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Phenotypic clustering of dilated cardiomyopathy patients highlights important pathophysiological differences
Job A J Verdonschot1,2, Marco Merlo3, Fernando Dominguez4,5
1Department of Cardiology, Cardiovascular Research Institute (CARIM), Maastricht University Medical Center, PO Box 5800, 6202 AZ Maastricht, The Netherlands.
European Heart Journal
|November 6, 2020
Summary
Dilated cardiomyopathy (DCM) patients can be classified into four distinct phenogroups based on genetics, comorbidities, and cardiac function. This phenogrouping reveals unique molecular profiles and predicts patient outcomes, enabling personalized DCM treatment strategies.
Area of Science:
- Cardiology
- Genomics
- Personalized Medicine
Background:
- Dilated cardiomyopathy (DCM) presents a heterogeneous phenotype influenced by genetic and acquired factors.
- Current clinical decision-making for DCM relies on ejection fraction (EF) and NYHA classification, overlooking patient heterogeneity.
- There is a need to identify distinct DCM subgroups to understand underlying pathophysiology and improve treatment.
Purpose of the Study:
- To identify patient subgroups within DCM using phenotypic clustering.
- To integrate aetiologies, comorbidities, and cardiac function with cardiac transcript levels.
- To unveil pathophysiological differences between DCM subgroups for personalized treatment.
Main Methods:
- Unsupervised hierarchical clustering of principal components from 795 DCM patients.
- In-depth phenotyping including clinical data, imaging, and endomyocardial biopsies.
- RNA-sequencing of cardiac samples (n=91) to analyze molecular profiles.
- Decision tree modeling using clinical parameters for phenogroup classification.
Main Results:
- Four distinct phenogroups (PG) were identified: mild systolic dysfunction (PG1), auto-immune (PG2), genetic and arrhythmias (PG3), and severe systolic dysfunction (PG4).
- Distinct molecular profiles were observed per PG: pro-inflammatory (PG2), pro-fibrotic (PG3), and metabolic (PG4).
- Event-free survival differed significantly among the four phenogroups, independent of known predictors.
- A decision tree model accurately classified patients into phenogroups across independent cohorts (n=789).
Conclusions:
- The study identified four DCM phenogroups with distinct clinical presentations, molecular profiles, and outcomes.
- This phenogrouping approach facilitates a more personalized treatment strategy for DCM patients.
- The identified clinical parameters offer a feasible method for classifying DCM patients into actionable phenogroups.
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