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Diagnosis-Independent Alzheimer Disease Biomarker Signature in Cognitively Normal Elderly People
Geert De Meyer, PhD;
Fred Shapiro, MLS;
Hugo Vanderstichele, PhD;
Eugeen Vanmechelen, PhD;
Sebastiaan Engelborghs, MD, PhD;
Peter Paul De Deyn, MD, PhD;
Els Coart, PhD;
Oskar Hansson, MD;
Lennart Minthon, MD;
Henrik Zetterberg, MD, PhD;
Kaj Blennow, MD, PhD;
Leslie Shaw, PhD;
John Q. Trojanowski, MD, PhD; for the Alzheimer's Disease Neuroimaging Initiative
Arch Neurol. 2010;67(8):949-956. doi:10.1001/archneurol.2010.179
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 fluid–derived 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  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.
Author Affiliations: Stat-Gent CRESCENDO, Department of Applied Mathematics and Computer Science, Ghent University (Dr De Meyer), and Innogenetics NV (Mr Shapiro and Drs Vanderstichele, Vanmechelen, and Coart), Gent, and Department of Neurology and Memory Clinic, Middelheim General Hospital and Institute Born-Bunge, University of Antwerp, Antwerp (Drs Engelborghs and De Deyn), Belgium; Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö (Drs Hansson and Minthon), and Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at University of Gothenburg, Molndal (Drs Zetterberg and Blennow), Sweden; and Department of Pathology and Laboratory Medicine, Institute on Aging, Alzheimer's Disease Center, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia (Drs Shaw and Trojanowski).
Group Information: A list of the Alzheimer's Disease Neuroimaging Initiative members appears at http://www.adni-info.org/ISAB/ISABMembers.aspx.
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