Abstract
Ischemic stroke (IS) is the most prevalent type of stroke. The early diagnosis and prognosis of IS are crucial for successful therapy and early intervention. Metabolomics, a tool in systems biology based on several innovative technologies, can be used to identify disease biomarkers and unveil underlying pathophysiological processes. Accordingly, in recent years, an increasing number of studies have identified metabolites from cerebral ischemia patients and animal models that could improve the diagnosis of IS and prediction of its outcome. In this paper, metabolomic research is comprehensively reviewed with a focus on describing the metabolic changes and related pathways associated with IS. Most clinical studies use biofluids (e.g., blood or plasma) because their collection is minimally invasive and they are ideal for analyzing changes in metabolites in patients of IS. We review the application of animal models in metabolomic analyses aimed at investigating potential mechanisms of IS and developing novel therapeutic approaches. In addition, this review presents the strengths and limitations of current metabolomic studies on IS, providing a reference for future related studies.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The datasets supporting the conclusions of this article are available from the corresponding author.
References
Adibhatla RM, Hatcher JF (2008) Altered lipid metabolism in brain injury and disorders. Subcell Biochem 49:241–268. https://doi.org/10.1007/978-1-4020-8831-5_9
Au A (2018) Metabolomics and Lipidomics of Ischemic Stroke. Adv Clin Chem 85:31–69. https://doi.org/10.1016/bs.acc.2018.02.002
Azad RK, Shulaev V (2019) Metabolomics technology and bioinformatics for precision medicine. Br Bioinform 20:1957–1971. https://doi.org/10.1093/bib/bbx170
Cambiaghi A, Ferrario M, Masseroli M (2017) Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration. Br Bioinform 18:498–510. https://doi.org/10.1093/bib/bbw031
Castellanos M, Sobrino T, Pedraza S et al (2008) High plasma glutamate concentrations are associated with infarct growth in acute ischemic stroke. Neurology 71:1862–1868. https://doi.org/10.1212/01.wnl.0000326064.42186.7e
Chi NF, Chang TH, Lee CY et al (2021) Untargeted metabolomics predicts the functional outcome of ischemic stroke. J Formos Med Assoc 120:234–241. https://doi.org/10.1016/j.jfma.2020.04.026
Chumachenko MS, Waseem TV, Fedorovich SV (2022) Metabolomics and metabolites in ischemic stroke. Rev Neurosci 33:181–205. https://doi.org/10.1515/revneuro-2021-0048
Collaborators GBDLR of S, Feigin VL, Nguyen G et al (2018) Global, Regional, and Country-Specific Lifetime Risks of Stroke, 1990 and 2016. N Engl J Med 379:2429–2437. https://doi.org/10.1056/NEJMoa1804492
Dang VT, Huang A, Werstuck GH (2018) Untargeted Metabolomics in the Discovery of Novel Biomarkers and Therapeutic Targets for Atherosclerotic Cardiovascular Diseases. Cardiovasc Hematol Disord Drug Targets 18:166–175. https://doi.org/10.2174/1871529X18666180420170108
Donatti A, Canto AM, Godoi AB, et al (2020) Circulating Metabolites as Potential Biomarkers for Neurological Disorders-Metabolites in Neurological Disorders. Metabolites 10. https://doi.org/10.3390/metabo10100389
Everett JR, Holmes E, Veselkov KA et al (2019) A Unified Conceptual Framework for Metabolic Phenotyping in Diagnosis and Prognosis. Trends Pharmacol Sci 40:763–773. https://doi.org/10.1016/j.tips.2019.08.004
Fatahzadeh M, Glick M (2006) Stroke: epidemiology, classification, risk factors, complications, diagnosis, prevention, and medical and dental management. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 102:180–191. https://doi.org/10.1016/j.tripleo.2005.07.031
Fluri F, Schuhmann MK, Kleinschnitz C (2015) Animal models of ischemic stroke and their application in clinical research. Drug Des Devel Ther 9:3445–3454. https://doi.org/10.2147/DDDT.S56071
Goulart VAM, Sena MM, Mendes TO et al (2019) Amino Acid Biosignature in Plasma among Ischemic Stroke Subtypes. Biomed Res Int 2019:8480468. https://doi.org/10.1155/2019/8480468
Goyal M, Ospel JM, Menon B et al (2020) Challenging the Ischemic Core Concept in Acute Ischemic Stroke Imaging. Stroke 51:3147–3155. https://doi.org/10.1161/STROKEAHA.120.030620
Guo X, Li Z, Zhou Y et al (2019) Metabolic profile for prediction of ischemic stroke in Chinese hypertensive population. J Stroke Cerebrovasc Dis 28:1062–1069. https://doi.org/10.1016/j.jstrokecerebrovasdis.2018.12.035
Guo W, Wang Y, Fan M et al (2020) Integrating metabolomics and network pharmacology to explore the protective effect of gross saponins of Tribulus terrestris L. fruit against ischemic stroke in rat. J Ethnopharmacol 263:113202. https://doi.org/10.1016/j.jep.2020.113202
Hernandez-de-Diego R, Tarazona S, Martinez-Mira C et al (2018) PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data. Nucleic Acids Res 46:W503–W509. https://doi.org/10.1093/nar/gky466
Holmes MV, Millwood IY, Kartsonaki C et al (2018) Lipids, Lipoproteins, and Metabolites and Risk of Myocardial Infarction and Stroke. J Am Coll Cardiol 71:620–632. https://doi.org/10.1016/j.jacc.2017.12.006
Jia J, Zhang H, Liang X et al (2021) Application of Metabolomics to the Discovery of Biomarkers for Ischemic Stroke in the Murine Model: a Comparison with the Clinical Results. Mol Neurobiol 58:6415–6426. https://doi.org/10.1007/s12035-021-02535-2
Johnson CH, Ivanisevic J, Siuzdak G (2016) Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 17:451–459. https://doi.org/10.1038/nrm.2016.25
Jove M, Mauri-Capdevila G, Suarez I et al (2015) Metabolomics predicts stroke recurrence after transient ischemic attack. Neurology 84:36–45. https://doi.org/10.1212/WNL.0000000000001093
Kaiser EE, West FD (2020) Large animal ischemic stroke models: replicating human stroke pathophysiology. Neural Regen Res 15:1377–1387. https://doi.org/10.4103/1673-5374.274324
Kimberly WT, Wang Y, Pham L et al (2013) Metabolite profiling identifies a branched chain amino acid signature in acute cardioembolic stroke. Stroke 44:1389–1395. https://doi.org/10.1161/STROKEAHA.111.000397
Laborde CM, Mourino-Alvarez L, Akerstrom F et al (2012) Potential blood biomarkers for stroke. Expert Rev Proteomics 9:437–449. https://doi.org/10.1586/epr.12.33
Latchaw RE, Alberts MJ, Lev MH et al (2009) Recommendations for imaging of acute ischemic stroke: a scientific statement from the American Heart Association. Stroke 40:3646–3678. https://doi.org/10.1161/STROKEAHA.108.192616
Lee Y, Khan A, Hong S et al (2017) A metabolomic study on high-risk stroke patients determines low levels of serum lysine metabolites: a retrospective cohort study. Mol Biosyst 13:1109–1120. https://doi.org/10.1039/c6mb00732e
Li JT, Zeng N, Yan ZP et al (2021) A review of applications of metabolomics in osteoarthritis. Clin Rheumatol 40:2569–2579. https://doi.org/10.1007/s10067-020-05511-8
Liao S, Apaijai N, Chattipakorn N, Chattipakorn SC (2020) The possible roles of necroptosis during cerebral ischemia and ischemia / reperfusion injury. Arch Biochem Biophys 695:108629. https://doi.org/10.1016/j.abb.2020.108629
Lin B, Chen R, Wang Q et al (2021) Transcriptomic and metabolomic profiling reveals the protective effect of Acanthopanax senticosus (Rupr. & Maxim.) harms combined with Gastrodia Elata Blume on cerebral Ischemia- Reperfusion injury. Front Pharmacol 12:619076. https://doi.org/10.3389/fphar.2021.619076
Liu M, Liu X, Wang H et al (2016a) Metabolomics study on the effects of Buchang Naoxintong capsules for treating cerebral ischemia in rats using UPLC-Q/TOF-MS. J Ethnopharmacol 180:1–11. https://doi.org/10.1016/j.jep.2016.01.016
Liu M, Tang L, Liu X et al (2016b) An Evidence-Based Review of Related Metabolites and Metabolic Network Research on Cerebral Ischemia. Oxid Med Cell Longev 2016:9162074. https://doi.org/10.1155/2016/9162074
Liu P, Li R, Antonov AA et al (2017) Discovery of Metabolite Biomarkers for Acute Ischemic Stroke Progression. J Proteome Res 16:773–779. https://doi.org/10.1021/acs.jproteome.6b00779
Luo L, Kang J, He Q, et al (2019) A NMR-Based Metabonomics Approach to Determine Protective Effect of a Combination of Multiple Components Derived from Naodesheng on Ischemic Stroke Rats. Molecules 24. https://doi.org/10.3390/molecules24091831
Makris K, Haliassos A, Chondrogianni M, Tsivgoulis G (2018) Blood biomarkers in ischemic stroke: potential role and challenges in clinical practice and research. Crit Rev Clin Lab Sci 55:294–328. https://doi.org/10.1080/10408363.2018.1461190
Meng XE, Li N, Guo DZ et al (2015) High plasma glutamate levels are associated with poor functional outcome in acute ischemic stroke. Cell Mol Neurobiol 35:159–165. https://doi.org/10.1007/s10571-014-0107-0
Montaner J, Ramiro L, Simats A et al (2020) Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke. Nat Rev Neurol 16:247–264. https://doi.org/10.1038/s41582-020-0350-6
Nicholson JK, Connelly J, Lindon JC, Holmes E (2002) Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov 1:153–161. https://doi.org/10.1038/nrd728
Nicholson JK, Lindon JC (2008) Systems biology: Metabonomics. Nature 455:1054–1056. https://doi.org/10.1038/4551054a
Paik MJ, Li WY, Ahn YH et al (2009) The free fatty acid metabolome in cerebral ischemia following human mesenchymal stem cell transplantation in rats. Clin Chim Acta 402:25–30. https://doi.org/10.1016/j.cca.2008.12.022
Papes F, Surpili MJ, Langone F et al (2001) The essential amino acid lysine acts as precursor of glutamate in the mammalian central nervous system. FEBS Lett 488:34–38. https://doi.org/10.1016/s0014-5793(00)02401-7
Peng B, Li H, Peng XX (2015) Functional metabolomics: from biomarker discovery to metabolome reprogramming. Protein Cell 6:628–637. https://doi.org/10.1007/s13238-015-0185-x
Qureshi MI, Vorkas PA, Coupland AP et al (2017) Lessons from Metabonomics on the Neurobiology of Stroke. Neuroscientist 23:374–382. https://doi.org/10.1177/1073858416673327
Rinschen MM, Ivanisevic J, Giera M, Siuzdak G (2019) Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol 20:353–367. https://doi.org/10.1038/s41580-019-0108-4
Saorin A, Di Gregorio E, Miolo G, et al (2020) Emerging Role of Metabolomics in Ovarian Cancer Diagnosis. Metabolites 10. https://doi.org/10.3390/metabo10100419
Shin TH, Lee DY, Basith S, et al (2020) Metabolome Changes in Cerebral Ischemia. Cells 9. https://doi.org/10.3390/cells9071630
Sidorov E, Bejar C, Xu C et al (2020a) Potential Metabolite Biomarkers for Acute Versus Chronic Stage of Ischemic Stroke: A Pilot Study. J Stroke Cerebrovasc Dis 29:104618. https://doi.org/10.1016/j.jstrokecerebrovasdis.2019.104618
Sidorov E, Sanghera DK, Vanamala JKP (2019) Biomarker for Ischemic Stroke Using Metabolome: A Clinician Perspective. J Stroke 21:31–41. https://doi.org/10.5853/jos.2018.03454
Sidorov EV, Bejar C, Xu C et al (2020b) Novel Metabolites as Potential Indicators of Ischemic Infarction Volume: a Pilot Study. Transl Stroke Res. https://doi.org/10.1007/s12975-020-00876-z
Sommer CJ (2017) Ischemic stroke: experimental models and reality. Acta Neuropathol 133:245–261. https://doi.org/10.1007/s00401-017-1667-0
Suissa L, Guigonis JM, Graslin F et al (2020) Metabolome of cerebral thrombi reveals an association between high glycemia at stroke onset and good clinical outcome. Metabolites 10:1–15. https://doi.org/10.3390/metabo10120483
Sun D, Tiedt S, Yu B et al (2019a) A prospective study of serum metabolites and risk of ischemic stroke. Neurology 92:e1890–e1898. https://doi.org/10.1212/WNL.0000000000007279
Sun R, Li Y, Cai M et al (2019b) Discovery of a new biomarker pattern for differential diagnosis of acute ischemic stroke using targeted metabolomics. Front Neurol 10:1011. https://doi.org/10.3389/fneur.2019.01011
Sun H, Zhao J, Zhong D, Li G (2017) Potential serum biomarkers and metabonomic profiling of serum in ischemic stroke patients using UPLC/Q-TOF MS/MS. PLoS ONE 12:e0189009. https://doi.org/10.1371/journal.pone.0189009
Tiedt S, Brandmaier S, Kollmeier H et al (2020) Circulating Metabolites Differentiate Acute Ischemic Stroke from Stroke Mimics. Ann Neurol 88:736–746. https://doi.org/10.1002/ana.25859
Tokarz J, Adamski J, Rizner TL (2020) Metabolomics for Diagnosis and Prognosis of Uterine Diseases? A Systematic Review. J Pers Med 10. https://doi.org/10.3390/jpm10040294
Tokarz J, Haid M, Cecil A et al (2017) Endocrinology Meets Metabolomics: Achievements, Pitfalls, and Challenges. Trends Endocrinol Metab 28:705–721. https://doi.org/10.1016/j.tem.2017.07.001
Ussher JR, Elmariah S, Gerszten RE, Dyck JR (2016) The Emerging Role of Metabolomics in the Diagnosis and Prognosis of Cardiovascular Disease. J Am Coll Cardiol 68:2850–2870. https://doi.org/10.1016/j.jacc.2016.09.972
Vorkas PA, Shalhoub J, Lewis MR et al (2016) Metabolic Phenotypes of Carotid Atherosclerotic Plaques Relate to Stroke Risk: An Exploratory Study. Eur J Vasc Endovasc Surg 52:5–10. https://doi.org/10.1016/j.ejvs.2016.01.022
Wang D, Kong J, Wu J et al (2017) GC-MS-based metabolomics identifies an amino acid signature of acute ischemic stroke. Neurosci Lett 642:7–13. https://doi.org/10.1016/j.neulet.2017.01.039
Wang Y, Wang Y, Li M et al (2013) (1)H NMR-based metabolomics exploring biomarkers in rat cerebrospinal fluid after cerebral ischemia/reperfusion. Mol Biosyst 9:431–439. https://doi.org/10.1039/c2mb25224d
Wang Y, Wang YG, Ma TF et al (2014) Dynamic metabolites profile of cerebral ischemia/reperfusion revealed by (1)H NMR-based metabolomics contributes to potential biomarkers. Int J Clin Exp Pathol 7:4067–4075
Wang Y, Zhao H, Liu Y, et al (2019) GC-MS-Based Metabolomics to Reveal the Protective Effect of Gross Saponins of Tribulus terrestris Fruit against Ischemic Stroke in Rat. Molecules 24. https://doi.org/10.3390/molecules24040793
Wesley UV, Bhute VJ, Hatcher JF et al (2019) Local and systemic metabolic alterations in brain, plasma, and liver of rats in response to aging and ischemic stroke, as detected by nuclear magnetic resonance (NMR) spectroscopy. Neurochem Int 127:113–124. https://doi.org/10.1016/j.neuint.2019.01.025
Wishart DS (2019) Metabolomics for Investigating Physiological and Pathophysiological Processes. Physiol Rev 99:1819–1875. https://doi.org/10.1152/physrev.00035.2018
Wu L, Chen C, Li Y et al (2020) UPLC-Q-TOF/MS-Based Serum Metabolomics Reveals the Anti-Ischemic Stroke Mechanism of Nuciferine in MCAO Rats. ACS Omega 5:33433–33444. https://doi.org/10.1021/acsomega.0c05388
Wu MY, Yiang GT, Liao WT et al (2018) Current Mechanistic Concepts in Ischemia and Reperfusion Injury. Cell Physiol Biochem 46:1650–1667. https://doi.org/10.1159/000489241
Yang L, Wang Y, Cai H et al (2020) Application of metabolomics in the diagnosis of breast cancer: a systematic review. J Cancer 11:2540–2551. https://doi.org/10.7150/jca.37604
Zahoor I, Rui B, Khan J et al (2021) An emerging potential of metabolomics in multiple sclerosis: a comprehensive overview. Cell Mol Life Sci 78:3181–3203. https://doi.org/10.1007/s00018-020-03733-2
Zhang Y, Li J, Zhang X et al (2021) Advances of Mechanisms-Related Metabolomics in Parkinson’s Disease. Front Neurosci 15:614251. https://doi.org/10.3389/fnins.2021.614251
Zhou B, Xiao JF, Tuli L, Ressom HW (2012) LC-MS-based metabolomics. Mol Biosyst 8:470–481. https://doi.org/10.1039/c1mb05350g
Funding
This study is supported by the National Natural Science Foundation of China (82173648), Innovative Talent Support Plan of the Medical and Health Technology Project in Zhejiang Province(2021422878), Internal Fund of Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences(2020YJY0212), Sanming Project of Medicine in Shenzhen (SZSM201803080), Zhejiang Provincial Public Service and Application Research Foundation (LGF20H250001 and GC22H264267), Public Welfare Foundation of Ningbo (2021S108), Ningbo Science and Technology Innovation 2025 Specific Project (2020Z096), and Shenzhen Nanshan District Science and Technology Bureau (2020075).
Author information
Authors and Affiliations
Contributions
Meng Jiajia, Zhang Ruijie and Han Liyuan wrote and revised the manuscript. All authors participated in critical revision of the manuscript and approved the final version.
Corresponding authors
Ethics declarations
Ethics approval
Not applicable.
Consent to participate
Not applicable for animal studies.
Consent for publication
Not applicable.
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhang, R., Meng, J., Wang, X. et al. Metabolomics of ischemic stroke: insights into risk prediction and mechanisms. Metab Brain Dis 37, 2163–2180 (2022). https://doi.org/10.1007/s11011-022-01011-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11011-022-01011-7