TY - JOUR
T1 - Detection of Alzheimer's disease at mild cognitive impairment and disease progression using autoantibodies as blood-based biomarkers
AU - Alzheimer's Disease Neuroimaging Initiative
AU - DeMarshall, Cassandra A.
AU - Nagele, Eric P.
AU - Sarkar, Abhirup
AU - Acharya, Nimish K.
AU - Godsey, George
AU - Goldwaser, Eric L.
AU - Kosciuk, Mary
AU - Thayasivam, Umashanger
AU - Han, Min
AU - Belinka, Benjamin
AU - Nagele, Robert G.
N1 - Funding Information:
Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) ( National Institutes of Health Grant U01 AG024904 ) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging , the National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Funding Information:
This research was supported in part by the Osteopathic Heritage Foundation and the Michael J. Fox Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the article.
PY - 2016
Y1 - 2016
N2 - Introduction: There is an urgent need to identify biomarkers that can accurately detect and diagnose Alzheimer's disease (AD). Autoantibodies are abundant and ubiquitous in human sera and have been previously demonstrated as disease-specific biomarkers capable of accurately diagnosing mild-moderate stages of AD and Parkinson's disease. Methods: Sera from 236 subjects, including 50 mild cognitive impairment (MCI) subjects with confirmed low CSF Aβ42 levels, were screened with human protein microarrays to identify potential biomarkers for MCI. Autoantibody biomarker performance was evaluated using Random Forest and Receiver Operating Characteristic curves. Results: Autoantibody biomarkers can differentiate MCI patients from age-matched and gender-matched controls with an overall accuracy, sensitivity, and specificity of 100.0%. They were also capable of differentiating MCI patients from those with mild-moderate AD and other neurologic and non-neurologic controls with high accuracy. Discussion: Autoantibodies can be used as noninvasive and effective blood-based biomarkers for early diagnosis and staging of AD.
AB - Introduction: There is an urgent need to identify biomarkers that can accurately detect and diagnose Alzheimer's disease (AD). Autoantibodies are abundant and ubiquitous in human sera and have been previously demonstrated as disease-specific biomarkers capable of accurately diagnosing mild-moderate stages of AD and Parkinson's disease. Methods: Sera from 236 subjects, including 50 mild cognitive impairment (MCI) subjects with confirmed low CSF Aβ42 levels, were screened with human protein microarrays to identify potential biomarkers for MCI. Autoantibody biomarker performance was evaluated using Random Forest and Receiver Operating Characteristic curves. Results: Autoantibody biomarkers can differentiate MCI patients from age-matched and gender-matched controls with an overall accuracy, sensitivity, and specificity of 100.0%. They were also capable of differentiating MCI patients from those with mild-moderate AD and other neurologic and non-neurologic controls with high accuracy. Discussion: Autoantibodies can be used as noninvasive and effective blood-based biomarkers for early diagnosis and staging of AD.
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U2 - 10.1016/j.dadm.2016.03.002
DO - 10.1016/j.dadm.2016.03.002
M3 - Article
AN - SCOPUS:84966318506
VL - 3
SP - 51
EP - 62
JO - Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
JF - Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
SN - 2352-8729
ER -