Abstract Background and purpose Biomarkers may provide insight into the molecular mechanisms underlying cardiac remodelling and dysfunction. Using a targeted proteomic approach, we aimed to identify circulating biomarkers associated with early-stage heart failure and extract a proteome-based risk classifier for this condition. Methods 575 community-based participants (mean age, 57 years; 51.7% women) underwent echocardiography and proteomic profiling (CVD II panel, Olink Proteomics). We applied partial least squares-discriminant analysis (PLS-DA) and a machine learning algorithm (extreme gradient boosting, XGBoost) to identify key proteins associated with echocardiographic abnormalities. We used Gaussian Mixture modelling for unbiased clustering to construct phenogroups based on influential proteins in PLS-DA and XGBoost. Results Of 87 proteins, 13 were important in PLS-DA and XGBoost modelling for detection of left ventricular (LV) remodelling, LV diastolic dysfunction and/or left atrial reservoir dysfunction: placenta growth factor, kidney injury molecule-1, prostasin, angiotensin-converting enzyme-2, galectin-9, cathepsin L1, matrix metalloproteinase-7, TNFR superfamily members 10A, 10B and 11A, interleukins-6 and 16 and alpha-1-microglobulin/bikunin precursor. Based on these proteins, the clustering algorithm divided the cohort into two distinct phenogroups, with each cluster grouping individuals with a similar protein profile. Participants belonging to the second cluster (n=118) were characterized by an unfavourable cardiovascular risk profile and adverse cardiac structure and function. The adjusted risk of presenting cardiac maladaptation was higher in this phenogroup than in the other cluster (P<0.0001). Conclusion We identified proteins reflecting renal function, extracellular matrix remodelling, angiogenesis and inflammation to be associated with echocardiographic signs of early-stage heart failure. Focused proteomic phenomapping discriminated individuals at high risk for cardiac maladaptation in the community. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Research Foundation Flanders