Alzheimer's Disease Severity, Objectively Determined and Measured

• Activity Number: 48
• Type: Invited
• Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Alden Gross* and Jeannie M. Leoutsakos and Marilyn Albert and Dan M. Mungas and Richard Norman Jones
• Companies: Johns Hopkins Bloomberg School of Public Health and The Johns Hopkins University and The Johns Hopkins University and University of California at Davis and Brown University
• Keywords: alzheimer's disease ; latent variable modeling ; SEM ; ADNI ; BIOCARD ; biomarkers

Abstract

AD biomarkers from brain imaging, neuropathology, and cognitive testing provide distinct information about pathophysiology of AD over segments of the clinical spectrum from preclinical to clinical AD. We derived a measure of AD severity based on several biomarkers using latent variable modeling. We used data from ADNI (N=2861) and validated findings in BIOCARD (N=347). We evaluated criterion validity for distinguishing diagnostic groups and construct validity by evaulating rates of change in AD severity. The AD severity factor distinguishes AD from cognitively normal (AUC=0.94) and cognitively normal from MCI (AUC=0.87). Among ADNI MCI, worsening scores predict faster conversion to AD (HR=1.17, 95%CI: 1.13,1.22). The pace of change in AD severity is steepest among converters, with persisting differences by baseline diagnostic status. Results suggest our content-valid latent variable measurement model is a reasonable approach for grading AD severity across a broad spectrum.