiffer J, Pejoska S, Penny L, Perez K, Pertile K, Phal P, Raniga P, Rembach A, Restrepo C, Riley M, Roberts B, Robertson J, Rodrigues M, Rooney A, Rumble R, Ryan T, Salvado O, Samuel M, Saunders I, Savage G, Silbert B, Sohrabi H, Syrette J, Szoeke C, Taddei K, Taddei T, Tegg M, Thomas P, Trivedi D, Trounson B, Tuckfield A, Varghese J, Veljanovski R, Verdile G, Villemagne V, Volitakis I, Vovos M, Vrantsidis F, Walker S, Watt A, Weinborn M, Wilson A, Wilson B, Woodward M, Yastrubetskaya O, Yates P, Zhang P.

Author information

1
CSIRO Preventative Health Flagship: Mathematics, Informatics and Statistics, Perth, WA, Australia.
2
Mental Health Research Institute (MHRI), The University of Melbourne, Parkville, VIC, Australia.
3
CSIRO Preventative Health Flagship: Mathematics, Informatics and Statistics, North Ryde, NSW, Australia.
4
1] Centre of Excellence for Alzheimer's Disease Research & Care, School of Medical Sciences, Edith Cowan University, Joondalup, WA, Australia [2] Sir James McCusker Alzheimer's Disease Research Unit, Hollywood Private Hospital, Perth, WA, Australia.
5
1] Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia [2] National Ageing Research Institute, Parkville, VIC, Australia.
6
Victorian Research Laboratory, National ICT of Australia (NICTA), Melbourne, VIC, Australia.
7
CSIRO Preventative Health Flagship: Mathematics, Informatics and Statistics, Herston, QLD, Australia.
8
Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia.
9
CSIRO Preventative Health Flagship: Health and Life Sciences, Urrbrae, SA, Australia.
10
1] Department of Nuclear Medicine and Centre for PET, Austin Health, Heidelberg, VIC, Australia [2] Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, VIC, Australia.
11
CSIRO Preventative Health Flagship: Information and Communication Technology, Herston, QLD, Australia.
12
CSIRO Preventative Health Flagship: Materials Science and Engineering, Parkville, VIC, Australia.
13
Department of Nuclear Medicine and Centre for PET, Austin Health, Heidelberg, VIC, Australia.

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.

PMID:
23628985
DOI:
10.1038/mp.2013.40
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Nature Publishing Group
Loading ...
Support Center