Serum Metabolomics Study Based on LC-MS and Antihypertensive Effect of Uncaria on Spontaneously Hypertensive Rats.
Ana LiuYan-Jun ChuXiaoming WangRuixue YuHaiQiang JiangYun-Lun LiHonglei ZhouLi-Li GongWen-Qing YangJianqing JuPublished in: Evidence-based complementary and alternative medicine : eCAM (2018)
Our previous studies have shown that Uncaria has an important role in lowering blood pressure, but its intervention mechanism has not been clarified completely in the metabolic level. Therefore, in this study, a combination method of HPLC-TOF/MS-based metabolomics and multivariate statistical analyses was employed to explore the mechanism and evaluate the antihypertensive effect of Uncaria. Serum samples were analyzed and identified by HPLC-TOF/MS, while the acquired data was further processed by partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to discover the perturbed metabolites. A clear cluster among the different groups was obtained, and 7 significantly changed potential biomarkers were screened out. These biomarkers were mainly associated with lipid metabolism (dihydroceramide, ceramide, PC, LysoPC, and TXA2) and vitamin and amino acids metabolism (nicotinamide riboside, 5-HTP). The result indicated that Uncaria could decrease the blood pressure effectively, partially by regulating the above biomarkers and metabolic pathways. Analyzing and verifying the specific biomarkers, further understanding of the therapeutic mechanism and antihypertensive effect of Uncaria was acquired. Metabolomics provided a new insight into estimate of the therapeutic effect and dissection of the potential mechanisms of traditional Chinese medicine (TCM) in treating hypertension.
Keyphrases
- blood pressure
- hypertensive patients
- mass spectrometry
- ms ms
- heart rate
- simultaneous determination
- randomized controlled trial
- blood glucose
- high performance liquid chromatography
- machine learning
- liquid chromatography
- electronic health record
- big data
- skeletal muscle
- weight loss
- artificial intelligence
- solid phase extraction
- deep learning