Identifying Age-Related Protein Mechanisms of Alzheimer's Disease Amyloidosis from Cerebrospinal Fluid Proteomics Using a Novel Machine Learning Approach

Abstract Alzheimer’s disease (AD) is clinically characterized by progressive memory loss and cognitive decline, with aging as the primary risk factor. Early AD pathology includes accumulation of amyloid-beta (Aß) in plaques. This study aims to investigate the molecular mechanisms by which aging contributes to brain amyloidosis using a machine learning-based approach on large proteomic datasets from cerebrospinal fluid (CSF). To accomplish this, we trained a machine learning model to predict CSF Aß42/40, a key biomarker for amyloidosis. Our modified elastic net model using adaptive feature selection achieved robust accuracy (Pearson Correlation of 0.86) predicting CSF Aß42/40 in our validation cohort. Pathway analysis of the model-utilized proteins (and proteins highly correlated to them) revealed age-associated alterations potentially linked to amyloidosis, particularly highlighting dysregulated autophagy and membrane trafficking pathways. These findings suggest that impaired autophagosome-lysosome fusion and endosomal processing may drive the decline in Aß clearance with aging. Our study highlights the power of machine learning in biomarker approximation and biological prediction, enabling insights into multiple diseases.