Heart failure with preserved ejection fraction is clinically heterogeneous, complicating efforts to identify patients at highest risk. Here, the authors show that integrating proteomics with machine learning reveals two biologically distinct subtypes with different prognoses, including a high-risk inflammatory cardiorenal phenotype.