Abstract
Background: Early detection of neurodegenerative diseases (NDDs) like Alzheimer's and Parkinson's is crucial for timely intervention and management, yet remains a significant clinical challenge. Current diagnostic methods often rely on symptomatic presentation, leading to delayed diagnosis. This study evaluates the efficacy of various emerging biomarker panels for enhancing early NDD detection. Methods: We conducted a comprehensive review and comparative analysis of published research on biomarker panels for NDDs, focusing on proteomic, genetic, imaging, and fluid-based biomarkers. Data from studies published up to February 2024 were analyzed, with an emphasis on sensitivity, specificity, and clinical applicability. Machine learning approaches for integrating multimodal biomarker data were also considered. Results: Several promising biomarker panels are emerging. Proteomic analyses of cerebrospinal fluid and blood have identified key proteins such as amyloid-beta and tau species, alongside inflammatory markers (Cattaneo et al., 2016). Extracellular vesicles, particularly neuronal exosomes in saliva, show potential for detecting NDD-associated molecular signatures (Sharma et al., 2023; Yáñez‐Mó et al., 2015). Advanced neuroimaging techniques, including quantitative susceptibility mapping (QSM), offer distinct biomarkers for differentiating NDD subtypes (Nikparast et al., 2022). Machine learning algorithms show promise in integrating these diverse data types for improved diagnostic accuracy (Nallore et al., 2023; Fanijo et al., 2023). However, challenges remain in standardization and validation across diverse populations. Conclusion: Emerging biomarker panels, particularly multimodal approaches leveraging advanced analytics, hold significant promise for revolutionizing the early detection of neurodegenerative diseases. Further validation and standardization are essential for their translation into routine clinical practice.