Journal
PLOS ONE
Volume 6, Issue 12, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0028092
Keywords
-
Categories
Funding
- state of Texas through the Texas Council on Alzheimer's Disease and Related Disorders
- NIH, NIA [P30AG12300]
- Cynthia and George Mitchell Foundation
- CH Foundation
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- 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.
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- U.S. Food and Drug Administration
- NIH [P30 AG010129, K01 AG030514]
- Dana Foundation
Ask authors/readers for more resources
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/A beta 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available