Protein Biomarkers of New-Onset Heart Failure: Insights From the Heart Omics and Ageing Cohort, the Atherosclerosis Risk in Communities Study, and the Framingham Heart Study

Background: We sought to identify protein biomarkers of new-onset heart failure (HF) in 3 independent cohorts (HOMAGE cohort [Heart Omics and Ageing], ARIC study [Atherosclerosis Risk in Communities], and FHS [Framingham Heart Study]) and assess if and to what extent they improve HF risk prediction compared to clinical risk factors alone. Methods: A nested case-control design was used with cases (incident HF) and controls (without HF) matched on age and sex within each cohort. Plasma concentrations of 276 proteins were measured at baseline in ARIC (250 cases/250 controls), FHS (191/191), and HOMAGE cohort (562/871). Results: In single protein analysis, after adjusting for matching variables and clinical risk factors (and correcting for multiple testing), 62 proteins were associated with incident HF in ARIC, 16 in FHS, and 116 in HOMAGE cohort. Proteins associated with incident HF in all cohorts were BNP (brain natriuretic peptide), NT-proBNP (N-terminal pro-B-type natriuretic peptide), eukaryotic translation initiation factor 4E-BP1 (4E-binding protein 1), hepatocyte growth factor (HGF), Gal-9 (galectin-9), TGF-alpha (transforming growth factor alpha), THBS2 (thrombospondin-2), and U-PAR (urokinase plasminogen activator surface receptor). The increment in C-index for incident HF based on a multiprotein biomarker approach, in addition to clinical risk factors and NT-proBNP, was 11.1% (7.5%–14.7%) in ARIC, 5.9% (2.6%–9.2%) in FHS, and 7.5% (5.4%–9.5%) in HOMAGE cohort, all P<0.001), each of which was a larger increase than that for NT-proBNP on top of clinical risk factors. Complex network analysis revealed a number of overrepresented pathways related to inflammation (eg, tumor necrosis factor and interleukin) and remodeling (eg, extracellular matrix and apoptosis). Conclusions: A multiprotein biomarker approach improves prediction of incident HF when added to natriuretic peptides and clinical risk factors.

[1]  M. Devignes,et al.  Insulin-like growth factor binding protein 2: A prognostic biomarker for heart failure hardly redundant with natriuretic peptides. , 2019, International journal of cardiology.

[2]  A. Mebazaa,et al.  Proteomic Bioprofiles and Mechanistic Pathways of Progression to Heart Failure. , 2019, Circulation. Heart failure.

[3]  Deepak L. Bhatt,et al.  Dapagliflozin and Cardiovascular Outcomes in Type 2 Diabetes , 2019, The New England journal of medicine.

[4]  Daniel Levy,et al.  Protein Biomarkers of Cardiovascular Disease and Mortality in the Community , 2018, Journal of the American Heart Association.

[5]  E. Ingelsson,et al.  Circulating proteins as predictors of incident heart failure in the elderly , 2018, European journal of heart failure.

[6]  Harry Hemingway,et al.  Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals , 2017, The Lancet.

[7]  H. White,et al.  Predictors of incident heart failure in patients after an acute coronary syndrome: The LIPID heart failure risk-prediction model. , 2017, International journal of cardiology.

[8]  Gerasimos S Filippatos,et al.  2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. , 2017, Journal of the American College of Cardiology.

[9]  Anne Newman,et al.  Risk for Incident Heart Failure: A Subject‐Level Meta‐Analysis From the Heart “OMics” in AGEing (HOMAGE) Study , 2017, Journal of the American Heart Association.

[10]  Gerasimos S Filippatos,et al.  2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. , 2017, Journal of cardiac failure.

[11]  Robert E. Gerszten,et al.  Emerging Affinity-Based Proteomic Technologies for Large-Scale Plasma Profiling in Cardiovascular Disease , 2017, Circulation.

[12]  G. Fonarow,et al.  2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. , 2017, Circulation.

[13]  Volkmar Falk,et al.  2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure , 2016, Revista espanola de cardiologia.

[14]  Neil Pearce,et al.  Analysis of matched case-control studies , 2016, British Medical Journal.

[15]  C. Yancy,et al.  Population Risk Prediction Models for Incident Heart Failure: A Systematic Review , 2015, Circulation. Heart failure.

[16]  A. Mebazaa,et al.  Heart ‘omics’ in AGEing (HOMAGE): design, research objectives and characteristics of the common database , 2014, Journal of biomedical research.

[17]  O. Melander,et al.  Increased plasma level of soluble urokinase plasminogen activator receptor is associated with incidence of heart failure but not atrial fibrillation , 2014, European journal of heart failure.

[18]  W. Paulus,et al.  A novel paradigm for heart failure with preserved ejection fraction: comorbidities drive myocardial dysfunction and remodeling through coronary microvascular endothelial inflammation. , 2013, Journal of the American College of Cardiology.

[19]  D. Houston,et al.  The role of metabolic syndrome, adiposity, and inflammation in physical performance in the Health ABC Study. , 2013, The journals of gerontology. Series A, Biological sciences and medical sciences.

[20]  D. Levy,et al.  Prognostic Utility of Novel Biomarkers of Cardiovascular Stress: The Framingham Heart Study , 2012, Circulation.

[21]  F. Forastiere,et al.  Prevalence of preclinical and clinical heart failure in the elderly. A population‐based study in Central Italy , 2012, European journal of heart failure.

[22]  Gary D. Bader,et al.  clusterMaker: a multi-algorithm clustering plugin for Cytoscape , 2011, BMC Bioinformatics.

[23]  Martin Lundberg,et al.  Homogeneous antibody-based proximity extension assays provide sensitive and specific detection of low-abundant proteins in human blood , 2011, Nucleic acids research.

[24]  A. Cohen-Solal,et al.  Predictors of clinical outcomes in elderly patients with heart failure , 2011, European journal of heart failure.

[25]  Allan Kuchinsky,et al.  GLay: community structure analysis of biological networks , 2010, Bioinform..

[26]  Peter J Diggle,et al.  On the Operational Characteristics of the Benjamini and Hochberg False Discovery Rate Procedure , 2007, Statistical applications in genetics and molecular biology.

[27]  M. Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[29]  J. McMurray,et al.  Changes in notions about heart failure , 2001, The Lancet.

[30]  Simon Stewart,et al.  Epidemiology, aetiology, and prognosis of heart failure , 2000, Heart.

[31]  P. Macfarlane,et al.  The design of a prospective study of Pravastatin in the Elderly at Risk (PROSPER). PROSPER Study Group. PROspective Study of Pravastatin in the Elderly at Risk. , 1999, The American journal of cardiology.

[32]  A. Folsom,et al.  The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. , 1989, American journal of epidemiology.

[33]  H. Klepzig [The department of cardiology]. , 1957, Die Medizinische.