Journal
ALZHEIMER DISEASE & ASSOCIATED DISORDERS
Volume 27, Issue 3, Pages 233-243Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/WAD.0b013e31826d597a
Keywords
Alzheimer; mild cognitive impairment; ADNI; Rules-Based Medicine; machine learning; biomarker; random forest; proteomic
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Funding
- Abbott
- National Institutes of Health
- AstraZeneca AB
- Bayer Schering Pharma AG
- Bristol-Myers Squibb
- Eisai Global Clinical Development
- Elan Corporation
- Genentech
- GE Healthcare
- GlaxoSmithKline
- Innogenetics
- Johnson Johnson
- Eli Lilly and Co.
- Merck and Co. Inc.
- Novartis AG
- Pfizer Inc.
- F. Hoffmann-La Roche
- Schering-Plough
- Synarc Inc.
- Wyeth
- Abbott Laboratories
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Previous studies that have examined the potential for plasma markers to serve as biomarkers for Alzheimer disease (AD) have studied single analytes and focused on the amyloid- and isoforms and have failed to yield conclusive results. In this study, we performed a multivariate analysis of 146 plasma analytes (the Human DiscoveryMAP v 1.0 from Rules-Based Medicine) in 527 subjects with AD, mild cognitive impairment (MCI), or cognitively normal elderly subjects from the Alzheimer's Disease Neuroimaging Initiative database. We identified 4 different proteomic signatures, each using 5 to 14 analytes, that differentiate AD from control patients with sensitivity and specificity ranging from 74% to 85%. Five analytes were common to all 4 signatures: apolipoprotein A-II, apolipoprotein E, serum glutamic oxaloacetic transaminase, -1-microglobulin, and brain natriuretic peptide. None of the signatures adequately predicted progression from MCI to AD over a 12- and 24-month period. A new panel of analytes, optimized to predict MCI to AD conversion, was able to provide 55% to 60% predictive accuracy. These data suggest that a simple panel of plasma analytes may provide an adjunctive tool to differentiate AD from controls, may provide mechanistic insights to the etiology of AD, but cannot adequately predict MCI to AD conversion.
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