Abstract
BACKGROUND: Tears are an easily accessible biofluid that reflects both emotional states and disease conditions. They are particularly enriched in extracellular vesicles (EVs), which carry proteins and nucleic acids relevant to neurological health. This makes tear EVs a promising source for biomarker discovery. However, limited sample volume and variability pose challenges for identifying reliable biomarkers for clinical diagnosis.
RESULTS: We present AI-driven Biomarker Learning for the Early Diagnosis of Neurodegenerative Diseases (ABLEDx), which applies a conditional variational autoencoder (cVAE) to enhance proteomic analysis of tear EVs. This approach effectively addresses sample limitations and improves the identification of disease-associated biomarkers. Our results reveal that tear EVs capture molecular signals along the eye-brain axis, reflecting contributions from both ocular and central nervous system cells. ABLEDx identified clinically relevant protein modules, which were consistently elevated in patients with neurodegenerative diseases. Moreover, we recognize that KRAS is highly expressed in patients with Alzheimer's disease, Parkinson's disease, and ocular myasthenia gravis, and tear-EV-associated LRG1 and HSPG2 exhibit differentiation between Alzheimer's disease and Parkinson's disease.
CONCLUSIONS: ABLEDx demonstrates the utility of combining AI with tear-EV proteomics for non-invasive biomarker discovery. This strategy enables early and real-time detection of neurodegenerative and ocular diseases, offering new opportunities for clinical diagnostics and translational medicine.