Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2011;6(12):e28092.
doi: 10.1371/journal.pone.0028092. Epub 2011 Dec 7.

A blood-based screening tool for Alzheimer's disease that spans serum and plasma: findings from TARC and ADNI

Sid E O'Bryant  1 , Guanghua Xiao, Robert Barber, Ryan Huebinger, Kirk Wilhelmsen, Melissa Edwards, Neill Graff-Radford, Rachelle Doody, Ramon Diaz-Arrastia; Texas Alzheimer's Research & Care Consortium; Alzheimer's Disease Neuroimaging Initiative
Collaborators, Affiliations

A blood-based screening tool for Alzheimer's disease that spans serum and plasma: findings from TARC and ADNI

Sid E O'Bryant et al. PLoS One. 2011.

Abstract

Context: There is no rapid and cost effective tool that can be implemented as a front-line screening tool for Alzheimer's disease (AD) at the population level.

Objective: To generate and cross-validate a blood-based screener for AD that yields acceptable accuracy across both serum and plasma.

Design, setting, participants: Analysis of serum biomarker proteins were conducted on 197 Alzheimer's disease (AD) participants and 199 control participants from the Texas Alzheimer's Research Consortium (TARC) with further analysis conducted on plasma proteins from 112 AD and 52 control participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The full algorithm was derived from a biomarker risk score, clinical lab (glucose, triglycerides, total cholesterol, homocysteine), and demographic (age, gender, education, APOE*E4 status) data.

Major outcome measures: Alzheimer's disease.

Results: 11 proteins met our criteria and were utilized for the biomarker risk score. The random forest (RF) biomarker risk score from the TARC serum samples (training set) yielded adequate accuracy in the ADNI plasma sample (training set) (AUC = 0.70, sensitivity (SN) = 0.54 and specificity (SP) = 0.78), which was below that obtained from ADNI cerebral spinal fluid (CSF) analyses (t-tau/Aβ ratio AUC = 0.92). However, the full algorithm yielded excellent accuracy (AUC = 0.88, SN = 0.75, and SP = 0.91). The likelihood ratio of having AD based on a positive test finding (LR+) = 7.03 (SE = 1.17; 95% CI = 4.49-14.47), the likelihood ratio of not having AD based on the algorithm (LR-) = 3.55 (SE = 1.15; 2.22-5.71), and the odds ratio of AD were calculated in the ADNI cohort (OR) = 28.70 (1.55; 95% CI = 11.86-69.47).

Conclusions: It is possible to create a blood-based screening algorithm that works across both serum and plasma that provides a comparable screening accuracy to that obtained from CSF analyses.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have the following competing interest: In the TARC, a patent has been submitted on this blood-based screener. There are no other products in development or marketed products to declare. This does not alter the authors' adherence to all PLoS ONE policies on sharing data and materials, as detailed online in the guide for authors. ADNI has received funding from the following commercial sources: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc. This does not alter the authors' adherence to all PLoS ONE policies on sharing data and materials, as detailed online in the guide for authors. ADNI data is freely available to any interested scientists.

Figures

Figure 1
Figure 1. The density plot the Pearson's correlation coefficients between serum and plasma in TARC cohort.
We used Mclust (model-based clustering algorithm [21]) package in R to fit the data and discovered two clusters in the correlation coefficients: one (red) corresponding to low correlation and the other (blue) corresponding to high correlation. The threshold value that separated these two clusters most effectively is 0.75. The black line is the density plot of all biomarkers. The dots represent the correlation coefficients of the biomarkers and the color indicates the cluster membership.
Figure 2
Figure 2. Outline of methods.
Figure 3
Figure 3. ROC curve for serum-plasma based biomarker algorithm.
Each line represents the AUC of the respective portions of the algorithm with the yellow line reflecting chance.
See this image and copyright information in PMC

References

    1. Thal LJ, Kantarci K, Reiman EM, Klunk WE, Weiner MW, et al. The role of biomarkers in clinical trials for Alzheimer disease. Alzheimer Disease & Associated Disorders. 2006;20:6–15. - PMC - PubMed
    1. O'Bryant SE, Xiao G, Barber R, Reisch J, Doody R, et al. A serum protein-based algorithm for the detection of Alzheimer disease. Archives of Neurology. 2010;67:1077–1081. - PMC - PubMed
    1. O'Bryant S, Xiao G, Barber R, Reisch J, Doody R, et al. for the Texas Alzheimer's Research Consortium. A serum protein-based algorithm for the detection of Alzheiemr's disease. Arch Neurol in press - PMC - PubMed
    1. Schneider P, Hampel H, Buerger K. Biological marker candidates of alzheimer's disease in blood, plasma, and serum. CNS Neuroscience and Therapeutics. 2009;15:358–374. - PMC - PubMed
    1. O'Bryant S, Xiao G, Barber R, Reisch J, Hall J, et al. A blood based algorithm for the detection of Alzheimer's disease. Dementia and Geriatric Cognitive Disorders. 2011;32:55–62. - PMC - PubMed

Publication types

MeSH terms