Comparative Screening of Alzheimer's Disease, Lewy Body Dementia, and Frontotemporal Dementia Using miRNA and Machine Learning.

Abstract

Current dementia diagnostic methods can be costly, invasive, or limited in their ability to distinguish between disorders with overlapping clinical symptoms. Dysregulated microRNAs (miRNAs) have emerged as promising noninvasive biomarkers for neurodegenerative disease, but individual miRNA changes alone may not capture the complex molecular patterns needed for accurate disease classification. Machine learning provides a way to integrate multiple layers of miRNA-derived information and identify disease-specific biomarker signatures. In this study, we developed machine learning models to classify dysregulated miRNAs associated with Alzheimer's disease dementia (AD), Lewy body dementia (LBD), and frontotemporal dementia (FTD). Each miRNA was represented using sequence-derived descriptors, predicted gene targets, and KEGG pathway features. The highest-performing models trained on AD, LBD, and FTD achieved 10-fold cross-validation accuracies of 90.6%, 92.9%, and 100%, respectively. When evaluated on independent datasets, the AD, LBD, and FTD models achieved accuracies of 88.9%, 77.8%, and 90.9%, respectively. Cross-disease testing showed reduced performance when models were applied across dementia types, suggesting partially disease-specific miRNA patterns while also indicating overlap among the molecular signatures of AD, LBD, and FTD. These results suggest that machine learning-based integration of miRNA sequence, target-gene, and pathway information can improve the identification of dementia-associated biomarker signatures and may support the future development of noninvasive diagnostic tools for dementia.