Serum proteomics classified DCM into 4 subtypes and predicted 3-year recurrence and death with 91.3% and 73.4% accuracy; trimetazidine response predicted with 81.5% accuracy.
Can serum proteomic profiling classify subtypes, predict prognosis, and predict trimetazidine efficacy in patients with dilated cardiomyopathy?
Serum proteomic profiling in dilated cardiomyopathy enables distinct biological subtype classification and provides highly accurate predictive models for 3-year recurrence, mortality, and response to trimetazidine.
Absolute Event Rate: 0% vs 0%
Abstract Dilated cardiomyopathy (DCM) is a subtype of cardiomyopathy characterized by its heterogeneous etiology. There is a pressing need for guidance in clinical treatment and prognosis prediction. In this study, we conducted a comprehensive profiling of serum proteome in DCM patients, comparing it with both healthy individuals and patients with ischemic cardiomyopathy (ICM), to identify serological characteristics unique to DCM. By analyzing serum proteomics data from 113 DCM patients, we successfully classified DCM into four distinct subtypes: Catabolism and Immunity, Cell Adhesion and Regeneration, Immunity and Inflammation, and Metabolism and Antibiotic Response. Although these subtypes did not show significant associations with prognosis, they may reflect the complex etiology underlying DCM. Additionally, we identified a protein interaction network centered around BCHE, APOC3, and TTR, which was significantly correlated with better prognosis. Conversely, a network involving FCRL3, JCHAIN, and lGKV3-20 was linked to poorer outcomes and increased mortality risk. Notably, four proteins-H6PD, FGB, FGG, and OlT3-were significantly associated with recurrence of DCM events, highlighting their potential as biomarkers for monitoring disease progression. Based on these findings, we developed prediction models for recurrence and death within a three-year period, achieving accuracies of 91.3% and 73.4%, respectively. Furthermore, we identified some other proteins, such as ANG, as potential independent indicators for predicting the efficacy of trimetazidine therapy, leading to establishment of a predictive model specifically for trimetazidine efficacy, which achieved a prediction accuracy of 81.5%. These results not only deepen our understanding of the biological underpinnings of dilated cardiomyopathy (DCM) but also emphasize the importance of serum proteomics in improving the prognostic management of DCM patients, providing a potential basis for more personalized therapeutic strategies. Figure Captions Fig. 1 A Study design overview with control (n=30), ICM (n=30), and DCM (n=113) groups, including 2-3 years of follow-up and a proteomic analysis workflow. B Venn diagram showing the overlap of identified proteins among control, ICM, and DCM groups. C Intensity distribution of identified proteins across DCM, ICM, and control groups. D-E Volcano plots showing protein expression differences between DCM (or ICM) and control groups. F Relative intensity of differentially expressed proteins between DCM and control groups. Significant differences between groups are indicated by asterisks (*p 0.05, **p 0.01, ***p 0.001, ****p 0.0001). Fig. 2 A Heatmap showing clinical characteristics and proteomic data across four DCM subtypes. B Functional annotation of proteins associated with each DCM subtype, categorized into four biological process subgroups. C-F Heatmaps of proteins specific to subtypes 1, 2, 3, and 4.Study Design and Proteomic Profile Subtype Classification of DCM
Chen et al. (Sat,) reported a other. Serum proteomics classified DCM into 4 subtypes and predicted 3-year recurrence and death with 91.3% and 73.4% accuracy; trimetazidine response predicted with 81.5% accuracy.
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