Dynamic urinary metabolomics analysis based on UHPLC-Q-TOF/MS to investigate the potential biomarkers of blood stasis syndrome and the effects of Danggui Sini decoction

Blood stasis syndrome (BSS) is one of the common syndromes in traditional Chinese medicine (TCM). It involves abnormal blood circulation, which can progress to produce many severe diseases. Danggui Sini decoction (DSD) is a classical TCM prescription frequently used to treat BSS by decreasing blood stasis and improving blood circulation. However, understanding of the therapeutic mechanism of DSD during the development of BSS is still limited, as the development of BSS is a slow dynamic process. Therefore, a dynamic urinary metabolomics analysis based on ultra-high-performance liquid chromatography-quadrupole-time of flight tandem mass spectrometry (UHPLC-Q-TOF/MS) combined with multivariate statistical analysis was used to explore the distinctive metabolic patterns of BSS development and the efficacy of DSD. The dynamic changes of endogenous metabolites over time revealed the progression of BSS and allowed the overall efficacy of DSD in rats with BSS to be evaluated. The effects of the DSD compatibilities were also explored. A total of 21 metabolites were identified during the development of BSS. They are involved in the metabolic pathways of tryptophan metabolism, phenylalanine metabolism, riboflavin metabolism, nicotinate and nicotinamide metabolism, pentose and glucuronate interconversions, histidine metabolism, steroid hormone biosynthesis, and starch and sucrose metabolism. A receiver operating characteristic (ROC) curve analysis showed that 10 metabolites with an area under the curve (AUC) value >0.9, which can be used as potential biomarkers for the diagnosis of BSS. In conclusion, a dynamic urinary metabolomics approach was applied to identify potential biomarkers of the development of BSS and to clarify the therapeutic mechanism of DSD in BSS. The results could provide a theoretical basis for further research on the therapeutic mechanism of DSD. (C) 2019 Elsevier B.V. All rights reserved.