Exploring potential biomarkers of coronary heart disease treated by Jing Zhi Guan Xin Pian using high-throughput metabolomics

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Abstract

Coronary heart disease (CHD) is a relatively complex disease characterized by narrowing of the arterial lumen and reduction of blood flow to the heart. There is no effective early diagnosis and prevention method. Jing Zhi Guan Xin Pian (JZGXP) is a new preparation prepared from the effective extract of Guanxin II. It is made of five components of traditional Chinese medicine and functions by promoting blood circulation and removing blood stasis and is used for the treatment of CHD and angina pectoris. In our study, a CHD rat model was prepared using a high-fat diet combined with intraperitoneal injection of vitamin D3. Clinical biochemical indexes (TG, CHO and HDL-C), histopathology (coronary and myocardial tissue), electrocardiogram and cardiac indexes were used to evaluate the efficacy of JZGXP in the treatment of CHD model rats. UPLC-HDMS-based metabolomics techniques were used to find metabolic profiles, biomarkers and related metabolic pathways in CHD models and to evaluate the effects of JZGXP on them. At the same time, the targets of JZGXP for the treatment of CHD were analyzed. Our study ultimately identified 25 biomarkers associated with CHD models. Further studies found that these 25 biomarkers involved 9 metabolic pathways in the body and found that JZGXP can recall 21 biomarkers in the urine of model rats and these biomarkers involve nine metabolic pathways. Finally, the targets of JZGXP for the treatment of CHD were β-alanine metabolism and tyrosine metabolism, i.e. amino acids metabolism. This study showed that metabolomics technology is effective for exploring potential biomarkers associated with syndromes or diseases and the therapeutic mechanisms of a traditional Chinese medicine formulation.

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Sun, H., Li, X. na, Zhang, A. hua, Zhang, K. ming, Yan, G. li, Han, Y., … Wang, X. jun. (2019). Exploring potential biomarkers of coronary heart disease treated by Jing Zhi Guan Xin Pian using high-throughput metabolomics. RSC Advances, 9(20), 11420–11432. https://doi.org/10.1039/C8RA10557J

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