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Comparative Study
. 2011 Feb 8;76(6):501-10.
doi: 10.1212/WNL.0b013e31820af900. Epub 2011 Jan 12.

Improving CSF biomarker accuracy in predicting prevalent and incident Alzheimer disease

C M Roe  1 , 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
Affiliations
Comparative Study

Improving CSF biomarker accuracy in predicting prevalent and incident Alzheimer disease

C M Roe et al. Neurology. .

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 Aβ(42), tau, ptau(181), tau/Aβ(42), and ptau(181)/Aβ(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 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.

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Figures

Figure 1
Figure 1. Discriminating persons with dementia of the Alzheimer type from those with normal cognition
(A) Aβ42, (B) Tau, (C) Ptau181, (D) Tau/Aβ42, (E) Ptau181/Aβ42. Increase in area under the receiver operating characteristic curve as each variable is added to the expanded model for each biomarker. GDS = Geriatric Depression Scale; nWBV = normalized whole brain volume.
Figure 2
Figure 2. Predicting time to incident dementia of the Alzheimer type from cognitive normality at baseline
Increase in concordance probability estimate with the addition of age and education in the expanded biomarker models.
See this image and copyright information in PMC

Comment in

References

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