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Precision Phenotyping of Dilated Cardiomyopathy Using Multidimensional Data
Upasana Tayal1, Job A J Verdonschot2, Mark R Hazebroek3
1National Heart Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton Hospital (Guy's and St Thomas's NHS Foundation Trust), London, United Kingdom.
Insights
Researchers identified three new dilated cardiomyopathy (DCM) subtypes using multiparametric data. These novel DCM subphenotypes improve patient stratification and prognosis prediction beyond ejection fraction.
Area of Science:
- Cardiology
- Genetics
- Biomarkers
Background:
- Dilated cardiomyopathy (DCM) is a complex heart condition with diverse causes and poor outcomes.
- Current methods for stratifying DCM patients, like ejection fraction, are insufficient for predicting adverse events.
Purpose of the Study:
- To identify novel, reproducible DCM subphenotypes using comprehensive patient data.
- To improve patient stratification and risk prediction in DCM.
Main Methods:
- Utilized longitudinal data from UK and Dutch DCM cohorts (n=665) with clinical, genetic, cardiovascular magnetic resonance, and proteomic assessments.
- Applied machine learning (profile regression) to identify subtypes and penalized multinomial logistic regression for validation.
- Compared novel DCM groupings against conventional risk measures using nested Cox models.
Main Results:
- Identified three distinct DCM subtypes: profibrotic metabolic, mild nonfibrotic, and biventricular impairment.
- Prognosis significantly differed among subtypes in both derivation and validation cohorts (P < 0.0001).
- The profibrotic metabolic subtype showed higher rates of diabetes, myocardial fibrosis, and elevated creatinine. Five variables (ventricular volumes, atrial volume, fibrosis, creatinine) were sufficient for classification, improving predictive accuracy (C-statistic from 0.60 to 0.76). Interleukin-4 receptor-alpha emerged as a novel prognostic biomarker.
Conclusions:
- Discovered three reproducible, mechanistically distinct DCM subtypes using accessible clinical and biological data.
- These subtypes offer added prognostic value beyond traditional risk models for dilated cardiomyopathy.
- The identified DCM subphenotypes may facilitate personalized treatment strategies and improve patient selection for novel interventions, advancing precision medicine in cardiology.
Background:
Dilated cardiomyopathy (DCM) is a final common manifestation of heterogenous etiologies. Adverse outcomes highlight the need for disease stratification beyond ejection fraction.
Objectives:
The purpose of this study was to identify novel, reproducible subphenotypes of DCM using multiparametric data for improved patient stratification.
Methods:
Longitudinal, observational UK-derivation (n = 426; median age 54 years; 67% men) and Dutch-validation (n = 239; median age 56 years; 64% men) cohorts of DCM patients (enrolled 2009-2016) with clinical, genetic, cardiovascular magnetic resonance, and proteomic assessments. Machine learning with profile regression identified novel disease subtypes. Penalized multinomial logistic regression was used for validation. Nested Cox models compared novel groupings to conventional risk measures. Primary composite outcome was cardiovascular death, heart failure, or arrhythmia events (median follow-up 4 years).
Results:
In total, 3 novel DCM subtypes were identified: profibrotic metabolic, mild nonfibrotic, and biventricular impairment. Prognosis differed between subtypes in both the derivation (P < 0.0001) and validation cohorts. The novel profibrotic metabolic subtype had more diabetes, universal myocardial fibrosis, preserved right ventricular function, and elevated creatinine. For clinical application, 5 variables were sufficient for classification (left and right ventricular end-systolic volumes, left atrial volume, myocardial fibrosis, and creatinine). Adding the novel DCM subtype improved the C-statistic from 0.60 to 0.76. Interleukin-4 receptor-alpha was identified as a novel prognostic biomarker in derivation (HR: 3.6; 95% CI: 1.9-6.5; P = 0.00002) and validation cohorts (HR: 1.94; 95% CI: 1.3-2.8; P = 0.00005).
Conclusions:
Three reproducible, mechanistically distinct DCM subtypes were identified using widely available clinical and biological data, adding prognostic value to traditional risk models. They may improve patient selection for novel interventions, thereby enabling precision medicine.
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