Toward Preventive Alzheimer’s Risk Screening with Cell-Subtype-Aware Brain-Blood Graph Neural Network

Abstract

Early Alzheimer’s disease (AD) pathology begins decades before symptoms emerge, yet over 75% of the at-risk population lacks access to non-invasive screening methods. Current diagnostic tools like PET imaging and cerebrospinal fluid sampling are costly, invasive, and poorly suited for large-scale, proactive brain health monitoring. This research introduces a cell-subtype-aware brain-blood gene modeling framework that reframes AD assessment from reactive diagnosis to preventive risk evaluation for sustained cognitive health. Using graph neural networks, blood RNA-seq profiles are anchored to sex-specific, single-cell brain transcriptomics across neuronal layers, enhancing biological fidelity and interpretability. Explicit control of APOE4 genotype, age, sex, and education preserves meaningful variation while suppressing systemic noise. Gene set enrichment analysis confirmed pathways in neurodegeneration, inflammation, oxidative phosphorylation, and sensory function, with brain-derived signals reproducibly detected in blood. Sex-stratified analyses revealed female-specific signatures linked to addiction and mood regulation, pathogen-driven immune responses, and nutrient-based neuroprotection. This research identifies a blood-based gene panel for AD risk: GFAP, TREM2, C1QC, C1QB, PLCG2, TXNIP, CD163, CAMK1D, DAPK1, CCND3, LRP10, and COQ10A. By coupling fine-grained brain biology with interpretable AI, this work enables equitable, population-scale early risk identification, supporting proactive interventions to maintain cognitive function and delay disease onset.