Metabolomics is the large-scale study of small molecule metabolites within biological systems. In neurodegeneration research, metabolomics provides a functional readout of cellular metabolism and has emerged as a powerful approach for discovering biomarkers, understanding disease mechanisms, and identifying therapeutic targets.
Metabolomics complements genomics, proteomics, and transcriptomics by measuring the end products of cellular processes. The metabolome reflects the combined influence of genetic variation, environmental factors, and disease pathology, making it particularly valuable for understanding complex neurodegenerative disorders like Alzheimer's disease (AD) and Parkinson's disease (PD)[1].
Multiple metabolite alterations have been identified in AD:
PD-specific metabolic changes include:
MWAS examine the relationship between the metabolome and disease phenotypes. Key findings include:
NMR spectroscopy offers several advantages for metabolomics:
Mass spectrometry-based approaches provide:
| Metabolite | Disease | Direction | Clinical Relevance |
|---|---|---|---|
| Sphingomyelin | AD | Decreased | Diagnostic biomarker |
| Phosphatidylserine | AD | Decreased | Disease progression |
| Uric acid | PD | Decreased | Risk factor |
| CoQ10 | PD | Decreased | Therapeutic target |
| Lactate | AD/PD | Increased | Energy metabolism |
| Glutathione | PD | Decreased | Oxidative stress |
CSF metabolomics provides direct insight into brain metabolism:
Peripheral biomarkers offer practical advantages:
Metabolomics data integrates with other omics layers:
Metabolomic approaches support therapeutic development:
Patti GJ, Yanes O, Siuzdak G. 'Metabolomics: the apogee of the omics trilogy'. 2012. ↩︎
Cheng H, Wang M, Li JL, et al. Sphingolipid metabolism in Alzheimer's disease. 2023. ↩︎
Whiley L, Langford J, Zetterberg H, et al. Metabolomic biomarkers for Alzheimer's disease. 2021. ↩︎
González-Domínguez R, García-Barrera T, Gómez-Ariza JL. Metabolomic approaches to investigate metabolic alterations in Alzheimer's disease. 2015. ↩︎
Mahajan UV, Varma VR, Huang J, et al. Metabolomic signatures of Alzheimer's disease. 2022. ↩︎
Weisskopf MG, O'Reilly E, Chen H, Schwarzschild MA, Ascherio A. Plasma urate and risk of Parkinson's disease. 2007. ↩︎
Shults CW, Oakes D, Kieburtz K, et al. Effects of coenzyme Q10 in early Parkinson disease. 2002. ↩︎
Sian J, Dexter DT, Lees AJ, et al. Glutathione depletion in the substantia nigra. 1994. ↩︎
Arnold M, Nho K, Kueider-Paisley A, et al. Metabolomics for Alzheimer's disease. 2020. ↩︎
Tynkkynen J, Chouraki V, van der Lee SJ, et al. Metabolic profiling of incident diabetes. 2018. ↩︎
Shin SY, Fauman EB, Petersen AK, et al. An atlas of genetic influences on human blood metabolites. 2014. ↩︎
Emwas AH. The strengths and weaknesses of NMR spectroscopy and mass spectrometry. 2015. ↩︎
Dettmer K, Aronov PA, Hammock BD. Mass spectrometry-based metabolomics. 2007. ↩︎
Lin MT, Beal MF. Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases. 2006. ↩︎
Zheng H, Zhang C, Luo W, et al. CSF neurotransmitter metabolites in Alzheimer's disease. 2021. ↩︎
Mapstone M, Cheema AK, Fiandaca MS, et al. Plasma phospholipids identify antecedent memory impairment. 2014. ↩︎
Ryu do Y, Song SH, Lee KA, et al. Blood metabolomics for biomarker discovery in Parkinson's disease. 2020. ↩︎
Suhre K, Shin SY, Petersen AK, et al. Human metabolic individuality in biomedical and pharmaceutical research. 2011. ↩︎
Krumsiek J, Suhre K, Illig T, Adamski J, Theis FJ. Bayesian multivariate analysis of metabolomics data. 2011. ↩︎
Patti GJ, Tautenhahn R, Rinehart D, et al. 'A view from on high: NIST metabolomics standards'. 2012. ↩︎
Kaddurah-Daouk R, Kristal BS, Weinshilboum RM. 'Metabolomics: a global biochemical approach to drug response and disease'. 2008. ↩︎