Distinguishing Intracerebral Hemorrhage from Acute Cerebral Infarction through Metabolomics
| Autor: | Pitong Sun, Yanzhao Li, Xue Wu, Xiaoyu Sun, Peng Gao, Mo Hong, Jin-Hui Song, Dongfeng Deng, Liang Yan, Zhang Xuxin |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Adult
Male medicine.medical_specialty 030501 epidemiology Sensitivity and Specificity Diagnosis Differential 03 medical and health sciences 0302 clinical medicine Metabolomics Internal medicine Acute cerebral infarction medicine Humans cardiovascular diseases Stroke Aged Cerebral Hemorrhage Aged 80 and over Intracerebral hemorrhage Training set medicine.diagnostic_test business.industry Reproducibility of Results Magnetic resonance imaging Cerebral Infarction General Medicine Glutarylcarnitine Middle Aged medicine.disease Magnetic Resonance Imaging Confidence interval Case-Control Studies Cardiology Female Dried Blood Spot Testing Neural Networks Computer Tomography X-Ray Computed 0305 other medical science business Biomarkers 030217 neurology & neurosurgery |
| Zdroj: | Revista de investigaci�n Cl�nica. 69 |
| ISSN: | 0034-8376 |
| DOI: | 10.24875/ric.17002348 |
| Popis: | 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 usingdried 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. |
| Databáze: | OpenAIRE |
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