Kiran Buddharaju, M. Javali, A. Mehta, R. Srinivasa, P. Acharya
{"title":"Role of S100β Glial Protein as a Serological Marker for Analysis of Acute Ischemic Stroke","authors":"Kiran Buddharaju, M. Javali, A. Mehta, R. Srinivasa, P. Acharya","doi":"10.1177/25166085211011284","DOIUrl":null,"url":null,"abstract":"Background: Stroke is a major cause of neurological disability, which can be often predicted with serological markers. Glial-derived S100β protein is a potential biomarker for cerebral ischemia and may be helpful in predicting the severity, outcome, and recovery of stroke. Aim: This study aimed to study the role of S100β glial protein as a serological marker in predicting the severity of acute ischemic stroke (AIS), outcome, and functional recovery after 1 month. Methods: A hospital-based prospective case control study included 43 consecutive patients, >18 years old, who were admitted with acute middle cerebral artery (MCA) territory infarcts within 72 h of onset of neurological deficits. Control group comprised of 43 age-matched asymptomatic volunteers. Independent t-test and chi square test were used to compare the means and evaluate the association between protein level and various parameters. P ≤ .05 was statistically significant. Results: S100β protein level in AIS patients was significantly higher compared to controls (P < .05). Elevated serum S100β protein level was found to be associated with larger infarct volumes, higher National Institute Health Stroke Scale scores, and higher modified Rankin Scale scores at admission (P < .05). Patients with higher S100β protein levels at admission had poor recovery at 1 month compared to patients having normal S100β protein levels. Conclusion: S100β protein levels at admission after an acute MCA territory infarct may be used as a reliable serological tool in predicting the severity, outcome, and functional recovery in stroke.","PeriodicalId":93323,"journal":{"name":"Journal of stroke medicine","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of stroke medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/25166085211011284","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Stroke is a major cause of neurological disability, which can be often predicted with serological markers. Glial-derived S100β protein is a potential biomarker for cerebral ischemia and may be helpful in predicting the severity, outcome, and recovery of stroke. Aim: This study aimed to study the role of S100β glial protein as a serological marker in predicting the severity of acute ischemic stroke (AIS), outcome, and functional recovery after 1 month. Methods: A hospital-based prospective case control study included 43 consecutive patients, >18 years old, who were admitted with acute middle cerebral artery (MCA) territory infarcts within 72 h of onset of neurological deficits. Control group comprised of 43 age-matched asymptomatic volunteers. Independent t-test and chi square test were used to compare the means and evaluate the association between protein level and various parameters. P ≤ .05 was statistically significant. Results: S100β protein level in AIS patients was significantly higher compared to controls (P < .05). Elevated serum S100β protein level was found to be associated with larger infarct volumes, higher National Institute Health Stroke Scale scores, and higher modified Rankin Scale scores at admission (P < .05). Patients with higher S100β protein levels at admission had poor recovery at 1 month compared to patients having normal S100β protein levels. Conclusion: S100β protein levels at admission after an acute MCA territory infarct may be used as a reliable serological tool in predicting the severity, outcome, and functional recovery in stroke.