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 beta-amyloid protein 1-42, total tau protein, and phosphorylated tau(181P) 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 beta-amyloid protein 1-42/phosphorylated tau(181P) 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 epsilon4 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.