Molecular Pathology of Neurodegenerative Disease: Multi-Omics Fluid Biomarkers, Analytical Standardization, and AI-Enabled Precision Diagnostics

Neurodegenerative diseases are biologically heterogeneous disorders in which pathological protein accumulation, axonal injury, synaptic dysfunction, glial activation, and genetic variation evolve before conventional clinical phenotypes become fully established. This review examines these disorders through a molecular-pathology framework that links biomarker biology to specimen type, pre-analytical handling, analytical platform, clinical interpretation, and therapeutic stratification. Established fluid biomarkers, including amyloid-β42/40, phosphorylated tau (p-tau217, p-tau181, and p-tau231), neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP), are evaluated alongside disease-specific assays for α-synuclein and emerging TDP-43, synaptic, and endo-lysosomal markers. We critically compare mass spectrometry, single-molecule array, proximity extension, nucleic-acid-linked immunoassay, automated immunoassay, and seed-amplification approaches, emphasizing analytical sensitivity, multiplexing, standardization, and readiness for clinical deployment. The review then considers how proteomics, transcriptomic signals, genetic information, imaging, digital phenotypes, and machine learning can be integrated without obscuring biological interpretability. Across biomarker classes, the major translational bottleneck is no longer analytical detection alone; it is demonstration that a measured signal is biologically attributable to central nervous system pathology, reproducible across platforms and populations, and useful for a defined clinical decision. We therefore propose a pathology-to-clinic framework organized around four linked requirements: analytical validity, biological validity, clinical validity, and clinical utility. This framework prioritizes standardized pre-analytical workflows, orthogonal confirmation, externally validated thresholds, diverse population testing, and prospective evidence that biomarker-guided decisions improve patient management. The resulting approach positions molecular pathology, not algorithmic prediction alone, as the interpretive bridge between multi-omics discovery and precision diagnosis.