Metabolite Signatures of Neurodegeneration: Alzheimer’s and Parkinson’s Disease from CSF and Plasma Metabolomics to Clinically Applicable Diagnostic Algorithms in the Medical Laboratory

Alzheimer’s disease (AD) and Parkinson’s disease (PD) represent the most prevalent neurodegenerative disorders worldwide and contribute significantly to morbidity, mortality, and healthcare burden. Early and accurate diagnosis remains challenging, particularly during the prodromal stages when clinical symptoms are subtle. Advances in metabolomics have provided new opportunities to identify small-molecule biomarkers in cerebrospinal fluid (CSF) and plasma that reflect underlying metabolic disturbances associated with neurodegeneration. Methods:This narrative review summarizes evidence published between 2005 and 2024 regarding metabolomic alterations in AD and PD. Studies investigating CSF and plasma metabolite signatures, metabolic pathway alterations, analytical platforms, and biomarker discovery approaches were reviewed. Additionally, the potential translation of metabolomic findings into clinically applicable diagnostic algorithms in medical laboratory practice was evaluated. Results: Multiple metabolomic studies consistently demonstrate disturbances in energy metabolism, lipid metabolism, amino acid metabolism, oxidative stress pathways, and tryptophan-kynurenine metabolism in both AD and PD. Metabolite panels involving phosphatidylcholines, sphingomyelins, glutamate, branched-chain amino acids, acylcarnitines, and urate have shown diagnostic accuracy with area-under-the-curve values ranging from 0.78 to 0.95. Conclusion: Metabolomic profiling of CSF and plasma represents a promising strategy for early detection and disease stratification in neurodegenerative disorders. However, significant challenges remain in translating metabolomic biomarkers into routine clinical diagnostics. Standardization of analytical workflows, multicenter validation, and integration with established protein biomarkers will be critical for successful clinical implementation.