Estimating Progression of Alzheimer's Disease with Extracellular Vesicle-Related Multi-Omics Risk Models

Xiao Zhang¹Sanoji Wijenayake²Shakhawat Hossain³Qian Liu^4*

• ¹Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
• ²Department of Biology, The University of Winnipeg, Winnipeg, Manitoba, Canada
• ³Department of Mathematics and Statistics, The University of Winnipeg, Winnipeg, Canada
• ⁴The Department of Applied Computer Science, University of Winnipeg, Winnipeg, Canada

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 progression in transcriptomics data. These genes were selected using Cox regression, aided by the Least Absolute Shrinkage and Selection Operator (LASSO), and were used to construct three risk models at different omics levels. Gene signatures from the significant risk factors were used as biomarkers for further evaluation, including gene set enrichment analysis (GSEA) and drug perturbation analysis.Results: Nine EV-related genes were identified as significant risk factors. All three risk models predicted high/low risk groups with significant separation in Kaplan-Meier analysis. Training the transcriptomics risk models on EV-related genes yielded better AD classification results than using all genes in an independent dataset. GSEA evaluated Mitophagy as a significant pathway.Four drugs showed therapeutic potential based on Connectivity Map analysis.The proposed risk score model demonstrates a novel approach to AD using EVrelated large-scale multi-omics data. Potential biomarkers and pathways related to AD were identified for further investigation. Drug candidates were identified for further evaluation in biological experiments, potentially transported to targeted tissues via bioengineered EVs.• The use of EV-related genetic risk factors for AD prognosis produced more accurate risk models when compared to using general risk factors.• Evaluation of significant EV-related risk factors revealed mitophagy as a relevant pathway and penfluridol as a potential repurposed treatment that can be used to treat AD.• EV-related multi-omics data integration allows for a more comprehensive characterization of AD across biological layers.