Mechanism of Chinese Medicine Herbs Effects on Chronic Heart Failure Based on Metabolic Profiling

Kuo Gao, Huihui Zhao, Jian Gao, Binyu Wen, Caixia Jia, Zhiyong Wang, Feilong Zhang, Jinping Wang, Hua Xie, Juan Wang, Wei Wang, Jianxin Chen, Kuo Gao, Huihui Zhao, Jian Gao, Binyu Wen, Caixia Jia, Zhiyong Wang, Feilong Zhang, Jinping Wang, Hua Xie, Juan Wang, Wei Wang, Jianxin Chen

Abstract

Chronic heart failure (CHF) is a major public health problem in huge population worldwide. The detailed understanding of CHF mechanism is still limited. Zheng (syndrome) is the criterion of diagnosis and therapeutic in Traditional Chinese Medicine (TCM). Syndrome prediction may be a better approach for understanding of CHF mechanism basis and its treatment. The authors studied disturbed metabolic biomarkers to construct a predicting mode to assess the diagnostic value of different syndrome of CHF and explore the Chinese herbal medicine (CHM) efficacy on CHF patients. A cohort of 110 patients from 11 independent centers was studied and all patients were divided into 3 groups according to TCM syndrome differentiation: group of Qi deficiency syndrome, group of Qi deficiency and Blood stasis syndrome, and group of Qi deficiency and Blood stasis and Water retention syndrome. Plasma metabolomic profiles were determined by UPLC-TOF/MS and analyzed by multivariate statistics. About 6 representative metabolites were highly possible to be associated with CHF, 4, 7, and 5 metabolites with Qi deficiency syndrome, Qi deficiency and Blood stasis syndrome, and Qi deficiency and Blood stasis and Water retention syndrome (VIP > 1, p < 0.05). The diagnostic model was further constructed based on the metabolites to diagnose other CHF patients with satisfying sensitivity and specificity (sensitivity and specificity are 97.1 and 80.6% for CHF group vs. NH group; 97.1 and 80.0% for QD group vs. NH group; 97.1 and 79.5% for QB group vs. NH group; 97.1 and 88.9% for QBW group vs. NH group), validating the robustness of plasma metabolic profiling to diagnostic strategy. By comparison of the metabolic profiles, 9 biomarkers, 2-arachidonoylglycerophosphocholine, LysoPE 16:0, PS 21:0, LysoPE 20:4, LysoPE 18:0, linoleic acid, LysoPE 18:2, 4-hydroxybenzenesulfonic acid, and LysoPE 22:6, may be especially for the effect of CHM granules. A predicting model was attempted to construct and predict patient based on the related symptoms of CHF and the potential biomarkers regulated by CHM were explored. This trial was registered with NCT01939236 (https://clinicaltrials.gov/).

Keywords: UPLC-TOF/MS; chronic heart failure; metabolomics; syndrome; traditional Chinese medicine.

Figures

Figure 1
Figure 1
Study Design. 6MWT, 6 Min Walking Test; CHF, Chronic heart failure; CHM, Chinese herbal medicine; LVEF, Left Ventricular Ejection Fraction; NH, Normal healthy group; QD, Qi deficiency group; QB, Qi deficiency and Blood stasis group; QBW, Qi deficiency and Blood stasis and Water retention group.
Figure 2
Figure 2
OPLS-DA Score Plots among CHF, QD, QB, QBW, and NH. OPLS-DA Score Plots compared (A) CHF vs. NH, (B) QD vs. NH, (C) QB vs. NH, and (D) QBW vs. NH. CHF, Chronic heart failure; NH, Normal healthy group; QD, Qi deficiency group; QB, Qi deficiency and Blood stasis group; QBW, Qi deficiency and Blood stasis and Water retention group.
Figure 3
Figure 3
Diagnostic Outcomes and Predictive Accuracies. The diagnostic outcomes in the discovery phase are shown via the receiver-operating characteristic (ROC) curves for comparison between (A) CHF vs. NH, (B) QD vs. NH, (C) QB vs. NH, and (D) QBW vs. NH. The predictive accuracies by the biomarkers in validation phase and validation sets were compared between (E) CHF vs. NH, (F) QD vs. NH, (G) QB vs. NH, and (H) QBW vs. NH. AUC, Area Under the Curve; CHF, Chronic heart failure; NH, Normal healthy group; QD, Qi deficiency group; QB, Qi deficiency and Blood stasis group; QBW, Qi deficiency and Blood stasis and Water retention group.
Figure 4
Figure 4
Result of 6MWT and ejection fraction results. (A) 6MWT result between Prior-CHM group and Prior-Placebo group & Post-CHM group and Post-Placebo group, #P < 0.05 for Post-CHM group and Post-Placebo group. (B) Ejection fraction result between Prior-CHM group and Prior-Placebo group & Post-CHM group and Post-Placebo group, *P < 0.05 for Post-CHM group and Post-Placebo group. 6MWT, 6 Min Walking Test; CHM, Chinese herbal medicine; LVEF, Left Ventricular Ejection Fraction.
Figure 5
Figure 5
UPLC-QTOF-MS characteristic chromatogram of CHM granules (6 herbs). 1.Albiflorin; 2.Paeoniflorin; 3.Baicalin; 4.Ononin; 5.Tanshinol B; 6.Calycosin; 7.Formononetin; 8. Rhamnocitrin; 9.Dihydrotanshinone I; 10.Cryptotanshinone; 11.Dimethyl Lithospermate; 12.Tanshindiol B; 13.3-Indole Carboxylic Acid Glucuronide; 14.Beta-Carotene; 15.2-Nitrophenyl Beta-D-Glucuronide.
Figure 6
Figure 6
PLS-DA & OPLS-DA Score Plots and Treatment & five-fold Cross Validation Outcomes. PLS-DA Score Plots compared (A) Prior-CHM vs. Prior-Placebo vs. NH, (C) Post-CHM vs. Post-Placebo vs. NH. OPLS-DA Score Plots compared (B) Prior-CHM vs. Prior-Placebo, (D) Post-CHM vs. Post-Placebo. The treatment outcomes are shown via the receiver-operating characteristic (ROC) curves for comparison between (E) Post-CHM vs. Prior-CHM, (F) Post-Placebo vs. Prior- Placebo. The five-fold Cross Validation Outcomes were for (G) Post-CHM vs. Prior-CHM, (H) Post-Placebo vs. Prior-Placebo. AUC, Area Under the Curve; CHM, Chinese herbal medicine; NH, Normal healthy group. Green, NH; Yellow, Prior-Placebo; Blue, Prior-CHM; Red, Post-Placebo; Purple, Post-CHM.
Figure 7
Figure 7
Heat map of differential metabolites. NH, Normal healthy group; QD, Qi deficiency group; QB, Qi deficiency and Blood stasis group; QBW, Qi deficiency and Blood stasis and Water retention group.
Figure 8
Figure 8
Metabolism-Protein Networks. Related proteins in Arachidonic acid metabolism pathway.

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