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Distinguishing Intracerebral Hemorrhage from Acute Cerebral Infarction through Metabolomics
Xuxin Zhang1, Yanzhao Li1, Yan Liang1
1Department of Neurosurgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning, China.
Insights
Metabolomic analysis successfully identified 11 biomarkers to differentiate acute cerebral infarction (ACI) from intracerebral hemorrhage (ICH). This approach offers a promising tool for rapid stroke diagnosis and treatment selection.
Area of Science:
- Biochemistry
- Medical Diagnostics
- Neuroscience
Background:
- Acute cerebral infarction (ACI) and intracerebral hemorrhage (ICH) are critical cerebrovascular diseases with overlapping symptoms but distinct treatments.
- Accurate differentiation is crucial for effective stroke management and patient outcomes.
Purpose of the Study:
- To identify reliable biomarkers for distinguishing between ACI and ICH.
- To develop a diagnostic model for rapid stroke subtyping.
Main Methods:
- Analyzed metabolites in blood samples from 129 ACI patients, 128 ICH patients, and 65 controls using mass spectrometry.
- Employed multivariate statistical analysis to screen for differentiating biomarkers.
- Constructed and validated an artificial neural network model for classification.
Main Results:
- Identified 11 key metabolites and metabolite ratios, including 3-hydroxylbutyrylcarnitine and glutarylcarnitine (C5DC), as potential biomarkers.
- The developed artificial neural network model achieved a sensitivity of 0.84 and specificity of 0.77 in an external test set.
- Demonstrated the model's effectiveness in differentiating ACI from ICH.
Conclusions:
- Metabolomic profiling provides a valuable method for the rapid and accurate differentiation of stroke types.
- This approach supports timely diagnosis, enabling appropriate therapeutic strategies for ACI and ICH.
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.
