Plasma Metabolomic Profiles Differentiate Patients With Dilated Cardiomyopathy and Ischemic Cardiomyopathy

Figures

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  Figure 1. Workflow of the present study. In general, HF patients with different etiology (38 patients with DCM and 18 patients with ICM, respectively), and 20 healthy controls were enrolled in this study. An untargeted metabolomic panel was applied to the plasma samples of these individuals. We comparatively analyzed the plasma metabolomics profiling of the 3 groups to clarify the differences in HF patients with different etiology from the perspective of circulation metabolomics.

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  Figure 2. Score plots of PLSDA model in positive and negative ion modes. In the three-dimensional score plots, each plot represents a sample. They showed a distinct classification in healthy controls (gray) and patients with DCM (blue) and ICM (red) in positive ion mode (A) and negative ion mode (B).

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  Figure 3. Differential metabolites between patients with DCM, ICM, and healthy controls. Volcano plots for dysregulated endogenous metabolites between patients with DCM and healthy controls in positive ion mode (A) and negative ion mode (B). Volcano plots for dysregulated endogenous metabolites between patients with ICM and healthy controls in positive ion mode (C) and negative ion mode (D). Each plot represents a metabolite. The differentiating metabolites with adjusted P < 0.05 were highlighted in color. The venn diagram displayed the differential metabolites shared with DCM and ICM and the specific metabolites to be significant only in one comparison in in positive ion mode (E) and negative ion mode (F).

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  Figure 4. Dysregulated pathway observed in DCM and ICM. Dysregulated pathways involved in the pathogenesis of patients with DCM (A) or ICM (B). Significant pathways (P < 0.05) were highlighted.

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  Figure 5. The disease specific dysregulated metabolites distinguish patients with DCM from patients with ICM. (A) LASSO coefficient profiles of the disease-specific features. A coefficient profile plot was produced against the log(λ) sequence. Dotted vertical line was drawn at the optimal λ at minimum criteria and 1 standard error (1-SE criteria). The model at 1-SE criteria was selected as the final model with 6 non-zero coefficients. (B) Tuning parameter (λ) selection in the LASSO model used 10-fold cross-validation via minimum criteria. The binomial deviance was plotted vs. log (λ). (C) Bar plot of the 6 metabolites in DCM and ICM and HC. Student's t-test for two group comparison. (D) Plot of the area under curve for the created biomarker model in training and testing dataset by cross validation under 500 random replications. Data are presented as mean ± SD in bar plots. (E) The ROC created by a combination of six selected features based on whole dataset. *P < 0.05, **P < 0.01, ***P < 0.001.