Neurology International (Sep 2022)

Stroke and Emerging Blood Biomarkers: A Clinical Prospective

  • Aimilios Gkantzios,
  • Dimitrios Tsiptsios,
  • Stella Karatzetzou,
  • Sofia Kitmeridou,
  • Vaia Karapepera,
  • Erasmia Giannakou,
  • Penelope Vlotinou,
  • Nikolaos Aggelousis,
  • Konstantinos Vadikolias

DOI
https://doi.org/10.3390/neurolint14040065
Journal volume & issue
Vol. 14, no. 4
pp. 784 – 803

Abstract

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Stroke constitutes the primary source of adult functional disability, exhibiting a paramount socioeconomic burden. Thus, it is of great importance that the prediction of stroke outcome be both prompt and accurate. Although modern neuroimaging and neurophysiological techniques are accessible, easily available blood biomarkers reflecting underlying stroke-related pathophysiological processes, including glial and/or neuronal death, neuroendocrine responses, inflammation, increased oxidative stress, blood–brain barrier disruption, endothelial dysfunction, and hemostasis, are required in order to facilitate stroke prognosis. A literature search of two databases (MEDLINE and Science Direct) was conducted in order to trace all relevant studies published between 1 January 2010 and 31 December 2021 that focused on the clinical utility of brain natriuretic peptide, glial fibrillary acidic protein, the red cell distribution width, the neutrophil-to-lymphocyte ratio, matrix metalloproteinase-9, and aquaporin-4 as prognostic tools in stroke survivors. Only full-text articles published in English were included. Twenty-eight articles were identified and are included in this review. All studied blood-derived biomarkers proved to be valuable prognostic tools poststroke, the clinical implementation of which may accurately predict the survivors’ functional outcomes, thus significantly enhancing the rehabilitation efficiency of stroke patients. Along with already utilized clinical, neurophysiological, and neuroimaging biomarkers, a blood-derived multi-biomarker panel is proposed as a reasonable approach to enhance the predictive power of stroke prognostic models.

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