Characteristics of Blood Metabolic Profile in Coronary Heart Disease, Dilated Cardiomyopathy and Valvular Heart Disease Induced Heart Failure

Figure 1. Workflow of the present study. In general, HF patients with different etiology (67 CHD patients, 16 VHD patients, and 13 DCM patient, respectively), and 96 healthy controls were enrolled in our study. A targeted metabolomics panel was applied to the plasma of these individuals. We comparatively analyzed the plasma metabolomics data of the four groups to elucidate the differences among these patients with different etiology.

Figure 2. (A) Heatmap of significantly differential metabolites between HF patients and control group, depicting the levels of metabolites in each sample and the variation in each metabolite between samples, ranging from blue (low intensity or downregulation) to yellow and red (high intensity or upregulation). (B) Volcano plot for significantly differentially metabolites between the two groups. Red dots indicate that the metabolite was significantly changed in terms of the P-value and log2 fold-change value; gray circles indicate that the metabolite level was not significantly altered in terms of P-value. (C) PCA and (D) PLS-DA analysis of 49 metabolites, the green dots represent HF patients while the red dots represent control group. (E) VIP scores of differential metabolites. (F) Correlation analysis of all metabolites, ranging from blue (low correlation) to red (high correlation).

Figure 3. Boxplot of significantly differential metabolites between HF patients and control group. Some representatively differentiated metabolites with P < 0.05 were showed (****P < 0.0001).

Figure 4. Diagnostic effectiveness of differential metabolites between HF patients and control group. (A) The receiver operating characteristic (ROC) curve of significantly changed metabolites (AUC > 0.8 were showed). (B) Correlation matrix of these differential metabolites. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.

Figure 5. Differential metabolites differentiate patients with CHD from patients with VHD. (A) PLS-DA analysis of 49 metabolites between CHD and VHD patients. The green dots represent VHD patients while the red dots represent VHD patients. (B) Asp, Val, C14-OH, C20 changed significantly in VHD and CHD patients (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001).

Figure 6. Differential metabolites differentiate patients with CHD from patients with DCM. (A) PLS-DA analysis of 49 metabolites between CHD and DCM patients. The green dots represent DCM patients while the red dots represent CHD patients. (B) Asp, Arg, Cit, Glu, Pip, C14-OH, changed significantly in CHD and DCM patients. (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001).

Figure 7. Correlation among clinical features and significantly differentially metabolites in HF patients and control groups. (A) Correlation among Cit, clinical renal function features (BNP, BUN, and CRE) and serum lipids index (ChoL and LDL). (B) Correlation among C14, LVEF% and clinical renal function features (BNP, BUN, and CRE). (C) Correlation among LAD, C22, and Arg. (D) Correlation among LVEF%, C14 and Arg. Data were analyzed by linear regression in figure.