A flexible modeling approach for biomarker‐based computation of absolute risk of Alzheimer's disease dementia
Sarah M. Hartz, Jessica Mozersky, Suzanne E. Schindler, Erin Linnenbringer, Junwei Wang, Brian A. Gordon, Cyrus A. Raji, Krista L. Moulder, Tim West, Tammie L.S. Benzinger, Carlos Cruchaga, Jason Hassenstab, Laura J. Bierut, Chengjie Xiong, John C. Morris
Abstract
INTRODUCTION: As Alzheimer's disease (AD) biomarkers rapidly develop, tools are needed that accurately and effectively communicate risk of AD dementia. METHODS: We analyzed longitudinal data from >10,000 cognitively unimpaired older adults. Five-year risk of AD dementia was modeled using survival analysis. RESULTS: A demographic model was developed and validated on independent data with area under the receiver operating characteristic curve (AUC) for 5-year prediction of AD dementia of 0.79. Clinical and cognitive variables (AUC = 0.79), and apolipoprotein E genotype (AUC = 0.76) were added to the demographic model. We then incorporated the risk computed from the demographic model with hazard ratios computed from independent data for amyloid positron emission tomography status and magnetic resonance imaging hippocampal volume (AUC = 0.84), and for plasma amyloid beta (Aβ)42/Aβ40 (AUC = 0.82). DISCUSSION: An adaptive tool was developed and validated to compute absolute risks of AD dementia. This approach allows for improved accuracy and communication of AD risk among cognitively unimpaired older adults.