Biosensors for early detection of neurodegenerative diseases: Analytical challenges and innovations

Neurodegenerative diseases (NDs) pose a significant global health burden, with their prevalence expected to surpass cancer and cardiovascular diseases in the coming decades. Early detection is crucial for improving clinical outcomes, as substantial neuronal damage often occurs before overt symptoms appear. Biosensors have emerged as promising diagnostic tools for NDs, offering advantages such as high sensitivity, specificity, portability, and potential for point-of-care integration. This review provides a comprehensive overview of biosensor technologies for early ND detection, focusing on analytical challenges and recent innovations. Key ND biomarkers, including amyloid-beta, tau, alpha-synuclein, and neurofilament light chain, are discussed in the context of their diagnostic utility and detection challenges in complex biofluids. Biosensor platforms are categorized based on their transduction mechanisms, highlighting advancements in electrochemical, optical, piezoelectric, magnetoresistive, and field-effect transistor-based systems. The integration of nanotechnology and advanced biomaterials, such as gold nanoparticles, graphene, carbon nanotubes, quantum dots, hydrogels, and molecularly imprinted polymers, is explored as a means to enhance sensitivity, selectivity, and miniaturization. Sample processing techniques and analytical challenges associated with cerebrospinal fluid, blood, saliva, and urine are critically examined to promote reliable, safe, and scalable diagnostic practices. Innovations in integrated systems, lab-on-a-chip devices, multiplexing, and smartphone-based readout are highlighted as key trends driving the development of affordable, resilient, and decentralized diagnostic solutions, particularly for resource-limited and underserved settings. The review also discusses emerging frontiers, including wearable biosensors, AI-assisted data analytics, interdisciplinary collaboration, and translational pathways for clinical implementation. Future perspectives emphasize the need for interdisciplinary collaboration to bridge the gap between technological innovation and real-world clinical adoption, ultimately enabling early intervention, personalized disease management, and improved patient outcomes in the fight against neurodegenerative diseases.