A Serum Protein-Based Algorithm for the Detection of Alzheimer Disease

Objective: To develop an algorithm that separates patients with Alzheimer disease (AD) from controls. Design: Longitudinal case-control study. Setting: The Texas Alzheimer's Research Consortium project. Patients: We analyzed serum protein based multiplex biomarker data from 197 patients diagnosed with AD and 203 controls. Main Outcome Measure: The total sample was randomized equally into training and test sets and random forest methods were applied to the training set to create a biomarker risk score. Results: The biomarker risk score had a sensitivity and specificity of 0.80 and 0.91, respectively, and an area un-der the curve of 0.91 in detecting AD. When age, sex, education, and APOE status were added to the algorithm, the sensitivity, specificity, and area under the curve were 0.94, 0.84, and 0.95, respectively. Conclusions: These initial data suggest that serum protein-based biomarkers can be combined with clinical information to accurately classify AD. A disproportionate number of inflammatory and vascular markers were weighted most heavily in the analyses. Additionally, these markers consistently distinguished cases from controls in significant analysis of microarray, logistic regression, and Wilcoxon analyses, suggesting the existence of an inflammatory-related endophenotype of AD that may provide targeted therapeutic opportunities for this subset of patients.