A new ANMerge-based blood transcriptomic resource to support Alzheimer’s disease research
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.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-cell receptor biology. Several genes linked to AD-associated neuroinflammatory pathways, including BLNK , TREM2 , and MS4A1 , showed significant enrichment. Concatenation of transcriptomics and MRI models modestly improved classification performance (AUC; MRI=0.922 vs. transcriptomics-MRI=0.930).
DISCUSSION
We provide a new large-scale and technically robust blood AD transcriptomic dataset, highlighting details of molecular sexual dimorphism in AD and potential literature false positives, while providing a novel resource for future multimodal ML and genomic studies.
