Zhang X, Li Y, Liang Y, Sun P, Wu X, Song J, Sun X, Hong M, Gao P, Deng D

Language: English
References: 32
Page: 319-328
PDF size: 425.99 Kb.

ABSTRACT

Background: Acute cerebral infarction (ACI) and intracerebral hemorrhage (ICH) are potentially lethal cerebrovascular diseases that seriously impact public health. ACI and ICH share several common clinical manifestations but have totally divergent therapeutic strategies. A poor diagnosis can affect stroke treatment. Objective: To screen for biomarkers to differentiate ICH from ACI, we enrolled 129 ACI and 128 ICH patients and 65 healthy individuals as controls. Methods: Patients with stroke were diagnosed by computed tomography/magnetic resonance imaging, and their blood samples were obtained by fingertip puncture within 2-12 h after stroke initiation. We compared changes in metabolites between ACI and ICH using dried blood spot-based direct infusion mass spectrometry technology for differentiating ICH from ACI. Results: Through multivariate statistical approaches, 11 biomarkers including 3-hydroxylbutyrylcarnitine, glutarylcarnitine (C5DC), myristoylcarnitine, 3-hydroxypalmitoylcarnitine, tyrosine/citrulline (Cit), valine/phenylalanine, C5DC/3-hydroxyisovalerylcarnitine, C5DC/palmitoylcarnitine, hydroxystearoylcarnitine, ratio of sum of C0, C2, C3, C16, and C18:1 to Cit, and propionylcarnitine/methionine were screened. An artificial neural network model was constructed based on these parameters. A training set was evaluated by cross-validation method. The accuracy of this model was checked by an external test set showing a sensitivity of 0.8400 (95% confidence interval [CI], 0.7394-0.9406) and specificity of 0.7692 (95% CI, 0.6536-0.8848). Conclusion: This study confirmed that metabolomic analysis is a promising tool for rapid and timely stroke differentiation and prediction based on differential metabolites.

REFERENCES

1. Towfighi A, Saver JL. Stroke declines from third to fourth leading cause of death in the United States: Historical perspective and challenges ahead. Stroke. 2011;42:2351-5.

2. O’Donnell MJ, Xavier D, Liu L, et al. Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): A case-control study. Lancet. 2010; 376:112-23.

3. Hu SL, Feng H, Xi GH. Hyperbaric oxygen therapy and preconditioning for ischemic and hemorrhagic stroke. Med Gas Res. 2016;6:232-6.

4. van der Worp HB, van Gijn J. Clinical practice. Acute ischemic stroke. N Engl J Med. 2007;357:572-9.

5. Luo J. New progress on treating acute cerebral infarction. Pract J Cardiac Cereb Pneumal Vasc Dis. 2010;18:825-9.

6. Keep RF, Hua Y, Xi G. Intracerebral haemorrhage: Mechanisms of injury and therapeutic targets. Lancet Neurol. 2012;11:720-31.

7. Zhang J, Yang Y, Sun H, Xing Y. Hemorrhagic transformation after cerebral infarction: Current concepts and challenges. Ann Transl Med. 2014;2:81.

8. Martinez B, Peplow PV. Blood microRNAs as potential diagnostic markers for hemorrhagic stroke. Neural Regen Res. 2017; 12:13-18.

9. Graham GD, Blamire AM, Howseman AM, et al. Proton magnetic resonance spectroscopy of cerebral lactate and other metabolites in stroke patients. Stroke. 1992;23:333-40.

10. Jung JY, Lee HS, Kang DG, et al. 1H-NMR-based metabolomics study of cerebral infarction. Stroke. 2011;42:1282-8.

11. Gowda GA, Zhang S, Gu H, et al. Metabolomics-based methods for early disease diagnostics. Expert Rev Mol Diagn. 2008;8:617-33.

12. Jiang Z, Sun J, Liang Q, et al. A metabonomic approach applied to predict patients with cerebral infarction. Talanta. 2011; 84:298-304.

13. Shah NM, Hawwa AF, Millership JS, Collier PS, McElnay JC. A simple bioanalytical method for the quantification of antiepileptic drugs in dried blood spots. J Chromatogr B Analyt Technol Biomed Life Sci. 2013;923:65-73.

14. Therrell BL, Adams J. Newborn screening in North America. J Inherit Metab Dis. 2007;30:447-65.

15. Zhao D, Han L, He Z, Zhang J, Zhang Y. Identification of the plasma metabolomics as early diagnostic markers between biliary atresia and neonatal hepatitis syndrome. PLoS One. 2014;9:e85694.

16. Wang Q, Sun T, Cao Y, et al. A dried blood spot mass spectrometry metabolomic approach for rapid breast cancer detection. Onco Targets Ther. 2016;9:1389-98.

17. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J Roy Stat Soc B. 1995;57:289-300.

18. Cawley GC, Talbot NL. Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers. Pattern Recognit. 2003;36:2585-92.

19. Ivanisevic J, Siuzdak G. The role of metabolomics in brain metabolism research. J Neuroimmune Pharmacol. 2015;10:391-5.

20. Sun H, Zhang A, Yan G, et al. Metabolomic analysis of key regulatory metabolites in hepatitis C virus-infected tree shrews. Mol Cell Proteomics. 2013;12:710-9.

21. Iadecola C, Anrather J. Stroke research at a crossroad: Asking the brain for directions. Nat Neurosci. 2011;14:1363-8.

22. Kimberly WT, Wang Y, Pham L, Furie KL, Gerszten RE. Metabolite profiling identifies a branched chain amino acid signature in acute cardioembolic stroke. Stroke. 2013;44:1389-95.

23. Hosinian M, Qujeq D, Ahmadi Ahangar A. The relation between GABA and L-Arginine levels with some stroke risk factors in acute ischemic stroke patients. Int J Mol Cell Med. 2016; 5:100-5.

24. Nishimura H, Rosenblum WI, Nelson GH, Boynton S. Agents that modify EDRF formation alter antiplatelet properties of brain arteriolar endothelium in vivo. Am J Physiol. 1991;261: H15-21.

25. Lim HK, Lim HK, Ryoo S, et al. Mitochondrial arginase II constrains endothelial NOS-3 activity. Am J Physiol Heart Circ Physiol. 2007;293:H3317-24.

26. Steppan J, Nyhan D, Berkowitz DE. Development of novel arginase inhibitors for therapy of endothelial dysfunction. Front Immunol. 2013;4:278.

27. Wang D, Kong J, Wu J, Wang X, Lai M. GC-MS-based metabolomics identifies an amino acid signature of acute ischemic stroke. Neurosci Lett. 2017;642:7-13.

28. Liu P, Li R, Antonov AA, Wang L, Li W. Discovery of metabolite biomarkers for acute ischemic stroke progression. J Proteome Res. 2017;16:773-9.

29. Jian WX, Yuan ZK, Huang XP. Detection and analysis on plasma metabolomics in patient with coronary heart disease of Xinblood stasis syndrome pattern. Zhongguo Zhong Xi Yi Jie He Za Zhi. 2010;30:579-84.

30. Fernstrom JD. Branched-chain amino acids and brain function. J Nutr. 2005;135:1539S-46.

31. Huang Y, Zhou M, Sun H, Wang Y. Branched-chain amino acid metabolism in heart disease: An epiphenomenon or a real culprit? Cardiovasc Res. 2011;90:220-3.

32. Menazza S, Wong R, Nguyen T, et al. CypD(-/-) hearts have altered levels of proteins involved in Krebs cycle, branch chain amino acid degradation and pyruvate metabolism. J Mol Cell Cardiol. 2013;56:81-90.