Data-driven identification of endophenotypes of Alzheimer's disease progression: implications for clinical trials and therapeutic interventions.

ABSTRACT:

Background

Given the complex and progressive nature of Alzheimer's disease (AD), a precision medicine approach for diagnosis and treatment requires the identification of patient subgroups with biomedically distinct and actionable phenotype definitions.

Methods

Longitudinal patient-level data for 1160 AD patients receiving placebo or no treatment with a follow-up of up to 18 months were extracted from an integrated clinical trials dataset. We used latent class mixed modelling (LCMM) to identify patient subgroups demonstrating distinct patterns of change over time in disease severity, as measured by the Alzheimer's Disease Assessment Scale-cognitive subscale score. The optimal number of subgroups (classes) was selected by the model which had the lowest Bayesian Information Cri

SUBMITTER: Geifman N 

PROVIDER: S-EPMC6389228 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature