Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012).
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
Alzheimer's disease -- Risk factors, Biochemical markers
Introduction: Characterization of longitudinal trajectories of biomarkers implicated in sporadic Alzheimer's disease (AD) in decades prior to clinical diagnosis is important for disease prevention and monitoring.
Methods: We used a multivariate Bayesian model to temporally align 1369 AD Neuroimaging Initiative participants based on the similarity of their longitudinal biomarker measures and estimated a quantitative template of the temporal evolution cerebrospinal fluid (CSF) Aβ1-42, p-tau181p, and t-tau, hippocampal volume, brain glucose metabolism, and cognitive measurements. We computed biomarker trajectories as a function of time to AD dementia, and predicted AD dementia onset age in a disjoint sample.
Results: Quantitative template showed earliest changes in verbal memory, followed by CSF Aβ1-42, hippocampal volume, and p-tau181p. Mean error in predicted AD dementia onset age was < 1.5 years.
Discussion: Our method provides a quantitative approach for characterizing the natural history of AD starting at preclinical stages despite the lack of individual-level longitudinal data spanning the entire disease timeline.
Bilgel, Murat and Jedynak, Bruno, "Predicting Time to Dementia Using a Quantitative Template of Disease Progression" (2019). Portland Institute for Computational Science Publications. 13.