Model-based stratification of progression along the Alzheimer disease continuum highlights the centrality of biomarker synergies.

ABSTRACT:

Background

The progression rates of Alzheimer's disease (AD) are variable and dynamic, yet the mechanisms that contribute to heterogeneity in progression rates remain ill-understood. Particularly, the role of synergies in pathological processes reflected by biomarkers for amyloid-beta ('A'), tau ('T'), and neurodegeneration ('N') in progression along the AD continuum is not fully understood.

Methods

Here, we used a combination of model and data-driven approaches to address this question. Working with a large dataset (N = 321 across the training and testing cohorts), we first applied unsupervised clustering on longitudinal cognitive assessments to divide individuals on the AD continuum into those showing fast vs. moderate decline. Next, we developed a deep learning model that

SUBMITTER: Sadiq MU 

PROVIDER: S-EPMC8787915 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature