Title
Diagnosis-independent Alzheimer disease biomarker signature in cognitively normal elderly people Diagnosis-independent Alzheimer disease biomarker signature in cognitively normal elderly people
Author
Faculty/Department
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
article
Publication
Chicago, Ill. ,
Subject
Human medicine
Source (journal)
Archives of neurology / American Medical Association. - Chicago, Ill., 1960 - 2012
Volume/pages
67(2010) :8 , p. 949-956
ISSN
0003-9942
1538-3687
ISI
000280809000006
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Objective To identify biomarker patterns typical for Alzheimer disease (AD) in an independent, unsupervised way, without using information on the clinical diagnosis. Design Mixture modeling approach. Setting Alzheimer's Disease Neuroimaging Initiative database. Patients or Other Participants Cognitively normal persons, patients with AD, and individuals with mild cognitive impairment. Main Outcome Measures Cerebrospinal fluidderived β-amyloid protein 1-42, total tau protein, and phosphorylated tau181P protein concentrations were used as biomarkers on a clinically well-characterized data set. The outcome of the qualification analysis was validated on 2 additional data sets, 1 of which was autopsy confirmed. Results Using the US Alzheimer's Disease Neuroimaging Initiative data set, a cerebrospinal fluid β-amyloid protein 1-42/phosphorylated tau181P biomarker mixture model identified 1 feature linked to AD, while the other matched the "healthy" status. The AD signature was found in 90%, 72%, and 36% of patients in the AD, mild cognitive impairment, and cognitively normal groups, respectively. The cognitively normal group with the AD signature was enriched in apolipoprotein E {varepsilon}4 allele carriers. Results were validated on 2 other data sets. In 1 study consisting of 68 autopsy-confirmed AD cases, 64 of 68 patients (94% sensitivity) were correctly classified with the AD feature. In another data set with patients (n = 57) with mild cognitive impairment followed up for 5 years, the model showed a sensitivity of 100% in patients progressing to AD. Conclusions The mixture modeling approach, totally independent of clinical AD diagnosis, correctly classified patients with AD. The unexpected presence of the AD signature in more than one-third of cognitively normal subjects suggests that AD pathology is active and detectable earlier than has heretofore been envisioned.
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