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Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke

Subjects

Abstract

Despite many years of research, no biomarkers for stroke are available to use in clinical practice. Progress in high-throughput technologies has provided new opportunities to understand the pathophysiology of this complex disease, and these studies have generated large amounts of data and information at different molecular levels. The integration of these multi-omics data means that thousands of proteins (proteomics), genes (genomics), RNAs (transcriptomics) and metabolites (metabolomics) can be studied simultaneously, revealing interaction networks between the molecular levels. Integrated analysis of multi-omics data will provide useful insight into stroke pathogenesis, identification of therapeutic targets and biomarker discovery. In this Review, we detail current knowledge on the pathology of stroke and the current status of biomarker research in stroke. We summarize how proteomics, metabolomics, transcriptomics and genomics are all contributing to the identification of new candidate biomarkers that could be developed and used in clinical stroke management.

Key points

  • Biomarkers of stroke could improve diagnosis and management, but standardization or harmonization of procedures is needed before translation of biomarkers to clinical practice to ensure results are comparable and reliable.

  • Studies of the proteome of the brain, cerebrospinal fluid and brain extracellular fluid after ischaemic stroke have led to identification of candidate biomarkers for stroke management.

  • Most studies of stroke genetics have focused on common or low-frequency single-nucleotide polymorphisms; other types of variation, such as rare single-nucleotide variants or structural variations, have been insufficiently explored.

  • Changes in RNA levels in stroke have the potential to aid stroke diagnosis and provide insight into stroke aetiology.

  • Circulating metabolites provide information about local and systemic events after stroke, and therefore could serve as biomarkers of stroke and for differentiation of major stroke aetiologies.

  • Integrated analysis of data obtained with different omics approaches will enable implementation of biomarkers at several stages in the stroke care pathway, with the potential to transform stroke management.

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Fig. 1: Schematic representation of a multi-omics approach to the study of stroke.
Fig. 2: Mechanisms by which genetic variants can influence the risk of stroke and stroke outcomes.

References

  1. 1.

    Benjamin, E. J. et al. Heart disease and stroke statistics—2019 update: a report from the American Heart Association. Circulation 139, e56–e528 (2019).

    Article  Google Scholar 

  2. 2.

    Ramiro, L., Simats, A., García-Berrocoso, T. & Montaner, J. Inflammatory molecules might become both biomarkers and therapeutic targets for stroke management. Ther. Adv. Neurol. Disord. 11, 1–24 (2018).

    Article  CAS  Google Scholar 

  3. 3.

    Kunz, A. et al. Effects of ultraearly intravenous thrombolysis on outcomes in ischemic stroke: the STEMO (Stroke Emergency Mobile) group. Circulation 135, 1765–1767 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  4. 4.

    Senn, R., Elkind, M. S. V., Montaner, J., Christ-Crain, M. & Katan, M. Potential role of blood biomarkers in the management of nontraumatic intracerebral hemorrhage. Cerebrovasc. Dis. 38, 6 (2014).

    Article  CAS  Google Scholar 

  5. 5.

    Dirnagl, U., Iadecola, C. & Moskowitz, M. A. Pathobiology of ischaemic stroke: an integrated view. Trends Neurosci. 22, 391–397 (1999).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  6. 6.

    Wetterling, F. et al. Investigating potentially salvageable penumbra tissue in an in vivo model of transient ischemic stroke using sodium, diffusion, and perfusion magnetic resonance imaging. BMC Neurosci. 17, 1–10 (2016).

    Article  CAS  Google Scholar 

  7. 7.

    Chamorro, Á. et al. The immunology of acute stroke. Nat. Rev. Neurol. 8, 401–410 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  8. 8.

    Iadecola, C. & Anrather, J. The immunology of stroke: from mechanisms to translation. Nat. Med. 17, 796–808 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Simats, A., García-Berrocoso, T. & Montaner, J. Neuroinflammatory biomarkers: from stroke diagnosis and prognosis to therapy. Biochim. Biophys. Acta 1862, 411–424 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  10. 10.

    Yan, Y.-Y. et al. Immune cells after ischemic stroke onset: roles, migration, and target intervention. J. Mol. Neurosci. 66, 342–355 (2018).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  11. 11.

    Sas, A., Horvath, L., Olah, C. & Valikovics, A. in Mechanisms of Neuroinflammation (ed. Aranda Abreu, G. E.) 119–144 (IntechOpen, 2017).

  12. 12.

    Perez-de-Puig, I. et al. Neutrophil recruitment to the brain in mouse and human ischemic stroke. Acta Neuropathol. 129, 239–257 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  13. 13.

    Lakhan, S. E., Kirchgessner, A., Tepper, D. & Leonard, A. Matrix metalloproteinases and blood–brain barrier disruption in acute ischemic stroke. Front. Neurol. 4, 32 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Lambertsen, K. L., Finsen, B. & Clausen, B. H. Post-stroke inflammation — target or tool for therapy? Acta Neuropathol. 137, 693–714 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Pennypacker, K. R. Targeting the peripheral inflammatory response to stroke: role of the spleen. Transl Stroke Res. 5, 635–637 (2015).

    Article  Google Scholar 

  16. 16.

    Liu, Q. et al. Brain ischemia suppresses immunity in the periphery and brain via different neurogenic innervations. Immunity 46, 474–487 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  17. 17.

    Chavan, S. S., Pavlov, V. A. & Tracey, K. J. Mechanisms and therapeutic relevance of neuro-immune communication. Immunity 46, 927–942 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Dirnagl, U. et al. Stroke-induced immunodepression: experimental evidence and clinical relevance. Stroke 38, 770–773 (2007).

    PubMed  Article  PubMed Central  Google Scholar 

  19. 19.

    Esmaeili, A., Dadkhahfar, S., Fadakar, K. & Rezaei, N. Post-stroke immunodeficiency: effects of sensitization and tolerization to brain antigens. Int. Rev. Immunol. 31, 396–409 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  20. 20.

    Shi, K., Wood, K., Shi, F. D., Wang, X. & Liu, Q. Stroke-induced immunosuppression and poststroke infection. Stroke Vasc. Neurol. 3, 34–41 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Westendorp, W. F., Nederkoorn, P. J., Vermeij, J.-D., Dijkgraaf, M. G. & van de Beek, D. Post-stroke infection: a systematic review and meta-analysis. BMC Neurol. 11, 110 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Montaner, J. et al. A panel of biomarkers including caspase-3 and D-dimer may differentiate acute stroke from stroke-mimicking conditions in the emergency department. J. Intern. Med. 270, 166–174 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  23. 23.

    Laskowitz, D. T., Kasner, S. E., Saver, J., Remmel, K. S. & Jauch, E. C. Clinical usefulness of a biomarker-based diagnostic test for acute stroke: the Biomarker Rapid Assessment in Ischemic Injury (BRAIN) study. Stroke 40, 77–85 (2009).

    PubMed  Article  PubMed Central  Google Scholar 

  24. 24.

    Bustamante, A. et al. Blood biomarkers for the early diagnosis of stroke: the Stroke-Chip study. Stroke 48, 2419–2425 (2017). This is the largest study of biomarkers in acute stroke diagnosis and provides promising candidates for future panels.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  25. 25.

    Penn, A. M. et al. Validation of a proteomic biomarker panel to diagnose minor-stroke and transient ischaemic attack: phase 2 of SpecTRA, a large scale translational study. Biomarkers 23, 793–803 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    Ebinger, M. et al. Effect of the use of ambulance-based thrombolysis on time to thrombolysis in acute ischemic stroke: a randomized clinical trial. JAMA 311, 1622–1631 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  27. 27.

    Eng, L. F., Ghirnikar, R. S. & Lee, Y. L. Glial fibrillary acidic protein: GFAP-thirty-one years (1969–2000). Neurochem. Res. 25, 1439–1451 (2000).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  28. 28.

    Perry, L. A. et al. Glial fibrillary acidic protein for the early diagnosis of intracerebral hemorrhage: systematic review and meta-analysis of diagnostic test accuracy. Int. J. Stroke 14, 390–399 (2018). This paper presents a meta-analysis of the accuracy of GFAP, one of the most promising candidates for diagnosis of acute ICH.

    PubMed  Article  PubMed Central  Google Scholar 

  29. 29.

    Llombart, V. et al. Plasmatic retinol-binding protein 4 and glial fibrillary acidic protein as biomarkers to differentiate ischemic stroke and intracerebral hemorrhage. J. Neurochem. 136, 416–424 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  30. 30.

    Montaner, J. et al. Matrix metalloproteinase expression is related to hemorrhagic transformation after cardioembolic stroke. Stroke 32, 2762–2767 (2001).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  31. 31.

    Castellanos, M. et al. Plasma metalloproteinase-9 concentration predicts hemorrhagic transformation in acute ischemic stroke. Stroke 34, 40–45 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  32. 32.

    Yuan, R. et al. Predictive value of plasma matrix metalloproteinase-9 concentrations for spontaneous haemorrhagic transformation in patients with acute ischaemic stroke: a cohort study in Chinese patients. J. Clin. Neurosci. 58, 108–112 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  33. 33.

    Wang, L. et al. The accuracy of serum matrix metalloproteinase-9 for predicting hemorrhagic transformation after acute ischemic stroke: a systematic review and meta-analysis. J. Stroke Cerebrovasc. Dis. 27, 1653–1665 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  34. 34.

    Castellanos, M. et al. Plasma cellular-fibronectin concentration predicts hemorrhagic transformation after thrombolytic therapy in acute ischemic stroke. Stroke 35, 1671–1676 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  35. 35.

    Hernandez-Guillamon, M. et al. Plasma VAP-1/SSAO activity predicts intracranial hemorrhages and adverse neurological outcome after tissue plasminogen activator treatment in stroke. Stroke 41, 1528–1535 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  36. 36.

    Ribo, M. et al. Admission fibrinolytic profile is associated with symptomatic hemorrhagic transformation in stroke patients treated with tissue plasminogen activator. Stroke 35, 2123–2127 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  37. 37.

    Bustamante, A. et al. Usefulness of ADAMTS13 to predict response to recanalization therapies in acute ischemic stroke. Neurology 90, e995–e1004 (2018). This pilot study considers the usefulness of blood biomarkers to guide both intravenous and endovascular reperfusion therapies.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    De Marchis, G. M. et al. Copeptin adds prognostic information after ischemic stroke: results from the CoRisk study. Neurology 80, 1278–1286 (2013).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  39. 39.

    Bustamante, A. et al. Ischemic stroke outcome: a review of the influence of post-stroke complications within the different scenarios of stroke care. Eur. J. Intern. Med. 29, 9–21 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  40. 40.

    Hoffmann, S. et al. Stroke-induced immunodepression and dysphagia independently predict stroke-associated pneumonia — the PREDICT study. J. Cereb. Blood Flow Metab. 37, 3671–3682 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  41. 41.

    Wang, C. et al. Procalcitonin is a stronger predictor of long-term functional outcome and mortality than high-sensitivity C-reactive protein in patients with ischemic stroke. Mol. Neurobiol. 53, 1509–1517 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  42. 42.

    Ulm, L. et al. The randomized controlled STRAWINSKI trial: procalcitonin-guided antibiotic therapy after stroke. Front. Neurol. 8, 153 (2017). This clinical trial with a remarkable design might represent an example of how to implement biomarkers in clinical practice, despite negative findings.

    PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Azurmendi, L. et al. Proteomic discovery and verification of serum amyloid A as a predictor marker of patients at risk of post-stroke infection: a pilot study. Clin. Proteomics 14, 27 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Hart, R. G. et al. Embolic strokes of undetermined source: the case for a new clinical construct. Lancet Neurol. 13, 429–438 (2014).

    PubMed  Article  PubMed Central  Google Scholar 

  45. 45.

    Llombart, V. et al. B-type natriuretic peptides help in cardioembolic stroke diagnosis: pooled data meta-analysis. Stroke 46, 1187–1195 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  46. 46.

    Bai, J., Sun, H., Xie, L., Zhu, Y. & Feng, Y. Detection of cardioembolic stroke with B-type natriuretic peptide or N-terminal pro-BNP: a comparative diagnostic meta-analysis. Int. J. Neurosci. 128, 1100–1108 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  47. 47.

    Santamarina, E. et al. Biomarker level improves the diagnosis of embolic source in ischemic stroke of unknown origin. J. Neurol. 259, 2538–2545 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  48. 48.

    Kamel, H. et al. The AtRial Cardiopathy and Antithrombotic Drugs In prevention After cryptogenic stroke randomized trial: rationale and methods. Int. J. Stroke 14, 207–214 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  49. 49.

    Engdahl, J. et al. Stepwise mass screening for atrial fibrillation using N-terminal pro B-type natriuretic peptide: the STROKESTOP II study design. Europace 19, 297–302 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  50. 50.

    Ward, G., Simpson, A., Boscato, L. & Hickman, P. E. The investigation of interferences in immunoassay. Clin. Biochem. 50, 1306–1311 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  51. 51.

    Andreasson, U. et al. A practical guide to immunoassay method validation. Front. Neurol. 6, 179 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Schimmel, H., Zegers, I. & Emons, H. Standardization of protein biomarker measurements: is it feasible? Scand. J. Clin. Lab. Invest. Suppl. 70, 27–33 (2010).

    CAS  Article  Google Scholar 

  53. 53.

    Harpaz, D., Eltzov, E., Seet, R. C. S., Marks, R. S. & Tok, A. I. Y. Point-of-care-testing in acute stroke management: an unmet need ripe for technological harvest. Biosensors 7, 30 (2017).

    PubMed Central  Article  Google Scholar 

  54. 54.

    Cuadrado, E. et al. Vascular MMP-9/TIMP-2 and neuronal MMP-10 up-regulation in human brain after stroke: a combined laser microdissection and protein array study. J. Proteome Res. 8, 3191–3197 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  55. 55.

    Cuadrado, E. et al. The proteome of human brain after ischemic stroke. J. Neuropathol. Exp. Neurol. 69, 1105–1115 (2010). This study is the first to describe the whole brain proteome after ischaemic stroke.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  56. 56.

    García-Berrocoso, T. et al. From brain to blood: new biomarkers for ischemic stroke prognosis. J. Proteom. 94, 138–148 (2013).

    Article  CAS  Google Scholar 

  57. 57.

    Yuan, D., Liu, C. & Hu, B. Dysfunction of membrane trafficking leads to ischemia–reperfusion injury after transient cerebral ischemia. Transl Stroke Res. 9, 215–222 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  58. 58.

    García-Berrocoso, T. et al. Single cell immuno-laser microdissection coupled to label-free proteomics to reveal the proteotypes of human brain cells after ischemia. Mol. Cell. Proteom. 17, 175–189 (2018).

    Article  Google Scholar 

  59. 59.

    Dayon, L. et al. Brain extracellular fluid protein changes in acute stroke patients. J. Proteome Res. 10, 1043–1051 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  60. 60.

    Engelborghs, S. et al. Consensus guidelines for lumbar puncture in patients with neurological diseases. Alzheimers Dement. 8, 111–126 (2017).

    Google Scholar 

  61. 61.

    Domingues, R., Bruniera, G., Brunale, F., Mangueira, C. & Senne, C. Lumbar puncture in patients using anticoagulants and antiplatelet agents. Arq. Neuropsiquiatr. 74, 679–86 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  62. 62.

    Zimmermann-Ivol, C. G. et al. Fatty acid binding protein as a serum marker for the early diagnosis of stroke. Mol. Cell. Proteomics 3, 66–72 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  63. 63.

    Lescuyer, P. et al. Identification of post-mortem cerebrospinal fluid proteins as potential biomarkers of ischemia and neurodegeneration. Proteomics 4, 2234–2241 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  64. 64.

    Dayon, L. et al. Relative quantification of proteins in human cerebrospinal fluids by MS/MS using 6-plex isobaric tags. Anal. Chem. 8, 2921–2931 (2008).

    Article  CAS  Google Scholar 

  65. 65.

    Simats, A. et al. Characterization of the rat cerebrospinal fluid proteome following acute cerebral ischemia using an aptamer-based proteomic technology. Sci. Rep. 8, 7899 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  66. 66.

    Anderson, N. L. & Anderson, N. G. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteomics 1, 845–867 (2002).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  67. 67.

    Agouni, A. et al. There is selective increase in pro-thrombotic circulating extracellular vesicles in acute ischemic stroke and transient ischemic attack: a study of patients from the Middle East and Southeast Asia. Front. Neurol. 10, 251 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Ciccocioppo, F., Lanuti, P., Centonze, D., Miscia, S. & Marchisio, M. The link among neurological diseases: extracellular vesicles as a possible brain injury footprint. Neurosignals 27, 25–39 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  69. 69.

    Turck, N. et al. A multiparameter panel method for outcome prediction following aneurysmal subarachnoid hemorrhage. Intensive Care Med. 36, 107–115 (2010).

    PubMed  Article  PubMed Central  Google Scholar 

  70. 70.

    Woo, D. et al. Top research priorities for stroke genetics. Lancet Neurol. 17, 663–665 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  71. 71.

    Psaty, B. M. et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (charge) consortium design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ. Cardiovasc.Genet. 2, 73–80 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  72. 72.

    Malik, R. et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet. 50, 524–537 (2018). This study is the latest and largest GWAS of stroke, including 72,147 patients with stroke, that identified 32 loci associated with stroke.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. 73.

    Malik, R. et al. Genome-wide meta-analysis identifies 3 novel loci associated with stroke. Ann. Neurol. 84, 934–939 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. 74.

    Okazaki, S. et al. Moyamoya disease susceptibility variant RNF213 p.R4810K increases the risk of ischemic stroke attributable to large-artery atherosclerosis. Circulation 139, 295–298 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  75. 75.

    Kubo, M. et al. A nonsynonymous SNP in PRKCH (protein kinase C η) increases the risk of cerebral infarction. Nat. Genet. 39, 212–217 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  76. 76.

    Woo, D. et al. Meta-analysis of genome-wide association studies identifies 1q22 as a susceptibility locus for intracerebral hemorrhage. Am. J. Hum. Genet. 94, 511–521 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  77. 77.

    Traylor, M. et al. A novel MMP12 locus is associated with large artery atherosclerotic stroke using a genome-wide age-at-onset informed approach. PLoS Genet. 10, e1004469 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  78. 78.

    Traylor, M. et al. Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE collaboration): a meta-analysis of genome-wide association studies. Lancet Neurol. 11, 951–962 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    Debette, S. et al. Common variation in PHACTR1 is associated with susceptibility to cervical artery dissection. Nat. Genet. 47, 78–83 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  80. 80.

    Malik, R. et al. ISGC Analysis Group, METASTROKE collaboration, Wellcome Trust Case Control Consortium 2 (WTCCC2), NINDS Stroke Genetics Network (SiGN). Low-frequency and common genetic variation in ischemic stroke: the METASTROKE collaboration. Neurology 86, 1217–1226 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. 81.

    Bellenguez, C. et al. Genome-wide association study identifies a variant in HDAC9 associated with large vessel ischemic stroke. Nat. Genet. 44, 328–333 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  82. 82.

    Holliday, E. G. et al. Common variants at 6p21.1 are associated with large artery atherosclerotic stroke. Nat. Genet. 44, 1147–1151 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  83. 83.

    Kilarski, L. L. et al. Meta-analysis in more than 17,900 cases of ischemic stroke reveals a novel association at 12q24.12. Neurology 83, 678–85 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  84. 84.

    Ikram, M. A. et al. Genomewide association studies of stroke. N. Engl. J. Med. 360, 1718–1728 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Biffi, A. et al. APOE genotype and extent of bleeding and outcome in lobar intracerebral haemorrhage: a genetic association study. Lancet Neurol. 10, 702–709 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  86. 86.

    Cheng, Y. C. et al. Genome-wide association analysis of young-onset stroke identifies a locus on chromosome 10q25 near HABP2. Stroke 47, 307–316 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  87. 87.

    Gretarsdottir, S. et al. Risk variants for atrial fibrillation on chromosome 4q25 associate with ischemic stroke. Ann. Neurol. 64, 402–409 (2008).

    PubMed  Article  PubMed Central  Google Scholar 

  88. 88.

    Carty, C. L. et al. Meta-analysis of genome-wide association studies identifies genetic risk factors for stroke in African Americans. Stroke 46, 2063–2068 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Gudbjartsson, D. F. et al. A sequence variant in ZFHX3 on 16q22 associates with a trial fibrillation and ischemic stroke. Nat. Genet. 41, 876–878 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  90. 90.

    Rosand, J. et al. Loci associated with ischaemic stroke and its subtypes (SiGN): a genome-wide association study. Lancet Neurol. 15, 174–184 (2016).

    Article  CAS  Google Scholar 

  91. 91.

    Chauhan, G. et al. Identification of additional risk loci for stroke and small vessel disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 15, 695–707 (2016).

    Article  Google Scholar 

  92. 92.

    Verhaaren, B. F. J. et al. Multiethnic genome-wide association study of cerebral white matter hyperintensities on MRI. Circ. Cardiovasc. Genet. 8, 398–409 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  93. 93.

    Cole, J. W. et al. Genetics of the thrombomodulin–endothelial cell protein C receptor system and the risk of early-onset ischemic stroke. PLoS One 13, e0206554 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  94. 94.

    Rannikmäe, K. et al. Common variation in COL4A1/COL4A2 is associated with sporadic cerebral small vessel disease. Neurology 84, 918–926 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  95. 95.

    Gupta, R. M. et al. A genetic variant associated with five vascular diseases is a distal regulator of endothelin-1 gene expression. Cell 170, 522–533 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  96. 96.

    Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. 97.

    Söderholm, M. et al. International Stroke Genetics Consortium, the NINDS-SiGN Consortium, and the Genetics of Ischaemic Stroke Functional Outcome (GISCOME) Network. Genome-wide association meta-analysis of functional outcome after ischemic stroke. Neurology 92, e1271–e1283 (2019). This meta-analysis describes genetic variants associated with functional outcomes 3 months after stroke.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  98. 98.

    Marini, S. et al. 17p12 influences hematoma volume and outcome in spontaneous intracerebral hemorrhage. Stroke 49, 1618–1625 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. 99.

    Dorothea Pfeiffer, Bowang et al. Genetic imbalance is associated with functional outcome after ischemic stroke. Stroke 50, 298–304 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  100. 100.

    Ibanez, L. et al. Overlap in the genetic architecture of stroke risk, early neurological changes, and cardiovascular risk factors. Stroke 50, 1339–1345 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  101. 101.

    Schlunk, F. & Greenberg, S. M. The pathophysiology of intracerebral hemorrhage formation and expansion. Transl Stroke Res. 6, 257–263 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  102. 102.

    Pulit, S. L. et al. Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes. Neurol. Genet. 4, e293 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  103. 103.

    Dick, K. J. et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet 383, 1990–1998 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  104. 104.

    Liang, L. et al. An epigenome-wide association study of total serum immunoglobulin E concentration. Nature 520, 670–674 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  105. 105.

    Gallego-Fabrega, C. et al. PPM1A methylation is associated with vascular recurrence in aspirin-treated patients. Stroke 47, 1926–1929 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  106. 106.

    Soriano-Tárraga, C. et al. Global DNA methylation of ischemic stroke subtypes. PLoS One 9, e96543 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  107. 107.

    Soriano-Tárraga, C. et al. Biological age is better than chronological as predictor of 3-month outcome in ischemic stroke. Neurology 89, 830–836 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  108. 108.

    Mishra, A. et al. Association of variants in HTRA1 and NOTCH3 with MRI-defined extremes of cerebral small vessel disease in older subjects. Brain 142, 1009–1023 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  109. 109.

    Raffield, L. M. et al. Coagulation factor VIII: relationship to cardiovascular disease risk and whole genome sequence and epigenome-wide analysis in African Americans. J. Thromb. Haemost. https://doi.org/10.1111/jth.14741 (2020).

  110. 110.

    Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570, 514–518 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  111. 111.

    Musunuru, K. et al. The expressed genome in cardiovascular diseases and stroke: refinement, diagnosis, and prediction: a scientific statement from the American Heart Association. Circ. Cardiovasc. Genet. 10, e000037 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. 112.

    Moore, D. F. et al. Using peripheral blood mononuclear cells to determine a gene expression profile of acute ischemic stroke: a pilot investigation. Circulation 11, 212–221 (2005).

    Article  CAS  Google Scholar 

  113. 113.

    Stamova, B. et al. Gene expression profiling of blood for the prediction of ischemic stroke. Stroke 41, 2171–2177 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  114. 114.

    Stamova, B. et al. The intracerebral hemorrhage blood transcriptome in humans differs from the ischemic stroke and vascular risk factor control blood transcriptomes. J. Cereb. Blood Flow Metab. 39, 1818–1835 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  115. 115.

    Jickling, G. C. et al. Signatures of cardioembolic and large-vessel ischemic stroke. Ann. Neurol. 68, 681–692 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  116. 116.

    Xu, H. et al. Gene expression in peripheral blood differs after cardioembolic compared with large-vessel atherosclerotic stroke: biomarkers for the etiology of ischemic stroke. J. Cereb. Blood Flow. Metab. 28, 1320–1328 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  117. 117.

    Jickling, G. C. et al. Profiles of lacunar and nonlacunar stroke. Ann. Neurol. 70, 477–485 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  118. 118.

    Jickling, G. C. et al. Prediction of cardioembolic, arterial, and lacunar causes of cryptogenic stroke by gene expression and infarct location. Stroke 43, 2036–2041 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  119. 119.

    Jickling, G. C. et al. Leukocyte response is regulated by microRNA let7i in patients with acute ischemic stroke. Neurology 87, 2198–2205 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  120. 120.

    Gilles, M. E. & Slack, F. J. Let-7 microRNA as a potential therapeutic target with implications for immunotherapy. Expert Opin. Ther. Targets 22, 929–939 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  121. 121.

    Tiedt, S. et al. RNA-seq identifies circulating MIR-125a-5p, MIR-125b-5p, and MIR-143-3p as potential biomarkers for acute ischemic stroke. Circ. Res. 121, 970–980 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  122. 122.

    Eyileten, C. et al. MicroRNAs as diagnostic and prognostic biomarkers in ischemic stroke — a comprehensive review and bioinformatic analysis. Cells 7, 249 (2018).

    CAS  PubMed Central  Article  Google Scholar 

  123. 123.

    Tiedt, S. & D., M. Role of non-coding RNAs in stroke. Stroke 49, 3098–3106 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  124. 124.

    Jickling, G. C. et al. MicroRNA expression in peripheral blood cells following acute ischemic stroke and their predicted gene targets. PLoS One 9, e99283 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  125. 125.

    Huang, S. et al. Identification of blood let-7e-5p as a biomarker for ischemic stroke. PLoS One 11, e0163951 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  126. 126.

    Tan, K. S. et al. Expression profile of microRNAs in young stroke patients. PLoS One 4, e7689 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  127. 127.

    Sepramaniam, S. et al. Circulating microRNAs as biomarkers of acute stroke. Int. J. Mol. Sci. 15, 1418–1432 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  128. 128.

    Dykstra-Aiello, C. et al. Altered expression of long noncoding RNAs in blood after ischemic stroke and proximity to putative stroke risk loci. Stroke 47, 2896–2903 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  129. 129.

    Deng, Q.-W. et al. Differential long noncoding RNA expressions in peripheral blood mononuclear cells for detection of acute ischemic stroke. Clin. Sci. 132, 159701501614 (2018).

    Article  Google Scholar 

  130. 130.

    Wang, W. et al. Integrated analysis of lncRNA-mRNA co-expression profiles in patients with Moyamoya disease. Sci. Rep. 7, 42421 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  131. 131.

    Holdt, L. M. & Teupser, D. Long Noncoding R. N. A. ANRIL: Lnc-ing genetic variation at the chromosome 9p21 locus to molecular mechanisms of atherosclerosis. Front. Cardiovasc. Med. 5, 145 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  132. 132.

    Wang, J. et al. Predictive value of long noncoding RNA ZFAS1 in patients with ischemic stroke. Clin. Exp. Hypertens. 41, 615–621 (2018).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  133. 133.

    Wishart, D. S. et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  134. 134.

    Shah, S. H., Kraus, W. E. & Newgard, C. B. Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases form and function. Circulation 126, 1110–1120 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  135. 135.

    McGarrah, R. W., Crown, S. B., Zhang, G. F., Shah, S. H. & Newgard, C. B. Cardiovascular metabolomics. Circ. Res. 122, 1238–1258 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  136. 136.

    Marklund, M. et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Fatty Acids and Outcomes Research Consortium (FORCE). Biomarkers of dietary omega-6 fatty acids and incident cardiovascular disease and mortality. Circulation 139, 2422–2436 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  137. 137.

    Stegemann, C. et al. Lipidomics profiling and risk of cardiovascular disease in the prospective population-based Bruneck study. Circulation 129, 1821–1831 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  138. 138.

    Sun, D. et al. A prospective study of serum metabolites and risk of ischemic stroke. Neurology 92, e1890–e1898 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  139. 139.

    Hu, Z. et al. Rapid and sensitive differentiating ischemic and hemorrhagic strokes by dried blood spot based direct injection mass spectrometry metabolomics analysis. J. Clin. Lab. Anal. 30, 823–830 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  140. 140.

    Sheth, S. A., Iavarone, A. T., Liebeskind, D. S., Won, S. J. & Swanson, R. A. Targeted lipid profiling discovers plasma biomarkers of acute brain injury. PLoS One 10, e0129735 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  141. 141.

    Kimberly, W. T., Wang, Y., Pham, L., Furie, K. L. & Gerszten, R. E. Metabolite profiling identifies a branched chain amino acid signature in acute cardioembolic stroke. Stroke 44, 1389–1395 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  142. 142.

    Choi, J. Y. et al. High free fatty acid level is associated with recurrent stroke in cardioembolic stroke patients. Neurology 82, 1142–1148 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  143. 143.

    Adams, H. P. et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke 24, 35–41 (1993).

    PubMed  Article  PubMed Central  Google Scholar 

  144. 144.

    Ay, H. et al. An evidence-based causative classification system for acute ischemic stroke. Ann. Neurol. 58, 688–697 (2005).

    PubMed  Article  PubMed Central  Google Scholar 

  145. 145.

    Nelson, S. E., Ament, Z., Wolcott, Z., Gerszten, R. E. & Kimberly, W. T. Succinate links atrial dysfunction and cardioembolic stroke. Neurology 92, e802–e810 (2019). This study links energy metabolites, such as succinate, to cardioembolic stroke and structural cardiac abnormalities for the first time, thereby supporting the abnormal atrial substrate model of cardioembolic stroke.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  146. 146.

    Kamel, H., Okin, P. M., Elkind, M. S. V. & Iadecola, C. Atrial fibrillation and mechanisms of stroke: time for a new model. Stroke 47, 895–900 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  147. 147.

    Kennedy, J. et al. Rapid screening and identification of novel psychoactive substances using PaperSpray interfaced to high resolution mass spectrometry. Clin. Mass Spectrom. 1, 3–10 (2016).

    Article  Google Scholar 

  148. 148.

    Land, W. H. et al. Partial least squares (PLS) applied to medical bioinformatics. Procedia Comput. Sci. 6, 273–278 (2011).

    Article  Google Scholar 

  149. 149.

    de Tayrac, M., Lê, S., Aubry, M., Mosser, J. & Husson, F. Simultaneous analysis of distinct omics data sets with integration of biological knowledge: multiple factor analysis approach. BMC Genomics 10, 32 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  150. 150.

    Cisek, K., Krochmal, M., Klein, J. & Mischak, H. The application of multi-omics and systems biology to identify therapeutic targets in chronic kidney disease. Nephrol. Dial. Transpl. 31, 2003–2011 (2016).

    Article  Google Scholar 

  151. 151.

    Hopewell, J. C. & Clarke, R. Emerging risk factors for stroke: what have we learned from Mendelian randomization studies? Stroke 47, 1673–1678 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  152. 152.

    Larsson, S. C. et al. Serum 25-hydroxyvitamin D concentrations and ischemic stroke and its subtypes. A Mendelian randomization study. Stroke 49, 2508–2511 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  153. 153.

    Larsson, S. C., Traylor, M. & Markus, H. S. Homocysteine and small vessel stroke: a Mendelian randomization analysis. Ann. Neurol. 85, 495–501 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  154. 154.

    van der Laan, S. W. et al. Cystatin C and cardiovascular disease: a Mendelian randomization study. J. Am. Coll. Cardiol. 68, 934–945 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  155. 155.

    Georgakis, M. K. et al. Genetically determined levels of circulating cytokines and risk of stroke. Circulation 139, 256–268 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  156. 156.

    Marios, K. et al. Circulating monocyte chemoattractant protein-1 and risk of stroke: meta-analysis of population-based studies involving 17 180 individuals. Circ. Res. 125, 773–782 (2019).

    Article  CAS  Google Scholar 

  157. 157.

    Sun, L. et al. Causal associations of blood lipids with risk of ischemic stroke and intracerebral hemorrhage in Chinese adults. Nat. Med. 25, 569–574 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  158. 158.

    Hartwig, F. P., Davies, N. M., Hemani, G. & Davey Smith, G. Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int. J. Epidemiol. 45, 1717–1726 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  159. 159.

    Ference, B. A. et al. Variation in PCSK9 and HMGCR and risk of cardiovascular disease and diabetes. N. Engl. J. Med. 375, 2144–2153 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  160. 160.

    Mokry, L. E., Ahmad, O., Forgetta, V., Thanassoulis, G. & Richards, J. B. Mendelian randomisation applied to drug development in cardiovascular disease: a review. J. Med. Genet. 52, 71–79 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  161. 161.

    Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  162. 162.

    Verbanck, M., Chen, C. Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  163. 163.

    Bayat Mokhtari, R. et al. Combination therapy in combating cancer. Oncotarget 8, 38022–38043 (2017).

    PubMed  Google Scholar 

  164. 164.

    Palmer, A. C. & Sorger, P. K. Combination cancer therapy can confer benefit via patient-to-patient variability without drug additivity or synergy. Cell 14, 1678–1691 (2017).

    Article  CAS  Google Scholar 

  165. 165.

    West, L. et al. A novel classification of lung cancer into molecular subtypes. PLoS One 7, e31906 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  166. 166.

    Collisson, E. A. et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat. Med. 17, 500–503 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  167. 167.

    Higdon, R. et al. The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders. OMICS 19, 197–208 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  168. 168.

    Sertbas, M. & Ulgen, K. O. Unlocking human brain metabolism by genome-scale and multiomics metabolic models: relevance for neurology research, health, and disease. OMICS 22, 455–467 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  169. 169.

    Traylor, M. et al. Genetics of stroke in a UK African ancestry case–control study: South London Ethnicity and Stroke Study. Neurol. Genet. 3, e142 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  170. 170.

    Zakai, N. A. et al. D-dimer and the risk of stroke and coronary heart disease. The REasons for Geographic and Racial Differences in Stroke (REGARDS) Study. Thromb Haemost 117, 618–624 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  171. 171.

    Zhan, X. et al. Transient ischemic attacks characterized by RNA profiles in blood. Neurology 77, 1718–1724 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  172. 172.

    Jickling, G. C. et al. Ischemic transient neurological events identified by immune response to cerebral ischemia. Stroke 43, 1006–1012 (2012). This study describes the role of RNA in predicting the cause of stroke when the cause remains unclear or cryptogenic.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  173. 173.

    Azghandi, S. et al. Deficiency of the stroke relevant HDAC9 gene attenuates atherosclerosis in accord with allele-specific effects at 7p21.1. Stroke 46, 197–202 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  174. 174.

    Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  175. 175.

    Sakaue, S. & Okada, Y. GREP: genome for REPositioning drugs. Bioinformatics 35, 3821–3823 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

The Neurovascular Research Laboratory acknowledges funding from grants PI15/00354 and PI18/00804 from Fondo de Investigaciones Sanitarias of the Instituto de Salud Carlos III (co-financed by the European Regional Development Fund, FEDER). The Neurovascular Research Laboratory is also a member of the Spanish stroke research network INVICTUS+ (RD16/0019). L.R. is supported by a predoctoral fellowship grant from the Instituto de Salud Carlos III (IFI17/00012). A.B. is supported by a Juan Rodes research grant from Instituto de Salud Carlos III (JR16/00008).

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All authors contributed to the development of the manuscript, wrote sections of the manuscript, approved the final version and are responsible for the content.

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Correspondence to Joan Montaner.

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Members of the neurovascular research laboratory (A.S., A.B., J.M. and L.R.) are inventors of a family of patents for biomarkers to differentiate ischaemic from haemorrhagic stroke, to predict stroke outcome and to establish stroke aetiology. J.-C.S. and J.M. are co-founders of ABCDx, a spin-off company of the University of Geneva (http://www.abcdx.ch).

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Nature Reviews Neurology thanks J. Meschia, B. Worrall and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Ischaemic core

The region of the brain with the most severe blood flow deficits (blood flow below 10–25%), resulting in rapid progression of cell death.

Embolic stroke of undetermined source

Ischaemic stroke with an unknown origin; these strokes are non-lacunar and non-atherosclerotic strokes of an undetermined embolic source.

Overfitting

A phenomenon that occurs when a statistical model describes random error or noise instead of the underlying relationship.

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Montaner, J., Ramiro, L., Simats, A. et al. Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke. Nat Rev Neurol 16, 247–264 (2020). https://doi.org/10.1038/s41582-020-0350-6

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