Author: Cauwenberghs, Nicholas; SabovÄik, FrantiÅ¡ek; Magnus, Alessio; Haddad, Francois; Kuznetsova, Tatiana
Title: Proteomic profiling for detection of earlyâ€stage heart failure in the community Cord-id: 15i9mjbf Document date: 2021_5_29
ID: 15i9mjbf
Snippet: 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 communityâ€based participants (mean age, 57 years; 51.7% women) underwent echocardiography and proteomic profiling (CVD II panel, Olink Proteomics). We applied partial least squaresâ€discriminant analysis (PLSâ€DA) and a ma
Document: 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 communityâ€based participants (mean age, 57 years; 51.7% women) underwent echocardiography and proteomic profiling (CVD II panel, Olink Proteomics). We applied partial least squaresâ€discriminant analysis (PLSâ€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 PLSâ€DA and XGBoost. Of 87 proteins, 13 were important in PLSâ€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 moleculeâ€1, prostasin, angiotensinâ€converting enzymeâ€2, galectinâ€9, cathepsin L1, matrix metalloproteinaseâ€7, tumour necrosis factor receptor superfamily members 10A, 10B, and 11A, interleukins 6 and 16, and α1â€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 earlyâ€stage heart failure. Proteomic phenomapping discriminated individuals at high risk for cardiac remodelling and dysfunction.
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