TY - JOUR
T1 - A blood-based screening tool for Alzheimer's disease that spans serum and plasma
T2 - Findings from TARC and ADNI
AU - O'Bryant, Sid E.
AU - Xiao, Guanghua
AU - Barber, Robert
AU - Huebinger, Ryan
AU - Wilhelmsen, Kirk
AU - Edwards, Melissa
AU - Graff-Radford, Neill
AU - Doody, Rachelle
AU - Diaz-Arrastia, Ramon
N1 - Funding Information:
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.
PY - 2011/12/7
Y1 - 2011/12/7
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pone.0028092
DO - 10.1371/journal.pone.0028092
M3 - Article
C2 - 22163278
AN - SCOPUS:82855163989
SN - 1932-6203
VL - 6
JO - PLoS One
JF - PLoS One
IS - 12
M1 - e28092
ER -