Background: Alzheimer's disease neuropathologic changes (AD-NC) are important to identify people with high risk for AD dementia (ADD) and subtyping ADD.
Objective: Develop imputation models based on clinical measures to infer AD-NC.
Methods: We used penalized generalized linear regression to train imputation models for four AD-NC traits (amyloid-β, tangles, global AD pathology, and pathologic AD) in Rush Memory and Aging Project decedents, using clinical measures at the last visit prior to death as predictors. We validated these models by inferring AD-NC traits with clinical measures at the last visit prior to death for independent Religious Orders Study (ROS) decedents. We inferred baseline AD-NC traits for all ROS participants at study entry, and then tested if inferred AD-NC traits at study entry predicted incident ADD and postmortem pathologic AD.
Results: Inferred AD-NC traits at the last visit prior to death were related to postmortem measures with R2 = (0.188,0.316,0.262) respectively for amyloid-β, tangles, and global AD pathology, and prediction Area Under the receiver operating characteristic Curve (AUC) 0.765 for pathologic AD. Inferred baseline levels of all four AD-NC traits predicted ADD. The strongest prediction was obtained by the inferred baseline probabilities of pathologic AD with AUC = (0.919,0.896) for predicting the development of ADD in 3 and 5 years from baseline. The inferred baseline levels of all four AD-NC traits significantly discriminated pathologic AD profiled eight years later with p-values < 1.4×10-10.
Conclusions: Inferred AD-NC traits based on clinical measures may provide effective AD biomarkers that can estimate the burden of AD-NC traits in aging adults.
Keywords: Alzheimer’s disease; Alzheimer’s disease neurologic change; computational modeling; dementia; pathology.