Identification of Biomarkers for Right Ventricular Dysfunction in Idiopathic Dilated Cardiomyopathy Via Urinary Proteomics and Machine Learning

Figures

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  Figure 1. Volcano plot depicting the differentially expressed proteins between RVD and non‐RVD. Proteins upregulated in RVD vs non‐RVD are shown in red and downregulated in blue. Dotted lines indicate the thresholds for a 1.5‐fold change in expression (vertical) and statistical significance (P adjusted=0.05; horizontal). Gene names are shown instead of the full‐length protein names. RVD indicates right ventricular dysfunction.

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  Figure 2. Correlations of differential urinary proteins with clinical and imaging parameters in patients with iDCM. Correlogram showing pairwise Pearson correlations between the abundance levels of differentially expressed urinary proteins (columns) and key clinical/imaging traits (rows) in patients with iDCM. Only significant correlations are marked with asterisks (*P<0.05; **P<0.01; ***P<0.001). Cell colors represent the strength and direction of Pearson’s correlation coefficient (blue=negative, red=positive), and only significance levels are overlaid. Protein names are ordered based on hierarchical clustering. E/e indicates early mitral inflow to mitral annular early diastolic velocity ratio; eGFR, estimated glomerular filtration rate; iDCM, idiopathic dilated cardiomyopathy; IVS, interventricular septal thickness; LAD, left atrial diameter; LGE, late gadolinium enhancement; LVEDVI, left ventricular end‐diastolic volume index; LVEF, left ventricular ejection fraction; LVMI, left ventricular mass index; LVPW, left ventricular posterior wall thickness; MR, mitral regurgitation; NP, N‐terminal pro‐B‐type natriuretic peptide; NYHA, New York Heart Association class; PASP, pulmonary artery…

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  Figure 3. Functional enrichment analysis of differentially expressed proteins associated with right ventricular dysfunction. A, Gene Ontology enrichment analysis reveals significant enrichment in biological processes related to antigen processing, metabolism, and cell signaling, cellular components such as organelles and exosomes, and molecular functions including transport and metabolic regulation. B, Kyoto Encyclopedia of Genes and Genomes pathway analysis shows significant enrichment in metabolic pathways, immune‐related pathways, endocytosis, and proteasome function. BP indicates biological processes; CC, cellular components; GO, Gene Ontology; MF, molecular functions; and NAD, nicotinamide adenine dinucleotide.

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  Figure 4. Feature selection of diagnostic biomarkers for RVD in patients with DCM. LASSO regression was performed on the training set to identify informative protein features. A total of 11 proteins were retained using the λ.1se criterion. Among these, GSK3A, MVB12B, and RARRES1 were selected for the final diagnostic model based on both statistical performance and biological relevance. A, LASSO path plot showing the coefficient profiles of 15 candidate proteins across a range of log(λ) values. The 2 vertical dashed lines indicate λ.min (yielding the minimum cross‐validated error) and λ.1se (yielding the most parsimonious model within 1 SE of λ.min); the latter was selected for final model construction. B, Tenfold cross‐validation plot displaying binomial deviance vs log(λ). The same 2 vertical lines denote λ.min and λ.1se, with λ.1se chosen to balance model simplicity and performance. C, Correlation matrix of the 11 LASSO‐selected DEPs, with color intensity representing the strength of the Spearman’s correlation (red for positive, blue for negative). DCM indicates dilated cardiomyopathy; DEP, differentially expressed proteins; GSK3A, glycogen synthase kinase 3 alpha; LASSO, least absolute…

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  Figure 5. Diagnostic performance of the three‐protein model and comparison with NT‐proBNP, TAPSE, and their combination in training and validation data sets. The diagnostic model integrating GSK3A, MVB12B, and RARRES1 demonstrated superior performance compared with individual proteins, NT‐proBNP, TAPSE, and the NT‐proBNP + TAPSE combination, achieving high AUC, sensitivity, and specificity in both training and validation sets. A, ROC curves comparing individual proteins, the 3‐protein model, NT‐proBNP, TAPSE, and the NT‐proBNP + TAPSE combination in the training set. B, ROC curves comparing the same models in the validation set. AUC indicates area under the curve; GSK3A, glycogen synthase kinase 3 alpha; MVB12B, multivesicular body subunit 12B; NT‐proBNP, N‐terminal pro‐brain natriuretic peptide; RARRES1, retinoic acid receptor responder protein 1; ROC, receiver operating characteristics; and TAPSE, tricuspid annular plane systolic excursion.

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  Figure 6. Kaplan–Meier survival curves based on a Cox regression‐derived risk score incorporating 3 urinary biomarkers (GSK3A, MVB12B, and RARRES1) for predicting adverse outcomes in iDCM. Patients were stratified into high‐ and low‐risk groups using the median risk score. A, For the composite end point of cardiovascular death, LVAD implantation, heart transplantation and heart failure rehospitalization (cutoff=0.86), the high‐risk group exhibited a significantly higher risk compared with the low‐risk group (HR, 3.24 [95% CI, 1.56–6.71], P=0.002). B, For cardiovascular death, LVAD implantation, or heart transplantation (cutoff=0.872), the high‐risk group showed significantly worse outcomes (HR, 4.17 [95% CI, 1.17–14.79], P=0.027). C, For heart failure hospitalization (cutoff=0.973), the high‐risk group also exhibited a significantly increased risk (HR, 2.56 [95% CI, 1.03–6.34], P=0.043). GSK3A indicates glycogen synthase kinase 3 alpha; HR, hazard ratio; iDCM, idiopathic dilated cardiomyopathy; LVAD, left ventricular assist device; MVB12B, multivesicular body subunit 12B; and RARRES1, retinoic acid receptor responder protein 1.