A new ANMerge-based blood transcriptomic resource to support Alzheimer's disease research.

ABSTRACT:

Introduction

Alzheimer's disease (AD) has greater prevalence in women and lacks effective treatments. Integrating multimodal data using machine learning (ML) may help improve diagnostics and prognostics.

Methods

We produced a large and updatable blood transcriptomic dataset (n=1021, with n=317 replicates). Technical robustness was assessed using sampling-at-random, batch adjustment and classification metrics. Transcriptomic and MRI features were concatenated to develop models for AD classification.

Results

Reprofiling of blood transcriptomics resolved previous technical artefacts (sampling-at-random AUC; Legacy=0.732 vs. New=0.567). AD-associated molecular pathways were influenced by cell counts and sex, including unchanged mitochondrial DNA-encoded RNA and altered B-

SUBMITTER: Mohamed Ismail N 

PROVIDER: S-EPMC12622077 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature