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.

中文翻译：

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使用多维数据对扩张型心肌病进行精确表型分析

背景

扩张型心肌病（DCM）是异质病因的最终常见表现。不良结果凸显了射血分数之外的疾病分层的必要性。

目标

本研究的目的是利用多参数数据确定 DCM 的新型、可重复的亚表型，以改善患者分层。

方法

纵向、观察性英国推导（n = 426；中位年龄 54 岁；67% 男性）和荷兰验证（n = 239；中位年龄 56 岁；64% 男性）DCM 患者队列（2009-2016 年入组）临床症状、遗传、心血管磁共振和蛋白质组学评估。机器学习与轮廓回归识别出新的疾病亚型。使用惩罚多项逻辑回归进行验证。嵌套 Cox 模型将新颖的分组与传统的风险度量进行了比较。主要复合结局是心血管死亡、心力衰竭或心律失常事件（中位随访 4 年）。

结果

总共确定了 3 种新的 DCM 亚型：促纤维化代谢亚型、轻度非纤维化亚型和双心室功能障碍。推导组 ( P < 0.0001) 和验证组中各亚型的预后均存在差异。新的促纤维化代谢亚型具有更多的糖尿病、普遍的心肌纤维化、保留的右心室功能和升高的肌酐。对于临床应用，5 个变量足以进行分类（左心室和右心室收缩末期容积、左心房容积、心肌纤维化和肌酐）。添加新的 DCM 亚型将 C 统计量从 0.60 提高到 0.76。在推导（HR：3.6；95% CI：1.9-6.5；P = 0.00002）和验证队列（HR：1.94；95% CI：1.3-2.8；P = 0.00005）。

结论

利用广泛可用的临床和生物学数据确定了三种可重复的、机制上不同的 DCM 亚型，为传统风险模型增加了预后价值。它们可以改善患者对新型干预措施的选择，从而实现精准医疗。