Heart failure is a multifaceted clinical syndrome, in which the heart fails to supply adequate blood to meet the body's 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.

Philipp S. Wild reports grants from Bayer AG; non-financial grants from Philips Medical Systems; grants and consulting fees from Boehringer Ingelheim, Novartis AG, Sanofi-Aventis GmbH, and Daiichi Sankyo Europe GmbH; grants and consulting and lecturing fees from Bayer Healthcare Pharmaceuticals; lecturing fees from Pfizer Inc. and Bristol Myers Squibb; consulting fees from AstraZeneca plc; consulting fees and non-financial support from DiaSorin; and non-financial support from I.E.M.

This work was funded by the German Federal Ministry for Education and Research (BMBF) as part of the DIASyM project to L.B, P.S.W and E.A under grant numbers 161L0219, 161L0217A, 031L0217A, 031L0217B, 03ZU1202EB and 16LW0241K. Additionally, this paper is supported by FAIR (Future Artificial Intelligence Research) project to E.C., funded by the NextGenerationEU program within the PNRR-PE-AI scheme (M4C2, Investment 1.3, Line on Artificial Intelligence).

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The study was conducted in accordance with the Declaration of Helsinki. The local ethics committee (Ethical Committee N° 2019-14744) of the medical association of Rhineland-Palatinate State approved the study with a decision number 837.319.12 (8420-F).

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