Estimating progression of Alzheimer's disease with extracellular vesicle-related multi-omics risk models.

ABSTRACT:

Background

Alzheimer's Disease (AD) is heterogeneous and shows complex interconnected pathways at various biological levels. Risk scores contribute greatly to disease prognosis and biomarker discovery but typically represent generic risk factors. However, large-scale multi-omics data can generate individualized risk factors. Filtering these risk factors with brain-derived extracellular vesicles (EVs) could yield key pathologic pathways and vesicular vehicles for treatment delivery.

Methods

A list of 460 EV-related genes was curated from brain tissue samples in the ExoCarta database. This list was used to select genes from transcriptomics, proteomics, and DNA methylation data. Significant risk factors included demographic features (age, sex) and genes significant for progress

SUBMITTER: Zhang X 

PROVIDER: S-EPMC12328399 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature