Endotyping is an emerging concept in which diseases are classified into distinct subtypes based on underlying molecular mechanisms. Heart failure (HF) is a complex clinical syndrome that encompasses multiple endotypes with differential risks of adverse events, and varying responses to treatment. Identifying these distinct endotypes requires molecular-level investigation involving multi-“omics” approaches, including genomics, transcriptomics, proteomics, and metabolomics. The derivation of these HF endotypes has important implications in promoting individualized treatment and facilitating more targeted selection of patients for clinical trials, as well as in potentially revealing new pathways of disease that may serve as therapeutic targets. One challenge in the integrated analysis of high-throughput omics and detailed clinical data is that it requires the ability to handle “big data”, a task for which machine learning is well suited. In particular, unsupervised machine learning has the ability to uncover novel endotypes of disease in an unbiased approach. In this review, we will discuss recent efforts to identify HF endotypes and cover approaches involving proteomics, transcriptomics, and genomics, with a focus on machine-learning methods.

内分型是一个新兴概念，其中根据潜在的分子机制将疾病分为不同的亚型。心力衰竭 (HF) 是一种复杂的临床综合征，包括多种内型，具有不同的不良事件风险和不同的治疗反应。识别这些不同的内型需要涉及多“组学”方法的分子水平研究，包括基因组学、转录组学、蛋白质组学和代谢组学。这些 HF 内型的推导对于促进个体化治疗和促进更有针对性地选择患者进行临床试验以及潜在地揭示可作为治疗靶点的新疾病途径具有重要意义。高通量组学和详细临床数据综合分析的一个挑战是它需要处理“大数据”的能力，而机器学习非常适合这项任务。特别是，无监督机器学习能够以无偏见的方法发现新的疾病内型。在这篇综述中，我们将讨论最近为识别 HF 内型所做的努力，并涵盖涉及蛋白质组学、转录组学和基因组学的方法，重点是机器学习方法。