The clinical diagnosis of neurodegenerative diseases is notoriously inaccurate and current methods are often expensive, time-consuming, or invasive. Simple inexpensive and noninvasive methods of diagnosis could provide valuable support for clinicians when combined with cognitive assessment scores. Biological processes leading to neuropathology progress silently for years and are reflected in both the central nervous system and vascular peripheral system. A blood-based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. In this study, we set out to discover a small set of blood transcripts that can be used to distinguish healthy individuals from those with Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, Friedreich’s ataxia, or frontotemporal dementia. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood and discovered small sets of transcripts that distinguish a number of neurodegenerative diseases with high sensitivity and specificity. We validated the usefulness of blood RNA transcriptomics for the classification of neurodegenerative diseases. Information about features selected for the classification can direct the development of possible treatment strategies.

Understanding of the biology of Alzheimer's disease (AD) has long been fragmented, with various investigators concentrating on amyloid beta (Aβ) or tau, inflammation, cell death pathways, misfolded proteins, glia, and more. Yet data from multiple authors has repeatedly shown altered expression of myriad genes related to these seemingly disparate phenomena. In 2022, Morgan et al. organized the massive data on changes in AD in a meticulous survey of the literature and related these changes to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Their data showed that 91% of the known KEGG pathways are involved in AD and that many of these pathways are represented by the known cellular/molecular phenomena of AD. Such data then raise the fundamental question: What mechanism(s) may be responsible for such widespread changes in gene expression? We review evidence for a unifying model based on sequestrations in stress granules and alteration of nucleocytoplasmic transport in AD. Highlights: In Alzheimer's disease (AD), critical changes take place in neurons before the appearance of plaques or tangles. Addressing these early changes provides a path to early detection and effective intervention in AD.