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February 08, 2011; 76 (6) Articles

Improving CSF biomarker accuracy in predicting prevalent and incident Alzheimer disease

C.M. Roe, A.M. Fagan, M.M. Williams, N. Ghoshal, M. Aeschleman, E.A. Grant, D.S. Marcus, M.A. Mintun, D.M. Holtzman, J.C. Morris
First published January 12, 2011, DOI: https://doi.org/10.1212/WNL.0b013e31820af900
C.M. Roe
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A.M. Fagan
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M.M. Williams
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N. Ghoshal
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M. Aeschleman
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E.A. Grant
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Citation
Improving CSF biomarker accuracy in predicting prevalent and incident Alzheimer disease
C.M. Roe, A.M. Fagan, M.M. Williams, N. Ghoshal, M. Aeschleman, E.A. Grant, D.S. Marcus, M.A. Mintun, D.M. Holtzman, J.C. Morris
Neurology Feb 2011, 76 (6) 501-510; DOI: 10.1212/WNL.0b013e31820af900

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Abstract

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 Aβ42, tau, ptau181, tau/Aβ42, and ptau181/Aβ42.

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 ε4 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.

Footnotes

  • Study funding: Supported by the NIH (NINDS P30 NS057105, NIA P50 AG005681, P01 AG003991, and P01 AG026276); the NCRR (KL2RR024994 and 1UL1RR024992); and the Charles F. and Joanne Knight Alzheimer's Research Initiative of the Washington University Alzheimer's Disease Research Center.

  • Editorial, page 496

  • Supplemental data at www.neurology.org

  • AD
    Alzheimer disease
    AUC
    area under the receiver operating characteristic curve
    CDR
    Clinical Dementia Rating
    CPE
    concordance probability estimate
    CS
    collateral source
    DAT
    dementia of the Alzheimer type
    GDS
    Geriatric Depression Scale
    LP
    lumbar puncture
    MMSE
    Mini Mental-State Examination
    nWBV
    normalized whole brain volume
    ROC
    receiver operating characteristic

  • Received May 14, 2010.
  • Accepted September 20, 2010.
  • Copyright © 2011 by AAN Enterprises, Inc.
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