Time-dependent metabolomics study of cerebral ischemia-reperfusion and its treatment: focus on the combination of traditional Chinese medicine and Western medicine.

Cerebral ischemia is a common cerebrovascular disease with high mortality, and thrombolysis can cause more severe reperfusion injury. In clinical practice, Ginkgo biloba dispersible tablets combined with nimodipine have been widely used to reduce cerebral ischemia-reperfusion injury, but the mechanism has not been clearly elucidated. To explore this relationship, the change in metabolism between a sham operation group, a model group and an administration group was analyzed for the period after cerebral ischemia. Biochemical assays were used to assess injury extent and the therapeutic effects of different dosing regimens. A metabolomics method based on ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was developed to screen biomarkers in plasma of rats and analyze abnormal metabolic pathways. Using statistical analysis, corticosterone, glutamine, oleic acid, isoleucine, phenylalanine and sphingomyelin (d18:1/16:0) were screened as diagnostic biomarkers. The metabolic pathways perturbed by cerebral ischemia-reperfusion involved phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, alpha-linolenic acid metabolism, retinol metabolism, alanine, aspartate and glutamate metabolism, and glycerophospholipid metabolism. Analysis of the adjustment of biomarkers at different time points showed that the best time to evaluate the efficacy of combined administration is about 6 h after administration. Both pathological characteristics and metabolomics confirmed the better effect of the combined group than the individual groups. In this study, a non-targeted metabolomics method was developed to explore the mechanism of action of the combination of traditional Chinese and Western medicine in cerebral ischemia-reperfusion treatment, providing a theoretical basis for disease prognosis and treatment options. Graphical abstract.