Metabolomic Biomarkers in Neurodegenerative Disease

Metabolomic biomarkers have become indispensable in elucidating the biochemical underpinnings of neurodegenerative diseases and in advancing their diagnosis, prognosis and therapeutic monitoring. By profiling small-molecule metabolites in biofluids and tissues, metabolomics captures the dynamic interplay between genetic predisposition, environmental exposures and pathological processes. In disorders such as Parkinson’s disease, Alzheimer’s disease, amyotrophic lateral sclerosis and multiple sclerosis, characteristic perturbations include mitochondrial dysfunction, oxidative stress, dysregulated lipid and amino-acid metabolism, and neuroinflammatory cascades. Analytical platforms—chiefly liquid chromatography–mass spectrometry and nuclear magnetic resonance spectroscopy—enable both untargeted discovery of novel metabolic signatures and targeted quantification of predefined metabolites. Metabolomic biomarkers facilitate early detection by revealing subtle shifts in metabolic networks before overt clinical manifestations, support stratification of disease subtypes through distinct metabolic fingerprints, and guide personalised interventions by monitoring response to disease-modifying therapies. Integration with other modalities, including neuroimaging, genomics and digital phenotyping, is forging a holistic framework for precision neurology and for identifying mechanistic links between peripheral metabolic changes and central neurodegeneration.

Research from Nature Portfolio

Seminal work in non-invasive biofluids has defined foundational metabolic signatures of Parkinson’s disease. Comprehensive urinary profiling revealed progressive alterations in branched-chain amino acids, tryptophan and steroid metabolites that correlate with disease stage, demonstrating the feasibility of urine as a source of diagnostic biomarkers. Subsequent double-cohort plasma analyses identified a consistent decrement of long-chain acylcarnitines in early-stage Parkinson’s disease, implicating suppressed mitochondrial β-oxidation as an early pathogenic event and offering high diagnostic specificity. More recently, lipidomic analysis of sebum has uncovered alterations in the carnitine shuttle, sphingolipid metabolism and fatty-acid biosynthesis in patients with Parkinson’s disease, highlighting skin-derived lipids as accessible reporters of central lipid dysregulation and reinforcing the concept of peripheral metabolite changes reflecting central nervous system pathology.

Metabolomic Biomarkers in Neurodegenerative Disease publication trend

The graph below shows the total number of articles in metabolomic biomarkers in neurodegenerative disease across all publications each year (not limited to Nature Index journals).

Technical terms

Metabolomics: The large-scale study of small-molecule metabolites within cells, tissues or biofluids.

Biomarker: A measurable indicator of physiological or pathological processes or responses to an intervention.

Untargeted metabolomics: A comprehensive approach that seeks to detect as many metabolites as possible without prior selection.

Targeted metabolomics: The quantitative analysis of predefined metabolites of interest.

Liquid chromatography–mass spectrometry (LC–MS): An analytical technique that separates metabolites by chromatography and measures their mass-to-charge ratio.

Nuclear magnetic resonance (NMR) spectroscopy: A method that identifies and quantifies metabolites based on the magnetic properties of atomic nuclei.

Cerebrospinal fluid (CSF): The clear fluid surrounding the brain and spinal cord, reflecting central nervous system biochemistry.

References

1. Comprehensive urinary metabolomic profiling and identification of potential noninvasive marker for idiopathic Parkinson’s disease. Scientific Reports (2015).
2. Decreased long-chain acylcarnitines from insufficient β-oxidation as potential early diagnostic markers for Parkinson’s disease. Scientific Reports (2017).
3. Metabolomics of sebum reveals lipid dysregulation in Parkinson’s disease. Nature Communications (2021).
4. Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease. npj Digital Medicine (2024).
5. Untargeted serum metabolomics reveals novel metabolite associations and disruptions in amino acid and lipid metabolism in Parkinson’s disease. Molecular Neurodegeneration (2023).
6. Comprehensive analysis of the cerebrospinal fluid and serum metabolome in neurological diseases. Journal of Neuroinflammation (2024).
7. Precision neurology. Ageing Research Reviews (2024).