Aims Heart failure with preserved ejection fraction (HFpEF) has become the predominant form of heart failure and a leading cause of global cardiovascular morbidity and mortality. Due to its heterogeneous nature, HFpEF presents substantial challenges in diagnosis and management. Given the limited treatment options and lifestyle‐associated comorbidities, early identification is crucial for establishing effective preventive strategies. Here, we introduce and validate a machine learning‐based multi‐omics approach that integrates clinical and molecular data to detect and characterize HFpEF. Methods and results A supervised classifier was trained on a stratified subset of UK Biobank participants ( n = 401 917) to identify phenotypic profiles associated with subsequent symptom‐defined HFpEF during longitudinal follow‐up. Model performance was validated in a non‐overlapping hold‐out subset from all 22 UK Biobank assessment centres ( n = 100 446; 6726 HFpEF cases; 7394 with multi‐omics data). The classifier demonstrated robust discriminatory performance, with a receiver operating characteristic area under the curve (ROC AUC) of 0.931 (95% confidence interval CI 0.930–0.931), a sensitivity of 0.857 (95% CI 0.855–0.860) and a specificity of 0.847 (95% CI 0.846–0.847). It identified individuals who subsequently developed HFpEF an average of 6.3 ± 3.9 years before symptom onset in asymptomatic individuals. Similarity network fusion (SNF) identified distinct subgroups, including a high‐risk cluster characterized by elevated mortality and dysregulated inflammatory pathways, which was distinguishable with high accuracy (ROC AUC 0.988; 95% CI 0.985–0.990). Conclusions We identified HFpEF phenotypes at an early stage, often several years before the onset of clinical symptoms, when the disease trajectory may still be amenable to modification. The molecular characterization provides novel insights into the underlying disease complexity and enables more refined risk stratification.
Versnjak et al. (Mon,) studied this question.