Abstract
AIMS: Biomarkers may provide insights into molecular mechanisms underlying heart remodelling and dysfunction. Using a targeted proteomic approach, we aimed to identify circulating biomarkers associated with early stages of heart failure. METHODS AND RESULTS: A total of 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. Of 87 proteins, 13 were important in PLS-DA and XGBoost modelling for detection of left ventricular remodelling, left ventricular diastolic dysfunction, and/or left atrial reservoir dysfunction: placental growth factor, kidney injury molecule-1, prostasin, angiotensin-converting enzyme-2, galectin-9, cathepsin L1, matrix metalloproteinase-7, tumour necrosis factor receptor superfamily members 10A, 10B, and 11A, interleukins 6 and 16, and α1-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 echocardiographic abnormalities was higher in this phenogroup than in the other (P < 0.0001). CONCLUSIONS: We identified proteins related to renal function, extracellular matrix remodelling, angiogenesis, and inflammation to be associated with echocardiographic signs of early-stage heart failure. Proteomic phenomapping discriminated individuals at high risk for cardiac remodelling and dysfunction.