Predictive serum biomarkers of patients with cerebral infarction.

OBJECTIVES: Stroke is the third most common cause of death and also causes seizures and disability. Biomarkers are abnormal signal indicators at the biological level that are present before the organism is seriously affected and are more sensitive to early diagnosis than are traditional imaging methods. Early diagnosis of stroke can prevent the progression of the disease. However, there are currently no widely accepted biomarkers for stroke that have been applied clinically. METHODS: A serum metabonomics method based on ultra-high-performance liquid chromatography-quadrupole-time of flight tandem mass spectrometry (UPLC-Q-TOF/MS) was used to identify potential biomarkers and metabolic pathways of cerebral infarction. The receiver-operating characteristic (ROC) curve was used to verify the diagnostic and classification abilities of the biomarkers, and a support vector machine (SVM) model was developed for the prediction of cerebral infarction. RESULTS: Principal component analysis revealed a clear separation between the normal and cerebral infarction groups. A total of 13 potential serum biomarkers were identified, which were mainly involved in linoleic acid metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; tyrosine metabolism; arachidonic acid metabolism; and fatty acid biosynthesis. The ROC curve analysis showed that the potential biomarkers had high specificity and sensitivity for the diagnosis of cerebral infarction. The SVM model had good diagnostic ability and could accurately distinguish the control group from the cerebral infarction group. DISCUSSION: The metabonomics approach may be a useful bioanalytical method for understanding the pathophysiology of cerebral infarction and may provide an experimental basis for the development of clinical biomarkers for stroke.

Urinary metabonomics study of anti-depressive mechanisms of Millettia speciosa Champ on rats with chronic unpredictable mild stress-induced depression.

As a traditional Chinese medicine (TCM), Millettia speciosa Champ (MSC), exerts a wide range of pharmacological activities. Our research group previously found that MSC has antidepressant effects, but the specific antidepressant mechanisms remain unclear. Therefore, in this study, urine metabolomics based on ultra-performance liquid chromatography/quadrupole time of flight mass spectrometry (UPLC-Q-TOF/MS) combined with pharmacodynamics was used to explore the pathogenesis of depression and the antidepressant effects of MSC. The results showed that MSC treatment could significantly improve chronic unpredictable mild stress (CUMS)-induced depression. Urine metabolic showed that the profiles of the CUMS model group were significantly separated from the control group, while the drug-treated groups were closer to the control group, especially the MSC group treated with a 14 g/kg dose of MSC. Furthermore, 9 metabolites, including glutaric acid, L-isoleucine, L-Dopa, sebacic acid, 3-methylhistidine, allantoin, caprylic acid, tryptophol, and 2-phenylethanol glucuronide, were identified as potential biomarkers of depression. Metabolic pathway analysis showed that these potential biomarkers were mainly involved in valine, leucine, and isoleucine biosynthesis, aminoacyl-tRNA biosynthesis, valine, leucine and isoleucine degradation, tyrosine metabolism, histidine metabolism, fatty acid biosynthesis, and pentose and glucuronate interconversions. Through Receiver operating characteristic (ROC) analysis and Pearson correlation analysis, the combination of L-isoleucine, sebacic acid, and allantoin, were further screened out as potential pharmacodynamic biomarkers associated with the efficacy of MSC. This study suggests that the integration of metabolomics with pharmacodynamics helps to further understand the pathogenesis of depression and provides novel insight into the efficacy of TCM.