Stroke · 2011
1H-NMR-based metabolomics study of cerebral infarction.
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Jung Jee Youn, Lee Ho-Sub, Kang Dae-Gill, Kim No Soo, Cha Min Ho, Bang Ok-Sun, Ryu Do Hyun, Hwang Geum-Sook
Аннотация
BACKGROUND AND PURPOSE: Stroke is one of the leading causes of adult disability and death in developing countries. However, early diagnosis is difficult and no reliable biomarker is currently available. Thus, we applied a 1H-NMR metabolomics approach to investigate the altered metabolic pattern in plasma and urine from patients with cerebral infarctions and sought to identify metabolic biomarkers associated with stroke.
METHODS: Metabolic profiles of plasma and urine from patients with cerebral infarctions, especially small vessel occlusion, were investigated using 1H-NMR spectroscopy coupled with multivariate statistical analysis, such as principal components analysis and orthogonal partial least-squares discriminant analysis.
RESULTS: Multivariate statistical analysis showed a significant separation between patients and healthy individuals. The plasma of stroke patients was characterized by the increased excretion of lactate, pyruvate, glycolate, and formate, and by the decreased excretion of glutamine and methanol; the urine of stroke patients was characterized by decreased levels of citrate, hippurate, and glycine. These metabolites detected from plasma and urine of patients with cerebral infarctions were associated with anaerobic glycolysis, folic acid deficiency, and hyperhomocysteinemia. Furthermore, the presence of cerebral infarction in the external validation model was predicted with high accuracy.
CONCLUSIONS: These data demonstrate that a metabolomics approach may be useful for the effective diagnosis of cerebral infarction and for the further understanding of stroke pathogenesis.
METHODS: Metabolic profiles of plasma and urine from patients with cerebral infarctions, especially small vessel occlusion, were investigated using 1H-NMR spectroscopy coupled with multivariate statistical analysis, such as principal components analysis and orthogonal partial least-squares discriminant analysis.
RESULTS: Multivariate statistical analysis showed a significant separation between patients and healthy individuals. The plasma of stroke patients was characterized by the increased excretion of lactate, pyruvate, glycolate, and formate, and by the decreased excretion of glutamine and methanol; the urine of stroke patients was characterized by decreased levels of citrate, hippurate, and glycine. These metabolites detected from plasma and urine of patients with cerebral infarctions were associated with anaerobic glycolysis, folic acid deficiency, and hyperhomocysteinemia. Furthermore, the presence of cerebral infarction in the external validation model was predicted with high accuracy.
CONCLUSIONS: These data demonstrate that a metabolomics approach may be useful for the effective diagnosis of cerebral infarction and for the further understanding of stroke pathogenesis.