Endotyping in Heart Failure　― Identifying Mechanistically Meaningful Subtypes of Disease ―

Abstract

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.

The Relationship between Angiotensin–Neprilysin Treatment, Echocardiographic Parameters, and NT-proBNP Levels in HFpEF Patients with Acute Decompensated Heart Failure

“…The worldwide public health concern of heart failure (HF) affects around 2% of people in developed countries [ 1 , 2 ], resulting in symptoms associated with insufficient cardiac output [ 2 , 3 ]. The three subtypes of heart failure are HF with reduced left ventricular ejection fraction (HFrEF) EF <40% (amended to ≤ 35%), HF with preserved LVEF (HFpEF) in patients with an EF greater than 50%, LV diastolic dysfunction, and evidence of structural heart disease (HFmrEF) in patients with an LVEF of 40-49%, diastolic dysfunction, increased BNP concentrations, and evidence of structural heart disease [ 4 , 5 ]. The etiology and pathophysiological characteristics of HFpEF are complex and heterogeneous [ 6 , 7 ], and even individuals specializing in HF may have difficulty accurately diagnosing this disease.…”

Comprehensive Transcriptomics Profiling of MicroRNA Reveals Plasma Circulating Biomarkers of Hypertrophic Cardiomyopathy and Dysregulated Signaling Pathways

“…Many sncRNAs have been found to play an important role in the regulation of gene transcription, epigenetics, and posttranslational messenger RNA processing. 6,36 A number of studies have shown that concentrations of circulating sncRNAs can be measured noninvasively in the blood and change in response to an array of acute and chronic disease states. 7,8,[37][38][39] Among sncRNAs, miRNAs are particularly attractive candidates as biomarkers of disease.…”

404‐error “Disease not found”: Unleashing the translational potential of ‐omics approaches beyond traditional disease classification in heart failure research

“…'Multi-omics' technology is necessary to adequately understand the complex molecular milieu of human diseases. Molecular profiling can classify diseases like HF by analysing the molecular signatures associated with the disease at various omics levels 30,31 (Table 1).…”

“…‘Multi‐omics’ technology is necessary to adequately understand the complex molecular milieu of human diseases. Molecular profiling can classify diseases like HF by analysing the molecular signatures associated with the disease at various omics levels 30,31 ( Table 1 ). The integration and deep understanding of these molecular patterns allow for identifying distinct HF pathophysiological changes but also a general perspective of the behaviour of the syndrome ( Figure ) 32 …”