Unbiased multi-omics network-based data integration allows clinically relevant outcome-predicting clustering of individuals with heart failure

Heart failure is a multifaceted clinical syndrome, in which the heart fails to supply adequate blood to meet the bodys oxygen and nutrients needs. Evidence indicates multi-level molecular shifts in heart failure subjects, necessitating unbiased molecular stratification of patients with heart failure. This study utilized AI-based multimodal integration method to analyse 359 lipids and 538 proteins measured in participants of the MyoVasc heart failure cohort. Patient similarity networks were constructed, and spectral clustering, an unsupervised machine learning technique, identified clinically relevant subgroups predictive of patient outcomes. Comparative analyses of cluster-defining proteins and lipids revealed molecular-level insights into heart failure clinical subtypes. In addition to metabolic dysfunctions such as diabetes mellitus, the clinical profiles and outcomes of the identified eight subgroups also showed kidney and liver function indicators. The unbiased molecular characterization was particularly notable in clusters lacking clear, established clinical distinctions, suggesting novel insights into previously uncharacterized patient subgroups. The results show that network-based integration enables to unbiasedly characterize novel molecular subgroups, providing a foundation for improved understanding and management of heart failure.