Journal
NEUROLOGY
Volume 76, Issue 6, Pages 501-510Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1212/WNL.0b013e31820af900
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Funding
- NIH [NINDS P30 NS057105, NIA P50 AG005681, P01 AG003991, P01 AG026276]
- NCRR [KL2RR024994, 1UL1RR024992]
- Charles F. and Joanne Knight Alzheimer's Research Initiative of the Washington University Alzheimer's Disease Research Center
- NIH/NIA
- Charles and Joanne Knight Alzheimer Research Initiative of the Knight Alzheimer's Disease Research Center
- Eli Lilly and Company
- Bristol-Myers Squibb
- Elan Corporation/Janssen
- Wyeth/Pfizer Inc
- Novartis
- NIH (NIA/NINDS)
- US Department of Defense
- AstraZeneca
- Pfizer Inc.
- Elan Corporation
- Forest Laboratories, Inc.
- Cure Alzheimer's Fund
- Fidelity Foundation
- Wyeth
- Avid Radiopharmaceuticals
- Dana Foundation
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Objective: To investigate factors, including cognitive and brain reserve, which may independently predict prevalent and incident dementia of the Alzheimer type (DAT) and to determine whether inclusion of identified factors increases the predictive accuracy of the CSF biomarkers A beta(42), tau, ptau(181), tau/A beta(42), and ptau(181)/A beta(42). Methods: Logistic regression identified variables that predicted prevalent DAT when considered together with each CSF biomarker in a cross-sectional sample of 201 participants with normal cognition and 46 with DAT. The area under the receiver operating characteristic curve (AUC) from the resulting model was compared with the AUC generated using the biomarker alone. In a second sample with normal cognition at baseline and longitudinal data available (n = 213), Cox proportional hazards models identified variables that predicted incident DAT together with each biomarker, and the models' concordance probability estimate (CPE), which was compared to the CPE generated using the biomarker alone. Results: APOE genotype including an epsilon 4 allele, male gender, and smaller normalized whole brain volumes (nWBV) were cross-sectionally associated with DAT when considered together with every biomarker. In the longitudinal sample (mean follow-up = 3.2 years), 14 participants (6.6%) developed DAT. Older age predicted a faster time to DAT in every model, and greater education predicted a slower time in 4 of 5 models. Inclusion of ancillary variables resulted in better cross-sectional prediction of DAT for all biomarkers (p < 0.0021), and better longitudinal prediction for 4 of 5 biomarkers (p < 0.0022). Conclusions: The predictive accuracy of CSF biomarkers is improved by including age, education, and nWBV in analyses. Neurology (R) 2011; 76: 501-510
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